| description abstract | In recent decades, there has been a rapid rate of development of hydropower in China. The unprecedented rate of expansion, development scale, and large numbers of hydropower plants have posed a challenge to the operation of large-scale cascaded hydropower systems (OLCHSs), which has become one of the most important factors in ensuring the security and economic operation of the power grid in China. In this paper, a long-term optimal operation model is developed for the purpose of maximizing the total generated energy of cascaded hydropower plants. To solve the OLCHS problem effectively, an elite-guide particle swarm optimization (EGPSO) algorithm is proposed in this paper. An external archive set, which can preserve elite solutions during the evolution process, is employed to provide flying directions for particles. Since the OLCHS problem is a high-dimensional, nonlinear, multistage, and stringent constraint optimal problem, the proposed algorithm introduces three new innovations: a layer-partition approach is presented to divide the decision vectors into small ones according to the reservoir’s relative position and hydraulic connection. Meanwhile, the initial solutions are generated in the proposed contractively feasible region so that operation results do not rely so much on the initial solution. To deal with the multiconstraint coupling problem, a constraint-corridor method is adapted to handle these complex constraints in the cascaded hydropower system. Finally, this novel strategy is applied successfully to solve the optimal operation of a large-scaled cascaded hydropower system in the upper reaches of the Yangtze River. Compared with the conventional method, the proposed EGPSO has a competitive performance in not only simulation results but also computing time, which offers a new approach to solving high-dimensional and complicated problems of optimizing reservoir dispatching. | |
