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Greater adoption of renewable energy technologies by households is a key component of decarbonization and energy transition goals. Although existing literature has examined how sociodemographic characteristics, "green" preferences, and peer effects impact adoption of new energy technology, the role of behavioral preferences has not been adequately studied. In this paper, we examine the effect of two types of behavioral preferences, namely the degree of risk tolerance (risk preference) and attitude toward delayed reward (time preference) on the contract decision to lease or own a solar photovoltaic (PV) system. We develop a theoretical framework to show that the effect of risk and time preferences on the relative utilities from the two contracts is monotonic: Lower risk aversion and lower discount rate (more patience) imply a higher chance of solar PV ownership. To test these predictions empirically, we first estimate preference parameters (risk aversion and discount rate) from laboratory data collected from solar PV adopters. We then combine the parameter estimates with data on actual solar PV contract choice to examine the relationship between solar PV adopters' time and risk preferences and their lease‐versus‐own choice. Our regression results confirm that less risk averse individuals have a higher tendency to choose the ownership option, whereas more patient individuals are (weakly) more likely to own their solar PV systems. These findings contribute to a greater understanding of the role of behavioral factors in household decisions related to energy technologies. [ABSTRACT FROM AUTHOR]

Renewable energy generation, especially in the form of rooftop solar photovoltaic (PV) systems, is expected to play an important role in South Africa's future energy mix. The national government, along with various municipalities at local government level, are introducing regulatory incentives to promote the uptake of solar PV systems in the private sector. These incentives include feed-in tariffs, capital subsidies and tax benefits. In addition to the regulatory incentives, there are non-regulatory drivers that motivate private property owners to pursue solar PV systems. These drivers include environmental considerations, cost savings, energy security, tenant requirements, and green-energy finance. This study examines the various regulatory incentives to pursue solar PV systems available to private property owners located in the City of Cape Town Metropolitan Municipality. The City of Cape Town is selected as a single case-study area as the municipality creates a conducive environment through its regulatory framework for private property owners to pursue private embedded generation systems. The study examines the different incentives applicable to residential and non-residential property owners. The research also establishes to what extent the regulatory incentives influence private property owners to pursue solar PV systems compared to non-regulatory drivers and benefits. The study reveals that the various regulatory incentives differ for residential and non-residential property owners. These differences impact the extent to which the regulatory incentives motivate particular private property owners to pursue solar PV systems. The research suggests that, although regulatory incentives play a role in private property owners' decision-making process, the non-regulatory drivers are the main motivating factors for private property owners pursuing solar PV systems. [ABSTRACT FROM AUTHOR]

Poor access to electricity in rural communities has been linked to a poor educational system, as electricity is essential for supporting laboratories, technical practice, and long study hours for students. Therefore, this work presents the techno-economic analysis of a hybrid solar PV–agro-wastes (syngas) energy system for electricity, heat, and cooling generation to improve energy access in rural schools. The system is located in Ghana at Tuna (lat. 9°29′18.28″ N and long. 2°25′51.02″ W) and serves a secondary school for enhanced quality education. The system relies on agro-waste (gasifier-generator) and sunlight (solar PV), with a battery energy storage system, to meet the school's energy demand. The study employs HOMER Pro Version 3.16.2 software to comprehensively analyze technical, economic, and environmental aspects. The system can generate 221,621 kWh of electricity (at a unit cost of electricity of 0.295 EUR/kWh) and 110,896 kWh of thermal energy yearly. The cost of electricity from the proposed system is cheaper than the cost of electricity from an equivalent diesel generator at 0.380 EUR/kWh. The thermal energy can meet the heating demand of the school in addition to powering a vapor absorption chiller. The system is environmentally friendly, with the capacity to sink 0.526 kg of CO2 yearly. Government policies that moderate interest rates for bioenergy/solar PV systems and social solution on feedstock pricing will favor the economic sustainability of the proposed system. The system will address the energy access challenge (SDG 7), enhance the quality of education (SDG 4), and contribute to climate mitigation through carbon sequestration (SDG 13). [ABSTRACT FROM AUTHOR]

