Hybrid machine learning based energy policy and management in the renewable-based microgrids considering hybrid electric vehicle charging demand

Abstract

In this paper, a secured energy management architecture for both islanded and grid-connected operation modes of renewable hybrid AC-DC microgrids considering various renewable energy sources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) is proposed. In this architecture, whale optimization algorithm (WOA) is employed to minimize the operation cost of the grid and also an intrusion detection system (IDS) based on the sequential hypothesis testing (SHT) approach is introduced to detect identity-based cyber-attacks (i.e. Sybil attack, masquerading attack) on the wireless-based advanced metering infrastructures (AMI). The proposed IDS makes use of the received signal strength (RSS) value to distinguish different signal sources and detect cyber-attacks. The feasibility and performance of the proposed architecture are tested on a practical hybrid microgrid, which is constructed based on the IEEE 33-bus test system. the results are provided for both islanded and grid-connected operation modes.