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Mediterranean European cities are characterized by high population density and limited space for large-scale implementation of renewable energy installations. This paper addresses the optimization of renewable energy installations in Mediterranean dwellings with the scope of increasing their energy contribution and cost-effectiveness. In a case study for Malta, the three technologies studied were solar photovoltaics, solar water heating, and heat pump water heating. Technical and economic analyses were performed on a number of reference configurations using Polysun software (version 2022.8). Sensitivity analyses were also conducted to study the impact that different technical and economic factors have on the performance of the configurations considered. Finally, comparisons were made between the techno-economic results obtained from the reference and sensitivity analyses. Based on data collected, the presence of renewable energy source (RES) technologies in the residential sector of Malta was characterized and correlated with the types of dwellings considered. Among the results obtained, it was found that although a solar RES installation may experience some shading, this does not mean that it is rendered economically unfeasible. Moreover, from the simulations conducted, electrical energy storage technology was considered as too premature unless strongly subsidized, making economic sense only in specific circumstances. On the other hand, although heat pump water heating technology is also relatively modern, it was concluded to be the most beneficial in terms of both energy yield and economic benefit, generally speaking. Furthermore, it was determined that in a higher occupancy dwelling, solar water heating (SWH) and heat pump water heating (HPWH) result in considerably more attractive energy savings. [ABSTRACT FROM AUTHOR]

The effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs). [ABSTRACT FROM AUTHOR]

The main two reasons for the shift to renewable energy are the depletion and the harmful effects of non-renewable energy, such as burning coal and fossil fuels. As a result, the use of solar cells as renewable energy resources, that prioritise high efficiency while lowering production costs, can provide a promising future for sustaining energy resources. Solar cells have been developed starting with the monocrystalline and polycrystalline solar cells (first generation), non-crystalline and thin film solar cells (second generation) and organic solar cells (third generation). The dye-sensitized solar cell (DSSC) is a new type of solar cell that has gained popularity due to its ability to convert energy at a low cost, ease of fabrication, and non-toxic nature. This paper explores the use of fungal colours as natural dyes for DSSC due to their wide range of colours, large yields, sustainable supply and non-toxic. Natural fungal colours can be extracted using proper extraction conditions and methods. Although fungal dyes have several limitations like instability, non-resistance to harsh conditions, colour loss during processing or storage and limited profitable yield, they still have promising future prospects to overcome the poor interaction of natural sensitizer with semiconductor together with other advantages of fungal colour. DSSC with dye from crude extracts from Cortinarius fungi exhibited the highest efficiency of 0.64% among reported DSSC with fungal dye. [ABSTRACT FROM AUTHOR]

The penetration of distributed renewable energy sources degrades the protection of microgrids, which leads to incorrect data flow in the energy systems. It is critical to detect faults, types of defects and location of faults in order to improve the protection and security of microgrids. To cater this issue in hybrid renewable energy system, a novel fault detection scheme is adopted using artificial intelligence. The renewable energy based microgrid system is implemented in the IEEE 13 bus power network to obtain the normal and faulty voltage and current data.. The system is simulated using MatLab/Simulink platform. From the time series data, the features are decomposed using empirical wavelet transform (EWT). First, EWT evaluates the frequency components in the signal, then calculates the bounds and gets the basis of the oscillating components. The obtained samples are classified using a Hybrid Convolutional Recurrent Neural Network (HCRNN) and optimized by the Pelican Optimization Algorithm. The 11 types of faults are identified along with the location of fault in the system is obtained. The results are compared with the existing methods and found that the proposed method has improved the fault sample detection accuracy by 1.56%. [ABSTRACT FROM AUTHOR]

