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A two-stage multi-objective optimal scheduling in the integrated energy system with We-Energy modeling

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  • Zhang, Ning
  • Sun, Qiuye
  • Yang, Lingxiao

Abstract

This paper proposes a two-stage multi-objective optimal scheduling strategy (TMOS) based on the innovative mathematical model of We-Energy (WE) in the integrated energy system (IES). WE, as a new-style energy unit with full duplex and multi-energy carrier coupling interaction, is necessary to provide a mathematical model to solve the schema translation problem for the scheduling of the WE. Therefore, a WE mathematical model based on Hadamard Product is presented which can clearly show the dynamic properties of internal elements and the full duplex characteristic of the WE. Namely, the optimization model for the WE can be easily and compactly established by utilizing the proposed method. Furthermore, in order to reduce the unfavorable effects of the renewable energy (RE) uncertainty and realize the energy management of the WE, a TMOS on account of the proposed mathematical model is presented to dispatch the WE operation. The comprehensive impact of multiple significant operation indicators is considered in TMOS which conventional methods ignored. The economic benefit and customer satisfaction can be improved by the first-stage of TMOS according to the energy price and the day-ahead forecasting of RE generation. Meanwhile, the TMOS can reduce the impact of the RE prediction error to realize the real-time power balancing and ensure the security operation by regulating the components of the WE in the second-stage dispatch. The proposed strategy is demonstrated by two example cases, where the performance of the TMOS is observed. The consequences of the cases are analyzed in view of the energy exchanges with networks and the outputs of elements in the presented condition. Moreover, the contrast of the proposed optimal scheduling with another traditional optimal method is also discussed in the paper. As the results shown in the cases, the TMOS based on the innovative WE model balances the forecast error and has more benefits in networks influence, customer satisfaction and residual capacity indicator.

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  • Zhang, Ning & Sun, Qiuye & Yang, Lingxiao, 2021. "A two-stage multi-objective optimal scheduling in the integrated energy system with We-Energy modeling," Energy, Elsevier, vol. 215(PB).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pb:s0360544220322283
    DOI: 10.1016/j.energy.2020.119121
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    1. Yang, Hongming & Xiong, Tonglin & Qiu, Jing & Qiu, Duo & Dong, Zhao Yang, 2016. "Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response," Applied Energy, Elsevier, vol. 167(C), pages 353-365.
    2. Najafi, Arsalan & Falaghi, Hamid & Contreras, Javier & Ramezani, Maryam, 2016. "Medium-term energy hub management subject to electricity price and wind uncertainty," Applied Energy, Elsevier, vol. 168(C), pages 418-433.
    3. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
    4. Wang, Jiawei & You, Shi & Zong, Yi & Cai, Hanmin & Træholt, Chresten & Dong, Zhao Yang, 2019. "Investigation of real-time flexibility of combined heat and power plants in district heating applications," Applied Energy, Elsevier, vol. 237(C), pages 196-209.
    5. Sheikhi, Aras & Bahrami, Shahab & Ranjbar, Ali Mohammad, 2015. "An autonomous demand response program for electricity and natural gas networks in smart energy hubs," Energy, Elsevier, vol. 89(C), pages 490-499.
    6. Chuanjia Han & Bo Yang & Tao Bao & Tao Yu & Xiaoshun Zhang, 2017. "Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer," Energies, MDPI, vol. 10(5), pages 1-24, May.
    7. Yang, Yulong & Wu, Kai & Long, Hongyu & Gao, Jianchao & Yan, Xu & Kato, Takeyoshi & Suzuoki, Yasuo, 2014. "Integrated electricity and heating demand-side management for wind power integration in China," Energy, Elsevier, vol. 78(C), pages 235-246.
    8. Kamyab, Farhad & Bahrami, Shahab, 2016. "Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets," Energy, Elsevier, vol. 106(C), pages 343-355.
    9. Ommen, Torben & Markussen, Wiebke Brix & Elmegaard, Brian, 2014. "Comparison of linear, mixed integer and non-linear programming methods in energy system dispatch modelling," Energy, Elsevier, vol. 74(C), pages 109-118.
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    Cited by:

    1. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    2. Yin, Linfei & Zhao, Lulin, 2021. "Rejectable deep differential dynamic programming for real-time integrated generation dispatch and control of micro-grids," Energy, Elsevier, vol. 225(C).
    3. Dezhou Kong & Jianru Jing & Tingyue Gu & Xuanyue Wei & Xingning Sa & Yimin Yang & Zhiang Zhang, 2023. "Theoretical Analysis of Integrated Community Energy Systems (ICES) Considering Integrated Demand Response (IDR): A Review of the System Modelling and Optimization," Energies, MDPI, vol. 16(10), pages 1-22, May.
    4. 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.
    5. Pan, Chenyun & Fan, Hongtao & Zhang, Ruixiang & Sun, Jie & Wang, Yu & Sun, Yaojie, 2023. "An improved multi-timescale coordinated control strategy for an integrated energy system with a hybrid energy storage system," Applied Energy, Elsevier, vol. 343(C).

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