Search Results (269)

Maximizing the optimal scheduling capability of a virtual power plant (VPP) over its aggregated resources is crucial for increasing its revenue. However, the limited dispatchable resources in single-energy VPPs hinder maximum economic efficiency. To address this issue, in this paper, a multienergy virtual power plant (MEVPP), which aggregates distributed electrical, thermal, and demand-side flexible resources, is introduced. Furthermore, a low-carbon economic dispatch strategy model is proposed for the coordinated operation of the MEVPP with shared energy storage. First, an MEVPP model incorporating shared energy storage is constructed, with equipment modeling developed from both electrical and thermal dimensions. Second, a low-carbon dispatch strategy that incorporates multiple types of demand responses is formulated, accounting for the effects of electrical and thermal demand responses, as well as carbon emissions, on dispatch. The simulation results demonstrate that, compared with models that do not consider the multienergy demand response, the proposed model reduces system operating costs to 54.2% and system carbon emissions to 42%. Additionally, the MEVPP can leverage energy storage by charging during low-price periods and discharging during high-price periods, thereby enabling low-carbon and economically viable system operation. This study offers valuable insights for the optimized operation of MEVPP systems. Full article

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

The increasing penetration of microgrids (MGs) in modern power distribution systems requires advanced operational strategies to ensure both economic efficiency and technical reliability. This study developed an optimal economic framework for battery energy storage in MG connected to distribution systems in order to minimize operational costs while considering renewable integration and battery charging and discharging cost and degradation cost as well, and their impact on grid technical constraint. An MG is interconnected to the IEEE-33 radial distribution feeder through an additional bus, where the BESS operates to minimize the total operating cost over a 24 h horizon. The formulation captures the charging and discharging dynamics of the BESS, BESS degradation, state-of-charge constraints, electricity price signals, and the network’s operational limits. The optimization problem is solved using Mixed Integer Linear Program (MILP) to obtain the optimal scheduling of BESS charging and discharging which minimizes the total operating cost and maintains grid constraint within the allowable limit by optimizing the power exchange between the MG and the distribution grid. Simulation results showed that the proposed approach reduces operational costs and optimize grid power exchange, while maintaining technical reliability of the distribution system by enhancing its voltage profiles, improving its feeder loading capability, and reducing the system losses. This study provides a practical tool for enhancing both economic and technical performance in MG-connected distribution systems. Full article

To achieve the optimal energy allocation between the auxiliary power unit (APU) and battery of connected automated range-extended electric vehicle (CAR-EEV), the hierarchical eco-driving control with dynamic game energy management were investigated and the optimization design of APU working mode was carried out from a multi-objective perspective. Initially, the acceleration and speed of the host vehicle were adjusted in real time, based on the driving status of the preceding vehicle, and the ecological driving speed was obtained in the adaptive car-following eco-driving mode. The dynamic game energy management strategy was proposed, leveraging the real-time interactive information between the vehicle and the traffic environment, and intelligently allocating and scheduling the energy flow within the powertrain. Dynamic game optimization was adopted to achieve dynamic decision-making and control optimization on whether to switch the APU operating speed or not. The multi-objective optimization analyses are carried out based on the weight coefficient matrix. The hierarchical dynamic game energy management strategy based on eco-driving speed (HDGEMS) is implemented through dynamic games and exhibits excellent performance. This strategy enables dynamic adjustment of power distribution between the APU and the battery, thereby allowing the APU to operate efficiently under optimal operating conditions. Meanwhile, it effectively reduces secondary charging losses and the dynamic switching time of the APU, and ultimately achieves energy optimization. Eventually, the results of simulation and experimental thoroughly indicated that economy improvement, emission reduction, and battery life enhancement of CAR-EEV were effectively kept in balance under the control of the proposed HDGEMS with intelligent optimization mode. New research ideas and technical directions are provided for the field of EMS, which is expected to promote technological progress in the industry. Full article

