Search Results (514)

The article analyses the hosting capacity of low-voltage (LV) power grids for connecting distributed energy sources (DER), mainly photovoltaic installations (PV), considering technical limitations imposed by power system operating conditions. The main objective of the research was to develop a simple equation that enables the quick estimation of the maximum power of an energy source that can be safely connected at a given point in the network without causing excessive voltage rise or overloading the transformer and line cable. The analysis was performed on the basis of relevant calculation formulas and simulations carried out in DIgSILENT PowerFactory, where a representative low-voltage grid model was developed. The network model included four transformer power ratings (40, 63, 100, and 160 kVA) and four cable cross-sections (25, 35, 50, and 70 mm²), which made it possible to assess the impact of these parameters on grid hosting capacity as a function of the distance from the transformer station. Based on this, the P_HCI indicator was developed to determine the hosting capacity of a low-voltage network, using only the transformer rating and the length and cross-section of the line for the calculations. A comparison of the results obtained using the proposed equation with detailed calculations showed that the approximation error does not exceed 15%, which confirms the high accuracy and practical applicability of the proposed approach. Full article

The precise identification of photovoltaic power stations is essential for advancing the assessment of energy infrastructure and for the efficient management of land resources. To address the need for spatially explicit data on photovoltaic (PV) development in arid and semi-arid regions amid green energy transitions, particularly in the context of identification challenges induced by the widespread distribution of bare ground, this study optimized a remote sensing-based identification method integrating Principal Component Analysis (PCA), automated sampling via Google Earth Engine (GEE), and deep learning models, and applied it to Qinghai Province, one of China’s largest PV regions. The results showed that HRNetv2 (validation Dice = 0.9463) outperformed UNet (0.9328), Attention UNet (0.9399), and HRNet + OCR (0.9184) in small-sample (1871 training samples) PV segmentation; the PV installed area during 2020–2024 accounted for 63.5% of the total pre-2024 area (~607 km²), exceeding the cumulative area before 2019, with projects predominantly distributed in areas with elevation less than 2500 m and slope less than 2°; bare land dominated PV land use (88.7%), followed by grassland (6.9%) and shrubland (3.9%), and PV construction contributed to desert greening by modifying microclimates. The study concludes that its optimized method effectively supports PV spatial identification, and the revealed PV distribution and land use patterns provide scientific guidance for synergistic PV development and ecological conservation in arid regions, while acknowledging limitations in generalizability to other regions due to Qinghai-specific data, suggesting future algorithm refinement and expanded research scales. Full article

This study investigates the optimal operation of distribution systems incorporating Photovoltaic (PV) units, Electric Vehicle Charging Stations (EVCSs), and DSTATCOM devices using the Starfish Optimization Algorithm (SFOA). The main goal of the SFOA is to minimize a combined function that encompasses three key objectives: reducing system losses, increasing PV capacity, and enhancing EVCS power. By applying the SFOA within a multi-objective optimization framework, the optimal locations and sizes of PV units, EVCSs, and DSTATCOMs are identified to meet these objectives. This study analyzes and compares several case studies with different numbers of EVCSs, focusing on the operation of a modified 51-bus distribution system over 24 h. Results show that PV hosting energy increases to 21.73, 23.83, and 29.22 MWh for cases with 1, 2, and 3 EVCSs, respectively. EVCS energy also rises to 12.41, 19.50, and 37.23 MWh for the same cases. The corresponding optimized DSTATCOM reactive powers are 11.02, 12.02, and 13.74 MVarh. Throughout all cases, system constraints—such as voltage limits, utility current, and power flow equations—remain within acceptable ranges. The findings demonstrate the SFOA’s effectiveness in optimizing distribution systems with various devices, ensuring efficient operation and meeting all key objectives while adhering to system constraints. Full article

