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• Published: 18 April 2024

Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis

• Shahjahan Alias Sarang 1 ,
• Muhammad Amir Raza 1 ,
• Madeeha Panhwar 1 ,
• Malhar Khan 1 ,
• Ghulam Abbas 2 ,
• Ezzeddine Touti 3 ,
• Abdullah Altamimi 4 , 5 &
• Andika Aji Wijaya 6  

Scientific Reports volume  14 , Article number:  8944 ( 2024 ) Cite this article

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• Electrical and electronic engineering
• Energy infrastructure
• Energy science and technology
• Engineering

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems.

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Introduction.

All developed and underdeveloped nations can utilize renewable natural resources such as sunlight, wind, water, and geothermal heat by utilizing renewable energy technologies. They can generate electricity, heat buildings and commercial spaces, and power automobiles. Since renewable energy technologies don't emit greenhouse gases or other pollutants, they are better for the environment than conventional fossil fuels 1 . They also aid in lowering our dependency on imported energy. Some of the most popular renewable energy sources are solar, wind, hydro, geothermal, and biomass energy. PV solar power systems have the potential to contribute significantly to supplying the world's energy demands in the future. They create zero emissions of greenhouse gases and are clean, renewable energy sources. This makes it a wise decision to lessen our reliance on fossil fuels and slow down global warming. Systems using solar photovoltaic energy are also getting cheaper and more effective. The cost of solar panels has dropped significantly in recent years, and the efficiency of solar cells has also grown 2 . Now, solar photovoltaic systems can generate more power for a lower cost. PV solar energy systems are not only reasonably priced and effective but also incredibly adaptable. Installation options include the roof, the ground, and even the water. This implies that they may be applied in many settings, regardless of temperature or topography. Solar photovoltaic systems have a wide range of benefits. They can aid in lowering greenhouse gas emissions, dependency on fossil fuels, and energy costs 3 . During power outages, they can also offer backup power. The potential for solar photovoltaic systems to significantly contribute to the global energy mix is expanding as solar photovoltaic technology advances and costs drop. Future residential, commercial, and transportation energy needs may be mostly met by solar power systems.

A solar PV system uses solar panels or cells to capture sunlight and turn it into electrical power. Solar panels and solar cells, which respond to photons, or solar energy particles, with various solar spectrum wavelengths, are made from semiconductor materials. A solar inverter, solar tracking system, battery, mounting, cabling, and electrical accessories are examples of additional components that solar PV systems could be included to enhance functionality and use. Direct Current (DC) power is produced in a photovoltaic system using solar panels, which absorb sunlight 4 . The inverter then converts the DC power into Alternating Current (AC) electricity that may be used in your residence or place of business. In addition, batteries are needed to store electrical energy in the event that the system is connected to the grid or directly uses generated electricity. Batteries are only required if the user demands electricity at night. Due to its sustainability once installed, PV systems have the benefit of low running costs. Because they are incredibly durable and designed to endure for many years, they require very little maintenance 5 . Lastly, they don't require any lubricants because they are mechanically motionless immobile systems. Despite being the most widely utilized kind of renewable energy, photovoltaic systems have a number of shortcomings that are being investigated and fixed 6 . Solar PV systems are dependent on sunlight to generate electricity. Therefore, Extreme weather events, rain, snow, and cloud cover all have an impact on how much power solar panels can produce. Although photovoltaic systems are the most widely used kind of renewable energy, they have a number of shortcomings that are being investigated and fixed. The fact that photovoltaic technology is so dependent on the weather, or more precisely, one of the most significant shortcomings of the technology, is the amount and direction of sunlight impacting the panel surface. It is, hence, erratic and unreliable 7 . Additionally, the photovoltaic system's conversion rate or efficiency is low when compared to other power-generating systems. A significant number of solar panels must be erected because a single solar panel's efficiency is low, and adding more solar panels would increase the required land area. For every system, especially complicated systems like solar PV systems where the variable solar irradiation causes voltage instability and frequency deviation, regulation and control are two fundamental building elements. There must be certain regulating techniques and monitoring systems in place to provide a dependable and effective supply from the solar PV system, which further drives up the cost 8 .

A solar PV array's performance and output can be significantly impacted by shading. The smooth passage of sunlight onto the surface of PV cells is disrupted when shadows fall on a solar panel. These shadows could be cast by nearby objects such as trees, buildings, or even debris. The effect of such shading is twofold: it reduces the overall irradiance reaching the shaded cells and introduces electrical bypass pathways 9 . The reduction in irradiance limits the amount of light available for conversion into electrical energy, ultimately lowering the power output of the shaded panels. Sunlight, normally uniform across the surface of the solar array, becomes fragmented, creating an uneven distribution of energy absorption. The impacted cells' ability to generate electricity is severely reduced as a result, which lowers the system's overall efficiency. Partial shading affects the MPPT algorithm's performance. The solar panel cannot get continuous sunshine because of weather fluctuations, climatic variations, and variations in the angle at which solar radiation strikes the panel. Therefore, it is essential to use an MPPT technique that can maximize solar panel power depending on the weather at the time. The shading issue affects the power and current versus voltage curves 10 .

The MPPT method is used in PV systems to boost a solar panel's power output. It serves the purpose of ensuring that the solar panel is producing the highest amount of electrical power when it is functioning at its maximum power point (MPP), which is located on the current–voltage (I–V) curve 11 . The power output of solar panels fluctuates based on the operating conditions because of their non-linear I–V curve, as shown in Fig.  1 . MPPT is employed in PV systems to boost overall efficiency and energy production. Temperature, shade, and the quantity of sunshine received are a few instances of variables influencing the MPP. MPPT algorithms continuously monitor the MPP by adjusting the operating voltage and current of the solar panel to extract the maximum amount of electricity possible 12 . PV systems employ MPPT to boost overall efficiency and energy output. Higher energy output may be achieved by running the solar panel at its MPP, which allows for greater power harvesting from the sun. This is especially important when the solar panel is connected to a battery or grid since it makes the best use of the solar energy that is currently available and improves the system's performance 13 .

Graph showing power and voltage relationship 14 .

This work aims to make a substantial contribution to the field of solar energy systems and control algorithms.

Specifically, it evaluates a highly advanced PV model for MPPT tacking.

Our focus extends to the rigorous evaluation of ten distinct MPPT controllers, including conventional methods such as INC, P&O, INC-PSO, P&O-PSO and advanced approaches employing AI such as ANN, FLC, ANFIS, PSO, ANN-PSO and FLC-PSO.

The crux of our contribution lies in the comprehensive comparative analysis of these controllers, assessing key performance parameters such as maximum output voltage, extracted maximum power, time response dynamics, design complexity, and system stability.

The ultimate goal of this research is to guide the scientific community in selecting and optimizing MPPT algorithms for improved solar energy harvesting.

Related work on MPPT techniques

A state-of-the-art literature review is conducted to analyze the research gap and present the novelty of the proposed technique. The Study presents a novel MPPT method utilizing Artificial Neural Networks (ANN) to efficiently track the maximum power generated by a PV panel. The proposed ANN-based MPPT algorithm demonstrates rapid and accurate adaptation to changing meteorological conditions, including variations in temperature and solar radiation. Comprehensive research includes the design and modeling of a PV system structure in conjunction with the ANN-MPPT controller. The main goal of the study is to develop a high-performance ANN-based MPPT controller for solar applications.

In 15 , the authors introduced the Fuzzy Logic (FL) MPPT algorithm, a novel fuzzy logic-based method for monitoring the maximum power point of PV arrays. Unlike standard FL-MPPT methods that employ the change in slope of P–V characteristics, the proposed technique uses a new parameter termed "Ea" that is generated from I–V characteristics. This additional parameter improves tracking performance in a variety of environmental conditions (ECs) and increases the precision with which duty ratio changes may be computed. Using the "Ea" parameter, the approach successfully distinguishes between the operating point's placement in the Voltage Source Region (VSR) or Current Source Region (CSR) and its proximity to the MPP region.

Another study highlights the importance of MPPT controllers for optimizing the performance of solar (PV) modules 16 . The authors present a comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller architecture, an FL power controller, a PV module, an ANFIS reference model, and a DC-DC boost converter. Through simulations in the MATLAB/Simulink environment, the proposed ANFIS-based MPPT controller successfully harvests the maximum power from the PV module under a variety of weather circumstances, contributing significantly to the advancement of MPPT methods for solar energy systems.

Based on the literature study, the hardware implementation of the Incremental Conductance (INC) approach for MPPT in PV systems using Arduino boards is an important field of study in renewable energy. Numerous studies have examined the efficacy of MPPT techniques, and INC has emerged as a viable tool due to its ability to follow the MPP in quickly changing environmental settings. Researchers have investigated the integration of INC with DC-DC Boost converters, PV panels, and resistive loads in order to optimize energy harvesting in PV systems. Previous studies have also highlighted the importance of doing simulation trials using tools like MATLAB/Simulink to confirm the algorithm's performance prior to putting it on Arduino hardware. A strong experimental validation of INC-based MPPT controllers has been made possible by the incorporation of spatial Simulink packages, such as the "support package for Arduino hardware," despite the popularity of Arduino-based MPPT systems' low cost and accessibility. Overall, the research studies have concluded that the successful application of the INC algorithm in obtaining effective MPP extraction from PV panels will pave the way for its future use in renewable energy systems 17 .

The invention and improvement of MPPT algorithms, which are essential for effectively capturing the Global Maximum Power Point (GMPP) even in scenarios involving partial shade of PV arrays, is a key factor in improving the efficiency of PV systems. The Particle Swarm Optimization (PSO) algorithm stands out as a soft computing strategy among several MPPT methods. Hardware simplicity and independence from the installed PV system are two benefits of conventional PSO-based MPPT trackers. Nevertheless, choosing the parameters for the PSO to enable the successful extraction of the GMPP is a significant difficulty in real PSO deployment. The lack of a defined methodology for choosing the best parameters in PSO-based MPPT controllers for PV systems is revealed by an analysis of the current literature. The study conducted by 18 closes this gap by putting forth a methodical and logical approach to determine the optimal PSO algorithm settings, accounting for factors such as solar panel arrangement, DC-DC converter topology, and even associated battery parameters. Furthermore, the work presents a novel method for determining the optimal sample period to maximize the performance of digital MPPT controllers. The modified PSO algorithm, along with its customized parameters, best satisfies the requirements of MPPT control for PV systems, representing a significant step towards increasing the overall effectiveness of such systems.

In the realm of PV arrays, tracking the MPP is one area where achieving greater energy conversion efficiency is always crucial. This objective is particularly crucial when partially shaded conditions exist. In these situations, the power–voltage (P–V) characteristic curve of PV arrays exhibits multiple peaks, making it challenging for conventional MPPT techniques to distinguish between the local MPPs and the global MPPs. Research on developing strategies for efficiently monitoring the GMPP while reducing the adverse effects of partial shadow has increased significantly. PSO, one of these methods, has become popular because of its quick tracking abilities and flexibility in various environmental circumstances. However, a number of changes and improvements have evolved to overcome several flaws in traditional PSO techniques. In 19 , the authors perform a comprehensive and comparative study of various PSO-based methods, considering significant factors such as particle initialization criteria, search space exploration, convergence speed, initial parameter settings, performance with and without partial shading, and overall efficacy. Comprehensive simulation tests utilizing MATLAB code and the Simulink package extensively evaluate the suitability of these methodologies for real PV systems running under varied operating circumstances.

In the work done by 20 , they investigate the behavior of a photovoltaic system under various environmental conditions, such as intermittent variations in the atmosphere. The primary objective is to improve the system's performance by contrasting two MPPT algorithms: the PSO and the P&O algorithm. The efficiency, stability, speed, and robustness of the algorithms are tested in a range of atmospheric conditions. Simulation data shows that the PSO algorithm outperforms the P&O approach, highlighting its superior efficiency in maximizing power generation under a variety of environmental situations. Their study provides insightful recommendations for enhancing the efficiency of solar systems and emphasizes the critical need for selecting the appropriate MPPT algorithms for better energy harvesting.

Another study by 21 aims to enhance the performance of microgrid systems by creating a self-adapting energy management model that integrates optimal ANN. The proposed model is composed of a series of artificial neural networks that have been optimized individually through the application of PSO. The model aims to estimate and provide essential data to the energy management system to enhance the microgrid's integration of various energy sources. After being constructed in MATLAB/Simulink, the model is validated using experimental data.

A novel hybrid Fuzzy Particle Swarm Optimization (FPSO) technique, in conjunction with a photovoltaic-fed shunt active power filter, is proposed by 22 to increase power quality and produce clean electricity. MPPT is a function of the FPSO system, which tracks the MPP and extracts as much energy as possible from the PV system. Fuzzy logic and synchronous reference frame theory govern the photovoltaic-fed shunt active power filter, which connects the boost converter output to the grid. The results demonstrate that the recommended controller performs well in a variety of load circumstances, resulting in improved power quality and more environmentally friendly electricity distribution.

Techniques for MPPT are essential for optimizing PV systems' efficiency and performance. The rapid expansion of the PV sector has led to the suggestion of various MPPT approaches. One of these, the INC approach, locates the MPP with a high rate of convergence, although it suffers from significant ripple under continuous radiation. In contrast, the PSO method has a slower rate of convergence than the INC method but a lower ripple output power. A new approach that integrates and combines the INC and PSO methodologies is proposed by 23 in order to make use of the benefits of both approaches. This innovative method takes advantage of the INC method's rapid convergence in response to radiative changes and the PSO method's stability and excellent accuracy under conditions of continuous irradiation. The suggested strategy attempts to boost the MPPT performance for PV systems, increasing their general efficiency and stability, by utilizing these technologies in concert.

The numerous MPPT strategies used in solar systems are thoroughly examined in this literature review, which classifies them into conventional, intelligent, optimization, and hybrid methodologies. While offering simplicity and cost-effectiveness, traditional MPPT techniques like P&O and INC may have slow tracking and oscillations. Fuzzy logic and neural networks are intelligent ways that improve tracking speed and accuracy but may need a lot of processing power. High tracking efficiency is provided by optimization-based approaches like P&O with the Newton–Raphson method, although they can be computationally demanding. Hybrid methods, which combine components of many methodologies, offer a compromise between complexity and efficiency. Although hybrid methods are most effective, they are typically more complicated and expensive, making conventional and intelligent techniques appealing alternatives.

The availability of different methods presents issues for maintaining continuous power generation from solar PV systems and ensuring the usage of optimum MPPT controllers. As a result, a thorough comparison study is required. In line with current research trends in renewable energy, our study is to perform a thorough comparative analysis of these controllers by applying real environments and a variety of weather conditions 24 .

Conventional MPPT techniques

Conventional MPPT methods are fundamental approaches used in solar energy system optimization with the goal of improving PV system efficiency. Of these, the most often used are INC and P&O due to their ease of use and integration. These methods work by dynamically modifying the PV system's operating point in order to track the MPP in a range of environmental circumstances. While INC is recognized for its rapid convergence and adaptability to dynamic changes, P&O offers a straightforward approach, albeit with potential accuracy challenges in certain scenarios 25 . Practitioners must have a thorough understanding of these traditional MPPT approaches in order to design and implement PV systems with the best possible energy extraction.

Introduction to incremental conductance

INC MPPT is a widely used technique for MPPT in PV systems. It functions by comparing the INC (dI/dV) and instantaneous conductance (I/V) of the PV module 26 . The main idea behind this technique is to compute the power of the PV module in relation to voltage and ensure that the result is balanced to zero. This equilibrium condition is important because it shows that the system is operating at the MPP, where the output power is maximum. The INC MPPT algorithm effectively and dynamically tracks the MPP, maximizing the PV system's energy extraction under fluctuating environmental and load conditions by continuously monitoring and adjusting the operating voltage or current based on the balance between instantaneous and incremental conductance 27 . As a result of its capacity to raise the general effectiveness and performance of solar PV systems, the INC MPPT technology has become quite well-liked. The research flow diagram of the INC controller is given in Fig.  2 .

Shows the flow diagram of INC.

Working principle of INC MPPT controller

As a result of employing the differential of the operating point, or dp/dv, the INC algorithm exhibits a remarkable ability to follow the MPP in spite of continuously changing climatic conditions. This method is predicated on the fundamental notion that the power derivative with respect to voltage or current equals zero at the maximum power point 28 . As the weather changes, the INC algorithm dynamically adjusts the operating point in the direction of the MPP. It is crucial to emphasize that, on the left side of the MPP, power exhibits an increasing trend with voltage, while on the right side of the MPP, power exhibits a declining trend with voltage. The INC algorithm takes advantage of these unique tendencies to maximize system power extraction, ensuring improved energy efficiency and performance. Thus, the INC algorithm can be defined mathematically in Eq. ( 1 ) as follows:

Because it offers the following advantages, the INC MPPT controller is a popular choice for optimizing energy extraction from PV solar systems.

Advantages of INC MPPT controller

The MPP can be efficiently tracked by the INC MPPT controller across a range of weather conditions and load variations. Ensuring that the PV system is running at the MPP raises energy conversion efficiency. The INC algorithm can readily adapt to variations in temperature and solar irradiation because of its quick reaction time. The controller's reactivity allows it to maintain system operation at the MPP even in the face of fast environmental changes. The INC MPPT controller accurately tracks the MPP by comparing the instantaneous conductance (I/V) with the incremental conductance (dI/dV) 29 . The technology continually extracts the greatest amount of power from the PV array thanks to this accurate tracking.

Disadvantages of INC MPPT controller

Unfortunately, the INC MPPT controller has a number of limitations and disadvantages that need to be considered: Oscillations around the MPP may occasionally be seen on the INC MPPT controller, particularly when the PV system is running under dynamic or quickly changing conditions 30 . These oscillations have the potential to cause minor power losses and possible damage to power electronic components by forcing an unnecessary operating point transition. Partial shade situations might provide challenges for INC MPPT controllers since they rely on comparing incremental and instantaneous conductance to calculate the MPP. The controller may become confused by many MPPs brought on by partial shadow, which could cause it to pursue less-than-ideal locations or become stuck in local peaks. Because the INC method calculates the derivative of power with respect to voltage, errors in current and voltage measurements may occur. Noisy data can result in incorrect derivative computations, making it more difficult for the controller to properly follow the MPP. Temperature variations may affect the INC algorithm's performance, which could affect how accurately it tracks the MPP 31 . Calibration or temperature correction techniques can be required to counteract this effect.

Introduction to perturb and observe

In photovoltaic systems, one of the most used MPPT algorithms is the P&O algorithm. Its basic idea is to gradually alter the PV system's operating point while closely observing how the power output changes in response. The operating point is changed to improve power output after reaching the maximum power point 32 . Due to its simplicity and ease of implementation, the P&O algorithm is preferred. It is important to keep in mind, though, that it has a somewhat slow tracking speed and that it might not reach the global maximum power point. The flow diagram of the P&O controller is given in Fig.  3 .

Shows the flow diagram of P&O 33 .

Working principle of P&O

The first step of the method is to establish the PV system's initial operating point. Any point on the PV module's current–voltage (I–V) curve can serve as this. The algorithm modifies the voltage or duty cycle (if the system is based on a DC–DC converter) to slightly disrupt the operating point. Usually tiny in size, this perturbation is selected according to the desired pace of convergence and the properties of the system. The algorithm calculates the power output change upon perturbation 26 . At the initial functioning point, it contrasts the new and past power outputs. The program then decides what to do based on the power output change that was noticed. The program keeps perturbing in the same direction to keep going in the direction of the maximum power point if the power grows. In the event that power drops, the algorithm returns to the highest power point by reversing the perturbation direction. Until the algorithm determines the operating point where the power output is at its maximum, the perturbation and observation processes are repeated iteratively. The algorithm determines that the system has reached the maximum power point when it observes a change in power output that is very close to zero or below a predefined threshold 34 . After that, the P&O algorithm modifies the operating point to remain at the maximum power point and keeps an eye on the system all the time to ensure peak power extraction—especially in the event of changing load or weather.

It's mandatory to note that the simplicity of the P&O algorithm allows for easy implementation, but it also brings certain limitations, such as oscillations and sensitivity to partial shading or dynamic weather conditions.

Advantages of P&O

Due to its ease of implementation, the P&O method is a popular option for small-scale photovoltaic systems with constrained computing and hardware resources. The technique is economical for realistic MPPT implementations since it requires little extra hardware and only uses simple sensors to detect changes in power output 35 . P&O functions in real-time, continuously modifying the PV system's operating point to track the maximum power point in a variety of environmental circumstances.

Disadvantages of P&O

Around the maximum power point, P&O may behave oscillatory. This would force the system to continually alternate between higher and lower power points, which would result in less-than-ideal efficiency. P&O's incremental perturbation strategy may cause a delayed convergence to the maximum power point, particularly when the weather is changing quickly. Due to its reliance on tiny perturbations, the algorithm can be inaccurate or inefficient, particularly when the PV system is subjected to partial shadowing or unsuitable climatic conditions 36 . Sometimes, P&O will converge to a local maximum power point rather than the global maximum, which will reduce the total power output.

Introduction to incremental conductance—particle swarm optimization (INC-PSO) hybrid MPPT

In order to maximize the efficiency of PV systems, MPPT is a crucial approach that tracks the operating point that yields the maximum power output from the PV module under a variety of environmental conditions. PSO and INC are two popular MPPT algorithm techniques. Every approach has advantages and disadvantages of its own. While the PSO method is competent at locating the GMPP, it can occasionally be slow. The INC approach is quick and effective, but it can become stuck in local maxima. The INC and PSO algorithms are combined in a hybrid MPPT algorithm that has been developed to overcome the shortcomings of separate approaches. The hybrid MPPT algorithm INC-PSO seeks to capitalize on the advantages of both INC and PSO to achieve faster and more accurate tracking of the GMPP under dynamic irradiance conditions.

Working principle of INC-PSO hybrid MPPT

The INC-PSO hybrid MPPT algorithm combines the PSO algorithm's global search power with the INC algorithm's local search capabilities. The PSO method is utilized to refine the operating point and converge to the GMPP, whereas the INC algorithm does a quick initial search to find the area around the GMPP 37 .

