Economic Load Dispatch of A Multi-Area Power System Using Multi-Agent Distributed Optimization Based on Genetic Algorithm

Abstract

This study presents a new methodology for distributed multi-agent optimization utilizing a genetic algorithm to address Multi-Area Economic Dispatch Problem (MAEDP) in a power system. While numerous studies have been conducted on various optimization methods for distributed multi-agent systems, this paper proposes a model for solving the optimal economic dispatch equations in different areas of the power system in a distributed and coordinated manner. In this model, each area is represented by an agent responsible for coordinating data exchange with other areas and solving the generation dispatch equations within its own area. The coordination model between agents and areas is described in the form of an algorithm, whereby the exchanged data values converge after several iterations, and the final solution to the problem is obtained from the perspective of each agent. The objective of each agent in each area is to minimize generation costs and meet its own area's load demand while maintaining voltage profiles. Each agent sets the power generation values of resources in each area using the genetic algorithm rules and then solves the distributed power flow equations using the proposed method. Upon achieving convergence, each agent evaluates all operational constraints within its designated region, calculates the associated generation cost, and shares the cost value to other agents, thereby facilitating the computation of the total cost for each agent. This process continues until the best possible solution is found. The results of implementing the proposed model and algorithm on several different test networks of power systems demonstrate the capability and effectiveness of the method in decomposing the optimal economic dispatch problem into smaller sub-problems and then finding the final optimal solution through simultaneous solving with agent consensus in coordinated steps.