Exploring Models of Electricity Price Forecasting: Case Study on A FCAS Market

Abstract

VPPs (Virtual Power Plants) play an important role in balancing supply and demand. In order to make VPP revenue, it is necessary to forecast market prices and bidding energy for supply and demand adjustment markets, called FCAS (Frequency Control Ancillary Service) markets. However, price forecasting for FCAS markets is still challenging because they have multiple different response times and one price, directly and indirectly, influences each other. There is no study on electricity price forecasting in FCAS markets, and a novel forecasting model considering not only its price but also the other prices of the different response times is necessary. This work presents a market price forecasting model for a FCAS market by exploring the forecasting models derived from a wholesale market, and then it takes into account the markets with different response times as well as the target one from AEMO (Australian Energy Market Operator). Through the experiments, our forecasting model achieves 7.8$/MWh of RMSE on the electricity price in AEMO’s 6-Second-Raise market. The proposed forecasting model reduces RMSE by 80% compared to the forecast price published by AEMO.