Comparative Analysis of Continuous and Hybrid Binary-Continuous Particle Swarm Optimization for Optimal Economic Operation of a Microgrid

Microgrids (MGs) are a key solution to future power generation systems to produce smarter, cleaner, and more efficient electricity. They can also enhance the reliability, resilience, and security of power systems. The optimal economic operation for MGs typically relies on an optimization framework that can be attractively modeled using both continuous and binary variables. The particle swarm optimization (PSO) algorithm is a perfect candidate for this purpose since it can efficiently perform an informed search for finding the optimal economic operation using continuous and binary versions. In this regard, this paper investigates continuous and binary versions of PSO algorithms for the optimal economic operation of an MG. The optimization model is carefully developed based on the operation of the conventional generator. We first design an ordinary optimization model to optimize continuous operations using continuous PSO (CPSO). Then, we propose a hybrid binary-continuous optimization model to define the optimal ON/OFF operations using hybrid binary-continuous PSO (HBPSO). In addition, offline and real-time optimizations are proposed to evaluate the effectiveness of the mentioned algorithms. The simulation outcomes clearly indicate that both the CPSO and HBPSO algorithms can achieve the optimal operation of the MG in real time, resulting in a cost reduction of up to 7% compared to the offline results.