Mitigating CO2 emissions is essential to reduce climate change and its adverse effects on ecosystems. Photovoltaic electricity is 30 times less carbon-intensive than coal-based electricity, making solar PV an attractive option in reducing electricity demand from fossil-fuel-based sources. This study looks into utilizing solar PV electricity production on a large university campus in an effort to reduce CO2 emissions. The study involved investigating 153 buildings on the campus, spanning nine years of data, from 2015 to 2023. The study comprised four key phases. In the first phase, PVWatts gathered data to predict PV-generated energy. This was the foundation for Phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity. The SHAP (SHapley Additive exPlanations) technique was incorporated into the proposed framework to enhance model explainability. Phase III involved calculating historical CO2 emissions based on past energy consumption data, providing a baseline for comparison. A meta-learning algorithm was implemented in Phase IV to project future CO2 emissions post-solar PV installation. This comparison estimated a potential emissions reduction and assessed the university's progress toward its net-zero emissions goals. The study's findings suggest that solar PV implementation could reduce the campus's CO2 footprint by approximately 18% for the studied cluster of buildings, supporting sustainability and cleaner energy use on the campus. [ABSTRACT FROM AUTHOR]

This paper proposes a high-voltage (HV) gain non-inverting an improved Z-source (ZS)-derived non-isolated boost converter topology. The achievement of HV gain is facilitated through the astute's integration of a ZS, a switched capacitor (SC), and a boost cell that operates with a reduced duty cycle. In comparison with earlier developed topologies, the proposed topology uses fewer components and provides HV gain. In addition to this, the converter reduces voltage stress on semiconductors and passive components. As a result, low-voltage rating (small Rds(on)) switches and diodes can be used to reduce conduction and reverse recovery losses, respectively. The converter emphasises on reducing device count while increasing device utilisation factor and accommodating a broad range of input variations; thus, it has potential application in HV low-power distributed photovoltaic (PV) systems. Additionally, the converter draws continuous input current from the low-voltage PV terminal. The proposed converter's operating principle is thoroughly examined in stead state. A 400 W prototype has been developed to verify the operational efficiency and theoretical assertions of the converter, with a switching frequency of 100 kHz. The converter obtained a peak efficiency of 93.25%. [ABSTRACT FROM AUTHOR]

The modern power systems incorporate high penetration of renewable is a large, composite, interconnected network with dynamic behavior. The small disturbances occurring in the system may induce low-frequency oscillations (LFOs) in the system. If the (LFOs) are not suppressed within a stipulated time, it may cause system islanding or even blackouts. Hence, it is essential to investigate the behavior of the system under various levels of disturbances and control action must be taken to damp these oscillations. The established approach to damping the LFOs is by installing power system stabilizers (PSS). PSS uses the local signals from generators to control the oscillations. The dominant source of inter-area oscillations in power systems is due to overloaded weak interconnected lines, converter-interfaced generation, and the action of the high gain exciter present in the system. Consequently, wide area control is needed to control the inter-area oscillations existent in the system. This paper developed a coordinated design of conventional PSS, static compensator, renewable converters, and wide area controller for damping the local and inter-area oscillations in renewable incorporated power systems. The performance of the developed controller is evaluated through the time domain analysis and eigenvalue analysis. A comparison of the introduced controller has been done with other standard conventional methods. The choice of input signals for the wide area controller from the wide-area measurement system is done based on the controllability index. Additionally, the location of the controller must be identified to dampen the inter-area oscillations in the system. In this paper, the controllability index is calculated to find out the highly affected wide area signals for considering it as the feedback signal to a developed controller. The location of the controller is recognized by computing the participation factor. The developed controller has experimented on renewable incorporated large study power systems when time delay and noise are present in wide area signals. [ABSTRACT FROM AUTHOR]

The increasing demand for cleaner energy solutions has led to the exploration of renewable energy sources, particularly solar photovoltaic (PV) technology. This research focuses on the thermal management of solar PV systems in Tembalang Village, Central Java, Indonesia, where the efficiency of PV panels is significantly affected by temperature fluctuations caused by environmental factors such as pollution, wind speed, humidity, solar radiation, and ambient temperature. The study highlights the importance of effective thermal management systems to enhance the energy efficiency of PV panels, which typically operate at an average efficiency of around 20%. As temperatures rise, the efficiency of these panels can drop by 10% to 25%, necessitating the implementation of cooling technologies. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development. [ABSTRACT FROM AUTHOR]

The advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid. [ABSTRACT FROM AUTHOR]

