Search Results (376)

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

Continuous carbon anode roasting in industrial parks requires a stable high-temperature heat supply and remains highly dependent on grid electricity and natural gas. However, existing energy-system studies rarely coordinate hydrogen production and storage, volumetric hydrogen blending, and temperature-graded waste-heat recovery under continuous production constraints. To address this gap, this study proposes an electricity–heat–gas–hydrogen integrated energy system for carbon anode industrial parks and develops a 24 h multi-objective scheduling model. The model coordinates heat demands at different temperature levels with hourly electricity and hydrogen flows, using surplus photovoltaic power to produce hydrogen for later high-load periods. The selected scheme achieves a daily volumetric hydrogen-blending ratio of 10.79%, with an operating cost of 82,985.75 CNY and carbon emissions of 54,371.53 kg. Relative to an otherwise equivalent non-hydrogen configuration, hydrogen coupling provides additional reductions of 7.2% in operating cost and 2.3% in carbon emissions. Compared with a basic conventional configuration, operating cost and carbon emissions decrease by 27.9% and 26.0%, respectively. Cascade recovery also increases the daily waste-heat utilization rate by approximately 30 percentage points. These results show that the proposed scheduling framework can coordinate hydrogen utilization and graded waste-heat recovery while maintaining continuous carbon anode production. Full article

A transition towards sustainable transportation requires efficient integration of electric vehicles (EVs) with renewable energy sources. This work proposes a two-stage Parking Lot Energy Management Scheme (PLEMS) to minimize charging costs while maximizing solar photovoltaic utilization in commercial parking facilities. In the first stage, the optimization phase is formulated using a mixed-integer linear programming (MILP) that minimizes the overall cost of EV charging while ensuring maximum utilization of locally available PV energy. In the second stage, a gated recurrent unit (GRU)-based deep learning model performs state of charge (SOC) forecasting for EVs parked in the parking lot. Using the predicted SOC for the next time step, the system decides whether each EV will be charged or discharged, ensuring consistency with the cost-optimal MILP strategy from the first stage. The proposed PLEMS achieves up to 62% daily cost savings in charging compared to uncoordinated direct grid charging. However, this cost saving is the outcome of proposed optimization as well as the integration of PV panels in power grid. When compared with nine similar vehicles to grid (V2G)-enabled approaches from the literature, which report cost savings ranging from 9.73% to 52%, the proposed framework shows an improvement of 10% to 52% over these methods. This hybrid MILP-GRU framework offers practical V2G operation and high scalability for large EV fleets in solar-powered smart parking lots. Full article

This study evaluates a disturbance-informed, planning-level workflow for assessing steady-state feeder performance and post-disturbance frequency security in renewable-rich distribution networks. The workflow links feeder operation in DIgSILENT PowerFactory to reduced-order frequency-security screening in OpenModelica and PSAT, with Pandapower used as an independent steady-state cross-check. The IEEE 33-bus feeder includes distributed photovoltaic units, DFIG-based wind generation, and a grid-forming battery energy storage system (BESS). Hourly feeder time-series results are used to identify renewable-output deficits, and each deficit is converted from MW to the common 10 MVA dynamic-system base before being applied as a conservative step disturbance. The steady-state validation gives a maximum voltage mismatch of 0.0155 pu, within the adopted 2% screening limit. The cross-tool frequency benchmark shows close agreement for nadir and settling time, while RoCoF is interpreted conservatively because of its sensitivity to numerical differentiation and event implementation. As renewable penetration increases from 0% to 100%, the minimum bus voltage remains close to 1.0 pu (0.9999–0.9991 pu), the maximum bus voltage rises from 1.0295 pu to 1.0826 pu, and feeder losses increase from 0.0246 MW to 0.1531 MW. The 24 h assessment gives a maximum daily voltage of 1.0755 pu at 100% penetration, while loading remains below thermal limits. For the corrected 75% severe event (0.3048 MW; −0.0305 pu on the 10 MVA base), the 2 MW droop-plus-FFR case improves the nadir from 49.9695 Hz without support to 49.9924 Hz. Voltage therefore becomes the earliest binding screening constraint from 50% penetration onward, whereas thermal loading and supported frequency nadir remain non-binding under the studied conditions. Full article