The INC-PSO hybrid MPPT algorithm operates as follows:

Initialization Initialize the INC and PSO algorithms with appropriate parameters.

Measurement Measure the current (I) and voltage (V) of the PV module.

INC Algorithm Calculate the incremental conductance (dI/dV) using the measured I and V values.

PSO Algorithm Update the positions and velocities of the particles in the PSO swarm based on the current operating point and the global best position.

Operating Point Adjustment Adjust the duty cycle of the DC–DC converter based on the output of the INC algorithm and the position of the best particle in the PSO swarm.

Convergence Check Check if the convergence criteria are met. If not, repeat steps 2–5.

Advantages of INC-PSO hybrid MPPT

The INC-PSO hybrid MPPT algorithm offers several advantages over traditional MPPT methods 38 , including:

Faster tracking speed The INC algorithm provides fast initial searching, while the PSO algorithm ensures convergence to the GMPP.

Improved accuracy The combination of INC and PSO algorithms enhances the accuracy of GMPP tracking, especially under rapidly changing irradiance conditions.

Reduced sensitivity to noise The INC algorithm is less sensitive to noise compared to other MPPT methods.

Robustness to partial shading The INC-PSO hybrid MPPT algorithm is more robust to partial shading conditions compared to traditional methods.

Disadvantages of INC-PSO hybrid MPPT

The INC-PSO hybrid MPPT algorithm also has some disadvantages compared to traditional methods 39 :

Increased computational complexity The PSO algorithm introduces additional computational complexity compared to simpler MPPT methods.

Parameter tuning The performance of the INC-PSO hybrid MPPT algorithm depends on the tuning of parameters for both INC and PSO algorithms.

Potential instability Under certain conditions, the INC algorithm may cause oscillations around the MPP, which can affect the stability of the PV system.

Introduction of perturb and observe-particle swarm optimization (P&O-PSO) hybrid MPPT

MPPT algorithms are crucial for ensuring that PV systems operate at their maximum power output. The other innovative approach in this domain is the integration of the P&O algorithm with PSO, creating a P&O-PSO hybrid MPPT system.

Working principle of P&O-PSO

The P&O-PSO hybrid MPPT algorithm amalgamates the simplicity of the P&O algorithm with the global optimization capabilities of PSO. The Perturb and Observe component is responsible for perturbing the operating point of the PV system, observing the resulting power change, and determining the direction in which the power increases. This local optimization method is complemented by the global search capabilities of PSO, which adjusts the step size and perturbation direction dynamically based on the collective intelligence of particles in the swarm. The PSO component enhances the P&O algorithm by providing a more robust and adaptive mechanism for tracking the MPP under various operating conditions, including changes in solar irradiance and temperature.

Advantage of P&O-PSO

The integration of PSO with P&O improves the accuracy of the MPPT algorithm, ensuring a more precise and reliable tracking of the MPP under diverse environmental conditions. Additionally, PSO's ability to explore the solution space globally enhances the P&O algorithm's local optimization, making the hybrid approach suitable for complex and dynamic PV system operating conditions. The P&O-PSO hybrid is adept at adapting to changes in solar irradiance and temperature, providing a responsive solution for PV systems in fluctuating environments. Finally, the global search capabilities of PSO contribute to faster convergence towards the MPP, minimizing tracking time and maximizing the energy harvesting efficiency of the PV system 40 .

Disadvantages of P&O-PSO

The incorporation of PSO may introduce additional computational overhead, potentially impacting real-time performance, especially in resource-constrained applications. On the other hand, tuning the parameters of both P&O and PSO components is critical, and the performance of the hybrid system may be sensitive to the initial conditions and the selected parameter values. Also, implementing and fine-tuning a P&O-PSO hybrid MPPT algorithm may require a deep understanding of both P&O and PSO techniques, potentially posing challenges for practitioners 41 .

Artificial intelligence (AI) based MPPT techniques

AI-based controllers represent a cutting-edge paradigm in the optimization of solar energy systems, revolutionizing the field of MPPT. These controllers leverage advanced techniques such as ANN, FLC, PSO, and ANFIS. It introduces a sophisticated layer of intelligence to the MPPT process. By harnessing the power of AI, these controllers autonomously adapt to complex and dynamic environmental conditions, offering a higher degree of accuracy and efficiency in tracking the MPP of PV systems. The integration of AI-based controllers contributes to improved performance, adaptability, and robustness, positioning them as pivotal tools in the quest for enhanced solar energy harvesting. A nuanced comprehension of these AI-based approaches is indispensable for researchers and engineers seeking to propel the advancement of intelligent MPPT strategies in the realm of renewable energy systems.

Introduction to artificial neural network (ANN)

A neural network is a highly efficient, parallel-distributed processor capable of acquiring and utilizing experiential knowledge. Like the human brain, it learns through a process of acquiring synaptic weights, which represent the inter-neuron connection strengths and store the acquired knowledge. Neural networks are well-suited for handling large and complex systems with numerous interconnected parameters, as they prioritize important inputs and filter out less significant data. Among the various learning algorithms for neural networks, the most popular and powerful one is back-propagation and its variants. This error-correction learning rule involves two passes through the network's layers: a forward pass and a backward pass. Neural networks possess remarkable capabilities in processing data, resembling the functioning of the human brain. They excel in handling intricate systems by focusing on crucial inputs and can learn from experiences using advanced learning algorithms such as back-propagation. Artificial neural networks offer numerous advantages, encompassing robust functionality, rapid convergence, resilience to non-linear systems, and the capability for offline training. The flow diagram of the ANN controller is given in Fig.  4 .

Shows the flow diagram of ANN 42 .

Working principle of ANN

An ANN consists of artificial neurons that act as nodes in a weighted directed graph. The connections between the inputs and outputs of neurons are made by the directed edges with set weights. The ANN receives input signals from external sources in the form of image-representing patterns and vectors. The strength of the inter-neuronal connections throughout the entire ANN is then determined by multiplying these inputs by matching weights 42 . The computation unit handles the weighted inputs, computing their sum. Subsequently, the output is activated by making it non-zero and scaled to the system's desired response using a bias or other mechanisms. The bias input holds a weight of one, remaining consistent across all connections. The total number of weighted inputs may vary from zero to infinity. The activation function plays an important role in transmitting the sum of weighted inputs, in some cases, limiting the response to the desired range. The activation function constitutes a collection of transfer functions, collaboratively achieving the intended effect. Numerous types of activation functions exist, with linear and nonlinear sets being the most prevalent among them.

Supervised learning is a well-established learning paradigm employed in neural networks, wherein the network is guided to acquire knowledge by leveraging known target outputs. Throughout the supervised training phase, the optimization algorithm Stochastic Gradient Descent (SGD) is utilized to iteratively update the values of the hidden units (c) and weights (w), with the main goal being the minimization of the error function E. This error function measures the difference between the target outputs and the predicted outputs produced by the network, which motivates iterative parameter adjustments to boost the network's predictive power and performance. The iterative nature of SGD facilitates the network's convergence towards local minima on the error surface, hence enhancing the model's accuracy and generalization when presented with new input data 43 . The rule for center learning is given in Eq. ( 2 )

where the cost function, E = 12∑(kd − k)2, k d  shows the actual MPP voltage.

Learning technique of ANN

Advantages of ann.

ANNs offer a powerful and flexible tool for various tasks due to their numerous advantages. ANNs are well suited to handling issues with detailed patterns and interactions because they can model complex, non-linear relationships between inputs and outputs. ANNs can update their internal parameters (weights and biases) over time to increase performance by learning from data. ANNs' capacity to learn from examples enables them to generalize and produce precise forecasts on brand-new, untried data 44 . The parallelization of ANN computations allows them to process several inputs at once. When used in hardware implementations, this parallel processing capacity can result in significant speed increases for some tasks. ANNs can handle noisy or incomplete data, as they can learn to recognize relevant patterns despite uncertainties in the input. ANNs eliminate the need for laborious manual feature engineering by automatically learning to extract pertinent features from raw data. Large and complicated datasets can be handled by ANNs by scaling. Deep Learning architectures have demonstrated remarkable performance in handling vast amounts of data. ANNs can be updated and retrained to adapt to changing data distributions or new requirements, allowing them to remain relevant in dynamic scenarios. ANNs can be integrated with other algorithms or techniques to enhance their capabilities and achieve even more sophisticated outcomes.

Disadvantages of ANN

ANNs have many benefits, but they also have significant drawbacks and restrictions. ANNs are frequently regarded as "black boxes," which denotes their lack of interpretability. It can be difficult to comprehend how a network makes a certain choice or prediction, particularly in deep and complicated topologies. Overfitting is a condition in which an ANN performs incredibly well on training data but is unable to generalize to new, unknown data. When a network gets overly complicated or when there is not enough or noisy training data, overfitting may happen. It can be costly and time-consuming to train large-scale ANNs, especially deep neural networks, which require specialized hardware like GPUs or TPUs. ANNs heavily rely on large amounts of labeled training data. Acquiring and preparing such datasets can be laborious and costly, especially in domains with limited data availability. Selecting an appropriate architecture and tuning hyper-parameters can be challenging and often requires trial and error, making the design process time-consuming. Deep Learning models can have significant memory requirements, making them less suitable for deployment in resource-constrained environments.

Introduction to adaptive neuro-fuzzy inference system (ANFIS)

By fusing the benefits of ANN and FLC, the ANFIS controller creates a controller with outstanding capabilities. These controllers are especially well-suited for nonlinear systems like SPV modules because they exhibit rapid reactivity and excellent efficiency under a variety of weather conditions 45 . The benefit of the ANFIS controller is that it can reduce the complexity associated with conventional fuzzy controllers by automatically designing rules and membership functions during the learning and training process 46 . These issues are successfully handled by the ANFIS controller's neural network and fuzzy logic integration. The ANFIS controller effectively handles the variability in irradiance and temperature conditions by utilizing the adaptability and learning capacity of neural networks and the interpretability of fuzzy logic. As a result, it ensures that SPV modules respond quickly and work at their best regardless of the weather. A controller with improved accuracy, robustness, and efficiency is produced by this special fusion of neural networks and fuzzy logic, making it an appealing option for managing solar photovoltaic systems.

Working principle of ANFIS

Inputs for the ANFIS model include solar irradiation, surrounding temperature, PV array voltage, and PV array current. A flexible and optimized inference system is produced as a result of the vital role the ANN plays in assisting the tuning of the rule table and the membership functions. Through a learning process, the ANFIS inference system successfully optimizes nonlinear functions by efficiently aligning with a collection of fuzzy rule books. It is highly suited for regulating systems with built-in uncertainties and nonlinearity since this combination of ANN and fuzzy logic creates a robust and flexible control system that can handle complicated and interrelated data. The ANFIS controller effectively captures the system's dynamic character, enabling accurate and effective decision-making depending on the inputs 47 . Utilizing the benefits of both ANN and FLC, the ANFIS approach emerges as a comprehensive and sophisticated solution, improving performance and flexibility for a variety of real-world applications. The flow diagram of the ANFIS controller is given in Fig.  5 . The fuzzy rule sets for a two-input ()–one output () FIS can be given in Eq. ( 3 ): The 1st rule is that if is A 1 and y is B 1 , then,

Shows the flow diagram of ANFIS 46 .

Advantages of ANFIS

Combining the advantages of fuzzy logic and neural networks, ANFIS can manage complicated and unpredictable systems with ease, which makes it useful in a variety of applications like pattern recognition, decision-making, and control systems. As a result of its ability to adapt and learn from data, ANFIS is able to fine-tune its settings and improve its performance depending on the particular issue it is trying to solve. Through a learning/training process, ANFIS may build fuzzy rules and membership functions automatically, eliminating the need for human rule construction, which can be difficult and time-consuming. ANFIS can offer interpretability in the form of fuzzy rules and membership functions, in contrast to standard black-box machine learning models, enabling users to comprehend the decision-making process, and offering insights into the behavior of the system. ANFIS can handle complicated and non-linear systems because it is effective at modeling and approximating nonlinear interactions between inputs and outputs. When new information becomes available or the system's behavior changes, ANFIS can be updated and retrained, which enables it to continue to be useful in dynamic situations.

Disadvantages of ANFIS

It can be difficult to set up an ANFIS model, especially for people who are unfamiliar with both neural networks and fuzzy logic. It can take a while to define suitable fuzzy sets, membership functions, and rules, and domain knowledge is necessary. For ANFIS to effectively optimize its parameters, a large amount of training data is often needed. Large and representative datasets can be difficult to acquire and occasionally impractical. Particularly for big and complicated models, the ANFIS training procedure can be computationally taxing. Speeding up the training process might require the use of strong computing resources. The general behavior of the model can be difficult to understand even with the help of fuzzy rules and membership functions in ANFIS, especially in complicated structures with lots of layers and parameters. The initial values of ANFIS's parameter throughout the training process can have an impact on how well it performs. Finding appropriate initial settings may be essential for getting the best results. Similar to fuzzy logic-based systems, ANFIS may not be able to handle data uncertainty or probabilistic models.

Introduction to particle swarm optimization (PSO)

To effectively monitor the MPP of a PV system, it is advised to integrate a PSO method, which is also referred to as cooperative particles. This PSO technique aims to solve the nonlinear system optimization problem by utilizing a swarm of Np particles. The cooperative particles of the PSO algorithm work together to find and follow the MPP, guaranteeing the PV system's optimal performance 48 .

Working principle of PSO algorithm

This strategy is based on a series of five crucial steps :

Initialization The Np particle swarm is initially initialized by the PSO algorithm with random coordinates and velocities throughout the search space. Every particle is a potential answer to the optimization issue.

Evaluation This stage involves computing the objective function, which rates how well each particle's solution performs. The objective function of the PV system controller may be based on power output or efficiency.

Update Personal Best (PBest) Based on how well it performed, each particle updates its unique best-known position (PBest). So far, the particle has found the best solution represented by this PBest.

Update Global Best (GBest) The best-performing particle in the swarm, as determined by the objective function, is identified as the global best (GBest). This particle's position serves as the optimal solution found by the entire swarm.

Update Velocities and Positions The particles adjust their velocities and positions using information from both their PBest and GBest. This velocity update allows the swarm to explore the search space effectively and converge toward the MPP.

Until a predetermined stopping criterion is satisfied—such as reaching a maximum number of iterations or attaining a desirable level of optimization—these five processes are performed iteratively. By working together, the cooperative particles in the PSO algorithm track the PV system's MPP and guarantee its effective and efficient operation 49 . The flow diagram of the PSO controller is given in Fig.  6 .

Shows the flow diagram of PSO.

Advantages of PSO technique

The PSO method is a well-liked option for resolving optimization issues since it has a number of benefits. Comparatively speaking to other optimization techniques, PSO is reasonably simple to comprehend and apply. Its premise is social behavior-inspired, making it simple to understand and put into practice. PSO is appropriate for complex, multi-modal optimization problems with numerous optimal solutions dispersed throughout the search space because it may look for global optima. Particularly in the early iterations, PSO can swiftly converge to a nearly optimal solution. It is useful for issues with a vast search space because of this attribute. PSO performs effectively in real-world scenarios with uncertainties because of its robustness in handling noisy objective functions and restrictions. The objective function's gradient or Hessian is not necessary for PSO because it is a derivative-free optimization technique. This qualifies it for non-differentiable optimization.

Disadvantages of PSO technique

Although PSO offers many benefits, it also has several drawbacks and restrictions: Premature convergence is a problem in PSO, where the swarm becomes locked in local optima and ignores other interesting areas of the search space. For some difficult situations, this restriction might prevent the algorithm from locating the global optimum. PSO does not ensure convergence to the ideal solution, unlike certain other optimization methods. The features of the problem and the selection of algorithmic parameters can affect the convergence behaviors performance may be impacted by the selection of control parameters, including the swarm size, cognitive and social coefficients, and inertia weight. Finding the right parameter values can be time-consuming and difficult. For discrete or mixed-variable optimization issues, PSO may need to be modified or adjusted from its initial design for continuous optimization problems. PSO's handling of discrete variables can be challenging and can produce unsatisfactory results.

Introduction to fuzzy logic controller (FLC)

An artificial intelligence system called FLC has emerged that uses fuzzy logic principles to make decisions depending on input and output parameters. Due to its ability to accommodate many input variables and efficiently consider the dynamic nature of the system, FLC outperforms other MPPT approaches in terms of capabilities. Due to this quality, FLC is more adaptable and durable when it comes to maximizing power extraction from solar panels under various environmental circumstances. Fuzzy logic is used in the FLC-based MPPT algorithm to enable adaptive adaptation and response to change solar irradiance, temperature, and partial shade circumstances, which can be difficult for traditional MPPT methods to handle 50 .

Working principle of FLC

FLC typically comprises three fundamental stages: fuzzification, rule base, and de-fuzzification. The controller takes variations of error and errors as inputs and produces the duty ratio variation of the DC/DC Boost converter as the output. The inputs of the fuzzy controller are defined by Eq. ( 1 ) and Eq. ( 4 ):

The error in this case is denoted by "e(t)", which is the ratio of the power change (ΔP(t)) to the voltage change (ΔV(t)) between successive time steps (t and t − 1). With the help of this formulation, the fuzzy logic controller can efficiently ascertain the proper duty ratio modification for the DC/DC Boost converter, guaranteeing optimal power point tracking, and analyze and interpret fluctuations in the system's performance. Our thesis delves into an in-depth examination of the FLC-based MPPT control, exploring its usefulness, benefits, and possibilities for enhancing the effectiveness and flexibility of solar energy systems 51 .

Fuzzy rules

The fuzzy rule base is a collection of pre-established rules that aid in figuring out the DC/DC Boost converter's duty ratio depending on error variations and rate of change. The rule base, which is made up of 25 fuzzy control rules, is arranged in Table  1 . Each rule is a mix of language variables that determine the output (duty ratio) and the inputs (error and its rate of change).

The five different fuzzy levels used for the inputs and output variables are: NB (Negative Big), NS (Negative Small), ZE (Zero), PS (Positive Small) and PB (Positive Big).

Considering the first three rules in the table:

IF error is NB (Negative Big) AND error rate of change is NB (Negative Big), THEN the duty ratio is PB (Positive Big).

IF the error is NB (Negative Big) AND the error rate of change is NS (Negative Small), THEN the duty ratio is PB (Positive Big).

IF error is NB (Negative Big) AND error rate of change is ZE (Zero), THEN the duty ratio is PB (Positive Big).

These rules indicate that when the error is significantly negative (NB) and its rate of change is also significantly negative (NB), the duty ratio of the DC/DC Boost converter should be increased significantly positive (PB). Similarly, in the other two rules, when the error is negative (NB) and its rate of change is either negative small (NS) or zero (ZE), the duty ratio should be adjusted to positive big (PB).

The remaining fuzzy rules in the table follow a similar pattern, representing different combinations of input linguistic variables to determine the appropriate output (duty ratio) to optimize the power extraction from the photovoltaic system. Fuzzy logic allows for precise and flexible control decisions, accommodating various environmental conditions and ensuring effective MPPT operation 49 . The linguistic variables help in representing the system's behavior in human-readable terms, facilitating the interpretation of the control rules, and enhancing the FLC's adaptability and performance 52 .

Advantages of fuzzy logic controller

Due to its intrinsic resilience, FLC can withstand fluctuations and disruptions in the working circumstances of the PV system, including shifts in temperature, partial shade, and solar irradiation. By integrating several input variables, FLC allows the controller to consider a greater range of parameters and factors affecting the PV system's operation. PV systems typically exhibit nonlinear behavior due to variations in temperature and irradiation. FLC can successfully manage this non-linearity, enabling precise and effective MPPT tracking in practical settings. The FLC controller employs a rule-based methodology that can include expert knowledge or rules that are data-driven and based on system behavior 53 . This adaptability allows the controller to gather information particular to a certain domain and boost overall performance. Adjust the duty ratio of the DC/DC Boost converter continuously to comply with the fuzzy control rules. By ensuring that the PV system is running at or near its maximum power point, FLC increases energy efficiency and power production. FLC operates in real-time, making it feasible to respond swiftly to changing environmental conditions. For efficient MPPT tracking, this real-time capacity is crucial when weather or load conditions suddenly change.

Disadvantages of FLC

Constructing the fuzzy rule base can be challenging and time-consuming, especially for complex systems with multiple inputs and outputs. Expert knowledge or data-driven approaches are required to develop the rules, which might involve trial-and-error iterations for optimal performance. As the number of input variables and fuzzy sets increases, the rule base can become quite large and complex. Managing and maintaining a large rule base may become cumbersome, affecting the controller's computational efficiency.

Introduction to ANN-PSO hybrid MPPT

ANN-based MPPTs are capable of learning and adapting to complex patterns, but they can be slow and require significant training data. PSO is efficient and can find global optima, but it can be sensitive to parameter settings. A hybrid MPPT algorithm that blends ANN and PSO has been developed to overcome the shortcomings of individual approaches. The goal of the ANN-PSO hybrid MPPT method is to monitor the MPP under dynamic irradiance conditions more quickly and accurately by utilizing the advantages of both ANN and PSO 54

Working principle of ANN-PSO hybrid MPPT

The hybrid MPPT algorithm known as ANN-PSO combines the ANN's capacity for pattern recognition with the PSO algorithm's capacity for global optimization. The relationship between the power output and operating point of a PV module is taught to the ANN using a huge dataset of PV module I-V curves. Next, the operational point is adjusted using the PSO algorithm, which converges to the MPP 55 , 56 , 57 .

The ANN-PSO hybrid MPPT algorithm operates as follows:

Initialization Initialize the ANN and PSO algorithms with appropriate parameters.

ANN Processing Feed the measured I and V values into the ANN to obtain an estimated MPP voltage (V_MPP).

PSO Algorithm Update the positions and velocities of the particles in the PSO swarm based on the current operating point, the estimated V_MPP, and the global best position.

Operating Point Adjustment Adjust the duty cycle of the DC-DC converter based on the output of the PSO algorithm.