Against the backdrop of high-penetration distributed photovoltaic (DPV) integration into distribution networks, the limited measurability of small-scale DPV systems poses significant challenges to accurately estimating the baseline load of electric vehicle (EV) clusters. To address this issue, effective forecasting of DPV power output becomes essential. This paper proposes a domain-adversarial architecture for ultra-short-term DPV power prediction, designed to support baseline load estimation for EV clusters. The power output of DPV systems is influenced by scattered geographical distribution and abrupt weather changes, leading to complex spatiotemporal distribution shifts. These shifts result in a notable decline in the generalization capability of traditional models that rely on historical statistical patterns. To enhance the robustness of models in complex and dynamic environments, this paper proposes a domain-adversarial architecture for ultra-short-term DPV power forecasting, explicitly designed to address spatiotemporal distribution shifts by extracting spatiotemporal invariant features robust to distribution shifts. First, a Graph Attention Network (GAT) is utilized to capture spatial dependencies among PV stations, characterizing asynchronous power fluctuations caused by factors such as cloud movement. Next, the spatiotemporally fused features generated by the GAT are adaptively partitioned into multiple distribution domains using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), providing pseudo-supervised signals for subsequent adversarial learning. Finally, a Temporal Convolutional Network (TCN)-based domain-adversarial mechanism is introduced, where gradient reversal training forces the feature extractor to discard domain-specific characteristics, thereby effectively extracting spatiotemporal invariant features across domains. Experimental results on real-world distributed PV datasets validate the effectiveness of the proposed method in improving prediction accuracy and generalization capability under transitional weather conditions. Full article

The rapid adoption of electric vehicles (EVs) has increased the need for sustainable charging infrastructure supported by renewable energy. This study presents a comprehensive techno-economic and environmental analysis of private EV charging stations in Kuwait powered by grid-connected solar and wind systems using the HOMER Pro 3.18.4 optimization software. Four configurations—grid-only, grid–solar, grid–wind, and grid–solar–wind—were modelled and evaluated in terms of energy output, cost performance, and carbon emission reduction under Kuwait’s climatic conditions. HOMER simulated 484 systems, of which 244 were technically feasible. The optimal configuration, combining grid, 5 kW photovoltaic (PV) (BEIJIAYI 600 W panels), and a 5.1 kW AWS wind turbine, achieved a renewable fraction of 78%, reducing grid dependency by 78.1% and annual CO₂ emissions by approximately 7027 kg. Although the hybrid system required a higher initial investment (USD 7662) than the grid-only setup (USD 1765), it achieved the lowest Levelized Cost of Energy (LCOE = USD 0.017/kWh) and long-term cost competitiveness through reduced operating expenses. Sensitivity analysis confirmed the hybrid system’s robustness against ±15% variations in wind speed and ±10% changes in solar irradiance. The results highlight that hybrid solar–wind systems can effectively mitigate intermittency through diurnal complementarity, where daytime solar generation and nighttime wind activity ensure continuous supply. The findings demonstrate that integrating renewables into Kuwait’s EV charging infrastructure enhances economic viability, energy security, and environmental sustainability. The study provides practical insights to guide renewable policy development, pilot deployment, and smart grid integration under Kuwait Vision 2030’s clean-energy framework. Full article

Aiming at the dynamic characteristics and stability of smart distribution network stations under the combined effect of the uncertainty of new energy output and the control logic of power electronics, an adaptive dimensionally increasing linear dynamic modeling method based on Koopman theory is proposed. Firstly, a regional nonlinear model of an intelligent transformer integrating photovoltaic, wind power, battery, hydrogen fuel cell, and synchronous generator is constructed. The control logic of the virtual synchronous generator is then integrated to characterize the dynamic response of the power electronic interface. Secondly, by constructing a set of nonlinear observation functions, including high-order polynomials, exponents, and periodic functions, the dimensional upgrade mapping of the system state is carried out. The dynamic mode decomposition algorithm is adopted to adaptively extract the dominant dynamic modes in the dimensional upgrade space, achieving global linear approximation of complex nonlinear dynamical systems. Finally, the simulation example results show that the average RMAE error of the Koopman method proposed in this paper in voltage spatiotemporal reconstruction is 0.1419, and the maximum RMSE error is 0.1915, significantly improving the accuracy and stability of dynamic modeling. Full article

