Optimizing trading decisions of wind power plants with hybrid energy storage systems using backwards approximate dynamic programming

On most modern energy markets, electricity is traded in advance and a power producer has to commit to deliver a certain amount of electricity some time before the actual delivery. This is especially difficult for power producers with renewable energy sources that are stochastic (like wind and solar). Thus, short-term electricity storages like batteries are used to increase flexibility. By contrast, long-term storages allow to exploit price fluctuations over time, but have a comparably bad efficiency over short periods of time. In this paper, we consider the decision problem of a power producer who sells electricity from wind turbines on the continuous intraday market and possesses two storage devices: a battery and a hydrogen based storage system. The problem is solved with a backwards approximate dynamic programming algorithm with optimal computing budget allocation. Numerical results show the algorithm's high solution quality. Furthermore, tests on real-world data demonstrate the value of using both storage types and investigate the effect of the storage parameters on profit.

Automated summary

This paper addresses the decision problem of a power producer selling electricity from wind turbines on the continuous intraday market, considering the use of batteries and hydrogen-based storage systems. The results show that employing a backward approximate dynamic programming algorithm can provide high-quality solutions, and tests on different storage parameters reveal their impact on profit.