Search Results (6,867)

Due to increasing demand and consumption of electricity, as well as the need to decarbonize and mitigate climate change, solar energy is an important factor in the transition to emission-free energy sources. This study focuses on identifying the most suitable locations for the construction of large solar photovoltaic (PV) power plants while respecting environmental, economic, and technical standards. The study area covers the mainland part of Southeastern Europe (796,039 km²), including the following countries: Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, North Macedonia, Albania, Greece, Bulgaria, Romania, Moldova, and Türkiye. Using geographic information systems (GIS) and remote sensing methods, nine factors (topographic, climatic, hydrological, ecological, vegetation, and anthropogenic) were analyzed with a spatial resolution of 100 m. A fuzzy analytic hierarchy process (F-AHP) pairwise comparison matrix was constructed to quantify the relative importance of the selected criteria. The F-AHP weighting results indicate that photovoltaic output (17.9%) and land use (15.7%) are the most important among the evaluated criteria. The results show that 6.7% of Southeastern Europe is very highly suitable for installing solar PV plants, with the most suitable areas located in Moldova (14.5%) and Greece (10.5%). Through spatial analysis of the final results, 24 of the most suitable locations for large-scale solar PV power plant development were identified, with a potential to generate approximately 30.2 TWh of electricity annually. In such a scenario, the forecast indicates that 24 large-scale solar power plants would supply electricity to more than 6.7 million households, corresponding to over 17 million inhabitants. The final spatial patterns provide decision-makers at the international level with a significantly more effective basis for planning solar energy development in order to increase the share of green energy and clean technologies in this part of Europe. Full article

This proof-of-concept study proposes a photovoltaic transparent vacuum insulation panel (PV-TVIP) and evaluates its heat transfer and power generation characteristics with increased temperatures, and light transmission characteristics for visible light and ultraviolet wavelengths. The study was conducted with a climate-controlled chamber mimicking the common temperature range of Sapporo, Japan. The average TVIP heat flux was measured to be 65–75 W/m² with a U-value of 1.95–2.3 W/(m²∙K). Compared to earlier measurements to see the effect of seasonal atmospheric conditions to the quality of the TVIP, it was determined that the TVIP manufactured during winter conducted less heat, assumed to be caused by decreased humidity. Placing the PV between the TVIP and a glass pane increased the operating temperature by 26.06 °C and decreased power generation by 13%. Afterwards, the transparency of the TVIP and PV-TVIP were measured under a bright light therapy lamp, showing that TVIP reduced the amount of most visible light wavelengths by 50% and the PV-TVIP by 90%. UV radiation was respectively reduced by approximately 78% and 100%. The results show that while PV-TVIP shows potential as a BAPV window retrofit solution, its manufacturing requires optimized, low-humidity conditions during all phases of the manufacturing process. Full article

This study evaluates the resource, technical, economic, and CO₂ mitigation potentials of terrestrial hydropower, wind power, and photovoltaic (PV) power in China under historical and future SSP(Shared Socioeconomic Pathways) climate scenarios. By integrating hydro-meteorological observations, land-use information, digital elevation data, nature-reserve constraints, and CMIP6 climate outputs, we estimate renewable-energy potentials through a consistent national-scale screening framework and cost–supply curve analysis. The results show clear spatial heterogeneity among the three energy sources. Hydropower potential is concentrated mainly in the Yangtze River basin, Pearl River basin, and Southwestern International Rivers. Wind-power potential is relatively high in northwestern, northeastern, and plateau regions, while PV potential is particularly large in northwestern, northern, northeastern, and selected southeastern regions. Under the adopted assumptions, PV shows the largest resource and technical potential, followed by wind power and hydropower; however, this ranking reflects resource potential rather than comprehensive deployment superiority. Practical development is also constrained by ecological flow requirements, land-use competition, grid integration, storage demand, transmission capacity, curtailment risk, and regional demand matching. The findings provide a national-scale comparative reference for renewable-energy planning and CO₂ mitigation, while highlighting the need for future work that incorporates dynamic land use, system-level integration costs, detailed turbine or power-curve modeling, and dynamic grid-emission factors. Full article

