Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2023 , Vol. 57 >Issue 9: 1126 - 1136

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.378

New Type Power System and the Integrated Energy

A Microgrid Energy Management Strategy Considering Carbon Quota Guided Demand Response

  • Enyu JIANG ,
  • Yu CHEN ,
  • Zhengjing SHI ,
  • Zhecheng WU ,
  • Shunfu LIN ,
  • Dongdong LI
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  • 1. College of Electrical Engineering, Shanghai Electric Power University, Shanghai 200082, China
    2. Shandong Nuclear Power Co., Ltd., Yantai 264000, Shandong, China

Received date: 2022-09-29

  Revised date: 2022-12-24

  Accepted date: 2022-12-29

  Online published: 2023-05-23

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Abstract

In order to reduce the forecast output error caused by the randomness and volatility of renewable energy in microgrid operation, a microgrid energy management strategy considering carbon quota guided demand response is proposed. A two-layer model predictive control (MPC) energy management model is constructed. The upper layer guides electric vehicles to participate in the demand response of microgrid by constructing a carbon emission quota mechanism to realize the economic operation of microgrid and reduce carbon emissions. The lower layer uses the model predictive control rolling optimization and the power fluctuation caused by the prediction error of renewable energy is suppressed by the short time scale model predictive control. The results of calculation analysis show that the proposed energy management strategy can effectively guide electric vehicles or other controllable loads to participate in demand response and realize low-carbon economic dispatch and stable operation of microgrid.

Key words： microgrid; energy management; model predictive control (MPC); carbon quota; demand response

Cite this article

Enyu JIANG , Yu CHEN , Zhengjing SHI , Zhecheng WU , Shunfu LIN , Dongdong LI . A Microgrid Energy Management Strategy Considering Carbon Quota Guided Demand Response[J]. Journal of Shanghai Jiaotong University, 2023 , 57(9) : 1126 -1136 . DOI: 10.16183/j.cnki.jsjtu.2022.378

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