Advantages of ANN-PSO hybrid MPPT

Comparing the ANN-PSO hybrid MPPT algorithm to conventional MPPT techniques reveals a number of benefits. Based on the patterns it has learned, it offers quick initial searching, and the PSO guarantees convergence to the MPP. Furthermore, the accuracy of MPP tracking is improved by the combination of ANN and PSO algorithms, particularly in situations where irradiance is changing quickly. ANN sensitivity to noise is lower than that of other MPPT techniques. Additionally, compared to conventional techniques, the ANN-PSO hybrid MPPT algorithm is more resilient to partial shading circumstances.

Disadvantages of ANN-PSO hybrid MPPT

Comparing the ANN-PSO hybrid MPPT algorithm to other techniques reveals a few more drawbacks. In comparison to more straightforward MPPT techniques, the PSO algorithm adds computing complexity, and the ANN-PSO hybrid MPPT methodology's performance is contingent upon the caliber of the training data that was utilized to educate the ANN. The ANN may not adapt well to real-world situations if it is overfitted to the training set 58 .

Introduction of FLC-PSO hybrid MPPT

Algorithms for MPPT are essential for maximizing these systems' power output. FLC and PSO are used to create a hybrid MPPT system, which is another creative approach to hybrid MPPTs.

Working principle FLC-PSO

The FLC-PSO hybrid MPPT algorithm effectively tracks a PV system's MPP by fusing the adaptive properties of fuzzy logic with the global optimization powers of PSO. A rule-based decision-making mechanism offered by fuzzy logic enables the algorithm to adjust to shifting environmental circumstances. PSO quickly converges towards the MPP by optimizing the fuzzy control parameters 59 . The method continuously adapts the PV system's operating point to dynamic variations in solar irradiance and temperature by iteratively adjusting it based on feedback from the fuzzy controller and PSO's global search capabilities.

Advantages of FLC-PSO

Because fuzzy-PSO hybrid MPPT is so good at adjusting to changing environmental factors, it can be used in places where temperatures and sun radiation fluctuate. Furthermore, the PSO component makes it possible for the algorithm to globally explore the solution space, guaranteeing that the MPP is correctly discovered even under challenging and dynamic operating circumstances. As a result, the algorithm continuously runs close to the MPP by constantly modifying the control parameters, which increases energy extraction and boosts system efficiency as a whole 60 , 61 , 62 . Finally, by integrating the benefits of PSO and fuzzy logic, the hybrid approach mitigates the drawbacks of individual algorithms and strengthens the robustness of the MPPT system.

Disadvantages of FLC-PSO

The algorithm's hybrid design may result in higher computational complexity, particularly in situations where real-time performance is essential. Furthermore, it can be difficult to fine-tune the settings of FLC and PSO components, necessitating a deep comprehension of the dynamics and performance traits of the system. The algorithm's performance might be affected by the starting parameters and conditions, which could necessitate recalibration in reaction to adjustments made to system elements or external circumstances.

MPPT controllers play a crucial role in optimizing the efficiency of solar photovoltaic systems. Here are the advantages and disadvantages of conventional and artificial MPPT controllers:

Advantages of conventional controllers:

Simplicity Traditional MPPT methods are straightforward and easy to implement.

Efficiency They can effectively track a single maximum power point (MPP) under uniform illumination.

Disadvantages of Conventional Controllers:

Limitations in Partial Shading Traditional methods struggle to distinguish between local and global peaks in partial shading scenarios, limiting their efficiency.

Complexity in Variable Conditions They may not perform optimally in variable weather conditions.

Advantages of AI based controllers:

Enhanced Tracking Performance Advanced MPPT controllers, such as fuzzy logic-based controllers, offer superior tracking performance.

Efficiency They can outperform standard methods in terms of efficiency and performance.

Disadvantages of AL based controllers:

Increased Complexity Advanced controllers are more sophisticated, requiring a higher level of technical expertise for installation and maintenance.

Cost They may come at a higher cost due to their complexity and advanced features.

Advanced MPPT controllers, utilizing soft computing, bio-inspired, or artificial intelligence techniques, offer improved efficiency and performance but require more expertise and investment.

Conventional MPPT controllers are known for their simplicity and ease of implementation, they may struggle in scenarios like partial shading. On the other hand, advanced MPPT controllers provide enhanced tracking performance but come with increased complexity and cost implications. The choice between conventional and artificial MPPT controllers depends on the specific requirements and conditions of the solar photovoltaic system.

Artificial MPPT controllers differ from conventional ones in several key aspects based on the provided sources:

Classification:

Conventional MPPT Controllers Conventional MPPT controllers are categorized as traditional methods that are relatively simple and commonly used in solar systems

Artificial MPPT Controllers Artificial MPPT controllers are classified as advanced techniques that leverage AI or hybrid-based methods for enhanced performance and adaptability

Technology:

Conventional MPPT Controllers Conventional controllers rely on basic algorithms like P&O and INC for tracking the MPP of solar panels

Artificial MPPT Controllers Artificial controllers utilize advanced technologies such as ANN, ANFIS, or fuzzy logic for more precise and efficient tracking, especially in non-uniform weather conditions and partial shading scenarios

Performance:

Conventional MPPT Controllers Traditional methods may have limitations in dynamic weather conditions and partial shading scenarios, affecting their efficiency and adaptability

Artificial MPPT Controllers Artificial controllers offer superior tracking performance, robustness, and adaptability to varying conditions, making them more efficient and effective in optimizing solar system performance

Adaptability:

Conventional MPPT Controllers Conventional controllers are generally simpler and may struggle in scenarios like partial shading or rapidly changing weather conditions

Artificial MPPT Controllers Artificial controllers excel in adapting to non-uniform weather conditions, making them more suitable for maximizing power generation in challenging environments

In essence, artificial MPPT controllers stand out from conventional ones due to their utilization of advanced technologies like AI, which enable them to offer superior performance, adaptability to various conditions, and robustness in optimizing solar system efficiency.

The comprehensive analysis of conventional and artificial intelligence-based controllers provides valuable insights into the nuanced trade-offs between performance and cost across various MPPT algorithms, aiding in informed decision-making for solar power systems. Further analysis of all controllers is given in Table  2 .

MPPT techniques play a pivotal role in harnessing complete solar energy. As we conclude our exploration into the realm of solar energy systems, it becomes evident that the effective implementation of MPPT strategies is paramount in unlocking the true power and promise of solar energy on a worldwide scale. The presented research aimed to conduct a comprehensive analysis of both individual and hybrid MPPT techniques for efficient solar power generation. The primary focus is on evaluating the efficacy of PV systems in tracking the Maximum Power, aiming to determine the optimal approach for maximizing power production. The study explores various MPPT algorithms, including PSO, FLC, ANN, INC, P&O, and hybrid techniques such as ANFIS. Additionally, combinations of PSO with INC, P&O, Fuzzy, and ANN are examined to provide a comprehensive understanding of their performance in enhancing solar energy system efficiency. The comparison encompasses key parameters such as cost, complexity, response time, stability, partial shading, and accuracy. The findings reveal that among conventional MPPT controllers, FPSO demonstrates superior performance, although at a higher cost. INC-PSO and P&O with PSO follow closely, exhibiting commendable efficiency. INC and P&O present a moderate performance. Furthermore, ANN and ANFIS excel among intelligent MPPT algorithms despite their higher cost. FLC emerges as a strong contender, offering optimal performance at an affordable price despite having a medium response time. Meanwhile, ANN-PSO and PSO deliver moderate performance and affordability.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

Photovoltaic

Maximum power point tracking

Artificial intelligence

Direct current

Alternating current

Maximum power point

Current–voltage

Artificial neural networks

Fuzzy logic

Environmental conditions

Voltage source region

Current source region

Adaptive neuro-fuzzy inference system

Incremental conductance

Global maximum power point

Particle swarm optimization

Power–voltage

Perturbation and observation

Fuzzy particle swarm optimization

Incremental conductance-particle swarm optimization

Perturb and observe-particle swarm optimization

Stochastic gradient descent

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The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number ‘‘NBU-FFR-2024-2448-10.’’

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Ezzeddine Touti

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Sarang, S.A., Raza, M.A., Panhwar, M. et al. Maximizing solar power generation through conventional and digital MPPT techniques: a comparative analysis. Sci Rep 14 , 8944 (2024). https://doi.org/10.1038/s41598-024-59776-z

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Recent advances and future prospects in energy harvesting technologies

Hiroyuki Akinaga 2,1

Published 23 October 2020 • © 2020 The Author(s). Published on behalf of The Japan Society of Applied Physics by IOP Publishing Ltd Japanese Journal of Applied Physics , Volume 59 , Number 11 Citation Hiroyuki Akinaga 2020 Jpn. J. Appl. Phys. 59 110201 DOI 10.35848/1347-4065/abbfa0

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2 This review includes the English translation of tutorial review paper published in the journal of the Japan Society of Applied Physics, Oyo Buturi, Vol. 89, No. 6, pp. 321-327 (2020) [in Japanese].

Hiroyuki Akinaga https://orcid.org/0000-0002-5521-3148

• Received 29 August 2020
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Energy harvesting technology is attracting attention as "enabling technology" that expands the use and opportunities of IoT utilization, enriches lives and enhances social resilience. This technology harvests energy that dissipates around us, in the form of electromagnetic waves, heat, vibration, etc. and converts it into easy-to-use electric energy. This paper describes the features of these technologies, recent topics and major challenges, and boldly predicts the future prospects of the development.

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1. Introduction

In the environment around us, we can "harvest" tiny amounts of dissipating energy and use it as available electric energy. This technology is known as energy harvesting. It is also attracting attention as a technology for achieving Goal 7 ("Ensure access to affordable, reliable, sustainable and modern energy for all") of the Sustainable Development Goals (SDGs) and strengthening the resilience of our society. 1 – 3 ) With the increasing sophistication of our society using IoT technology, we will inevitably enter the Trillion Sensor Universe, where networks consisting of one trillion sensors per year are envisioned. An initial prediction indicates that such an era will arrive within a few years. 4 ) In practice, it is difficult to connect each sensor to a power source individually, and therefore batteries have been continuously used for convenience despite their disadvantage in terms of power cost. However, it is actually impossible to keep replacing the batteries connected to sensors many times on the scale of the Trillion Sensor Universe. Thus, the social implementation of energy harvesting technology is becoming indispensable for sensing the environment or our own bodies.

To the best of our knowledge, the term "harvesting" has been used for photovoltaics in the visible light range since around the late 1980s. 5 ) In the 2000s, various energy harvesting technologies were reported. 6 , 7 ) Although the term "scavenging" was also used initially, it has recently fallen out of use, probably because its meaning is inappropriate. Figure 1 shows the overall scheme of energy harvesting technology for targets in the environment, such as electromagnetic waves, heat and vibrations. Here, I have classified energy harvesting technologies into four processes: (1) harvesting tiny amounts of energy in the environment, (2) converting the harvested energy into electric energy, (3) processing the energy in power conversion circuits and (4) utilizing the power for sensing, information processing and communication. In this article, I refer to all of these processes as energy harvesting technology. In the design of actual devices, each process should be designed in the direction opposite to the arrows in the figure. Among the energy harvesting technologies, solar cells are a well-known technology for yielding high output and have already been put into practice. However, energy harvesters are required to yield stable output from not only sunlight, but indoor light, and have been studied and developed by many researchers. 8 – 10 ) In general, the illuminance of indoor light is low and its spectrum is centered on the visible light range. Moreover, there are many types of solar cells, such as organic thin-film solar cells, 11 , 12 ) dye-sensitized solar cells 13 ) and perovskite solar cells. 14 – 16 ) The standardization of the method of evaluating the energy harvesting characteristics of each type of solar cell is essential for the promotion of research and development and the social implementation of the technologies. 17 )

Fig. 1.  (Color online) Survey of energy harvesting technologies. Technologies discussed in this paper are shown within the dashed line.

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Energy harvesting technology using solar cells has been reported in many scientific papers and explained in excellent review articles by various researchers. In this article, I focus on three technologies for vibrational, radio waves and thermoelectric energy harvesting. The specifications commonly required for energy harvesters that will be applied to IoT devices are small in size with a high output. In addition, environmental durability and operational reliability are required depending on their usage environment, implementation form and cost. Here, I will point out the academic and technical issues specific to each type of technology and introduce some recent topics. As shown in Fig. 1 , I assume that energy harvesting technology will be applied to the IoT field, for example, the processing and communication of sensor information, and exclude large environmental energy harvesters as well as geothermal, wave and wind power generation. Energy harvesting technologies for these energy sources and the harvesting of renewable energy were comprehensively reviewed in previous review articles, 18 , 19 ) which are recommended to the reader.

2. Vibrational energy harvesting

2.1. features of vibrational energy harvesting.

There are three methods of obtaining electric power from vibration as a kinetic energy source: electromagnetic, electrostatic and piezoelectric methods. 20 ) The electromagnetic method uses electromagnetic induction and inverse magnetostrictive effects. In the inverse magnetostrictive method, the magnetization state of a magnetostrictive material is controlled by applying a bias magnetic field using permanent magnets and then a strain is applied to the material to generate a change in magnetic flux, which is converted into electric power using a coil. 21 ) The electrostatic method includes electret-type vibrational energy harvesting using MEMS and triboelectric energy harvesting. 22 ) A charged electrode of a capacitor is vibrated to change the electrostatic capacitance and generate power. The piezoelectric effect refers to dielectric polarization, namely, surface charges appear, when a mechanical stress or strain is applied to a dielectric. Piezoelectric energy harvesters collect the electric energy generated when vibration is applied to these piezoelectric materials. 23 ) For these vibrational energy harvesters, design from the viewpoint of the type of vibration to be used for energy harvesting is important in addition to the specifications commonly required for energy harvesters. 24 ) In general, the frequency of vibrations in the environment is 200 Hz or lower. In the case of using vibration from infrastructure such as bridges and the human body, the frequency is only about 2–3 Hz. The acceleration of such vibration rarely reaches 10 m s −2 . The frequency and acceleration change at random in an actual environment. As is clear from the above-mentioned principles, the electromagnetic, electrostatic and piezoelectric methods have characteristic features in their output power, impedance and frequency response, and their research and development is carried out to use these features. For vibration in an actual environment, design to maximize the converted power by avoiding resonant conditions may be possible. Therefore, the features in the design of vibrational energy harvesters are impedance matching and maximization of energy conversion. For example, researchers are developing a vibrational energy harvester that efficiently harvests power from faint environmental vibrations with an acceleration of ∼0.1 g (1 g = 9.8 m s −2 ) and a frequency of 100 Hz or lower using MEMS technology. This device generates current through electrostatic induction when electrets on one of a pair of opposing comb electrodes with a gap of a few micrometers are charged and the electrodes are vibrated (Fig. 2 ). Advanced MEMS technology is required because the effective area increases due to the structure of comb electrodes. In another study, a voltage-boost rectifier was fabricated using CMOS integrated circuits and achieved a DC output of 3.3 V. The low threshold of the rectifier circuit enabled a roughly tenfold expansion of the frequency band for the energy harvesting of MEMS vibrational energy harvesters compared with the cases using p-n diode rectifier circuits. 25 )

Fig. 2.  (Color online) (a) Conceptual diagram of button battery type MEMS vibrational energy harvester. (b) MEMS vibrational energy harvester consisting of movable electrodes covered with electret. Electret is a material that has a permanent electrical charge.

2.2. Topics on research and development of materials and processes

For vibrational energy harvesting by electromagnetic and piezoelectric methods, research and development of the energy conversion materials shown in Fig. 1 has been intensively carried out. The typical magnetostrictive materials used in vibrational energy harvesting by the electromagnetic method based on inverse magnetostriction are the rare-earth–iron alloy Terfenol-D and the Fe–Ga alloy Galfenol. 26 ) On the other hand, materials with a higher cost competitiveness than those used in other vibrational energy harvesting methods have also been developed using elements with high abundance in the Earth's crust. For example, a vibrational energy harvester using FeCo alloys was reported. 27 ) As piezoelectric materials, perovskite-type composite oxides such as Pb(Zr,Ti)O 3 (PZT) are known to have excellent energy conversion characteristics. Various film deposition methods for composite oxides have been developed to increase their area and decrease the cost. 28 ) To further improve the performance of PZT, attempts to realize multilayered structures 29 ) and nanostructures 30 , 31 ) have been reported. More concretely, it was experimentally demonstrated that the polarization characteristics of PZT can be greatly changed by coating PZT nanorods with a metal. This suggests that the performance of ferroelectrics can be improved by downsizing materials to the nanometer order and controlling the charge shielding effect rather than by employing conventional approaches such as controlling the material composition and strain. 32 ) However, the use of lead is restricted by the Restriction of Hazardous Substances (RoHS) Directive because of its toxicity to the human body and the environment. 33 ) Research and development of lead-free piezoelectric materials has also been ongoing. 34 – 36 ) Moreover, piezoelectric vibrational energy harvesters with a flexible 3D structure fabricated by a microfabrication process are expected to cover low frequencies and achieve a large strain, and are attracting attention for use in wearable devices. 37 , 38 ) Ionic liquids, 39 ) fluorine-containing polymers, 40 , 41 ) parylene C 42 ) and hydroxyapatite 43 ) have been intensively developed as materials for use in electrostatic electret-type energy harvesters.

2.3. Major challenges and international standardization

As mentioned above, energy harvesters are required to have high environmental durability and operational reliability. However, in the case of piezoelectric energy harvesters, for example, the material properties may change during the manufacturing process, even if the piezoelectric effect is caused by intrinsic physical properties such as the crystal structure of the material. When a strain is repeatedly applied to a material, macroscopic cracks or grain boundary segregation may occur, resulting in a reduction in the amount of power generated. 44 ) Clarifying the mechanism behind the deterioration of materials that occurs during the conversion of kinetic energy into electric energy and taking countermeasures are challenges for vibrational energy harvesting technology.

The evaluation of material properties with high reproducibility is indispensable towards the practical application and commercialization of any devices including energy harvesters. As mentioned above, there are various combinations of methods and energy conversion materials for vibrational energy harvesters. Hence, we need to select the combination that provides the intended properties for applications, and common standards should be prepared. International standardization for vibrational energy harvesters is being carried out by Technical Committee (TC) 47 of the International Electrotechnical Commission (IEC). 45 , 46 ) Standardization not only provides the industry with a sound competitive environment, but promotes the dissemination of achievements in research and development. In particular, when new test methods for material properties are developed, their techniques should be actively standardized to propagate the use of vibrational energy harvesting technology.

3. Radio wave energy harvesting [Radio frequency (RF) energy harvesting] 47 – 49 )

Radio waves are a type of electromagnetic wave and are defined as electromagnetic waves with a frequency of ≤3 million MHz (3 THz) in the Radio Law. IEC similarly defines a radio wave as "an electromagnetic wave propagated in space without artificial guide and having by convention a frequency lower than 3000 GHz". It is clear from the fact that we can search using our smartphones at any time, even on an airplane or a train, that radio waves can be harvested everywhere. In most cases, the harvested radio waves are electromagnetic waves originally generated electrically, and those in a certain frequency band are selectively transmitted for specific purposes. Therefore, radio wave energy harvesting includes rectification. Moreover, wireless power supply systems that are commercially available for various applications transmit radio waves with energy, which is similar to radio wave energy harvesting. Radio wave energy harvesters should efficiently perform the processing explained in Fig. 1 with a compact antenna because they need to be small, similar to other energy harvesters, and the electric energy of the radio waves to be harvested is extremely small. Although the vibrational energy harvesters introduced in the previous section require a similar circuit design, radio wave energy harvesters require the solution of other problems because of the high frequency of radio waves. For example, to design a boosting circuit for energy storage, the problem of reduced efficiency of power conversion, or power loss, in circuits must be solved. Towards the 5 G era, energy harvesting from high-frequency bands is advantageous for reducing the size of antennas, but the circuit design must be optimized to satisfy the conflicting demands for each application.

Rectennas are antennas integrated with rectifier circuits and can convert harvested radio waves into DC power. A wide range of research and development of rectennas for radio wave energy harvesting has been conducted from device technology to rectenna evaluation. 50 – 52 ) For example, a high-sensitivity backward diode consisting of III–V semiconductor nanowires was developed as a rectifier that replaces the preceding GaAs Schottky barrier diodes and is expected to efficiently convert even sub- μ W-class weak radio wave energies into electric power (Fig. 3 ). 53 , 54 ) In addition, the rectification characteristics of diodes consisting of silicon-on-insulator (SOI) FETs with steep current characteristics, called p-n junction body-tie SOI-FETs, have been markedly improved, and they are expected to be applied to radio wave energy harvesting. 55 – 57 ) Moreover, flexible diodes made of molybdenum(IV) sulfide (MoS 2 ), a 2D semiconductor, were developed and proved to be promising for rectennas in the 2.4 GHz frequency band. 58 ) There is still much room for new materials and structures to bring about breakthroughs in terms of not only the development of diodes, but the advancement of small high-efficiency antennas. Future research and development is expected to lead to major breakthroughs in radio wave energy harvesting.

Fig. 3.  (Color online) (a) Conceptual diagram of microwave power harvesting. (b) Newly developed antenna and rectifier consisting of a backward diode.

Fig. 4.  (Color online) Schematic of p-type module for organic thermoelectric energy harvesting. PEDOT:PSS and TTF-TCNQ are used as the p-type and n-type thermoelectric materials, respectively, and these are alternately connected in series to form the p-type module. Voltage exceeding 250 mV is generated by providing a temperature difference between the upper and lower surfaces. N. Satoh, M. Otsuka, T. Ohki, A. Ohi, Y. Sakurai, Y. Yamashita and T. Mori: "Organic p-type thermoelectric module supported by photolithographic mold: a working hypothesis of sticky thermoelectric materials", Science and Technology of Advanced Materials, 19:1, 517-525, (2018), DOI: 10.1080/14686996.2018.1487239 Ref. 100 .

4. Thermoelectric energy harvesting

4.1. features of thermoelectric energy harvesting.

