Solving static and dynamic multi-area economic dispatch problems using an improved competitive swarm optimization algorithm

Multi-area economic dispatch (MAED) is an important non-linear optimization problem in power system operation. MAED involves multiple power generation areas, and minimizes the total fuel cost by determining the power generation within areas and the power interchange between areas. This paper proposes an improved competitive swarm optimization (ImCSO) algorithm to solve the MAED problems. The ImCSO algorithm introduces two improvements into competitive swarm optimization for performance enhancement. Firstly, a ranking paired learning strategy is adopted to enhance the learning efficiency of the loser particles; secondly, a differential evolution strategy is used to update and improve the winner particles. Combining a constraint repair technique, the proposed ImCSO algorithm is applied to solve 10-unit, 40-unit and 120-unit multi-area static economic dispatch and 40-unit multi-area dynamic economic dispatch problems. The solved MAED problems integrate comprehensive constraints such as valve point effect, multiple fuels, transmission loss, tie-line constraint, prohibited operating zone and ramp rate limit. Through comparison with other well-established optimization algorithms, it is observed that the ImCSO algorithm has superiority in terms of solution accuracy and reliability in solving the MAED problems. •A novel method ImCSO is proposed for solving multi-area economic dispatch (MAED).•Ranking paired learning and differential evolution strategies are adopted in ImCSO.•Static and dynamic MAED problems integrating comprehensive constraints are studied.•Simulation results show that ImCSO has superiority in solution accuracy and reliability.