Search Results (710)

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

At present, photovoltaic (PV) systems are becoming the core of low-carbon power systems, but their large-scale integration is still limited by weather-driven intermittency, heterogeneous data, equipment failures, operational uncertainty, and life-cycle sustainability requirements. Unlike specific task reviews that only focus on photovoltaic forecasting, fault diagnosis, or general artificial intelligence applications in renewable energy, this review develops an integrated data-driven perspective for machine learning and artificial intelligence in photovoltaic power generation systems. It links data governance, feature engineering, prediction, and uncertainty quantification, fault diagnosis and predictive maintenance, energy management, market participation, and carbon-aware optimization within a framework for photovoltaic systems. This review indicates that traditional machine learning, deep learning, graph learning, reinforcement learning, generative artificial intelligence, and physics-based artificial intelligence are suitable for different photovoltaic tasks based on data structure, time range, operational constraints, and deployment maturity. The main contribution is cross-task integration, which links the output of artificial intelligence models, including scheduling, storage scheduling, maintenance planning, virtual power plant operation, and low-carbon management, with actual decision-making. The review further identified the most critical deployment barriers, such as incomplete benchmarks, weak cross-site generalization, insufficient uncertainty calibration, limited interpretability, network security risks, and computational costs. The resulting methodological approach emphasizes data management, uncertainty awareness, physical constraints, decision orientation, and sustainability-driven photovoltaic intelligence. Full article

To address the limitations of traditional day-ahead bidding strategies in handling the time-varying uncertainty of photovoltaic output, and considering that single-point forecasts are insufficient for reliable risk-based decision-making, this paper proposes a day-ahead bidding strategy for PV power plants based on dynamic error-band optimization. First, a dynamic uncertainty quantification method based on dual-model prediction discrepancy is proposed. It couples two complementary forecasting mechanisms—Long Short-Term Memory, and Seasonal Autoregressive Integrated Moving Average—and utilizes the Dynamic Time Warping algorithm to extract their discrepancy as a dynamic input for subsequent risk assessment and decision-making. Secondly, based on this uncertainty indicator, a probabilistic mapping model is constructed to link prediction uncertainty to the risk of power violation, translating the abstract prediction discrepancy into a concrete economic risk probability. Finally, considering the trade-off between economic benefits and security, a dynamic error-band optimization mechanism is introduced to adaptively determine the bidding margin at different time periods. Case results for a 20 MW PV plant show that the dynamic strategy reduces the number of violation events to zero in the tested daily bidding case, compared with four violations under a fixed 5% error band and one violation under a fixed 10% error band. The corresponding economic revenue increases by 5.3% and 11.2% relative to the fixed 5% and fixed 10% strategies, respectively. Full article

With the advancement of the carbon peaking and carbon neutrality goals, large-scale grid integration of photovoltaic (PV) systems has become a core trend in the development of distribution networks (DNs). However, this high penetration breaks the inherent spatiotemporal symmetry of power flow in traditional DNs, leading to severe spatiotemporal imbalance issues, including voltage violations, reverse power flow, and a sharp increase in network power loss. To address these challenges, an optimized flexible control method for PV inverters in DNs considering cluster partitioning is proposed in this paper. First, a comprehensive performance index system integrating improved modularity, source-load matching degree, and voltage sensitivity is constructed, which quantifies the electrical coupling symmetry and source-load power symmetry within clusters, providing a rigorous quantitative basis for dynamic cluster partitioning. Moreover, based on a dynamic monitoring mechanism, an improved Particle Swarm Optimization algorithm for cluster partitioning is proposed to achieve the optimal cluster partitioning of DN nodes and the selection of key control nodes. Finally, a Q-V flexible control model of the inverter adapted to cluster control is established; thus, an optimization model with the objectives of minimizing voltage deviation, PV curtailment loss, and PV reactive power output is constructed. The distributed and efficient solution is performed using the Alternating Direction Method of Multipliers algorithm and the GUROBI solver. Simulation results based on the modified IEEE 123-node test feeder show that, compared with traditional methods, the proposed method improves the cluster partitioning effectiveness, ensures that the operating voltage deviation of the control system is within 5%, and reduces the PV curtailment loss of the system. Full article

In modern power systems, the rapid growth of renewable energy capacity, such as wind and solar photovoltaic (PV) power, has led to a decline in system equivalent inertia and primary frequency regulation margin. At the same time, net load fluctuations have intensified across multiple time scales, making it more likely for the RoCoF, frequency nadir, and quasi-steady-state frequency deviation to approach safety limits following disturbances. To achieve a balance between frequency security and economic operation, this paper proposes a two-stage stochastic frequency-security-constrained unit commitment (FSC-SUC) model tailored for scenarios with high renewable energy penetration. The day-ahead hourly dispatch stage jointly determines the on/off status and reference output of synchronous units and the reservation of slow frequency regulation capacity, as well as energy storage charging and discharging plans, SoC trajectories, and the reservation of fast frequency regulation capacity. The intraday minute-level real-time dispatch stage accommodates prediction errors through scenario-based rescheduling and ensures the deliverability of both slow and fast frequency regulation capabilities via commitment consistency constraints. To address the challenge of directly embedding frequency nadir constraints into mixed-integer optimization, this paper employs a modeling approach that combines analytical criteria with linear surrogate constraints. The RoCoF and quasi-steady-state frequency deviation are specified via aggregated analytical constraints, while the nadir is embedded into the main problem after generating samples offline using a simplified frequency response model and training a polyhedral linear surrogate for external approximation. The safety margin is then calibrated using high-quantile residuals from the validation set to ensure conservativeness. Case studies on the IEEE 33-bus system under different renewable penetration levels demonstrate that the proposed method significantly reduces the probability of frequency nadir violations and load-loss risk with only a modest cost increase while also improving coordination between fast and slow frequency regulation. Full article

