Search Results (7,008)

Rail stations have non-traction electricity and heat demands, requiring coordinated renewable generation, storage, and conversion. We develop a cloud–edge–terminal energy-management framework for an electricity–hydrogen–thermal system. A 24 h mixed-integer linear programming model schedules grid import, photovoltaic (PV) generation, battery and thermal storage, electrolysis, hydrogen compression and storage, and fuel-cell and heat-pump operation. Hydrogen inventory is represented by stored hydrogen mass, with tank pressure linked to hydrogen density through a pressure–density relation. We compare three systems: hydrogen-free, hydrogen-integrated, and hydrogen-free with electrically equivalent battery storage. Hourly schedules map 150 capacity-equivalent virtual terminals for minute-level verification under operational and communication disturbances. Hydrogen integration reduces objective value by 1.06%, operating cost by 0.34%, and peak grid import by 6.89% relative to hydrogen-free operation but increases daily grid electricity purchase and CO₂ emissions by approximately 3.09%. Sensitivity analyses show stronger benefits at higher PV penetration, negligible gains from tank-volume expansion beyond the storage range, and electrolyzer power as the constraint on renewable absorption at high PV capacities. Verification achieves a 98.503% average command–delivery success rate, a steady-state grid import error below 1 kW, and 2 min to self-heal faults. Hydrogen improves peak shaving and intertemporal energy shifting, whereas total electricity use and emissions depend on renewable availability and conversion limits. Full article

High penetration of converter-interfaced photovoltaic and wind generation reduces the effective inertia of islanded renewable energy systems and makes frequency stability more sensitive to load and renewable-power disturbances. This paper proposes a bounded adaptive droop framework for battery energy storage system (BESS) fast frequency response under low-inertia conditions, built around a bounded coordination mechanism. The framework coordinates frequency deviation, filtered rate of change of frequency (RoCoF), renewable-power fluctuation, and state of charge (SOC) to unify frequency-support capability with SOC availability and converter operating constraints. SOC-dependent available power limits, rated converter saturation, a ramp-rate limiter, RoCoF filtering, and measurement delay translate support demand into feasible power commands. A unit-consistent per-unit frequency-response model is combined with kW-level BESS power commands in MATLAB simulations. Compared with Fixed Droop, the proposed method reduces Max |Δf| and RMS Δf by 31.61% and 31.06%, respectively, on average across five deterministic cases, including four main disturbance cases and one charging case. In 30 Monte Carlo validation trials, the mean Max |Δf| decreases from 0.586 Hz to 0.330 Hz (43.66%), and the mean RMS Δf decreases from 0.416 Hz to 0.234 Hz (43.71%), with no SOC violation. Charging-protection tests further show that a fully charged BESS cannot absorb surplus renewable power, indicating the need for coordination with renewable curtailment, dump loads, secondary control, or reserve-SOC management. Full article

This article exposes how Chile permits photovoltaic plants on its land of highest agricultural capability, withdrawing the material basis of long-term food security for at least a generation, in full legality. Linking environmental-assessment records, fiscal cadastral microdata and the CIREN capability survey, I document 135 approved plants sited dominantly on Class I–III soil, 5836 declared hectares; elite soil covers 18.2% of the farmed landscape yet hosts 42.8% of the approved plants, 2.35 times its share. That the system processes this tension without friction reveals the socio-legal infrastructure of property, whose dephysicalisation erases the land’s food-producing capability from every register of decision. The regime fuses three securities (legal certainty, juridical security and land-tenure security), serving as the foundation of the energy transition. The fusion shields the proprietor and confines all protection to the voluntary, visible in the compensations proponents offer when the Agriculture and Livestock Service (SAG) objects. A documentary census of the permitting files supplies the new evidence. The SAG objects in writing to the loss of productive agricultural land in 64 of 136 approved files, and no objection prevents approval; in 21 a favourable qualification is issued with the objection still standing. The Service refutes, with measurements of its own, proponents who declare their soil poor, up to Class I, and the projects are approved nonetheless. If rural land is to be protected as a public good, voluntariness must yield to obligation. Full article