In recent years, research on the intention to adopt solar photovoltaic technology has yielded rich results. However, controversy still exists regarding the key antecedents of households' intention to adopt solar photovoltaic technologies. To clarify the critical factors influencing the intention to adopt solar photovoltaic technology and potential moderating variables, this study utilized meta-analysis to perform a quantitative literature analysis on 29 empirical articles. It identified eight key influencing factors and tested the moderating effects of two variables: sample size and research area. The results show that "Attitude" and "Government Incentive" are moderately correlated with the intention to adopt. "Social Influence", "Product Knowledge", "Effort Expectancy", "Perceived Cost-benefit", "Performance Expectancy", and "Perceived Behavioral Control" are weakly correlated with the adoption intention. The study also found that using the sample size and research area as moderating variables can partly reveal differences between various studies. Overall, the findings of this study offer theoretical guidance for subsequent in-depth studies and support for the practical promotion of solar photovoltaic technology. [ABSTRACT FROM AUTHOR]

Due to the intermittent electricity production, the battery takes an important role in the off-grid PV systems by storing excess electricity production. The stored electricity is then used when the PV system generates less electricity than what is demanded. However, the current price and lifetime of the battery make the electricity cost of the off-grid PV system expensive. Therefore, the capacity of components must be designed to fulfill the demand at the lowest electricity cost. In this study, two strategies in the design of off-grid PV systems to fulfill the same demand are compared. The first strategy is to employ the PV module with proper capacity, which means the annual energy production equals the annual energy demand, but it needs a huge capacity battery to store the accumulation of the excess energy. The second strategy is to use the large capacity of the PV module, which considers the lowest energy production day, but it requires the small capacity of the battery. The results show that the electricity cost of the second strategy is only 29.0 % of the first strategy. However, it dumps 50.1 % of the annual produced electricity. [ABSTRACT FROM AUTHOR]

The United Nations' Sustainable Development Goals (SDGs) highlight the importance of affordable and clean energy sources. Solar energy is a perfect example, being both renewable and abundant. Its popularity shows no signs of slowing down, with solar photovoltaic (PV) panels being the primary technology for converting sunlight into electricity. Advancements are continuously being made to ensure cost-effectiveness, high-performing cells, extended lifespans, and minimal maintenance requirements. This study focuses on identifying suitable locations for implementing solar PV systems at the University Malaysia Pahang Al Sultan Abdullah (UMPSA), Pekan campus including buildings, water bodies, and forest areas. A combined technical and economic analysis is conducted using Helioscope for simulations and the Photovoltaic Geographic Information System (PVGIS) for economic considerations. Helioscope simulation examine case studies for PV installations in forested areas, lakes, and buildings. This approach provides comprehensive estimations of solar photovoltaic potential, annual cost savings, electricity costs, and greenhouse gas emission reductions. Based on land coverage percentages, Floatovoltaics have a large solar PV capacity of 32.3 Megawatts (MW); forest-based photovoltaics (Forestvoltaics) achieve maximum yearly savings of RM 37,268,550; and Building Applied Photovoltaics (BAPV) have the lowest CO emissions and net carbon dioxide reduction compared to other plant sizes. It also clarifies the purpose of using both software tools to achieve a comprehensive understanding of both technical and economic aspects. Graphic Abstract [ABSTRACT FROM AUTHOR]

Corrosion is a phenomenon that occurs on pipes, reinforced concrete structures, and storage tanks and causes a major impact on the facility structures and can have a major impact on a facility's structural integrity. This can result in a serious failure in the system and lead to substantial economic losses. One of the solutions widely used to eliminate the corrosion effects is by applying cathodic protection, which depends on direct current as the supply potential. The technique of cathodic protection is used to control corrosion in the utilisation of reinforced concrete structures, pipelines, storage tanks, etc. A photovoltaic cathodic protection system is normally used as an energy source to supply the system. This research reviews the technique utilised for applying solar photovoltaics in powering systems of cathodic protection. Subsequently, it highlights the methods of cathodic protection systems, sacrificial anode cathodic protection and the impressed current cathodic protection. Finally, it is indicated that applying solar photovoltaics in powering cathodic protection systems has great efficacy in controlling the corrosion in the facility's equipment in a smarter, controlled way. Furthermore, this study provides significant insight into the designing and operating the domain of solar photovoltaic systems that power cathode protection systems. [ABSTRACT FROM AUTHOR]

