A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator
With the rising extension of renewable energies, the intraday electricity markets have recorded a growing popularity amongst traders as well as electric utilities to cope with the induced volatility of the energy supply. Through their short trading horizon and continuous nature, the intraday markets...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-05-01
|
| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546822000039 |
| _version_ | 1818244833131626496 |
|---|---|
| author | Malte Lehna Björn Hoppmann Christoph Scholz René Heinrich |
| author_facet | Malte Lehna Björn Hoppmann Christoph Scholz René Heinrich |
| author_sort | Malte Lehna |
| collection | DOAJ |
| description | With the rising extension of renewable energies, the intraday electricity markets have recorded a growing popularity amongst traders as well as electric utilities to cope with the induced volatility of the energy supply. Through their short trading horizon and continuous nature, the intraday markets offer the ability to adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice. Producers of renewable energies utilize the intraday market to lower their forecast risk, by modifying their provided capacities based on current forecasts. However, the market dynamics are complex due to the fact that the power grids have to remain stable and electricity is only partly storable. Consequently, robust and intelligent trading strategies are required that are capable to operate in the intraday market. In this work, we propose a novel autonomous trading approach based on Deep Reinforcement Learning (DRL) algorithms as a possible solution. For this purpose, we model the intraday trade as a Markov Decision Process (MDP) and employ the Proximal Policy Optimization (PPO) algorithm as our DRL approach. A simulation framework is introduced that enables the trading of the continuous intraday price in a resolution of one minute steps. We test our framework in a case study from the perspective of a wind park operator. We include next to general trade information both price and wind forecasts. On a test scenario of German intraday trading results from 2018, we are able to outperform multiple baselines with at least 45.24% improvement, showing the advantage of the DRL algorithm. However, we also discuss limitations and enhancements of the DRL agent, in order to increase the performance in future works. |
| first_indexed | 2024-12-12T14:23:18Z |
| format | Article |
| id | doaj.art-ec8e79f3922a4d4ba1ee28441376f9ce |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-12-12T14:23:18Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-ec8e79f3922a4d4ba1ee28441376f9ce2022-12-22T00:21:45ZengElsevierEnergy and AI2666-54682022-05-018100139A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operatorMalte Lehna0Björn Hoppmann1Christoph Scholz2René Heinrich3Corresponding author.; Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, GermanyFraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, GermanyFraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, GermanyFraunhofer Institute for Energy Economics and Energy System Technology (IEE), Königstor 59, 34119 Kassel, GermanyWith the rising extension of renewable energies, the intraday electricity markets have recorded a growing popularity amongst traders as well as electric utilities to cope with the induced volatility of the energy supply. Through their short trading horizon and continuous nature, the intraday markets offer the ability to adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice. Producers of renewable energies utilize the intraday market to lower their forecast risk, by modifying their provided capacities based on current forecasts. However, the market dynamics are complex due to the fact that the power grids have to remain stable and electricity is only partly storable. Consequently, robust and intelligent trading strategies are required that are capable to operate in the intraday market. In this work, we propose a novel autonomous trading approach based on Deep Reinforcement Learning (DRL) algorithms as a possible solution. For this purpose, we model the intraday trade as a Markov Decision Process (MDP) and employ the Proximal Policy Optimization (PPO) algorithm as our DRL approach. A simulation framework is introduced that enables the trading of the continuous intraday price in a resolution of one minute steps. We test our framework in a case study from the perspective of a wind park operator. We include next to general trade information both price and wind forecasts. On a test scenario of German intraday trading results from 2018, we are able to outperform multiple baselines with at least 45.24% improvement, showing the advantage of the DRL algorithm. However, we also discuss limitations and enhancements of the DRL agent, in order to increase the performance in future works.http://www.sciencedirect.com/science/article/pii/S2666546822000039Deep Reinforcement LearningGerman intraday electricity tradingDeep neural networksMarkov Decision ProcessProximal Policy OptimizationElectricity price forecast |
| spellingShingle | Malte Lehna Björn Hoppmann Christoph Scholz René Heinrich A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator Energy and AI Deep Reinforcement Learning German intraday electricity trading Deep neural networks Markov Decision Process Proximal Policy Optimization Electricity price forecast |
| title | A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator |
| title_full | A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator |
| title_fullStr | A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator |
| title_full_unstemmed | A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator |
| title_short | A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator |
| title_sort | reinforcement learning approach for the continuous electricity market of germany trading from the perspective of a wind park operator |
| topic | Deep Reinforcement Learning German intraday electricity trading Deep neural networks Markov Decision Process Proximal Policy Optimization Electricity price forecast |
| url | http://www.sciencedirect.com/science/article/pii/S2666546822000039 |
| work_keys_str_mv | AT maltelehna areinforcementlearningapproachforthecontinuouselectricitymarketofgermanytradingfromtheperspectiveofawindparkoperator AT bjornhoppmann areinforcementlearningapproachforthecontinuouselectricitymarketofgermanytradingfromtheperspectiveofawindparkoperator AT christophscholz areinforcementlearningapproachforthecontinuouselectricitymarketofgermanytradingfromtheperspectiveofawindparkoperator AT reneheinrich areinforcementlearningapproachforthecontinuouselectricitymarketofgermanytradingfromtheperspectiveofawindparkoperator AT maltelehna reinforcementlearningapproachforthecontinuouselectricitymarketofgermanytradingfromtheperspectiveofawindparkoperator AT bjornhoppmann reinforcementlearningapproachforthecontinuouselectricitymarketofgermanytradingfromtheperspectiveofawindparkoperator AT christophscholz reinforcementlearningapproachforthecontinuouselectricitymarketofgermanytradingfromtheperspectiveofawindparkoperator AT reneheinrich reinforcementlearningapproachforthecontinuouselectricitymarketofgermanytradingfromtheperspectiveofawindparkoperator |