Day-ahead optimization for smart energy management of multi-microgrid using a stochastic-robust model

The growing popularity of hydrogen fuel cell vehicles (HFCVs) and electric vehicles (EVs) has led to the widespread adoption of multi-energy microgrids (MEMGs), which seamlessly integrate hydrogen refueling station systems (HRSS) and electric vehicle parking lots (EVPLs). Power-to-hydrogen (P2H2) technology has been instrumental in enabling this transition. To further enhance the efficiency and reliability of MEMG systems, a network structure known as a multi-microgrid (MMG) has emerged. This research introduces a robust decentralized framework for energy management, with a focus on optimizing day-ahead planning for interconnected microgrids (MGs). The MMG configuration includes hydrogen provider companies (HPCs) and electricity markets, integrating cutting-edge technologies such as power-to-heat (P2H) units, P2H2 units, combined heat and power (CHP) units, and various energy storage systems (ESSs). Maintaining data privacy is a key concern for interconnected MGs operating within an MMG. To address this, the study proposes the use of a search and rescue optimization (SARO) algorithm, which strengthens local and global search capabilities while safeguarding data privacy. Furthermore, the MMG integrates a demand response program (DRP) that efficiently manages electricity consumption through price signals, leading to greater cost-effectiveness and energy efficiency. Simulation results confirm the effectiveness of the proposed decentralized model in meeting diverse energy requirements, even in challenging scenarios with fluctuating electricity market prices.