Search Results (2,571)

When photovoltaic (PV) power plants are connected to weak alternating current (AC) grids, the interaction between the plant and grid may induce wideband oscillation, posing a serious threat to the stability of grid-connected PV systems. To address this problem, this paper proposes an oscillation suppression method based on adaptive supplementary damping control of a Static Var Generator (SVG). First, a sequence impedance model of a PV power plant integrated with an SVG is established, and the Nyquist criterion is employed to analyze the mechanism underlying wideband oscillations. Then, a supplementary damping controller implemented in the SVG is designed to reshape the impedance characteristics of the PV power plant and enhance system damping. Furthermore, a Variational Mode Decomposition–Prony modal identification algorithm is introduced to extract oscillation mode information in real time. Based on the identified oscillation frequency, the parameters of the damping controller are adaptively adjusted, thereby improving the suppression capability for wideband oscillations with varying frequencies. Finally, a grid-connected PV power plant model with an SVG is developed, and the performance of the proposed adaptive suppression strategy is compared with that of conventional supplementary damping control. The results demonstrate that the proposed strategy provides stronger robustness and adaptability, effectively suppresses wideband oscillations under different operating conditions, and improves the stability of grid-connected PV systems. Full article

With global demand for renewable energy increasing, the penetration of photovoltaic (PV) systems in power networks has risen significantly, introducing new challenges to microgrid stability. This study focuses on solar inverters using grid-forming (GFM) control, investigating their performance in black-start scenarios and in stabilizing microgrids with battery energy storage systems (BESSs). A MATLAB Simulink microgrid model integrating PV, BESS, and GFM inverters was developed to simulate scenarios including black start, load variation, grid synchronization, and power adjustment. Control techniques such as droop control, proportional–integral (PI) control, Clarke and Park transformations, and phase-locked loops (PLLs) were applied for precise regulation of voltage, frequency, and power. Results show that GFM inverters effectively stabilize voltage and frequency during load changes and PV grid connection, maintaining voltage between 0.96–1.003 p.u. and frequency within 59.87–60.07 Hz. The findings confirm the feasibility of GFM control for coordinated PV–BESS operation and support stable microgrid operation under high renewable penetration. Full article

The move to sustainable energy systems has increased the requirement for comprehensive decision support frameworks that are uncertainty-aware to guide the selection of solar power plant sites. The rapid growth of investments in solar energy has increased the demand for systematic and accurate decision-support tools to choose the best sites for photovoltaic (PV) power facilities. The selection of solar power plant sites is a complicated multi-criteria decision-making (MCDM) problem that involves technical, economic, environmental, social, and technological aspects. The process is typically associated with ambiguity and incomplete knowledge of experts. To overcome these problems, this paper offers an interval-valued neutrosophic fuzzy TOPSIS (IVN-TOPSIS) method, which extends the standard TOPSIS methodology by including truth, indeterminacy, and falsity membership degrees as interval values. The methodology is utilized in a real case study in the Mediterranean region of Türkiye, comprising three provinces with great potential: Antalya, Mersin, and Adana. An assessment of a complete set of environmental, economic, social, and technological criteria is performed using expert judgments stated in interval-valued neutrosophic language assessments. They were incorporated into a Geographic Information System (GIS) to produce a suitability map indicating the most suitable sites for the facility. The suggested approach is different from the traditional crisp or fuzzy MCDM techniques since it clearly models the degrees of truth, indeterminacy, and falsehood, thus providing a more detailed representation of the expert evaluations. According to the data, Mersin is the most ideal site for the construction of a solar power plant, followed by Antalya, and the least suitable site is Adana. The results suggest that sustainable solar energy planning must go beyond technical resource potential and include integrated and uncertainty-aware assessments. The suggested IVN-TOPSIS framework can serve as a powerful decision-support tool to policymakers, planners, and investors that wish to encourage regionally balanced and sustainable renewable energy development. Full article