We examine technology adoption and consumer welfare disparities across demographic groups using data from an online solar photovoltaic (PV) marketplace. Low-income households are 25% less likely to purchase solar through the platform and obtain 53% lower expected consumer surplus than high-income households. Moreover, Black and Hispanic households are relatively less likely to purchase solar through the platform and obtain lower consumer surplus than White and Asian households. We develop a method to decompose the drivers of consumer welfare disparities between demographic groups. Differences in demand fully account for the consumer surplus disparities between high- and low-income households and between White and Hispanic households. However, supply-side factors explain 37% of the consumer surplus gap between White and Black households. Black households get relatively fewer bids and face higher prices, and installers have higher implied costs to serve them. Finally, we assess counterfactuals that offer targeted price discounts to certain demographic groups. [ABSTRACT FROM AUTHOR]

Poor access to electricity in rural communities has been linked to a poor educational system, as electricity is essential for supporting laboratories, technical practice, and long study hours for students. Therefore, this work presents the techno-economic analysis of a hybrid solar PV–agro-wastes (syngas) energy system for electricity, heat, and cooling generation to improve energy access in rural schools. The system is located in Ghana at Tuna (lat. 9°29′18.28″ N and long. 2°25′51.02″ W) and serves a secondary school for enhanced quality education. The system relies on agro-waste (gasifier-generator) and sunlight (solar PV), with a battery energy storage system, to meet the school’s energy demand. The study employs HOMER Pro Version 3.16.2 software to comprehensively analyze technical, economic, and environmental aspects. The system can generate 221,621 kWh of electricity (at a unit cost of electricity of 0.295 EUR/kWh) and 110,896 kWh of thermal energy yearly. The cost of electricity from the proposed system is cheaper than the cost of electricity from an equivalent diesel generator at 0.380 EUR/kWh. The thermal energy can meet the heating demand of the school in addition to powering a vapor absorption chiller. The system is environmentally friendly, with the capacity to sink 0.526 kg of CO2 yearly. Government policies that moderate interest rates for bioenergy/solar PV systems and social solution on feedstock pricing will favor the economic sustainability of the proposed system. The system will address the energy access challenge (SDG 7), enhance the quality of education (SDG 4), and contribute to climate mitigation through carbon sequestration (SDG 13).

The main challenge in many countries is how to deal with climate change, particularly reducing CO2 emissions which is the most crucial aspect. Therefore, this paper focuses on adopting renewable energy in a fuel cell microgrid connected with hybrid energy storage systems. A control management approach based on state machines is introduced to regulate the combined renewable generation with hybrid energy storage systems. The operational performance of the microgrid has been evaluated under various conditions, including different weather patterns and the energy needs of local villagers. The key contribution of this work is aimed at addressing the region's unique issues in rural electrification and climate change mitigation. The results of microgrid stability in energy supply, mainly in off-grid scenarios affirm the system's practical viability and reliability.

In an effort to mitigate anthropogenic climate impacts the U.S. has established ambitious Nationally Determined Contribution (NDC) targets, aiming to reduce greenhouse gas emissions by 50% before 2030 and achieving net-zero emissions by 2050. Enhanced rock weathering (ERW)—the artificial enhancement of chemical weathering of rocks to accelerate atmospheric CO2 capture—is now widely seen as a potentially promising carbon dioxide removal (CDR) strategy that could help to achieve U.S. climate goals. Grinding rocks to smaller particle size, which can help to facilitate more rapid and efficient CO2 removal, is the most energy-demanding and cost-intensive step in the ERW life cycle. As a result, accurate life cycle analysis of ERW requires regional constraints on the factors influencing the energetic and economic demands of feedstock grinding for ERW. Here, we perform a state-level geospatial analysis to quantify how carbon footprints, costs, and energy demands vary among regions of the coterminous U.S. in relation to particle size and regional electricity mix. We find that CO2 emissions from the grinding process are regionally variable but relatively small compared to the CDR potential of ERW, with national averages ranging between ~5–35 kgCO2 trock−1 for modal particle sizes between ~10–100  μm. The energy cost for feedstock grinding also varies regionally but is relatively small, with national average costs for grinding of roughly 0.95–5.81 $ trock−1 using grid mix power and 1.35–8.26 $ trock−1 (levelized) for solar PV for the same particle size range. Overall energy requirements for grinding are also modest, with the demand for grinding 1 Gt of feedstock representing less than 2% of annual national electricity supply. In addition, both cost and overall energy demand are projected to decline over time. These results suggest that incorporating feedstock grinding into ERW deployment at scale in the coterminous U.S. should generally have only modest impacts on lifecycle emissions, cost-effectiveness, and energy efficiency. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed generation (DG) controller of a PV-based microgrid. An independent DG controller (IDGC) is designed for PV applications to improve Maximum-Power Point Tracking (MPPT). The Extreme-Learning Machine (ELM)-based MPPT method exactly estimates the controller's reference input, such as the voltage and current at the MPP. Feedback controls employ linear PI schemes or nonlinear, intricate techniques. Here, the converter controller is an IDGC that is improved by directly measuring the converter duty cycle and PWM index in a single DG PV-based MG. It introduces a fast-learning Extreme-Learning Machine (ELM) using the Moore–Penrose pseudo-inverse technique and online sequential ridge methods for robust control reference (CR) estimation. This approach ensures the stability of the microgrid during PV uncertainties and various operational conditions. The internal DG control approach improves the stability of the microgrid during a three-phase fault at the load bus, partial shading, irradiance changes, islanding operations, and load changes. The model is designed and simulated on the MATLAB/SIMULINK platform, and some of the results are validated on a hardware-in-the-loop (HIL) platform. [ABSTRACT FROM AUTHOR]