Various surveys have shown that we waste at least 70% of our primary energy, which dissipates as waste heat. 59 ) A survey reported that the temperature of the dissipating heat is mostly below 100 °C. The minute energy of such heat can be harvested and converted into electric energy by thermoelectric energy harvesting, which is attracting attention as a technology for providing an independent power supply for various IoT devices. 60 , 61 ) When a temperature difference ΔT is given to the two ends of a conductor, a voltage ΔV proportional to ΔT is generated. This effect is called the Seebeck effect, and the proportionality coefficient S (=ΔV/ΔT) is called the Seebeck coefficient. Thermoelectric materials are frequently evaluated by the dimensionless figure of merit ZT = (S 2 σ /κ)T, where σ is the electrical conductivity and κ is the thermal conductivity. Here, S 2 σ in the numerator is called the power factor (PF) and indicates how much power W can be obtained with a temperature difference of 1 °C per unit length. 62 ) The thermoelectric conversion efficiency increases with ZT, and it is required to exceed 1 for practical purposes. As shown in Fig. 1 , the properties of energy conversion materials should in general be metallic in order to decrease the contact resistance of the electrodes, and furthermore in the case of thermoelectrics to be favorable for ZT and lower the resistance of power generation from harvested heat. However, ordinary metals cannot achieve a high thermoelectric conversion efficiency in general because the number of free electrons does not greatly change with the temperature and the Wiedemann–Franz law, stating that σ is proportional to κ, holds. Moreover, S, σ and κ in the equation for ZT are functions of carrier concentration and are difficult to control independently. Thus, various breakthroughs are needed to improve the thermoelectric properties of materials.

4.2. Topics on development of materials 63 )

As a means of solving a bottleneck that prevents the increase in ZT, the selective enhancement of phonon scattering by microstructures has been attempted. 64 – 67 ) This involves reducing only κ ph on the basis of the assumption that the thermal conductivity κ is the sum of the contributions of electrons and phonons to κ, that is, κ e and κ ph , respectively. This attempt is also known as a material design guideline called "phonon glass electron crystal". 68 , 69 ) In addition, researchers have attempted to improve the effective thermoelectric properties of materials by controlling their specific crystal structures 70 , 71 ) and utilizing microfabrication technology, which means the use of nanostructures in a broad sense. 72 , 73 ) Moreover, the introduction of magnetic ions is one of the most promising methods. 74 , 75 ) For example, the increase in the effective mass of carriers has been attempted through the formation of polarons by spin–orbit interaction. The use of 2D materials 76 – 80 ) and band engineering 81 , 82 ) have also been studied intensively with the aim of enhancing the PF. Recently, significant progress has been made in improving ZT by band engineering. A report showed that the ZT determined from the thermal properties of thin-film Heusler alloys based on Fe 2 V 0.8 W 0.2 Al, which was measured by multiple laboratories while confirming reproducibility, has reached 5 in the range of 350–400 K. 83 ) This was observed in a thin-film material with a metastable crystal structure in its bulk state. It is highly expected that completely new thermoelectric materials can be developed by controlling the crystal phase in thermal non-equilibrium conditions. 84 )

Flexible organic materials are attracting attention as thermoelectric materials for wearable applications. 85 – 87 ) Even if the thermoelectric figure of merit of organic materials is inferior to that of inorganic materials, organic materials can be practically used by increasing their area to obtain the power needed. Carbon nanotube thin films have been studied and developed as thermoelectric materials because stable n-type doping can be achieved. High PFs of carbon nanotube thin films at RT have been reported. 88 – 90 ) Silicon and its compounds are drawing attention as thermoelectric materials developed from human- and environmentally friendly elements. 91 – 93 ) A p-type SiGe layer deposited on a flexible substrate in a low-temperature process has achieved a high PF of 280 μ W m −1  K −2 . 94 ) Conductive oxides have not been focused on as thermoelectric materials because of their low carrier mobility and high thermal conductivity. In 1997, however, excellent thermoelectric properties were discovered in the layered cobalt oxide Na 2 CoO 4 95 ) and then in (Ca 2 CoO 3 ) x CoO 2 , 96 ) SrTiO 3 , and so forth. 97 , 98 ) As a result, conductive oxides have attracted much attention as materials for energy harvesters, which are required to have low environmental impact and high environmental durability. Not only the crystal structure, but the electron and phonon properties of oxides can be easily controlled by element substitution. Recent trends of research on conductive oxides include (1) the improvement of the Seebeck coefficient by the control of orbital degeneracy, (2) the suppression of the phonon contribution to thermal conductivity using the fluctuation of the orbital degree of freedom and (3) the suppression of thermal conductivity with the use of interfaces (Fig. 4 ). 99 , 100 )

There have been various advances in techniques for generating, detecting and controlling spin current in the field of spintronics. A new research field that emerged from this field is spin caloritronics, which studies the control of spin current with heat. This was triggered by the discovery of the spin Seebeck effect in 2008, in which spin current is generated by a temperature difference. 101 ) Subsequently, an electromotive force (spin voltage) was observed in a magnetic insulator, contributing to significant academic progress. 102 ) Moreover, research on spin caloritronics has been carried out for the development of flexible applications and the further improvement of their performance. 103 – 108 ) In the Seebeck effect, an electromotive force is generated in the same direction as the temperature difference. In contrast, the temperature difference, magnetization and the electromotive force (Nernst voltage) are all perpendicular to each other in the Nernst effect. When the Nernst effect is used, it should be possible to obtain effectively large power by forming a module itself into a sheet. The Nernst effect in magnetic materials is called the anomalous Nernst effect, which has been observed in various materials. 109 – 112 ) For example, the giant anomalous Nernst effect observed in Co 2 MnGa is related to the topology of the electronic structure called the Weyl point and has been explained as a quantum critical phenomenon. 113 ) Graphene, a 2D material, is well known as a typical material that has a Dirac cone in the energy-band structure and exhibits topological properties. Considering the fact that two-dimensionalization contributes to improving the thermoelectric properties of ordinary thermoelectric materials, topology is expected to play a major role in the design of spin caloritronics materials. In practice, an anomalous Nernst effect due to the topology of the electronic structure was observed in the antiferromagnetic manganese alloy Mn 3 Sn, enabling the harvesting of energy using spin current at RT in zero magnetic field. 114 )

4.3. Major challenges and thermophysical metrology

Although various thermoelectric materials have been reported, a considerable number of them show a sharp decrease in the figure of merit near RT. This is why Bi 2 Te 3 with good thermoelectric properties near RT is almost the only choice of material for the practical IoT applications targeted in this article. Furthermore, it becomes difficult to stably obtain a sufficiently large temperature difference near RT. Hence, the improvement of the harvesting technology explained in Fig. 1 is required. However, the degradation of thermoelectric materials and electrode interfaces is alleviated at low temperatures, which is advantageous from the viewpoint of reliability. Thermoelectric materials are assumed to be used at relatively high temperatures and their degradation mechanism has been investigated from the viewpoint of ionic conductivity. 115 , 116 ) The clarification of the degradation mechanism of thermoelectric materials assuming their use for IoT applications will be future work. In the search for new materials that will be important in the future, some cases of using materials informatics for the search have been reported. Indeed, structures that can reduce thermal conductivity, and new material systems have been discovered by machine learning, indicating that materials informatics will be a powerful tool for searching for materials. 117 – 120 ) Research activities to obtain directions for material development from various data on thermoelectric properties will become increasingly popular. 121 ) Completely new research fields have emerged in phonon engineering. 122 – 124 ) Researchers have realized the control of thermal conduction in phononic crystals with a periodic nanostructure as well as thermal collection using a lens structure with a radial array of holes. 125 – 128 ) A technique for selectively generating heat at the nanometer scale using plasmons has also been developed. 129 ) Techniques related to the control of the heat current will have a large ripple effect and are expected to achieve further advances in the future.

When the heat current of thin-film thermoelectric materials is controlled using miniaturized structures, an accurate measurement of the thermophysical properties in the miniaturized structures is a major difficulty. For example, in a previous study, the temperature of a sample was measured with a nanometer spatial resolution by electron energy-loss spectroscopy in a transmission electron microscope on the basis of the dependence of the plasmon peak shift on the temperature of the sample. 130 ) However, the accurate measurement is impossible unless the plasmon peaks are sufficiently sharp. To solve this problem, microscopy techniques have been applied 131 ) and methods based on scanning probe microscopy have been developed. 132 ) A technique for calculating the temperature change and thermal effusivity at the tip of a probe and simultaneously mapping the Seebeck coefficient and thermal conductivity from spatial information on the potential difference has also been reported and is expected to be indispensable for evaluating the reliability of thermoelectric energy harvesters. 133 , 134 ) A thermoreflectance method has become a popular means of evaluating the thermal diffusivity and thermal conductivity of thin films. 135 , 136 ) One side of a thin film is instantaneously heated by pulsed light to detect the temperature response at a position a certain distance from the film. This temperature response is measured as the temperature dependence of the reflected intensity of the probe light, and its time dependence is analyzed to determine the thermal diffusivity and thermal permeability. The analysis of the experimental data obtained from reference samples enables the calculation of the thermal resistance induced at the interface between the position of the incident light and the measurement position of the reflected intensity. Furthermore, the interface of a thin film on the side of the substrate can be directly heated when long-wavelength light that penetrates through the substrate is used. Therefore, the thermal properties of the thin film in the thickness direction can be clarified by applying the thermoreflectance method to the surface of the thin film. 83 ) To increase the use of thermoelectric energy harvesting technology in society, it is necessary to establish techniques for evaluating the properties of materials with a sufficiently high accuracy as well as evaluation protocols to ensure their reproducibility. For example, the measurement of the Seebeck coefficient requires the measurement of the electromotive force, but the internal resistance of voltmeters is not infinite. Moreover, a measurement sample is always connected to an electrode, and the reference value of the electrode must be considered, that is, the Seebeck coefficient cannot be defined with the values of the sample alone. Namely, the absolute value of the Seebeck coefficient includes uncertainty if the electromotive force is measured just as it is. 137 – 139 ) The result of a round-robin test showed that the uncertainty of the figure of merit ZT was as large as ∼20%. 140 ) Recently, a research group has superimposed AC on DC and developed a measurement method to obtain the Seebeck coefficient from the Thomson coefficient, which is determined from a sample alone. 141 ) In the future, the development of reference samples that exhibit stable properties in a desired temperature range is expected, 142 ) and the establishment of international standards for the evaluation protocols for materials and modules is desired.

5. Conclusion

Focusing on vibrational, radio wave and thermoelectric energy harvesting, I described their technical features, recent topics and future challenges. In particular, for vibrational and thermoelectric energy harvesters, it is necessary to develop environmentally friendly and highly reliable materials, and standardize the techniques for evaluating their properties. I hope that early adopters will appear in unexpected fields in the near future if sufficiently mature energy harvesting technology stimulates academic research and development activities, and provides commercial value to meet user demands. As the trend of low power consumption further continues in IoT-related technology, energy management techniques and evaluation kits will be developed for the connection of energy harvesters to this technology. 143 , 144 ) Energy harvesting technology is expected to play a leading role as a technical enabler in the advancement of smart cities and societies, and in various fields such as advanced medicine. 145 , 146 )

As a final remark, I will predict the future of energy harvesting technology. As shown in Fig. 1 , the energy harvested from electromagnetic waves, heat and vibrations is used for sensing the environment and processing the information for communication. Such ambient energy is "information" itself of the environment. Therefore, energy harvesters' signals can be analyzed to find latent regularities in the environment by machine learning, and these regularities can be used as data for predicting the future of the environment. 147 ) Figure 5 shows a gait position sensor that uses machine learning for the analysis of signals obtained from a vibrational energy harvester. This sensor is expected to be used as a technology for predicting the usage status of transportation infrastructure by the analysis of the obtained data. Vibrational energy harvesters can convert the movement of the human body into electric energy and enable visualization of such a movement using light-emitting diodes. In practice, this technology has promoted the integration of art and science through entertainment. 148 ) As an extension of such technical development, there may be a future where artificial intelligence (AI) acquires physical knowledge and sensation by perceptually learning tactile information, as shown in Fig. 6 . 149 , 150 ) The AI implemented in a robot can obtain visual information as an image that contains kinetic information converted into light intensity. In addition, the AI learns as visual information of the motion commanded by the AI itself does not necessarily produce the result intended by control signals because vibrational energy harvesters are independent power supplies. I have described the future prospects of the applications of energy harvesting technology assuming that the visual sense plays an important role in acquiring neonatal somatic sensation. I hope that this article inspires the reader to ponder whether such applications will be possible in the future.

Fig. 5.  (Color online) Example of a piezoelectric vibrational energy harvester. We have succeeded in realizing a gait position sensor by utilizing machine learning. In addition, by using it with vibration power generation, the wireless and infrastructure-built-in-type sensor system becomes possible. It is expected that the utilization of transportation infrastructure can be analyzed from the gait signal.

Fig. 6.  (Color online) Possible future of energy harvesting technology. AI implemented in a robot with a motor function perceptually learns the movement of the arm as visual information by using the vibrational energy harvester and LED installed on the robot. AI acquires the physical knowledge and sensation.

Acknowledgments

For providing the details of the energy harvesting technologies described in this article, I would like to acknowledge the fruitful discussions with colleagues of the Strategic Basic Research Program, "Scientific Innovation for Energy Harvesting Technology", Japan Science and Technology Agency, Prof. Jiro Ida, Prof. Isaku Kanno, Prof. Kenji Taniguchi, Prof. Takashi Nakajima, Prof. Yoshiyuki Nonoguchi, Prof. Masahiro Nomura, Prof. Junichiro Shiomi and Prof. Tsuyohiko Fujigaya; especially Dr. Kenichi Kawaguchi, Prof. Michihiko Suhara, Prof. Hiroshi Toshiyoshi and Prof. Takao Mori for the definitive discussions. I would like to thank Dr. Shinji Aramaki and Dr. Yuji Yoshida for the discussion on R&D trends for indoor solar cells and energy harvesters. I would also like to thank Dr. Noriyuki Uchida and Dr. Yasutaka Amagai for their valuable discussions on thermal property measurement technologies. Figures 2 , 3 and 5 are reprinted with the kind permission of Prof. Toshiyoshi, Dr. Kawaguchi and Prof. Suhara, and Prof. Nakajima, respectively.

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Article Contents

Introduction, photovoltaics-based solar cogeneration, power cycle-based solar cogeneration, comparison of energy and exergy efficiencies and system applications, conclusions, authors’ contributions, data availability, a. appendix.

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Efficient approaches for harvesting solar energy in cogeneration: a review

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Jian Yao, Wenjie Liu, Yifan Jiang, Sihang Zheng, Yao Zhao, Yanjun Dai, Junjie Zhu, Vojislav Novakovic, Efficient approaches for harvesting solar energy in cogeneration: a review, Oxford Open Energy , Volume 1, 2022, oiab004, https://doi.org/10.1093/ooenergy/oiab004

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Stronger urbanization will increase the demand for power and thermal energy to meet the new energy service requirements, which often leads to higher fossil fuels use and emissions. Renewable energy utilization has high potential in urban context to reduce carbon emissions. Solar energy in particular has proved to be promising renewable source due to its ubiquity, abundance and sustainability. Efficient utilization of solar energy for cogeneration is an important application in the built environment, with wide applicability. This review provides a comprehensive state-of-the-art analysis of solar energy for combined heat and power supply based on the available literature. Different approaches to solar cogeneration are classified and critically reviewed. The review shows that efficient solar cogeneration methods could significantly improve the utilization efficiency of solar energy.

The increase of population and social progress has led to increased consumption of fossil fuel, causing higher carbon emissions and being the main reason for global climate change. Developing renewable energy is an important strategy to mitigate the energy and environmental crisis, but also effectively alleviate environmental pollution. For example, China has initiated policies to stabilize carbon emissions by 2030 and reach carbon neutrality by 2060 [ 1 ]. Many countries have also put forward targeted energy strategies on renewable energy for carbon emission reductions. For instance, the European Union has issued a new directive to promote renewable energy use, which sets a target of 32% of total final energy demand for renewable energy by 2030 [ 2 ]. According to the 2019 Federal Energy Regulatory Commission, renewable energy in the United States provided 23% of the electricity of the whole country, and it generated more electricity than coal for the first time [ 3 ]. Japan’s Energy Basic Plan aims to increase the share of renewable energy to 22–24% by 2030 and nuclear power to 20–22% and decrease the share of fossil fuels to 56% [ 4 ]. According to the Statistical Review of World Energy, 3.2 EJ (exajoule) of renewable energy was produced from renewable energy sources in 2019, the largest increase in all energy sources in 2019 [ 5 ].

The efficient utilization of renewable energy would significantly mitigate the detrimental impacts from fossil fuel consumption. Solar energy is the most promising source among the existing renewable energy sources due to its merits including ubiquity, abundance and sustainability [ 6 ]. Moreover, the environmental effect of using solar energy is minimal as it does not generate greenhouse gases or polluting gases. Its market share of global energy is positively increasing [ 7 ]. Electricity as commercial energy is the first target for renewable energy conversion. Examples of methods to convert solar energy into electricity are photovoltaics, combined power cycle with concentrating solar collectors (CSCs), photochemistry, etc. Some of the solar radiation is converted to waste heat during the electrical energy generation processes, e.g. in concentrated solar power (CSP) plants. The solar energy utilization efficiency could be maximized if the waste heat could be used properly, e.g. through combined heat and power schemes. On the other hand, the heat and power generated by solar energy could then meet higher shares of the energy demand in the built environment. Therefore, efficient solar cogeneration has a high potential for applications within the building sector.

Classification of approaches on solar cogeneration

Photovoltaic effect and solar power cycle are the mainstream approaches of solar-electricity conversion, and these two approaches are more efficient and mature than photochemistry methods. Thus, this review focuses on the efficient approaches to harvesting solar energy for solar cogeneration.

The efficient solar cogeneration process could be mainly divided into two categories: photovoltaic effect-based solar cogeneration and power cycle-based solar cogeneration.

The photovoltaic effect-based solar cogeneration technology refers to photovoltaic–thermal (PVT) technology. It could be classified into liquid-based PVT module, air-based PVT module and refrigerant-based PVT module. Moreover, the spectral beam splitting PVT module is recently proposed to utilize the full spectrum of solar irradiation according to the wavelength. The liquid-based PVT module could be further divided into water (including antifreeze), nanofluids and heat transfer oil-based PVT modules. The refrigerant-based PVT module includes the direct expansion PVT module and heat pipe-based PVT module. In addition, nanofluids-based spectral beam splitter, nano-film-based spectral beam splitter and semitransparent PV cell-based spectral beam splitter are the three sub-categories of the spectral beam splitting PVT module.

The power cycle-based solar cogeneration uses the turbine to generate electricity while the waste heat of the working medium could be used to generate useful thermal energy for domestic or industry applications [ 8 ]. According to the different power cycles, it could be classified as Brayton cycle, Stirling cycle and Rankine cycle-based solar cogeneration [ 9 ]. The driven temperatures are different of three kinds of power cycles, notwithstanding, the temperature demands are all above 100°C. Thus, the CSC is employed in these kinds of solar cogeneration systems to provide a high-temperature heat source. The linear concentrator, central receiver concentrator and dish mirrors are commonly adopted as CSC.

The classification of approaches for solar cogeneration is shown in Fig. 1 .

As shown in Fig. 2a , the photovoltaic effect could only be excited by the corresponding wavelength range of the solar spectrum (mainly the visible light) [ 10 ]. The rest of the solar irradiation would convert to waste heat and resulting in the temperature rise of the solar cells [ 11 ]. The high operating temperature of the solar cells would significantly diminish their electrical efficiency while causing deterioration in their service life [ 12 ]. Moreover, the waste heat absorbed by the PV module dissipates in the environment without appropriate usage. Thus, the proposal of PVT technology [ 13 ] could solve this problem as shown in Fig. 2b . The heat transfer medium could extract heat from the solar cells and transfer the waste heat to useful thermal energy. Meanwhile, the lower working temperature of the PV module would benefit its electrical efficiency. In this regard, the PVT module could realize solar cogeneration with high comprehensive solar energy utilization efficiency. The PVT technology could be classified into four categories based on the working medium, which would be reviewed specifically in the following subsections.

(a) Spectral utilization of the photovoltaic effect-based solar cogeneration. (b) Schematic diagram of the PVT module

(a) Upper and rear side of the water-based PVT module. (b) Photograph of the installation. [ 23 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Cross-section view of the structure of the water-based PVT module. (b–e) Four arrangements of PVT modules. [ 25 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Conductive layer. (b) Solar cell unit. (c) Structure of the building-integrated PVT module. [ 27 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Mini-channel PVT module. [ 28 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

The roll-bond panel-based water type PVT module. [ 29 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Schematic diagram of the three systems. (b) Photograph of the three systems. [ 30 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Summary of water-based PVT systems

Liquid-based PVT

Water (including antifreeze), nanofluids and heat transfer oil are four working mediums of the liquid-based PVT module. These four cooling fluids have different characteristics and applications, thus, the structures of the liquid-based PVT would be diversified.

Water-based PVT module

The water-based PVT module uses water as the working medium, and water performs better in the heat transfer characteristic than air due to its large heat capacity [ 14 ]. However, the sealing requirements would be improved, which lead to an increasing system cost [ 15 ]. In addition, the usage of pure water would cause the freezing pipe bursting problem in cold climate regions [ 16 ]. In this regard, antifreeze (a solution of water and ethylene glycol) could be adopted to avoid the freezing problem [ 17 – 19 ]. Numerous researchers have proposed various structures of the water-based PVT module and their corresponding mathematical models to improve the thermal and electrical efficiencies [ 20 – 22 ].

