Search Results (1,964)

The integration of photovoltaic (PV) generation with alkaline water electrolyzers (AWE) in DC microgrids offers a highly promising pathway for green hydrogen production. However, the inherent volatility of solar power often induces transient voltage ripples and power surges, degrading the electrolyzer stack and destabilizing the common DC bus. To overcome this, this study proposes a hierarchical multimodal coordinated control strategy tailored for a four-port (PV–Storage–Grid–Hydrogen) DC microgrid. The proposed framework leverages multi-port synergetic coordination among the PV array, battery storage, and grid-interfacing converters to actively buffer extreme power mismatches, thereby ensuring the constant regulation of the DC bus voltage. Through comprehensive time-domain simulations under worst-case step-change boundary conditions, the large-signal transient stability of the proposed strategy is quantitatively verified. Under extreme disturbances, the system successfully confines DC bus voltage deviations to within safe operational boundaries with a rapid settling time, effectively avoiding typical inverter overvoltage trip thresholds. Furthermore, the adaptive power regulation algorithm maintains precise steady-state power tracking. By utilizing a gradient-based flag variable, the system seamlessly transitions between maximum power point tracking (MPPT) and active power-limiting modes, ensuring continuous equipment protection, stable high-purity hydrogen yield, and uninterrupted microgrid stability. Full article

Solar power plants are among the most widely used renewable energy sources today. Varying radiation levels from region to region, and similarly varying consumption depending on the user within a given region, make the optimal sizing of these plants challenging. In this study, a machine learning-based surrogate model for the real-time sizing optimization of solar power plants, trained with a completely original dataset, has been developed. In the first stage, 500 different solar power plant installation scenarios were synthetically generated and evaluated in HOMER, and the obtained optimal sizing outputs were used as training targets for the proposed surrogate model rather than real operational data. The results obtained by applying various machine learning methods to the generated dataset are presented comparatively. Among 7 different machine learning models, XGBoost, Gradient Boosting, and LightGBM demonstrated the best performance. The developed model achieved an average R² score of 0.9425 for a total of 3 targets, while target-specific performance showed R² scores of 0.9747 for inverters, 0.9365 for PV panels, and 0.9165 for batteries. This model serves as a computationally efficient surrogate of the HOMER optimization process, enabling high-accuracy real-time predictions while significantly reducing the computational burden associated with intensive mathematical calculations, iterative procedures, and complex search spaces. Full article

Mountain photovoltaic (PV) stations are increasingly deployed in complex terrain, where generation is jointly controlled by solar-resource variability, near-surface meteorology, and local topography. However, the quantitative contribution of topographic factors to regional-scale PV generation remains insufficiently evaluated, and many prediction studies rely on single-station or short-term records. In this study, monthly measured generation from 118 standardized village-level mountain PV stations in Badong County, western Hubei Province, China (2019–2021), was integrated with Solargis Global Horizontal Irradiance (GHI)-related solar-resource data, high-resolution gridded meteorological data, a 25 m digital elevation model, seasonal-cycle variables, and historical-generation features. After seasonally grouped median-absolute-deviation (MAD) outlier screening, GIS-based spatial matching, terrain extraction, and viewshed-derived shading analysis, regression models and climatology baselines were compared under both chronological validation and station-exclusion spatial cross-validation. Under the strict chronological validation, CatBoost achieved the best temporal performance among the tested models (R² = 0.3119, MAE = 2719.7 kWh, RMSE = 3245.6 kWh), slightly outperforming the monthly climatology baseline. In the station-exclusion spatial cross-validation, XGBoost achieved the highest mean R² (0.8659), indicating good spatial transferability to unseen stations. Correlation and partial-correlation analyses showed that the temperature-related variable group and monthly radiation were the dominant meteorological controls, whereas elevation, slope, and terrain shading showed weak direct correlations with monthly generation for already-sited stations. Annual 90% prediction intervals were further estimated using residual bootstrapping, with an empirical coverage of 94.9%. The proposed framework provides a practical basis for monthly generation forecasting and operational assessment of already-built distributed PV stations in mountainous regions, while its application to greenfield site selection requires additional site engineering and near-field obstruction information. Full article

