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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 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]

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]

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]

This study examines the photovoltaic (PV) energy output and levelized cost of energy (LCOE) in seven regions of Tanzania across five different tilt adjustments of 1 MW PV systems. The one-diode model equations and the PVsyst 7.2 software were used in the simulation. The results reveal variations in energy output and LCOE among the regions and tilt adjustments indicating a strong correlation between PV energy output and solar irradiance incident on the PV panel. For horizontal mounting, the annual energy output ranges from 1229 MWh/year in Kilimanjaro to 1977 MWh/year in Iringa. Among the three optimal tilt adjustments, annually, monthly and seasonal, the last two are predicted to yield larger energy outputs, whereas the two axis tracking configuration consistently provides the maximal energy output in all regions, ranging from 1533 MWh/year in Kilimanjaro to 2762 MWh/year in Iringa. The LCOE analysis demonstrates the cost-effectiveness of solar PV systems compared to grid-connected and isolated mini-grid tariffs. The LCOE values across the regions and tilt adjustments range from $0.07/kWh to $0.16/kWh. In comparison, the tariff for grid-connected solar PV is $0.165/kWh, while for isolated mini-grids; it is $0.181/kWh. The monthly optimal tilt configuration proves to be the most cost-effective option for energy generation in multiple regions, as it consistently exhibits the lowest energy cost compared to the other four configurations. The results provide valuable insights into the performance and economic feasibility of various system setups. Through meticulous simulation and data analysis, we have gained a comprehensive understanding of the factors influencing energy generation and costs in the context of solar photovoltaic systems.

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) 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]

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]

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]

Institutions of higher learning in South Africa are grappling with occasional power outages in lecture halls, leading to disruptions in academic activities and occasional loss of lecture hours. Therefore, this study presents a comprehensive evaluation of a grid-connected solar PV/Li-ion battery microgrid (μG) system aimed at maintaining a constant power supply to selected lecture halls at a university in the Western Cape, South Africa. The microgrid design, modelling, and simulations, conducted in the MATLAB/Simulink environment, include the solar PV, Li-ion battery, energy consumption of the lecture halls (load profile), boost converter, bidirectional converter, and grid. Using the detailed design, modelling, and simulation, the study evaluates the economic and environmental impacts of integrating μGs, focusing on enhancing energy reliability, reducing operational costs, and mitigating CO2 emissions. The results indicate that integrating the microgrid resulted in a significant 51% reduction in energy cost and a decrease in greenhouse gas emissions by 530 kgCO2e per hour. In Cases 2 and 4, where the battery supplied power, the annual battery degradation costs are 6.08% and 14.9% of the initial cost, respectively. The μG ensures an uninterrupted power supply and improves the overall reliability of the university's energy infrastructure. It promotes environmental sustainability goals of zero emissions and maintains continuous academic activities during grid outages. Furthermore, it fosters a conducive learning environment, supports innovation and creativity in sustainable energy technologies, and sets a standard for other higher education institutions to integrate renewable energy-powered μGs. [ABSTRACT FROM AUTHOR]

With the advancements in electric vehicles in the market, it is necessary to rephrase the converter for effective gain. The proposed Two Input – Two Output (TITO) converter accepts input from two sources: Battery and Solar PV panel included with non-isolated buck, boost, and buck-boost DC–DC converters. Also, the proposed TITO converter topology uses only three switches when compared to conventional multiport converters where the number of semiconductor switches is higher. The conventional converter has a higher number of components, whereas the proposed converter has a reduced number of conduction components as well as switching semiconductors. As a result, there is a reduction in energy lost. The proposed topology is designed and simulated in MATLAB Simulink and observed with a lower ripple voltage during buck, boost and buck-boost mode. Experimental setup results are also obtained from a prototype of the proposed converter, which confirms the theoretical considerations and effectiveness of the proposed topology. [ABSTRACT FROM AUTHOR]

For the application of renewable energy at the airport, the use of solar power requires certainty of the electricity produced. The certainty of electricity generated from solar power can be predicted using machine learning methods. Predictions made on PV electrical power output are based on historical data from direct measurements of solar PV parameters, including solar radiation and PV panel temperature. Various types of machine learning methods for predicting PV output power have been used in previous studies with different evaluation values of prediction results. In this study, the author conducted a hybrid K-NN method with LSTM to predict the PV electrical power of solar PV output with solar radiation parameters and PV panel temperature. After making predictions using this method, excellent RSME results were obtained with a value of 0.015424830635781967. The results of the PV output power value graph in this prediction are also very good, where the predicted value is close to the value of the testing data or actual data. [ABSTRACT FROM AUTHOR]

