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Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program

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  • Yu Huang

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Shuqin Li

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Peng Ding

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Yan Zhang

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Kai Yang

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Weiting Zhang

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

Abstract

An MECS (multiple energy carrier system) could meet diverse energy needs owing to the integration of different energy carriers, while the distinction of quality of different energy resources should be taken into account during the operation stage, in addition the economic principle. Hence, in this paper, the concept of exergy is adopted to evaluate each energy carrier, and an economic–exergetic optimal scheduling model is formulated into a mixed integer linear programming (MILP) problem with the implementation of a real-time pricing (RTP)-based demand response (DR) program. Moreover, a multi-objective (MO) operation strategy is applied to this scheduling model, which is divided into two parts. First, the ε-constraint method is employed to cope with the MILP problem to obtain the Pareto front by using the state-of-the-art CPLEX solver under the General Algebraic Modeling System (GAMS) environment. Then, a preferred solution selection strategy is introduced to make a trade-off between the economic and exergetic objectives. A test system is investigated on a typical summer day, and the optimal dispatch results are compared to validate the effectiveness of the proposed model and MO operation strategy with and without DR. It is concluded that the MECS operator could more rationally allocate different energy carriers and decrease energy cost and exergy input simultaneously with the consideration of the DR scheme.

Suggested Citation

  • Yu Huang & Shuqin Li & Peng Ding & Yan Zhang & Kai Yang & Weiting Zhang, 2019. "Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program," Energies, MDPI, vol. 12(20), pages 1-21, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3995-:d:278587
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    References listed on IDEAS

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    1. Yi, Ji Hyun & Ko, Woong & Park, Jong-Keun & Park, Hyeongon, 2018. "Impact of carbon emission constraint on design of small scale multi-energy system," Energy, Elsevier, vol. 161(C), pages 792-808.
    2. Li, Zhengmao & Xu, Yan, 2018. "Optimal coordinated energy dispatch of a multi-energy microgrid in grid-connected and islanded modes," Applied Energy, Elsevier, vol. 210(C), pages 974-986.
    3. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    4. Chow, T.T. & Pei, G. & Fong, K.F. & Lin, Z. & Chan, A.L.S. & Ji, J., 2009. "Energy and exergy analysis of photovoltaic-thermal collector with and without glass cover," Applied Energy, Elsevier, vol. 86(3), pages 310-316, March.
    5. Aghamohamadi, Mehrdad & Hajiabadi, Mohammad Ebrahim & Samadi, Mahdi, 2018. "A novel approach to multi energy system operation in response to DR programs; an application to incentive-based and time-based schemes," Energy, Elsevier, vol. 156(C), pages 534-547.
    6. Ahmadi, Pouria & Rosen, Marc A. & Dincer, Ibrahim, 2012. "Multi-objective exergy-based optimization of a polygeneration energy system using an evolutionary algorithm," Energy, Elsevier, vol. 46(1), pages 21-31.
    7. Ahmadi, Pouria & Dincer, Ibrahim, 2010. "Exergoenvironmental analysis and optimization of a cogeneration plant system using Multimodal Genetic Algorithm (MGA)," Energy, Elsevier, vol. 35(12), pages 5161-5172.
    8. Khorsand, Hosein & Seifi, Ali Reza, 2018. "Probabilistic energy flow for multi-carrier energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 989-997.
    9. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
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    Cited by:

    1. Yu Huang & Weizhen Hou & Yiran Huang & Jiayu Li & Qixian Li & Dongfeng Wang & Yan Zhang, 2020. "Multi-Objective Optimal Operation for Steam Power Scheduling Based on Economic and Exergetic Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Li, Rong & Guo, Su & Yang, Yong & Liu, Deyou, 2020. "Optimal sizing of wind/ concentrated solar plant/ electric heater hybrid renewable energy system based on two-stage stochastic programming," Energy, Elsevier, vol. 209(C).
    3. Changrong Liu & Hanqing Wang & Zhiqiang Liu & Zhiyong Wang & Sheng Yang, 2021. "Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.
    4. Yong Yang & Rong Li, 2020. "Techno-Economic Optimization of an Off-Grid Solar/Wind/Battery Hybrid System with a Novel Multi-Objective Differential Evolution Algorithm," Energies, MDPI, vol. 13(7), pages 1-16, April.

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