Search Results (1,620)

There is a strong global increase in the installation of renewable energy power plants, due to increasing energy demand in the electricity generation sector and fast cost reduction. Recent studies indicate that the installation and operation of photovoltaic (PV) power plants have negligible microclimatic effects, although there are minor effects on night temperature in some cases, which, however, do not justify climate or environmental change. The development of solar energy and the installation and operation of PV power plants serve as a key solution for the energy transition to reduce carbon emissions and to address global warming. Despite the benefit of emission reduction, the deployment of solar energy through the installation of solar power plants causes land cover changes and may have minor effects on the surface energy balance by modifying roughness and albedo, biodiversity by disturbing habitats, and water resources by requiring water for cooling and cleaning. These changes may also lead to minor climatic, ecological, and social impacts. The objective of the paper consists of assessing the potential microclimatic effects of photovoltaic power plants based on satellite-based land surface temperature (LST) analyses. Specifically, the potential change in the land surface temperature, both under photovoltaic panels and on the panels, in relation to the temperature of the surrounding area is being examined in this study. The implementation is conducted in Mediterranean ecosystems, which are considered vulnerable agroecosystems due to increased climate variability. The final Landsat-based time series analysis further supports this synthesis, reporting that monthly LST differences between the PV Park and surrounding area are negligible and do not indicate a meaningful microclimate alteration attributable to PV operations. Accordingly, the evidence supports the core conclusion: utility-scale PV deployment does not constitute a driver of climate change, and the documented effects are best understood as localized surface–atmosphere energy-balance perturbations whose sign and magnitude depend on land cover, seasonality, and operation. Full article

This paper deals with the optimal integration of power plants, including a storage device. For such systems, numerous structures are possible, involving different numbers of heat exchangers, and for each of them, optimal operating temperatures need to be found. Moreover, the heat-storage system can be located at different temperature levels, offering another degree of freedom when optimizing the whole system. If process simulators are presently very powerful tools for optimizing complex processes, they need to propose a primary design before any optimization steps. Finite-Dimension Thermodynamics (FDT) could help engineers to propose this primary design, close to the optimal one. To this aim, the FDT method is generalized for power-generation systems including a storage device and any number of heat exchangers. The optimization step consists of maximizing the power generation submitted to the thermodynamics constraints (first and second laws) related to each heat exchanger, power block, and thermal storage system. Two types of heat transfer law are studied and compared: Newton’s law (K × ΔT) and phenomenological law issued from thermodynamics of irreversible processes (L × Δ(1/T)). Remarkable results have been found: (i) all the studied structures lead to the Curzon–Ahlborn efficiency when optimized with Newton’s law, (ii) for the same driving source (same high temperature and same power), and without any storage system, the output power production varies as N⁻², N being the number of the heat exchangers, (iii) Charge and discharge times scenarios have a big impact on the optimal operating temperatures and on the resulting daily energy production. Efficiencies of operational plants, including nuclear or solar plants and ORC, are finally compared with the theoretical efficiency found at the maximum power point. This shows that FDT provides a good assessment of the actual efficiency of existing power plants. Full article