The global shift towards sustainable energy has positioned photovoltaic (PV) systems as a critical component in the renewable energy landscape. However, maintaining the efficiency and longevity of these systems requires effective fault detection and diagnosis mechanisms. Traditional methods, relying on manual inspections and standard electrical measurements, have proven inadequate, especially for large-scale solar installations. The emergence of machine learning (ML) and deep learning (DL) has sparked significant interest in developing computational strategies to enhance the identification and classification of PV system faults. Despite these advancements, challenges remain, particularly due to the limited availability of public datasets for PV fault detection and the complexity of existing artificial-intelligence (AI)-based methods. This study distinguishes itself by proposing a novel AI-based approach that optimizes fault detection and classification in PV systems, addressing existing gaps in AI-driven fault detection, especially in terms of thermal imaging and current–voltage (I-V) curve analysis. This comprehensive survey identifies emerging trends in AI-driven PV fault detection, highlights the most advanced methodologies, and proposes a novel AI-based approach to enhance fault detection and classification capabilities. The findings aim to advance the state of technology in this field, offering insights into more efficient and practical solutions for PV system fault management. [ABSTRACT FROM AUTHOR]

In the current century, electrical networks have witnessed great developments and continuous increases in the demand for fossil fuels based energy, leading to an excessive rise in the total production cost (TPC), as well as the pollutant (toxic) gases emitted by thermal plants. Under this circumstances, energy supply from different resources became necessary, such as renewable energy sources (RES) as an alternative solution. This latter, however, characterized with uncertainty nature in its operation principle, especially when operator system wants to define the optimal contribution of each resource in an effort to ensure economic and enhanced reliability of grid. This paper presents an Enhanced version of Kepler optimization algorithm (EKOA) to solve the problem of stochastic optimal power flow (SOPF) in a most efficient way incorporating wind power generators and solar photovoltaic with different objective functions, the stochastic nature of wind speed and solar is modeled using Weibull and lognormal probability density functions respectively. To prove the effectiveness of the proposed EKOA, various case studies were carried out on two test systems IEEE 30-bus system and Algerian power system 114-bus, obtained results were evaluated in comparison with those obtained using the original KOA and other methods published in the literatures. Thus, shows the effectiveness and superiority of the efficient EKOA over other optimizers to solve complex problem. The incorporation of RES resulted in a significant 2.39% decrease in production cost, showcasing EKOA's efficiency with a $780/h, compared to KOA's $781/h, for IEEE 30-bus system. For the DZA 114-bus system revealed even more substantial reductions, with EKOA achieving an impressive 12.6% reduction, and KOA following closely with a 12.4% decrease in production cost. [ABSTRACT FROM AUTHOR]

The demand for the integration of renewable energy sources (RESs) with the existing distribution grid is increasing rapidly because of the growing power requirement. The variable power generation from RESs and changing power demand make it necessary to integrate energy storage units. To get stable and trouble-free operation in both transient state and steady state, a combination of battery and supercapacitor storage devices are considered in this work as hybrid energy storage devices (HESDs). But to ensure the power balance in the grid-connected microgrid is a critical aspect nowadays. Hence, a real power management algorithm is proposed in this work to ensure a balance between energy production and demand and provide stability in the microgrid. Both simulation and experimental implementation of the proposed scheme confirm the efficacy and smooth operation of the hybrid microgrid. [ABSTRACT FROM AUTHOR]

