Trading Strategies on Local Electricity Markets Using Agent‐Based Modelling and Reinforcement Learning: Vectors to Expand Energy Communities

Energy communities (ECs) members have a high degree of heterogeneity. Agent‐based modelling (ABM) allows for the modelling of each entity as an agent with distinct characteristics and decision rules. This is particularly useful when the heterogeneity of agents affects outcomes. Market‐based models handle competition and supply–demand interactions and often rely on aggregate or average behaviours, which might overlook important nuances due to individual differences. In this paper, we combine ABM with reinforcement learning (RL) and market models to trade the surplus and demand at the local electricity markets (LEMs) level embedding PyMarket and Mesa packages. It explores integrating electric vehicles, heating and flexibility as vectors to expand ECs using an RL agent to optimally schedule these devices for efficient bidding. Two strategies are proposed: S1‐RL agent predicts only the bidding price and S2‐RL agent predicts both price and quantity. The RL agent in S2 optimizes load to increase demand during local generation intervals, utilizing local surplus more effectively. Financially, S2 outperforms S1, offering more flexibility and optimization in LEMs trading. In summer, the financial savings (FST) increase from 11.58% in S1 to 17.88% in S2, while, in winter, they increase from 4.21% to 5.38%. The efficiency of trading is better, and the traded quantity doubles in summer in S2 compared to S1.