Battery energy storage systems can reduce the operating cost of active distribution networks while supporting voltage control through their power electronic converters. This paper develops an application-specific multi-operator Differential Evolution (DE) framework for the coordinated active and reactive power scheduling of distributed battery energy storage systems in radial distribution networks with photovoltaic generation. The optimization model minimizes the daily operating cost associated with conventional energy supply, photovoltaic and storage operation and maintenance, and battery degradation. Candidate schedules encode hourly active and reactive power references for three storage converters, producing a 144 dimensional decision vector for a 24 h horizon. Each candidate is repaired to satisfy active power, state of charge, terminal energy, and converter apparent power limits before being evaluated through an alternating current power flow based on matrix successive approximations. The search framework generates three competing trial schedules per target individual by combining established best-guided, random, and current-to-random DE mutation families with a discrete parameter pool, a common feasibility-repair operator, and greedy selection after AC network evaluation. The method is tested on modified 33-node and 69-node active distribution networks and compared with AJAYA, genetic algorithm, multiverse optimizer, and particle swarm optimization. In the deterministic 33-node case, Differential Evolution obtains the lowest best cost, USD 6846.206, and the largest best cost reduction, 2.1838 percent. The scenario study performs separate deterministic optimizations for pre-generated operating realizations and is therefore interpreted as a scenario-conditioned sensitivity assessment rather than as stochastic or robust optimization of one here-and-now schedule. In this assessment, DE achieves the largest average savings: 2.3487 percent in the 33-node network and 2.9314 percent in the 69-node network. Voltage magnitudes, branch loading, converter ratings, and cyclic state of charge constraints are satisfied in all evaluated cases. The results identify the proposed framework as a competitive day-ahead solver within the evaluated cases, while no claim of global optimality or universal superiority over alternative optimizers is made. Full article

Small catamaran unmanned surface vessels (USVs) under wave action experience continuous roll and pitch, causing the solar irradiance on deck-mounted photovoltaic (PV) panels to vary and thereby affecting power output. The quantitative effect of wave-induced hull motion on PV power fluctuation and daily energy yield remains insufficiently quantified, and the separate contributions of roll and pitch remain unquantified. We developed a coupled PV power simulation model for a catamaran USV and applied it to six scenarios combining three sea states (Hs = 0.6, 1.0, and 1.5 m) with two wave directions (0°and 90°). Under the simulated clear-sky conditions, the attitude-induced daily energy loss rate did not exceed 1.3% across all scenarios and remained below 0.2% in following seas. Wave direction strongly controlled the power output characteristics: at a given sea state, the coefficient of variation of power under beam seas was 2.3–2.8 times that under following seas, and the maximum loss rate was 6.5 times the corresponding following-sea value. The realized motion contribution was wave--dependent, with roll dominating under beam seas and pitch dominating under following seas. Under the prescribed equal-amplitude motions, roll exhibited approximately five times the fluctuation sensitivity of pitch, whereas the dominant contribution under irregular waves depended on wave direction. Power fluctuation followed a diurnal pattern, stronger in the early morning and late afternoon and weaker around midday. These results suggest that roll mitigation may be prioritized in PV system design for catamaran USVs, while reducing beam-sea exposure may be considered in route planning to improve power stability. Full article

Cold-climate dwellings can face coincident electricity and domestic hot-water shortfalls in winter, when solar availability is at its lowest. This study evaluates an integrated residential system for Aomori, Japan, combining photovoltaics, evacuated-tube solar water heating, and battery storage with electrolysis, compressed-hydrogen storage, and a PEM fuel cell operated in combined-heat-and-power mode. Building on a screening-level annual-balance analysis, a coupled annual TRNSYS simulation with a 0.125 h time step resolved battery dispatch, electrolyzer part-load operation, hydrogen compression and finite storage, seasonal fuel-cell operation, and heat recovery. The results show that the principal value of seasonal hydrogen lies in improving winter supply adequacy, dispatchability, and heat recovery rather than annual conversion efficiency. Fuel-cell heat recovery increased the number of days satisfying the hot-water screening indicator—a daily mean tank temperature of at least 43 °C—from 221 to 332. A reserve-aware criterion identified a 225 W electrolyzer operating-power cap as the positive-reserve case; 205 W was near-cyclic with a negligible margin, whereas the original 475 W cap was substantially oversized. The hydrogen pathway remained markedly less efficient than direct photovoltaic and solar-thermal use, and the estimated storage hardware’s lower bound substantially exceeded the break-even capital ceiling supported by the annual operating value. Seasonal hydrogen can therefore strengthen winter energy adequacy and heat recovery but is not yet cost-effective at the single-dwelling scale under the investigated conditions. Full article

