Abstract
In recent years so-called stochastic power producers (with portfolios including wind and solar power generation
capacities) are increasingly asked to participate in electricity markets under the same rules than for conventional generators. Stochastic power producers may act strategically in order to decrease expected penalties induced by imbalances. Many alternative offering strategies based on forecasts in various forms are available in the literature. However, they assume some form of knowledge of future market state and potential balancingprices. In contrast here, we explore whether algorithms could readily learn from market data and deduce how to offer strategically in order to maximize expected market revenues. Our analysis shows that a direct reinforcement learning algorithm can track the nominal level of the optimal quantile
forecast to trade in the day-ahead market, while yielding higher revenues than existing benchmark strategies
capacities) are increasingly asked to participate in electricity markets under the same rules than for conventional generators. Stochastic power producers may act strategically in order to decrease expected penalties induced by imbalances. Many alternative offering strategies based on forecasts in various forms are available in the literature. However, they assume some form of knowledge of future market state and potential balancingprices. In contrast here, we explore whether algorithms could readily learn from market data and deduce how to offer strategically in order to maximize expected market revenues. Our analysis shows that a direct reinforcement learning algorithm can track the nominal level of the optimal quantile
forecast to trade in the day-ahead market, while yielding higher revenues than existing benchmark strategies
| Original language | English |
|---|---|
| Title of host publication | Proceedings of European Electricity Market Conference 2016 |
| Number of pages | 5 |
| Publisher | IEEE |
| Publication date | 2016 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 13th International Conference on the European Energy market - Faculty of Engineering of University of Porto, Porto, Portugal Duration: 6 Jun 2016 → 9 Jun 2016 Conference number: 13 https://ieeexplore.ieee.org/xpl/conhome/7514734/proceeding |
Conference
| Conference | 13th International Conference on the European Energy market |
|---|---|
| Number | 13 |
| Location | Faculty of Engineering of University of Porto |
| Country/Territory | Portugal |
| City | Porto |
| Period | 06/06/2016 → 09/06/2016 |
| Internet address |
Keywords
- Renewable energy
- Strategic offering
- Stochastic optimization
- Reinforcement learning
- Direct learning
