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Energy Management for Smart Multi-Energy Complementary Micro-Grid in the Presence of Demand Response

Author

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  • Yongli Wang

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

  • Yujing Huang

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

  • Yudong Wang

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

  • Haiyang Yu

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

  • Ruiwen Li

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

  • Shanshan Song

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

Abstract

With the application and the rapid advancement of smart grid technology, the practical application and operation status of multi-energy complementary microgrids have been widely investigated. In the paper presented, the optimal operation of a solar unit, a storage battery and combined cooling, heating and power is studied via an economic optimization model implemented in General Algebraic Modeling Systems (GAMS). The model represents an optimization strategy for the economic operation of a microgrid considering demand response programs in different scenarios, and it is intended for the targets of minimizing the operating cost of the microgrid and maximizing the efficiency of renewable energy utilization. In addition, a multi-time electricity price response model based on user behavior and satisfaction is established, and the core value of the model is to describe the mechanism and effect of participation in electricity price demand response. In order to verify the accuracy of the model proposed, we design the dispatch strategy of a microgrid under different states considering demand response, and use genetic algorithm to solve the optimization problems. On the other hand, the application of methodology to a real case study in Suzhou demonstrates the effectiveness of this model to solve the economic dispatch of the microgrid’s renewable energy park.

Suggested Citation

  • Yongli Wang & Yujing Huang & Yudong Wang & Haiyang Yu & Ruiwen Li & Shanshan Song, 2018. "Energy Management for Smart Multi-Energy Complementary Micro-Grid in the Presence of Demand Response," Energies, MDPI, vol. 11(4), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:974-:d:141783
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    References listed on IDEAS

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    3. Rajamand, Sahbasadat, 2020. "Effect of demand response program of loads in cost optimization of microgrid considering uncertain parameters in PV/WT, market price and load demand," Energy, Elsevier, vol. 194(C).
    4. Tengfei Ma & Junyong Wu & Liangliang Hao & Huaguang Yan & Dezhi Li, 2018. "A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach," Energies, MDPI, vol. 11(10), pages 1-19, October.
    5. Liu, Jin-peng & Zhang, Teng-xi & Zhu, Jiang & Ma, Tian-nan, 2018. "Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration," Energy, Elsevier, vol. 164(C), pages 560-574.
    6. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    7. Qiwei Xu & Jianshu Huang & Yue Han & Yun Yang & Lingyan Luo, 2020. "A Study on Electric Vehicles Participating in the Load Regulation of Urban Complexes," Energies, MDPI, vol. 13(11), pages 1-23, June.
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    9. Dezhou Kong & Jianru Jing & Tingyue Gu & Xuanyue Wei & Xingning Sa & Yimin Yang & Zhiang Zhang, 2023. "Theoretical Analysis of Integrated Community Energy Systems (ICES) Considering Integrated Demand Response (IDR): A Review of the System Modelling and Optimization," Energies, MDPI, vol. 16(10), pages 1-22, May.
    10. Wang, Yongli & Huang, Yujing & Wang, Yudong & Zeng, Ming & Yu, Haiyang & Li, Fang & Zhang, Fuli, 2018. "Optimal scheduling of the RIES considering time-based demand response programs with energy price," Energy, Elsevier, vol. 164(C), pages 773-793.
    11. He Huang & DaPeng Liang & Zhen Tong, 2018. "Integrated Energy Micro-Grid Planning Using Electricity, Heating and Cooling Demands," Energies, MDPI, vol. 11(10), pages 1-20, October.
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    13. Temitope Adeyemi-Kayode & Sanjay Misra & Hope Orovwode & Anthony Adoghe, 2022. "Modeling the Next Decade of Energy Sustainability: A Case of a Developing Country," Energies, MDPI, vol. 15(14), pages 1-19, July.

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