Individual Thermal Generator and Battery Storage Bidding Strategies Based on Robust Optimization

Abstract

Bidding in the day-ahead market encompasses uncertainty on market prices. To properly address this issue, dedicated optimal bidding models are constructed. Traditionally, these models have been derived for generating units, in particular thermal generators. Recently, optimal bidding models have been updated to account for specifics of energy storage, foremost battery storage. Batteries are significantly different devices than generators. On one hand, a battery can both purchase and sell electricity with practically instant change in its output power. On the other hand, a battery is energy-limited, which makes its profit very sensitive to optimal scheduling. In this paper, we examine the existing and derive new robust optimization-based optimal bidding models individually for a thermal generator and a battery storage. The models are examined in terms of the expected profit by applying the obtained bidding curves and (dis)charging schedules to actual realizations of uncertainty. Moreover, we examine the effect of the range of uncertainty caused by the selection of input scenarios. Based on the presented case studies, we form conclusions on the effectiveness of the robust optimization approach for this type of problems.