Search Results (217)

With the increasing penetration of behind-the-meter photovoltaic generation and distributed energy storage, distribution system operators usually observe only the net load at the point of common coupling, while the actual user load and energy storage charging/discharging power are difficult to measure directly. To address this problem, this paper proposes a mode-aware constrained inverse optimization method for behind-the-meter distributed energy storage power estimation under fixed time-of-use tariffs. The proposed method uses net load, photovoltaic power, and tariff information as inputs and estimates the hidden user load, storage power, SOC trajectory, and dominant storage arbitrage mode. A mode-aware joint representation model is developed by introducing single-cycle and dual-cycle charge–discharge templates, daily action intensity factors, mode weights, and local correction terms. In addition, power limits, SOC dynamics, SOC bounds, daily energy balance constraints, tariff-response consistency, and mode selection penalty are incorporated into the inverse optimization framework to improve the physical feasibility and interpretability of the estimation results. Case studies are conducted using a 40-day hybrid dataset with a 1 h sampling interval and a 70%/30% training/testing split. The dataset is constructed from park-level user load and photovoltaic data, while the storage power profile is reconstructed according to typical time-of-use arbitrage operation. For the main dual-cycle testing case, the NRMSEs of storage power, user load, and net load are 14.75%, 3.90%, and 3.76%, respectively. The results show that the proposed method can recover the main variation trend of hidden storage power under the studied fixed time-of-use tariff scenario and provides a preliminary basis for park-level storage monitoring and flexible resource perception. Full article

In modern power systems with high penetration of renewable energy, the efficient interaction between demand-side flexible resources and the power grid has become a key approach to mitigating renewable generation fluctuations. As a typical flexible load, air conditioning loads exhibit significant potential for renewable energy utilization due to their large scale, low cost, and fast response capability. However, existing strategies for photovoltaic (PV) accommodation fail to fully consider the coordinated scheduling between heterogeneous air conditioning clusters and energy storage systems, and lack explicit modeling of the dynamic response of air conditioning loads. As a result, they are unable to effectively address the requirements induced by renewable energy fluctuations. To address these issues, this paper proposes a coordinated scheduling strategy for heterogeneous air conditioning clusters considering dynamic response characteristics, aimed at PV fluctuation smoothing. A hierarchical framework of “fixed-frequency priority, variable-frequency compensation, and energy storage backup” is developed. By incorporating response dynamics into the scheduling process, power–energy complementarity between air conditioning clusters and energy storage systems is achieved. Experimental results demonstrate that the proposed strategy improves the PV fluctuation smoothing rate from 77.16% to 100%, significantly enhancing the local PV accommodation capability within the park. 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

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 large-scale integration of photovoltaic (PV) generation and electric vehicles (EVs) into distribution networks introduces significant operational challenges, including voltage fluctuations, increased energy losses, and feeder congestion. While previous studies have addressed distribution system reconfiguration (DSR), PV scheduling, or EV intelligent parking lot (IPL) management separately, no unified framework exists that simultaneously optimizes all three flexibility tools. This research therefore aims to develop a coordinated scheduling framework that minimizes both energy losses and voltage deviations over a 24 h horizon. For solving the mathematical formulation, an Enhanced Grey Wolf Optimizer (EGWO) is developed using the concepts of dynamic neighborhood influence and self-adaptive convergence factor to prevent the issue of premature convergence and dynamic balancing of the algorithm during the search process. Simulation results on the IEEE 33-bus system across five scenarios quantify the benefits of each control layer. DSR alone reduces daily energy loss by 30.41%. Photovoltaic scheduling alone reduces loss by 15.40%. When combined, PV scheduling and DSR achieve a 38.29% loss reduction, demonstrating strong synergy. Full integration including IPL further improves voltage deviation by 40.26% compared to the base case, while maintaining loss reduction at 36.20%. Full article

Institutional shuttle fleets with fixed routes and predictable terminal parking are well-suited to charging photovoltaic–battery energy storage system (PV–BESS) charging for sustainable campus mobility. However, siting and sizing are often solved numerically without identifying the physical constraints that determine the optimum. This study develops a sustainability-oriented framework for converting a 10-van diesel shuttle fleet at Rajamangala University of Technology Lanna into an electric fleet supported by distributed PV–BESS charging stations. A centralized one-station layout is compared with a distributed two-station layout, and a closed-form active-constraint sizing rule is derived using Karush–Kuhn–Tucker (KKT) analysis. Results show that the distributed configuration eliminates dead-run travel and provides higher lifecycle value than the centralized case. KKT analysis identifies two binding constraints: the PV rooftop-area limit and the BESS one-day autonomy requirement. Under base-case assumptions, the transition achieves positive lifecycle value and substantial CO₂ reduction relative to the diesel baseline. Monte Carlo analysis confirms financial robustness within the uncertainty ranges, while deterministic stress tests show sensitivity to diesel prices, PV electricity credit values, discount rate, and fleet utilization. The framework provides an interpretable decision-support method for institutional fleet electrification in solar-rich campus settings, contributing to SDGs 7, 11, and 13 through clean-energy adoption, sustainable transportation, and CO₂-emission reduction. Full article

Addressing the challenge of power supply stability caused by the intermittent nature of photovoltaic power generation in off-grid microgrids, this study uses a commercial park in Wuhan as a case study and optimizes the capacity configuration of a photovoltaic–storage–hydrogen fuel cell hybrid microgrid system based on HOMER Pro software. First, a topology of the off-grid microgrid is constructed, comprising photovoltaic (PV), lithium-ion batteries, hydrogen fuel cells, and a diesel generator as backup. The power output characteristics, efficiency curves, and life-cycle cost models of each component are accurately established. On this basis, two typical operation strategies, namely Load Following (LF) and Cycle Charging (CC), are proposed and compared. The influence of different strategies on the optimal capacity configuration and operational economics is systematically analyzed, and the Cycle Charging strategy is identified as the optimal operation strategy for this scenario. Subsequently, a multi-scenario capacity optimization design is further conducted based on the optimal operation strategy. The minimization of net present cost (NPC) is taken as the primary objective, while multiple evaluation indicators such as renewable fraction (RF), levelized cost of electricity (LCOE), energy storage cycle life degradation, and system redundancy rate are comprehensively considered. The results show that, while ensuring 100% power supply reliability, the proposed model reduces the net present cost (NPC) by approximately 14.4% compared with the conventional PV-storage scheme. The renewable fraction (RF) reaches 95.8%, while the reliance on lithium-ion battery capacity is significantly reduced (battery capacity configuration decreased by 24.3%). This effectively extends the energy storage lifespan and enhances the overall economic and environmental benefits. The results provide a theoretical basis and technical reference for the planning and design of off-grid microgrids with high penetration of renewable energy. Full article

The industrial sector accounts for a significant share of global energy consumption and greenhouse gas emissions, making the optimal operation of industrial parks a key pathway for sustainable energy transition. This study proposes a day-ahead coordinated optimal scheduling framework for a multi-sector Industrial Park Energy Hub (IPEH) that integrates electricity, heating, and cooling systems with renewable generation and multi-energy storage. The model captures sectoral diversity across industrial, commercial, residential, and administrative sectors, enabling coordinated inter-sector operation through electricity and heating energy sharing. The scheduling problem minimizes total operating cost, including penalties for greenhouse gas (GHG) emissions and for power curtailment from photovoltaics (PV) and wind turbines (WT), while considering the physical constraints of the heating network and power tie-lines. The optimization problem is solved using the CPLEX solver in MATLAB. Results under three scenarios show that, compared with independent operation, electricity sharing alone reduces operating cost by 3.22% and renewable curtailment by 58.19%. Coordinated electricity and heat exchange further improves system performance, achieving a 6.95% reduction in operating cost, a 58.19% decrease in renewable energy curtailment, and emission reductions of 18.11% for CO₂, 23.80% for SO₂, and 38.42% for NO_x. Full article

