A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability. (December 2015) - Jiang, Shanhe
Highlights: WTEED model to coordinate power allocation from thermal and wind units is established. GAEPSO is proposed and updates its velocity by both PSO velocity and GSA acceleration. Performance of GAEPSO for solving conventional and wind–thermal test system is assessed. Results obtained demonstrate the effectiveness and superiority of the proposed GAEPSO. Abstract: To reduce the pollutant atmospheric emission level, a Wind–thermal Economic Emission Dispatch (WTEED) model considering the coordination of power allocation from thermal and wind power generators is established. Among the model formulation, the fuel cost and emission level of thermal units and the operating cost caused by wind power availability are comprehensively investigated here. Also, the cost of wind energy including overestimation and underestimation of available wind power using Weibull-based probability density function is also given in a closed-form expression according to the incomplete gamma function to characterize the impact of wind power. To seek the optimum fuel cost, optimum emission level and best compromise solution, a newly developed optimization approach, known as gravitational acceleration enhanced particle swarm optimization algorithm (GAEPSO), has been adopted to solve the model in this work. The approach adopts co-evolutionary technique to simultaneously update particles velocity with PSO velocity and GSA acceleration and fully incorporates the ability of exploration in PSO and the ability of exploitation in GSA. GAEPSO, therefore, is expected to obtain an efficient balance between exploration and exploitation. The potential of the proposed algorithm is assessed in terms of the minimum fuel cost, minimum emission and best compromise solution obtained for conventional thermal generators and modified wind–thermal generators test systems. The results obtained validate the feasibility and effectiveness of the proposed algorithm compared to PSO, GSA and other recently developed approaches. Both the Pareto-optimal set and the convergence speed of the proposed algorithm are also found to be better than, or at least comparable to other algorithms.