Renewable energy is becoming more attractive as traditional fossil fuels are rapidly depleted and expensive, and their use would release pollutants. Power systems that use both wind and solar energy are more reliable and efficient than those that utilize only one energy. Hybrid renewable energy systems (HRES) are viable for remote areas operating in standalone mode. This paper aims to present the state-of-the-art research on off-grid solar-wind hybrid energy systems over the last two decades. More than 1500 published articles extracted from the Web of Science are analyzed by bibliometric methods and processed by CiteSpace to present the results with figures and tables. Productive countries and highly cited authors are identified, and hot topics with hotspot articles are shown in landscape and timeline views. Emerging trends and new developments related to techno-economic analysis and microgrids, as well as the application of HOMER software, are predicted based on the analysis of citation bursts. Furthermore, the opportunities of hybrid energy systems for sustainable development are discussed, and challenges and possible solutions are proposed. The study of this paper provides researchers with a comprehensive understanding and intuitive representation of standalone solar-wind hybrid energy systems. [ABSTRACT FROM AUTHOR]

There is a serious need for reducing the carbon dioxide emissions due to the increase in the global warming. Besides, owing to the unavailability of clean energy sources throughout an entire the year, hybrid renewable energy systems (HRESs) are required. On other hand, the importance of optimal HRES design is to achieve a low cost with using a high green energy. Helioscope and HOMER Pro software were used to design a small grid-connected model and estimate the consumption energy for optimization. The analysis of the system showed how a grid-connected PV system with a battery backup affected on the total energy costs. In addition, the role of power supply irregularity from the national grid was highlighted by calculating the likelihood of a power outage and its impact on HRES. The results showed the internal rate of return (IRR) is 13%, and the return on investment (ROI) is around 9%. Also, the value of renewable fraction was around 63.4%. In conclusion, the proposed system was an efficient according to the energy consumption. This case study can extend to be applied in any country, especially the countries have longer summer like Iraq. [ABSTRACT FROM AUTHOR]

Many isolated rural communities lack basic electricity services and associated modern amenities. One proposed solution is the deployment of containerized microgrids, which are clusters of generation and storage assets packaged in a container for easy deployment. However, few works have described approaches for designing such solutions. This paper presents a five-step method for designing a containerized photovoltaic-based microgrid for isolated areas. The method includes defining system design requirements and constraints (technical, environmental, and legal), conducting preliminary studies on solar radiation and load profiles, selecting equipment, designing the control system, and performing a basic economic analysis. This method is verified in three scenarios of Colombian Non-Interconnected Zones (NIZs), resulting in a solution that can effectively provide electricity to the isolated communities, primarily from solar energy, store surplus generation in batteries, and minimize diesel backup generator use. The results show that the solutions can be scaled to feed larger loads and can be applied in other contexts, such as emergency supply after natural disasters.

This study investigates the application of a PID controller with a fractional derivative component (PIDDμ), optimized using the stochastic fractional search (SFS) technique, for load frequency control (LFC) in power systems. Additionally, solar PV and battery energy storage systems (BESS) and flywheel energy storage systems (FESS) are integrated as backup sources to enhance the LFC performance. To assess the effectiveness of the proposed approach, three error metrics—Integral Time Absolute Error (ITAE), Integral Time Multiplied Square Error (ITSE), and Integral of Absolute Error (IAE)—are evaluated. The results are compared with recent optimization-based LFC techniques across various scenarios. It is found that the SFS-PIDDμ achieves IAE=0.006786, ITSE=1.07e-05, and ITAE=0.01853, which are significantly lower than the error values produced by other LFC techniques. Furthermore, integrating solar PV, along with BESS and FESS, further reduces these error values. The graphical LFC responses of the SFS-PIDDμ outperform other optimization-based LFC techniques. The robustness of the approach is also validated against random load changes of varying magnitudes and for broader parametric variations.

This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1 % and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit.