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Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response

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  • Yuan, Guanxiu
  • Gao, Yan
  • Ye, Bei

Abstract

With the penetration of multiple distributed energy sources, demand side management (DSM) of the regional integrated energy system (RIES) becomes more complicated in the energy market. Real-time pricing (RTP) is an effective method for DSM, which can flexibly guide the supply and demand sides to adjust their behavior to participate in demand response (DR). In this paper, a hierarchical energy system is studied including multiple RIESs with multiple energy dispatch and supplement. To maximize the social welfare, a bilevel programming model is developed, in which the upper level aims at maximizing the profits of the supplier, and the lower level aims at maximizing the RIESs' welfare. Then, the proposed bilevel model is transformed into a mixed integer quadratic programming model using duality theory and Karush-Kuhn-Tucker conditions. Furthermore, the RTP strategy is obtained, and the optimal energy scheme of RIES is given in the solution. Compared simulations in different scenarios, the total social welfare is increased by about 14.12%, the peak-to-valley difference of power load and carbon emissions are reduced by 16.99% and 5.7% respectively after DR. The results show that the proposed bilevel model under the RTP is conducive to social economy and environment.

Suggested Citation

  • Yuan, Guanxiu & Gao, Yan & Ye, Bei, 2021. "Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response," Renewable Energy, Elsevier, vol. 179(C), pages 1424-1446.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:1424-1446
    DOI: 10.1016/j.renene.2021.07.036
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    2. Lyu, Xiangmei & Liu, Tianqi & Liu, Xuan & He, Chuan & Nan, Lu & Zeng, Hong, 2023. "Low-carbon robust economic dispatch of park-level integrated energy system considering price-based demand response and vehicle-to-grid," Energy, Elsevier, vol. 263(PB).
    3. Gejirifu De & Xinlei Wang & Xueqin Tian & Tong Xu & Zhongfu Tan, 2022. "A Collaborative Optimization Model for Integrated Energy System Considering Multi-Load Demand Response," Energies, MDPI, vol. 15(6), pages 1-26, March.
    4. Dai, Yeming & Sun, Xilian & Qi, Yao & Leng, Mingming, 2021. "A real-time, personalized consumption-based pricing scheme for the consumptions of traditional and renewable energies," Renewable Energy, Elsevier, vol. 180(C), pages 452-466.
    5. Meng, Anbo & Xu, Xuancong & Zhang, Zhan & Zeng, Cong & Liang, Ruduo & Zhang, Zheng & Wang, Xiaolin & Yan, Baiping & Yin, Hao & Luo, Jianqiang, 2022. "Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy," Energy, Elsevier, vol. 258(C).
    6. Han, Ouzhu & Ding, Tao & Zhang, Xiaosheng & Mu, Chenggang & He, Xinran & Zhang, Hongji & Jia, Wenhao & Ma, Zhoujun, 2023. "A shared energy storage business model for data center clusters considering renewable energy uncertainties," Renewable Energy, Elsevier, vol. 202(C), pages 1273-1290.
    7. Xia, Tangbin & Si, Guojin & Shi, Guo & Zhang, Kaigan & Xi, Lifeng, 2022. "Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization," Applied Energy, Elsevier, vol. 314(C).
    8. Jieran Feng & Junpei Nan & Chao Wang & Ke Sun & Xu Deng & Hao Zhou, 2022. "Source-Load Coordinated Low-Carbon Economic Dispatch of Electric-Gas Integrated Energy System Based on Carbon Emission Flow Theory," Energies, MDPI, vol. 15(10), pages 1-24, May.
    9. Dong, Haiyan & Fu, Yanbo & Jia, Qingquan & Zhang, Tie & Meng, Dequn, 2023. "Low carbon optimization of integrated energy microgrid based on life cycle analysis method and multi time scale energy storage," Renewable Energy, Elsevier, vol. 206(C), pages 60-71.
    10. Dai, Yeming & Yang, Xinyu & Leng, Mingming, 2022. "Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    11. Wang, Yudong & Hu, Junjie, 2023. "Two-stage energy management method of integrated energy system considering pre-transaction behavior of energy service provider and users," Energy, Elsevier, vol. 271(C).

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