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

Author

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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)

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.

Suggested Citation

  • Yuhong Wang & Xu Zhou & Yunxiang Shi & Zongsheng Zheng & Qi Zeng & Lei Chen & Bo Xiang & Rui Huang, 2021. "Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN," Energies, MDPI, vol. 14(19), pages 1-28, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6073-:d:641831
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    References listed on IDEAS

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    1. Annas Fauzy & Cheng-Dar Yue & Chien-Cheng Tu & Ta-Hui Lin, 2021. "Understanding the Potential of Wind Farm Exploitation in Tropical Island Countries: A Case for Indonesia," Energies, MDPI, vol. 14(9), pages 1-26, May.
    2. Ming Tang & Jian Wang & Xiaohua Wang, 2020. "Adaptable Source-Grid Planning for High Penetration of Renewable Energy Integrated System," Energies, MDPI, vol. 13(13), pages 1-26, June.
    3. Wenshi Wang & Houqi Dong & Yangfan Luo & Changhao Zhang & Bo Zeng & Fuqiang Xu & Ming Zeng, 2021. "An Interval Optimization-Based Approach for Electric–Heat–Gas Coupled Energy System Planning Considering the Correlation between Uncertainties," Energies, MDPI, vol. 14(9), pages 1-24, April.
    4. Da Li & Shijie Zhang & Yunhan Xiao, 2020. "Interval Optimization-Based Optimal Design of Distributed Energy Resource Systems under Uncertainties," Energies, MDPI, vol. 13(13), pages 1-18, July.
    5. Álvaro García-Cerezo & Luis Baringo & Raquel García-Bertrand, 2020. "Representative Days for Expansion Decisions in Power Systems," Energies, MDPI, vol. 13(2), pages 1-18, January.
    6. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
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    Cited by:

    1. Xinghua Wang & Fucheng Zhong & Yilin Xu & Xixian Liu & Zezhong Li & Jianan Liu & Zhuoli Zhao, 2023. "Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics," Energies, MDPI, vol. 16(18), pages 1-19, September.
    2. Mohamed M. Refaat & Shady H. E. Abdel Aleem & Yousry Atia & Ziad M. Ali & Adel El-Shahat & Mahmoud M. Sayed, 2021. "A Mathematical Approach to Simultaneously Plan Generation and Transmission Expansion Based on Fault Current Limiters and Reliability Constraints," Mathematics, MDPI, vol. 9(21), pages 1-21, November.
    3. Changrong Liu & Hanqing Wang & Zhiqiang Liu & Zhiyong Wang & Sheng Yang, 2021. "Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.
    4. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    5. Gabriel Pesántez & Wilian Guamán & José Córdova & Miguel Torres & Pablo Benalcazar, 2024. "Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies," Energies, MDPI, vol. 17(9), pages 1-25, May.

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