Statistical modelling of electricity prices in Day-Ahead markets and impact on Storage Revenues

Abstract

As renewable penetration increases in future power systems, electricity storage becomes vital to stabilise its performance. However, for electricity storage to become competitive in electricity markets, suitable price signals need to exist in day- ahead and intraday markets. This means that there is a strong need for sufficient price spread in the electricity prices of these markets. Fundamental economic dispatch models hardly reproduce the price spreads observed in real data. Thus, there is a strong need for either strategic bidding or the existence of unit commitments models, where dynamic constraints are enabled for all generator types, to achieve this level of detail. In this paper, we examine and compare these two methods to identify which model has a better fit to real price spreads. The impact of price spreads on battery storage revenues is also examined. Results show that strategic bidding of generators leads to higher price spreads and therefore, storage revenues.