The simplest structure of the water-based thermal collector is the sheet-and-tube type, the tube (copper tube is commonly used) is welded in the absorbing plate. Then, the sheet-and-tube collector is attached to the backside of the PV module to form the water-based PVT module. Bakker et al . [ 23 ] applied this kind of cooling component to improve the heat transfer between the solar cells and water. As shown in Fig. 3 , they installed 25 m 2 of water-based PVT module on the top of the roof for comparative experiments with the conventional PV module and solar collector. The experimental results indicated that this kind of system could meet the electricity and hot water demand of a single-family in Dutch. In terms of this kind of water-based PVT module, Vokas et al . [ 24 ] proposed a theoretical model to predict thermal and electrical performance. For a building with an installed area of 30 m 2 , the solar fraction of the PVT module could reach 47.79%. However, the thermal efficiency of the PVT module is lower than the conventional solar collector by 9%. Moreover, they investigated the payback period of the system is around 4.6 years.

(a) Linear-type fluid channel. (b) Spiral-type fluid channel. [ 46 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Photograph of the PVT module, which uses copper oxide as nanoparticles. [ 48 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

The water-based PVT modules are commonly set in serial, different arrangements would influence the hydraulic, thermal and electrical performance of the system. Therefore, Dubey et al . [ 25 ] have done research about the arrangement style and fill factor of the PVT module. The structure of the PVT module and four different forms of PVT module arrangement are shown in Fig. 4 . The results show that the b-form arrangement is more suitable for the family that takes heat as the first demand while the levelized cost of heat is the lowest among the four arrangement forms. In the country, the e-form arrangement is more preferable for the family, which takes electricity as the first demand. This kind of system is applicable for the suburban and rural areas where both heat and electricity are needed. Based on the above system, Mishra et al . [ 26 ] analysed the energy conversion efficiency and exergy efficiency of the system under the constant water tank temperature mode. The simulation results showed that the annual average exergy efficiency of the system using the e-form is 39.16% higher than that of the traditional flat collector system.

Chen et al . [ 27 ] assembled a lab-scale sheet-and-tube PVT module, and its structure is shown in Fig. 5 . During the test of the components, it is found that higher irradiation conditions and greater mass flow of circulating water could improve the power generation efficiency. However, the heat collection efficiency of 620 W/m 2 solar radiation intensity is 4–8% higher than that of 1000 W/m 2 solar radiation intensity under different mass flow rates of circulating water. The highest power generation efficiency of the module is 15.82%, when the irradiation intensity is 1000 W/m 2 , the corresponding heat collection efficiency is 59.41%.

The mini-channel thermal collector is another kind of structure for the PVT module, and the mini-channel could enlarge the heat transfer area remarkably and thereby increase the thermal efficiency of the PVT module. Zhou et al . [ 28 ] evaluated the comprehensive performance of the mini-channel PVT module (as shown in Fig. 6 ) in summer mode. The experimental and simulated electrical efficiencies are 11.5% and 12.6%, respectively, while the thermal efficiencies are 46.8% and 48%, respectively. The outlet water temperature of the experiments could reach 59.3°C.

The demerit of the sheet-and-tube structure of the water-based PVT module is that the heat transfer area is limited due to the welding process. The low thermal conductive coefficient would have an adverse effect on the thermal performance of the water-based PVT module. Therefore, the heat transfer area between the working fluid and the absorbing plate should be extended. In this regard, the roll-bond thermal collector has been used in the PVT module for a higher heat transfer coefficient.

From this aspect, Aste et al . [ 29 ] proposed a roll-bond panel-based PVT module to improve the thermal efficiency of the water-based PVT module. As shown in Fig. 7 , the area of the PVT module is 1.62 m 2 , while the pump power is 230 W. The roll-bond panel has larger flow resistance than the sheet-and-tube PVT module. The experimental results indicated that the electrical efficiency of the PVT module is around 12.8–13.5% while the thermal efficiency is around 20.8–32.9%. The temperature uniformity of the water-based PVT module is not well, and in this case, the maximum temperature difference reached 30°C.

The operation of a traditional water-based PVT system needs a water pump to circulate the working fluid. The pump power is determined by the flow resistance of the fluid channel in the whole system, and it would be a significant power consumption of the PVT system. Thus, some researchers have proposed the PVT system without a water pump, the system has a natural circulation of water driven by gravity and density difference. As shown in Fig. 8 , He et al . [ 30 ] comparatively conducted experiments to evaluate the performance of conventional flat plate solar collectors, PV modules and the PVT module. The experimental results showed that the thermal efficiency of the PVT module is slightly lower than that of a conventional flat plate solar collector, and the total efficiency is much higher than that of a flat plate collector and single PV module (the type of single PV module is the same as the PVT module). The natural circulation PVT water collector has no moving parts and no power consumption. Its thermal efficiency fluctuates between 38% and 43.51% and its electrical efficiency fluctuates between 9.01% and 12.51%.

The summary of the water-based PVT systems has shown in Table 1 .

Nanofluids-based PVT module

The nanofluids have an outstanding heat transfer coefficient compared with water, which would improve the thermal efficiency of the PVT module significantly. The nanoparticles below 100 nm are added to the base fluid to form the nanofluids. It was firstly proposed by Choi et al . [ 39 ] and the characteristics of nanofluids have been studied and developed in the past decades. It was found that the type of nanoparticles [ 40 ], the volume fraction of nanoparticles [ 41 ], the particle size [ 42 ], the type of base liquid [ 43 ] and other factors [ 44 ] would influence the performance of the nanofluids to varying degrees [ 45 ].

Karami et al . [ 46 ] used the boehmite nanofluids to cool the solar cells and they designed two different fluid channel patterns (as shown in Fig. 9 ) to enhance the thermal efficiency. The average temperature of linear and spiral type decreases by 39.70% and 53.76%, respectively, when the nano-fluid concentration is 0.1 wt%. For linear and helical channels, the efficiency of the nanofluidic cooled PV modules increased by 20.57% and 37.67%, respectively. Sardarabadi et al . [ 47 ] found that the total energy efficiency of 1 wt% nanoparticles in the nanofluid increased by 3.6%, and that of 3 wt% nanoparticles increased by 7.9%. The thermal efficiency of 1 wt% and 3 wt% nanofluids increased by 7.6% and 12.8%, respectively. The total exergy energy in the PVT module of pure water, silicon/water nanofluids (1 wt%) and silicon/water nanofluids (3 wt%) were increased by 19.36%, 22.61% and 24.31%, respectively.

(a) Schematic diagram of the system. (b) Cross-section view of the magnetic nanofluids-based PVT module. [ 49 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Photograph of the nanofluids-based PVT system. (b) Schematic diagram of the nanofluids-based PVT system. [ 50 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Michael et al . [ 48 ] directly embedded the absorber plate beneath the PV module to reduce the thermal conductive resistance between the solar cells and working fluid. The experimental rig is shown in Fig. 10 . They synthesized copper oxide nanoparticles and prepared copper oxide/water nanofluids with a volume concentration of 0.05%, and it could effectively reduce the temperature by 13.82%. The thermal efficiency is increased by 45% by using nanofluids as heat transfer fluid. The temperature of the PV module increases due to the high thermal conductivity of nanofluids. Thus, the output power and electrical efficiency of the PVT system decrease under high working temperatures.

Except for the traditional nanoparticles, Ghadiri et al . [ 49 ] proposed a magnetic nanoparticle (Fe 3 O 4 )-based nanofluid. The magnetic fluid could be magnetized by applying a magnetic field to the working fluid. The experimental setup has shown in Fig. 11 . The effect of magnetic nanofluids on the overall efficiency of the PVT system was studied by placing magnetic nanofluids under constant and variable magnetic fields. The overall efficiency of the PVT system could be increased by 50% and reach 45% when adding 3% ferric oxide compared with distilled water as the cooling liquid phase under the same conditions (50 Hz alternating frequency).

Rejeb et al . [ 50 ] numerically and experimentally investigated the thermoelectric properties of nanofluids-based PVT modules with sheet-and-tube structure collectors. The experimental rig and schematic diagrams have shown in Fig. 12 . They studied the combination of alumina and copper nanoparticles with water and glycol as the base solution. The effects of 0.1, 0.2 and 0.4 wt% concentration of nanoparticles on the performance of the PVT system were studied. It is found that the thermal and electrical properties of PVT modules are improved with the increase of the concentration of nanoparticles, and the performance of water-based nanofluids is better than that of ethylene glycol nanofluids.

Structure of the sheet-and-tube PVT module. [ 51 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Structure of the concentrating PVT module with directly cooling. [ 57 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Photograph of the concentrating PVT module with directly cooling. [ 58 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

The simulation system of the heat transfer oil-based PVT module. (APPLICABLE SOCIETY COPYRIGHT OWNER)

Ag and Al 2 O 3 are other options to form the nanofluids, thus, Khanjari et al . [ 51 ] studied the performance of the Ag/water and Al 2 O3/water nanofluids. They investigated the effects of volume concentration of nanoparticles and inlet flow rate on efficiency and heat transfer enhancement. Figure 13 shows the structure of the sheet-and-tube PVT module. The results showed that the efficiency and heat transfer coefficient increase with the increase of the volume concentration of nanoparticles. Al 2 O 3 /water nanofluids and Ag/water nanofluids have the maximum enhancement of heat transfer coefficient of 12% and 43%, respectively. The heat transfer performance of Al 2 O 3 /water nanofluids and Ag/water nanofluids is 8–10% and 28–45% higher than that of pure water, respectively, when the volume fraction is 5%.

Surface morphology of synthesized MXene (Ti3C2). [ 60 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Table 2 summarizes the specific parameters of the nanofluids-based PVT system.

Summary of nanofluids-based PVT systems

Heat transfer oil-based PVT module

The heat transfer oil is another kind of working medium of the liquid-based PVT module. It is not commonly used as the cooling fluid of the PVT module due to its poor hydraulic behavior [ 55 , 56 ]. Notwithstanding, the heat transfer oil has its own merits including a wide working temperature range and low electric conductivity. Thus, the heat transfer oil-based PVT module is more suitable for the concentrating PVT module.

For instance, Ji et al . [ 57 ] proposed a transmissive concentrator PVT module that uses silicone oil to cool the solar cells and further realize solar cogeneration. As shown in Fig. 14 , the concentrating system would increase the temperature of solar cells significantly, thus, the conventional heat transfer fluid is not suitable. The heat transfer oil (silicon oil which used in this study) could stay steady when it absorbs heat from the high-temperature solar cells (around 119°C). The maximum temperature of the thermal receiver could reach 180°C and this kind of system could utilize 86.1% of the solar irradiation. Moreover, Codd et al . [ 58 ] experimentally tested the performance of the concentrating PVT module with directly cooling silicon oil (as shown in Fig. 15 ), and the results indicated that the total efficiency could reach 85.1% ± 3%, and 138 W of electrical power at 304 suns.

Schematic diagram of the typical CPVT system based on the silicon oil/MXene fluid. [ 61 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Summary of heat transfer oil-based PVT systems

Samylingam et al . [ 59 ] proposed another heat transfer fluid based on olein palm oil (OPO), which could improve the thermal conductivity significantly compared to pure water (as shown in Fig. 16 ). They further added the MXene (Ti 3 C 2 ) into the OPO to enhance the heat transfer coefficient of the working fluid. It was found that the heat transfer coefficient could increase by 9% of the MXene-OPO fluid compared with the MXene-water fluid. Moreover, the employment of the novel heat transfer fluid could reduce the working temperature of the solar cells by 40% compared with the single PV module (the type of single PV module is the same as the PVT module).

Based on the abovementioned system, Rubbi et al . [ 60 ] optimized the performance of the PVT module through the Soybean oil/MXene heat transfer fluid. The CFD model was established to evaluate the thermal and electrical efficiencies of the novel PVT module. The thermal conductivity of the Soybean oil/MXene could be improved by 60.82% compared with the pure Soybean oil. The remarkable thermal performance of the heat transfer oil could decrease the working temperature of the solar cells and increase the electrical efficiency of the solar cells by 15.44% compared with the water/alumina fluid. The structure of the MXene particle is shown in Fig. 17 . Aslfattahi et al . [ 61 ] experimentally investigated the thermal and electrical performance of the Silicon oil/MXene-based PVT module. Figure 18 shows the schematic diagram of this system, they found that the maximum electrical efficiency could reach 17.8% while the thermal efficiency is 60%.

Table 3 presents the summary of the heat transfer oil-based PVT module.

Various structures of the air-based PVT module. [ 62 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Cross-section view of the V-shape air-based PVT module. (b) Photograph of the V-shape fluid channel. [ 63 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Four kinds of air-based bifacial PVT modules. [ 64 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Novel structured air-based PVT module. (b) Experimental setup of the system. [ 65 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Air-based PVT module

The air is free to acquire for the air-based PVT module, and the structure of this kind of PVT module is more simple and reliable. The sealing requirement of the airflow channel is lower than that of the liquid-based PVT module’s fluid channel, thus, the air-based PVT module has the advantage in the initial cost. In addition, the low heat capacity of the air could make a remarkable temperature rise of the air during the operation. Moreover, the air-based PVT module does not have the problem of freezing the working medium compared with the liquid-based PVT module. The high-temperature air could be used for space heating, solar dryers, dehumidification, and industry process preheating, etc. Numerous studies have been conducted in terms of mathematical models, boundary conditions and structures to evaluate and improve the performance of the air-based PVT module.

Tonui et al . [ 62 ] designed different structures of the air-based PVT module as shown in Fig. 19 , including glass-covered, uncovered, the channel with fins, thin metal sheet structure, etc. They established the corresponding mathematical models of each structure and experimentally validated their correctness. The adoption of fin and thin metal sheets could efficiently increase the heat transfer area between the air and the PV module and then improve the thermal efficiency of the PVT collector. The results indicated that the arrangement of fin and the thin metal sheet would not rise the initial cost significantly, but it could improve the comprehensive thermal and electrical efficiencies remarkably.

From the aspect of structure design, Fudholi et al . [ 63 ] developed a V-shape thermal collector for air-based PVT collectors. The cross-section view of the V-shape PVT module and its photograph have shown in Fig. 20 . The numerical model was proposed and verified through the experiments. It was found that the error between the simulation and the experiments was within 5.49%. The exergy efficiency of this kind of PVT module is 12.89%.

The bifacial PV module was adopted to form the PVT module by Ooshaksaraei et al . [ 64 ] as shown in Fig. 21 and experimentally studied the performance of different structures. The experimental results showed that the comprehensive conversion efficiency of the air-based bifacial PVT module with the double-channel in the same direction is 51–67%. The single-channel air-based bifacial PVT module has a minimum conversion efficiency of 28–49%. However, due to the use of dual flow channels, the fluid on the upper surface of the PV module and the cover plate will affect the direct radiation incident to the surface of the PV module. Therefore, it is recommended to use a single-flow channel PVT air collector if electricity is preferred, and the dual-flow channel air-based bifacial PVT module in the same direction should be adopted if heat generation is preferred. In addition, the single-channel air-based bifacial PVT module has the highest exergy efficiency of 8.2–8.4%, followed by the dual-channel co-directed air-based bifacial PVT module with the exergy efficiency of 7.2–8%.

To further increase the heat transfer coefficient of the air-based PVT module, Gholampour et al . [ 65 ] designed a novel structured air-based PVT module as shown in Fig. 22 . The air enters the channel from all sides for heat exchange with the back of the PV module, and the heated air enters the outlet channel through the suction effect of the fan to take heat away. Through CFD simulation and experimental approaches, it is found that the equivalent thermal efficiency is taken as the index to obtain the optimal value of air suction velocity and PV coverage under different conditions, which provides guidance for designers to design air-based PVT modules schemes in the future.

(a) Cross-section view of the multiple inlets air-based PVT module. (b) Photograph of the system. [ 66 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Structure of the roof-type air-based PVT module. (b) Photograph of the PVT system. [ 68 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Photograph of the building integrated roof type air-based PVT module. (b) Cross-section view of the PVT module. (c) Schematic diagram of the roof type air-based PVT module. [ 73 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Yang et al . [ 66 ] improved on the basis of the original PVT air collector, multiple air inlets were set up at the assembly installation place and metal mesh heat collecting parts were installed at the outlet of the air flow passage. The cross-section view of the structure and photographs have shown in Fig. 23 . The experimental results revealed that the multi-inlet and metal mesh heat collecting parts could effectively improve the thermal efficiency, but also improve the working temperature of the air. Therefore, the working temperature of the PV module would also be increased and its electrical efficiency would be reduced. Thus, power generation or heat generation should be given priority according to different demands, and then the system should be designed and optimized accordingly. In addition, they [ 67 ] further studied the transmittance of this kind of PVT module. During the experiment, they found that the dual-inlet air-based PVT module with translucent PV modules improved the thermal efficiency by 7.6% compared with the dual-inlet air-based PVT module with opaque PV modules, and the dual-inlet air-based PVT module increased the thermal efficiency by 5% compared with the single-inlet air-based PVT module.

The air-based PVT module is suitable for building integration, Tiwari et al . [ 68 ] integrated the circular PV module with the air channel to form the roof air-based PVT module, and they used the PVT module to preheat the biogas. Figure 24a shows the structure of the roof-type air-based PVT module. The surface coverage is low due to the circular PV module unit, so more solar radiation could directly heat the air in the flow channel. Figure 24b shows the experimental rig. The experimental results showed that the thermal output of roof-type air-based PVT modules equipped with 1.27 square meters of components reaches 11.18 kWh under good irradiation conditions.

Figure 25 presents another kind of building integrated roof type air-based PVT module, the air flows from downside to upside and the tilt angle is 35 degrees. In the case of an effective building area of 65 m 2 , the annual electricity generation of the system is 16 209 kWh and the heat generation is 1531 kWh, and the average thermal efficiency is 53.7%. Based on this component structure form, Bambrook et al . [ 69 ] changed the mass flow rate of inlet air through experiments to explore the optimal mass flow rate of inlet air to achieve the highest comprehensive efficiency of the system. The experimental results showed that the power generation and thermal efficiency of the system are the highest at the mass flow rate of 0.03–0.05 kg/s. In addition, the electrical efficiency of the system fluctuates between 10.6% and 12.2%, and the thermal efficiency fluctuates between 28% and 55%. In addition to optimizing the operating parameters, Farshchimonfared et al . [ 70 , 71 ] optimized and analysed the physical structure parameters of air-based PVT modules. They used modules of 10, 15, 25 and 30 m 2 with ratios of 0.5, 1, 1.5 and 2, respectively. The optimal mass flow rate to component area ratio is 0.021 kg/s, and the optimal flow height is 0.026–0.09 m when the inlet and outlet temperature rises to 10°C. In addition, Singh et al . [ 72 ] optimized the performance of air-based PVT module by genetic algorithm, and analysed the effects of runner length and width, inlet velocity, inlet temperature, glass thickness and backplane thickness on component efficiency, respectively. The maximum power generation efficiency is 14.15%, and the thermal efficiency is 49.11% when the exergy efficiency is taken as the target.

Table 4 presents the summary of air-based PVT modules.

Summary of air-based PVT modules

Cross-section view of the direct expansion PVT module. [ 80 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Refrigerant-based PVT module

The liquid-based PVT module and air-based PVT module apply the sensible heat working fluid to collect heat, while the refrigerant-based PVT module applies the latent heat working fluid. The cooling ability of the latent heat working fluid is better than the sensible heat working fluid due to the higher heat transfer coefficient. In this subsection, the refrigerant-based PVT module would be reviewed accordingly.

(a) Cross-section view of the triangular tube-type sheet-and-tube collector. (b) Photograph of the system. [ 82 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Cross-section view of the novel evacuated PVT collector/evaporator. (b) Vertical view of the component. [ 83 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Direct expansion PVT module

The direct expansion PVT module means that the working medium (refrigerant) directly evaporates in the fluid channel and extracts heat from the solar cells through the phase transition process. The evaporation channel is attached to the PV module to form the direct expansion PVT module. Furthermore, the direct expansion PVT module is utilized as an evaporator. The solar-assisted PVT heat pump system consists of PVT module, compressor, expansion valve, and condenser. The refrigerant absorbs heat in the PVT module and then it would be compressed to a high-temperature and high-pressure state. Afterward, the refrigerant releases the heat to the heat storage medium [water, phase change material (PCM), etc.] during the condensation process. The condensed refrigerant enters the PVT collector/evaporator after cooling and reducing pressure through the expansion valve and then evaporates again through the heat absorption to complete the whole thermodynamic cycle. The thermal efficiency of the direct expansion PVT module is improved effectively due to the evaporation of refrigerants. However, the phase change process of the refrigerant is the two-phase flow, which would lead to a complex design of the fluid channel. The high flow resistance, uneven flow distribution and uneven temperature distribution would have adversely affected the performance of the PVT module. In this regard, various experiments and simulations have been studied to attain a higher comprehensive solar energy utilization efficiency.

Ji et al . [ 80 , 81 ] developed a sheet-and-tube PVT collector/evaporator, the fluid channel tube is welded to the heat-collecting aluminum metal sheet, which collects the heat from the back of the solar cells and transfers it to the copper tube through thermal conduction. The working medium in the copper tube collects the heat through the convection heat transfer process, the cross-section view of this kind of PVT module is shown in Fig. 26 . They coupled the component with the heat pump system to form the direct expansion solar-assisted PVT heat pump system. According to the experimental results, the maximum COP of the system could reach 8.4 while the average COP is 5.4, and the average electrical efficiency of the PVT module is about 13.4%. In addition, Ji et al . [ 81 ] developed a distributed dynamic model to describe the direct expansion solar-assisted PVT heat pump system. The model could calculate the refrigerant conditions, such as pressure, temperature, quality, enthalpy, etc. under given environmental conditions including ambient temperature, irradiation intensity, wind speed, etc. The simulation results showed that the electrical efficiency is 12% and the thermal efficiency is 50%.

The circular copper tube type sheet-and-tube collector has a demerit, which is that the heat transfer area between the copper tube and the absorbing plate is small. Therefore, Mohanraj et al . [ 82 ] designed a triangular tube-type sheet-and-tube collector and compared its performance with the conventional circular copper tube. Figure 27 shows the component structure and the experimental rig. In addition to experimental tests, they also used artificial neural network algorithms to make predictions about the system. In practical tests in India, it was found that the use of triangular flow channels resulted in significant improvements in system efficiency, such as a 3–7% increase in thermal efficiency, a 3–5% increase in COP and a 4–13% increase in electrical efficiency.

