Optimization of single and multi-areas economic dispatch problems based on evolutionary particle swarm optimization algorithm

Economic dispatch (ED) is a non-convex, non-linear, and non-smooth optimization problem that determines the optimal output power of generation units to meet the forecasted demand from an economic point of view. The objective of this study is to develop and examine the applicability of a newly developed evolutionary particle swarm optimization (E-PSO) algorithm for optimization of the ED problem, where practical constraints, namely, valve-point effects, prohibited operating zones, multiple fuel usage, dynamic ramp rate limits, transmission losses, tie-line capacity, and spinning reserve are considered. In the developed E-PSO algorithm, three operators including mutation, crossover, and selection are applied to enable the search process to skip local optimal points and enhance computational efficiency. To further enhance the performance of the algorithm, an approach is proposed to dynamically adjust the inertia, cognitive, and social weight coefficients to improve exploration and exploitation for smooth convergence. Upon validation of the E-PSO algorithm by means of standard benchmark functions, four case studies including isolated and interconnected power systems are examined and the results are compared with those from other algorithms. The findings show that the proposed features enable the E-PSO algorithm to successfully optimize the ED problem in lower simulation time, while all constraints are met.