In recent years, research communities have shown significant interest in solar energy systems and their cooling. While using cells to generate power, cooling systems are often used for solar cells (SCs) to enhance their efficiency and lifespan. However, during this conversion process, they can generate heat. This heat can affect the performance of solar cells in both advantageous and detrimental ways. Cooling cells and coordinating their use are vital to energy efficiency and longevity, which can help save energy, reduce energy costs, and achieve global emission targets. The primary objective of this review is to provide a thorough and comparative analysis of recent developments in solar cell cooling. In addition, the research discussed here reviews and compares various cooling systems that can be used to improve cell performance, including active cooling and passive cooling. The outcomes reveal that phase-change materials (PCMs) help address critical economic goals, such as reducing the cost of PV degradation, while enhancing the lifespan of solar cells and improving their efficiency, reliability, and quality. Active PCMs offer precise control, while passive PCMs are simpler and more efficient in terms of energy use, but they offer less control over temperature. Moreover, an innovative review of advanced cooling methods is presented, highlighting their potential to improve the efficiency of solar cells. [ABSTRACT FROM AUTHOR]

Abstract The main two reasons for the shift to renewable energy are the depletion and the harmful effects of non-renewable energy, such as burning coal and fossil fuels. As a result, the use of solar cells as renewable energy resources, that prioritise high efficiency while lowering production costs, can provide a promising future for sustaining energy resources. Solar cells have been developed starting with the monocrystalline and polycrystalline solar cells (first generation), non-crystalline and thin film solar cells (second generation) and organic solar cells (third generation). The dye-sensitized solar cell (DSSC) is a new type of solar cell that has gained popularity due to its ability to convert energy at a low cost, ease of fabrication, and non-toxic nature. This paper explores the use of fungal colours as natural dyes for DSSC due to their wide range of colours, large yields, sustainable supply and non-toxic. Natural fungal colours can be extracted using proper extraction conditions and methods. Although fungal dyes have several limitations like instability, non-resistance to harsh conditions, colour loss during processing or storage and limited profitable yield, they still have promising future prospects to overcome the poor interaction of natural sensitizer with semiconductor together with other advantages of fungal colour. DSSC with dye from crude extracts from Cortinarius fungi exhibited the highest efficiency of 0.64% among reported DSSC with fungal dye.

Abstract Energy scarcity in the world and the pollutants resulting from excessive use of conventional energy aroused the need for sustainable alternatives that are environment friendly. A multi-use thermoelectric refrigerator powered by solar energy to obtain the lowest consumption with the highest efficiency. The designed refrigerator is based on the Peltier effect using Peltier units where a temperature difference is created between the junctions by applying a voltage difference across the junction. This study investigates the performance of a refrigerator cooling system powered by a photovoltaic (PV) system. The research aims to assess the efficiency, effectiveness, and feasibility of utilizing solar energy to drive refrigeration, particularly in off-grid or environmentally conscious applications. Through a comprehensive experimental setup and data analysis, the study examines energy consumption, cooling efficiency, and overall system performance under varying conditions. The findings contribute valuable insights into the potential of PV-powered refrigerators as sustainable cooling solutions. It relies on a control unit that measures the resulting temperature to determine the appropriate connection mode to give the highest cooling efficiency. The average solar radiation when operating for 8 h, for the different seasons of the year was 149.5, 67.5, 119.3, and 118.3 w/m2 in summer, winter, spring, and fall, respectively. The average cooling energy consumption was 107.25, 137.04, 107, and 138.08 w for temperatures (20 ± 1, 15 ± 1, 20 ± 2, and 15 ± 2) °C respectively that proof solar radiation is sufficient to produce energy for the summer of cooling temperatures up to 15 °C, while in the spring and fall it is sufficient to 20 °C. The Fast not Eco mode is the least energy consuming and the fastest cooling, it can be used for rapid cooling at a short time less than an hour. The best mode in the case of continuous operation is the case of as next Eco mode cooling temperature of 20 ± 0.1 °C. The MATLAB Simulink model was developed to reduce the design cycle and facilitate the integration of solar photovoltaic with the TEC. The optimal operating point is identified through simulation and validated through experimental analysis, the optimal COP was 71.089% by Response surface methodology (RSM).

