An Agent Based Model Applied to a Local Energy Market (LEM) Considering Demand Response (DR) and Its Interaction with the Wholesale Market (WSM)

The expected development and massification of Local Energy Markets (LEM), in particular the ones associated with Renewable Energy Communities, poses new challenges, and requires new operations strategies to their promoters, aggregators, and end-consumers. One of the mechanisms that can be used to speed up the spreading of this kind of market is the use of Demand Response (DR) programs since they can be designed to increase the community's savings and profits. In this framework, the end customers are induced to change their normal consumption patterns by temporarily reducing and/or shifting their electricity consumption away from periods with low local generation in response to a signal from a service provider, i.e., aggregator. To this purpose, this paper presents an Agent Based Model (ABM) using the Q-Learning mechanism to implement and to simulate a LEM and its interaction with the Wholesale Market (WSM), using also and incentive-based DR program. The overall objective of this design is to decrease average energy costs by moving the demand to periods of large availability of wind or solar resources or to store energy for future use. The developed model was tested considering real data regarding energy consumption and PV generation. The proposed paper describes and discusses the obtained market strategy and the profits that can be obtained with this approach.