The intensification of the urban heat island effect poses a critical threat to the preservation and habitability of compact historic districts. The Alameda de Hércules in Seville exemplifies this vulnerability, where the intersection of heritage protection and extreme Mediterranean summers limits conventional climate adaptation. This study conducts a multi-temporal evaluation of the square’s climate resilience, spanning from its configuration prior to major 21st-century renovations to its current state and future projections, proposing future interventions. By integrating advanced microclimatic simulation and high-fidelity energy modeling, the research assesses a dual-function strategy: the improvement of the thermal environment while implementing non-intrusive photovoltaic pavements (PVPs) for energy generation. Environmental parameters, including air temperature, mean radiant temperature (MRT), and the universal thermal climate index (UTCI), were analyzed alongside the renewable energy potential of the site’s mobility infrastructure. Four heritage-sensitive interventions were tested: PV-integrated bicycle lanes, shading canopies, reflective pavement, and permeable paved grass. The results demonstrate that the canopies and paved grass zones can lower surface temperature up to 3.7–4.3 °C, reduce UTCI stress up to 2.3–3.0 °C, and decline MRT up to 10.6 °C. These values correspond to the maximum reductions achieved in specific zones. However, the PVP can locally increase surface temperature by about 4.7 °C and the reflective pavements increase MRT by around 10.4 °C, while generating an estimated annual energy yield of 174.19 MWh. The analysis under future climate projections suggests that these strategies remain equally effective under future scenarios. These findings confirm that PV-integrated urban surfaces offer a viable, reversible, and replicable approach to retrofitting historic public spaces, harmonizing climate-adaptive cooling with decentralized energy production without compromising the site’s cultural significance. Full article

The occupational health implications of solar photovoltaic (PV) recycling remain critically under-investigated. This study assessed occupational exposure across the PV recycling value chain in Taiwan, evaluating primary mechanical dismantling and downstream reuse sectors (glass milling and controlled low-strength material [CLSM] batching). Area and personal samples were analyzed for total dust, respirable dust, and trace heavy metals. Results indicated that primary mechanical crushing yielded relatively low ambient dust and negligible toxic heavy metal (e.g., Pb, Cd) aerosols, attributed to the macroscopic ductility of metallic ribbons and EVA shock-absorbing properties. Conversely, a critical “hazard transfer” phenomenon was empirically identified downstream, where intensive secondary grinding and aggregate blending in the downstream reuse sector (glass milling and CLSM batching) systematically shifted the aerodynamic particle size distribution, causing the respirable dust fraction to surge to 38.9–72.6%. The pursuit of zero-waste material circularity inadvertently amplifies highly dispersive, respirable dust hazards in downstream sectors, necessitating targeted occupational exposure controls. Full article

This paper proposes an optimal operational framework for enhancing the economic, technical, and environmental performance of a renewable energy-based microgrid. The proposed system integrates photovoltaic (PV) generation, wind turbines (WTs), battery energy storage systems (BESSs), diesel generators (DGs), and utility grid interaction. Three multi-objective optimization algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Genetic Algorithm (MOGA), and Multi-Objective Celestial Orbit Optimization (MOCOO), are employed to minimize the total operating cost and grid dependency. The obtained results demonstrate that MOPSO achieves the best techno-economic performance with a minimum operating microgrid cost of 2.2 M$/year and a low grid dependency ratio of 0.0333. The operational analysis confirms that the proposed renewable-priority scheduling strategy significantly reduces operational emissions and reliance on the utility grid through coordinated BESS charging/discharging and efficiency-aware DG dispatch. The microgrid (MG) achieves zero-emission operation during operating periods dominated by renewable generation. Furthermore, the DG operates within an efficiency range of 36.8–39.3%, improving fuel utilization and reducing unnecessary emissions. The battery degradation analysis indicates high lifetime cycle capability under shallow depth-of-discharge operation, demonstrating improved long-term operational sustainability. Overall, the proposed framework provides a reliable and economically balanced solution for sustainable microgrid energy management. Full article

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application models such as agricultural PV integration, photovoltaic greenhouses, solar-powered irrigation, and livestock energy supply, the demand for the safe operation of photovoltaic systems in agricultural production scenarios is becoming increasingly prominent. To address the difficulty in fully characterizing the multi-scale dynamic features and local amplitude disturbances of DC SAF signals, this paper proposes a SAF detection method based on multi-scale generalized amplitude-aware permutation entropy (MS-GAAPE). The method extracts MS-GAAPE from arc current signals at various scales using sliding window-based generalized coarse-graining, which preserves temporal sequence information while improving the characterization of local amplitude variations. Particle swarm optimization (PSO) is applied to optimize these multi-scale features, strengthening fault-related information and reducing interference. The optimized features are then processed by a support vector machine (SVM) for SAF detection. The dataset used contains 50,000 samples covering transient conditions such as voltage fluctuations and is divided into a training set and an independent test set in a 70% to 30% ratio. The training set is utilized for feature parameter determination, feature weight optimization, and classification model construction, while the independent test set is reserved solely for final performance evaluation. Experimental results demonstrate that the proposed method achieves excellent detection performance under various operating conditions and load levels, with an accuracy of 99.32% and a total detection time of 103.62 ms, meeting the requirements of the UL1699B standard, thus showcasing strong real-time detection capability and potential for embedded implementation. Full article

