Applying machine learning to electricity price forecasting in simulated energy market scenarios

Policy packages, such as the "European Green Deal", call for a substantial restructuring of the power plant park. This, in combination with more flexible demand, will result in novel electricity price dynamics. These can be studied using, e.g., agent-based models which simulate bidding decisions of market actors, thereby uncovering emergent market phenomena. For their bidding decisions, simulated actors - just like real-world actors - require accurate market price forecasts. Techniques to obtain such forecasts need to be applicable to vastly different future electricity market scenarios, ideally without the need of scenario-specific retraining. This is a major difference compared to real-world electricity market forecasting, which is based on minimal variations in the underlying energy system. Despite the long track record in this field, it is not sufficiently clear which methods are suitable for forecasting simulated future electricity markets in greatly varying scenarios and technology mixes. To address this gap, we assess the applicability of different forecasting techniques to price time series generated by simulations of the future electricity market. We then evaluate the forecast accuracy of two recent machine learning architectures, namely N-BEATS and Temporal Fusion Transformers, based on parameter combinations with significant expansions of renewable energy and flexibility option capacity. As expected, the results demonstrate that machine learning exhibits superior accuracy compared to na & iuml;ve benchmarks. Particularly, when future covariates, such as residual load, are employed, the mean absolute error consistently remains below 1.40 EUR/MWh. This may be attributed to reduced inner complexity of simulated electricity prices compared to real-world market dynamics. Our findings demonstrate that machine learning can provide reliable forecasts of future electricity prices and that retraining may not be necessary even with widely varying shares of renewable energy and flexibility capacity. These forecasting methods could therefore be effectively employed in electricity market simulations in the context of the energy transition.