A fundamental Game Theoretic model and approximate global Nash Equilibria computation for European Spot Power Markets

TL;DR

This paper models European spot power markets using a game-theoretic approach, introducing a novel risk aversion concept and iterative algorithms to compute approximate global Nash equilibria, validated with real market data.

Contribution

It introduces a new risk aversion model based on worst-case scenarios and develops iterative schemes for global equilibrium computation in power markets.

Findings

The model accurately captures historical price series.

Strategic bidding is prevalent in the CWE market.

The approach provides insights into market participants' behavior.

Abstract

Spot electricity markets are considered under a Game-Theoretic framework, where risk averse players submit orders to the market clearing mechanism to maximise their own utility. Consistent with the current practice in Europe, the market clearing mechanism is modelled as a Social Welfare Maximisation problem, with zonal pricing, and we consider inflexible demand, physical constraints of the electricity grid, and capacity-constrained producers. A novel type of non-parametric risk aversion based on a defined worst case scenario is introduced, and this reduces the dimensionality of the strategy variables and ensures boundedness of prices. By leveraging these properties we devise Jacobi and Gauss-Seidel iterative schemes for computation of approximate global Nash Equilibria, which are in contrast to derivative based local equilibria. Our methodology is applied to the real world data of Central Western European (CWE) Spot Market during the 2019-2020 period, and offers a good representation of the historical time series of prices. By also solving for the assumption of truthful bidding, we devise a simple method based on hypothesis testing to infer if and when producers are bidding strategically (instead of truthfully), and we find evidence suggesting that strategic bidding may be fairly pronounced in the CWE region.