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Collaborative optimization scheduling of integrated energy system considering user dissatisfaction

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  • Ma, Kai
  • Zhang, Rencai
  • Yang, Jie
  • Song, Debao

Abstract

Traditional industrial parks generally have the problem of energy waste and low energy utilization. In this paper, we study a multi-energy collaborative optimization problem between integrated energy system (IES) energy scheduling and production control of plant, which considers the change of user dissatisfaction caused by load adjustment after industrial users participate in multi-energy demand response (MEDR). The collaborative optimization problem is described as a quadratic programming (QP) problem, including energy equipment output and production equipment operation load under constraints. The QP problem is solved by Cplex solver. An IES framework is constructed and its components are modeled separately. Then, based on Taguchi loss function and Fanger thermal comfort model, a collaborative optimization model of plant IES considering the user dissatisfaction is proposed. Furthermore, the collaborative optimization model is applied to control the IES and plant production. Simulation results show that the proposed optimization model can reduce the energy cost and user dissatisfaction, and improve the energy efficiency.

Suggested Citation

  • Ma, Kai & Zhang, Rencai & Yang, Jie & Song, Debao, 2023. "Collaborative optimization scheduling of integrated energy system considering user dissatisfaction," Energy, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223007053
    DOI: 10.1016/j.energy.2023.127311
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    References listed on IDEAS

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    1. Acuña, Luceny Guzmán & Padilla, Ricardo Vasquez & Mercado, Alcides Santander, 2017. "Measuring reliability of hybrid photovoltaic-wind energy systems: A new indicator," Renewable Energy, Elsevier, vol. 106(C), pages 68-77.
    2. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    3. Ekren, Orhan & Ekren, Banu Y., 2010. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing," Applied Energy, Elsevier, vol. 87(2), pages 592-598, February.
    4. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    5. Kourkoumpas, Dimitrios-Sotirios & Benekos, Georgios & Nikolopoulos, Nikolaos & Karellas, Sotirios & Grammelis, Panagiotis & Kakaras, Emmanouel, 2018. "A review of key environmental and energy performance indicators for the case of renewable energy systems when integrated with storage solutions," Applied Energy, Elsevier, vol. 231(C), pages 380-398.
    6. Derakhshan, Ghasem & Shayanfar, Heidar Ali & Kazemi, Ahad, 2016. "The optimization of demand response programs in smart grids," Energy Policy, Elsevier, vol. 94(C), pages 295-306.
    7. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote island," Applied Energy, Elsevier, vol. 121(C), pages 149-158.
    8. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    9. Cho, Heejin & Mago, Pedro J. & Luck, Rogelio & Chamra, Louay M., 2009. "Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme," Applied Energy, Elsevier, vol. 86(12), pages 2540-2549, December.
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    Cited by:

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    2. Jia, Zhiyang & Jin, Xinqiao & Lyu, Yuan & Xue, Qi & Du, Zhimin, 2023. "A robust capacity configuration selection method of multiple-chiller system concerned with the uncertainty of annual hourly load profile," Energy, Elsevier, vol. 282(C).
    3. Lin, Xiaojie & Lin, Xueru & Zhong, Wei & Zhou, Yi, 2024. "Multi-time scale dynamic operation optimization method for industrial park electricity-heat-gas integrated energy system considering demand elasticity," Energy, Elsevier, vol. 293(C).
    4. Lili Mo & Zeyu Deng & Haoyong Chen & Junkun Lan, 2023. "Multi-Objective Co-Operative Game-Based Optimization for Park-Level Integrated Energy System Based on Exergy-Economic Analysis," Energies, MDPI, vol. 16(24), pages 1-19, December.
    5. Yang, Meng & Liu, Yisheng, 2023. "Research on multi-energy collaborative operation optimization of integrated energy system considering carbon trading and demand response," Energy, Elsevier, vol. 283(C).

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