In accordance with the European common rules for the internal electricity market, suppliers offer end users contracts with dynamic energy prices. To reduce energy costs, prosumers must manage their installations with energy storage devices (ESSs). The authors propose a novel control strategy with a relatively simple simulation-based algorithm that effectively reduces daily energy costs by managing the ESS charging and discharging schedule under different types of dynamic energy tariffs. The algorithm operates in a running window mode to ensure ongoing control updates in response to the changing conditions of the prosumer’s installation operation and dynamically changing energy prices. A feature of the control system is its ability to regulate the power exchanged with the supply network in response to an external signal from a superior control system or a network operator. This feature allows the control system to participate in regulatory services provided by the prosumer to the DSO. The effectiveness of the proposed control algorithm was verified in the PSCAD V4 Professional environment and with the MS Excel SOLVER for Office 365 optimisation tool. The results showed good accuracy with respect to the cost reduction algorithm and confirmed that the additional regulatory service can be effectively implemented within the same prosumer ESS control system. Full article

This work proposes an intelligent strategy for the coordinated management of active and reactive power in Battery Energy Storage Systems (BESSs) within AC microgrids operating under both grid-connected (GCM) and islanded (IM) modes to minimize daily operational costs. The problem is formulated as a mixed-variable optimization model that explicitly leverages the control capabilities of BESS power converters. To solve it, a Parallel Particle Swarm Optimization (PPSO) algorithm is employed, coupled with a Successive Approximation (SA) power flow solver. The proposed approach was benchmarked against parallel implementations of the Crow Search Algorithm (PCSA) and the JAYA algorithm (PJAYA), both in parallel, using a realistic 33-node AC microgrid test system based on real demand and photovoltaic generation profiles from Medellín, Colombia. The strategy was evaluated under both deterministic conditions (average daily profiles) and stochastic scenarios (100 daily profiles with uncertainty). The proposed framework is evaluated on a 33-bus AC microgrid that operates in both grid-connected and islanded modes, with a battery energy storage system dispatched at both active and reactive power levels subject to network, state-of-charge, and power-rating constraints. Three population-based optimization algorithms are used to coordinate BESS schedules, and their performance is compared based on daily operating cost, BESS cycling, and voltage profile quality. Quantitatively, the PPSO strategy achieved cost reductions of 2.39% in GCM and 1.62% in IM under deterministic conditions, with a standard deviation of only 0.0200% in GCM and 0.2962% in IM. In stochastic scenarios with 100 uncertainty profiles, PPSO maintained its robustness, reaching average reductions of 2.77% in GCM and 1.53% in IM. PPSO exhibited consistent robustness and efficient performance, reaching the highest average cost reductions with low variability and short execution times in both operating modes. These findings indicate that the method is well-suited for real-time implementation and contributes to improving economic outcomes and operational reliability in grid-connected and islanded microgrid configurations. The case study results show that the different strategies yield distinct trade-offs between economic performance and computational effort, while all solutions satisfy the technical limits of the microgrid. Full article

To address the challenges of climate change mitigation and operational flexibility in active distribution networks (ADNs) amid high renewable energy penetration, this paper proposes a low-carbon economic dispatch framework integrating demand-side carbon regulation and cyber–physical system (CPS)-enabled shared energy storage. First, a consumer-side emission penalty mechanism is developed by fusing a carbon emission flow (CEF) model with price elasticity coefficients. This mechanism embeds carbon costs into end-user electricity pricing, guiding users to adjust consumption patterns (e.g., reducing usage during high-carbon-intensity periods) and shifting partial carbon responsibility to the demand side. Second, a CPS-based shared energy storage mechanism is constructed, featuring a three-layer architecture (physical layer, control decision layer, security layer) that aggregates distributed energy storage (DES) resources into a unified, schedulable pool. A cooperative, game-based profit-sharing strategy using Shapley values is adopted to allocate benefits based on each DES participant’s marginal contribution, ensuring fairness and motivating resource pooling. Finally, a unified mixed-integer linear programming (MILP) optimization model is formulated for ADNs, co-optimizing locational marginal prices, DES state-of-charge trajectories, and demand curtailment to minimize operational costs and carbon emissions simultaneously. Simulations on a modified IEEE 33-bus system demonstrate that the proposed framework reduces carbon emissions by 4.5–4.7% and renewable energy curtailment by 71.1–71.3% compared to traditional dispatch methods, while lowering system operational costs by 6.6–6.8%. The results confirm its effectiveness in enhancing ADN’s low-carbon performance, renewable energy integration, and economic efficiency. Full article

