Using Multi-objective Particle Swarm Optimization to Solve Dynamic Economic Emission Dispatch Considering Wind Power and Electric Vehicles

Abstract

As a kind of clean energy, wind power can reduce the fuel cost and pollution emission of tradition thermal power generators effectively. As a transportation, electric vehicle (EV) can not only save energy, but also protect the environment. However, the large-scale development of EVs will increase the load pressure on the power grid. Therefore, in order to deal with the rapid development of wind power and EVs, a multi-objective dynamic economic emission dispatch model with wind power and EVs is proposed considering both the total fuel cost and pollution emission objectives in this paper. The two-lbests based MOPSO (2LB-MOPSO) with constraint handling method is developed to optimize the proposed model. The 10-unit system, 100 wind turbines and 50000 EVs are employed as the test case to demonstrate the performance of the 2LB-MOPSO in the proposed model. In addition, other evolutionary algorithms are compared with 2LB-MOPSO. The simulation results show that 2LB-MOPSO is superior in solving the complex constrained DEED problem, and the proposed model can guide the charging and discharging behavior of EVs to serve the power grid.