Maximum power point tracking (MPPT) is essential for photovoltaic (PV) efficiency under dynamic environments. This paper presents a comparative analysis of three MPPT algorithms: incremental conductance (INC), Q-Learning (QL) reinforcement learning, and Transformer-inspired Machine Learning (TML) applied to a photovoltaic (PV) array configured in a 1S×3P with a DC-DC boost converter designed for a 48 V DC output. Simulations were performed under four scenarios in MATLAB/Simulink: standard test conditions (STC), irradiance variation, temperature variation, and combined wide-range variations. At STC, all three exceed 99% tracking efficiency, with QL achieving the highest efficiency, 99.914%; TML having the best output voltage regulation (48.071 V); and INC converging fastest (5.4 ms). Under dynamic irradiance variations, QL attained the highest average tracking efficiency (91.85%) and average output power (481.4 W), whereas INC converged within 36.9 ms. Under temperature variations, TML achieved the highest average tracking efficiency (97.448%) and average power output (549.30 W), while INC maintained the fastest convergence rate (68.6 ms). With wide-range combined variation, QL achieves the highest average tracking efficiency (91.32%) and output power (469.9 W), outperforming TML (84.06%) and INC (57.39%) by 7.3 and 33.9 percentage points, respectively; INC converges fastest (33.1 ms) but delivers 64.1% less output power than QL. The simulation results demonstrate that artificial intelligence-driven algorithms can significantly improve maximum power point tracking (MPPT) under dynamic conditions. To establish real-world viability, future work requires hardware-in-the-loop (HIL) testing and experimental validation. Full article

This scientific article presents a comprehensive case study detailing the design of a fully off-grid household in Poland, utilizing an energy solution that combines high-pressure hydrogen energy storage and photovoltaic (PV) technology. In response to the growing demand for sustainable and self-sufficient energy sources, the current study investigates the efficiency and yearly energy balance of this innovative system. The off-grid household is powered by a hybrid system that seamlessly integrates PV panels to harness solar energy and a high-pressure hydrogen energy storage system for long-term energy management. The presented case study examines the design and performance of a system integrating solar energy production with hydrogen storage. Through an analysis of real-world data and operational parameters, this research contributes valuable insights into the viability of such an off-grid solution in Polish environmental conditions. These findings provided an interesting approach to off-grid residential systems, offering a glimpse into the possible future of residential energetic autonomy in the pursuit of a greener and more resilient energy landscape. Full article

Although offshore photovoltaic (PV) systems have attracted increasing interest as a solution to land-use limitations, the influence of offshore-specific dynamic environmental conditions on PV performance remains insufficiently understood. Existing studies have primarily focused on static operating conditions or general energy yield comparisons between bifacial and monofacial PV technologies, while the combined effects of wave-induced motion, module tilt-angle, and sea-surface albedo on offshore PV performance have received limited attention. To address this gap, this study develops a parametric simulation framework to investigate the sensitivity of offshore bifacial photovoltaic (biPV) and monofacial photovoltaic (moPV) arrays to key offshore environmental and operational parameters. Given the scarcity of long-term operational data for offshore PV installations, a hypothetical offshore plant located in the Yellow Sea, China, is considered using real meteorological inputs. In this study, 16 kWp offshore biPV and moPV arrays are modeled and compared in terms of their performance through three case studies examining wave motions, tilt-angle variations, and surface albedo effects. Performance metrics such as maximum irradiance, total energy yield, energy yield losses, wave-induced power loss, and bifacial gain (BG) are analyzed and compared. The findings indicate that increasing wave motion diminishes the total energy yield due to higher tilt-angle fluctuations. Nevertheless, the biPV array regularly outperforms the moPV array because of the effect of the rear-side irradiance. The tilt angle analysis reveals a trade-off between energy yield and BG, with BG increasing from 0.05% to over 10% as the tilt angle increases from 10° to 45°. Higher surface albedo further enhances bifacial performance, increasing BG from 4.5% to 17.8% for albedo values of 0.05 and 0.25, respectively. Full article

