The transition to decentralized renewable energy systems has highlighted the role of AC microgrids and battery energy storage systems in achieving operational efficiency and sustainability. This study proposes an improved energy management system for AC MGs based on a tuned Parallel Population-Based Genetic Algorithm for the optimal operation of batteries under variable generation and demand. The optimization framework minimizes power losses, emissions, and economic costs through a master–slave strategy, employing hourly power flow via successive approximations for technical evaluation. A comprehensive assessment is carried out under both grid-connected and islanded operation modes using a common test bed, centered on a flexible slack bus capable of adapting to either mode. Comparative analyses against Particle Swarm Optimization and the Vortex Search Algorithm demonstrate the superior accuracy, stability, and computational efficiency of the proposed methodology. In grid-connected mode, the Parallel Population-Based Genetic Algorithm achieves average reductions of 1.421% in operational cost, 4.383% in power losses, and 0.183% in CO2 emissions, while maintaining standard deviations below 0.02%. In islanded mode, it attains reductions of 0.131%, 4.469%, and 0.184%, respectively. The improvement in cost relative to the benchmark exact methods is 0.00158%. Simulations on a simplified 33-node AC MG with actual demand and generation profiles confirm significant improvements across all performance metrics compared to previous research works.
Figueroa-Saavedra et al. (Sat,) studied this question.
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