When a fuel briquette is pressed using solar electricity in summer and burned for heating in winter, the briquette functions as a seasonal energy store—without batteries, self-discharge, or capital investment in storage infrastructure. This paper quantifies such “indirect energy storage” at an operating briquette production facility in Sumy, Ukraine, using 2024 operational data from a 34 kW hybrid solar power plant integrated into the production process without battery storage under continental climate conditions (50°55′ N) and full-scale military conflict. The objective was to determine the contribution of the solar power plant (SPP) to energy supply, analyse the structure of electricity consumption, and quantify the mechanism of indirect accumulation of renewable energy through transformation into solid biofuels. The study tested two hypotheses: (H1) that integration of a solar power plant into industrial daytime operation (6:00–22:00) achieves a self-consumption rate close to 100%, displacing grid electricity without curtailment or storage losses; and (H2) that the solar fraction embedded in produced briquettes constitutes a quantifiable mechanism of indirect seasonal energy storage despite a temporal mismatch between solar peaks (summer) and product demand (winter). Methods included statistical analysis of monthly and intraday operational data; Pearson correlation analysis between solar generation and production cycles; energy audit of production processes; decomposition of specific consumption into pressing and packaging components; and a simple economic assessment (NPV, IRR, LCOE, payback) with sensitivity analysis. Annual production reached 1222.975 t of briquettes. Total specific electricity consumption (including two short packaging campaigns in June and July only) was 141.3 ± 12.6 kWh/t (CV = 8.9%). After deducting 4962 kWh of dedicated packaging electricity (2.9% of annual consumption), the specific consumption for briquette pressing alone was 136.7 ± 5.0 kWh/t (CV = 3.7%)—within the European benchmark range of 80–150 kWh/t for wood densification, with tight monthly variation indicating a stable, well-tuned pressing operation throughout the year. The SPP supplied 18.3% of total annual electricity, peaking at 33.06% in May and averaging 29.95% from March to August. Intraday analysis of 530 five-minute intervals confirmed a 100% self-consumption rate across all seasons (H1 supported). A total of 223.4 t of briquettes containing accumulated solar energy were produced during the spring–summer period. A weak negative correlation (r = −0.28) between monthly SPP generation and briquette production was observed but did not reach statistical significance (p = 0.385); this descriptive—rather than causal—relationship is consistent with the expected temporal shift between summer surpluses and winter demand, and is itself a signature of indirect rather than direct energy coupling (H2 supported in a descriptive sense). The compound efficiency along the solar-to-stored-fuel chain was estimated at approximately 68%, providing a quantitative indicator for the indirect-storage concept. Economic analysis yielded a simple payback period of about 3 years, NPV (20 yr, 12%) ≈ 1.15 million UAH, IRR ≈ 33%, and LCOE ≈ 3.28 UAH/kWh—61% below the prevailing industrial tariff of 8.45 UAH/kWh—with sensitivity analysis showing positive NPV across ±20% variation in electricity price and ±15% in CAPEX. To the best of the authors’ knowledge, this is the first empirical quantification of biomass-solar integration as a seasonal energy buffer operating without battery storage. The solar energy accumulated in briquettes is sufficient to heat 56–74 households for a full winter season. Regional scaling of the present configuration—under explicit assumptions of comparable facility sizes and operating regimes—could in principle provide fuel for 15,000–20,000 households (8–12% of regional heating needs during energy crises). These findings are directly relevant to post-conflict energy recovery and to regions where attacks on energy infrastructure have left solid biofuels as the primary available heating source. Full article

To mitigate the effects of climate change, the world must significantly reduce its reliance on fossil fuels to lower greenhouse gas emissions. The nuclear power and renewable energy sources, such as solar, wind, water, waste, and geothermal energy, emit minimal to no greenhouse gases or pollutants during operation. These sources are considered crucial for combating climate change and supporting sustainable development. However, the production of electricity, like most industries, generates waste. Comparisons show clear differences: fossil fuel plants produce the largest total waste mass (primarily combustion ash, flue gas desulfurization residues, and wastewater sludge), while nuclear facilities generate a minimal volume but high-activity spent fuel and long-lived radioactive materials. Solar PV systems generate significant end-of-life electronic waste and glass encapsulant, and wind turbines yield moderate composite blade residues. Hydropower sediment management and geothermal scaling contribute unique waste streams of local concern. Regardless of the energy source, responsible waste management is critical to minimize environmental impacts. This article explores the sustainability of low-carbon energy sources, specifically focusing on waste management with the aim of highlighting the need of implementing targeted strategies such as advanced recycling and material substitution in order to minimize environmental impacts and enhance the circularity of low-carbon energy systems. Full article

The modern power system is characterized by large-scale networks, diverse types of sources and loads, and complex grid structures. Virtual Power Plants (VPPs) are proposed to address the operation problem after the integration of Distributed Energy Resources (DERs). Optimization problems in the VPP operation are predominantly mixed-integer programming (MIP) problems belonging to the class of NP-hard problems, motivating the application of quantum computers. Focusing on the VPP optimal dispatch problem under wind and solar uncertainty, we employ the Model Predictive Control (MPC) framework to conduct the VPP intraday rolling dispatch. The classical model and the Quadratic Unconstrained Binary Optimization (QUBO) model for the MPC-based intraday rolling dispatch problem are formulated, respectively. The QUBO formulation of the VPP dispatch problem renders it directly solvable by a specialized quantum computer based on dissipative optical systems: the Coherent Ising Machine (CIM). Compared with the benchmark classical solvers, the experimental results demonstrate the significant computational time reduction capability of CIM. Specifically, compared to Gurobi, Simulated Annealing and Tabu Search, the CIM achieves relative computational time reductions of $75.25\%$, $99.95\%$ and $99.96\%$, respectively, while maintaining competitive solution quality. Our work demonstrates the applicability of CIM and its acceleration potential in VPP intraday rolling dispatch, paving the way for the practical application of specialized photonic quantum computers in smart grids. Full article