Under the goals of carbon peaking and carbon neutrality and the development of zero-carbon parks, the continuous expansion of distributed photovoltaic (PV) installations has made grid-export constraints increasingly prominent. To investigate their influence on energy storage configuration and system operation, this paper incorporates the grid-export ratio constraint into the planning and scheduling process of a park-level PV-storage-charging system. A bilevel optimization model is established, in which the upper level minimizes the annual total cost (ATC), while the lower level minimizes the annual operating cost (AOC), considering time-of-use electricity prices, PV curtailment penalty, power shortage penalty, and battery degradation cost. The model is solved by a genetic algorithm (GA) and CPLEX. The results show that, for the studied industrial park, the 20% grid-export ratio is an important case-specific turning point under the given PV capacity, load level, electricity price, storage cost, and grid-connection conditions. Compared with the scheme without energy storage, the scheme with energy storage achieves lower PV curtailment and better economic performance. Sensitivity analyses further show that the PV curtailment penalty coefficient, energy storage investment cost, and PV installed capacity affect the optimal storage configuration and system economics. This study can provide a reference for energy storage planning and operation optimization of park-level PV-storage-charging systems under grid-export constraints. Full article

As electricity demand increases with social development, the inadequate upgrade of distribution network infrastructure fails to meet peak demand. To address this issue, introducing source-load distributed resources to enhance power system flexibility has become a development trend that reduces distribution equipment retrofitting costs while satisfying peak grid demand. To this end, this paper proposes an optimal aggregation and scheduling strategy for distributed resources based on zonotopes. First, a Monte Carlo simulation-based scenario generation model is developed to supplement the scenario set for uncertain photovoltaic output. Second, a distributed resource aggregation method using zonotopes is proposed to determine the adjustable range of distributed resource clusters. Finally, an industrial park case study is conducted to validate the superiority of the proposed strategy in coordinating source-load distributed resources for peak-load shaving, achieving a peak shaving rate of 40.09% during peak demand periods. Full article

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

With the high penetration of distributed photovoltaic and storage systems, active distribution grids are prone to experiencing “active power surplus and reactive power shortage” during the evening peak, leading to voltage sags at the network end. Although electric vehicle (EV) grid-connected inverters possess four-quadrant reactive power regulation capabilities without causing the additional chemical cyclic aging of the battery cells, existing dispatch systems often treat them as unconditional response resources, overlooking users’ actual willingness to cede control and the associated strategic interactions. To address this, this paper proposes a “grid-load” coordinated reactive power optimization strategy that accounts for EV users’ willingness to respond: a Logit model incorporating price incentives, initial energy consumption, and parking duration is constructed based on discrete choice theory. By combining a truncated normal distribution with the Monte Carlo method to eliminate micro-sampling errors, a model of the expected reactive power capacity of charging stations under dynamic incentives is established; considering the physical constraints of SVCs and EVs, a scalarized single-objective optimization model is constructed with grid loss-equivalent costs, ancillary service costs, and voltage deviation as objectives, and solved using an improved particle swarm optimization algorithm with linearly decreasing weights. Simulations on a modified 33-node IEEE system incorporating storage indicate that this strategy can assign optimal compensation prices to each node based on the spatial value of reactive power. Compared to traditional single-voltage regulation and fixed subsidies, it not only stabilizes the grid-wide voltage within a safe range but also avoids overcompensation, achieving global optimization of both power quality and economic efficiency. Full article

