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Bi-level optimal low-carbon economic dispatch for an industrial park with consideration of multi-energy price incentives

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  • Gu, Haifei
  • Li, Yang
  • Yu, Jie
  • Wu, Chen
  • Song, Tianli
  • Xu, Jinzhou

Abstract

Under the retail electricity market reform and the development of demand-side integrated energy systems in China, the Integrated Energy Service Agency (IESA) is responsible for purchasing energy from the external market and supplying it to multi-energy users (MEUs). However, with the increase in the types of MEUs, the IESA has gained more sales options. How to meet the required MEU participation level in the integrated demand response (IDR) plan to ensure that the IESA sets the optimal integrated energy price is an urgent problem. In this paper, a bi-level optimal low-carbon economic dispatch model for an industrial park is proposed considering multi-energy price incentives; at the upper level, the model takes the optimal net income of the IESA as the target, and the carbon emission constraints of the real-time unit integrated energy supply are considered, so that the IESA can reasonably dispatch a comprehensive energy supply, optimize the operation of energy conversion equipment, and set reasonable energy selling prices based on energy prices in the external market. At the lower level, the model takes the minimum integrated energy cost to MEUs as the goal. MEUs take the initiative to obtain retail energy price signals, formulate optimal multi-energy use strategies, and actively participate in the IDR plan. At the same time, coordination between the upper and lower levels helps to optimize the price of the energy sold and the power used by the IDR and is therefore used to achieve the overall environmental and economic requirements of the industrial park. The prime dual path following the interior point method is used to solve the nonlinear, multidimensional, and double-iterative optimization model, and three typical examples are used to illustrate that the model and method can improve the net income of the IESA, ensure the economic and environmental protection of the cooperative multi-energy operation, and encourage MEUs to actively participate in the IDR plan.

Suggested Citation

  • Gu, Haifei & Li, Yang & Yu, Jie & Wu, Chen & Song, Tianli & Xu, Jinzhou, 2020. "Bi-level optimal low-carbon economic dispatch for an industrial park with consideration of multi-energy price incentives," Applied Energy, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:appene:v:262:y:2020:i:c:s0306261919319634
    DOI: 10.1016/j.apenergy.2019.114276
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    2. Gengshun Liu & Xinfu Song & Chaoshan Xin & Tianbao Liang & Yang Li & Kun Liu, 2024. "Edge–Cloud Collaborative Optimization Scheduling of an Industrial Park Integrated Energy System," Sustainability, MDPI, vol. 16(5), pages 1-18, February.
    3. Mu, Yunfei & Wang, Congshan & Cao, Yan & Jia, Hongjie & Zhang, Qingzhu & Yu, Xiaodan, 2022. "A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices," Energy, Elsevier, vol. 247(C).
    4. 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.
    5. Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
    6. Yao, Wenliang & Wang, Chengfu & Yang, Ming & Wang, Kang & Dong, Xiaoming & Zhang, Zhenwei, 2023. "A tri-layer decision-making framework for IES considering the interaction of integrated demand response and multi-energy market clearing," Applied Energy, Elsevier, vol. 342(C).
    7. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "Low-carbon economic dispatch and energy sharing method of multiple Integrated Energy Systems from the perspective of System of Systems," Energy, Elsevier, vol. 244(PA).
    8. Fan, Linyuan & Ji, Dandan & Lin, Geng & Lin, Peng & Liu, Lixi, 2023. "Information gap-based multi-objective optimization of a virtual energy hub plant considering a developed demand response model," Energy, Elsevier, vol. 276(C).
    9. Baoqun Zhang & Cheng Gong & Yan Wang & Longfei Ma & Dongying Zhang & Shiwei Xia, 2023. "Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation," Energies, MDPI, vol. 16(14), pages 1-15, July.
    10. Lv, Si & Wei, Zhinong & Chen, Sheng & Sun, Guoqiang & Wang, Dan, 2021. "Integrated demand response for congestion alleviation in coupled power and transportation networks," Applied Energy, Elsevier, vol. 283(C).
    11. Shi, Zhengkun & Yang, Yongbiao & Xu, Qingshan & Wu, Chenyu & Hua, Kui, 2023. "A low-carbon economic dispatch for integrated energy systems with CCUS considering multi-time-scale allocation of carbon allowance," Applied Energy, Elsevier, vol. 351(C).
    12. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    13. Xingyun Yan & Lingyu Wang & Mingzhu Fang & Jie Hu, 2022. "How Can Industrial Parks Achieve Carbon Neutrality? Literature Review and Research Prospect Based on the CiteSpace Knowledge Map," Sustainability, MDPI, vol. 15(1), pages 1-29, December.

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