To systematically assess the regional impacts of new-energy base (NEB) development, this study proposes a comprehensive evaluation model integrating the Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight Method (EWM), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A 26-indicator framework across environmental, technological, economic, and social (ETES) dimensions was constructed. Empirical analysis of representative cases was carried out using game-theoretic integration of FAHP and EWM to derive indicator weights. Furthermore, an obstacle degree model was employed to identify key constraints. Three representative NEBs in Xinjiang Province were selected for analysis, including a medium-scale wind-PV hybrid base (Case A), a large-scale PV project with standalone storage (Case B), and a wind power expansion project (Case C). The results validate the scientific robustness of the ETES framework, with combined weighting indicating that economic criteria hold the highest priority. The case assessments reveal that Case B attained the highest relative closeness in the TOPSIS ranking, whereas Cases A and C performed less favorably due to significant deviations from ideal indicator values. Obstacle analysis further identified distinct limiting factors. These findings offer a theoretical basis and practical insights for analogous renewable energy initiatives, particularly in regions facing complex sustainability trade-offs. Full article

Against the backdrop of the global energy transition towards clean, low-carbon sources and China’s “carbon peak, carbon neutrality” strategic goals, distributed photovoltaic (PV) power generation is being integrated into distribution networks at large scale and with a high penetration level. This trend profoundly changes the configuration and operational characteristics of traditional distribution networks, posing challenges in system planning, operation control, power quality, and economics. This paper innovatively treats the step-up transformers of multiple distributed PV stations as a “distributed generation collection network” that requires coordinated optimization and constructs an integer linear programming (ILP) model aimed at minimizing the total life-cycle cost. The model deeply integrates engineering practice, incorporates nonlinear construction, installation, operation, and maintenance costs related to cluster size, as well as power transmission costs proportional to distance, and it employs piecewise cost functions to accurately capture economies of scale. This research achieves a system-level coordination framework that moves beyond single-device optimization, reducing system costs for step-up transformer deployment in distributed PV stations under complex terrain conditions. Full article

As the global energy transition accelerates, distribution systems are integrating increasing shares of inverter-interfaced renewables, making reliable voltage support a key operational requirement. In grid-connected microgrids, especially weak radial feeders in rural and remote areas, voltage-reactive power (Volt/Var) control must coordinate multiple inverters under uncertainty from photovoltaic (PV) intermittency, load volatility, and point-of-common-coupling (PCC) disturbances. Existing droop, model-based optimization, and non-graph reinforcement learning (RL) approaches often rely on fixed rules or do not explicitly exploit electrical topology, which limits adaptive coordination. To address this gap, we propose a topology-aware graph reinforcement learning framework for voltage-reactive power control in grid-connected microgrids under uncertainty. The method encodes node states with a graph convolutional network (GCN) and learns coordinated PV/storage reactive-power actions via proximal policy optimization (PPO) with a multi-objective reward balancing voltage quality, control effort, and action smoothness. In a controlled comparison against a multilayer perceptron (MLP)-PPO baseline with identical action space, reward, and PPO objective, our method reduces voltage violation rate (VVR) from 0.0316 ± 0.0086 to 0.0048 ± 0.0019. Additional validation on a modified IEEE 33-bus feeder further reduces VVR from 0.00726 for MLP-PPO and 0.02999 for Droop control to 0.00095, supporting the effectiveness of topology-aware state representation on a larger radial benchmark feeder. Full article

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

Photovoltaic (PV) power forecasting plays a central role in power system operation, electricity markets, and the integration of high shares of renewable energy. Over the past decade, forecasting approaches have evolved from classical statistical time-series models to advanced machine learning and deep learning architectures. This review analyses 119 studies published between 2016 and 2026, providing a structured assessment of PV forecasting methodologies, including model types, data requirements, validation strategies, and performance evaluation practices. Beyond summarizing existing approaches, the paper identifies three major methodological gaps in the literature: (i) fragmentation of evaluation metrics, which limits cross-study comparability; (ii) insufficient reporting of data preprocessing procedures and temporal leakage prevention; and (iii) limited integration of forecasting accuracy with economic and operational performance metrics. A systematic comparison of representative studies is conducted to highlight dominant modelling trends and persistent limitations. Beyond a descriptive summary, this review highlights significant limitations in methodological reporting across the 119 studies analysed, particularly regarding temporal leakage prevention in Deep Learning-based forecasting. To address these issues, we introduce a reproducibility checklist and propose a strategic roadmap aimed at strengthening the link between statistical accuracy (e.g., RMSE/MAE) and operational relevance in electricity markets. Full article

