Optimal Power Flow-Based Combined Economic and Emission Dispatch Problems Using Hybrid PSGWO Algorithm

Abstract

This paper proposes a new and efficient hybrid approach combining two meta-heuristic methods for solving economic and emission dispatch problems. We used particle swarm optimization (PSO)-based gray wolf optimization to solve this problem. Additionally, the nonlinear control parameter is employed to balance the global search and local search ability of the algorithm and improve the convergence speed of the algorithm. At the same time, the idea of PSO is introduced, which utilizes the best value of the individual and the best value of the wolf pack to update the position information of each gray wolf. This method preserves the best position information of the individual and avoids the algorithm falling into a local optimum. The optimal power flow (OPF)-based CEED problem is formulated with the combination of fuel cost, fuel emission with penalty function, real power loss and voltage deviation. The proposed approach is implemented in MATLAB working platform and tested by IEEE 30 bus system with different test cases. Moreover, the CEED problem-solving performance of PSGWO algorithm is examined by 3-unit, 6-unit and 40-unit test systems. Then, the obtained results are compared with conventional methods to highlight the benefits of the proposed algorithm in reducing the fuel cost, fuel emission, voltage deviation and power losses, respectively. The experimental results show that the proposed approach provides accurate solutions for all types of objective solutions.