A Hybrid Data-and-simulation-based Analysis of the Participants' Behavior Potential in the Italian Spot Electricity Markets

As mature electricity markets, the Italian spot ones provide substantial liquidity of the electrical trades through 3 interconnected markets with different pricing schemes. In this paper, we propose a framework for analyzing the behavior potential of the market players in the multi-market context to investigate if a different resources allocation in various spot market can increase the player's profit. Starting from forecast initial strategies obtained by a machine learning algorithm based on public data, bidding strategies for maximizing profit of players are iteratively derived by solving multiple market-clearings and settlements simultaneously. By simulating three cases, we find that small generators tend to use a survival-driven bidding approach, putting most of their production capacity on uniform price markets to guarantee a stable income. By contrast, due to the higher adjustment capabilities and risk resistances, large generators can take some risk to increase profits. Finally, with the player-specific strategy suggested by the framework, 180 operators are able to increase their profit by 139% on average.