ENHANCING MICROGRID OPERATION BY INCORPORATING DEMAND RESPONSE USING EVOLUTIONARY OPTIMIZATION ALGORITHMS

Abstract

With the rise of distributed energy resources, microgrids (MGs) offer a resilient solution for local energy supply and smart management. This thesis proposes an enhanced MG operation strategy by integrating demand response (DR) programs with evolutionary optimization algorithms. Three methods, Imperialist Competitive Algorithm (ICA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO)—are applied individually and in hybrid forms. The ICA-GA hybrid is tested on the IEEE 33-bus system, while ICA-PSO is applied to the IEEE 37-bus network. Simulations are conducted in MATLAB. Results show that hybrid approaches outperform conventional methods in voltage regulation, power loss reduction, and system efficiency. This work demonstrates the potential of combining DR strategies with evolutionary algorithms to optimize MG performance and support reliable, cost-effective power distribution.