Structure of the flat-box-based PVT module. [ 84 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a–d) Four fluid channel patterns of the roll-bond panel. (e) Cross-section view of the roll-bond panel-based PVT module. [ 85 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Roof-type PVT module. [ 87 ] (b) Building integrating PVT module. [ 88 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) [ 93 ] Heat pipe-based PVT module. (b) [ 94 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Novel structured heat pipe-based PVT module. [ 98 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Summary of direct expansion PVT modules

To further improve the thermal efficiency of the PVT module, Chen et al . [ 83 ] developed a novel structured evacuated PVT collector/evaporator as shown in Fig. 28 . They adopted it to the solar-assisted heat pump system for water heating. The simulation results indicated that the average monthly thermal efficiency is 75.2% and the electrical efficiency is 15.5%. In addition, the average COP of the system is 5.35. The performance of the component decreases slightly when the irradiation intensity is low, so this kind of structure has a better application prospect in high latitude areas.

The flat-box absorber is another structure of the thermal collector, Mastrullo et al . [ 84 ] proposed a new structure of the PVT collector/evaporator with a flat-box absorber as shown in Fig. 29 . The arranged air gap could lower the thermal loss of the PVT module and then more waste heat could be absorbed by the working fluids. The simulated highest electrical efficiency could reach 14.2% while the thermal efficiency is 52.0%. The electrical efficiency is 13.7% when the highest thermal efficiency (84.3%) is attained. The temperature of the flat-box absorber would decrease with the increase of the fill factor of the solar cell units.

Except for the sheet-and-tube type and flat-box type thermal collector, the roll-bond panel type thermal collector is a promising method to realize high efficient thermal collection. The heat transfer area between the working fluids and the absorbing plate could be enlarged significantly due to its special structure. Yao et al . [ 85 ] proposed the roll-bond panel-based PVT module with an optimized fluid channel pattern. The various fluid channel patterns of the roll-bond panel and the cross-section view of the PVT module have shown in Fig. 30 . The temperature uniformity, electrical and thermal efficiencies and hydraulic performance have been evaluated numerically and comparatively. The simulation results indicated that the temperature uniformity of the fluid channel Pattern 2 is the best with the lowest flow resistance. It was found that the optimized fluid channel pattern could reduce the solar cells’ working temperature by up to 47.3°C and increase its electrical efficiency by 46.5% on a typical summer day. In this circumstance, the average system COP is 4.37, and the average temperature difference of the PVT module could be controlled within 4.6°C, while the average electrical and thermal efficiencies could reach 16.7% and 47.6%, respectively. Furthermore, they [ 86 ] conducted experiments to evaluate the performance of the PVT module, it was found that the average electrical and thermal efficiencies could reach 17.93% and 109.4%, respectively.

From the building integrating aspect, the roll-bond PVT module could also be used as a roof. For instance, Tsai et al . [ 87 ] integrated the direct expansion PVT module to the building façade as the roof as shown in Fig. 31a . The structure of the thermal collector is the sheet-and-tube type, and the refrigerant working medium evaporates and collects heat in the tube, taking away the heat of the PV module. The experimental results showed that the thermal efficiency of the component fluctuates between 73.6% and 74.2%, while the electrical efficiency fluctuates between 12.2% and 12.5% under dynamic operating conditions. The COP of the system fluctuates between 7.07% and 7.10 and the power produced by the PV module could be used by the compressor in self-operating mode. Similarly, Shao et al . [ 88 ] also developed a building attached type direct expansion solar-assisted PVT heat pump system, using the components as building roofs to provide power and heat for the building. Figure 31b shows the building integrated PVT collector/evaporator, and the collector type is roll-bond panel [ 89 ]. The advantages of the roll-bond panel are stable performance and high thermal efficiency. According to the experimental results, the average electrical efficiency and thermal efficiency of the system are 11.67% and 60.17%, respectively, and the average COP of the system is 3.7.

Table 5 presents the summary of the direct expansion PVT module and the specific parameters of the solar-assisted PVT heat pump system.

Heat pipe-based PVT module

A heat pipe encapsulates the working fluid (refrigerant) in the tube and the cold end would absorb heat through the evaporating process while the hot end would release the heat through the condensing process. The hot side is commonly linked to the heat exchanger and transfer the heat to heat storage material (water, air, PCM, etc.). The secondary heat exchange process would mildly decrease the thermal efficiency of the system. Notwithstanding, the useful heat could be used for space heating, domestic hot water usage, residential heating, etc. Moreover, few researchers have composed the heat pipe PVT module with the thermoelectric device for higher electricity output.

As shown in Fig. 32a , Wu et al . [ 93 ] developed a core type heat pipe-based PVT module, they attached the heat pipe to the absorb plate with thermal conductive glue, and the heat absorbed by the heat pipe was transferred through secondary heat exchange to realize heat generation. They developed a solution based on the Number of Transfer Units (NTU) (heat element) method and analysed the effects of medium inlet temperature, solar cells’ fill factor and mass flow rate of the medium inlet on system performance. The simulation results showed that the temperature difference between heat pipe-based PVT module and PV module is less than 2.5°C. Its thermal efficiency, electrical efficiency and exergy efficiency are 63.65%, 8.45% and 10.26%, respectively. Gang et al . [ 94 , 95 ] adopted a heat pipe with another structure as a primary heat collector, as shown in Fig. 32b . The thermal and electrical properties of the components under dynamic conditions were analysed by the finite element method and verified by experiments. They used water as the heat collecting medium for secondary heat transfer, and the results showed that the highest thermal and electrical efficiencies of its components are 41.9% and 9.4%, respectively. In addition, the components used in the system could also be integrated to the building [ 96 ].

This kind of heat pipe-based PVT module was coupled with PCM storage by Sweidan et al . [ 97 ] for water heating. They arranged the encapsulated PCM sphere in the water storage tank while the hot side of the heat pipe was also arranged in the tank. The working liquid in the heat pipe evaporates after collecting heat from the PV module and releases heat at the condensation end to heat the hot water in the water tank. The existence of PCM could stabilize the water temperature. They simulated the optimal number of PV modules and the quality of PCMs for building heating. The simulation results showed that the highest electrical efficiency is 12.23% and the highest thermal efficiency is 35.3% in January. Moreover, the payback period of the system is 13.7 years.

Moradgholi et al . [ 98 ] developed another kind of heat pipe-based PVT module and the structure has shown in Fig. 33 . Their siphon heat pipe absorbs the waste heat generated by a monocrystalline silicon solar cell module using a phase change process inside the pipe. In spring, the working medium with methanol as the system has a slope of 30°, and in summer, the working medium with acetone as the system has a slope of 40°. The temperature of the PV modules drops by up to 15°C in the developed heat pipe-based PVT system. The experimental results showed that the electrical efficiency and thermal efficiency are increased by 5.67% and 16.35% respectively in spring, and by 7.7% and 45.14% in summer. Compared with the single PV module system (the type of single PV module is the same as the PVT module), the total efficiency is 15.3% and 44.38% higher in spring and summer, respectively.

Hu et al . [ 99 ] experimentally studied the thermal and electrical properties of components at different inclinations for the performance of heat pipe-based PVT modules with wickless and wire-meshed heat pipes. Two heat pipe-based PVT systems were tested at inclination angles of 20° and 40°, as shown in Fig. 34 . The experimental results showed that under the angle of 20°, the heat transfer resistance of the wickless heat pipe is large, so its component performance is poor. The inclination angle has little effect on the performance of the PVT system with wire-meshed heat pipe. The thermal efficiency of PVT systems based on wickless heat pipe and PVT systems based on wickless heat pipe is 52.8% and 51.5%, respectively, when the tilt angle is 40°. The efficiency of the wickless heat pipe is higher at a latitude above 20°, and the efficiency of heat pipe with the core is higher at a latitude below 20°.

(a) Wickless heat pipe. (b) Wire-meshed heat pipe. [ 99 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Except for the conventional circular type heat pipe, Deng et al . [ 100 ] adopted the microchannel heat pipe to form the heat pipe-based PVT module as shown in Fig. 35 . The microchannel contains micro fins, and its upper and lower surfaces are relatively flat, making it easier to fit with the PV module. The acetone acts as the working fluid of the heat pipe-based PVT module. The performance of the developed PVT system was tested under typical working conditions for four days. The results showed that the maximum electrical efficiency, thermal efficiency and average total efficiency are 14.65%, 33.07% and 45.38%, respectively.

Microchannel heat pipe-based PVT module. [ 100 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

The heat pipe-based PVT module could also be used for building integration. PVT component is combined with the building to form the building integrated heat pipe-based PVT module, which can not only be used as the building envelope but also increase the thermal power output of the building and realize energy self-supply. In this regard, Jouhara et al . [ 101 ] developed a building roof type heat pipe-based PVT module, as shown in Fig. 36 and the experimental tests were carried out. They tested the performance of the PVT system without PV, without cooling and with cooling. A mixture of 60% water and 40% ethylene glycol is used as the working liquid of the heat pipe. The thermal conversion rates were 50% and 64% for systems with and without PV layers, respectively, when tested on three identical systems. The electrical efficiency of the heat pipe-based PVT module was increased by 15%.

(a) Structure of the PVT heat mat. (b) Photograph of the roof-type heat pipe-based PVT module. [ 101 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Table 6 presents a summary of heat pipe-based PVT modules.

Summary of heat pipe-based PVT modules

Spectral beam splitting PVT module

Different from the conventional PVT module, the spectral beam splitting PVT module adopts the splitter to utilize solar irradiation in different wavelengths. The solar energy would convert to electricity and waste heat (accumulated in solar cells) simultaneously of conventional PVT module, then the thermal collector absorbs heat from the solar cells to realize cogeneration. However, the spectral beam splitting PVT module could generate electricity and useful heat without convert to waste heat. The solar irradiation that could not excite the photovoltaic effect would convert to useful thermal energy directly due to the spectral beam splitter. The spectral beam splitting PVT module could decouple the photovoltaic and photothermal conversion process and realize efficient solar cogeneration. The spectral beam splitter could be mainly separated into three categories: nanofluids-based spectral beam splitter, nano-film-based spectral beam splitter and semitransparent PV cell-based spectral beam splitter [ 106 ]. The schematic diagrams of different spectral beam splitting PVT modules are shown in Fig. 37 .

Schematic diagrams of different spectral beam splitting PVT modules [ 106 ]. (APPLICABLE SOCIETY COPYRIGHT OWNER)

Nanofluids-based spectral beam splitter

As shown in Fig. 37a , the nanofluids are employed as the spectral beam splitter as well as the heat collecting fluid. The infrared band of the solar irradiation (1109–2500 nm) would directly be absorbed by the nanofluids while the visible light (400–1109 nm) could penetrate the nanofluids and excite the photovoltaic effect. The nanofluids are arranged on the top of the PV module and this structure would decrease the electrical efficiency of the solar cells. The nanoparticles used in the nanofluids would cause sedimentation, leakage and pipe plugging problems. In this regard, numerous studies have been conducted to overcome the demerits of the nanofluids-based spectral beam splitter.

The typical structure of the nanofluids-based spectral beam splitter for PVT usage is shown in Fig. 38 . Ramdani and Ould-Lahoucine [ 107 ] numerically investigated the energy and exergy performance of this kind of PVT module. In their study, the water is regarded as the natural filter which could absorb infrared radiation. They developed the CFD model and conducted parametric analysis, the simulation results indicated that the thermal efficiency of the proposed PVT module could reach 17–40% while the electrical efficiency could reach 11.6–12.4%.

Cross-section view of the nanofluids beam splitter-based PVT module. [ 107 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Moreover, Al-Shohani et al . [ 108 , 109 ] comparatively investigated the thermal and electrical performance of the PVT module with/without optical water filter as shown in Fig. 39 . Different thicknesses of the water filter would influence the energy efficiency of the PVT module. The experimental results showed that the thicker water filter could reduce the working temperature of the solar cells more remarkably, but also would cause higher optical loss. The electrical efficiency of the PVT module with a 5 cm water filter has the maximum thermal efficiency (42%), but its electrical efficiency is the minimum (8%) due to the absorption of visible light of the water filter, while the total efficiency could reach above 50%.

Photograph of the PVT module with and without an optical water filter. [ 108 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Experimental rig for testing the PVT module. [ 110 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Pure water is easy to attain but its optical characteristics are not suitable for PVT usage. Thus, Han et al . [ 110 ] proposed five categories of nanofluids including inorganic aqueous salt, glycol, silicone oil, synthetic oil and mineral oil to improve the optical performance of the PVT module. The experimental rig has shown in Fig. 40 . Moreover, they adopted two types of solar cells (silicon cell and GaAs cell) for the PVT module. It was found that the electrical efficiency of the GaAs cell-based PVT module (13.1%) is lower than that of the silicon cell-based PVT module (15.7%) while the thermal efficiency is around 41%.

The fluid channel structure would influence the performance of the PVT module, thus, Rosa-Clot et al . [ 111 ] experimentally studied the S-shaped fluid channel type nanofluids beam splitter-based PVT module (as shown in Fig. 41 ). The field test revealed that the thermal efficiency varies from 30–60% while the electrical efficiency is around 13.19%. This kind of structure could improve the thermal and electrical efficiencies significantly while the maximum outlet temperature of the working fluid could reach 50°C.

To further improve the performance of the PVT module, Xiao et al . [ 112 ] investigated the influence of the S-shaped fluid channel’s structure on the system performance and temperature uniformity as shown in Fig. 42 . Model A has the best temperature uniformity under the same working conditions while its electrical and thermal efficiencies are 9.36% and 77.6%, respectively. However, the electrical efficiency of model C is the highest, which is 12.64%, but its thermal efficiency is the lowest, which is 71.15%. Therefore, different structures are recommended regarding the demand (electricity or heat).

Structure of the S-shaped fluid channel type nanofluids beam splitter-based PVT module. [ 111 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a–b) Structure of the S-shaped PVT module. (c) Temperature distribution of different fluid channel structure-based PVT modules. [ 112 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Photograph of the nanofluids beam splitter-based PVT module. [ 113 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Prototype of the spectral splitting concentrating PVT system. [ 114 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

The PCM/nanofluids beam splitter-based PVT module. [ 115 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

The system configuration of the nano-film-based solar power generation system. [ 116 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Schematic diagram of the nano-film filter-based solar cogeneration system. [ 117 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Schematic diagram of the concentrated nano-film splitter-based PVT system. [ 118 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Schematic diagram of the solar CPVT system. [ 119 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

(a) Cross-section view of the nano-film beam splitter-based PVT system. (b) Photograph of the system configuration. [ 120 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Structure of the PV-TE system. [ 122 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Configuration of typical power cycle-based solar cogeneration system

The configuration of the ORC with regenerator. [ 125 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

ETC-FPC driven Rankine cycle-based solar cogeneration system. [ 128 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Two kinds of solar-driven ORC cogeneration systems. [ 130 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Schematic of the proposed systems. (a) LHP-based solar CHP system with the single-stage turbine. (b) LHP-based solar CHP system with double stage turbine. [ 131 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Simplified schematic view of the three HPG configurations under comparison. [ 132 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Schematic of the solar dish Stirling micro-CHP system. [ 133 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Schematic layout of solar-powered Stirling engine for micro-cogeneration. [ 134 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Schematic picture of the m-CHP system under development within the DiGeSPo project. [ 136 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Schematic diagram of the new CCHP system with transcritical CO2 driven by solar energy. [ 139 ] (APPLICABLE SOCIETY COPYRIGHT OWNER)

Comparison of the electrical and thermal efficiencies of different solar cogeneration approaches

Comparison of the exergy efficiency and temperature of supply heating of different solar cogeneration approaches

Joshi and Dhoble [ 113 ] experimentally investigated theperformance of water, silicon oil and coconut oil-basedbeam splitter for PVT application. The experiment rig is shown in Fig. 43 . The maximum averagethermal and electrical efficiencies are 79% and 14%,respectively. It was found that the optical and thermalperformance of these three filters is similar for the PVTmodule.

To further improve the electricity and heat output, Zhang et al . [ 114 ] proposed and manufactured the spectral splitting concentrating PVT system and experimentally studied its performance. The structure of this prototype is shown in Fig. 44 . The maximum thermal and electrical efficiencies are 52% and 9.6%, respectively, when the diffuse irradiance ratios are 15.0%. Moreover, the adoption of the beam flitter could reduce the solar cells’ temperature by about 12°C. The outlet fluid temperature could reach 70°C when the concentration ratio is 15.

In another case, Yazdanifard et al . [ 115 ] employed the PCM as the first optical filter while the absorptive liquid as the second optical filter. The multi-layer structure of the proposed PVT module is shown in Fig. 45 . The numerical results showed that the electrical efficiency of the PVT module could reach 14.5% while the thermal efficiency of the PVT module is 46.5%. The nanofluids could maintain the temperature of the solar cells around 27°C while the temperature increase of the nanofluids is 7.2°C.

Table 7 summarizes the specific parameters of the nanofluids beam splitter-based PVT module.

Summary of nanofluids beam splitter-based PVT modules

Nano-film-based spectral beam splitter

As shown in Fig. 37b , the nano-film could realize spectral beam splitting benefit from its structure, and it could be used to separate the solar irradiation. The visible light (400–1109 nm) would be reflected by the nano-film when it is incident while the infrared light could penetrate the nano-film. Thus, the reflected visible light is used to generate electricity while the transmitted infrared light is used to generate heat. The PV module and thermal collector should be installed separately, which would cause a complex system arrangement. In this regard, the nano-film-based PVT module is not suitable for large-scale utilization due to the size limitation of the nano-film. The manufacturing process of the nano-film is not mature while the cost is high, which leads to poor economic performance. However, this kind of PVT module could realize high comprehensive solar energy utilization efficiency, and it has its suitable applications.

Shou et al . [ 116 ] developed a broadband TiO 2 /SiO 2 optical thin-film filter for solar power generation, the system configuration is shown in Fig. 46 . The thin-film filter could split the solar irradiation and reflect the visible light while penetrating the infrared light. The visible light could be used to generate electricity for the PV module, and the infrared light is also designed to generate electricity through the thermoelectric device. The maximum electrical efficiency of this system could reach 17.0% when the heterostructure cell is adopted, and the thermoelectric device could improve the overall electrical efficiency by 4.15%.

Crisostomo et al . [ 117 ] experimentally tested the performance of the SiN x /SiO 2 thin-film filters for PVT application. The test rig is shown in Fig. 47 . The linear Fresnel mirror is adopted to concentrate the incoming solar irradiation. The arrangement degree of the nano-film filter would affect the electrical efficiency of the solar cells. The testing maximum electrical efficiency of the solar cells could reach about 25% under 45 degrees arrangement while it is only 17% with 20 degrees. The total solar energy utilization efficiency could reach 85.6% of this system.

A similar structure as shown in Fig. 48 has been developed by Ling et al . [ 118 ] to generate heat and power simultaneously. The CdTe solar cell is adopted to generate electricity and it shows outstanding solar-to-electricity efficiency (39%, including thermochemical contribution). The electrical efficiency of the PV module is 15.5–19.5% while the thermal efficiency is around 60%. The cost analysis revealed that the specific cost of solar electricity is $0.20/kWh.

In another case, Wang et al . [ 119 ] designed and analysed the nano-film beam splitter-based PVT system as shown in Fig. 49 in terms of the thermodynamic aspect. They concluded that the overall optical efficiency is 66.2%. The thermodynamic analysis results revealed that the electrical efficiency of the PVT module could reach 26.6% while the overall efficiency could reach 30.5%.

Except for the linear concentration type, the point concentration Fresnel lens is adopted by Liang et al . [ 120 ] for small-scale usage as shown in Fig. 50 . The SiO2/TiO2 nano-film is used to split the solar irradiation, the experimental results showed that the electrical efficiency of the PV module is 13–16%, which is 9.4% higher than the conventional PV module. The overall energy efficiency and exergy efficiency of this study are 15.95% and 20.3%, respectively. Moreover, they [ 121 ] designed the two-axis sun tracking system for the nano-film beam splitter PVT module. The nano-film achieved a high reflectance (≥96.8%) for the visible light and a high transmittance (85%) at 1100–2500 nm. In this system, the overall energy efficiency could reach 22.72%.

Summary of nano-film beam splitter-based PVT modules

Table 8 presents the summary of nano-film beam splitter-based PVT modules.

Semitransparent PV cell-based spectral beam splitter

As shown in Fig. 37c , the semitransparent PV cell refers to the technology that utilizes PV cells with the transparent electrode (TCO) [ 106 ]. The semiconductor material shows transparent characteristics when the incoming photons could not excite the photovoltaic effect while it would strongly absorb the incident visible light and convert it to electricity. The long-wavelength spectrum of the incoming solar irradiation would transmit the semitransparent PV cell and be absorbed by the thermal collector. The initial cost of this kind of PVT module would significantly increase and the electrical efficiency of the solar cells could not maintain at a high level.

However, the research about the semitransparent PV cell-based spectral beam splitter is not rich. Several researchers coupled the semitransparent PV cell with the thermoelectric (TE) device to further improve the electrical efficiency of the system. For instance, Zhou et al . [ 122 ] proposed the concentrated PV-TE system as shown in Fig. 51 to realize power generation, and other researchers [ 123 , 124 ] have investigated the nano-structure of the PV module to improve the utilization efficiency of the incoming solar irradiation. Nevertheless, the usage of the semitransparent PV cell for PVT application is scarce.

Different from photovoltaics, CSP is another pathway using solar energy for electricity generation. Coupling CSCs with the boiler power cycle, typical CSP systems concentrate the solar irradiation to heat the working fluids. The heated working fluids (of which the temperature is higher than 100°C) directly or indirectly drive the power cycle to generate electricity. Besides, the lower-grade thermal energy released from the condenser can be retrieved for domestic or industrial uses, which enables solar cogeneration based on the power cycle.

