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A robust aggregate model and the two-stage solution method to incorporate energy intensive enterprises in power system unit commitment

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  • Jin, Hongyang
  • Li, Zhengshuo
  • Sun, Hongbin
  • Guo, Qinglai
  • Chen, Runze
  • Wang, Bin

Abstract

Incorporating energy-intensive enterprises (EIEs) in power system unit commitments is beneficial for both power systems and EIEs. However, power systems and EIEs are typically operated by different entities, making it impractical to incorporate a full EIE model in the unit commitment because of the increasing model dimensions and consequent calculation efficiency and privacy issues. Therefore, a robust aggregate model (RAM) of the EIE is developed in this paper to simplify the detailed EIE model as a conventional generator; which ensures the robustness of the simplified model, i.e., all possible dispatch signals from system operators can be realized by EIEs. The flexibility of EIEs and the corresponding cost are also retained in the RAM. To obtain the technical parameters of the RAM, a two-stage robust problem is developed, and a modified column-constraint-generation solution method is proposed. In addition, the economic parameters of the RAM are obtained based on the upper convex hull of an EIE’s maximum cost under given dispatch signals. With the proposed RAM, the dimensions and calculation time of the unit commitment incorporating EIEs are drastically decreased, and the EIE can reduce production costs while the system operator can reduce operational costs and increase wind integration.

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  • Jin, Hongyang & Li, Zhengshuo & Sun, Hongbin & Guo, Qinglai & Chen, Runze & Wang, Bin, 2017. "A robust aggregate model and the two-stage solution method to incorporate energy intensive enterprises in power system unit commitment," Applied Energy, Elsevier, vol. 206(C), pages 1364-1378.
    • Handle: RePEc:eee:appene:v:206:y:2017:i:c:p:1364-1378
    DOI: 10.1016/j.apenergy.2017.10.004
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    as
    1. Nezamoddini, Nasim & Wang, Yong, 2017. "Real-time electricity pricing for industrial customers: Survey and case studies in the United States," Applied Energy, Elsevier, vol. 195(C), pages 1023-1037.
    2. Chen, F. & Huang, G.H. & Fan, Y.R. & Chen, J.P., 2017. "A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning," Applied Energy, Elsevier, vol. 187(C), pages 291-309.
    3. Ayón, X. & Gruber, J.K. & Hayes, B.P. & Usaola, J. & Prodanović, M., 2017. "An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands," Applied Energy, Elsevier, vol. 198(C), pages 1-11.
    4. Zhang, X.Y. & Huang, G.H. & Zhu, H. & Li, Y.P., 2017. "A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties," Energy, Elsevier, vol. 123(C), pages 664-676.
    5. Chen, Runze & Sun, Hongbin & Guo, Qinglai & Jin, Hongyang & Wu, Wenchuan & Zhang, Boming, 2015. "Profit-seeking energy-intensive enterprises participating in power system scheduling: Model and mechanism," Applied Energy, Elsevier, vol. 158(C), pages 263-274.
    6. Jiang, Bo & Farid, Amro M. & Youcef-Toumi, Kamal, 2015. "Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches," Applied Energy, Elsevier, vol. 156(C), pages 642-654.
    7. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    8. Mohan, Vivek & Singh, Jai Govind & Ongsakul, Weerakorn, 2015. "An efficient two stage stochastic optimal energy and reserve management in a microgrid," Applied Energy, Elsevier, vol. 160(C), pages 28-38.
    9. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2016. "Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy," Applied Energy, Elsevier, vol. 164(C), pages 590-606.
    10. Yuan-Zhang Sun & Jin Lin & Yong-Hua Song & Jian Xu & Xiao-Ming Li & Jian-Xun Dong, 2012. "An Industrial System Powered by Wind and Coal for Aluminum Production: A Case Study of Technical Demonstration and Economic Feasibility," Energies, MDPI, vol. 5(11), pages 1-26, November.
    11. Park, Heejung & Baldick, Ross, 2016. "Multi-year stochastic generation capacity expansion planning under environmental energy policy," Applied Energy, Elsevier, vol. 183(C), pages 737-745.
    12. Hu, Qing & Huang, Guohe & Cai, Yanpeng & Huang, Ying, 2011. "Feasibility-based inexact fuzzy programming for electric power generation systems planning under dual uncertainties," Applied Energy, Elsevier, vol. 88(12), pages 4642-4654.
    13. Middelberg, Arno & Zhang, Jiangfeng & Xia, Xiaohua, 2009. "An optimal control model for load shifting - With application in the energy management of a colliery," Applied Energy, Elsevier, vol. 86(7-8), pages 1266-1273, July.
    14. Moradi-Dalvand, M. & Mohammadi-Ivatloo, B. & Amjady, N. & Zareipour, H. & Mazhab-Jafari, A., 2015. "Self-scheduling of a wind producer based on Information Gap Decision Theory," Energy, Elsevier, vol. 81(C), pages 588-600.
    15. Pan, Zhaoguang & Guo, Qinglai & Sun, Hongbin, 2017. "Feasible region method based integrated heat and electricity dispatch considering building thermal inertia," Applied Energy, Elsevier, vol. 192(C), pages 395-407.
    16. Ashok, S. & Banerjee, R., 2000. "Load-management applications for the industrial sector," Applied Energy, Elsevier, vol. 66(2), pages 105-111, June.
    17. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
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    2. Liao, Shiwu & Yao, Wei & Han, Xingning & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu & He, Haibo, 2019. "An improved two-stage optimization for network and load recovery during power system restoration," Applied Energy, Elsevier, vol. 249(C), pages 265-275.
    3. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
    4. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    5. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    6. Ma, Huan & Chen, Qun & Hu, Bo & Sun, Qinhan & Li, Tie & Wang, Shunjiang, 2021. "A compact model to coordinate flexibility and efficiency for decomposed scheduling of integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    7. Qiu, Haifeng & Gu, Wei & Pan, Jing & Xu, Bin & Xu, Yinliang & Fan, Miao & Wu, Zhi, 2018. "Multi-interval-uncertainty constrained robust dispatch for AC/DC hybrid microgrids with dynamic energy storage degradation," Applied Energy, Elsevier, vol. 228(C), pages 205-214.

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