Water scarcity in arid regions poses a significant challenge for the maintenance of Concentrated Solar Power (CSP) plants, where mirror cleaning consumes substantial resources. This study proposes a systematic methodological framework to bridge the gap between laboratory-scale surface characterization and engineering-scale water consumption. Through a gravimetric approach based on the physical principles of droplet retention (Furmidge theory), the water-saving potential of self-cleaning coatings has been quantified. Experimental results on 100 cm² specimens demonstrate that hydrophobic coatings can reduce residual water from 0.52 L/m² to approximately 0.24 L/m², achieving a water-saving potential of over 50%. The model incorporates a site-specific soiling factor (f_dirt) and was validated using field data from the ENEASHIP pilot plant. This approach provides a promising predictive tool for plant operators to optimize cleaning strategies and reduce operational costs, offering a scalable methodology for the solar thermal industry. Full article

Aiming to mitigate renewable energy curtailment and curb the carbon emissions of traditional thermal power units (TPUs), this paper proposes a stochastic optimal scheduling of a multi-energy complementary base considering multi-resource reserve and TPU doped with ammonia-concentrated solar power coordination. Firstly, the proton exchange membrane (PEM) electrolyzer (EL) and coal-to-hydrogen (C2H) technology are combined to produce hydrogen, and a mixed-hydrogen-source ammonia production model is constructed. The low-carbon characteristics of ammonia gas are used for thermal power mixed ammonia combustion. Secondly, to alleviate the operational burden on TPUs, a collaborative operating framework integrating a concentrating solar power (CSP) plant, an electric heater (EH), and an ammonia-coal co-fired power unit (ACCPU) is introduced. Furthermore, its low-carbon mechanisms during both peak and off-peak load intervals are thoroughly investigated. Thirdly, the ‘electricity–hydrogen–ammonia’ conversion link inside the deep excavation base and the reserve potential of the CSP plant are constructed, and a variety of flexible resource collaborative reserve models are constructed. Building upon this foundation, to account for the diverse uncertainties associated with load demand, wind, and PV generation, a fuzzy chance-constrained programming method is formulated. Seeking to enhance economic efficiency, the framework focuses on lowering the aggregate operational expenditures. Ultimately, the example results demonstrate that the presented approach effectively expands the accommodation capacity for renewable energy, lowers the base’s carbon emission, and alleviates the operational strain on TPUs. Full article

Large portions of rural population in South Africa lack access to basic water and sanitation. This advocates for urgent interventions in support of water supply. This study assessed the performance of solar-powered groundwater pumping systems established at nine pilot sites in rural areas of Greater Giyani Municipality (Limpopo, South Africa). Performance assessment indicators, namely weather, groundwater abstraction, power supply, water supply, water quality, number of beneficiaries and farm productivity, were monitored (2023–2024). Increased groundwater abstraction reduced groundwater levels by 0.4–11 m, depending on the monitored borehole. This was replenished by above-average rainfall in 2023 (≈650 mm). Power supply and pump discharge rates were stable with generally low fluctuations at recommended pumping rates (0.5–2.0 L s⁻¹). Groundwater quality was generally fit to marginal for irrigation and drinking. High levels of NO₃⁻ and total organic carbon, especially in the proximity of villages, mandated the installation of mini water treatment plants for drinking water. The implementation of solar-powered groundwater pumping schemes was generally successful, with more than 5000 villagers benefiting directly from the interventions, whilst smallholder farms turned into commercial and financially viable enterprises. Long-term monitoring of bio-physical and socio-economic drivers is essential to ensure long-term sustainability of the solar-powered groundwater pumping systems. Full article