As Fig. 52 illustrates, a typical power cycle-based solar cogeneration system consists of the solar field, thermal energy storage (TES) system and heat and power generation (HPG) section. The solar field is composed of an array of solar collectors to concentrate solar irradiation. TES system stores the thermal energy and conveys it to the HPG section, serving as an optional buffer to overcome the intermittency of solar irradiation. HPG sections, comprising power cycle and SPG subsections, generate thermal energy with lower grade (for direct use or driving absorption/adsorption heat pump) and electricity simultaneously.

The CSP systems differ in cycle type. The power cycles functioning in the power cycle-based solar cogeneration systems mainly include the Rankine cycle, Stirling cycle and Brayton cycle.

Rankine cycle-based solar cogeneration

Rankine cycle, especially organic Rankine cycle (ORC), is the dominant type of thermodynamic cycle functioning in the power cycle-based solar cogeneration systems. The concentrated solar irradiation heats the working fluid directly or indirectly (through a heat exchanger) and drives the Rankine cycle to generate electricity and heat.

Several types of solar collectors can serve in the solar field of Rankine cycle-based solar cogeneration system. Do Ango [ 125 ] et al . proposed a small-scale (electricity output <3 kW) solar cogeneration system ( Fig. 53 ), combining linear Fresnel collectors (as a solar field) and ORC with regenerator (as HPG sections). The test bench was manufactured ( Fig. 54 ), and an experiment was implemented to investigate the performance of the cogeneration system. The system offered an electrical efficiency of about 5% in the test condition.

Freeman et al . [ 126 ] proposed a small-scale solar ORC system for CHP with evacuated flat-plate collectors (EFPCs) or evacuated tube heat pipe collectors (ETHPCs) as a solar field. Freeman et al . simulated this proposed system with a collector area of 15 m 2 to access and compare the performance of systems with different collectors. The results presented the superiority of EFPC over ETHPC as a solar field and reported overall electrical efficiencies of 4.4–6.4% in the UK and of 6.3–7.3% in Cyprus under the simulation conditions. The system with EFPC was expected to have the potential to provide 3 h continuous 1-kW electricity output in Cyprus in January.

Borunda et al . [ 127 ] proposed a direct-feed solar cogeneration system coupling ORC and parabolic trough solar power plant. A case study (based on the meteorological data from Almeria) was conducted by TYNSYS to access the performance under different configurations. Under the simulation conditions, the electrical/thermal efficiency was estimated to be 6.79–8.35%/48.64–59.80% for different configurations, the overall exergy efficiency was estimated to be 24.87–30.58%.

Bellos et al . [ 128 ] designed an ORC solar cogeneration system with the combination of ETCs (evacuated tube collectors) and FPCs (flat plate collectors) as the solar field ( Fig. 55 ). A thermodynamic model based on EES was developed to investigate the performance variation of the system with the variation of heat production power. When the heating production varies from 5 kW to 35 kW, the overall energy efficiency was estimated to vary from 7.51% to 23.47%, while the exergy efficiency was ranged from 4.34% to 4.6%, respectively.

Eterafi et al . [ 129 ] conceptualized a solar cogeneration system combining dish collectors solar field and ORC for solar CHP. A TYNSYS-based simulation was implemented to evaluate the operation of the system daily and monthly. The required lowest inlet temperature was calculated at 266.1°C. After optimization, the average thermal and electric efficiency of the integrated system in July could reach 62.53% and 12.88%, respectively.

Zhang et al . [ 130 ] conducted an exergy analysis of two kinds of the solar cogeneration system, namely series mode and parallel mode ( Fig. 56 ), in Lhasa. Besides, three different collectors were investigated respectively based on different modes: FPC, ETC and PTC. The analysis was performed by Matlab and REFPROP to determine the optimal operation mode and collectors under different conditions.

HPG sections with different configurations are expected to influence the performance of the Rankine cycle-based solar cogeneration system. Beygzadeh et al . [ 131 ] conducted a thermodynamic comparison between solar ORC cogeneration systems (solar field: heat pipe collectors) with single-stage and double-stage turbines ( Fig. 57 ). The one-stage system was estimated to present a thermal/electrical/exergy efficiency of 63.39%/8.37%/11.22% with n-hexane as the working fluid for the single-stage system, and 63.36%/8.4%/11.26% for double stage system.

Cocco et al . [ 132 ] performed an exergy analysis by Matlab-based simulation to compare three different configurations ( Fig. 58 ) of HPG sections of an ORC solar cogeneration system with Linear Fresnel collectors as a solar field. The results suggested the superiority of HPG-A configuration for a small power-to-heat ratio and higher outlet water temperature.

Stirling cycle-based solar cogeneration

Stirling cycle, as a technology received renewed attention and investigation recently, also functions in some conception of power cycle-based solar cogeneration system. Stirling engine is especially suitable for distributed micro-CHP systems due to its advantages of miniaturization, adaptability to different heat sources and high efficiency.

Moghadam et al . [ 133 ] conceptualized a solar dish Stirling cogeneration system to provide energy demand for a residential building. Figure 59 briefly demonstrates the system configuration. 3E analysis was conducted to investigate and optimize the system performance. The conditions of fives Iran cities were discussed. The results estimated the highest electrical/overall efficiency of 34.5%/78.4% in Tabriz.

Ferreira et al . [ 134 ] proposed a micro solar cogeneration system equipped with a Stirling engine and dish collectors. Figure 60 briefly presents the system configuration. The proposed system was expected to output 1–5 kW of electricity and 2–35 kW of thermal power and to provide hot water with a temperature of 343 K. The system was estimated to offer an electrical/total efficiency of 26.2%/98.1% after optimization. Ferreira et al . [ 135 ] also proposed a solar-driven Stirling cycle cogeneration system for domestic use, reported an expected electrical/thermal efficiency of 23.91%/74.1%, with the output hot water temperature as 333 K.

Crema et al . [ 136 ] proposed and demonstrated a micro solar cogeneration system ( Fig. 61 ) combining parabolic trough collectors with a Stirling engine. Figure 61 presented the solar field and Stirling engine of the demonstration plant. The demonstration activities provided the best efficiency of 47%.

Brayton cycle-based solar cogeneration

Brayton cycle with supercritical carbon dioxide has higher thermal efficiency compared with Rankine cycle and is also reported to function in the power cycle-based solar cogeneration systems. Brayton cycle-based solar cogeneration system is suitable for large-scale CHP systems given the large volume of the devices and high working pressure.

The Brayton cycle could be utilized as the power cycle of the solar-assisted power generation system. Wang et al . [ 137 ] reported a Dish Brayton system for power generation with solar energy serving on preheating. However, the solar-driven Brayton cycle providing CHP/CCHP was rarely reported. Sharan et al . proposed a solar-driven Brayton cycle providing cogeneration of electricity and heat-driven desalination [ 138 ]. Wang et al . [ 139 ] conceptualized a solar-driven Brayton cycle combined with ejector refrigeration ( Fig. 62 ) for CCHP. Driven by trough collectors, this system provided a thermal efficiency of 53.0%, and net electricity output of 0.109 kW (the input solar irradiation was 135.277 kW).

Table 9 presents the summary of power cycle-based solar cogeneration systems.

Summary of power cycle-based solar cogeneration systems

The different approaches to harvesting solar energy for cogeneration have various energy and exergy efficiencies. In this regard, the applications for each technology method are differed according to the output temperature range. Thus, the comparison of energy and exergy efficiencies and suitable system applications is introduced in this section.

Energy and exergy efficiencies of different systems

Figure 63 illustrates the comparison results of the electrical and thermal efficiencies of different solar cogeneration approaches. The thermal efficiency of the liquid, air and spectral beam splitter-based PVT modules varies from 20% to 80% while the electrical efficiency is in the range of 5–18%, which is determined by the solar cells’ type and operating temperature. The liquid, air and spectral beam splitter-based PVT modules commonly adopt the working fluids that use sensible heat to collect heat. Thus, the heat transfer coefficient would be limited by the temperature difference between the solar cells and the working fluids. In this regard, the refrigerant-based PVT module that uses latent heat as a major method to extract heat performs better in thermal efficiency. Therefore, the higher thermal efficiency that is above 100% could be attained when the refrigerant evaporating temperature is lower than the ambient temperature. The electrical efficiency according to the field tests of the photovoltaic effect-based solar cogeneration system is generally below 20%. The conversion efficiency of the photovoltaic effect would limit the electrical efficiency of the PVT module, and the high operating temperature of the solar cells would have an adverse effect on its efficiency. In this regard, the power cycle-based solar cogeneration system shows better performance in electrical efficiency. For instance, the electrical efficiency of the Stirling cycle-based solar cogeneration system and Brayton cycle-based solar cogeneration system could reach around 35% and 42%, respectively, but the electrical efficiency of the Rankine cycle-based solar cogeneration system is around 12%. The thermal efficiency of the power cycle-based solar cogeneration system is around 50%. To be noted, the thermal efficiency of the Brayton cycle-based solar cogeneration system has not been reported clearly. Not all points within the rectangle range could be obtained. The rectangle range exists only to better demonstrate the electrical and thermal efficiencies of each technology.

The exergy efficiency and temperature of supply heating are shown in Fig. 64 . The power cycle-based solar cogeneration system has higher exergy efficiency than most photovoltaic effect-based solar cogeneration systems. Nevertheless, the exergy efficiency of the PVT module using CSC could be improved. The temperature of supply heating of power cycle-based solar cogeneration system could reach above 100°C while the outlet temperature of PVT module is generally below 80°C due to the limitation of the solar cells’ temperature. In this regard, the power cycle-based solar cogeneration system has better performance; however, the construction, initial cost and maintenance of this kind of system are much higher than the photovoltaic effect-based solar cogeneration system. To be noted, not all points within the rectangle range could be obtained. The rectangle range exists only to better demonstrate the exergy efficiency and temperature of supply heating of each technology.

System applications

The system applications would differ considering the system scale, installation area, temperature of thermal energy output, usage requirement, etc. Table 10 summarizes the system applications (focus on thermal energy usage) of different solar cogeneration approaches. Small scale system is suitable for household usage, while middle scale system is preferable for public buildings, factories, schools, etc., and a large-scale system is recommended for a district, village, community, etc.

Summary of system applications of various solar cogeneration approaches [ 11 ]

In this article, efficient approaches to harvesting solar energy for cogeneration have been reviewed. The photovoltaics-based solar cogeneration systems and power cycle-based solar cogeneration systems have been introduced, classified and analysed. Furthermore, the comparison of energy and exergy efficiencies and system applications identified suitable applications for each technology analysed.

The power cycle-based solar cogeneration system could reach higher exergy efficiency with high-grade thermal energy output. However, the initial cost, system installation, system safety and maintenance difficulty would limit the application of this technology. Therefore, the power cycle-based solar cogeneration system is recommended for middle and large-scale applications such as district heating, power supply, etc. On the contrary, the photovoltaic-based solar cogeneration systems (known as PVT) are more applicable for small-scale use. The compact system arrangement and flexible installation area make the PVT system a promising application in distributed use systems (households, single buildings, etc.). In terms of delivery temperature, the power cycle-based solar cogeneration system has a higher outlet temperature (>100°C) while the temperature of supply of the PVT system is generally below 80°C. Thus, the power cycle-based solar cogeneration system is more versatile and the high-grade thermal energy could also be used for industrial pre-heating, desalination, district heating, etc. The photovoltaic-based solar cogeneration system is more preferable for domestic hot water supply, residential heating, spacing heating, etc.

The photovoltaics-based solar cogeneration system is suitable for urban areas while the power cycle-based solar cogeneration system is preferable in suburbs, but each technology has its merits, depending on the perspective chosen. In the future, more efficient and lower-cost technologies could be developed to realize solar cogeneration, for instance, higher efficiency PV modules, efficient solar thermal systems, etc. Distributed solar energy utilization technologies could be further expanded in cities. Therefore, efficient solar cogeneration methods could significantly reduce the demand for fossil fuels usage, decrease carbon emissions and contribute to sustainable development.

STUDY FUNDING

This publication has been jointly written within the cooperative project ‘Key technologies and demonstration of combined cooling, heating and power generation for low-carbon neighborhoods/buildings with clean energy—ChiNoZEN’. The authors gratefully acknowledge the funding support from the Ministry of Science and Technology of China (MOST project number 2019YFE0104900) and from the Research Council of Norway (NRC project number 304191—ENERGIX).

CONFLICT OF INTEREST

None declared.

Jian Yao is responsible for the methodology, investigation, data collation and plotting and writing of the original draft. Wenjie Liu is responsible for the methodology, investigation and data collation and plotting. Yifan Jiang is responsible for the methodology and data collation and plotting. Sihang Zheng is responsible for the methodology and data collation and plotting. Yao Zhao is responsible for the methodology, investigation and data collation and plotting. Yanjun Dai is responsible for the conceptualization, supervision, funding acquisition and writing of the review and editing. Junjie Zhu is responsible for the supervision, methodology and writing of the review and editing. Vojislav Novakovic is responsible for the supervision, methodology and writing of the review and editing.

The data used to support the findings of this study are available from the corresponding author upon request.

Congress TtSMPs The Fourteenth Five-Year Plan of Shanghai Municipality for National Economic and Social Development and the Outline of the 2035 Long-Term Goals . China : Shanghai Municipal Government , 2021

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Solar Panels for Low Power Energy Harvesting

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• Cite this conference paper

• Maritza Nuñez   ORCID: orcid.org/0000-0002-4972-0573 11 ,
• Carlos Gordón   ORCID: orcid.org/0000-0002-8031-2658 11 ,
• Clara Sánchez   ORCID: orcid.org/0000-0003-0499-4789 11 &
• Myriam Cumbajín   ORCID: orcid.org/0000-0001-9993-7095 12  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 678))

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• International Conference on Computer Science, Electronics and Industrial Engineering (CSEI)

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Solar panels are widely used nowadays to capture solar radiation and generate voltage, so they are being used for Energy Harvesting applications. The present work carries out the study of low power solar panels for energy storage applications, together with the DC-DC conversion and storage stage. The methodology carried out has been the design, simulation, fabrication and characterization of the elements that form the system. The elements that make up the system are 4 solar panels of 2.4 V and 80 mA, a voltage regulator element and rechargeable batteries. As a result, both in simulation and measurement, the mixed configuration (series-parallel) is the one that provides the best characteristics for its use, with a voltage of 4.57 V and a current of 127.3 mA, obtaining at the converter output a voltage of 19.44 V, concluding that the system meets the design expectations with which it was made, collecting energy, raising it and storing it, providing promising results for future applications.

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Acknowledgment

The authors thank the invaluable contribution of the Technological University Indoamerica, for his support in conducting the research project “ESTUDIO DE ALGORITMOS HIBRIDOS DE APRENDIZAJE AUTOMATICO PARA LA PREDICCIÓN DE GENERACIÓN DE ENERGÍAS RENOVABLES”, Project Code: 281.230.2022. Also, the authors thank the Technical University of Ambato and the “Dirección de Investigación y Desarrollo” (DIDE) for their support in conducting this research, in the execution of the project “Captación de Energía Limpia de Baja Potencia para Alimentación de Dispositivos de Quinta Generación (5G)”, approved by resolution “Nro. UTA-CONIN-2022-0015-R”. Project code: SFFISEI 07.

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GITED Research Group. Facultad de Ingeniería en Sistemas, Electrónica e Industrial, Universidad Técnica de Ambato, UTA, 180207, Ambato, Ecuador

Maritza Nuñez, Carlos Gordón & Clara Sánchez

SISAu Research Center, Facultad de Ingeniería y Tecnologías de la Información y la Comunicación, Universidad Tecnológica Indoamérica, UTI, 180103, Ambato, Ecuador

Myriam Cumbajín

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Nuñez, M., Gordón, C., Sánchez, C., Cumbajín, M. (2023). Solar Panels for Low Power Energy Harvesting. In: Garcia, M.V., Gordón-Gallegos, C. (eds) CSEI: International Conference on Computer Science, Electronics and Industrial Engineering (CSEI). CSEI 2022. Lecture Notes in Networks and Systems, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-031-30592-4_21

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Harvesting the Sun: On-Farm Opportunities and Challenges for Solar Development

At a glance, key challenge, policy insight, introduction.

By 2040, the amount of land needed to meet the United States’ growing energy requirements will increase by 27%, directly affecting an estimated 200,000 square kilometers (sq. km.) of land with new energy development (Trainor et al. 2016, 1-16). This is the projected result of both a changing energy portfolio and increasing demand.

Over the last decade, for example, advances in drilling technology have unlocked considerable energy potential from 1.3 million sq. km. of land—roughly twice the size of Alaska—that had previously been ill-suited for conventional oil and gas development. Fossil fuel production demands constant land expansion as available resources are depleted. Production will continue to encroach on new land for as long as demand for these fuels persists.

If the U.S. sets ambitious targets for renewable energy development, with the ultimate goal of reaching net-zero carbon emissions from energy by 2050, the share of land directly affected by new renewable energy infrastructure would increase dramatically (Larson et al. 2020, 1-345). Just meeting existing electricity demand with photovoltaics would require approximately 290,000 sq. km. of land, assuming 150 MW/ sq. km. of electricity generation, a capacity factor of 25%, as well as perfect load balancing (U.S. Energy Information Administration, n.d.). 1

In the lower 48 states, 63% of land is used for agricultural purposes (Economic Research Service, U. S. Department of Agriculture). As demand for energy infrastructure increases, land competition between energy and agricultural production will inevitably grow—as will the potential advantages of co-locating these land uses where possible.

We have already seen the shale boom drive mineral developers to many agricultural regions, and the result has been a surge in domestic fuel production and a significant secondary source of income for many farm operators (Hitaj and Suttles 2016, 1-47). In some states, like Oklahoma and Pennsylvania, oil and gas development leases provide up to 6% of gross cash farm income, and an even greater share of net income.

In 2014, more than 10% of farms in 9 American states received energy production-related payments. Average payments can be sizable, exceeding $150,000 in Pennsylvania and North Dakota. These leases can be immensely valuable to farm owners, since most American farms are small and depend on off-farm sources of income to remain operational (Economic Research Service, n.d; U. S. Department of Agriculture, n.d.).

Although the co-location of energy infrastructure on farmland has historically been mostly limited to oil and gas development, on-farm solar development is increasingly becoming a financially viable and environmentally friendly alternative on American farmland. On-farm solar development can help meet the country’s swelling demand for carbon-free energy, offer farmers and rural communities a consistent and long-term stream of income, and even boost agricultural productivity under the right circumstances.

However, realizing these co-benefits at scale will require a long-term commitment and innovative solutions from local, state, and federal policymakers. In this policy digest, we lay out why farmers choose to lease their mineral rights, the unpredictable costs of on-farm oil and gas development, and why solar could be a better alternative.

How Mineral Leasing Works

In the United States, most subsurface rights—the rights to minerals beneath the ground—are privately owned by individuals (Fitzgerald 2014, 1-7). Typically, energy companies interested in extracting minerals will lease land from owners rather than purchase the land outright. This is an effective way for the development companies to reduce capital expenses – reflecting their singular and short-term interest in the land.

The compensation structure for these land leases typically includes an upfront payment as well as a royalty, which reflects a share of the gross revenue of any oil or gas that is ultimately extracted (typically between 13 to 21%) (Brown et al. 2016, 23-38). Crucially, energy development companies can deduct expenses associated with transportation and processing from landowners’ royalty payments (Fitzgerald 2014, 1-7).

As one might expect, there is a substantial amount of information asymmetry between the lessee and the lessor in these arrangements. Company representatives have extensive experience negotiating agreements, while farm owners may only negotiate a mineral lease once in a lifetime. In addition, because oil and gas resources at a given site require expert analysis to approximate, the energy company generally has a more complete understanding of expected production than the mineral rights owner.

Fuel prices are also a considerable source of revenue variability. In 2020, for example, the U.S. government saw its royalties from mineral rights fall from $2 billion between March and June, compared to $4 billion in the same period last year, because of the precipitous drop in fuel prices and production induced by the COVID-19 pandemic (Knight 2020).

Landowners have little recourse in response to disruptions in expected revenue since contracts can last for many years. And when prices fall, the post-production costs deducted by developers can eat into farmers’ royalty checks, especially if their lease agreements do not address allowable deductions (Cusick and Sisk 2018).

Not only are oil and gas leases often uncertain value propositions, they also come with a number of serious economic and environmental risks for farmers (U.S. Geological Survey, n.d.). At best, development leads to increased traffic and noise pollution, and places increased demands on local water resources. At worst, oil and gas development leads to water and soil contamination and reduced land productivity (Environmental Protection Agency 2015, 1-25).

A typical shale gas well can use 2 to 4 million gallons of water during fracturing, the process by which gas resources are extricated from subterranean rock formations (U.S. Department of Energy 2009, 1-98). Wells can drive-up local water prices or compel farmers to modify their operations (Dutzik et al. 2012, 1-43).

Accidental water contamination or improper gas flaring can sicken or even kill livestock. Furthermore, according to a 2019 Energy Research & Social Science study that surveyed farmers in four midwestern states, many respondents reported relying on themselves or family to complete land reclamation efforts following oil or gas development (Haggerty et al. 2019, 84-92).

For farmers who own their mineral rights and are approached by a developer, the security of a secondary source of income—even one that comes with the uncertainties of energy land leases—can be attractive. The promise of additional revenue often outweighs the environmental risks, an indication of the substantial economic pressures many farmers face.

Yet for many rural communities, mineral leases may fail to provide much long-term benefit. The precise economic effect of natural gas development remains an area of active research. A 2016 study found that employment and wages can grow in the first four years of gas development, but decline to pre-boom levels over time (Komarek 2016, 1-17).

A 2014 study of the oil and gas boom in the American West in the 1970s and 1980s actually found that per capita incomes, following the bust, were 6% lower than pre-boom levels and that unemployment compensation remained elevated throughout the post-bust period (Jacobsen and Parker 2016, 1092-1128). The authors suggest that overspecialization in infrastructure and skills specific to the boom limited market participants’ ability to find new business and employment opportunities once the demand for extraction services receded and economic fundamentals changed.

