Power plant start-up scheduling: a reinforcement learning approach combined with evolutionary computation

Abstract

Power plant start-up scheduling is aimed at minimizing the start-up time while limiting turbine rotor stresses to acceptable values. In order to increase on-line performance of searching an optimal or near-optimal start-up schedule during power plant operation, we propose to integrate neural network-based reinforcement learning with evolutionary computation implemented by means of Genetic Algorithms (GA). GA guides reinforcement learning to learn optimal schedules with respect to a number of representative sets of stress limits prior to the start-up process. During start-up, GA combined with reinforcement learning will search an optimal or near-optimal start-up schedule at a given set of stress limits. This approach significantly reduces the time needed for learning and searching. On a SPARC station 20, experiments show that it can search an optimal or near-optimal schedule within tens of seconds of CPU time, a time range which should be acceptable in power plant operations.