This paper presents an integrated day-ahead battery energy storage system (BESS) scheduling framework for radial active distribution networks with photovoltaic generation. Its main contribution is a reproducible evaluation chain combining non-ideal state-of-charge (SoC) feasibility correction, sequential AC power-flow verification, consistent metaheuristic benchmarking, and post-dispatch battery-aging analysis. The operating-cost objective coordinates hourly BESS active-power exchanges while enforcing storage and AC-network constraints. A parallel Coyote Optimization Algorithm (COA) is compared with parallel GWO, GA, PSO, and MVO implementations under a common formulation, correction procedure, evaluator, and computational environment. Validation uses modified 33-, 69-, and 136-bus radial feeders: 100 independent runs for the deterministic 33-bus benchmark, 100 independently optimized Monte Carlo scenarios for the 69-bus assessment, and seven representative daily profiles for the 136-bus weekly case. COA achieved an average cost reduction of 1.0084%, with the lowest dispersion of $\sigma =0.0070\%$, in the 33-bus system; a mean scenario-wise reduction of 1.8337% in the 69-bus system; and a weekly reduction of 0.4402% in the 136-bus system. It obtained the lowest operating costs among the evaluated calibrated configurations, and all pairwise comparisons remained significant after Holm’s step-down adjustment applied separately within each system, although COA required greater computational effort than PSO. The reported schedules satisfied the imposed BESS and AC-network limits. Battery aging was evaluated only after scheduling and was not included in the optimization objective. The resulting cost-oriented schedules produced equivalent full-cycle values near 0.8 day⁻¹ and projected 80% SoH lifetimes of approximately 6–8 years. These results provide an AC-feasible basis for comparing economic performance and post-dispatch battery-health implications under the evaluated conditions. Full article

The increasing penetration of distributed energy resources and diverse load characteristics in interconnected multi-microgrid systems creates significant challenges for coordinated energy management and optimal resource planning. This study proposes a multi-objective optimization framework for the simultaneous sizing of photovoltaic (PV) systems and battery energy storage systems (BESSs), combined with coordinated energy management and bidirectional power exchange among residential, commercial, and industrial microgrids connected to the IEEE 33-bus distribution network. The framework incorporates 24 h load profiles, photovoltaic generation, time-of-use electricity pricing, and distribution network operational constraints. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is employed to simultaneously minimize the total daily cost, network power losses, and grid dependency while satisfying the voltage, feeder loading, and battery state-of-charge constraints. The economic objective combines the equivalent daily investment costs of PV and BESSs with daily operating costs using a capital recovery factor (CRF)-based formulation to ensure dimensional consistency. The best compromise solution is selected using the minimum normalized Euclidean distance to the ideal Pareto solution. Simulation results demonstrate that the proposed framework achieves a total daily cost of $13,412.52/day, total network power losses of 4179.1 kW, and grid dependency of 3262.28 kWh while maintaining all operational constraints within acceptable limits. Furthermore, coordinated PV–BESS operation improves voltage regulation, reduces feeder loading and network power losses, enhances renewable energy utilization, and decreases the reliance on the utility grid. The results demonstrate that the proposed framework provides an effective techno-economic approach for the coordinated planning and operation of interconnected multi-microgrid systems with high renewable energy penetration. Full article

Freely accessible weather APIs are an attractive input for photovoltaic (PV) power prediction, yet how their reference-based error carries through—under temporal aggregation—into day-ahead PV energy is rarely quantified before model development. This study audits eight freely accessible weather-data services—seven forecast services and the Meteostat observational archive—and finds that, for the endpoints, subscription tiers, and collection period evaluated here, only two of the forecast services expose hourly global horizontal irradiance (GHI), while a third advertised irradiance field returns empty—itself a material result for municipal integrators. The two irradiance-capable services are propagated through a common physical PV model of the 548 kWp east–west rooftop plant being built on a university campus in Bratislava, Slovakia, against independent references (CAMS irradiance; NASA POWER temperature and wind). Throughout, “error” denotes discrepancy against the reference, not measurement truth. Over a common 62-day spring–summer window, the day-ahead daily-energy mean absolute error (MAE, relative to mean reference daily energy) is 10–11% for both sources; Open-Meteo’s winter-inclusive own window raises its value to about 13%. On the matched window, Open-Meteo has the lower hourly GHI RMSE (121 vs. 128 W·m⁻²), yet this advantage does not carry through to day-ahead energy—the point-estimate ordering even changes—and neither matched-window difference is statistically resolved. Because daily aggregation rewards low bias over low scatter, this ordering change is already present in daily GHI energy—before the PV conversion—so hourly irradiance accuracy alone does not determine day-ahead energy. Propagated input-data auditing is therefore an advisable step before ML-based PV forecasting and day-ahead scheduling of urban distributed PV. Full article

