Safe Reinforcement Learning for Strategic Bidding of Virtual Power Plants in Day-Ahead Markets
This addresses the challenge of safe and efficient bidding for Virtual Power Plants in day-ahead markets, representing an incremental improvement with domain-specific enhancements.
The paper tackles the problem of strategic bidding for Virtual Power Plants in electricity markets by developing a safe reinforcement learning algorithm, which achieves competitive bidding policies without needing an accurate market model, as demonstrated in a case study on the IEEE 13-bus network.
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.