A Hybrid Method for Optimal Scheduling of Short-Term Electric Power Generation of Cascaded Hydroelectric Plants Based on Particle Swarm Optimization and Chance-Constrained Programming

A novel strategy for optimal scheduling of short-term electric power generation of cascaded hydroelectric plants based on particle swarm optimization (PSO) and chance-constrained programming is presented to maximize the expected profit at a given risk level in this paper. Based on chance-constrained programming, in which some specified probability are given to simulate some uncertainties, such as water inflows, electricity prices, unit status, and so on. This paper proposes a model for short-term scheduling optimization of cascaded hydro plants, which includes uncertainties, spatial-temporal constraints among cascaded reservoirs, etc. A hybrid particle swarm optimization (HPSO), which is embedded with evolutionary algorithms, is presented to use for the solution of global optimization problems. Catastrophe theory, which is concerned with natural evolutionary or survival-of-the-fittest, is utilized as an indication of the premature converge of PSO, and the positions of particles are further adjusted in the search space according to chaos optimization. In this way, each particle competes and cooperates with its neighbors. The proof shows that HPSO is guaranteed to converge to the global optimization solution with probability one. The model presented is solved by a combination method of HPSO and Monte Carlo simulation. Finally, a numerical example is served for demonstrating the feasibility of the method developed.