A Hybrid Crow Search Algorithm and Coalitional Game Theory Approach for Optimal Plug-in Electric Vehicles’ Integration in Networked Microgrids

Nowadays, owing to the advancement of technology, plug-in electric vehicles (PEVs) enable microgrids (MGs) to balance local generation and demand through vehicle-to-grid technology and increase MG reliability and flexibility. In this article, PEVs are considered as an energy storage system for MGs with the aim of improving the overall network operation and MGs’ individual utility rather than the efficiency and reliability of each MG. We propose a hybrid optimization approach that combines enhanced crow search algorithm (ECSA) with coalitional game theory to solve two key challenges: optimal PEV fleet scheduling and efficient power exchange between sets (MG+PEV). The algorithm operates in three stages: 1) ECSA optimizes PEV charging/discharging schedules using a novel dual-guidance position update mechanism that simultaneously moves toward promising solutions and away from poor ones, significantly improving convergence speed and solution quality compared to standard CSA through more balanced exploration-exploitation dynamics, 2) a cooperative game theory-based framework forms optimal trading coalitions among sets through auction-based power exchange, working synergistically with ECSA’s solutions to maximize system-wide benefits, and 3) the Shapley value method ensures fair profit distribution within coalitions. Comparative analysis demonstrates the algorithm’s superior performance against existing methods in reducing peak demand and operational costs while maintaining battery longevity and system stability.