Metaheuristic Optimization Techniques for Aggregator Profit Maximization and System Optimization for a Modified IEEE 14-Bus System with DER Integration in Electricity Market

Abstract

The integration of distributed energy resources (DERs) in modern power systems necessitates advanced optimization strategies to ensure both economic viability and technical reliability. This paper presents a comprehensive comparison of four metaheuristic algorithms: Rank-based Compact Evolutionary Design Using Mutual Dependency Algorithm (RCEDUMDA), Modified RCEDUMDA, Teaching-Learning-Based Optimization (TLBO), and Particle Swarm Optimization (PSO). The algorithms are implemented on a modified IEEE 14-bus system integrated with DERs including solar photovoltaic (PV), wind turbines, and energy storage systems (ESS). The primary objective is to maximize aggregator profit while minimizing system losses, voltage deviations, and optimizing DER and ESS utilization. Evaluation metrics include convergence behavior, computational efficiency, system stability, runtime, and resource utilization. Results show that Modified RCEDUMDA achieves the highest aggregator profit with enhanced stability and efficiency, making it a robust choice for smart grid optimization.