Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

Abstract

Local energy markets (LEMs) are proposed in recent years as a way to enable local prosumers and community to trade their electricity and have control over their electrical related resources by ensuring that electricity is traded closer to where it is produced. However, literature is still scarce with the most optimal and effective trading strategies for LEM design. In this work, we propose two reinforcement learning based intelligent bidding strategies for prosumers and consumers trading within an LEM. Our proposed models were evaluated of their performance by testing them in a German real case scenario. The simulation results show that intelligent bidding strategies create additional self sufficiency and market savings to the local community compared to the baseline strategy where the agents make their trading decision randomly without an intelligent agent. Moreover, modelling the intelligent agents to perform towards a common goal creates more share of individual savings for the prosumers and consumers compared to the classical intelligent bidding strategies employed in this work.

Keywords

• Bidding strategy
• energy community
• local energy markets
• Markov decision process
• peer-to-peer
• reinforcement learning