Perhaps most importantly, a closer look at payment statistics reveals that the financial rewards of oil and gas development are not equally available to all American farmers, and instead largely accrue to a small subset (Hitaj et al. 2018, 1-31). In 2014, the top 10% of farmers receiving oil and gas payments received 18 times more money than the bottom 50% of farmers receiving payments. The mean payment to all farmers receiving oil and gas royalties was $43,736, dwarfing the median payment of just $6,600.

Is Solar a Better Option?

For all of their economic risks and environmental harms, mineral leases demonstrate an opportunity for the co-location of energy and agricultural production. On-farm solar (or agrivoltaics) can offer farmers and rural landowners a smaller environmental footprint and fewer economic risks than oil and gas development, while still providing a reliable secondary source of income. As the country’s energy demand affects more and more land, agrivoltaics can also play a crucial role in accelerating the transition to renewables.

First and foremost, solar panels present almost no risk of soil or water contamination when installed and maintained properly. In terms of water consumption, photovoltaic maintenance only requires enough water to occasionally wash dust and grime from panel surfaces (Clarke 2014). Compared to an oil or gas well, this water use is negligible.

Further, solar panels produce no additional toxic waste, and aside from soil disturbance during installation or removal, they have little long-term impact on the productivity of the land on which they are sited. While larger solar installations can have negative effects on soil and vegetation, there are a number of measures—like careful siting, prudent landscaping, and re-vegetation—that can mitigate these concerns (Dhar et al. 2020, 134602). In general, solar panels have a dramatically more favorable environmental profile than traditional sources of power generation (Turney and Fthenakis 2011, 3261-3270).

Solar power is also a flexible, reliable, and scalable source of energy, especially on agricultural land. Whereas oil and gas wells require a minimum of 5-10 acres of land, solar can be deployed to whatever scale a farm owner desires or is able to accommodate (MineralWise, n.d.). This means that solar can be developed on land that is already unused or unirrigated by farmers, minimizing disruptions to existing farm production.

In 2011, the National Renewable Energy Laboratory estimated that Colorado had over 1,200 sq. km. of non-irrigated corners of center-pivot irrigation fields (Roberts 2011, 1-11). This land could, in theory, support 890 sq. km. of solar fields without compromising agricultural productivity.

While a farmer’s opportunity to capitalize on mineral rights is entirely dependent on whether or not there is an accessible oil or gas basin, photovoltaics are an economically viable investment for landowners across the country, and solar power is at its most productive (Adeh et al. 2019, 11442) when installed on croplands (McDonnell 2020). While temperature and average cloud cover determine the capacity factor of cells, solar is already being successfully deployed from Arizona to Maine.

Solar power is also immune to hyperbolic declines in production, as is possible with oil and gas drilling (see Figure 1) (U.S. Energy Information Administration 2020). Instead, solar leases are long-term (Moore 2017), typically lasting around 20 years, with fixed rental contracts instead of royalties (White). This reduces the economic risk borne by landowners, and while there is certainly risk associated with long-term agreements, the fixed payment structure—as well as fairly predictable life-cycle costs—can help farmers avoid imbalanced negotiations with developers and plan for the future (Xiarchos and Vick 2011, 1-86).

In some cases, revenue from solar development can eclipse the revenue generated by harvest yields (Bookwalter 2019), though other studies have suggested that payback periods for on-farm solar projects are still too long (Colorado State University Extension, n.d., 45-48).

Still, the benefits of solar panels on farmland could extend far beyond simply providing a supplementary income source; they can, in the best case, actively enhance farm operations and improve agricultural yield. Agrivoltaics—the siting of elevated solar panels above crops, which continue to be cultivated—can confer a number of synergistic benefits, which oil and gas development cannot emulate (Barron-Gafford et al. 2019, 848-855).

Agrivoltaics are capable of reducing transpiration of water from plants and the evaporation of water from soil, thereby reducing farmers’ water use. Solar panels can also mitigate some of the light and heat stress that can have an adverse effect on crop photosynthesis.

Finally, transpired water has a cooling effect on solar panels, improving their efficiency by at least 1% (Tricoles 2017). While the effects of agrivoltaics on crop yield varies by species, some study results have shown a doubling in total fruit production and water efficiency in shade-tolerant and temperature-sensitive crops (Barron-Gafford et al. 2019, 848-855).

In the context of the wider economy, agrivoltaics can serve as a mitigant (Agostini et al. 2021, 116102) against market shocks or crop shortages and can help meet the energy demands of several farm operations such as pumping water, refrigeration, lighting, and sprinkler systems (Xiarchos and Vick 2011, 1-86). The benefits of agrivoltaics extend to livestock farming as well. The co-siting of photovoltaics on a rabbit farm, for example, was recently shown to reduce operating costs by up to 8%, increase revenue by 17%, and cut fencing-related costs (Lytle et al. 2021, 124476).

Remaining Challenges and Opportunities

The opportunities offered by on-farm solar development are considerable, especially when compared to mineral leases. However, there are some remaining economic and policy challenges that demand policy solutions before the full potential of co-locating agriculture and solar generation can be fully realized. These solutions would promote the (a) provision of public funds for rural energy development and incentive programs, (b) the circulation of tools and information that can help farmers make financially sound decisions, and (c) the implementation of streamlined land use policies to facilitate solar development and protect crop yields.

Fund Solar Projects

Continued public funding is necessary to encourage the adoption of solar resources and ensure that such projects make financial sense for farm operators . There are already a number of (Tennessee Department of Energy and Conservation 2020; Massachusetts Farm Energy Program, n.d.) state and federal funding opportunities for farmers interested in investing in on-farm renewable energy projects, including a 30% federal business energy investment tax credit and a 25% Rural Energy for America Program grant from the U.S. Department of Agriculture (neither of which are available to oil and gas developers) (Hay 2016, 1-27).

However, for agricultural land to host meaningful solar generation capacity and support a rapid transition to renewables, these funding opportunities ought to be accessible to a much wider community of farmers. Specifically, federal agencies, like the USDA, should direct greater public funding toward on-farm solar deployment. The availability of external funding is a significant determinant of the ultimate profitability and size of renewable energy systems adopted by farmers (Bazen and Brown 2009, 748-754; Beckman and Xiarchos 2013, 322-330).

In recent years, the federal government has aggressively stepped up its support of solar projects in rural America. Between 2002 and 2019, the USDA distributed over $7.7 billion in grant aid to support renewable energy development in rural communities (USDA, n.d.). Along with anaerobic digesters, solar projects have been the largest recipients of this USDA support in the past five years.

These targeted grants and loan guarantees have helped small businesses cut their energy costs and energy consumption (USDA 2019). In 2015 alone, solar projects financed by the USDA’s Rural Energy for America Program generated 530,000 MWhs of electricity (Hitaj and Suttles 2016, 1-47). Still, federal support for investment in agricultural infrastructure remains relatively modest and should be significantly expanded in order to meet changing energy demands.

Federal loan and grant programs still play a critical role in making solar development a profitable proposition for farm operators and in sustaining investor interest (Petrovich et al. 2021, 106856). In 2015 and 2016, Colorado State University conducted 30 solar assessments for farmers interested in renewable systems deployment in pivots—land left unused owing to center-pivot irrigation (Colorado State University Extension, n.d. 45-48). Those researchers found that the average solar array would have generated lifetime energy savings of $156,000, in addition to $23,000 in payments for excess electricity sold back to the grid. The up-front cost of the average solar array was $137,000, before incorporating any federal grants and tax credits. Accounting for such credits, the total cost would fall to $71,000, significantly reducing the payback period and resulting in a return on investment of 4.7%.

Inform Farmers

Farm operators and rural communities need to be empowered with the information to make financially and environmentally sound decisions regarding on-farm energy development. One of the central goals of policymakers interested in facilitating on-farm solar development should be to help clarify the full financial picture of a proposed project. Absent such support, it would be easy to discount on-farm renewable energy based on revenue figures alone: According to a USDA. analysis, in 2014, the average payment to farm operators for leasing wind rights was $8,287, substantially less than the average payment of $43,736 from oil and gas (Hitaj et al. 2018, 1-31). While the USDA did not consider revenues associated with on-farm solar projects in that study, modern solar and wind installations have similar costs/kW and capacity factors in the same ballpark, so landowners can expect lease revenue from solar projects to be similarly modest (Mey 2020; SolarLandLease, n.d.). It is worth noting, however, that wind power is significantly less energy dense than photovoltaic power in terms of kW/acre. This means that while costs/kW and capacity factors may be similar across both technologies, photovoltaics may offer farmers and developers the flexibility to generate more electricity from the same acreage.

Sound information and technical guidance, however, could allow on-farm solar projects to be financially viable investments that circumvent many of the risks associated with traditional oil and gas development. A key advantage of solar development, over oil or gas, is that solar radiation is much easier to estimate than subsurface mineral availability.

Whereas oil and gas are found in relatively dense pockets of geological formations and require extensive site exploration to uncover and approximate, solar radiation is easily mappable based on geographic location, local topography, and surrounding vegetation. In fact, the National Renewable Energy Laboratory offers a solar calculator tool online that allows users to estimate the performance of solar facilities, based on their location and other variables (National Renewable Energy Laboratory, n.d.).

But solar radiation is just one of several inputs. For farmers considering leasing their land for solar development, the value of their land and the range of possible per-acre rental fees they could collect is essential information needed for negotiations with developers. According to Strategic Solar Group, annual per-acre rents for larger tracts of solar land can range from $300 to $800 depending on a state’s average capacity factor and land availability (White).

To help farmers navigate these financial considerations—for land leases and personal projects alike—federally-supported, no-cost energy audits should be made available to all farm owners (New York State Energy Research and Development Authority, n.d.). These audits would help operators identify possible applications of solar power and understand the costs, savings, and payback periods of possible solar development (among other energy-saving and emissions-reducing measures).

Between 2016 and 2019, CSU Rural Energy Center administered its Farm Assessments for Solar Energy program, which provided 60 free evaluations to farmers about the feasibility of solar installations on their properties (Colorado State University Extension, n.d.). Colorado’s Energy Office also administers an Agricultural Energy Efficiency program, which provides free audits for eligible farms seeking to reduce energy expenses and implement cost-saving measures (Colorado Energy Office, n.d.).

Further expanding the reach of such programs—and leveraging emerging technologies such as LiDAR to improve and streamline auditing—could protect rural landowners in negotiations in a way that has never really been possible with oil and gas leases. These audits could give landowners confidence in moving forward with rental agreements or personal development projects—lending assurance that their investments are sound and ultimate revenues are fair, even if those revenues are relatively modest.

Clarify Policies

Land use, planning, and energy policies need to be clarified and made more consistent. On-farm solar development has the potential to directly compete with existing cropland if not planned and developed with sustained or improved agricultural productivity in mind. Finding this balance remains a major focus for state and local officials and policymakers (Bergan and Braun 2019).

The Grow Solar Initiative, a USDA-funded effort to boost the solar production potential of three Midwestern states, observes that regulatory and statutory inconsistencies for siting projects can be major obstacles to the growth of the solar industry (Grow Solar 2015). As the opportunities for shared land use become better understood, local and state governments need to outline clear and detailed guidelines for what constitutes appropriate and allowable shared use of agricultural land.

In 2019, for example, North Dakota’s Public Service Commission approved the construction of a 200 MW utility-scale project on 1,600 acres of prime farmland (Lee 2019). Under existing North Dakota laws, this land would have been excluded from development if the overall effect on agricultural yields was perceived to be too large. Existing laws, however, did not specifically prescribe what constituted a large effect on agricultural yields, so the commission had to deliberate (unearthing microform documents from the 1970s in the process) before reaching a decision.

Like North Dakota, Michigan has had to wrestle with decades-old laws blocking more rapid solar development. The state’s Farmland and Open Space Preservation Program, passed in 1975, requires participating farmers to maintain their farmland for agricultural uses in exchange for tax incentives and exemptions (Michigan Department of Agriculture and Rural Development). But the state decided in 2017 that commercial solar development was not a permissible activity on land preserved by the program, excluding one-third of the state’s farmland from solar electricity generation (the program covered 3 million acres in 2020) (Malewitz 2019; Michigan Department of Agriculture & Rural Development 2020, 1-20).

Farmers interested in solar development could have exited the program, but they would have had to pay back the previous seven years of tax credits along with 6% interest—a prohibitive barrier (Malewitz 2019). It took another two years before the state revised its policy, allowing solar development for commercial and personal purposes on preserved farmland (Michigan Department of Agriculture & Rural Development 2019).

Final Thoughts

The use of agricultural land for solar electricity generation can support the U.S. farm sector, strengthen rural economies, and facilitate the country’s energy transition. The shale gas revolution of the last decade has offered valuable lessons for farmers and energy developers about how energy lease agreements should be structured in order to both promote energy development and protect farmers, local resources, and surrounding ecosystems. On-farm solar power eliminates many of the most serious environmental risks of oil and gas development and can, if deployed correctly, increase the productivity of crops and livestock.

However, inconsistent regional land use policies, insufficient federal funding for development and research, and the inadequate availability of information mean that the full potential of solar development on American farmland has yet to be realized. The abundance of agricultural land in the United States could be a competitive advantage in national efforts to decarbonize, but until the necessary policy tools are leveraged, it is more likely to create unnecessary land competition.

Anuj Krishnamurthy

Oscar Serpell

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• Author’s calculation: sq. km. = (((annual electricity demand in MWh/365 days)/24hours)*4)/150 MW [ ↩ ]

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New solar cells break efficiency record – they could eventually supercharge how we get energy from the Sun

Associate Professor of Materials, University of Oxford

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The sight of solar panels installed on rooftops and large energy farms has become commonplace in many regions around the world. Even in grey and rainy UK, solar power is becoming a major player in electricity generation.

This surge in solar is fuelled by two key developments. First, scientists, engineers and those in industry are learning how to make solar panels by the billions. Every fabrication step is meticulously optimised to produce them very cheaply. The second and most significant is the relentless increase in the panels’ power conversion efficiency – a measure of how much sunlight can be transformed into electricity.

The higher the efficiency of solar panels , the cheaper the electricity. This might make you wonder: just how efficient can we expect solar energy to become? And will it make a dent in our energy bills?

Current commercially available solar panels convert about 20-22% of sunlight into electrical power. However, new research published in Nature has shown that future solar panels could reach efficiencies as high as 34% by exploiting a new technology called tandem solar cells. The research demonstrates a record power conversion efficiency for tandem solar cells.

What are tandem solar cells?

Traditional solar cells are made using a single material to absorb sunlight. Currently, almost all solar panels are made from silicon – the same material at the core of microchips. While silicon is a mature and reliable material, its efficiency is limited to about 29%.

To overcome this limit, scientists have turned to tandem solar cells, which stack two solar materials on top of each other to capture more of the Sun’s energy.

In the new nature paper, a team of researchers at the energy giant LONGi has reported a new tandem solar cell that combines silicon and perovskite materials. Thanks to their improved sunlight harvesting, the new perovskite-silicon tandem has achieved a world record 33.89% efficiency.

Perovskite solar materials, which were discovered less than two decades ago , have emerged as the ideal complement to the established silicon technology. The secret lies in their light absorption tuneability . Perovskite materials can capture high energy, blue light more efficiently than silicon.

In this way, energy losses are avoided and the total tandem efficiency increases. Other materials, called III-V semiconductors, have also been used in tandem cells and achieved higher efficiencies. The problem is they are hard to produce and expensive, so only small solar cells can be made in combination with focused light.

The scientific community is putting tremendous effort into perovskite solar cells. They have kept a phenomenal pace of development with efficiencies (for a single cell in the lab) rising from 14% to 26% in only 10 years. Such advancements enabled their integration into ultra-high-efficiency tandem solar cells, demonstrating a pathway to scale photovoltaic technology to the trillions of Watts the world needs to decarbonise our energy production.

The cost of solar electricity

The new record-breaking tandem cells can capture an additional 60% of solar energy. This means fewer panels are needed to produce the same energy, reducing installation costs and the land (or roof area) required for solar farms.

It also means that power plant operators will generate solar energy at a higher profit. However, due to the way that electricity prices are set in the UK , consumers may never notice a difference in their electricity bills. The real difference comes when you consider rooftop solar installations where the area is constrained and the space has to be exploited effectively.

The price of rooftop solar power is calculated based on two key measures. First, the total cost to install solar panels on your roof, and second, how much electricity they will generate over their 25 years of operation. While the installation cost is easy to obtain, the revenues from generating solar electricity at home are a bit more nuanced. You can save money by using less energy from the grid, especially in periods when it is costly, and you can also sell some of your surplus electricity back to the grid.

However, the grid operators will pay you a very small price for this electricity, so sometimes it is better to use a battery and store the energy so you can use it at night. Using average considerations for a typical British household, I have calculated the cash savings consumers would gain from rooftop solar electricity depending on the efficiency of the panels.

If we can improve panel efficiency from 22% to 34% without increasing the installation cost, savings in electricity bills will rise from £558ְ/year up to £709/year. A 27% bump in cash savings that would make solar rooftops extremely attractive, even in grey and cloudy Britain.

So when can we buy these new solar panels?

As research continues, considerable efforts are being made to scale up this technology and ensure its long-term durability. The record breaking tandem cells are made in laboratories and are smaller than a postage stamp. Translating such high performance to metre-square areas remains a vast challenge.

Yet, we are making progress. Earlier this month, Oxford PV, a solar manufacturer at the forefront of perovskite technology, announced the first sale of its newly developed tandem solar panels. They have successfully tackled the challenges of integrating two solar materials and making durable and reliable panels. While they are still far from 34% efficiencies, their work shows a promising route for next generation solar cells.

Another consideration is the sustainability of the materials used in tandem solar panels. Extracting and processing some of the minerals in solar panels can be hugely energy intensive . Besides silicon, perovskite solar cells require the elements lead, carbon, iodine and bromine as components to make them work properly. Connecting perovskite and silicon also requires scarce materials containing an element called indium , so there is plenty of research still required to address these difficulties.

Despite the challenges, the scientific and industrial community remains committed to developing tandem solar devices that could be integrated into almost anything: cars, buildings and planes.

The recent developments toward high efficiency perovskite-silicon tandem cells indicate a bright future for solar power, ensuring solar continues to play a more prominent role in the global transition to renewable energy.

• Renewable energy
• Environment
• Solar energy
• Green energy
• Perovskite solar cells

Economics Editor

Deputy Vice-Chancellor (Indigenous Strategy and Services)

Director and Chief of Staff, Indigenous Portfolio

Chief People & Culture Officer

Lecturer / senior lecturer in construction and project management.

A naphthalene–phenanthro[9,10- d ]imidazole-based π-conjugated molecule with a self-assembly-induced tuneable multiple fluorescence output exhibits artificial light-harvesting properties †

First published on 16th September 2024

Introduction

Results and discussion.

Spectral analysis

Morphological variation

To verify the hydrolytic stability of L1 , we recorded the UV-Vis absorption and steady-state emission spectra of the precursors imidazole-based aldehyde (A1) and naphthalene diamine (B1) in THF:water mixtures of varying polarity. Noticeable differences in the electronic spectral pattern of the starting aldehyde (A1) and diamine (B1) of L1 suggest that L1 is stable in aqueous media (Fig. S7, ESI † ). Furthermore, the emission colour of the parental aldehyde (A1) and the diamine (B1) in a THF:water mixture with varied polarity (Fig. S8, ESI † ) confirmed that the visually detectable changes in the emission colour of L1 upon altering the polarity of the medium are due to aggregation induced by changing the medium polarity and not due to hydrolytic decomposition.

DFT studies

Ab initio molecular dynamics

Light-harvesting properties

Materials and methods

Synthesis of l1, self-assembly of l1, high-resolution scanning electron microscopy (hr-sem), dynamic light scattering (dls) analysis, fourier transform infrared spectroscopy (ft-ir), uv-vis spectroscopy, fluorescence spectroscopy, x-ray diffraction (xrd) analysis, dft calculations.

Time-dependent (TD)-DFT calculations of the complex geometries were performed at the ωB97XD/def2-TZVPP level. The hybrid meta-GGA functional ωB97XD has a 100% fraction of HF exchange at long-range in addition to about 22% at short-range and also contains empirical dispersion terms. 64 Unlike PBE0, the long-range-corrected functional ωB97XD properly describes the ground and excited state properties of complex molecules. 68 The importance of long-range corrected functionals for charged systems and for describing CT states is explained in previous studies. 69,70 The ten lowest vertical excitation energies were calculated based upon time-dependent density functional theory (TDDFT), utilizing optimized geometries.

Periodic boundary condition calculations were performed using the Vienna Ab initio Simulation Package (VASP) using the plane-wave basis set. 71,72 All calculations were done on the PBE level of theory using Grimme empirical dispersion. 73 Ab initio MD simulations were performed using the isothermal–isobaric (NPT) ensemble until equilibrium was achieved. 74,75 The MD simulation was performed at 300 K using the Langevin thermostat and standard pressure, and the timestep was set to 1 fs.

Author contributions

Data availability, conflicts of interest, acknowledgements.

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Advancements and future prospects in ocean wave energy harvesting technology based on micro-energy technology.

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Yang, W.; Peng, J.; Chen, Q.; Zhao, S.; Zhuo, R.; Luo, Y.; Gao, L. Advancements and Future Prospects in Ocean Wave Energy Harvesting Technology Based on Micro-Energy Technology. Micromachines 2024 , 15 , 1199. https://doi.org/10.3390/mi15101199

Yang W, Peng J, Chen Q, Zhao S, Zhuo R, Luo Y, Gao L. Advancements and Future Prospects in Ocean Wave Energy Harvesting Technology Based on Micro-Energy Technology. Micromachines . 2024; 15(10):1199. https://doi.org/10.3390/mi15101199

Yang, Weihong, Jiaxin Peng, Qiulin Chen, Sicheng Zhao, Ran Zhuo, Yan Luo, and Lingxiao Gao. 2024. "Advancements and Future Prospects in Ocean Wave Energy Harvesting Technology Based on Micro-Energy Technology" Micromachines 15, no. 10: 1199. https://doi.org/10.3390/mi15101199

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