This paper presents a comprehensive review of state-of-the-art developments in electric vehicle (EV) charging technologies, charging stations, and charging protocols, with particular emphasis on their integration with renewable energy sources (RESs). EV chargers are generally classified into on-board and off-board configurations. This study examines recent designs and advanced control strategies for both AC/DC and DC/DC power conversion stages, highlighting key technical aspects, recent innovations, and existing challenges. Furthermore, it provides an in-depth discussion of emerging multiport EV charger architectures that integrate photovoltaic (PV) systems, energy storage units, EVs, and the power grid within a unified framework. A comparative analysis is also presented to evaluate various converter topologies and energy management strategies used in the AC/DC and DC/DC stages of EV charging systems. Critical performance indicators such as power rating, output voltage level, efficiency, economic feasibility, and system complexity are also discussed. A comprehensive comparison is conducted among 13 review papers between 2015 and 2026, identifying key trends, methodological differences, and common findings. Full article

Amid the global drive for carbon peaking and carbon neutrality, integrating renewable energy into building energy systems to mitigate photovoltaic (PV) intermittency and realize low-carbon energy supply has become a critical research frontier. This study proposes a novel dual-storage renewable energy system integrating solar energy storage system (SES), hydrogen energy storage system (HES), and an alkaline fuel cell (AFC). The model was validated using a two-story single-family residence as the case study, with residential load profiles and Xi’an’s climatic conditions considered under real-world scenarios. An adaptive energy management strategy is developed to dynamically coordinate PV utilization, hydrogen dispatch, and grid interaction, while recovering AFC waste heat to enhance overall efficiency. Targeting minimized lifecycle cost (LCC) and levelized cost of energy (LCOE), the GenOpt multi-objective optimization model optimizes key design parameters. Key results show 74.2% annual renewable energy penetration, 68.5% carbon reduction versus conventional systems, and robust seasonal operation: PV dominates summer supply (81.3% self-sufficiency), while AFC compensates in winter (62.4% hydrogen contribution). The system reduces annual grid dependence by 43.7% with a minimum LCOE of ~ 12.9 USD/MWh, bridging technical feasibility and economic practicality to provide actionable insights for building-scale renewable integration. Full article

In modern power systems with high penetration of renewable energy, the efficient interaction between demand-side flexible resources and the power grid has become a key approach to mitigating renewable generation fluctuations. As a typical flexible load, air conditioning loads exhibit significant potential for renewable energy utilization due to their large scale, low cost, and fast response capability. However, existing strategies for photovoltaic (PV) accommodation fail to fully consider the coordinated scheduling between heterogeneous air conditioning clusters and energy storage systems, and lack explicit modeling of the dynamic response of air conditioning loads. As a result, they are unable to effectively address the requirements induced by renewable energy fluctuations. To address these issues, this paper proposes a coordinated scheduling strategy for heterogeneous air conditioning clusters considering dynamic response characteristics, aimed at PV fluctuation smoothing. A hierarchical framework of “fixed-frequency priority, variable-frequency compensation, and energy storage backup” is developed. By incorporating response dynamics into the scheduling process, power–energy complementarity between air conditioning clusters and energy storage systems is achieved. Experimental results demonstrate that the proposed strategy improves the PV fluctuation smoothing rate from 77.16% to 100%, significantly enhancing the local PV accommodation capability within the park. Full article

Smart microgrids combining photovoltaic arrays, wind turbines, and battery storage generate telemetry that existing open-source monitoring tools cannot process with per-mechanism energy loss visibility in real time. This paper presents the design, implementation, and evaluation of an IoT monitoring framework. The framework incorporates a physics-based microgrid simulator, a hierarchical MQTT communication architecture, and a React-based web-based user interface that supports WebSocket-based real-time data visualization. The framework consists of ten containerized microservices that can be started with a single command: docker compose up -d. All stack performance testing was conducted using a simulated 1 h test case based on a 100 kWp PV system, 10 kW wind turbine, and 50 kWh battery-powered campus microgrid. Median P50 publisher-to-subscriber latency was 27.2 ms and 99th percentile (P99) latency was 48.3 ms, with 100% message delivery across 5840 test messages, with per-topic analysis revealing a 25 ms serialization-order effect in sequential MQTT publishing. Comparative analysis against nine existing platforms including OpenEMS, VOLTTRON, Eclipse Ditto, and pymgrid confirms that, among the platforms surveyed, none unifies physics-based loss telemetry, IoT communication, time-series storage, and real-time visualization in a single reproducible deployment. Full article