Expansion of photovoltaic infrastructure in arid regions raises concerns about soil microhabitat degradation. Very few studies have systematically compared these recovery alternatives in reshaping the soil fungal communities during early recovery. This study investigated short-term effects (less that two-year recovery) of PV infrastructure and restoration (natural/artificial) on soil fungal diversity and enzymatic activities in Ningxia desert steppe. A total of 243 soil samples were analyzed to assess fungal diversity, composition, enzyme activities, and co-occurrence networks. The restoration method significantly affected soil fungal α-diversity and β-diversity in the experimental solar park. Specifically, at each recovery site, soil depth showed significant effect on fungal α-diversity. However, on a fine scale, artificial restoration significantly increased fungal species richness across soil depths. Ascomycota dominated across different sites, followed by Basidiomycota and Mucoromycota. Shared core genera Fusarium, Mortierella, and Geminibasidium were determined in both recovery sites. Sucrase/phenol oxidase (natural) and catalase/sucrase (artificial) were identified as key fungal drivers according to Random Forest models. Co-occurrence analysis suggested neither artificial restoration nor natural restoration has attained the level of natural habitats. Networks of artificial subsoil and natural topsoil were closest to natural habitat. These results emphasize the impact of restoration and PV shading on fungal communities via spatial heterogeneity and enzyme dynamics during initial recovery stage, providing insights for semi-arid ecosystem management under PV development. Full article

Substations’ auxiliary systems support the station’s operational loads and are crucial for grid security, often requiring backup power to ensure uninterrupted operation. A new alternative for this backup power supply is a microgrid composed of photovoltaic (PV) generation and storage. This paper proposes an electric–hydrogen microgrid as backup power supply for substation auxiliary systems. This microgrid ensures power supply during emergencies, provides clean and stable energy for daily operations, and enhances environmental friendliness and profitability. Firstly, using a 220 kV substation as an example, the construction principles of the proposed backup power microgrid are introduced. Secondly, operation strategies under different scenarios are proposed, considering time-sharing tariffs and different weather conditions. Following this, the capacity configuration optimization model of the electric–hydrogen microgrid is proposed, incorporating critical thresholds for energy reserves to ensure system robustness under fault conditions. Finally, the Particle Swarm Optimization (PSO) algorithm is used to solve the problem, and a sensitivity analysis is performed on hydrogen market pricing to evaluate its impact on the system’s economic feasibility. The results indicate that the proposed electric–hydrogen microgrid is more economical and provides better fault power supply time than battery-only power supply. With the development of hydrogen energy storage technology, the economy of the proposed microgrid is expected to improve further in the future. Full article

Distributed photovoltaic power generation is volatile and intermittent, and its power generation is usually difficult to accurately predict. Previous studies have focused on physical or mathematical modeling methods, and it is difficult to grasp the complexity and variability of historical data, and the prediction accuracy is limited. To address these challenges, this paper proposes Solar-LLM, a novel prediction framework that adapts a pre-trained Large Language Model (LLM) for time-series forecasting. By freezing the core LLM and reprogramming only its input and output layers, Solar-LLM efficiently translates numerical time-series data into a format the model can understand. This approach leverages the LLM’s inherent ability to capture long-term dependencies and complex patterns, enabling effective learning even from limited data. Experiments conducted on a dataset from five photovoltaic power stations show that Solar-LLM significantly outperforms baseline models, proving it to be a highly effective and feasible solution for distributed PV power prediction. Full article

