Aug 2026· Sustainability· Vol 18, pp. 7823· 0 citations· 34 references
Abstract
With the advancement of net-zero targets and the large-scale deployment of distributed photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs) in residential sectors, home energy management systems (HEMS) have become central to improving end-use energy efficiency. However, reliance on synthetic data, simplified and homogeneous load modeling, and lack of coordination between dynamic pricing mechanisms and multi-device scheduling, make them practically inefficient. Furthermore, most studies address only unilateral user-side optimization while neglecting the distribution network operational constraints and omitting rigorous anti-arbitrage mechanisms to preclude speculative user behavior. To address these gaps, this paper proposes a data-driven coordinated scheduling framework for residential PV-BESS and multi-device systems formulated within bi-level game-theoretic architecture. The upper-level employs particle swarm optimization (PSO) to determine dynamic additional price signals for peak shaving and distribution network security, with explicit constraints on distribution transformer capacity, node voltage deviation, and load ramp rate adapted to three-user scenarios. The lower-level formulates a mixed-integer quadratic programming (MIQP) model to achieve multi-objective optimization of user electricity cost, thermal comfort, device usage preference, battery cycle degradation, and end-of-cycle energy balance. Simulation results for representative summer and winter days indicate that the proposed framework reduces user-side electricity cost by around 30%, elevates PV self-consumption rate to over 70%, and achieves about 20% peak load reduction with around 15% peak-valley difference narrowing on the grid side. All distribution network security constraints and anti-arbitrage rules are strictly satisfied. The framework effectively reconciles the objectives of both residential users and the distribution grid.
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.
This paper proposes a dual-layer coordinated framework that combines day-ahead battery energy storage system (BESS) scheduling with real-time Volt–VAr Control (VVC) for active distribution networks. The optimization minimizes distribution system technical losses while satisfying operational constraints related to voltage regulation, equipment loading, battery operation, voltage regulator (VR) tap commutation, and smart inverter (SI) operating limits defined by IEEE Std 1547-2018. The planning stage determines the optimal charging and discharging schedule of multiple BESS units over a 24-hour horizon, whereas the operational stage performs real-time VVC through the coordinated control of VRs, capacitor banks (CBs), and SI associated with distributed photovoltaic (DPV) and BESS units. The methodology was implemented in a Python–OpenDSS co-simulation environment and validated on a modified IEEE 34-bus feeder using real SCADA load measurements and solar irradiance data through daily and seasonal operating scenarios under both planning and actual operating conditions. Performance was also compared with conventional local VVC strategies. Results demonstrate that the proposed framework maintains voltages within prescribed limits, eliminates or substantially mitigates reverse power flow, reduces feeder peak demand, and significantly decreases network energy losses. Overall, the proposed strategy significantly enhances the operation of active distribution networks with high renewable energy penetration.
R. R. Biazzi, D. Bernardon, Maurício Sperandio· IEEE Access· 0 citations
This study proposes a comprehensive multi-objective optimization framework for demand-side management of a hybrid microgrid comprising photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS), a fuel cell (FC), and a grid connection. The framework simultaneously minimizes the Peak-to-Average Ratio (PAR) and total operating cost through dynamic load scheduling under real-time pricing (RTP). A renewable energy utilization strategy prioritizes clean energy dispatch, while an intelligent battery management scheme optimizes charging and discharging decisions according to renewable generation availability, load demand, and electricity price signals. To address the limitations of conventional weighted-sum optimization approaches, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a diverse and well-distributed Pareto front without requiring predefined objective weights. The proposed framework is evaluated under three energy system configurations: (i) grid-only operation, (ii) grid-integrated renewable energy and battery storage, and (iii) grid-integrated renewable energy, battery storage, and fuel-cell support. The results demonstrate that hybrid renewable energy configurations significantly improve both economic and operational performance compared with conventional grid-dependent operation. The proposed framework generated multiple Pareto-optimal operating strategies with different trade-offs between operating cost and PAR. The minimum-cost solution achieved an operating cost of 131.73 Cents, while a representative compromise solution achieved 155.98 Cents with improved demand-side management performance. Comparative evaluation against NSGA-II, MOPSO, SPEA2, and the Weighted Sum Method reveals that NSGA-III consistently achieves superior Pareto-front quality, convergence characteristics, solution diversity, and robustness across 30 independent trials. The findings demonstrate the effectiveness of NSGA-III for multi-objective energy management and provide a scalable optimization framework for enhancing the economic efficiency, operational flexibility, and sustainability of future smart microgrid systems.