This study analyzes the impact of thermal and electrical storage solutions coupled with Photovoltaic (PV) and Concentrating Solar Power (CSP) plants, proposing an innovative model to test a Hybrid Energy Storage System (HESS). The work presents an innovative Mixed Integer Linear Programming (MILP) model to determine the optimal configuration and operational strategy of a HESS within a grid-connected Microgrid (MG). The research focuses on the synergistic integration of PV with Lithium-ion Electrical Energy Storage (EES) and CSP with Thermal Energy Storage (TES). The MG includes dynamic residential, commercial, and hospital loads. The MILP model is optimized over a 24 h horizon across four season-representative days, utilizing a multi-criteria objective function that balances economic performance and CO₂ emissions via a weighting factor ω ∈ [0, 1]. Three distinct CSP options such as Parabolic Trough Collectors with varying Heat Transfer Fluids (molten salt or thermal oil) and TES types (direct and indirect dual-tank, or Phase Change Material) are analyzed, each coupled with a Rankine or Organic Rankine Cycle. Key constraints address energy balances, component efficiencies, power limits, and storage dynamics. The comprehensive results identify the most suitable technology portfolio mix and optimal hour-by-hour operational rules, providing transparent decision-making criteria based on storage size, process temperatures, and specific demand profiles. Full article

This study proposes a Long Short-Term Memory (LSTM)-based deep learning model for short-, medium-, and long-term forecasting of solar energy production. Approximately four years of hourly data from four photovoltaic power plants in Karabük were used. In addition to production data, meteorological and environmental variables were included through a multivariate forecasting approach. The model was tested under three scenarios at different time scales. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R²) metrics. Results showed high prediction accuracy, particularly with seasonal data, where R² values exceeded 0.90 in most cases. In forecasts based on monthly data, performance was more variable, and the shorter data window limited the model’s learning capacity. Long-term analyses indicated that the model successfully captured overall production trends and achieved high accuracy across all Photovoltaic (PV) systems. The findings also revealed that incorporating meteorological and environmental variables significantly improved prediction performance. In particular, air pollution parameters were effective in long-term production forecasting. Overall, the study demonstrates that Long Short-Term Memory (LSTM)-based models are reliable and effective tools for solar energy forecasting, with strong potential for energy planning and smart grid applications. Full article

The addition of renewable energy sources (RES), including photovoltaic (PV) and wind generation technology, has introduced new challenges and opportunities for modern power systems. This paper examines the functionality and reliability of a hybrid PV–-wind-integrated 9-bus power system evaluated in DIgSILENT PowerFactory. The system has been designed with two solar PV plants, two offshore wind farms, multiple loads, and transformer interconnections, and aims to evaluate steady-state, dynamic, and contingency behavior. The system was evaluated using load-flow, quasi-dynamic, and RMS simulations to assess power balance, voltage stability, and fault recovery. The outcomes indicated convergence, balanced power flow, and system resilience under single-contingency conditions. This paper shows the effectiveness of the power system simulation tool for analyzing hybrid renewable power systems. Full article

Different types of Virtual Power Plants (VPPs) play distinct roles within power systems. To scientifically evaluate the operational benefits of VPPs, this paper constructs a comprehensive evaluation framework based on combined weighting and the Evaluation based on Distance from Average Solution (EDAS) method. First, an evaluation index system is established encompassing four dimensions: economic, environmental, social, and technical. Subsequently, a hybrid model integrating DEMATEL, CRITIC, Game Theory, and EDAS is proposed. Specifically, the DEMATEL method is employed to analyze the causal relationships among indicators and determine subjective weights, while the CRITIC method is used to calculate objective weights. Game Theory is then applied to optimize the combination of weights, and the EDAS method is utilized to rank the alternatives. Empirical analysis of five VPP scenarios indicates that the renewable energy accommodation rate and hardware investment costs are the core driving factors affecting operational benefits. Specifically, the renewable-energy accommodation rate exhibits the highest combined weight of 0.08, and the hardware investment cost reaches 0.07. Among the scenarios, a wind-solar-storage hybrid VPP demonstrates the optimal comprehensive performance. The results are consistent with comparative methods such as TOPSIS, verifying the reliability of the proposed framework and providing a scientific reference for VPP investment decision-making. Full article

