Search Results (1,209)

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

The large-scale integration of renewable energy has significantly improved the low-carbon performance of power systems, but has also increased operational uncertainty. Under extreme weather conditions, wind and solar power may experience consecutive days of simultaneous output shortfalls—referred to as “renewable energy drought”—leading to persistently high net load and severe challenges to supply guarantee. To address this issue, this paper proposes a multi-source coordinated supply-guarantee dispatch strategy for consecutive-day renewable energy drought scenarios. First, net load is defined as the total system load minus the available wind and solar output. Based on magnitude and duration thresholds, renewable energy drought events are extracted from historical data to generate representative scarcity scenarios. Second, a multi-source coordinated optimization dispatch model is constructed, incorporating wind power, solar power, thermal units, battery energy storage, and pumped-storage hydro. The objective is to minimize the total system operating cost, which includes thermal fuel cost, start-up/shut-down costs, storage cycling cost, wind/solar curtailment penalty cost, and load shedding penalty cost. The load shedding penalty coefficient is set to a magnitude much higher than conventional costs to highlight the priority of supply guarantee. The model accounts for operational constraints such as minimum up/down times, deep regulation capability, ramping limits of thermal units, and charge/discharge power limits of storage. Taking a provincial power system in China for the year 2030 as a case study, a dispatch case covering four consecutive days (96 time periods) is designed. Based on a baseline scenario, eight groups of sensitivity analyses are conducted to comprehensively investigate the impacts of key factors on the supply-guarantee strategy, including: the minimum up/down time of thermal units, deep regulation capability, load shedding penalty cost, load level, rated energy capacity and charge/discharge efficiency of battery energy storage, rated energy capacity and pumping/generating efficiency of pumped-storage hydro, thermal fuel cost coefficient, and renewable energy capacity. Simulation results show that the proposed strategy can effectively coordinate multiple resources under consecutive-day drought conditions; reducing the minimum up/down time of thermal units improves supply flexibility but increases start-up/shut-down costs; enhancing deep regulation capability optimizes storage utilization and reduces total system cost; the load shedding penalty cost directly determines the trade-off between supply guarantee and economic efficiency; and as load level decreases by 5%, 10%, and 15%, the total system operating cost reduces by approximately 6.3%, 12.5%, and 18.8%, respectively. This study provides a quantitative method and technical support for supply-guarantee dispatch decisions and resource allocation in high-renewable power systems under persistent drought conditions. Full article

The coal chemical industry is a major emitter of carbon and pollutants in China, yet the synergistic potential of decarbonization options remains unclear. This study integrates life-cycle assessment (LCA) and techno-economic analysis (TEA) to evaluate the synergistic reduction potential of substituting conventional coal-based H₂/O₂ with renewable-powered electrolytic H₂/O₂ across eight scenarios for 2030 and 2050, explicitly accounting for green H₂ supply constraints. We find that full life-cycle emissions reached 1.29 Gt CO₂eq and 20.43 Mt of pollutants in 2023 (≈10% of national GHG emissions), projected to rise to 2.49 Gt and 41.46 Mt by 2050. While the theoretical maximum carbon reduction potential reaches 95%, a severe green H₂ supply gap limits near-term feasibility: achievable reductions are only 12% (carbon) and 1% (pollutants) by 2030, rising to 42% and 11% by 2050, with abatement costs of –380 billion to 3.6 trillion CNY. The wind- and solar-powered pathways are most cost-effective (marginal abatement costs as low as 195 CNY/t CO₂eq). We recommend prioritizing deployment in renewable-rich regions and aligning electrolysis scale-up with grid decarbonization to enable a pragmatic transition toward a green H₂-integrated coal chemical industry. 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