Due to the intermittent electricity production, the battery takes an important role in the off-grid PV systems by storing excess electricity production. The stored electricity is then used when the PV system generates less electricity than what is demanded. However, the current price and lifetime of the battery make the electricity cost of the off-grid PV system expensive. Therefore, the capacity of components must be designed to fulfill the demand at the lowest electricity cost. In this study, two strategies in the design of off-grid PV systems to fulfill the same demand are compared. The first strategy is to employ the PV module with proper capacity, which means the annual energy production equals the annual energy demand, but it needs a huge capacity battery to store the accumulation of the excess energy. The second strategy is to use the large capacity of the PV module, which considers the lowest energy production day, but it requires the small capacity of the battery. The results show that the electricity cost of the second strategy is only 29.0 % of the first strategy. However, it dumps 50.1 % of the annual produced electricity.

Abstract Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R 2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

Abstract Solar photovoltaic (PV) is a crucial renewable energy source in the fight against carbon dioxide emissions, aligning well with growing energy demands. However, solar PV efficiency naturally degrades over time, primarily due to uncontrollable outdoor factors such as irradiance, humidity, shading, soiling, aging, and temperature. These collectively lead to decreased efficiency in PV systems. Soiling on PV glass surfaces significantly impacts light penetration and subsequently reduces power generation. To combat this, a self-cleaning nano-calcium carbonate coating has been proposed. The effectiveness of this method is compared with a developed solar PV thermal (PV/T) system, evaluating both performance and cost-effectiveness. After six months of outdoor exposure, the coated glass solar PV achieved an efficiency of 7.6%, surpassing bare glass solar PV at 6.0%. Moreover, the coated glass solution boasts exceptional cost-effectiveness, incurring only an annual expense of 17.6 USD per panel compared to the PV/T system of 59.8 USD per panel. These findings highlight the potential of coatings to enhance solar PV performance and economics, particularly in addressing challenging uncontrollable factors like soiling.

This study analyses the PV potential in Mountain Rise, Durban, South Africa, considering three PV panel technologies and tilt angles in a 20-kWp PV system (c-Si, CdTe, and CIS cells; tilts of 30°, 10°, and 48° for year-round, winter, and summer conditions, respectively, with a fixed Azimuth of 0°). Computational modelling with Solargis Prospect, pvPlanner, and PV*SOL is utilized. Findings reveal the site's annual GHI (1636 kWh/m2) is lower than GTI at different tilts (30° = 1839.3 kWh/m2, 10°/48° = 1921 kWh/m2). Average ambient conditions include temperature (20.8 °C), wind speed (4.6 m/s), and relative humidity (78%). Transitioning from GHI to GTI yields a 12% irradiance increase at βopt (30°) and 15% at βwin/βsum (10°/48°). PV system performance in MWh at βopt is 30.3, 28.7, and 28.2 for CdTe, CIS, and c-Si, respectively, while at βwin/βsum, it is 32.1, 30.7, and 29.9. Average PR values in winter are 82.9%, 80%, and 78.8%, and in summer, they are 83.7%, 78.1%, and 76.9%. The study concludes that CdTe is the most technically viable PV cell, and a season-based tilt approach enhances electricity generation. Additionally, deploying PV systems of CdTe, CIS, and c-Si prevents CO2 emissions of 10,377, 10,409, and 11,082 kg/year, respectively

Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO2-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations. [ABSTRACT FROM AUTHOR]