This research investigates the use of municipal solid waste cremated fly ash as a viable substitute for natural sand in building methodologies, with a focus on sustainability. The waste material is used in the manufacturing of concrete roof tiles that are combined with solar PV systems, providing advantages in terms of both thermal comfort and improved energy efficiency. These tiles exhibit thermal insulation prowess by effectively preserving a 2-degree temperature differential and collecting heat from solar panels to enhance their energy-production efficiency. In order to enhance performance even further, aluminium foil is strategically placed on all four sides of the roof walls. The foil acts as a reflector, redirecting solar energy towards the tiles, which leads to a 5% boost in power generation. Particular alignments, such as positioning in an east-west or north-south direction, result in further enhancements in performance of 4% and 3%, respectively. This comprehensive approach not only confirms the use of waste materials for environmentally friendly construction but also emphasizes their crucial role in promoting energy-efficient building methods. [ABSTRACT FROM AUTHOR]

The depletion of conventional energy sources has led to an increase in interest in renewable energy across the globe. The usage of renewable energy has lowered economic risk in the electricity markets. This study presents an approach to utilize solar photovoltaic as a renewable energy source, fuel cells as the energy storage system, and Flexible AC Transmission networks (FACTS) to reduce system risk in deregulated networks. The difference between real and expected renewable energy data is the primary cause of disequilibrium pricing (DP) in the renewable energy-integrated system. Integration of the FCs with a Unified Power Flow Controller (UPFC) can play an important role in coping with the disequilibrium pricing, emphasizing optimizing profitability and societal welfare in a deregulated environment. The paper also evaluates the system voltage outline and LBMP (location-based marginal pricing) scenarios, both with and without the integration of solar power. Two distinct factors, i.e., Bus Sensitivity Index (BSI) and Line Congestion Factor (LCF), have been proposed to identify the key buses and lines for solar power and Unified Power Flow Controller installation in the system. The study also employs conditional-value-at-risk (CVaR) and value-at-risk (VaR) to assess the system's risk. Using a real-time IEEE 39-bus New England system, multiple optimization algorithms including Sequential Quadratic Programming and the Slime Mould Algorithm (SMA) are employed to estimate the financial risk of the considered system. This analysis demonstrates that the risk coefficient values improve with the placement of UPFC and fuel cells in the renewable incorporated system. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid method for energy management (EM) of a solar photovoltaic (PV) hybrid microgrid (MG) for the residential distribution system (DS). The proposed approach integrates the single candidate optimizer algorithm (SCOA), and gradient boost decision tree algorithm (GBDT), called the SCOA-GBDT algorithm. The main contribution of this manuscript is to (a) effectively achieve battery storage, solar PV, and loads to improve energy savings and lessen the loss of conversion; (b) effectively handle solar photovoltaic, battery storage, and loads to recognize cost-effective power distribution using the proposed technique; and (c) effectively handle weak photovoltaic power prevalent in the DC side of MG by an auxiliary-battery arrangement to preserve energy. The proposed energy management strategy lowers the conversion losses in the residential DS. Here, the dc loads are provided by a solar photovoltaic, the utility-grid provides the AC loads, and an auxiliary-battery bank is considered for storing the energy. Then, the performance of the proposed technique is done in MATLAB software and is compared to different existing approaches. From the simulation outcome, it is concluded that the proposed approach reduces costs and losses compared to the existing approaches. [ABSTRACT FROM AUTHOR]

Reliable energy sources are crucial for both economic growth and quality of life. In developing countries, where expensive fuels are often the primary energy source, governments are exploring innovative solutions like small‐scale, IoT‐based projects to achieve energy independence in buildings. This research investigates the integration of renewable energy technologies, statistical modeling, cloud computing, and IoT to develop a self‐managing energy system for buildings. The system prioritizes renewable sources, specifically monocrystalline solar cells with 20% efficiency for photovoltaic (PV) energy and flat plate collectors with 90% efficiency and minimal energy loss for thermal energy. Thermal energy is stored in paraffin wax, chosen for its high storage efficiency and thermal properties. The system also utilizes an absorption chiller with a high coefficient of performance (COP) to provide cooling using solar thermal energy. The building's energy loads are categorized as A, B, C, and D, each utilizing both PV and thermal energy. A SCADA system oversees the operation, monitoring the on–off status of these loads. The system is designed for continuous operation, with simulations conducted using Anaconda Jupyter Notebook and Python. This model aims to offer a sustainable and efficient energy solution for buildings, meeting energy demands while optimizing energy use. [ABSTRACT FROM AUTHOR]

