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Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization

Author

Listed:
  • Tong Xing

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Hongyu Lin

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Zhongfu Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Liwei Ju

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

To promote the utilization of distributed resources, this paper proposes a concept of a micro energy system (MES) and its core structure with energy production, conversion, and storage devices. In addition, the effect of demand–response on the operation of a MES is studied. Firstly, based on uncertainties of a MES, a probability distribution model is introduced. Secondly, with the objectives of maximizing operating revenue, and minimizing operational risk and carbon emissions, a multi-objective coordinated dispatching optimization model was constructed. To solve this model, this paper linearizes objective functions and constraints via fuzzy satisfaction theory, then establishes the input–output matrix of the model and calculates the optimal weight coefficients of different objective functions via the rough set method. Next, a comprehensive dispatching optimization model was built. Finally, data from a MES in Longgang commercial park, Shenzhen City, were introduced for a case study, and the results show that: (1) A MES can integrate different types of energy, such as wind, photovoltaics, and gas. A multi-energy cycle is achieved via energy conversion and storage devices, and different energy demands are satisfied. Demand–response from users in a MES achieves the optimization of source–load interaction. (2) The proposed model gives consideration to the multi-objectives of operating revenue, operational risk, and carbon emissions, and its optimal strategy is obtained by using the proposed solution algorithm. (3) Sensitivity analysis results showed that risks can be avoided, to varying degrees, via reasonable setting of confidence. Price-based demand–response and maximum total emission allowances can be used as indirect factors to influence the energy supply structure of a MES. In summary, the proposed model and solution algorithm are effective tools for different decision makers to conceive of dispatching strategies for different interests.

Suggested Citation

  • Tong Xing & Hongyu Lin & Zhongfu Tan & Liwei Ju, 2019. "Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization," Energies, MDPI, vol. 12(23), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4414-:d:289068
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    References listed on IDEAS

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    2. Yang Zhang & Zhenghui Fu & Yulei Xie & Qing Hu & Zheng Li & Huaicheng Guo, 2020. "A Comprehensive Forecasting–Optimization Analysis Framework for Environmental-Oriented Power System Management—A Case Study of Harbin City, China," Sustainability, MDPI, vol. 12(10), pages 1-26, May.
    3. Przemysław Motyl & Marcin Wikło & Julita Bukalska & Bartosz Piechnik & Rafał Kalbarczyk, 2020. "A New Design for Wood Stoves Based on Numerical Analysis and Experimental Research," Energies, MDPI, vol. 13(5), pages 1-11, February.
    4. Gao, Hongjun & Cai, Wenhui & He, Shuaijia & Liu, Chang & Liu, Junyong, 2023. "Stackelberg game based energy sharing for zero-carbon community considering reward and punishment of carbon emission," Energy, Elsevier, vol. 277(C).

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