This paper examines an algorithm and evaluates the upper limits of technical parameters for step-by-step management of forecast coverage for aggregated generation from solar power plants (SPPs) in Ukraine, given the high share of renewable energy sources in the integrated power system of Ukraine. The relevance of the research is due to the growth in the installed capacity of SPPs, stricter requirements for forecasting accuracy, and the full financial responsibility of producers for imbalances in accordance with the current electricity market model. The problem is formulated as a special case of a hierarchically controlled quasi-dynamic power system, accounting for technological, energy, and economic constraints. The objective function is defined as the minimisation of the total hourly measure of discrepancy between the forecast and actual volumes of electricity supplied, whilst ensuring power balance through energy storage systems and flexible generation. The numerical implementation was carried out using the “SOPS” software and information complex. The input data used were hourly indicators of the forecasted and actual generation of Ukraine’s solar power plants for 2021–2025, published by the state-owned enterprise “Guaranteed Buyer”. Hourly, daily and monthly operating parameters for aggregated solar power generation in 2025 have been calculated. The calculations show that the maximum hourly mismatch between forecasted and actual solar generation in 2025 reached 3116 MW, while the maximum daily mismatch exceeded 19.8 GWh. Under the assumed operating conditions, an energy storage system with 30,000 MWh capacity and flexible generation of up to 7500 MW enabled full imbalance compensation, achieving IMB(t) = 0 for all hourly intervals in the analysed case. The required volumes of flexible generation and the operating parameters of the storage systems have been determined. The practical significance of the results lies in their potential use for operational planning of the operating modes of solar power plants, energy storage systems, and flexible generation on a daily and hourly basis, as well as for justifying technical and economic decisions aimed at reducing imbalances. The results obtained confirm the effectiveness of the proposed step-by-step control algorithm and demonstrate the potential to minimise imbalances through the rational coordination of solar power plants, energy storage systems, and flexible generation capacities. Full article

This study examines the economics of blue and green hydrogen as feedstock for large industrial facilities in Southeast Asia. To understand how industries can adopt low-emission and renewable hydrogen, the levelised costs of blue and green hydrogen are calculated. Four pathways are examined, including a large-scale carbon capture and sequestration facility located a distance away from an existing steam methane reforming hydrogen plant, a gigawatt-scale electrolysis facility adjacent to a large industrial site fed by an adjacent solar photovoltaic electricity source, as well as two pathways with either remote electrolyser and solar photovoltaic, necessitating hydrogen transport and storage, or a remote solar photovoltaic source with a dedicated power transmission line. The region’s transition to green hydrogen must overcome the challenges of high renewable electricity costs, the need for large land banks for solar photovoltaic farms and efficient long-distance hydrogen transport solutions or power transmission lines. Moreover, the region must improve its inconsistent track record in implementing billion-dollar public–private projects within budget and on time. Full article

Accurate solar power forecasting is a key technology for efficient operation of photovoltaic (PV) power plants and safe grid dispatch. Under the “dual carbon” goals and the increasing share of renewable energy connected to the grid, ultra-short-term power forecasting is important for improving dispatch decisions and supporting system operation. To address the ultra-short-term forecasting task at two PV sites, this study develops an end-to-end framework that integrates machine learning, Bayesian optimisation, and SHAP-based interpretability. First, correlation analysis was performed on the datasets from the two sites to provide a foundation for subsequent model development. Next, seven forecasting models, including CatBoost, NGBoost, Random Forest (RF), AdaBoost, ARIMA, CNN-LSTM, and LSTM, were developed and uniformly optimised using Bayesian optimisation. Under a unified framework of data partitioning, optimisation budget, and evaluation metrics, the predictive performance of all models at the two sites was systematically assessed. The results show that the optimal model varies across sites: at Site 1, LSTM delivered the best performance, with test-set R², MSE, RMSE, and MAE values of 0.972, 17.610, 4.196, and 2.267, respectively; at Site 2, CatBoost achieved the best results, with corresponding values of 0.994, 0.385, 0.621, and 0.249, respectively. These findings highlight pronounced site-specific differences in model performance, indicating that different modeling approaches exhibit distinct adaptability under varying data characteristics and operational conditions. Further error analysis and SHAP interpretation indicate that solar irradiation and key meteorological variables are the main drivers of power output, and their effects are nonlinear, confirming the model’s ability to capture complex nonlinear relationships in PV power forecasting. Finally, a graphical user interface (GUI) tool was developed to support site selection, real-time forecasting, and parameter input, providing a practical and convenient solution for PV plant operation and grid dispatch. Full article