With the large-scale deployment of distributed photovoltaics (PVs) on the user side, integrated PV-storage systems have become a critical means to reduce electricity costs and enhance energy flexibility. However, the volatility of PV output and the dynamic nature of time-of-use (TOU) pricing render the economic viability of such systems highly dependent on the coordinated optimization of capacity configuration and operational strategies. To address this, a bi-level optimization model is developed. The upper level maximizes the equivalent annual economic benefit by determining the installed capacities of PV and storage, explicitly incorporating power-sensitive operation and maintenance costs. The lower level, formulated as a mixed-integer programming problem, minimizes the daily net electricity cost by optimizing charging/discharging schedules and grid interaction. The model is solved through an iterative hierarchical approach combining the chaotic sparrow search algorithm (CSSA) and the CPLEX solver. A case study using actual data from an industrial park demonstrates that, compared with scenarios without PV-storage and with PV only, the joint PV-storage configuration reduces total electricity costs by 17.3% and 4.5%, respectively. Furthermore, the asymmetric impacts of PV forecast errors on operational economics are quantitatively analyzed: when PV output is underestimated, the failure to pre-reserve accommodation capacity leads to an increase in electricity procurement costs of RMB 1927.84 compared with the ideal scenario. To mitigate this, a risk-aware fault-tolerant scheduling strategy is proposed, which reserves a 5% accommodation margin through conservative biasing, reducing the additional cost caused by forecast errors by 20.14% and significantly enhancing the system’s economic robustness under forecast uncertainty. Full article

The rapid electrification of road transport has increased interest in distributed energy strategies that reduce grid demand and support decarbonization. Vehicle-integrated photovoltaics (VIPV), including vehicle-applied photovoltaic configurations (VAPV), can generate electricity directly on the vehicle. This systematic review examines peer-reviewed VIPV literature published between 2015 and 2026, focusing on the distinction between theoretical photovoltaic generation and practically usable energy. A Scopus search conducted on 2 May 2026 identified 196 records, of which 88 studies were included after screening against predefined criteria. Due to heterogeneity in vehicle types, climates, technologies, modeling assumptions, and reported metrics, no meta-analysis was performed. Instead, the review applies a multi-layered framework covering climate, geometry, thermal effects, electrical mismatch, battery state-of-charge interactions, fleet-scale modeling, economics, and life-cycle implications. The evidence shows that VIPV is technically feasible and can deliver measurable energy yields, especially in high-irradiance regions and vehicles with favorable daytime parking exposure. However, useful contribution depends strongly on curvature losses, dynamic shading, electrical configuration, SOC limits, charging behavior, seasonality, and vehicle energy demand. Therefore, VIPV is best understood as a context-dependent supplementary energy strategy rather than a transformative standalone solution. Its strongest value lies in specific vehicle classes, climates, and usage patterns where on-board generation can reduce charging demand, support operational resilience, or improve distributed self-consumption. The review also proposes minimum reporting requirements for future studies, including annual energy yield, Wh/km contribution, PV area or capacity, mileage assumptions, SOC modeling, and curtailment treatment. The review was not formally registered, and no formal risk-of-bias or certainty assessment was applied. Full article

This paper presents a sizing and optimization methodology for LCL filters tailored to high-capacity modular power systems. The approach prioritizes the strategic selection of the resonance frequency, an asymmetric inductance design, and strict harmonic current limits. The methodology is validated through a case study simulation of a 126 MW photovoltaic plant in a region of Mexico, analyzing its 2.34 MW inverter architecture. The simulations show that precise capacitor sizing for reactive power management, combined with a passive resistive damping strategy, ensures compliance with grid interconnection standards (IEEE 1547) and power quality standards (IEC 61000). This approach simplifies practical implementation by eliminating the need for complex active damping control algorithms. Additionally, dynamic decoupling is validated through time-domain step responses, and frequency-domain sensitivity analysis confirms robust stability margins even under ±20% variations in passive parameters. Ultimately, the system achieves voltage total harmonic distortion (THD) levels below 0.18%, demonstrating a scalable solution for maintaining grid stability. Full article