Bidding in local electricity markets with cascading wholesale market integration

Abstract Local electricity markets are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, as such a new concept, how these local markets will be designed and integrated into existing market structures, and make the most profit from them, is still unclear. In this work, we propose a local market mechanism in which end-users (consumers, small producers, and prosumers) trade energy between peers. Due to possible low liquidity in the local market, the mechanism assumes that end-users fulfill their energy demands through bilateral contracts with an aggregator/retailer with access to the wholesale market. The allowed bids and offers in the local market are bounded by a feed-in tariff and an aggregator tariff guaranteeing that end-users get, at most, the expected cost without considering this market. The problem is modeled as a multi-leader single-follower bi-level optimization problem, in which the upper levels define the maximization of agent profits. In contrast, the lower level maximizes the energy traded in the local market. Due to the complexity of the matter, and lack of perfect information of end-users, we advocate the use of evolutionary computation, a branch of artificial intelligence that has been successfully applied to a wide variety of optimization problems. Throughout three different case studies considering end-users with distinct characteristics, we evaluated the performance of four different algorithms and assessed the benefits that local markets can bring to market participants. Results show that the proposed market mechanism provides overall costs improvements to market players of around 30–40% regarding a baseline where no local market is considered. However, the shift to local markets in energy procurement can affect the conventional retailer/aggregator role. Therefore, innovative business models should be devised for the successful implementation of local markets in the future.