A New Particle Swarm Optimization Algorithm to Hierarchy Multi-objective Optimization Problems and Its Application in Optimal Operation of Hydropower Stations

Some engineering optimization problems, such as multi-objective daily generation scheduling for the hydropower stations, the objective functions has obvious hierarchy and priority, simultaneously, the constraints is very complex. It is difficult to solve the operation problems by adopting traditional optimal technique. In this paper, a new particle swarm optimization algorithm solving hierarchy multi-objective timization problems is proposed. The algorithm can handle the level multi-objective optimization problem effectively. By adopting the adaptive inertia weight algorithm (AWA) and mutative scale local search algorithm (MSLSA), the convergence performance of the algorithm is improved. Then, A multi-objective daily generation scheduling model for the hydropower stations is established, in which two objective functions including maximization of peak-energy capacity benefits and maximization of power generation are involved. Finally, Multi-objective daily generation scheduling problem of the Three Gorges cascade hydropower system during low-flow period is studied with proposed algorithm to obtain the maximum peak-energy capacity benefits, as well as power generation benefits of three gorges cascade stations.