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Dissipativity based distributed economic model predictive control for residential microgrids with renewable energy generation and battery energy storage

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  • Zhang, Xinan
  • Bao, Jie
  • Wang, Ruigang
  • Zheng, Chaoxu
  • Skyllas-Kazacos, Maria

Abstract

The combination of renewable energy generation and battery energy storage has been widely recognized as a promising solution to the problems associated with variability of renewable energy in residential microgrid. However, due to the low renewable feed-in tariffs in many countries, microgrid users are generally not motivated to install expensive battery systems if they can only be used to satisfy the objective of grid operator. From this perspective, a microgrid power market that encourages users to install batteries for energy-trading will be helpful for the deployment of batteries. For such circumstances, this paper introduces a user-driven microgrid power market. The possible pricing schemes are discussed and an illustrative price controller is presented. The potential destabilizing effect of the collective trading behavior of users is analyzed. A novel dissipativity based distributed economic model prediction control approach is proposed to allow microgrid users to optimize their own benefits while ensuring the performance and stability of the residential microgrid. A simulation study with photovoltaic energy generation and Vanadium Redox batteries is presented to illustrate the efficacy of the proposed method.

Suggested Citation

  • Zhang, Xinan & Bao, Jie & Wang, Ruigang & Zheng, Chaoxu & Skyllas-Kazacos, Maria, 2017. "Dissipativity based distributed economic model predictive control for residential microgrids with renewable energy generation and battery energy storage," Renewable Energy, Elsevier, vol. 100(C), pages 18-34.
    • Handle: RePEc:eee:renene:v:100:y:2017:i:c:p:18-34
    DOI: 10.1016/j.renene.2016.05.006
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    Citations

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    Cited by:

    1. Xinan Zhang & Ruigang Wang & Jie Bao, 2018. "A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids," Mathematics, MDPI, vol. 6(4), pages 1-21, April.
    2. Rubén López-Rodríguez & Adriana Aguilera-González & Ionel Vechiu & Seddik Bacha, 2021. "Day-Ahead MPC Energy Management System for an Island Wind/Storage Hybrid Power Plant," Energies, MDPI, vol. 14(4), pages 1-33, February.
    3. Karol Bot & Inoussa Laouali & António Ruano & Maria da Graça Ruano, 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques," Energies, MDPI, vol. 14(18), pages 1-27, September.
    4. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    5. Jinghan Cui & Su Liu & Jinfeng Liu & Xiangjie Liu, 2018. "A Comparative Study of MPC and Economic MPC of Wind Energy Conversion Systems," Energies, MDPI, vol. 11(11), pages 1-23, November.
    6. Vieira, Filomeno M. & Moura, Pedro S. & de Almeida, Aníbal T., 2017. "Energy storage system for self-consumption of photovoltaic energy in residential zero energy buildings," Renewable Energy, Elsevier, vol. 103(C), pages 308-320.
    7. Hu, Jiefeng & Shan, Yinghao & Guerrero, Josep M. & Ioinovici, Adrian & Chan, Ka Wing & Rodriguez, Jose, 2021. "Model predictive control of microgrids – An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    8. Gulnar Shaimardanovna Kaliakparova & Y?lena Evgenevna Gridneva & Sara Sarsebekovna Assanova & Sandugash Babagalikyzy Sauranbay & Abdizhapar Djumanovich Saparbayev, 2020. "International Economic Cooperation of Central Asian Countries on Energy Efficiency and Use of Renewable Energy Sources," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 539-545.
    9. Jiefeng Hu & Ka Wai Eric Cheng, 2017. "Predictive Control of Power Electronics Converters in Renewable Energy Systems," Energies, MDPI, vol. 10(4), pages 1-14, April.
    10. Ihsan Ullah & Muhammad Babar Rasheed & Thamer Alquthami & Shahzadi Tayyaba, 2019. "A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid," Sustainability, MDPI, vol. 12(1), pages 1-36, December.
    11. Zhu, Zheng & Liu, Xiangjie & Kong, Xiaobing & Ma, Lele & Lee, Kwang Y. & Xu, Yuping, 2024. "PV/Hydrogen DC microgrid control using distributed economic model predictive control," Renewable Energy, Elsevier, vol. 222(C).
    12. Ashok Krishnan & L. P. M. I. Sampath & Y. S. Foo Eddy & H. B. Gooi, 2018. "Optimal Scheduling of a Microgrid Including Pump Scheduling and Network Constraints," Complexity, Hindawi, vol. 2018, pages 1-20, July.
    13. Kutaiba Sabah Nimma & Monaaf D. A. Al-Falahi & Hung Duc Nguyen & S. D. G. Jayasinghe & Thair S. Mahmoud & Michael Negnevitsky, 2018. "Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids," Energies, MDPI, vol. 11(4), pages 1-27, April.

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