Many challenges emerge in the life cycle of solar photovoltaic (PV) panels throughout the processes of their deployment and use in residential, commercial, industrial and transportation sectors. There is a growing need for total product recovery by recycling and reusing the solar panel base and other components in a way that is economically efficient and environmentally sound. This study highlights the urgency to develop and implement a suitable system for the collection and management of photovoltaic systems at their end-of-life cycle and the need for professional implementation of circular strategies in the solar PV value chain. To achieve this goal, a systematic literature review of 81 peer-reviewed articles, published in English between 2013 and 2023, was conducted. The main purpose of the analysis is to examine the value chain of the solar panels covering the period of design, construction, use, end of life, recovery or landfill. The two processes that are investigated include the extent of end-of-life management of PV panels and the extent of circular strategies to reach a sustainable and comprehensive business model. It is argued that the current obstacles faced by solar energy businesses create new opportunities and challenges for innovation within a circular PV industry, and appropriate policies and trained professionals are needed for the implementation of the Sustainable Development Goals (SDGs), including SDG12, in the solar PV value chain. [ABSTRACT FROM AUTHOR]

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Summary: An increase in generation from distributed energy sources like solar PV has motivated researchers to explore better electrical energy conversion solutions. Multilevel inverters with fewer component count and boosting capabilities contribute to a reliable and efficient single‐stage solution. This work proposes a reliable single‐source switched capacitor multilevel inverter capable of producing nine‐level boosted AC voltage with its stand‐alone and grid‐connected operation. It employs 11 switches, three diodes, and three switched capacitors. The main feature of the proposed topology is its ability to produce quadruple boosting with respect to the DC input voltage. Another advantage is the exhibition of low capacitor currents due to frequent charging durations in one operational cycle. The selection criterion for the switched capacitors is also presented. The topology's performance is evaluated in MATLAB/Simulink under various conditions and in PLECS environments for its efficiency. It was then realized on an experimental prototype in stand‐alone mode. The topology is then modified in terms of topology and modulation to mitigate the leakage current, thus making it suitable for grid‐connected operation. This operation is validated on the hardware‐in‐the‐loop platform. Further, the reliability analysis explores a high total mean time to failure (MTTFT) of nearly 253,164 hours/failures compared to recent switched capacitor topologies and an efficiency of above 95%. The low inrush currents, high MTTFT value, and better efficiency make it a suitable topology for renewable microgrids' stand‐alone and grid‐integrated operation. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems, coupled with thermo-electric cooling, have gained significant attention as an eco-friendly solution. To enhance energy efficiency and reduce the overall environmental impact of energy generation and consumption, it is a viable option. This study investigates the impact of critical parameters, namely inclination angle of solar panels, the type of material used in thermo-electric cooling modules, on the performance of a solar PV-thermo electric cooling system. In this research, the impact of inclination angles (15°, 20°, 25°) and two materials (Bismuth telluride and Peltium telluride) of thermoelectric were considered for this study. A comprehensive series of experiments were conducted to analyze the impact of varying inclination angles of the solar panels and material of thermoelectric cooler. The average incident irradiation, panel temperature and outpower of solar panel variation with time are presented. The optimum tilt angle of the solar panel is observed as 20° and material for thermoelectric cooler is Bismuth telluride. When using Bismuth telluride as a thermoelectric material with 20° tilt angle, the solar panel's temperature decreases by 14% and its outpower is augments by a maximum of 14.5%. The results presented here offer practical guidance for system design and operation, ultimately promoting the widespread adoption of this technology in a more environmentally conscious manner. [ABSTRACT FROM AUTHOR]