The rapid expansion of photovoltaic (PV) systems poses significant challenges to grid stability. Hybrid Energy Systems (HES) are intended to alleviate this volatility, yet their coordinated dispatch often remains suboptimal due to communication delays and ramp-rate constraints. Accurate ultra-short-term PV power forecasting is therefore essential, as it enables preemptive control and timely dispatch adjustments that unlock the full potential of HES. In this study, we propose a novel AI hybrid forecasting framework that integrates a rule-based model with a Decomposition Linear (DLinear) Long Short-Term Memory (LSTM) deep learning core, representing, to the best of our knowledge, a novel integration of a decomposition-based linear model (DLinear) with LSTM networks for ultra-short-term PV power forecasting. The DLinear component decomposes the time series into trend and remainder sequences, which are then independently modeled by separate LSTM networks to capture distinct dynamics. Using data from a 300 kWp PV power station, the framework achieves an average daily prediction accuracy exceeding 93% for both 5-min and 15-min horizons. The model reliably tracks power variations under sunny and rainy conditions, while under volatile cloudy weather its accuracy decreases but still captures essential fluctuation patterns. These results demonstrate the potential of the proposed framework for improving the dispatch and operational reliability of hybrid energy systems. However, further validation across additional seasons and sites is needed to establish broader generalizability. Full article

Residential photovoltaic generation and household demand are temporally mismatched, affecting grid dependence and local storage use. This study formulates a 24 h mixed-integer linear programming model for a grid-connected residential prosumer with rooftop PV, a stationary behind-the-meter battery, and an electric vehicle capable of vehicle-to-home operation. Four configurations are compared under common external inputs: no storage, battery only, V2H only, and a hybrid battery–V2H system. The model resolves the main power routes, enforces charging, discharging, and cyclic state-of-charge constraints, allows grid charging, and excludes storage-to-grid export. PV generation is reduced, and residential demand is increased through a prescribed deviation-scaling parameter evaluated at five levels for clear-day and synthetic partly cloudy profiles. Numerical consistency is checked using an independent no-storage calculation and equation residuals. At $\rho$ = 0.3 under the clear-day profile, the hybrid configuration reduces daily operating cost from USD 26.113 to USD 17.807, increases PV self-consumption from 69.684% to 89.121%, lowers utility purchase from 106.380 to 88.700 kWh/day, and reduces export from 36.170 to 12.980 kWh/day. Under the partly cloudy profile, all storage-based configurations reach 100% PV self-consumption and zero export, while the hybrid case retains the lowest operating cost. The results are conditional on the adopted capacities, continuous EV connection, tariff structure, and exclusion of degradation and investment costs. Full article

This paper proposes a manner of modeling properties of stationary and tracking photovoltaic installations. A SPICE-based model of such photovoltaic installations is proposed. With the use of this model, the properties of two photovoltaic installations located adjacent to each other are compared. The first is stationary, while the second is equipped with a solar-tracking system. The design of both installations is described. The results of measurements and computations for both installations are presented and compared. These results illustrate the daily changes in the generated power. These measurements and computations were conducted for selected days across four seasons. The energy produced by each installation in each month was also determined. The obtained results are discussed. It was demonstrated that the use of a solar tracker can significantly increase annual energy production, especially during months with a high solar trajectory. The presented research results demonstrate that the developed model provides accurate predictions of the performance of both photovoltaic installations and that their efficiency is strongly influenced by seasonal meteorological conditions. Full article

To address the elevated local peak loads, voltage degradation at weak nodes, increased line power flows, and rising operational costs caused by uncoordinated electric vehicle (EV) charging in shopping mall and workplace areas, this paper proposes a coordinated orderly charging method that integrates photovoltaic (PV) generation, an energy storage system (ESS), and EVs based on a two-stage modified bounty hunter optimizer (MIBHO). First, an uncoordinated EV charging load model for the shopping mall and workplace area is constructed using the Monte Carlo method to simulate vehicle arrival times, departure times, initial state of charge (SOC), and target SOC. Next, the mall base load, PV system, ESS, and EV charging station are integrated at node 18 of the IEEE 33-node distribution network, and an orderly charging optimization model is formulated incorporating vehicle time windows, SOC requirements, single-vehicle power limits, and station-level capacity constraints. The proposed MIBHO uses adaptive hierarchical block encoding for coarse search and a refined 24-dimensional hourly search. Without changing daily EV charging energy, it increases peak-PV charging from 52.41% to 69.46%, reduces losses from 4964.0 to 4892.5 kWh, lowers costs from CNY 805.12 to 718.03, and mitigates the evening peak. Classical and CEC2017 tests confirm its competitiveness. Full article

This study examines the electrical energy performance of a photovoltaic (PV) power plant with respect to the duration of the daylight period. The evaluated indicators are normalized to the initially installed capacity of the PV plant. During the operational period, a portion of the photovoltaic panels ceased operation. However, for the subsequent analysis periods, the calculations of the energy and power performance indicators were carried out with reference to the originally installed capacity in order to maintain consistency and comparability of the results. The energy yield and power output of the PV plant were evaluated on both a monthly and a daily basis for the total installed capacity and referred to unit inverter capacity. Tabulated datasets and graphical representations were used to provide both quantitative and visual comparisons of the PV system performance over the investigated period. Full article