A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator

• URL: http://arxiv.org/abs/2111.13609v1
• Date: Fri, 26 Nov 2021 17:17:27 GMT
• Title: A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator
• Authors: Malte Lehna and Bj\"orn Hoppmann and Ren\'e Heinrich and Christoph Scholz
• Abstract summary: We propose a novel autonomous trading approach based on Deep Reinforcement Learning (DRL) algorithms. We test our framework in a case study from the perspective of a wind park operator.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Problem (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.

Related papers

• Optimizing Quantile-based Trading Strategies in Electricity Arbitrage [0.0]
  This study delves into the optimization of day-ahead and balancing market trading, leveraging quantile-based forecasts. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets. Despite increased costs and narrower profit margins associated with higher-volume trading, the implementation of high-frequency strategies plays a significant role in maximizing profits.
  arXiv  Detail & Related papers  (2024-06-19T21:27:12Z)
• Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets [13.03742132147551]
  We develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services markets. Unlike conventional "black-box" DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS.
  arXiv  Detail & Related papers  (2024-02-29T12:41:54Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Deep Reinforcement Learning Approach for Trading Automation in The Stock Market [0.0]
  This paper presents a model to generate profitable trades in the stock market using Deep Reinforcement Learning (DRL) algorithms. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set.
  arXiv  Detail & Related papers  (2022-07-05T11:34:29Z)
• Probabilistic forecasting of German electricity imbalance prices [0.0]
  The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place.
  arXiv  Detail & Related papers  (2022-05-23T16:32:20Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)
• The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets [69.68068088508505]
  We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h. We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm. We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
  arXiv  Detail & Related papers  (2021-03-07T11:28:54Z)
• Challenges and Prospects of Negawatt Trading in Light of Recent Technological Developments [69.98112767404695]
  We review the challenges and prospects of negawatt trading in light of recent technological advancements. Grid interactive buildings and distributed ledger technologies can ensure active participation and fair pricing.
  arXiv  Detail & Related papers  (2020-07-16T13:41:14Z)
• A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding [69.37299910149981]
  A key component for the successful renewable energy sources integration is the usage of energy storage. We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market. An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
  arXiv  Detail & Related papers  (2020-04-13T13:50:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.