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

Energy storage systems (ESSs) installed alongside traditional photovoltaic (PV) power plants are primarily used to track planned output, which often results in low utilization rates and extended payback periods. Moreover, existing research inadequately addresses actual grid frequency fluctuation characteristics and lacks multi-timescale optimization frameworks. To address these issues, this paper proposes a day-ahead and intraday multi-market coordinated rolling optimization strategy that integrates energy market trading with Automatic Generation Control (AGC) frequency regulation services through a zone-based frequency regulation control strategy. The strategy first defines distinct regulation zones based on regional control deviations, enabling a dynamic power allocation approach for the energy storage system. Recognizing that conventional constant power control can lead to battery overcharging, over-discharging, and reduced cycle life, the strategy introduces state of charge (SOC)-based variable power charging and discharging constraint coefficients. These constraints ensure the battery operates safely within its optimal range. Furthermore, an electrochemical energy storage life decay model is developed to quantify battery degradation. To accommodate the uncertainty in PV output, Latin hypercube sampling is employed. A day-ahead dispatch model is established to maximize the system’s total daily operating revenue, and rolling optimization is applied during the intraday phase to correct deviations from the day-ahead forecast. Finally, simulation studies using actual data from a PV power plant demonstrate that the proposed strategy achieves a total daily revenue of 107,477 ¥, representing a 24.6% improvement over energy market-only participation; battery aging costs are reduced by 11.1% compared to the scenario without zone-based frequency regulation control. Results indicate that the proposed strategy effectively balances battery life degradation against market revenue, significantly improving the overall operational efficiency and economic viability of PV-storage hybrid systems. Full article

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

Accurate short-term photovoltaic (PV) power forecasting is important for grid dispatch and PV integration. However, PV power under complex weather conditions has strong fluctuation, non-stationarity, and multi-frequency coupling. These features make accurate forecasting difficult. This paper proposes a short-term PV power forecasting model named WOA-MS-TFformer-BiTCN. The model first constructs similar-day samples through daily feature extraction, Gaussian mixture clustering, and physical consistency correction. Then, the whale optimization algorithm (WOA) optimizes the key parameters of variational mode decomposition (VMD) and the forecasting network. VMD decomposes the original power sequence into modes with different frequency features. The multi-scale frequency-domain perception (MS) module extracts multi-scale frequency-domain features from these modes. TFformer captures global temporal relationships, while BiTCN models local dynamic changes. Experiments are conducted using PV data from Gansu, China. The Alice Springs PV dataset is used for cross-regional validation. The results show that the proposed model achieves the lowest MAE, RMSE and the highest R² in all 16 season-weather cases, corresponding to four seasons and four weather types, for the 15 min-ahead task. Its average MAE, RMSE and the highest R² are 0.5439, 0.7910, and 0.99898, respectively. The model also performs best on rainy samples from the Alice Springs dataset. In addition, prediction intervals based on validation-set residual quantiles provide uncertainty information for point forecasts. The results show that the proposed method improves the accuracy and stability of short-term PV power forecasting under complex weather conditions. Full article

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

The integration of photovoltaic (PV) and wind turbine (WT) systems into modern power grids demands not only accurate component-level models but also a holistic understanding of their coordinated operation. This review bridges the gap between low-level device physics and high-level system coordination, offering a dual perspective often overlooked in existing surveys that treat generation and management separately. We systematically analyze PV models, from single-diode equivalent circuits to data-driven approaches, and WT models, ranging from aerodynamic and mechanical representations to simplified electrical equivalents suitable for stability studies. Critically, we then shift focus to the system level by examining energy management systems (EMS) that enable hybrid PV–WT coordination. Unlike prior reviews that emphasize either component accuracy or dispatch strategies alone, this paper highlights the emerging synergy between hybrid PV–WT modeling and EMS architectures. By identifying mismatches between model fidelity and EMS requirements, this review maps a pathway towards more integrated hybrid renewable systems. The discussion synthesizes key trade-offs in scalability, uncertainty handling, and real-time feasibility, underscoring that true potential is unlocked only through intelligent integration of component models and control architectures. 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