Modeling Uncertainty of Day-ahead Market Prices for Energy Storage Aggregator

Electrical distribution system is transforming due to increased penetration of distributed generation and consumer connected storage units. With the introduction of favorable regulatory conditions, battery owners are expected to participate in electricity markets through aggregators to deliver various grid services. One such service is electricity generation/consumption offered through whole-sale day-ahead electricity markets. When offering this service, an aggregator optimizes the charging and discharging schedule of the batteries based on the market price forecasts to maximize the profits. Any uncertainty or error in the price forecasts will adversely impact the aggregator's profit. Researchers have proposed two different ways to reduce the impact of market price uncertainty on the profit: (a) Using the Conditional Value-at-Risk, and (b) Using Robust Optimization techniques. However, there is no comparative study between these two approaches in the literature to determine which is better. Further, it remains to be seen if there are other measures, apart from the forecast error, that can impact an aggregator's profitability. In this paper, we focus on answering these two specific questions using real world logs obtained from the day-ahead market of an European Power exchange.