Optimal Stochastic Scheduling of Plug-in Electric Vehicles as Mobile Energy Storage Systems for Resilience Enhancement of Multi-Agent Multi-Energy Networked Microgrids

Ahmadi, Seyed Ehsan, Marzband, Mousa, Ikpehai, Augustine and Abusorrah, Abdullah (2022) Optimal Stochastic Scheduling of Plug-in Electric Vehicles as Mobile Energy Storage Systems for Resilience Enhancement of Multi-Agent Multi-Energy Networked Microgrids. Journal of Energy Storage, 55 (Part B). p. 105566. ISSN 2352-152X

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Abstract

This paper presents an optimal scheduling of plug-in electric vehicles (PEVs) as mobile power sources for enhancing the resilience of multi-agent systems (MAS) with networked multi-energy microgrids (MEMGs). In each MEMG, suppliers, storage, and consumers of energy carriers of power, heat, and hydrogen are taken into account under the uncertainties of intermittent nature of renewable units, power/heat demands, and parking time of PEVs. In the case of contingencies, the proposed algorithm supplies energy to the on-fault MEMGs from normal-operated grid-connected MEMGs, using mobile PEVs. The procedure of selecting PEVs to supply energy to the on-fault MEMGs is performed in three stages. Initially, both on-fault and normaloperated MEMGs inform the central energy management system (EMS) about the amount of required energy and the amount of available energy from existing PEVs. Further, central EMS prioritizes the MEMGs among networked MEMGs to supply the energy support to the on-fault islanded MEMG. Lastly, the chosen MEMGs select their available efficient PEVs to supply energy to the on-fault islanded MEMG. Considering two diverse faulty case studies, the proposed technique is investigated in a MAS with four networked MEMGs. Simulated results demonstrate that the proposed algorithm enhances the resilience of MEMGs (over 25) even without a physical connection between the MEMGs.

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