Electricity Price Forecasting for Operational Scheduling of Behind-the-Meter Storage Systems

Electricity price forecast plays a key role in strategic behavior of participants in competitive electricity markets. With the growth of behind-the-meter energy storage, price forecasting becomes important in energy management and control of such small-scale storage systems. In this paper, a forecasting strategy is proposed for real-time electricity markets using publicly available market data. The proposed strategy uses high-resolution data along with hourly data as inputs of two separate forecasting models with different forecast horizons. Moreover, an intra-hour rolling horizon framework is proposed to provide accurate updates on price predictions. The proposed forecasting strategy has the capability to detect price spikes and capture severe price variations. The real data from Ontario's electricity market is used to evaluate the performance of the proposed forecasting strategy from the statistical point of view. The generated price forecasts are also applied to an optimization platform for operation scheduling of a battery energy storage system within a grid-connected micro-grid in Ontario to show the value of the proposed strategy from an economic perspective.