Optimal operation of multi-reservoir hydropower systems using enhanced comprehensive learning particle swarm optimization

Metaheuristics are promising optimization algorithms for tackling reservoir-system operation. Comprehensive learning particle swarm optimization (CLPSO) is a state-of-the-art metaheuristic that is strong in exploration. Recently we have proposed enhanced CLPSO (ECLPSO) to improve the exploitation performance of CLPSO. In this paper, we apply ECLPSO to the optimal operation of multi-reservoir hydropower systems. Two novel strategies are proposed to handle the various physical and operational constraints. First, the outflow and storage volume constraints are appropriately enforced to achieve a tradeoff between preserving diversity and facilitating convergence. Second, with the penalty function technique adopted to penalize the constraint violations and convert the original constrained problem into an unconstrained one, the penalty factor is dynamically adjusted in order to encourage exploration of the search space in the beginning and gradually guide the search to concentrate in the feasible region. The short-term scheduling of a 4-reservoir hydrothermal power system and the long-term planning of China's Xiluodu-Xiangjiaba-Threegorges 3-reservoir hydropower system are studied. Experimental results demonstrate that ECLPSO helps to robustly derive feasible high quality solutions for the two cases studied. The contribution to performance improvement by ECLPSO as well as the constraint enforcement and penalty factor adjustment strategies are analyzed.