Accurate forecasting of photovoltaic (PV) power is crucial for real-time grid balancing and storage optimization. However, the intermittent, noisy, and nonstationary nature of PV generation, together with cross-site interactions, makes multi-site intra-hour forecasting challenging. In this paper, we propose a unified approach for multi-site PV power forecasting named WGL (Wavelet–Graph Learning). Unlike prior studies that treat denoising and spatio-temporal modeling separately or predict each station independently, WGL forecasts all PV stations jointly while explicitly capturing their inherent spatio-temporal correlations. Within WGL, Learnable Wavelet Shrinkage (LWS) performs end-to-end noise suppression; a Temporal Multi-Scale Fine-grained Fusion (T-MSFF) module extracts complementary temporal patterns; and an attention fusion gate adaptively balances TCN and LSTM branches. For spatial coupling, graph self-attention (GSA) learns a sparse undirected graph among stations, and a Factorized Spatio-Temporal Attention (FSTA) efficiently models long-range interactions. Experiments on real-world multi-site PV datasets show that WGL consistently outperforms representative deep and graph-based baselines across intra-hour horizons, highlighting its effectiveness and deployment potential. Furthermore, a comprehensive analysis of influencing factors for scheme implementation—encompassing safety, reliability, economic rationality, management scientificity, and humanistic care—is conducted, providing a holistic assessment of the framework’s feasibility and potential impact in real-world power systems. Full article

Climate change and rising temperatures in cities due to the urban heat island (UHI) effect are causing increased heat stress and driving the development of efficient, sustainable outdoor cooling systems. The aim of this article was to analyze the integration of adiabatic air cooling systems with photovoltaic (PV) installations in the context of improving thermal comfort and energy autonomy. The study was conducted on the example of a bus station in Rzeszow (Poland), considering two system variants: indirect evaporative cooling and direct evaporative cooling. To assess the impact of comfort parameters on the number of hours of system operation, energy consumption, and operating costs, four upper thermal comfort limits were considered: 22 °C, 22.9 °C, 24 °C, and 25 °C. The results indicate that increasing the upper limit of thermal comfort reduces the operating time of the system and significantly reduces the demand for cooling—for example, increasing the thermal comfort range from 22.9 °C to 24 °C reduces useful energy by 41%. Assuming a thermal comfort range of 25 °C, the direct evaporative cooling system achieves full energy autonomy and is fully powered by photovoltaics. Life cycle analysis (LCA) and life cycle cost (LCC) confirmed the environmental and economic benefits of using higher thermal comfort values. The study highlights the potential of adiabatic cooling systems, in conjunction with a local photovoltaic installation, as an adaptive solution that improves thermal comfort in urban spaces with minimal energy consumption from the grid. Full article

(1) Background: Ultra-short-term photovoltaic (PV) power prediction is crucial for optimizing grid scheduling and enhancing energy utilization efficiency. Existing prediction methods face challenges of missing data, noise interference, and insufficient accuracy. (2) Methods: This study proposes a single-step hybrid neural network model integrating Temporal Convolutional Network (TCN), Temporal Shift Transformer (TST), and Bidirectional Gated Recurrent Unit (BiGRU) to achieve high-precision 15-minute-ahead PV power prediction, with a design aligned with symmetry principles. Data preprocessing uses Variational Mode Decomposition (VMD) and random forest interpolation to suppress noise and repair missing values. A symmetric parallel dual-branch feature extraction module is built: TCN-TST extracts local dynamics and long-term dependencies, while BiGRU captures global features. This symmetric structure matches the intra-day periodic symmetry of PV power (e.g., symmetric irradiance patterns around noon) and avoids bias from single-branch models. Tensor concatenation and an adaptive attention mechanism realize feature fusion and dynamic weighted output. (3) Results: Experiments on real data from a Xinjiang PV power station, with hyperparameter optimization (BiGRU units, activation function, TCN kernels, TST parameters), show that the model outperforms comparative models in MAE and R²—e.g., the MAE is 26.53% and 18.41% lower than that of TCN and Transforme. (4) Conclusions: The proposed method achieves a balance between accuracy and computational efficiency. It provides references for PV station operation, system scheduling, and grid stability. Full article