Agrivoltaic systems, which enable simultaneous crop production and solar photovoltaic (PV) electricity generation on the same land, can support climate mitigation, food security, and rural development. Leguminous crops like beans are globally important, yet there is limited performance studies on diverse agrivoltaic trials. This limits appropriate policy guidance. To overcome these limitations, this study assessed organic green bush bean performance under thirteen PV configurations with varying transparency and spectral properties, comparing both agricultural outcomes against national yields and policy standards. The results in vegetative metrics indicated that blue-spectrum thin-film and intermediate-transparency c-Si modules supported growth near German productivity thresholds. Although no agrivoltaic system matched national average yields, combining crop and energy revenues revealed substantial benefits: the 44%—transparent c-Si configuration generated 340% more total revenue than traditional farming, and the blue 70%—transparent thin-film system achieved 94% of national yield but 164% of conventional farm revenue per acre. Electricity generation gains outweighed modest crop reductions, highlighting strong synergies between food and energy. The results of this study highlights the potential of agrivoltaic systems to enhance land-use efficiency, support renewable energy expansion, and improve rural economic resilience, while underscoring the need for multi-year trials and site-specific controls to validate long-term sustainability outcomes. Full article

Accurate plant-level photovoltaic (PV) power estimation is important for performance monitoring, model benchmarking, and grid-integration studies. In bifacial horizontal single-axis tracking (HSAT) systems, this task is complicated by the coupled effects of front-side irradiance, rear-side irradiance, tracker position, and module temperature. This study proposes an algorithmic framework for same-time-step AC power estimation in a bifacial HSAT PV plant using field measurements of irradiance, tracker angle, module temperature, and inverter active power. The framework is not intended as an operational forecasting model because future irradiance and weather conditions are not predicted; instead, it evaluates how compact physics-based structure, interpretable nonlinear learning, and ensemble learning estimate measured AC power under nominal operating conditions. An empirical rear-to-front irradiance relationship was derived using solar-elevation bins and incorporated into a compact physics-based benchmark. This benchmark was compared with an additive Explainable Boosting Machine (EBM) and a Random Forest (RF) on a common test subset of 3916 observations. The physics-based model achieved an RMSE of 19.6 kW, an R² of 0.72, and an NRMSE of 0.38. The EBM improved these values to 17.09 kW, 0.786, and 0.334, respectively, while the RF achieved 15.96 kW, 0.814, and 0.312. Chronological validation showed weaker and more variable performance than randomized validation, indicating that temporal generalization remains challenging. Overall, the results support the use of interpretable PV-domain-guided learning as a transparent intermediate approach between compact physics-based modeling and more flexible ensemble regression. Full article

This study investigates an integrated ecological strategy to reduce electricity consumption in semi-collective housing located in the hot–arid climate of Biskra, Algeria, a region with high solar potential. The research combines photovoltaic (PV) electricity generation with passive thermal insulation using a locally sourced bio-based material derived from date palm fibers. The case study includes 104 dwellings within a residential complex of 350 units. Results show that monocrystalline PV panels (350 W) can produce approximately 479 kWh/panel/year. To meet the total annual electricity demand (504,712 kWh), around 1052 panels are required, corresponding to 1714 m² (13.8%) of the available building envelope. This installation area demonstrates the significant photovoltaic potential of the residential complex under hot–arid climatic conditions. Thermal analysis indicates that integrating a 5 cm palm fiber insulation layer increases thermal resistance from 2.06 to 2.62 m²·°C/W and reduces heat flux from 2.18 to 1.72 W/m². This improvement decreases conductive heat transfer through the envelope by approximately 21%, while numerical simulations indicate indoor temperature reductions of 4–8°C during summer conditions. These findings demonstrate that combining PV systems with bio-based insulation significantly enhances energy efficiency and thermal comfort in residential buildings under desert climatic conditions. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

The control of micro energy grids (MEGs) is characterized by volatility, uncertainty, and decentralization. Traditional power distribution algorithms, designed for centralized, dispatchable generators, are inadequate for MEG environments. Controllable load management provides peak shaving, load balancing, frequency regulation, and voltage stability, as well as fast balancing services for renewable energy grids in distributed power systems. A non-grid-tied inverter costs a fraction of its grid-tied counterpart for the same capacity. In the initial setting, one or more inverters are used. As the demand grows, more non-grid-tied inverters are added to the mix. Non-grid-tied inverters cannot be connected in parallel. There is no practical solution available in the market for the optimum utilization of this type of setting. Unlike a grid-tied microgrid, in non-grid-tied mode, a microgrid uses grid power only when needed, prioritizing renewable sources. This paper explores autonomous strategies for controlling and coordinating multiple renewable energy sources in MEG settings. It reviews and develops an algorithmic framework for optimal load distribution among multiple renewable sources, including solar photovoltaic (PV), wind turbines, and battery energy storage systems (BESSs). The proposed framework integrates resource forecasting, multi-objective optimization, and adaptive supervisory control to ensure stability, maximize renewable penetration, and minimize operational costs. Performance considerations, mathematical modelling, and potential implementation architectures are discussed. A hybrid approach, combining multiple algorithms, is therefore proposed. In this paper a real-life solution is proposed to a real-life problem. Full article

