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.

该论文提出了一种新颖的安全强化学习算法，用于虚拟电力厂在日前电力市场上的战略竞标，同时考虑了虚拟电力厂内部复杂的物理约束，并结合 DDPG 方法设计了投影安全屏障和奖励机制，最终在 IEEE 13-bus 网络上展示了提出方法的有效性。