Against the backdrop of the accelerating global low-carbon transition, the mismatch between intermittent renewable energy generation and inflexible energy demand has become a major bottleneck for sustainable energy development. To address this challenge, this paper proposes an AI-driven 4N4F (Four Networks and Four Flows) multi-network convergence system. The paper first clarifies the conceptual connotation and theoretical foundation of 4N4F integration and then systematically constructs an overall technical architecture built on three core pillars: intelligent connected electric vehicles as mobile energy nodes, intelligent networked building-integrated photovoltaics as stationary energy carriers, and an integrated energy management system as the global coordination hub. The effectiveness of the proposed framework is examined through representative engineering cases covering commercial buildings, hybrid energy storage, off-grid microgrids, and sustainable campuses. The results demonstrate that the 4N4F system breaks down traditional silos among energy, information, transportation, and human networks, significantly improving renewable energy utilization and overall energy efficiency. These findings highlight the framework’s scientific contribution as a new technical paradigm for modern energy-system construction and sustainable development. Full article

A hybrid pumped-storage power station (hereinafter referred to as the “hybrid station”) is developed by installing reversible pump–turbine units between cascade reservoirs, providing both conventional hydropower regulation and cross-seasonal energy storage capability. However, existing studies have mainly focused on its short-term peak-shaving function, while the potential of cross-seasonal water resource allocation remains insufficiently explored. This paper develops a medium-to-long-term cross-seasonal optimal dispatch model considering the seasonal regulation characteristics of hybrid pumped-storage stations, with the objective of minimizing residual-load variance. Seasonal reservoir storage rate-of-change constraints are introduced to represent the operation mechanism of storing water during wet seasons and releasing water during dry seasons. A Variable-Phase Progressive Optimality Algorithm (VPPOA) is proposed to solve the model. Three operation modes, including no hybrid storage, hybrid daily regulation, and hybrid seasonal regulation, are established for comprehensive evaluation under different hydrological conditions, renewable outputs, load variations, and algorithm performances. The results show that the proposed model achieves an annual cross-seasonal energy transfer of 848.8 GWh. Compared with the no-hybrid-storage scheme, the hybrid seasonal regulation scheme reduces the residual-load variance by 77.3%, increases the wind–PV accommodation rate from 40.3% to 96.6%, and decreases the wet-season wind–PV curtailment rate from 38.8% to 3.9%. Compared with the hybrid daily regulation scheme, it further reduces the residual-load variance by 26.1%, improves the wind–PV accommodation rate by 9.2 percentage points, and increases dry-season supply reliability from 93.2% to 100%. Moreover, VPPOA outperforms GA, PSO, and conventional POA, achieving a lower residual-load variance of 2861.05 MW². The results demonstrate that the cross-seasonal regulation capability of hybrid pumped-storage stations can effectively enhance renewable energy accommodation and power supply reliability, providing support for the flexible operation of new-type power systems. Full article

Accurate modeling of photovoltaic (PV) cell behavior under varying operational conditions is essential for optimizing energy yield and system reliability. This study presents an extended simulation-based methodology for analyzing monocrystalline silicon PV cells using a double-diode model (DDM) with a physics-based, temperature- and irradiance-dependent parameterization. Building on a SPICE-equivalent circuit formulation, the governing implicit DDM equation is solved numerically to regenerate every current–voltage (I–V) and power–voltage (P–V) curve, and all circuit, block and flow diagrams are redrawn as vector-quality figures. Beyond the deterministic analysis, the manuscript introduces two extensions: (i) a quantitative statistical analysis of the influence of temperature (T), irradiance (G) and series resistance $\left(R_s\right)$ on open-circuit voltage, short-circuit current, maximum power and fill factor, using linear/log-linear regression, a multiple linear regression model and a Pearson correlation analysis; and (ii) a machine-learning (ML) validation study in which a random-forest surrogate model is trained on a 600-point physics-consistent synthetic dataset spanning the full $(T, G, R_s)$ operating envelope and evaluated with a held-out test split and 5-fold cross-validation. The surrogate reproduces the DDM outputs with cross-validated coefficients of determination above 0.98 for maximum power, open-circuit voltage, short-circuit current and fill factor, confirming that the DDM response surface is smooth, learnable and suitable for fast surrogate-based design optimization and maximum-power-point-tracking (MPPT) algorithm testing. Simulated outputs at standard test conditions (25 °C, 1000 W/m², AM 1.5) are compared against manufacturer datasheet values, and residual errors are analyzed and attributed to specific modeling assumptions. Full article

