Enhancement of Hydroelectric Generation Scheduling Using Ant Colony System-Based Optimization Approaches

In this paper, an ant colony system (ACS)-based optimization approach is proposed for the enhancement of hydroelectric generation scheduling. To apply the method to solve this problem, the search space of multistage scheduling is first determined. Through a collection of cooperative agents called ants, the near-optimal solution to the scheduling problem can be effectively achieved. In the algorithm, the state transition rule, local pheromone-updating rule, and global pheromone-updating rule are all added to facilitate the computation. Because this method can operate the population of agents simultaneously, the process stagnation can be better prevented. The optimization capability can be thus significantly enhanced. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results demonstrate the feasibility and effectiveness of the method for the application considered.