Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies

[Display omitted] •Proposing a novel structure for multi-microgrids based on power, heat, and hydrogen.•Considering Power-to X technologies such as power-to-hydrogen and power-to-heat.•Applying a decentralized approach for optimal energy management of multi-microgrids.•Using a robust optimization method to deal with uncertainties in multi-microgrids. Nowadays, the enormous rising demand for hydrogen fuel cell vehicles (HFCVs) and electric vehicles (EVs) in the transportation sector has a significant contribution in growing of multi-energy microgrids (MEMGs) accompanied by hydrogen refueling stations (HRSs), EV parking lots (EVPLs) and power-to-hydrogen (P2H2) technologies. The competency to enhance the efficiency and the reliability in MEMG systems leads to form a networked structure called multi-microgrids (MMG). In this paper, a robust decentralized energy management framework is proposed for the optimal day-ahead scheduling of a set of interconnected hydrogen, heat, and power-based microgrids (MGs) in the presence of HRSs and EVPLs. The proposed MMG is a collaborative structure of hydrogen provider company (HPC) and electricity markets with novel technologies such as power-to-heat (P2H), power-to-hydrogen (P2H2), combined heat and power (CHP) units, multiple energy storages and demand response to improve the system flexibility in meeting multi-energy demands. The necessity of data privacy preservation methods for MGs has emerged when the interconnected MGs are operated as an MMG to satisfy different energy demands with minimum cost. Therefore, an iterative-based algorithm called the alternating direction method of multipliers (ADMM) is utilized to decompose the structure of the scheduling problem to minimize the total daily cost of the MMG system while protecting the data privacy of MEMGs. In the proposed structure, the robust optimization model is able to manage the uncertainty by considering the worst-case scenario for electricity price in different conservativeness levels as MEMGs are sensitive to electricity price fluctuations. Finally, the simulation results represent the effectiveness of the proposed decentralized model under the worst case of electricity market price to meet the demand for electricity, heat, and hydrogen.