Algorithm for Adaptive Intelligent Agent Trading Electric Power in Decentralized Autonomous Smart Grid

Abstract

In this paper, we propose a new learning model for decentralized autonomous smart grid involving adaptive trading agents which can sell and buy electric power effectively in a local electric power network. We name the electric power network i-Rene (inter intelligent renewable energy network). The trading agents manage the amount of electric power generated by solar panels or other renewable energies by trading electric power stored in a storage battery in a house. The agent learns a trading strategy by maximizing its utility. Based on the proposed system, we evaluated its price formation and effectiveness of the adaptive trading method through simulations. Additionally, we propose a new variable consumption model for decentralized autonomous smart grid involving living people consuming electric power and the adaptive trading agents. To model demand side management which can control the amount of electric power consumption, developing variable consumption model is essential. We added a variable consumption model to the i-Rene model. We evaluated its price formation and effectiveness of the decentralized autonomous smart grid to equalize fluctuating demand.