Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN

Yuhong Wang, Xu Zhou, Yunxiang Shi, Zongsheng Zheng, Qi Zeng, Lei Chen, Bo Xiang and Rui Huang
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Yuhong Wang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xu Zhou: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yunxiang Shi: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Zongsheng Zheng: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Qi Zeng: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Lei Chen: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Bo Xiang: State Grid Sichuan Comprehensive Energy Service Co., Ltd., Chengdu 610031, China
Rui Huang: State Grid Sichuan Comprehensive Energy Service Co., Ltd., Chengdu 610031, China

Energies, 2021, vol. 14, issue 19, 1-28

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.

Keywords: transmission network expansion planning (TNEP); deep reinforcement learning; uncertainty; wind power; multi-agent DDQN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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