Against the backdrop of rapidly growing distributed photovoltaics (DPVs) and mounting pressure on conventional frequency-regulation (FR) resources, this study proposes a day-ahead–intraday two-stage optimal scheduling strategy for aggregators of DPV + advanced energy storage participating in a joint energy–FR market. In the day-ahead stage (hourly resolution), a multi-aggregator-independent offering model is formulated that explicitly accounts for PV curtailment costs and storage operating/lifecycle costs. Subject to constraints on buy–sell transactions, PV output, storage charging/discharging power and state of charge (SOC), FR capacity, and power balance, the model co-optimizes energy and FR-capacity offers to maximize profit. In the intraday stage (15 min resolution), bidding deviation penalties are introduced, and a rolling optimization is employed to jointly adjust energy and FR dispatch/offers, reconfigure storage SOC in real time, reduce deviations from day-ahead schedules, and enhance economic performance. A three-aggregator case study indicates that, with deviation penalties considered, regulation-command tracking remains at a high level and PV utilization remains very high, while clearing costs decline and system frequency-response capability improves. The results demonstrate the proposed strategy’s implementability, economic efficiency, and scalability, enabling high-quality participation in ancillary services and promoting high-quality renewable integration under high-penetration distributed scenarios. Full article

Uncontrolled charging of large electric bus fleets can strain constrained power grids, such as South Africa’s. This study develops and evaluates a demand-oriented charging strategy for Golden Arrow Bus Services using a Mixed-Integer Linear Programming (MILP) model calibrated with real operating data. The model schedules fleet charging over an off-peak window to minimise the highest total demand charge (Notified Maximum Demand, NMD) while respecting arrival state of charge (SOC), Time-of-Use (ToU) tariffs, and ensuring all vehicles are fully charged before dispatch. Compared to the unmanaged baseline, the optimised schedules reduce the peak demand charge by 17%, keeping total depot demand below 1 MW and ensuring full fleet readiness. The strategy also eliminates all energy consumption during expensive peak-tariff windows in both winter and summer. Further analysis shows that raising the minimum arrival SOC reduces the required optimum per-bus demand approximately linearly (≈1.5 kW per +5% SOC), whereas widening the SOC arrival range increases demand variability. This MILP framework demonstrates that exploiting SOC diversity and modest charge capacity capping can significantly lower peak demand and operational costs, offering a validated model for depots in other capacity-constrained power systems. Full article

The widespread adoption of electric vehicles intensifies the spatiotemporal mismatch between charging demand and station capacity, leading to operational inefficiencies. This paper proposes a cooperative charging scheduling strategy based on a novel queuing model that integrates virtual charging piles and state variables to accurately estimate queuing time, overcoming the limitations of conventional methods. A bi-level optimization model is established to coordinate grid load balancing and station-level queue management. An adaptive large-neighborhood search algorithm combining heuristic rules with mathematical solving is developed for efficient solution. Numerical experiments demonstrate that the proposed strategy outperforms existing approaches by significantly increasing fulfilled charging demand and reducing queuing times with only minimal travel distance increase. Analysis further reveals a computational performance trade-off related to scheduling frequency, providing critical insights for practical implementation. Full article

The growing integration of plug-in electric vehicles (PEVs) into microgrids presents both challenges and opportunities, particularly through vehicle-to-grid (V2G) services. This paper proposes a dynamic horizon optimization (DHO) framework with adaptive pricing for real-time scheduling of PEVs in a renewable-powered microgrid. The system integrates solar and wind energy, V2G capabilities, and time-of-use (ToU) tariffs. The DHO strategy dynamically adjusts control horizons based on forecasted load, generation, and electricity prices, while considering battery health. A PEV-specific pricing scheme couples ToU tariffs with system marginal prices. Case studies on a microgrid with four heterogeneous EV charging stations show that the proposed method reduces peak load by 23.5%, lowers charging cost by 12.6%, and increases average final SoC by 12.5%. Additionally, it achieves a 6.2% reduction in carbon emissions and enables V2G revenue while considering battery longevity. Full article

