Iterative coupling of a fundamental electricity market model and an agent-based simulation model to reduce the efficiency gap

Fundamental optimization models can determine normative cost-optimal systems following theoretical assumptions of markets with perfect competition and perfect foresight. However, in real markets, deviations from ideal assumptions need to be considered for designing efficient policies: they may lead to higher costs than theoretically expected and contribute to the so-called “efficiency gap”. Agent-based simulation models can consider market inefficiencies but are not ideally suited for determining normative systems. This work aims to reduce the efficiency gap between theoretically cost-optimal system configurations and their implementations in non-perfect markets by coupling a fundamental electricity market model and an agent-based model. A fully automated iterative coupling is demonstrated, and its convergence behavior is investigated for two coupling parameters. Fast convergence is observed when integrating peak capacity usage from the previous agent-based simulation into the fundamental model. However, the efficiency gap is reduced only to limited extent as no information on hourly dispatch is transferred between the models. Conversely, when using hourly storage dispatch as coupling parameter, it takes longer to achieve convergence. In return, the efficiency gap is reduced to larger extent. The proposed iterative coupling thus allows the identification of realizable cost-optimal systems, which means that they are achievable under non-perfect market conditions.