The increasing integration of renewable energy resources in microgrids requires effective frameworks for energy management, system monitoring, and operational assessment. This study presents a simulation-based digital twin-oriented framework for a small-scale renewable energy microgrid with integrated energy management control. The framework consists of a solar photovoltaic (PV) system, a lithium-ion battery energy storage system, and a variable load implemented in a MATLAB/Simulink 2024b environment. Mathematical models are developed to represent PV generation, battery state-of-charge (SOC) dynamics, and load variations, while a rule-based energy management strategy is used to regulate power flow between generation, storage, and demand. An interactive dashboard is incorporated to provide dynamic visualization within the simulation environment of the system operation and key performance indicators. Simulation results show that the controller successfully maintains the battery SOC within the safe operating range of 30–90% and eliminates SOC constraint violations. Compared with uncontrolled operation, renewable energy utilization increases from 67.4% to 92.8%, overall system efficiency improves from 79.6% to 91.3%, and system reliability increases from 93.1% to 99.2%. The Loss of Power Supply Probability (LPSP) decreases from 0.069 to 0.008, while RMS power imbalance is reduced by 50.0%. Battery and converter losses decrease by 41.7% and 43%, respectively. These results demonstrate the effectiveness of the proposed framework in improving energy utilization, reliability, and operational stability while providing a foundation for future digital twin-enabled microgrid optimization and decision support applications. Full article

The residential sector accounts for a significant portion of global energy demand, which can be met through sustainable alternatives such as solar energy. This study evaluated the energy, exergy, environmental, and exergy-sustainability performance of three waste heat recovery configurations (double-loop organic Rankine cycle—DORC, Kalina cycle—KC, and organic Rankine cycle—ORC) coupled to a supercritical CO₂ Brayton cycle with intercooling and reheating, designed to meet the demand of a residential complex of 120 homes in the Colombian Caribbean region, built with four different materials, using a concentrated solar power tower as the heat source. Mass, energy, and exergy balances were performed, along with a life cycle analysis, sizing the systems to supply a cooling load of 133 kW. The results show that the three configurations meet the required demand, with energy efficiencies above 50%: sCO₂-DORC (51.7%), sCO₂-ORC (51.61%), and sCO₂-KC (51.32%), with a maximum exergy efficiency for sCO₂-DORC (24.3%). The environmental analysis indicates that the construction phase accounts for more than 95% of total emissions. Overall, the results confirm the viability of these configurations for residential applications, promoting the integration of renewable energies and supporting the regional energy transition. Full article

Solar power generation is highly sensitive to short-term weather variations, particularly under rapid cloud movement, leading to significant power fluctuations and challenges in stable energy dispatch. To address this issue, this study proposes an AI-based solar power prediction and power switching control system integrating a hybrid deep learning model with an embedded microcontroller. The model combines radar echo imagery and meteorological time-series data, where a convolutional neural network (CNN) extracts spatial cloud features and a gated recurrent unit (GRU) captures temporal dynamics for short-term irradiance forecasting. Based on the prediction results, the microcontroller performs real-time power source switching between solar and grid supply. Experimental results using real-world data from the Taiwan Central Weather Bureau demonstrate that the proposed system achieves reliable prediction performance and enables effective proactive energy management. These results suggest that the integration of AI-based forecasting and embedded control has potential for renewable energy utilization and power dispatch applications. By supporting intelligent energy management and more efficient use of photovoltaic energy resources, the proposed framework may contribute to sustainable energy utilization in photovoltaic systems. Full article

An improved availability–dependability–capability (ADC) assessment framework is proposed for evaluating the mission effectiveness of solar-powered unmanned surface vehicles (USVs) under the coupled effects of hardware state transitions and stochastic energy supply. Within the availability and dependability dimensions, a joint state space is constructed that couples hardware condition states with battery state of charge (SOC). A Monte Carlo simulation driven by real solar irradiance data is used to derive the initial probability distribution and the dynamic state transition matrix. In the capability dimension, a hierarchical index system is established, and a hierarchical Criteria Importance Through Intercriteria Correlation (CRITIC) method is introduced to achieve objective weighting that accounts for inter-index correlations across multiple levels. Case-study calculations for a typical task profile yield a baseline mission effectiveness of approximately 0.661934. Comparative analysis reveals that neglecting the dynamic evolution of the energy state overestimates effectiveness by 38.94%, whereas ignoring random hardware failures produces a deviation of roughly 9.48%. These findings indicate that, while energy-state dynamics impose the dominant constraint on mission performance, the contribution of hardware degradation remains substantial. The concurrent influence of both limiting factors confirms the necessity of an assessment framework that explicitly integrates hardware and energy states, as developed in this work. Full article

