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.
The inherent intermittency of renewable energy sources and the mismatch between generation and community load demand pose significant challenges to the reliability of microgrids. To address these issues, this paper proposes a robust multi-objective optimization framework for the capacity sizing of a Hybrid Renewable Energy System (HRES) integrating Wind, Photovoltaic (PV), Battery Energy Storage System (BESS), and a Power-to-Heat coupling subsystem. A rule-based Energy Management Strategy (EMS) is developed to coordinate the electrical and thermal power flows, prioritizing renewable utilization and storage efficiency. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is employed to solve the sizing problem, simultaneously minimizing the Net Present Cost (NPC) and the Loss of Energy Ratio (LER). A case study based on real-world hourly meteorological data and a large-scale community load profile demonstrates the proposed approach. The simulation results demonstrate that the MOEA/D algorithm effectively identifies a set of Pareto-optimal solutions, revealing the trade-off between economic investment and system reliability. The optimal compromise solution suggests a PV-dominated configuration (924 MW) supported by a hybrid storage system comprising 2478 MWh of battery and 585 MWh of thermal storage. This configuration achieves a high reliability with an LER of 1.92% and a lifecycle cost of $2.40 billion, proving the techno-economic feasibility of the proposed electric-thermal coupling scheme for community energy supply.
Pu-Zhuang Liu, Nor Azwan Bin Mohamed Kamari· IOP Conference Series: Earth...· 0 citations
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.
To solve the problems of low renewable energy utilization and high grid dependence in multi-park hybrid microgrids (MPHMs), this paper aims to develop a centralized optimization framework for the wind-solar-storage resource configuration to realize the coordinated operation of new energy suppliers, integrated energy service providers and end-users. An multi-objective economic dispatch model considering source-storage-load-grid coordination, time-of-use pricing and demand response incentive, as well as technical constraints of energy storage is established. An improved hybrid intelligent algorithm combining NSGA-III with enhanced solution selection based on clustering is designed to efficiently solve the optimization problem and obtain practical configurations. The proposed method is applied to a system including industrial, commercial and residential parks with different loads and renewable generation capacity. Simulation results indicate that the optimal energy storage configuration can eliminate the renewable curtailment in all parks and reduce both daily electricity purchasing cost from main grid and total daily power supply cost. These results demonstrate that coordinated planning can improve both sustainability and economy of MPHMs. Sensitivity analyses validate that the solution is robust to the variations of storage cost, incentive policy and renewable generation capacity. The proposed method performs better than non-storage scenario even in unfavorable cost conditions. The proposed scalable and practical method provides a valuable reference for optimizing multi-area microgrids and integrated energy systems.
Tsz-Sen Zhu· Mathematical Modeling and Al...· 0 citations
This paper proposes a day-ahead scheduling framework to analyze and optimize the impact of the coordinated active and reactive power management of wind turbines (WTs) and battery energy storage systems (BESSs) on the energy losses and CO2 emissions of AC microgrids (MGs). Within this framework, the BESS plays a central role by absorbing surplus renewable generation, mitigating curtailment, supporting voltage regulation, and ensuring a stable and reliable dispatch over a 24-hour horizon. A population-based genetic algorithm (PGA) is proposed as the main solution methodology, while particle swarm optimization (PSO) and the multiverse optimizer (MVO) are employed as benchmark methods for comparison. To ensure a fair assessment, all optimization techniques are implemented under the same parallel processing scheme, using the same decision-variable encoding, feasibility correction procedure, and hourly sequential AC power-flow method. The objective is to minimize network energy losses and CO2 emissions under both grid-connected and islanded operating modes. The proposed methodology is validated on 33-node and 69-node MGs, both evaluated under variable demand and wind-generation scenarios to capture the uncertainty and temporal variability associated with renewable production and load behavior. In addition, the BESS model includes charging/discharging efficiency, self-discharge effects, and battery lifetime assessment under the proposed operating scenarios, allowing a more realistic representation of storage performance. The optimization methods are evaluated over 100 independent runs using the best solution, average solution, standard deviation, and computational time as performance indicators. The results show that the proposed PGA provides the most robust and repeatable performance, while also highlighting the operational contribution of the BESS, reducing renewable curtailment, and guaranteeing compliance with all technical constraints, under deterministic baseline operation and under uncertain time-varying operating conditions in both test systems.
D. Sanín-Villa, Héctor Pinto Vega, Carlos R. Baier et al.· PLoS ONE· 0 citations
This study presents the techno-economic optimization of a hybrid backup system integrated within an off-grid microgrid framework with electric vehicle (EV) grid-interaction capability. A real-world case study from a remote region in Egypt is used to evaluate system performance under realistic operating conditions. The optimization problem is formulated to minimize the net present cost (NPC) while ensuring system reliability using a penalty-based loss of power supply probability (LPSP). The system integrates photovoltaic (PV), wind turbines (WT), battery energy storage systems (BESS), hydrogen energy storage systems (HESS), and EVs. The novelty of this work lies in the development of a coordinated multi-storage energy management strategy that integrates BESS, HESS, and constrained EV participation within a unified optimization framework. The results show that the BESS-only configuration achieves the lowest cost (NPC ≈ $20.33 billion), while the PV/WT/BESS/HESS configuration results in the highest cost (NPC ≈ $25.48 billion). The proposed PV/WT/BESS/HESS/EV configuration provides a balanced solution with an NPC of approximately $22.79 billion while maintaining near-zero LPSP. EV integration enhances system flexibility, reducing the required BESS capacity by 44.8% relative to the HESS-only configuration, while also lowering reliance on hydrogen-based long-term storage, thereby mitigating the high capital costs associated with extended-duration storage components. These findings demonstrate that coordinated multi-storage management significantly improves the techno-economic viability and operational resilience of large-scale off-grid microgrids in remote regions.
Noha Nabil Abd-Elhady, Mohammed Fathy Ahmed, Salama Abu-Zaid et al.· Scientific Reports· 0 citations
This paper presents a comparative study between hybrid particle swarm optimization (PSO) and genetic algorithm (GA) for energy management in residential microgrids equipped with photovoltaic generation, stationary battery storage, and bidirectional electric vehicles (V2G). The system comprises 40 apartments, 1000 m² solar panels, 1 MWh battery storage, and 15 electric vehicles with V2G capability. A multi-objective optimization framework minimizes daily operational costs while satisfying mobility requirements, state-of-charge constraints, and battery aging considerations. Simulation results demonstrate that hybrid PSO significantly outperforms GA, achieving a net daily profit of 279 C (compared to 100 C cost for GA) through strategic energy arbitrage and massive grid sales (2232 kWh/day vs 3.7 kWh/day for GA). The PSO-based approach achieves 28% energy autonomy while generating substantial revenue from feed-in tariffs. The methodology provides a scalable framework for real-world V2G-integrated microgrids, with ongoing experimental validation at the ESTACA V2G testbed.
Unknown authors· International Journal of App...· 0 citations
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