The rapid growth of electric vehicles (EVs) and photovoltaic (PV) generation creates substantial power peaks that strain local electrical infrastructure. Coordinated bidirectional charging can mitigate these challenges while delivering benefits such as lower costs, improved PV utilization, and reduced emissions. This paper develops a framework for fleet charging that combines station assignment with a two-stage scheduling approach. A heuristic assignment method allocates EVs to uni- and bidirectional charging stations, ensuring efficient use of limited infrastructure. Building on these assignments, charging power is optimized in two stages: a Mixed-Integer Linear Program (MILP) generates day-ahead schedules from forecasts, while an intraday heuristic-based MILP adapts them to unplanned arrivals and forecast errors through lightweight re-optimization. A Python -based simulator is developed to evaluate the framework under stochastic PV, load, price, and EV conditions. Results show that the approach reduces costs and emissions compared to alternative methods, improves the utilization of bidirectional infrastructure, scales efficiently to large fleets, and remains robust under significant uncertainty, highlighting its potential for practical deployment. Full article

In recent years, renewable energy generation (RPG) has experienced rapid growth, and large-scale hydro–wind–photovoltaic storage (HWPS) bases have been progressively developed in southwest China, where hydropower resources are abundant. Ensuring the small-signal stability of such large-scale integrated systems has become a critical challenge. While considerable research has focused on the small-signal stability of grid-connected wind, photovoltaic, or energy storage systems (ESSs), studies on the stability of large-scale HWPS bases remain limited. Moreover, emerging grid codes require power electronic devices to maintain synchronization under unbalanced grid conditions. The time-varying rotating transformations introduced by positive-sequence (PS) and negative-sequence (NS) control render the conventional Park transformation ineffective. To address these challenges, this study develops a linear time-periodic (LTP) model of a large-scale HWPS base using trajectory linearization. Based on Floquet theory, the impacts of RPG station and ESS control parameters on system stability are analyzed. The results reveal that under the considered scenario, these control parameters may induce oscillations over a relatively wide frequency range. Specifically, low PLL and DVC bandwidths (BWs) are associated with the risk of low-frequency oscillations, whereas excessively high BWs may lead to sub-synchronous oscillations. The validity of the analysis is verified through comparison with time-domain simulations of the nonlinear model. Full article

This study aims to provide an efficient framework for the coordinated integration of AC and DC chargers, intermittent solar Photovoltaic (PV) Distributed Generation (DG) units, and a Distribution Static Compensator (DSTATCOM) across residential, commercial, and industrial zones of a Radial Distribution Network (RDN) considering the benefits of various stakeholders: Electric Vehicle (EV) charging station owners, EV owners, and distribution network operators. The model uses a multi-zone planning method and healthy-bus strategy to allocate Electric Vehicle Charging Stations (EVCSs), Photovoltaic Distributed Generation (PVDG) units, and DSTATCOMs. The proposed framework optimally determines the numbers of EVCSs, PVDG units, and DSTATCOMs using Harris Hawk Optimization, considering the maximization of techno-economic benefits while satisfying all the security constraints. Further, to showcase the benefits from the perspective of EV owners, an EV waiting-time evaluation is performed. The simulation results show that integrating EVCSs (with both AC and DC chargers) with solar PVDG units and DSTATCOMs in the existing RDN improves the voltage profile, reduces power losses, and enhances cost-effectiveness compared to the system with only EVCSs. Furthermore, the zonal division ensures that charging infrastructure is distributed across the network increasing accessibility to the EV users. It is also observed that combining AC and DC chargers across the network provides overall benefits in terms of voltage profile, line loss, and waiting time as compared to a system with only AC or DC chargers. The proposed framework improves EV owners’ access and reduces waiting time, while supporting distribution network operators through enhanced grid stability and efficient integration of EV loads, PV generation, and DSTATCOM. Full article