Assessment of the broader applicability of a smart agent in peer-to-peer energy trading: A full factorial analysis of a multi-agent reinforcement learning solution

To realise the clean energy transition, peer-to-peer (P2P) renewable energy sharing markets have been proposed as one possible solution for achieving such a goal and are recognised as a potential path to achieving other goals such as affordable and reliable energy. Existing studies have shown that coordination at the micro level can be achieved by employing such P2P market structures. A pressing question concerns how to set the trade price such that the community coordinates in a way that maximises social welfare. A solution to this question based on multi-agent reinforcement learning (MARL) has been provided as a proof-of-concept in a single environment. However, various factors such as climate and community scale have been shown to affect the collective performance in such energy-sharing communities. In this work, to test the wider applicability of the proposed solution, a full factorial experiment based on the factors of climate, community scale, and price mechanism, is conducted to ascertain the response of the community w.r.t. the outputs: community self-sufficiency, total net-loss, and income equality. In short, we find that a community stands an odds of 2 to 1 in higher savings by adopting a smart agent.