Search Results (5,154)

To address the low-voltage violations that may occur in distribution networks with high penetration of distributed photovoltaic (PV) during sunset and evening peak periods under extreme high-temperature conditions, this paper establishes a source–grid–load electro-thermal coupling model that accounts for load thermal accumulation, transient conductor thermal inertia, temperature-dependent line impedance, and PV thermal derating. Based on a soft safety lower bound and a risk-preference utility function, the probability of voltage violation, violation depth, and expected violation duration are introduced to construct node-level and system-level comprehensive risk factors. The cumulant method combined with the Cornish–Fisher expansion is used to reconstruct the probability distribution of nodal voltages, enabling analytical risk calculation. Simulation results on the IEEE 33-bus system at 45 °C show that the proposed method can quantitatively reflect the temporal variations of nodal voltage distributions, physical violation depth, dimensionless severity utility, and expected violation duration, and identify weak nodes in the later part of the evening peak, providing a reference for risk early warning in distribution networks under extreme heat. Full article

Urban industrial parks have high electricity demand, and rooftop photovoltaic (PV)-battery systems can help reduce grid dependence and carbon emissions. However, battery degradation affects battery replacement timing and long-term economic performance, which should be considered in capacity sizing. This study proposes a degradation-aware techno-economic sizing method for rooftop PV-battery systems in urban industrial parks. GIS-based rooftop assessment, EnergyPlus load modeling, TRNSYS system simulation, battery SOH tracking, and NPV evaluation were integrated into one framework. A case study was conducted for an urban industrial park in Shanghai, China. The usable rooftop area was estimated as 113,208 m², corresponding to a PV capacity of approximately 18,765 kWp. The annual PV generation was 24.7 GWh, accounting for 24.7% of the park’s annual electricity demand. Battery capacities from 5000 to 40,000 kWh were evaluated. The results show that increasing battery capacity improves load shifting and reduces direct grid supply, but the marginal benefit gradually decreases. The maximum NPV is obtained at 30,000 kWh, with an NPV of 128.36 million CNY, a simple payback period of 4.6 years, and a discounted payback period of 6.0 years. The rooftop PV system achieves a 25-year CO₂ emission reduction of approximately 335,967 tCO₂ after considering PV degradation. Sensitivity analyses show that BES cost, tariff spread, and discount rate are key factors affecting the recommended capacity. Full article

Urban heat stress is intensifying under climate change, particularly in outdoor public spaces where conventional mechanical cooling is impractical. This study develops a climate-driven, system-level numerical framework to evaluate the pre-deployment feasibility of modular, solar-powered spray cooling canopies across 110 cities in Türkiye. Hourly Typical Meteorological Year (TMYx) weather files, representing a single typical year constructed from 2009 to 2023 source data, are used to estimate photovoltaic (PV) energy yield, electrical load, feasible misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. The misting operation is governed by a rule-based adaptive control strategy based on air temperature, relative humidity, and plane-of-array irradiance. To support transferable comparison, the cities are classified into six summer climate-archetype zones using k-means clustering of standardized climate variables, including temperature, humidity, irradiance, wind speed, and summer precipitation. Results show that evaporative cooling feasibility is governed primarily by humidity rather than temperature alone. Hot–Dry Inland cities exhibit the longest mean misting duration (501.90 h) and highest water demand (30,152 L per module), but the lowest PV-to-load autonomy ratio (1.55) because of high pump-driven electrical demand. In contrast, Humid Black Sea cities show minimal misting duration (11.43 h) and water use (465 L per module), but the highest autonomy ratio (39.68) due to very limited system activation. Thus, high autonomy does not necessarily indicate high cooling usefulness. The proposed framework provides a reproducible screening tool for identifying where PV-powered spray cooling canopies are climatically suitable, where water and PV sizing become limiting, and where alternative outdoor heat-mitigation strategies may be more appropriate. Full article

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

The integration of photovoltaic (PV) generation with alkaline water electrolyzers (AWE) in DC microgrids offers a highly promising pathway for green hydrogen production. However, the inherent volatility of solar power often induces transient voltage ripples and power surges, degrading the electrolyzer stack and destabilizing the common DC bus. To overcome this, this study proposes a hierarchical multimodal coordinated control strategy tailored for a four-port (PV–Storage–Grid–Hydrogen) DC microgrid. The proposed framework leverages multi-port synergetic coordination among the PV array, battery storage, and grid-interfacing converters to actively buffer extreme power mismatches, thereby ensuring the constant regulation of the DC bus voltage. Through comprehensive time-domain simulations under worst-case step-change boundary conditions, the large-signal transient stability of the proposed strategy is quantitatively verified. Under extreme disturbances, the system successfully confines DC bus voltage deviations to within safe operational boundaries with a rapid settling time, effectively avoiding typical inverter overvoltage trip thresholds. Furthermore, the adaptive power regulation algorithm maintains precise steady-state power tracking. By utilizing a gradient-based flag variable, the system seamlessly transitions between maximum power point tracking (MPPT) and active power-limiting modes, ensuring continuous equipment protection, stable high-purity hydrogen yield, and uninterrupted microgrid stability. Full article

