Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization

This paper presents an improved quantum-behaved particle swarm optimization (IQPSO) for short-term combined economic emission hydrothermal scheduling, which is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing emission cost. In this paper, quantum-behaved particle swarm optimization is improved employing heuristic strategies in order to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also devised to handle the reservoir storage volumes constraints. To show feasibility and effectiveness of the proposed method, different case studies, such as economic load scheduling (ELS), economic emission scheduling (EES) and combined economic emission scheduling (CEES) in hydrothermal scheduling, are carried out and the test results are compared with those of other methods reported in the literature. It is also very important to note that the proposed method is capable of yielding higher-quality solutions while strictly satisfying all constraints of the test system.