In recent years, global efforts towards a future with sustainable energy have intensified the development of renewable energy sources (RESs) such as offshore wind, solar photovoltaics (PVs), hydro, and geothermal. Concurrently, green hydrogen, produced via water electrolysis using these RESs, has been recognized as a promising solution to decarbonizing traditionally hard-to-abate sectors. Furthermore, hydrogen storage provides a long-duration energy storage approach to managing the intermittency of RESs, which ensures a reliable and stable electricity supply and supports electric grid operations with ancillary services like frequency and voltage regulation. Despite significant progress, the hydrogen economy remains nascent, with ongoing developments and persistent uncertainties in economic, technological, and regulatory aspects. This paper provides a comprehensive review of the green hydrogen value chain, encompassing production, transportation logistics, storage methodologies, and end-use applications, while identifying key research gaps. Particular emphasis is placed on the integration of green hydrogen into both grid-connected and islanded systems, with a focus on operational strategies to enhance grid resilience and efficiency over both the long and short terms. Moreover, this paper draws on global case studies from pioneering green hydrogen projects to inform strategies that can accelerate the adoption and large-scale deployment of green hydrogen technologies across diverse sectors and geographies. [ABSTRACT FROM AUTHOR]

Globally, the integration of electric vehicles (EVs) in the transportation sector represents a significant step towards achieving environmental decarbonization. This shift also introduces a new demand for electric power within the utility grid network. This study focuses on the design and development of a grid-connected renewable energy system tailored to meet the EV load demands in Taif, Kingdom of Saudi Arabia (KSA). The integration of renewable energy sources, specifically solar photovoltaic (SPV) and wind turbines (WT), is explored within the context of economic feasibility and system reliability. Key considerations include optimizing the system to efficiently handle the fluctuating demands of EV charging while minimizing reliance on conventional grid power. Economic analyses and reliability assessments are conducted to evaluate the feasibility and performance of the proposed renewable energy system. This article discusses the technical sizing of hybrid systems, energy reduction, and net present cost for the selected location. A rigorous sensitivity analysis is performed to determine the impact of major variables such as inflation rate, real discount rate, solar irradiation, and Lack of Power Supply Probability (LPSP) on system performance. The results demonstrate that the Pufferfish Optimization Algorithm (PFO) significantly outperforms other metaheuristic algorithms documented in the literature, as well as the HOMER software. The study found that the grid-connected renewable energy system is the best option for operating EV charging stations at the selected location. The findings underscore the potential for sustainable energy solutions in urban environments like Taif, highlighting the importance of integrating renewable energy technologies to meet growing energy demands with enhanced economic efficiency and system reliability. This initiative seeks to pave the way for a greener and more resilient energy infrastructure, aligning with global efforts towards sustainable development and clean transportation solutions. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) is a crucial renewable energy source in the fight against carbon dioxide emissions, aligning well with growing energy demands. However, solar PV efficiency naturally degrades over time, primarily due to uncontrollable outdoor factors such as irradiance, humidity, shading, soiling, aging, and temperature. These collectively lead to decreased efficiency in PV systems. Soiling on PV glass surfaces significantly impacts light penetration and subsequently reduces power generation. To combat this, a self-cleaning nano-calcium carbonate coating has been proposed. The effectiveness of this method is compared with a developed solar PV thermal (PV/T) system, evaluating both performance and cost-effectiveness. After six months of outdoor exposure, the coated glass solar PV achieved an efficiency of 7.6%, surpassing bare glass solar PV at 6.0%. Moreover, the coated glass solution boasts exceptional cost-effectiveness, incurring only an annual expense of 17.6 USD per panel compared to the PV/T system of 59.8 USD per panel. These findings highlight the potential of coatings to enhance solar PV performance and economics, particularly in addressing challenging uncontrollable factors like soiling. [ABSTRACT FROM AUTHOR]

