Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems

Abstract

Abstract The dearth of power generation from energy resources, environmental concerns and ever-increasing demand for electrical energy necessitate optimal economic dispatch with minimum costs and emissions. Due to the confined optimum convergence and non-convexity of realistic scenarios, classical optimization methods are not proficient to handle such problems. Instead, evolutionary optimization methods have gained more attention in recent years. Application of a new proposed double weighted particle swarm optimization (DWPSO) technique in solving non-convex combined emission economic dispatch (CEED) problems with wind power penetration and also solving non-convex multiple fuel option economic dispatch problem has been technologically proposed in this paper. The results on several case study systems are compared with other published methods in literature and confirm the effectiveness of DWPSO against other existing methods. DWPSO successfully reduces the production costs as well as hazardous emissions considering wind power penetration, selects the best fuel types of the generators and adjusts the feasible and optimum settings to allocate load demand to the online generation units in power system. The results demonstrate that using the proposed method can minimize the total generation costs and optimally satisfy the power demands in the grid while the computation performance remains satisfactory even in case of changes in the scale of the network.