Safe Reinforcement Learning for Strategic Bidding of Virtual Power Plants in Day-Ahead Markets
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Abstract (arXiv)
This paper presents a novel safe reinforcement learning algorithm for strategic bidding of Virtual Power Plants (VPPs) in day-ahead electricity markets. The proposed algorithm utilizes the Deep Deterministic Policy Gradient (DDPG) method to learn competitive bidding policies without requiring an accurate market model. Furthermore, to account for the complex internal physical constraints of VPPs we introduce two enhancements to the DDPG method. Firstly, a projection-based safety shield that restricts the agent's actions to the feasible space defined by the non-linear power flow equations and operating constraints of distributed energy resources is derived. Secondly, a penalty for the shield activation in the reward function that incentivizes the agent to learn a safer policy is introduced. A case study based on the IEEE 13-bus network demonstrates the effectiveness of the proposed approach in enabling the agent to learn a highly competitive, safe strategic policy.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 11 Jul 2023.
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8 of 29 references.
References · 8
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2020Recovery RL: Safe Reinforcement Learning with Learned Recovery Zonescited by 175
- 2020Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?cited by 34
- 2022Towards Safe Reinforcement Learning with a Safety Editor Policycited by 30
- 2019Probabilistic model predictive safety certification for learning-based controlcited by 22
- 2020A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systemscited by 4
- 2021A Learning-based Optimal Market Bidding Strategy for Price-Maker Energy Storagecited by 4
- 2022Multiple Ancillary Services Provision by Distributed Energy Resources in Active Distribution Networkscited by 1
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2 papers.