Hybrid renewable energy systems are regarded as one of the most promising solutions for the autonomous power supply of remote and weakly electrified sites, where diesel generation remains a costly and carbon-intensive energy source. This study presents the optimization of an off-grid PV/BESS/DGU microgrid for three representative regions of Kazakhstan—North, Central/East, and South/South-West—under different environmental scenarios. The aim of the study was to determine the optimal installed photovoltaic capacity, battery storage capacity, diesel generator rated power, and annual load coverage balance using the Generalized Reduced Gradient (GRG) method. The optimization was carried out using two objective functions: the conventional levelized cost of electricity, $LCOE$, and the environmentally adjusted cost of electricity, ${LCOE}_{\mathrm{e}\mathrm{n}\mathrm{v}}$, which includes the monetized cost of emissions associated with diesel generator operation. The model was formulated as a constrained nonlinear programming problem incorporating hourly energy balance, battery state-of-charge constraints, diesel generator operating constraints, and carbon price scenarios of 0, 25, 50, and 100 USD/tCO₂. The results show that an increase in the carbon price systematically shifts the optimum toward a higher share of photovoltaic generation and reduced diesel generator use in all regions. The strongest response is observed in the South/South-West region, followed by Central/East, whereas the North exhibits the lowest sensitivity due to the more pronounced seasonality of solar generation. Under the considered scenarios, the optimal PV capacity increases by approximately 24–28%, while the share of diesel generation in annual load coverage decreases by approximately 28% in the North, 44% in Central/East, and 61% in the South/South-West. At the same time, the rated diesel generator capacity remains unchanged in most scenarios, indicating the persistence of its backup function. The results confirm that the PV/BESS/DGU configuration constitutes a technically and economically justified baseline architecture for autonomous power supply under Kazakhstan’s conditions, while the inclusion of environmental costs supports the cost-effective displacement of diesel generation. The GRG method proved to be suitable for the transparent and efficient optimization of hybrid microgrid parameters. Full article

Water is an indispensable resource for the survival of all living organisms on Earth. However, many coastal villages continue to face challenges in accessing potable water, particularly during extended droughts. This comprehensive study evaluates the implementation and performance of a solar desalination system that employs photovoltaic (PV) panels and a parabolic solar concentrator to meet clean water demand in a drought-prone area of Indonesia. The system harnesses both solar-generated electricity and thermal energy to power an advanced desalination apparatus, effectively converting seawater into safe drinking water. Over a rigorous 4-month testing period, the device maintained an average steam outlet temperature of 105.9 °C, enabling a direct single-stage evaporation and condensation desalination process. Under optimal sunlight conditions, the system produced 1500 mL of purified water every 30 min, resulting in a total daily output of approximately 12 L (1500 mL × 8 cycles over 4 h). Laboratory analysis revealed a decrease in pH from 8.0 in raw seawater to 6.8 in treated water after post-treatment pH adjustment, meeting established safety standards for human consumption. Electrical conductivity measurements fell from 40–50 mS/cm to 480–500 µS/cm, confirming substantial salt removal. These results demonstrate the system’s capacity to generate potable water using sustainable energy sources and support circular economy principles by repurposing renewable resources for water desalination in water-scarce environments. Full article

Europe’s solar photovoltaic (PV) capacity is expanding rapidly, raising a key question: how much PV can each national electricity system actually absorb? Most existing assessments rely on annual or seasonal averages, which overlook the hour-by-hour match between PV generation and demand that ultimately limits feasible deployment. This study quantifies the demand-constrained PV potential of 38 European countries and how it varies across regions. Hourly PV generation is simulated in PVsyst and matched against national hourly demand from ENTSO-E. Feasible capacity is defined as the largest installation whose output never exceeds demand in any hour of the year. This system-level, time-resolved method yields operationally constrained estimates rather than purely physical potential. The 38 countries could feasibly deploy about 614 GWp of PV, generating around 678 TWh per year without exceeding hourly demand. Regional differences are pronounced: southern Europe benefits from superior solar resources, while northern and eastern regions face seasonal and infrastructural challenges. These findings underline the importance of grid modernization, energy storage, and cross-border integration. The estimates form a conservative baseline; they exclude drivers such as electric-vehicle (EV) deployment, demand-side flexibility, battery energy storage, latent demand growth, power export, and building-integrated photovoltaics (BIPV), whose inclusion would expand the feasible potential. This study offers a transparent comparative framework to guide policy, investment, and system planning for Europe’s carbon-neutral energy transition. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article

