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On-line reinforcement learning for optimization of real-life energy trading strategy

arXiv 2303.16266 ↗First posted 28 Mar 2023cs.LG q-fin.TRcited by 0

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Abstract (arXiv)

An increasing share of energy is produced from renewable sources by many small producers. The efficiency of those sources is volatile and, to some extent, random, exacerbating the problem of energy market balancing. In many countries, this balancing is done on the day-ahead (DA) energy markets. This paper considers automated trading on the DA energy market by a medium-sized prosumer. We model this activity as a Markov Decision Process and formalize a framework in which an applicable in real-life strategy can be optimized with off-line data. We design a trading strategy that is fed with the available environmental information that can impact future prices, including weather forecasts. We use state-of-the-art reinforcement learning (RL) algorithms to optimize this strategy. For comparison, we also synthesize simple parametric trading strategies and optimize them with an evolutionary algorithm. Results show that our RL-based strategy generates the highest market profits.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 52 references that are in the map. First posted on arXiv 28 Mar 2023.

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14 of 52 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  4. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
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  6. 2016The CMA Evolution Strategy: A Tutorialcited by 364
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  9. 2018A Dissection of Overfitting and Generalization in Continuous Reinforcement Learningcited by 86
  10. 2019Robust Reinforcement Learning for Continuous Control with Model Misspecificationcited by 76
  11. 2017Towards Generalization and Simplicity in Continuous Controlcited by 65
  12. 2020Robust Reinforcement Learning via Adversarial training with Langevin Dynamicscited by 45
2 more
  1. 2020Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learningcited by 6
  2. 2022Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learningcited by 4

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