IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6073-d641831.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6073/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6073/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baringo, Luis & Boffino, Luigi & Oggioni, Giorgia, 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units," Applied Energy, Elsevier, vol. 265(C).
    2. Merrick, James H. & Bistline, John E.T. & Blanford, Geoffrey J., 2024. "On representation of energy storage in electricity planning models," Energy Economics, Elsevier, vol. 136(C).
    3. Jia, Rui & Du, Kun & Song, Zhigang & Xu, Wei & Zheng, Feifei, 2024. "Scenario reduction-based simulation method for efficient serviceability assessment of earthquake-damaged water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    4. Victor H. Hinojosa & Joaquín Sepúlveda, 2020. "Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors," Energies, MDPI, vol. 13(13), pages 1-15, June.
    5. Georgios E. Arnaoutakis & Georgia Kefala & Eirini Dakanali & Dimitris Al. Katsaprakakis, 2022. "Combined Operation of Wind-Pumped Hydro Storage Plant with a Concentrating Solar Power Plant for Insular Systems: A Case Study for the Island of Rhodes," Energies, MDPI, vol. 15(18), pages 1-23, September.
    6. Niu, Jide & Li, Xiaoyuan & Tian, Zhe & Yang, Hongxing, 2023. "A framework for quantifying the value of information to mitigate risk in the optimal design of distributed energy systems under uncertainty," Applied Energy, Elsevier, vol. 350(C).
    7. Xinyu Sun & Hao Wu & Siqi Guo & Lingwei Zheng, 2022. "Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming," Energies, MDPI, vol. 15(18), pages 1-17, September.
    8. Thongsavanh Keokhoungning & Suttichai Premrudeepreechacharn & Wullapa Wongsinlatam & Ariya Namvong & Tawun Remsungnen & Nongram Mueanrit & Kanda Sorn-in & Satit Kravenkit & Apirat Siritaratiwat & Chav, 2022. "Transmission Network Expansion Planning with High-Penetration Solar Energy Using Particle Swarm Optimization in Lao PDR toward 2030," Energies, MDPI, vol. 15(22), pages 1-19, November.
    9. García-Cerezo, Álvaro & Baringo, Luis & García-Bertrand, Raquel, 2021. "Robust transmission network expansion planning considering non-convex operational constraints," Energy Economics, Elsevier, vol. 98(C).
    10. Tostado-Véliz, Marcos & Rezaee Jordehi, Ahmad & Amir Mansouri, Seyed & Jurado, Francisco, 2022. "Day-ahead scheduling of 100% isolated communities under uncertainties through a novel stochastic-robust model," Applied Energy, Elsevier, vol. 328(C).
    11. Gao, Hongjun & Zhao, Yinbo & He, Shuaijia & Liu, Junyong, 2023. "Demand response management of community integrated energy system: A multi-energy retail package perspective," Applied Energy, Elsevier, vol. 330(PA).
    12. Navarro, Andrés & Favereau, Marcel & Lorca, Álvaro & Olivares, Daniel & Negrete-Pincetic, Matías, 2024. "Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks," Applied Energy, Elsevier, vol. 358(C).
    13. Hamdi Abdi & Mansour Moradi & Sara Lumbreras, 2021. "Metaheuristics and Transmission Expansion Planning: A Comparative Case Study," Energies, MDPI, vol. 14(12), pages 1-23, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6073-:d:641831. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.