A multimicrogrid energy management model implementing an evolutionary game‐theoretic approach

Microgrids (MGs) are widely increasing to manage unequal electrical load requirements based on the infrastructure. The goal of this article is to manage energy in a centralized controller multimicrogrid (MMG) system operated at islanded mode. Renewable energy fluctuations in MG due to weather conditions build oscillation in MG operation modes. To solve this, a three-stage energy management MMG system is proposed. The proposed system is composed of operating mode prediction by measuring the weather conditions. In islanded mode, energy management is incorporated using a two-round fuzzy-based speed (TRFS) algorithm followed by evolutionary game theory and status updating by Markov chain. The TRFS algorithm takes into account voltage, frequency, power factor, total harmonic distortion, and loss of produced power probability parameters. The parallel processing of the TRFS algorithm reduces processing time, then a Stackelberg game with a quasi-oppositional symbiotic organisms search approach is carried out for power exchange. Markov chain based future prediction of MG states ensures detection of MG operating mode along with weather changes. Simulations are developed in MATLAB Simulink, and their outcomes show better performance than previous work whose results are evaluated in terms of load and generator output at two modes, power generated at individual MG and exchanged power.