Congestion Management Using Multi-Objective Glowworm Swarm Optimization Algorithm

Abstract

This paper proposes a novel congestion management (CM) approach within an optimal power flow (OPF) framework in the context of restructured power markets. The conventional OPF problem is modified to include a mechanism which enables the market players to compete and trade, and simultaneously ensuring the secured system operation. In this paper, both the centralized and bilateral dispatch strategies of system operator are considered. The proposed CM problem is formulated by considering the two objective functions. If the bidding prices in the market are not considered, then the first objective is to minimize to the total cost of generation. By considering the bidding prices in the market, the first objective function becomes the minimization of congestion rental in the system. The second objective function is to minimize the total transmission losses in the system. The proposed multi-objective based CM problem has been solved using the multi-objective glowworm swarm optimization (MO-GSO) algorithm. The standard IEEE 30 bus and IEEE 118 bus test systems are used to test the proposed CM approach. The results show the suitability of proposed MO-GSO algorithm for solving the multi-objective based CM problem and to generate a well distributed Pareto optimal set of considered two objective functions.