Enhanced Microgrid Energy Coordination Using Hybrid Particle Swarm Optimization with Bidirectional Electric Vehicle Integration

ESTACA LAB, ESTACA Paris-Saclay, Paris, France

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Abstract

The increasing penetration of renewable energy sources in residential distribution networks introduces significant operational challenges stemming from their inherent intermittency and temporal mismatch with consumption patterns. This paper presents an advanced optimization methodology that synergistically combines Particle Swarm Optimization with gradient-based techniques for intelligent energy resource management in residential microgrids. The proposed framework integrates photovoltaic generation, stationary battery storage, and electric vehicles equipped with bidirectional power transfer capabilities, addressing the complex multi-dimensional coordination problem while rigorously accounting for real-world constraints including stochastic mobility requirements, battery degradation mechanisms, time-varying electricity tariffs, and grid interaction limits. The computational approach employs a hybrid metaheuristic augmented with Sequential Quadratic Programming local search to solve the 24-hour scheduling problem encompassing 120 optimization variables. Key technical contributions include comprehensive probabilistic modeling of electric vehicle availability incorporating empirical travel patterns, multicriteria optimization balancing economic objectives, operational performance metrics, and battery health preservation through degradation cost functions, and sophisticated adaptive constraint handling mechanisms ensuring practical feasibility while maintaining solution quality. Performance evaluation conducted on a representative 20-household residential configuration demonstrates substantial improvements across multiple dimensions: 42.7% reduction in grid power purchases, 34.2% decrease in daily energy expenditures, and 78.4% utilization of locally generated photovoltaic energy. The framework maintains vehicle operational readiness with an average state-of-charge of 82.4% while delivering valuable ancillary grid support services through bidirectional power flow. Comparative analysis against conventional PSO, genetic algorithms, and MILP benchmarks reveals superior performance in solution quality (0.9% optimality gap), computational efficiency (124-second convergence), and constraint satisfaction. The hybrid methodology exhibits robust convergence characteristics and effective handling of non-linear constraints, validating its practical applicability for real-world microgrid implementations and contributing to the advancement of sustainable energy management systems.