To improve the reliability of microgrid operation and the capability for off-grid autonomous operation, this study proposes a robust reliability-based optimal configuration method for microgrid hybrid energy storage systems. Firstly, the operational characteristics of the hydrogen energy storage system are analyzed, and combined with the cooperative mechanism of the hydrogen energy storage and distributed power supply systems, the operation architecture of the microgrid electro-hydrogen coupling system is constructed. Secondly, according to the electro-hydrogen coupling characteristics and adjustment capability, a two-stage distributionally robust optimization configuration model of electro-hydrogen hybrid energy storage is constructed. In the first stage, the minimum investment and operation cost of the microgrid is the optimization goal. The optimal configuration model for the equipment capacity in the microgrid is constructed, and the distributed photovoltaic, wind turbine, battery, hydrogen storage tank, hydrogen fuel cell and electrolytic cell in the microgrid are fixed. In the second stage, according to the results of the capacity optimization configuration in the first stage, the operation reliability of the microgrid is taken as the optimization objective; 168 h is set as the operation cycle, and the extreme operation scenario for the new energy output from the microgrid is assumed to provide the capacity optimization boundary for the upper layer. Finally, the model is solved by the constraint generation algorithm and analyzed by an actual microgrid in a given area. The quantitative results demonstrate the validity of the proposed method: compared to systems without hydrogen energy storage, the duration of the continuous islanded operation is increased from approximately 302 h to 529 h (an improvement of approximately 75.2%), and the load-shedding duration is reduced from approximately 35 h to 7 h (an approximately 80% reduction). Full article

Water scarcity and groundwater salinization constrain irrigated agriculture in the Jordan Valley. This study evaluates a small-scale photovoltaic-powered brackish-water reverse osmosis (RO) desalination system successively upgraded from single-stage to two-stage RO operation and enhanced with ultrafiltration (UF) pre-treatment. The novelty of the study lies in the integrated assessment of these successive system configurations using field-based operational data. A screening gate-to-gate life-cycle assessment (LCA) and levelized cost of water (LCOW) analysis were performed for a functional unit of 1 m³ of irrigation-quality permeate. Three successive configurations were compared: (i) the baseline single-stage RO system, (ii) a two-stage RO system in which the first-stage concentrate was re-pressurized and treated in a second RO stage, and (iii) a UF-assisted two-stage RO system operated at up to 80% recovery. The configurations were compared using project inventory data for feedwater quality, recovery rate, energy demand, chemical consumption, materials and capital costs. The estimated climate-change impact of the UF-assisted PV-RO configuration was 0.9–1.4 kg CO₂-eq/m³. This impact is comparatively low for brackish-water RO and reflects the use of PV electricity, although direct comparison with the literature values is limited by differences in LCA boundaries and assumptions. Recovery increased from 35% for single-stage RO to 60% for two-stage RO and 80% for UF-assisted two-stage RO, reducing brine generation from 1.86 to 0.25 m³/m³ permeate. Economically, increasing RO membrane capacity reduced LCOW from 1.28 to 0.86 JD/m³, whereas the UF-RO configuration increased LCOW to 1.40 JD/m³ because of added capital cost. The results indicate that high recovery and solar power can improve environmental performance, but UF adoption should be justified by reliability, water-quality and membrane-lifetime benefits. Full article

Against the backdrop of the “dual-carbon” goals, the penetration of distributed photovoltaics (PV) in distribution networks continues to increase. Under the combined effects of stochastic PV generation and load variability, assessing PV hosting capacity in distribution networks under fault conditions has become increasingly challenging. To address the limited consideration of uncertainty and fault conditions in existing studies, this paper proposes a stochastic optimization framework for evaluating PV hosting capacity under fault conditions that considers load uncertainty and mobile shared energy storage (MSES). First, historical data are used to generate representative uncertainty scenarios through Latin hypercube sampling. An optimization model is then formulated subject to secure distribution network operation constraints to determine the optimal capacities and locations of distributed PV installations. Meanwhile, MSES is incorporated to further enhance PV hosting capacity through flexible spatial and temporal energy transfer. Finally, simulations on the modified IEEE 33-bus system demonstrate that MSES can effectively increase PV hosting capacity and renewable energy accommodation. However, under fault conditions, changes in voltage profiles and the emergence of reverse power flows further reduce PV hosting capacity. Although incorporating load uncertainty increases the system cost, the resulting PV hosting capacity is more representative of practical operating conditions, thereby providing quantitative decision-making support for the planning and operation of distribution networks with high PV penetration. Full article