This work presents a variable step Champernowne Adaptive Filter (VS-CAF) based control architecture for a grid-integrated solar photovoltaic system. The control structure is implemented to successfully synchronize the two-stage solar photovoltaic at the point of common coupling where the source and domestic loads are connected. The control architecture ensures solar power injection and improvised power quality at the unity power factor. Moreover, the control structure provides only the balanced positive sequence fundamental sinusoidal source currents at solar irradiation alteration and load disturbances. The VS-CAF is exploited to estimate the peak of the fundamental component out of the harmonics-rich domestic load currents. To examine the efficacy of the suggested control technique, a 40 kW solar photovoltaic is simulated using MATLAB/Simulink. The performance assessment of the grid-tied solar generation system is carried out at solar irradiation alteration, zero solar irradiation, and detachment of one phase of the three-phase load. Various simulation results are carried out to verify the successful integration of solar photovoltaics into the three-phase grid following the IEEE-519 standard and satisfactory performance during various working conditions. Moreover, the simulation results are also verified with real-time results taken from the Opal-RT simulator. [ABSTRACT FROM AUTHOR]

The escalating demand for energy and the mounting environmental impacts of fossil fuel usage necessitate a paradigm shift toward the integration of renewable energy sources (RES) as the core of the electrical energy sector. Solar photovoltaic (SPV) and wind-based generation have emerged as the most promising and viable solutions, leading to a significant increase in the RES-based microgrid penetration into the power grid. However, the grid-connected microgrid operation presents challenges to the stability of the main grid. Due to small aggregated physical inertia of these microgrid, there is a significant deviation in system inertia that contributes to low frequency oscillations (LFO). These oscillations have a significant risk to power system stability. In the proposed work, a grid-connected microgrid that incorporates a synchronous generator along with SPV and wind integration is investigated. A synchronized governor combined with a power system stabilizer features a cascade structure, encompassing tilt integral derivative and fractional-order proportional integral derivative with a filter to effectively mitigate the oscillations. Furthermore, this cascaded controller is optimized using the improved reptile search algorithm. The controller efficacy is validated in the MATLAB platform by subjecting it to random variations in reference torque. Comparative analysis against conventional controllers, including Proportional Integral, Lead-Lag and Fractional Lead-Lag, demonstrates superior damping efficiency of the proposed controller since it damps LFO within the time period of 15 s. [ABSTRACT FROM AUTHOR]

The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases. [ABSTRACT FROM AUTHOR]

This work quantifies the techno-economic performance of AC and DC residential building distribution. Two methods, utilising software and hardware configurations, are showcased to improve DC distribution: (i) a novel rule-based battery dual-objective operation (DOO) and (ii) a modular Master/Slave design of the grid-tied converter (GC). Both methods use the GC's load-dependent efficiency characteristic, eliminating partial-load operation and enhancing energy efficiency. The work uses measured annual PV and load data to evaluate the performance of the methods compared to AC and DC references. The techno-economic analysis includes the annual net electricity bill and monetised battery degradation. The results show that the DOO eliminates GC partial-load operation at the cost of increased battery usage, resulting in marginal net savings. In contrast, the modular converter design significantly reduces losses: −157 kWh/a (−31%) and −121 kWh/a (−26%), respectively, relative to the DC and AC references. For a parametric sweep of electricity price and discount rate, the Lifetime Operating Cost (LOC) comparison shows savings from DC of up to USD 575 compared to AC. [ABSTRACT FROM AUTHOR]

A metaheuristic algorithm named the Crystal Structure Algorithm (CrSA), which is inspired by the symmetric arrangement of atoms, molecules, or ions in crystalline minerals, has been used for the accurate modeling of Mono Passivated Emitter and Rear Cell (PERC) WSMD-545 and CS7L-590 MS solar photovoltaic (PV) modules. The suggested algorithm is a concise and parameter-free approach that does not need the identification of any intrinsic parameter during the optimization stage. It is based on crystal structure generation by combining the basis and lattice point. The proposed algorithm is adopted to minimize the sum of the squares of the errors at the maximum power point, as well as the short circuit and open circuit points. Several runs are carried out to examine the V-I characteristics of the PV panels under consideration and the nature of the derived parameters. The parameters generated by the proposed technique offer the lowest error over several executions, indicating that it should be implemented in the present scenario. To validate the performance of the proposed approach, convergence curves of Mono PERC WSMD-545 and CS7L-590 MS PV modules obtained using the CrSA are compared with the convergence curves obtained using the recent optimization algorithms (OAs) in the literature. It has been observed that the proposed approach exhibited the fastest rate of convergence on each of the PV panels. [ABSTRACT FROM AUTHOR]

