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Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks
طرح کنترل بهینه محدود مبتنی بر یادگیری تقویتی عمیق ایمن برای شبکه های توزیع فعال-2020 Reinforcement learning-based schemes are being recently applied for model-free voltage control in active distribution
networks. However, existing reinforcement learning methods face challenges when it comes to continuous
state and action spaces problems or problems with operation constraints. To address these limitations,
this paper proposes an optimal voltage control scheme based on the safe deep reinforcement learning. In this
scheme, the optimal voltage control problem is formulated as a constrained Markov decision process, in which
both state and action spaces are continuous. To solve this problem efficiently, the deep deterministic policy
gradient algorithm is utilized to learn the reactive power control policies, which determine the optimal control
actions from the states. In contrast to existing reinforcement learning methods, deep deterministic policy gradient
is naturally capable of addressing control problems with continuous state and action spaces. This is due to
the utilization of deep neural networks to approximate both value function and policy. In addition, in order to
handle the operation constraints in active distribution networks, a safe exploration approach is proposed to form
a safety layer, which is composed directly on top the deep deterministic policy gradient actor network. This
safety layer predicts the change in the constrained states and prevents the violation of active distribution net works operation constraints. Numerical simulations on modified IEEE test systems demonstrate that the proposed
scheme successfully maintains all bus voltage within the allowed range, and reduces the system loss by
15% compared to the no control case. Keywords: Active distribution network | Constraint satisfaction | Deep deterministic policy gradient | Optimal voltage control | Smart transformer |
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