Satisfying user preferences in community-based local energy markets — Auction-based clearing approaches

The advancing energy transition is leading to a constantly increasing number of prosumers and active participants in the energy system. Local energy markets are considered a promising approach to coordinate those active participants efficiently, increase acceptance among the population, and decrease grid expansion costs. However, energy models and markets do not consider heterogeneous user preferences which are considered crucial for the progressing energy transition. Therefore, this paper proposes new auction-based local energy market models that consider user preferences and users’ willingness to pay a premium for heterogeneous energy qualities. In order to account for the unknown and stochastic user behavior in such a new market setting, we simulate and compare existing and newly developed auction-based clearing algorithms with an unbiased Monte Carlo method and evaluate whether they fulfill pre-defined key characteristics. Based on the results, we conclude and identify a clearing algorithm that verifiably satisfies user preferences, considers willingness to pay a premium, increases local coverage of electricity, maintains individual rationality, and computational tractability. The presented clearing algorithms enable new market designs that can help to increase acceptance and accelerate the expansion of renewable energies. All market models and simulations are publicly available in the open-source repository lemlab. Future research will validate the presented results in a field trial in Germany with 20 households.