Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet
نویسندگان
چکیده
Based on a Double Deep-Q Network with deep ResNet (DDQN-ResNet), this paper proposes novel method for transmission network expansion planning (TNEP). Since TNEP is large scale and mixed-integer linear programming (MILP) problem, as the optimal constraints increase, numerical calculation heuristic learning-based methods suffer from heavy computational complexities in training. Besides, due to black box characteristic, solution processes of are inexplicable usually require repeated By using DDQN-ResNet, constructs high-performance flexible solve large-scale complex-constrained problem. Firstly, we form two-objective model, which one objective minimize comprehensive cost, another maximize reliability. The cost takes into account loss maintenance cost. reliability evaluated by expected energy not served (EENS) electrical betweenness. Secondly, task constructed based Markov decision process. abstracting task, environment obtained DDQN-ResNet. In addition, identify construction value lines, an agent establish Finally, perform static visualize reinforcement learning dynamic realized reusing training experience. validity flexibility DDQN-ResNet verified RTS 24-bus test system.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3083266