Learning Bidding Strategies in Local Electricity Markets using a Nature-Inspired Algorithm

Local electricity markets (LEM) are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, how these local markets will be integrated into existing market structures, and to make the most profit from them, is still unclear. In this work, we propose a LEM framework based on bi-level optimization. In the upper level, end-users aim at maximizing profits, while the lower level represents the clearing market process. Moreover, a cascade integration to the wholesale market through an aggregator that acts after the LEM has been cleared is considered. Learning strategies using only available information can be a powerful tool to take the most advantage of LEM. To this end, we advocate the use of ant colony optimization (ACO), a nature-inspired technique, similar to that employed in machine learning. By using ACO, consumers, producers and prosumers, can learn the best strategies to maximize their profits without sharing private information and based solely on their experience.