Hosseina Majid, Moghaddam Mahmoud Samiei, Hassannia Amir
Department of Electrical Engineering, Hakim Sabzevari University, Sabzevar, Iran.
Department of Electrical Engineering, Da.C., Islamic Azad University, Damghan, Iran.
Sci Rep. 2025 May 10;15(1):16297. doi: 10.1038/s41598-025-99974-x.
The increasing integration of distributed renewable energy sources (RES), energy storage systems (ESS), electric vehicle (EV) charging stations, and demand response (DR) mechanisms has significantly enhanced microgrid deployment. However, the operational complexity and vulnerability of islanded microgrids to disruptions, especially during renewable energy fluctuations, pose critical challenges. Existing approaches primarily focus on minimizing operational costs or emissions but fail to simultaneously address load curtailment, voltage stability, and resilience under uncertain conditions. In this paper, we propose a novel resilience-oriented energy and load management framework for island microgrids, integrating a multi-objective optimization function that explicitly minimizes load curtailment, energy losses, voltage deviations, emissions, and energy procurement costs while maximizing the utilization of renewable energy sources. Unlike conventional models that separately optimize demand-side management (DSM), distributed generation (DG), EV charging/discharging, and ESS scheduling, our approach incorporates a coordinated control strategy that jointly optimizes these elements alongside reactive power compensation devices such as capacitors and shunt reactors. To effectively solve this high-dimensional, nonlinear problem, we employ the Multi-objective Moth Flame Algorithm (MOMFA), an enhanced metaheuristic evolutionary algorithm designed to handle complex trade-offs between cost, reliability, and resilience. The superiority of MOMFA over conventional optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) is validated through simulations on a realistic 33-node microgrid under various renewable energy outage scenarios. The results demonstrate that the proposed framework achieves a 60% reduction in voltage deviation, an 81% decrease in energy losses, and an 86% reduction in CO₂ emissions, while ensuring zero load curtailment, even under severe outage conditions. The proposed method offers a scalable, real-time implementable solution for microgrid operators seeking to enhance resilience against renewable energy intermittency and optimize energy utilization. This work significantly advances state-of-the-art microgrid energy management by providing a holistic, multi-objective, and resilience-driven optimization strategy.
分布式可再生能源（RES）、储能系统（ESS）、电动汽车（EV）充电站和需求响应（DR）机制的日益融合，显著促进了微电网的部署。然而，孤岛微电网在运行方面的复杂性以及对干扰的脆弱性，尤其是在可再生能源波动期间，构成了严峻挑战。现有方法主要侧重于将运营成本或排放量降至最低，但未能同时解决在不确定条件下的负荷削减、电压稳定性和恢复力问题。在本文中，我们为孤岛微电网提出了一种新颖的面向恢复力的能源和负荷管理框架，集成了一个多目标优化函数，该函数明确将负荷削减、能量损耗、电压偏差、排放量和能源采购成本降至最低，同时最大限度地提高可再生能源的利用率。与分别优化需求侧管理（DSM）、分布式发电（DG）、电动汽车充电/放电和储能系统调度的传统模型不同，我们的方法纳入了一种协调控制策略，该策略与诸如电容器和并联电抗器等无功功率补偿装置一起联合优化这些要素。为了有效解决这个高维非线性问题，我们采用了多目标蛾火焰算法（MOMFA），这是一种增强的元启发式进化算法，旨在处理成本、可靠性和恢复力之间的复杂权衡。通过在一个实际的33节点微电网在各种可再生能源停电场景下的仿真，验证了MOMFA相对于遗传算法（GA）、粒子群优化（PSO）和灰狼优化（GWO）等传统优化技术的优越性。结果表明，所提出的框架即使在严重停电条件下，也能将电压偏差降低60%，能量损耗减少81%，二氧化碳排放量减少86%，同时确保零负荷削减。所提出的方法为寻求增强对可再生能源间歇性的恢复力并优化能源利用的微电网运营商提供了一种可扩展、实时可实施的解决方案。这项工作通过提供一种全面、多目标且由恢复力驱动的优化策略，显著推进了微电网能源管理的技术水平。
