Probabilistic solar power forecasting: An economic and technical evaluation of an optimal market bidding strategy

Abstract

Solar forecasting is a rapidly evolving field that can substantially contribute to the effective integration of large amounts of solar photovoltaic (PV) capacity into the electricity system. However, newly developed solar forecasting models are rarely tested in an operational context considering the intended application and objective. Besides, models are typically evaluated considering only technical error metrics, disregarding their economic value. This paper proposes an operational bidding strategy that optimizes the participation of a PV power plant in the electricity spot markets. To this end, a novel multistage stochastic optimization method is developed that considers the day-ahead, intraday, and imbalance markets. As the developed method utilizes a scenario generation algorithm, the proposed method can be adopted for a wide variety of related applications. The performance of the developed method is assessed using technical and economic metrics and compared to a reference method. The results demonstrate the effectiveness of the proposed bidding strategy, as it substantially outperforms the reference market bidding strategy. The findings also provide insights into the value of a multistage bidding method, as extending market participation from the day-ahead to the intraday market increases revenues by 22%, while halving the total imbalance. Additionally, the study examines the relationship between the technical and economic performance of solar power forecasting models, revealing a non-linear correlation.