Agrivoltaic systems configure photovoltaic power generation and agricultural production within the same land space, providing a new pathway for alleviating the conflict between energy development and farmland conservation; however, array shading and structural constraints also alter crop growth and agricultural equipment operating environments. Following the PRISMA process, this review searched studies published from 2010 to 2026 in the Web of Science Core Collection, Scopus, and China National Knowledge Infrastructure, and ultimately included 206 publications. The review focuses on array-induced environmental reconfiguration, equipment adaptation, light–thermal sensing and prediction, and coordinated agrivoltaic operation. Existing evidence indicates that the environmental effects of agrivoltaic systems are clearly influenced by climate and array configuration; in representative vertical or tracking systems, annual-scale photosynthetically active radiation decreased by approximately 11% to 34%. Mismatch among module height, row spacing, and implement width reduces field-operation efficiency, which fell to approximately 45% under severe mismatch in some experiments; elevated arrays also cause GNSS signal attenuation and increase the difficulty of continuous positioning beneath the panels. Because existing studies differ considerably in site conditions, evaluation indicators, and validation periods, the above results mainly reflect representative performance ranges. Matching criteria between photovoltaic arrays and agricultural machinery have not yet been established, long-term field-measured data in complex field environments are insufficient, model adaptability across regions is limited, and coordinated scheduling of agricultural production and photovoltaic operation and maintenance remains inadequate. Overall, coordinated design of arrays and agricultural machinery, multi-sensor fusion navigation, environmental prediction corrected for array structure, and hierarchical scheduling under safety constraints are the main development directions for intelligent coordinated operation of agrivoltaic systems. Full article

The increasing adoption of renewable energy technologies in residential buildings necessitates intelligent energy management strategies capable of improving energy efficiency while maintaining occupant comfort. This study presents a smart home energy management framework integrating a Heating, Ventilation, and Air Conditioning (HVAC) system, rooftop photovoltaic (PV) generation, and a battery energy storage system (BESS) using a computationally efficient rule-based control strategy. The framework was developed and evaluated in MATLAB/Simulink, where HVAC thermal dynamics, household load demand, PV generation, and battery state-of-charge (SOC) behavior were coordinated through hysteresis-based temperature control and SOC-constrained battery management. Simulation results demonstrated that the proposed controller effectively maintained indoor temperature within the prescribed comfort band while enhancing renewable energy utilization. The integrated PV–battery system achieved a PV self-consumption rate of 100%, supplied 90.64% of the total household energy demand from renewable sources, and reduced grid electricity imports from 0.921 kWh to 0.103 kWh, corresponding to an 88.81% reduction in grid dependency. The battery operated safely within an SOC range of 34.18–50.00%, resulting in a utilization swing of 15.82 percentage points. These findings demonstrate that the proposed rule-based framework provides a practical, low-complexity, and reliable solution for improving renewable energy utilization, reducing grid reliance, and supporting sustainable smart residential energy management. Full article

The open-winding dual inverter topology has the advantages of a multilevel output, high DC voltage utilization, large modulation freedom, and easy fault tolerance, and is widely used in photovoltaic grid-connected systems. The open-winding photovoltaic–energy-storage converter system studied in this paper adopts this topology. The system consists of photovoltaic and energy-storage units, and the two converters can independently deliver active power to the grid when the DC-bus voltages on both sides are balanced. To achieve active-power regulation and distribution between the photovoltaic side and the energy-storage side, this paper constructs a mathematical model of the open-winding dual-inverter photovoltaic–energy-storage system and proposes a power-allocation strategy for steady-state operation. Compared with the conventional SPWM, the proposed strategy introduces a reference-voltage allocation layer before the SPWM modules, enabling flexible power sharing while retaining the simple implementation of the conventional SPWM. The proposed strategy effectively guarantees the operational stability and grid-connected power quality of the photovoltaic–energy-storage converter. Finally, its feasibility and effectiveness are verified through simulation analysis and experimental testing. Full article