This article investigates heating options for poultry houses (or sheds) in order to meet their specific indoor air temperature requirements, with case studies conducted across Australia under conditions similar to those encountered worldwide. Hybrid geothermal–solar (PV)–gas heating systems with various configurations are proposed to minimise the lifecycle costs and GHG emissions of poultry shed heating, which involves six seven-week cycles per year. The baseload heating demand is satisfied using ground-source heat pumps (GSHPs), with solar photovoltaic panels generating the electricity needed. LPG burners satisfy the remaining heating demand. Integrating these systems with GSHPs aims to minimise the overall installation costs of the heating system. The primary focus is to curtail the costs and GHG emissions of poultry shed heating with these hybrid systems, considering three different electricity offsetting scenarios. It is found that a considerable reduction in the lifecycle cost (up to 55%) and GHG emissions (up to 50%) can be achieved when hybrid systems are used for heating. The Pareto front solutions for the systems are also determined. By comparing the Pareto front solutions for various scenarios, it is found that the shave factor, a measure of the GSHP proportion of the overall system, significantly influences the lifecycle cost, while the size and utilisation of the solar PV panels significantly affect the lifecycle GHG emissions. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) plants experience performance degradation due to environmental, operational, and material factors. Therefore, conducting a degradation analysis is essential to ensure the optimal performance and longevity of a solar PV plant. The main objective of this study is to investigate the impact of degradation on solar panels in various abnormal scenarios by evaluating performance indicators and electrical parameters of the PV plant. The core novelty of this research lies in the development of an integrated real-time degradation analysis framework for grid-interfaced solar PV plants, using both an I-V curve tracer and a thermal image camera in abnormal scenarios. This approach provides a comprehensive and precise assessment of the PV system's health by capturing detailed electrical characteristics and identifying thermal anomalies indicative of degradation. The key findings include the evaluation of electrical parameters and performance indicators such as peak power (Pmax), short-circuit current (Isc), open circuit voltage (Voc), Performance Ratio (PR), Fill Factor (FF), and the annual power degradation rate in various abnormal scenarios. An error analysis for all electrical parameters of the PV plant was conducted, comparing the percentage errors for each parameter with the panel parameters measured under standard test conditions. Finally, the performance metrics of the current study were compared with previous literature. [ABSTRACT FROM AUTHOR]

The rapid growth of distributed renewable energy penetration is promoting the evolution of the energy system toward decentralization and decentralized and digitized smart grids. This study was based on energy blockchain, and developed a dual-biding mechanism based on the real-time energy surplus and demand in the local smart grid, which is expected to enable reliable, affordable, and clean energy supply in smart communities. In the proposed system, economic benefits could be achieved by replacing fossil-fuel-based electricity with the high penetration of affordable solar PV electricity. The reduction of energy surplus realized by distributed energy production and P2P energy trading, within the smart grid results in less transmission loss and lower requirements for costly upgrading of existing grids. By adopting energy blockchain and smart contract technologies, energy secure trading with a low risk of privacy leakage could be accommodated. The prototype is examined through a case study, and the feasibility and efficiency of the proposed mechanism are further validated by scenario analysis. [ABSTRACT FROM AUTHOR]

Renewable energy generation, especially in the form of rooftop solar photovoltaic (PV) systems, is expected to play an important role in South Africa's future energy mix. The national government, along with various municipalities at local government level, are introducing regulatory incentives to promote the uptake of solar PV systems in the private sector. These incentives include feed-in tariffs, capital subsidies and tax benefits. In addition to the regulatory incentives, there are non-regulatory drivers that motivate private property owners to pursue solar PV systems. These drivers include environmental considerations, cost savings, energy security, tenant requirements, and green-energy finance. This study examines the various regulatory incentives to pursue solar PV systems available to private property owners located in the City of Cape Town Metropolitan Municipality. The City of Cape Town is selected as a single case-study area as the municipality creates a conducive environment through its regulatory framework for private property owners to pursue private embedded generation systems. The study examines the different incentives applicable to residential and non-residential property owners. The research also establishes to what extent the regulatory incentives influence private property owners to pursue solar PV systems compared to non-regulatory drivers and benefits. The study reveals that the various regulatory incentives differ for residential and non-residential property owners. These differences impact the extent to which the regulatory incentives motivate particular private property owners to pursue solar PV systems. The research suggests that, although regulatory incentives play a role in private property owners' decision-making process, the non-regulatory drivers are the main motivating factors for private property owners pursuing solar PV systems.

ABSTRACT Photovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development.