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Interactive game vector: A stochastic operation-based pricing mechanism for smart energy systems with coupled-microgrids

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  • Lu, Tianguang
  • Ai, Qian
  • Wang, Zhaoyu

Abstract

To accommodate the large scale of renewable energy resources now widely integrated into power systems, an interactive two-level pricing mechanism for coupled microgrids (MG) in a smart energy system is proposed that considers operational quality and renewable generation uncertainty. In the upper level of the pricing mechanism, the distribution energy market operator (DEMO) guarantees operational quality by trading energy with coupled microgrids, while the actual transactions between networked microgrids is performed at the lower level. Stochastic programming is applied to handle the uncertainty caused by large-scale renewable integration. An innovative time-varying game vector and energy transaction strategy deal with the spatio-temporal market interaction of the networked microgrids, where each microgrid is able to directly trade all types of energy with any other microgrid at any time. The proposed model is solved using a customized hierarchical genetic algorithm. Case studies on an IEEE bus test feeder and an existing energy system in China demonstrate the effectiveness of the proposed methodology.

Suggested Citation

  • Lu, Tianguang & Ai, Qian & Wang, Zhaoyu, 2018. "Interactive game vector: A stochastic operation-based pricing mechanism for smart energy systems with coupled-microgrids," Applied Energy, Elsevier, vol. 212(C), pages 1462-1475.
    • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:1462-1475
    DOI: 10.1016/j.apenergy.2017.12.096
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    3. Mahdi Azimian & Vahid Amir & Reza Habibifar & Hessam Golmohamadi, 2021. "Probabilistic Optimization of Networked Multi-Carrier Microgrids to Enhance Resilience Leveraging Demand Response Programs," Sustainability, MDPI, vol. 13(11), pages 1-30, May.
    4. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    5. Zhu, Dafeng & Yang, Bo & Liu, Qi & Ma, Kai & Zhu, Shanying & Ma, Chengbin & Guan, Xinping, 2020. "Energy trading in microgrids for synergies among electricity, hydrogen and heat networks," Applied Energy, Elsevier, vol. 272(C).
    6. Zhou, Dezhi & Wu, Chuantao & Sui, Quan & Lin, Xiangning & Li, Zhengtian, 2022. "A novel all-electric-ship-integrated energy cooperation coalition for multi-island microgrids," Applied Energy, Elsevier, vol. 320(C).
    7. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    8. Mei, Jie & Chen, Chen & Wang, Jianhui & Kirtley, James L., 2019. "Coalitional game theory based local power exchange algorithm for networked microgrids," Applied Energy, Elsevier, vol. 239(C), pages 133-141.
    9. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
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