Bidding in local electricity markets with cascading wholesale market integration

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