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- Title
Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN.
- Authors
Wang, Yuhong; Zhou, Xu; Shi, Yunxiang; Zheng, Zongsheng; Zeng, Qi; Chen, Lei; Xiang, Bo; Huang, Rui
- Abstract
This paper presents a multi-agent Double Deep Q Network (DDQN) based on deep reinforcement learning for solving the transmission network expansion planning (TNEP) of a high-penetration renewable energy source (RES) system considering uncertainty. First, a K-means algorithm that enhances the extraction quality of variable wind and load power uncertain characteristics is proposed. Its clustering objective function considers the cumulation and change rate of operation data. Then, based on the typical scenarios, we build a bi-level TNEP model that includes comprehensive cost, electrical betweenness, wind curtailment and load shedding to evaluate the stability and economy of the network. Finally, we propose a multi-agent DDQN that predicts the construction value of each line through interaction with the TNEP model, and then optimizes the line construction sequence. This training mechanism is more traceable and interpretable than the heuristic-based methods. Simultaneously, the experience reuse characteristic of multi-agent DDQN can be implemented in multi-scenario TNEP tasks without repeated training. Simulation results obtained in the modified IEEE 24-bus system and New England 39-bus system verify the effectiveness of the proposed method.
- Subjects
NEW England; WIND pressure; ELECTRICAL load shedding; DEEP learning; WIND power; RENEWABLE energy sources; REINFORCEMENT learning; K-means clustering
- Publication
Energies (19961073), 2021, Vol 14, Issue 19, p6073
- ISSN
1996-1073
- Publication type
Article
- DOI
10.3390/en14196073