This study presents a technical assessment of solar energy systems for integrated agricultural use and rural electrification. A model village comprising 30 households was considered, and high-resolution hourly load profiles were developed to characterize consumption dynamics, including peak demand and sectoral distribution across residential, agricultural, public, healthcare, and commercial users. A 60 kW photovoltaic (PV) system was designed in conjunction with an independent solar thermal installation for hot water supply. The system configuration was established through component sizing and numerical modeling, incorporating heat transfer mechanisms and operational constraints. Time-dependent simulations performed in MATLAB (R2022b) evaluated PV power output, battery storage cycling, and thermal system performance over a 24-h horizon. A comparative analysis of standalone PV, hybrid PV/T, and decoupled PV–thermal configurations was conducted based on performance and operational criteria. The results indicate that separated electrical and thermal subsystems achieve improved cost-effectiveness, enhanced reliability, and reduced maintenance requirements. The proposed approach demonstrates the technical viability of solar-based energy systems for rural applications, supporting energy autonomy, reduced fossil fuel dependence, and sustainable agricultural development. Full article

Alkaline water electrolysis (ALK) is essential for renewable energy integration, yet traditional models using a fixed minimum operating power often overestimate low-load flexibility by neglecting state-dependent safety boundaries. This study develops an electro-thermal-mass multiphysics dynamic model that treats the transient hydrogen content in oxygen (H₂-in-O₂) concentration as a first-principles state variable. Based on a quasi-steady-state safety balance, a dynamic minimum operating power constraint is derived to replace empirical static limits. A key feature of this model is the explicit coupling of Arrhenius thermal diffusion and pressure-differential-driven permeation during load transients, allowing the safety threshold to respond to real-time system states. Year-round simulations of a 30 MW industrial system under a wind–solar time series reveal that thermal inertia, with a time constant of approximately 4.2 h, induces a sustained mismatch between low-power operation and high system temperature. Under high-temperature and rapid-ramp conditions, the dynamic safety lower bound reaches 28.2% of the rated capacity, exceeding the conventional 20% static threshold by 8.2 percentage points. This deviation results in 378.3 MWh of implicit curtailment and 60.5 h of additional downtime annually, leading to a green hydrogen production deficit of approximately 42.2 t/year. This research provides a theoretical foundation and technical reference for the optimal control and flexibility assessment of industrial-scale green hydrogen systems under fluctuating power supply conditions. Full article

Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion network (SIL-MMFN) for detecting and classifying photovoltaic panel operating states in solar insecticidal lamps. The method combines time-series measurements with short-time Fourier transform (STFT)-based time–frequency images. A convolutional image branch extracts spatial features from time–frequency representations, whereas a bidirectional GRU branch with attention models temporal dependencies in the original signals. In addition, physics-informed features based on the illumination–current residual and output power are introduced to enhance discriminative fault information. Field data collected from four agricultural deployment nodes were used to classify normal, open-circuit, and mismatch states. Experimental results show that the proposed method achieved an accuracy of 97.5%, precision of 96.7%, recall of 97.8%, and macro-F1 score of 97.3%, outperforming single-modality and representative comparison models. The results indicate that multimodal fusion helps reduce confusion between open-circuit and mismatch faults and provides a potential approach for operating-state monitoring and maintenance of agricultural photovoltaic equipment. In this study, fault diagnosis refers to the detection and classification of photovoltaic panel operating states, including normal, open-circuit, and mismatch conditions. Full article