Grid-scale lithium-ion storage must deliver fast, reliable thermal control during dynamic grid services, yet high-fidelity thermal models are too slow for real-time use and inefficient cooling inflates energy and safety costs. This study develops and validates a reduced-order thermal modeling framework for grid-scale lithium-ion battery energy storage, targeting real-time thermal management. The framework uses proper orthogonal decomposition to capture dominant thermal dynamics across frequency regulation, peak shaving, and fast charging. Across scenarios, it delivers 15.2–22.3× computational speedups versus a detailed model while maintaining RMS temperature errors of 7.8 °C (frequency regulation), 34.4 °C (peak shaving), and 23.3 °C (fast charging). Spatial analysis identifies inter-zone temperature gradients up to 1.0 °C under severe loading, motivating targeted cooling strategies. Cooling energy scales nonlinearly with load intensity, from 5.44 kWh in frequency regulation to over 300 kWh in peak shaving, with cooling efficiencies spanning 17.27% to 8.94%. The reduced-order model achieves sub-0.1 s computational solve time per control cycle, suggesting feasibility for real-time integration into industrial battery-management systems under the tested simulation settings. Collectively, the results show that reduced-order thermal models can balance accuracy and computational efficiency for several grid services in the simulated scenarios, while high-power operation benefits from scenario-specific calibration and controller tuning. Practically, the benchmarks and workflow support decisions on predictive cooling schedules, temperature limits, and service prioritization to minimize parasitic energy. Full article

While battery-powered propulsion represents a promising pathway for inland waterway freight, its widespread adoption is hindered by range anxiety and high investment costs. Strategic energy replenishment has emerged as a critical and cost-effective solution to extend voyage endurance and mitigate these barriers. This paper introduces a novel approach to optimize energy replenishment strategies for inland electric ships that considers the possibility of adopting multiple technologies (charging and battery swapping) and partial replenishment. The proposed approach not only identifies optimal replenishment ports but also determines the technology to employ and the corresponding amount of energy to replenish for each operation, aimed at minimizing total replenishment costs. This problem is formulated as a mixed-integer linear programming model. A case study of a 700-TEU electric container ship operating on two routes along the Yangtze River validates the effectiveness of the proposed approach. The methodology demonstrates superior performance over existing approaches by significantly reducing replenishment costs and improving solution feasibility, particularly in scenarios with tight schedules and limited technology availability. Furthermore, a sensitivity analysis examines the impacts of key parameters, offering valuable strategic insights for industry stakeholders. Full article

The rapid growth of electric vehicle (EV) charging demand poses significant challenges to distribution networks (DNs), particularly during public holidays when concentrated peaks occur near scenic areas and urban transport hubs. These sudden surges can strain transformer capacity and compromise supply reliability. Fixed soft open points (SOPs) are costly and underutilized, limiting their effectiveness in DNs with multiple transformers and asynchronous peak loads. To address this, from the perspective of power supply companies, this study proposes a mobile soft open point (MSOP)-based approach to enhance the hosting capacity of EV charging stations. The method pre-installs a limited number of fast-access interfaces (FAIs) at candidate transformers and integrates a semi-rolling horizon optimization framework to gradually expand interface availability while scheduling MSOPs daily. An automatic peak period identification algorithm ensures optimization focuses on critical load periods. Case studies on a multi-feeder distribution system coupled with a realistic traffic network demonstrate that the proposed method effectively balances heterogeneous peak loads, matches limited interfaces with MSOPs, and enhances system-level hosting capacity. Compared with fixed SOP deployment, the strategy improves hosting capacity during peak periods while reducing construction costs. The results indicate that MSOPs provide a practical, flexible, and economically efficient solution for power supply companies to manage concentrated holiday charging surges in DNs. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article