ABSTRACT In this article, a two‐layer energy management system (EMS) is proposed for multi‐microgrid power systems, integrating a local EMS (LEMS) and a centralized EMS (CEMS) to coordinate microgrids (MGs) while reducing computational complexity. Each microgrid optimizes operational costs under uncertainty using probabilistic scenario‐based modelling, considering renewable generation variability, electric vehicle aggregators, controlled load shedding and loss of power supply probability (LPSP) constraints. The CEMS then performs overall optimization, including inter‐microgrid and grid power exchanges. The hierarchical coordination advantage lies in reducing decision variable dimensionality at the central level, enabling scalable and practically deployable energy management in real‐world multi‐microgrid systems. A multi‐objective hybrid Big Bang–Big Crunch algorithm (MO‐hBB–BC) solves the problem efficiently. Benchmarking with ZDT functions shows superior convergence, diversity and robustness versus NSGA‐II, multi‐objective grey wolf optimizer (MOGWO) and multi‐objective whale optimization algorithm (MOWOA) (average rank 1.2). Numerical results indicate stage one costs of €26,265 (6.7% reduction), emissions 141.36 kg and LPSP <2%; stage two with inter‐microgrid exchange reduces costs to €25,456, cuts emissions by 15.4%, and maintains reliability. Sensitivity analysis shows uncertainties can raise costs by 15.61%, whereas optimal load shedding achieves a 12.86% cost reduction. All the simulations were implemented in MATLAB R2023b environment on a workstation with an Intel Core i5 CPU (3.2 GHz), 8 GB DDR4 RAM and Windows 10 Pro operating system to ensure reproducibility and computational transparency.
Gorji et al. (Thu,) studied this question.
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