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

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.

Automated summary

The study focuses on establishing robust optimization-based optimal bidding models for thermal generators and battery storage, examining their expected profit by applying them to actual realizations of uncertainty. The impact of input scenario selection on the range of uncertainty is also investigated.