Mohd Bilal, Arshad Mohammad, Imdadullah et al.· Scientific Reports· 1 citation
: Large-scale integration of customer-side flexible resources and distributed resources can aggravate line congestion and voltage violations in active distribution networks, particularly under power supply guarantee scenarios. This paper develops a bi-level congestion management method that coordinates heterogeneous flexible resources through a Stackelberg game framework. Distributed energy storage, electric vehicles, interruptible loads, and time-shiftable loads are scheduled, and vehicle-to-grid capability is explicitly incorporated to enhance operational flexibility during critical supply periods. The model captures the interaction between the load aggregator (LA) and the distribution system operator (DSO): the LA optimizes the dispatch of aggregated flexible resources in response to price signals, while the DSO seeks to maximize social welfare subject to network security constraints. To solve the nested bi-level problem, an improved grey wolf optimizer (IGWO) with Tent chaotic initialization and nonlinear convergence control is employed. Simulations on a modified IEEE 33-bus system show that the proposed method can relieve line overloading, keep nodal voltages within allowable limits, smooth net-load fluctuations, and improve peak-shaving and valley-filling performance, thereby reducing social welfare losses. The results indicate that the method provides practical support for the secure and economic operation of active distribution networks and facilitates the effective integration of renewable generation and distributed storage.
C. Yuan, Zhu Liang, Ke Xu et al.· Energy Engineering· 0 citations
The electrification of residential demand through electric vehicles (EVs), heat pumps (HPs), photovoltaic (PV) systems, and battery energy storage systems (BESSs) creates new congestion challenges in low-voltage (LV) grids. This study evaluates a transparent, deterministic, and real-time-capable rule-based energy management system (EMS) for curative thermal congestion management within a §14a EnWG-oriented setting. The EMS is implemented in MATLAB/Simulink and tested on a representative four-feeder LV network supplying 56 households. Congestion is detected from maximum phase root-mean-square currents using conservative transformer and feeder thresholds. After a threshold is reached, the EMS first activates available BESS support and then applies simultaneous feeder-wide EV limitation, batched round-robin curtailment, or staged feeder-wide reduction toward 4.2 kW. In the uncontrolled case, the Feeder 3 and transformer overload areas are 62.84 Ah and 48.50 Ah, respectively. All controlled scenarios remove at least 98.70% of the feeder overload and eliminate the transformer overload within the reported numerical precision. The batched strategy requires 328.54 Ah of cumulative feeder-current reduction, compared with 977.34 Ah for simultaneous control and 816.00 Ah for staged control, and achieves the highest feeder-relief efficiency. It therefore provides a balanced trade-off between congestion relief and intervention intensity for the investigated deterministic case.
Sajjad Karami, P. Teimourzadeh Baboli, Christian Becker· Automation· 0 citations
Smart charging of electric-vehicle (EV) fleets must balance energy cost, transformer/feeder power limits, user satisfaction, and the operational value of on-site resources such as rooftop PV and battery energy storage systems (BESS). This work presents a scenario-based model predictive control (SB-SMPC) framework for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) coordination that minimizes the net operating cost while satisfying the system constraints. The controller explicitly models stochasticity in base load, PV generation, and electricity prices via sampled scenarios, and it also integrates demand charge cost for distribution grid services. EV service quality is guaranteed through departure energy targets, connection-time policies, and a minimum state of charge (SoC) floor. BESS dynamics, round-trip efficiency, terminal SoC targets, and battery degradation costs are included to capture battery storage economics. This study compares V2G operations with and without BESS across daily horizons. Results show that SB-SMPC systematically limits transformer import, curtails PV only when economically justified, and shifts charging to low-price periods while meeting EV energy requirements; enabling V2G further reduces net costs when energy export cost and demand charges are favorable. Comparative results (with/without BESS) reveal that BESS helps to reduce net electricity cost around 4% and grid peaks around 10% as compared to without BESS installation. Imposing high demand charges further cuts the peaks about 11%. The sensitivity analysis further confirmed the robustness of the proposed framework under varying load, PV, and price conditions.
Obaid Ur Rehman, Noman Ahmad, A. Uppal et al.· IEEE Access· 0 citations