The increasing frequency of extreme weather events, particularly hailstorms, driven by climate change, poses growing threats to the resilience, environmental sustainability, and long-term performance of photovoltaic (PV) systems. This study evaluates the environmental impacts of a 12 kWp rooftop PV installation in Brescia, northern Italy, through a comparative Life Cycle Assessment (LCA) of three system configurations: a standard unprotected system (Scenario A), one equipped with a retractable polycarbonate hail-protection panel with automated weather-sensor activation (Scenario B), and one using thicker reinforced front-glass modules (Scenario C). The analysis follows a cradle-to-gate plus operational maintenance phase (30-year horizon, excluding end-of-life) system boundary and employs the ReCiPe 2016 Midpoint (H) methodology across 18 environmental impact categories. A novel integration of the Social Discount Rate (SDR) to the LCA framework—constituting a Discounted LCA (D-LCA)—incorporates both temporal discounting and risk dimensions into the environmental evaluation. A structured PESTEL-based risk taxonomy is applied to derive scenario-specific SDRs, with the Environmental risk category as the key differentiator between configurations. The static LCA identifies Scenario A as the lowest-impact option, while the D-LCA framework reverses this ranking: Scenario C achieves the highest Net Present Value of Emissions, followed by Scenario A. A negative NPV-E for Scenario B reflects the temporal cost of a large, front-loaded construction debt rather than absolute environmental harm. D-LCA framework should be interpreted as a complement to the full 18-category static LCIA profile, not a replacement. These results demonstrate that risk-informed D-LCA provides a more policy-relevant environmental sustainability assessment than static LCA for long-lived energy infrastructure subject to climate-driven operational risks. 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

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

Solar energy has emerged as a promising alternative to fossil fuels for mitigating climate change; however, efficient photovoltaic (PV) operation requires continuous monitoring and accurate energy forecasting. This study proposes an intelligent IoT-based photovoltaic monitoring and short-term energy prediction system integrating real-time sensing, solar tracking, and machine learning techniques. A small-scale experimental prototype based on a 10 W photovoltaic panel was implemented to collect real-time data, including voltage, current, temperature, humidity, ultraviolet radiation, and dust accumulation during a 30-day monitoring period under outdoor conditions. The acquired data were transmitted through an IoT architecture based on the Arduino Uno and ESP32, programmed using Arduino IDE, and integrated with the Blynk cloud platform for real-time monitoring and analysis. To evaluate predictive performance, Random Forest, XGBoost, and LSTM models were trained and compared for photovoltaic energy forecasting. Experimental results showed that XGBoost achieved the best predictive performance, obtaining the lowest error values (MAE = 0.00077, RMSE = 0.001103) and the highest coefficient of determination (R² = 0.918), outperforming the other evaluated models. In addition, the proposed system enabled effective remote monitoring and degradation analysis associated with environmental conditions. The results demonstrate the potential of integrating IoT and machine learning for accurate short-term photovoltaic energy forecasting in small-scale experimental environments. Nevertheless, further long-term and large-scale validation is required to evaluate system robustness under operating conditions. Full article

In the context of rural revitalization and carbon neutrality, this study addresses energy inefficiency and thermal discomfort in existing rural housing by optimizing passive design strategies for the “SunnyInside” sunroom model. Using parametric simulation with Ladybug and Honeybee, a dynamic light-thermal coupling model was developed to evaluate climate-adaptive performance in two distinct Chinese climates: the cold climate of Datong and the hot-summer-cold-winter climate of Wuhan. Multi-objective optimization focused on orientation, overhang depth, and photovoltaic (PV) tilt angles to enhance ventilation, shading, and daylighting. Key findings include: (1) Optimal building orientations of 15° west of south (Datong) and 16° east of south (Wuhan); (2) A 1.5m overhang depth in Wuhan improved summer shading efficiency by 28.6% and extended thermal comfort duration by 15%; (3) PV tilt ranges of 29–36° (Datong) and 13–23° (Wuhan) maximized energy performance. These optimizations achieved a 19.3–24.7% improvement in comprehensive performance coefficients and reduced air conditioning energy consumption by 17.8–21.4 kWh/m² (with ≥82% photovoltaic conversion efficiency). The study demonstrates the effectiveness of parametric simulation and intelligent algorithms in refining climate-responsive rural housing renovations, providing quantitative guidelines for PV shading systems across diverse climatic zones. Full article

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. 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

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