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Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid

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  • Zhang, Xiaoshun
  • Bao, Tao
  • Yu, Tao
  • Yang, Bo
  • Han, Chuanjia

Abstract

This paper proposes a novel deep transfer Q-learning (DTQ) associated with a virtual leader-follower pattern for supply-demand Stackelberg game of smart grid. Each generator and load are regarded as an agent of a supplier and a demander, respectively, in which an economic dispatch (ED) and demand response (DR) can be simultaneously solved by DTQ. To maximize the total payoff of all the agents, a virtual leader-follower pattern is employed to achieve a reliable collaboration among the agents. Then, Q-learning with a cooperative swarm is adopted for the knowledge learning for each agent via appropriate explorations and exploitations in an unknown environment. Furthermore, the original extremely large-scale knowledge matrix can be efficiently decomposed into several simplified small-scale knowledge matrices through a binary state-action chain, while the continuous actions can be generated for continuous variables. Lastly, a deep belief network (DBN) is used for knowledge transfer, thus DTQ can effectively exploit the prior knowledge from source tasks so as to rapidly obtain an optimal solution of a new task. Case studies are carried out to evaluate the performance of DTQ for supply-demand Stackelberg game of smart grid on a 94-agent system and a practical Shenzhen power grid of southern China.

Suggested Citation

  • Zhang, Xiaoshun & Bao, Tao & Yu, Tao & Yang, Bo & Han, Chuanjia, 2017. "Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid," Energy, Elsevier, vol. 133(C), pages 348-365.
    • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:348-365
    DOI: 10.1016/j.energy.2017.05.114
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    Citations

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    Cited by:

    1. Lijing Zhu & Jingzhou Wang & Arash Farnoosh & Xunzhang Pan, 2021. "A Game-Theory Analysis of Electric Vehicle Adoption in Beijing under License Plate Control Policy," Working Papers hal-03500766, HAL.
    2. Hu, Chenxi & Zhang, Jun & Yuan, Hongxia & Gao, Tianlu & Jiang, Huaiguang & Yan, Jing & Wenzhong Gao, David & Wang, Fei-Yue, 2022. "Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19," Applied Energy, Elsevier, vol. 309(C).
    3. Zhang, Xiaoshun & Guo, Zhengxun & Pan, Feng & Yang, Yuyao & Li, Chuansheng, 2023. "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization," Energy, Elsevier, vol. 283(C).
    4. Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Coordination of resources at the edge of the electricity grid: Systematic review and taxonomy," Applied Energy, Elsevier, vol. 318(C).
    5. Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
    6. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    7. Luqin Fan & Jing Zhang & Yu He & Ying Liu & Tao Hu & Heng Zhang, 2021. "Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning," Energies, MDPI, vol. 14(3), pages 1-15, January.
    8. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    9. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    10. Xue Zhou & Jianan Shou & Weiwei Cui, 2022. "A Game-Theoretic Approach to Design Solar Power Generation/Storage Microgrid System for the Community in China," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    11. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
    12. Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility," Applied Energy, Elsevier, vol. 314(C).
    13. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    14. Zhang, Xiaoshun & Li, Shengnan & He, Tingyi & Yang, Bo & Yu, Tao & Li, Haofei & Jiang, Lin & Sun, Liming, 2019. "Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition," Energy, Elsevier, vol. 174(C), pages 1079-1090.

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