Namibia’s rural communities continue to experience limited and unreliable electricity access despite the potential of the country’s exceptional solar, wind, and biomass renewable energy resources. Conventional grid extension remains financially and technically impractical for dispersed off-grid settlements, underscoring the need for cost-effective, renewable-based alternatives. This paper presents a resource-driven design and multi-objective optimization framework for Hybrid Renewable Energy Systems (HRESs) tailored to Namibia’s off-grid communities. The proposed model integrates solar PV, wind turbines, biomass generators, and hydrogen-based fuel cells with a hybridized energy storage consisting of batteries, supercapacitors, and hydrogen tanks. Using the Non-dominated sorting Genetic Algorithm-II (NSGA-II), the system simultaneously minimizes Total Life Cycle Cost (TLCC), Levelized Cost of Electricity (LCOE), Loss of Power Supply Probability (LPSP), carbon dioxide (CO₂) emissions, and Wasted Renewable Energy (WRE). The framework is applied to three rural villages, Oluundje, Ombudiya, and Onguati, using high-resolution, site-specific renewable resource datasets and community-level load forecasts. The results demonstrate that resource-aligned configurations substantially improve system reliability (up to 99.28%), reduce LCOE (0.0023–0.0811 USD/kWh), and optimize dispatch behaviour across seasonal variations. Storage hybridization further enhances stability by balancing transient and long-duration deficits. Compared to existing diesel mini-grids, the optimized HRESs achieve markedly superior techno-economic and environmental performance. The proposed framework offers a scalable, adaptable, and policy-ready tool for accelerating sustainable rural electrification in Namibia. 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

Within a substantial segment of renewable energy systems, the production of wind energy and solar panels heavily relies on natural resources and weather conditions. The production of fresh energy could persist at a minimal level, leading to a scarcity of power and pushing the system into severe operational states, potentially triggering grave impacts on both production and functioning. Current studies typically employ novel energy production levels or weather benchmarks to assess extreme situation risks, making it challenging to delineate the risk variance in these scenarios from a supply-demand balance viewpoint. For this purpose, we suggest a method to evaluate risks in extreme operational situations. Initially, utilizing the ‘source-load’ random mismatch approach, this technique uncovers the distribution patterns of power supply and demand equilibrium in large-scale renewable energy systems, elucidating the variance in the intensity of diverse extreme situations. Next, the ALARP (As Low As Reasonably Practicable) standard is employed to categorize the risk associated with extreme operational situations, while the CVaR (Conditional Value at Risk) index characterizes the anticipated loss when the risk surpasses a specified limit. The likelihood of losing tail risk in areas of high risk is measured to establish a precise foundation for making risk-related decisions. Ultimately, a sample analysis is conducted, focusing on a substantial segment of the renewable energy power system. The findings indicate that the suggested technique is capable of precisely assessing the risk of imbalances in supply and demand due to severe operational situations. In contrast to a risk classification-based evaluation approach, this method more accurately mirrors the distribution traits of extreme situations in high-risk regions, offering practical assistance for adaptable system resource distribution and operational decision-making. Full article

Agrivoltaics is a promising technique, especially in view of the rapid population growth associated with the expansion of cultivated areas to satisfy the food demands of the population, and the increase in solar power plants, which require considerable space to supply the population with energy. Thus, the transition from agricultural to agrivoltaics systems and the transition from PV power plants to agrivoltaics systems can enable more efficient use of land for energy and agricultural production. However, the configuration of agrivoltaics systems, namely panel elevation, spacing between panels and between rows of panels, and panel size, defines the amount of material used. As a result, configuration can have a major impact on the environment. The aim of this study is to highlight the environmental impact from converting 1 ha of land used entirely for agricultural production to 1 ha of an agrivoltaic system, and from converting 1 ha of land used entirely for solar photovoltaic energy production to 1 ha of an agrivoltaic system through a life cycle assessment. Three different configurations of agrivoltaics systems are considered to assess the environmental potential of agrivoltaics configurations. This analysis is performed with SimaPro 9.4 software, using the ReCiPe Midpoint (H) method and the Eco-invent database. The study determined impacts on global warming, stratospheric ozone depletion, ionizing radiation, ozone formation, mineral resource scarcity, fossil resource scarcity, water consumption, and land use through the determination of the Land Equivalent Ratio (LER). The results show that impacts are highest for PV power plants, followed by the agrivoltaic system with the largest PV panels for all indicators, except for stratospheric ozone depletion, where impacts are highest for agrivoltaics and agricultural use systems. The results of the land evaluation showed that the agrivoltaic system Case 3 gave the best performance, with a Land Equivalent Ratio of 148.7%. Full article