The rapid electrification of urban freight transport requires new optimization approaches that jointly consider logistics operations and energy system constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that captures the interdependencies between vehicle operations, battery constraints, charging infrastructure availability and the temporal variability of photovoltaic energy. A multi-objective structure is adopted to minimize total energy costs and CO₂ emissions while maximizing the utilization of locally generated renewable energy. The model is evaluated using scenario-based simulations under three solar integration levels (0%, 30% and 60%). The results demonstrate that integrating solar energy into routing and charging decisions significantly reduces grid dependency, lowers emissions and improves overall system efficiency. Three types of charging stations are considered in the study (S1, S2, and S3), differing in photovoltaic (PV) energy penetration levels, ranging from conventional grid-based charging (S1) to high renewable integration stations (S3). The quantitative analysis reveals a clear resource and emission structure across the simulated scenarios. Incorporating charging stops grid-wide increases the total distance from theoretical routes to real tracks with stops to overcome the 120 kW battery limit. However, the integration of solar energy significantly alters the system’s environmental costs: total CO₂ emissions drop non-linearly by 33.4%, decreasing from 364.64 kg in the ‘Low Sun’ scenario to 243 kg in the ‘High Sun’ scenario. Furthermore, the localized impact shows that utilizing pure grid energy (S1) results in 405 kg of CO₂, while maximizing solar integration up to 60% (S3) reduces emissions to 162 kg. The sensitivity analysis showed how varying the share of solar energy at the two main stations (S2 and S3) affects the total CO₂ emissions, while maintaining the same routes. Three scenarios were examined: low (10% and 30%), base (30% and 60%) and high (50% and 90%) solar energy shares. As the share of solar energy in the system increases, a clear effect of emission reduction and energy cost optimization is observed. Full article

The deployment of rural electrification actions through off-grid mini-grid solutions is one of the most effective approaches to achieving universal access to electricity in an affordable, reliable, and sustainable way. To assess the sustainability of three mini-grid projects (Sembezea, Mawayela, and Dongane), this study applied a framework that integrates different methods (HOMER, LCA based on SimaPro, and Input–Output) and indicators under the economic, environmental, and social dimensions. Data for the analysis were obtained through site visits in the case study areas, a literature review, and the HOMER and ecoinvent databases. Sembezea and Mawayela were assessed based on their operational experience, whereas the Dongane biogas system is analyzed based on a projected household biodigester experience. The results of this study revealed the considerable benefits of biogas in generating local employment (506 employees) compared to wind/solar PV (98 employees) and hydro/solar PV (91 employees), as it is expected to require a considerable number of employees for feedstock collection for the digester, under the assumed scale and conditions. Additionally, in the long term, biogas would present the lowest cost of electricity at $0.22/kWh compared to wind/solar PV ($0.28/kWh) and hydro/solar PV ($0.60/kWh), thereby improving the ability of the local community to pay for electricity. In contrast, this study concluded that, in terms of environmental impact—particularly CO₂ emissions—biogas has relatively poor environmental performance (4.58 × 10⁻² kg CO₂ eq) compared to wind/solar PV (8.50 × 10⁻⁴ kg CO₂ eq) and hydro/solar PV (3.94 × 10⁻⁴ kg CO₂ eq) in the long term. Nevertheless, biogas presents carbon neutrality as an advantage, in the sense that the CO₂ released during its combustion is assumed to be carbon-neutral. By applying the framework to the aforementioned case studies, the extent to which it is possible to provide an integrated overview of the economic, environmental, and social aspects, as well as the impacts of different HRES options in line with the SDGs, is demonstrated. Full article

Transformerless inverters are increasingly becoming essential in renewable energy generation, particularly for grid-connected photovoltaic (PV) and other sustainable and alternative energy resources. The transformerless designs offer higher efficiency, compact size, and reduced cost compared to traditional inverters with bulky transformers. These inverters minimize energy losses and enable direct connection to the grid by removing the low-frequency transformer. This paper investigates a highly reliable single-phase common-ground inverter for solar panels and other alternative energy generation. The proposed PV inverter has the benefits of existing non-isolated common-ground PV inverters, including direct connection of an input source’s negative terminal to the AC neutral terminal, eliminating leakage ground currents. The inverter is an enhancement of the dual-buck inverter, incorporating one additional diode and a flying capacitor. The dual-buck structure with the inductor inserted between the inverter phase leg prevents short-circuiting. This increases the reliability of the entire power electronics system. Moreover, using external diodes to freewheel the current, the configuration has no reverse recovery issues, allowing power MOSFETs to be employed with safe commutation at higher DC-link voltage and achieve higher efficiency. Summarily, this design prevents short-circuit issues, enhancing reliability and efficiency, and relaxing pulse-width-modulation dead times. The derivation of the PV inverter is carefully analyzed. A 700 W prototype of power converter hardware has been built. The comparative study validates the operational performance, and the grid-connected experiment confirms its theoretical analysis. Experimental results of the hardware prototype are discussed to prove the feasibility and effectiveness of the proposed PV inverter. Full article