Implementing solar photovoltaic systems in the built environment (BEPV) is critical for the construction sector's contribution to mitigating climate change. While previous studies give various insights into innovation implementation, the challenges to value co-creation by construction actors remain underexplored. By studying the alignment of business ecosystems implementing BEPV in the Swedish built environment, we address this need. Drawing on the cumulative experience of construction actors, this study demonstrates how knowledge mislocation, knowledge deficits, cultural discrepancies, insufficient building codes, frequently changing regulations, and implementing a highly embedded innovation can disturb ecosystem alignment. The study contributes to the ecosystem literature, scholarly discussions of innovation implementation in complex construction projects, and the PV diffusion literature by offering insights into the realignment of ecosystems involving value co-creation by actors from previously unconnected industries. The study links PV diffusion research to the construction management literature by exploring the cumulative experience of implementation at the micro level. We highlight the significant industry reconfigurations required to accommodate a deeply embedded technological innovation, reconfigurations going beyond the challenges of implementing systemic innovations encumbered by fewer material dependencies. We also emphasize the critical importance of industrial interaction, coordination, and learning to accelerate the sustainability transition. [ABSTRACT FROM AUTHOR]

Summary A nonlinear control strategy for a solar photovoltaic (SPV) integrated three‐phase multifunctional converter (3PMFC) is proposed in this study. The proposed control approach integrates a Lyapunov function controller (LFC) for the voltage control loop and a model predictive controller (MPC) for the current tracking control loop. The required 3PMFC reference currents are calculated using a variable conductance factor (VCF) technique. The key highlights of the proposed control strategy are as follows: (1) LFC with only one control gain provides less steady‐state error than the proportional integrator (PI) with two control gains, (2) MPC generates the optimal switching signals for the associated converters in the system without using modulation stages, and (3) The VCF approach simplifies the mathematical formulation to derive the required reference current. The effectiveness of the proposed control technique was evaluated using different solar irradiation, DC loads, and AC loads (linear balanced, linear unbalanced, and nonlinear loads). The performance of the proposed control technique has been evaluated and validated in a dynamic operation scenario utilizing MATLAB/Simulink and a real‐time simulator (OPAL‐RT OP4510). The simulation results demonstrate that the proposed control approach outperforms the conventional control strategy in terms of grid current total harmonic distortion (THD) improvement, which is in accordance with the IEEE‐519 standard. The performance evaluated through simulation studies indicates that the 3PMFC can perfectly perform all of its grid‐supported multifunctional services, such as current harmonic compensation, reactive power support, and source current balance, along with injecting active power generated by the SPV into the utility system. [ABSTRACT FROM AUTHOR]

This study develops a new method to evaluate the economic viability of co-generation electric vehicle stations that concurrently generate electricity and hydrogen for charging battery electric vehicles and refueling hydrogen vehicles. The approach uniquely differentiates the costs associated with various energy outputs in co-generation stations and includes often-overlooked peripheral devices critical for accurate evaluation of the levelized cost of electricity (LCOE) and hydrogen (LCOH). The method was tested across three design configurations: two featuring single storage options (battery and fuel cell, respectively) and a third using hybrid storage employing both. Each configuration was modeled, simulated, and optimized using HOMER Pro 3.14.2 to determine the most optimal sizing solution. Then, based on the optimal sizing of each design, LCOE and LCOH were evaluated using the proposed method in this study. The analysis revealed that excluding often-overlooked peripheral devices could lead to a 27.7% error in LCOH evaluation, while the impact on LCOE was less than 1%. Among different configurations, the design with hybrid storage proved economically superior, achieving a total levelized cost of energy (TLCOE) for the entire system of USD 0.113/kWh, with the LCOE at USD 0.025/kWh and LCOH at USD 0.088/kWh (or USD 3.46/kg). Comparative analysis with state-of-the-art studies confirmed the accuracy of the proposed method. This study provides a more precise and holistic approach that can be leveraged for the feasibility analysis of electric vehicle stations globally, enhancing strategic decision-making in sustainable energy planning. [ABSTRACT FROM AUTHOR]

