Automatic Generation Control with Competing GENCOs-A Reinforcement Learning Based Approach

Abstract

Under deregulation, Automatic Generation Control (AGC) scheme may consider economic dispatch of Generating Companies (GENCOs) as well in addition to minimising the system frequency deviation. This will result in increased participation from GENCOs in AGC. GENCOs being private utilities, will have the private information like cost function etc which will not be shared with the system operator. This imposes a challenge on implementing classical economic dispatch problem in AGC scheme. The contribution of the paper is a game theoretic based model of AGC scheme that will ensure an optimal dispatch to competing GENCOs. This is achieved by defining an optimal participation factor for GENCOs that will minimize their cost of production. A game theoretic approach towards AGC as a dynamic game is presented and GENCOs are considered as Reinforcement learning agents. A Single Agent Q-Learning method along with pursuit algorithm is used at each GENCO agent to achieve the equilibrium point in AGC. The algorithm was studied with three competing GENCOs. Results show the suitability of the proposed AGC model and its ability to minimize the deviation in system frequency while ensuring economic operation of the system.