  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.