The uncertainties in source-side renewable power supply and user-side multi-energy demand pose significant challenges to coordinated scheduling in an electricity–heat–hydrogen integrated energy system (EHH-IES). A hierarchical scheduling approach for EHH-IES is introduced, with source–load forecasts serving as its basis. Traditional forecasting methods heavily rely on large amounts of training samples. To address the forecasting challenge in data-scarce scenarios, a frozen large language model assisted by variational mode decomposition is developed for joint source–load forecasting. During the scheduling process, conventional single-level reinforcement learning strategies are not sufficiently effective in dealing with the high-dimensional hybrid action space while satisfying the intricate operating constraints of the EHH-IES. Therefore, an imitation-learning-based hierarchical proximal policy optimization strategy is developed to decompose the scheduling task into system-level energy coordination and device-level action execution. The experimental evaluation shows that the proposed forecasting approach delivers improved forecasting accuracy in data-scarce scenarios, reducing the RMSE of photovoltaic power, wind power, electric load, heat load, and hydrogen load forecasting by 8.65%, 23.27%, 34.99%, 24.60%, and 39.38%, respectively, compared with the strongest baselines. The proposed scheduling strategy achieves the fastest convergence compared with the three benchmark methods while reducing the total operating cost by 21.3%, 7.1%, and 2.8%, respectively. Full article

This study investigates the aerodynamics and energy balance of a roof-mounted photovoltaic system on a vehicle’s energy consumption at different driving speeds. A 3D vehicle model, both with and without the PV system, was analyzed using aerodynamic simulation to obtain the data required for the evaluation. The results quantify the increase in aerodynamic drag and the corresponding rise in energy consumption caused by the PV system installation and determine the necessary reduction in cruising speed for the PV-equipped vehicle to match the energy consumption of the baseline configuration. The findings indicate that at low urban speeds, the energy generated by the PV system will add extra mileage, while at higher speeds, a moderate reduction in cruising speed can significantly mitigate the aerodynamic penalty. These insights help define the operational conditions under which roof-mounted photovoltaic systems provide a net benefit for electric or hybrid vehicles. Full article

Poland’s rapid expansion of renewable energy sources (RES) and of distributed photovoltaics (PVs) in particular has increasingly collided with a finite and unevenly distributed resource: available capacity in the electricity distribution grid. This paper examines grid-connection capacity as a spatial constraint on RES development in Poland, combining a national overview of grid congestion (2023–2026) with a quantitative case study of the 52 coherent 110 kV node groups administered by ENERGA-OPERATOR S.A. under the statutory reporting regime of Article 7(8l) of the Polish Energy Law. The node-group figures are not an observed annual series: the operator’s disclosure gives the capacity available in the base year of 2018, together with the capacity it planned to make available in each year up to 2023, so the analysis characterises the spatial distribution implied by the operator’s own five-year development plan rather than realised outcomes. Using descriptive statistics, a Gini coefficient, Lorenz curve analysis, and an original growth index (the ENERGA Grid-Connection Growth Index, EGCI) developed for this study, we show that available connection capacity is markedly unequally distributed across node groups (Gini = 0.48 in 2018, rising to 0.51 by the 2023 planning horizon) and that this inequality has a distinct regional pattern: the Olsztyn branch, corresponding to the Warmia–Mazury region (Warmińsko-mazurskie voivodeship), more than doubles its share of the operator’s total available capacity under the plan (from 12.4% to 29.3%, or from 75 MW to 365 MW in absolute terms), moving from the third-lowest to the highest planned capacity among the operator’s six branches, while two of its nine node groups—including the regional capital’s own—receive no increase at all under the plan. A voivodeship-level spatial analysis, mapped using verified administrative boundary data, shows a pattern consistent with this at a national scale: Warmia–Mazury has the second-lowest installed generation capacity of Poland’s 16 voivodeships despite favourable land and irradiation conditions for photovoltaic development. Bootstrap analysis confirms this robust level of inequality while indicating that, with 52 units, the five-year increase is best read as a consistent tendency rather than as a statistically established widening; the operator-level values are specific to ENERGA-OPERATOR and are not numerically generalisable to Poland’s other four operators. We discuss the implications of these findings for grid-investment planning, RES-integration policy (cable pooling, storage co-location, curtailment reduction), and the energy-security dimension of an increasingly decentralised, weather-dependent generation system, and we identify concrete directions for future quantitative and spatial research. Full article