Multiobjective Reinforcement Learning-Based Intelligent Approach for Optimization of Activation Rules in Automatic Generation Control

This paper proposes a novel hybrid intelligent approach to solve the dynamic optimization problem of activation rules for automatic generation control (AGC) based on multiobjective reinforcement learning (MORL) and small population-based particle swarm optimization (SPPSO). The activation rule for AGC is to dynamically allocate the AGC regulating commands among various AGC units, and subsequently, the secondary control reserve of those units can be activated. Therefore, the activation rule for AGC is vital to ensure the overall control performance of AGC schemes. In this paper, MORL is applied to provide a customized platform for interactive self-learning to maximize the long-run discounted reward, i.e., minimize the generation cost, regulating error, and emission from a long-term viewpoint. SPPSO is utilized to effectively and efficiently obtain the optimality of activation rule with a fast convergence speed to fulfill the real-time requirement of AGC activation. Furthermore, a novel analytic hierarchy process-based coordination factor is introduced to identify the optimum multi-objective tradeoff in various power system operation scenarios. At last, the validation of the proposed hybrid method has been demonstrated via comprehensive tests using practical data from the dispatch center of China Southern Power Grids.