Building day-ahead bidding functions for seasonal storage systems: A reinforcement learning approach
| Autor: | Fjo De Ridder, Bart De Schutter, Jesus Lago, Gowri Suryanarayana, Ecem Sogancioglu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Seasonal thermal energy storage Computer science business.industry 020208 electrical & electronic engineering 02 engineering and technology Bidding Energy Storage 7. Clean energy Electrical grid Reinforcement Learning Profit (economics) Renewable energy Bidding Functions 020901 industrial engineering & automation Electricity generation Control and Systems Engineering Seasonal Storage 0202 electrical engineering electronic engineering information engineering Reinforcement learning business |
| Zdroj: | IFAC-Papers IFAC-PapersOnLine, 52(4) |
| ISSN: | 2405-8963 1474-6670 |
| DOI: | 10.1016/j.ifacol.2019.08.258 |
| Popis: | Due to the increasing integration of renewable sources in the electrical grid, electricity generation is expected to become more uncertain. In this context, seasonal thermal energy storage systems (STESSs) are key to shift the delivery of renewable energy sources and tackle their uncertainty problems. In this paper, we propose an optimal controller for STESSs that, using reinforcement learning, builds bidding functions for the day-ahead market. In detail, considering that there is an uncertain energy demand that the STESS has to satisfy, the controller buys energy in the day-ahead market so that the uncertain demand is satisfied while the profits are maximized. Since prices are low during periods of large renewable energy generation (and vice versa), maximizing the profit of a STESS indirectly shifts the delivery of renewable energy to periods of high energy demand while reducing their uncertainty problems. To evaluate the proposed algorithm, we consider a real STESS providing different yearly-demand levels; then, we compare the performance of the controller to the theoretical upper bound, i.e. the optimal cost of buying energy given perfect knowledge of the demand and prices. The results indicate that the proposed controller performs reasonably well: despite the large uncertainty in prices and demand, the proposed controller obtains 70%-50% of the maximum gains given by the theoretical bound. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|