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This paper proposes a safe reinforcement learning algorithm for generationbidding decisions and unit maintenance scheduling in a competitive electricitymarket environment. In this problem, each unit aims to find a bidding strategythat maximizes its revenue while concurrently retaining its reliability byscheduling preventive maintenance. The maintenance scheduling provides somesafety constraints which should be satisfied at all times. Satisfying thecritical safety and reliability constraints while the generation units have anincomplete information of each others' bidding strategy is a challengingproblem. Bi-level optimization and reinforcement learning are state of the artapproaches for solving this type of problems. However, neither bi-leveloptimization nor reinforcement learning can handle the challenges of incompleteinformation and critical safety constraints. To tackle these challenges, wepropose the safe deep deterministic policy gradient reinforcement learningalgorithm which is based on a combination of reinforcement learning and apredicted safety filter. The case study demonstrates that the proposed approachcan achieve a higher profit compared to other state of the art methods whileconcurrently satisfying the system safety constraints.

Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units