This work presented power usage scheduling by engaging consumers in demand response program (DRP) with and without using renewable energy generation (REG). This power usage scheduling problem was modeled as an optimization problem, which was solved using an energy scheduler (ES) based on the crossover mutated enhanced wind-driven optimization (CMEWDO) algorithm. The CMEWDO was an enhanced wind-driven optimization (WDO) algorithm, where the optimal solution returned from WDO was fed to crossover and mutation operations to further achieve the global optimal solution. The developed CMEWDO algorithm was verified by comparing it with other algorithms like the whale optimization algorithm (WOA), enhanced differential evolution algorithm (EDE), and the WDO algorithm in aspects of the electricity bill and peak to average demand ratio (PADR) minimization without compromising consumers' comfort. Also, the developed CMEWDO algorithm has a lower computational time (measured in seconds) and a faster convergence rate (measured in number of iterations) than the standard WDO algorithm and other comparative algorithms. [ABSTRACT FROM AUTHOR]

Abstract The effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs).

Electric vehicles are crucial for sustainable transport and energy solutions, particularly in developing countries like Bangladesh where their popularity is rising. This study primarily focuses on the techno-economic design of a 300 kWp solar photovoltaic-powered electric vehicle charging station along the Dhaka-Mawa Expressway in Bangladesh, capable of charging 20 electric vehicles simultaneously. The design utilizes the commercially available software package PVsyst 7.2 to ensure the feasibility and efficiency of the charging infrastructure. The use of solar photovoltaics for electric vehicle charging, compared to traditional grid-based methods, offers substantial environmental benefits, including significant reductions in carbon emissions. This shift is driven by decreasing costs, improved module efficiencies, and increased environmental awareness. The estimated levelized cost of energy is calculated at 7.1556 BDT/kWh (BDT is Bangladeshi Taka), with an annual energy generation cost of 1436285.32 BDT. Over a projected lifespan of 25 years, the system is expected to replace 8065 tons of CO2 emissions with its own emissions totaling 537.56 tons, resulting in a net decrease of 6460.2 tons of CO2. This approach not only aligns with Bangladesh’s emission reduction goals but also exemplifies the potential for solar photovoltaic systems to enhance sustainability in transportation. It also emphasizes the importance of integrating renewable energy sources into the electric vehicle-based infrastructure to achieve true sustainability and supports the country’s commitment to combating climate change through technological innovation.

Developing countries, including Ghana, face challenges ensuring access to clean and reliable cooking fuels and technologies. Traditional biomass sources mainly used in most developing countries for cooking contribute to deforestation and indoor air pollution, necessitating a shift towards environmentally friendly alternatives. The study's primary objective is to evaluate the economic viability of using solar PV-based green hydrogen as a sustainable fuel for cooking in Ghana. The study adopted well-established equations to investigate the economic performance of the proposed system. The findings revealed that the levelized cost of hydrogen using the discounted cash flow approach is about 89 %, 155 %, and 190 % more than electricity, liquefied petroleum gas (LPG), and charcoal. This implies that using the hydrogen produced for cooking fuel is not cost-competitive compared to LPG, charcoal, and electricity. However, with sufficient capital subsidies to lower the upfront costs, the analysis suggests solar PV-based hydrogen could become an attractive alternative cooking fuel. In addition, switching from firewood to solar PV-based hydrogen for cooking yields the highest carbon dioxide (CO2) emissions savings across the cities analysed. Likewise, replacing charcoal with hydrogen also offers substantial CO2 emissions savings, though lower than switching from firewood. Correspondingly, switching from LPG to hydrogen produces lower CO2 emissions savings than firewood and charcoal. The study findings could contribute to the growing body of knowledge on sustainable energy solutions, offering practical insights for policymakers, researchers, and industry stakeholders seeking to promote clean cooking adoption in developing economies.