Optimization of Hybrid Dc and Ac Microgrid Configuration Using Chaos Artificial Bee Colony Algorithm

The increasing demand for reliable and resilient power supply, seamless renewable energy integration, cost reduction, and electrification of remote areas has led to the growing adoption of hybrid DC and AC microgrids. However, optimizing their configuration remains a critical challenge due to the uncertainties in renewable energy sources and varying load conditions. This paper proposes an optimization approach for determining the optimal configuration of a hybrid DC and AC microgrid, including the optimal number of solar photovoltaic (PV) panels, wind turbines, and total battery capacity, with the objective of minimizing the cost of energy (COE). To achieve this, a chaos artificial bee colony (chaos ABC) algorithm is proposed, leveraging chaotic maps to enhance global search capability and convergence speed. The proposed approach is compared against standard ABC, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm to validate its effectiveness. Numerical results demonstrate that the chaos ABC algorithm achieves better COE and total cost while improving convergence speed compared to other optimization techniques. The results confirm the superiority of the proposed approach in enhancing microgrid cost-efficiency and performance.