As the penetration rate of photovoltaic power generation continues to increase within new power systems, accurately forecasting regional PV power output has become critical to ensuring the safe and stable operation of power grids. Photovoltaic power generation exhibits significant spatio-temporal correlations, and traditional single-site forecasting methods struggle to fully capture the spatial dependencies among multiple PV plants within a region. To address this challenge, this study proposes a unified QR-STGAT probabilistic forecasting framework that jointly captures adaptive spatial dependencies via graph attention mechanisms and multi-scale temporal dynamics via a CNN-GRU architecture, while enabling end-to-end uncertainty quantification through integrated quantile regression. The framework is validated on 15 min resolution PV output data collected from five prefecture-level cities in Guangdong Province over a seven-month period from January to July 2025, and compared against baselines including BiLSTM and Transformer. Experimental results demonstrate that the proposed method reduces RMSE by up to 11.61% over baseline models and achieves a PICP of 93.05% at the 95% confidence level, providing a more reliable reference for power system dispatch decisions. 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

The increasing penetration of photovoltaic (PV) systems requires accurate and stable short-term forecasting to ensure reliable grid operation under operational constraints. This paper investigates short-horizon multi-step PV power forecasting using one full year of high-resolution (5 min) real-world data from a 111-kW grid-connected rooftop installation. The forecasting problem is formulated as a direct multi-output supervised learning task with a 30 min prediction horizon. A comprehensive comparative evaluation is conducted across baseline (persistence), tree-based (XGBoost), and deep learning architectures (LSTM, GRU, and Temporal Convolutional Networks—TCN). Results show that deep learning models significantly outperform conventional baselines, with LSTM achieving the lowest normalized RMSE (≈10.3%), while TCN provides a competitive trade-off between predictive accuracy, temporal stability, and computational efficiency. The direct multi-step formulation was adopted to reduce potential error propagation effects commonly observed in recursive forecasting approaches. Beyond forecasting accuracy, the study evaluates computational complexity and inference latency to assess practical deployability in resource-constrained environments. The proposed framework demonstrates that high-resolution real-world PV forecasting can achieve both strong predictive performance and operational feasibility. These findings contribute to the development of robust short-term forecasting strategies for distributed renewable energy systems operating under regulatory export constraints. Full article

This paper presents a novel approach to designing and implementing an Artificial Neural Network (ANN) for maximum power point tracking (MPPT), trained solely on unshaded photovoltaic (PV) manufacturer datasheets and capable of tracking and predicting the maximum power point (MPP) under changing shading conditions. This is also known as partial shading conditions (PSC). PSC arises when shade covers sections of the PV panel due to clouds, trees, dust, or man-made objects such as tall buildings. The proposed ANN-based MPPT technique addresses a common issue faced by conventional MPPT methods under PSC: inaccurate MPPT. PSC induces oscillations on the power-to-voltage curve, resulting in multiple local maxima (LMPPs). However, existing ANN-based MPPT methods are developed and trained on shaded PV datasets. This Neural Network (NN) tracking method complicates the training, development, and implementation processes. It increases the cost of development and requires physical, real-world data collection that requires hardware and a lot of time. All this can be avoided with unshaded PV datasheets. The input parameters used to train the model are temperature (T) and irradiance (G), and the output parameters are maximum power (Pmp) and maximum voltage (Vmp). The ANN-based MPPT technique demonstrated strong performance, accurately predicting the global MPP (GMPP) under PSC with high correlation and low prediction error. 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

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

The widespread deployment of photovoltaic (PV) inverters with digital control and communication systems has increased the power grid’s attack surface, making it more vulnerable to cyberattacks. This creates a need for locally implementable attack-detection methods that do not disrupt inverter operation. This paper therefore proposes an irradiance-driven natural watermarking approach for decentralized detection of false data injection (FDI) attacks on inverter terminal measurements. The approach leverages irradiance-driven DC-link voltage variations to watermark the inverter outputs, generating a non-removable signature in the true measurements. The proposed method is evaluated using a real-time hardware-in-the-loop model of a three-phase grid-following PV inverter that captures PV-array and grid-connection dynamics. Implementation robustness is further assessed on a separate hardware grid-forming inverter testbed with non-idealized components. In the tested cases, the detection model identifies noise-injection and replay attacks within $15\mathrm{ms}$, while otherwise undetectable model-based attacks are revealed when DC-link voltage variations between 5% and 10% occur. These experimental results demonstrate that irradiance-driven natural watermarking can reveal FDI attacks without affecting normal inverter operation. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article