Peer-to-peer transaction and energy consumption decisions for a community energy based on game theory and optimization techniques

Abstract

This work aims to understand the buying and selling decisions of the energy community agents when the degree of knowledge of future data is changed utilizing pricing following game theory and finding the decisions using an equilibrium model. The objective of the methodology is to find a fair price modeling the interaction between the agents to subsequently see an optimal policy of buying and selling energy between them and their interaction with a centralized power grid to minimize its cost. The above problem can be approached using a piecewise function to establish the price that respects the non-cooperative naturalness of the agents and the laws of economics, to subsequently build interdependent models for each agent, considering their interests, energy consumption, and production characteristics. After this, an equilibrium model is formulated to determine the efficient distribution of energy considering the interests of the energy agents in the community. Finally, three scenarios with different future knowledge are presented to understand how this affects decisions. It was found that the more information about the future is known, the more agents can propose strategies to obtain higher profits. Thus, a higher cost for the energy community is obtained when the agents do not know the future, and a lower total cost when they know the future perfectly.