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Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output

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  • Zheng, Lingwei
  • Zhou, Xingqiu
  • Qiu, Qi
  • Yang, Lan

Abstract

The intermittency and uncertainty output of renewable energy source (RES) poses a challenge for the optimal dispatch of an integrated energy system (IES). Many current studies focus on the time-domain characteristics of the RES prediction, while ignoring the time-frequency characteristics of the prediction, thus the operation is usually scheduled on a uniform time-step. Based on this context, an adaptive hybrid dispatch time-step (AHDT) determination method based on the time-frequency characteristics of the prediction is proposed. Different from the traditional time-step, AHDT is non-uniform. Firstly, the Hilbert-Huang transform is used to convert the predicted RES output data to the time-frequency. Next, a time-step function which reflects the mapping of time-step and instantaneous frequency is constructed to suggest an adaptive time-step in each dispatch segment. Then, an optimal day-ahead dispatch model of a typical IES with AHDT, which is essentially a mixed integer quadratic programming (MIQP) problem, is established. Finally, the day-ahead schedules of each unit are obtained by solving the MIQP problem. The simulations are conducted in a total of twenty randomly selected days. The results show that the AHDT method is superior to the uniform time-step method for its effect in reducing the operating cost and the optimization-calculating time cost.

Suggested Citation

  • Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
  • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315425
    DOI: 10.1016/j.energy.2020.118434
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    as
    1. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    2. Zhang, Yan & Fu, Lijun & Zhu, Wanlu & Bao, Xianqiang & Liu, Cang, 2018. "Robust model predictive control for optimal energy management of island microgrids with uncertainties," Energy, Elsevier, vol. 164(C), pages 1229-1241.
    3. Patteeuw, Dieter & Henze, Gregor P. & Helsen, Lieve, 2016. "Comparison of load shifting incentives for low-energy buildings with heat pumps to attain grid flexibility benefits," Applied Energy, Elsevier, vol. 167(C), pages 80-92.
    4. Li, Guoqing & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Bai, Linquan & Cui, Hantao & Li, Xiaojing, 2017. "Optimal dispatch strategy for integrated energy systems with CCHP and wind power," Applied Energy, Elsevier, vol. 192(C), pages 408-419.
    5. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Xu, Xiandong & Yu, Xiaodan, 2016. "Optimal day-ahead scheduling of integrated urban energy systems," Applied Energy, Elsevier, vol. 180(C), pages 1-13.
    6. Hung, Tzu-Chieh & Chong, John & Chan, Kuei-Yuan, 2017. "Reducing uncertainty accumulation in wind-integrated electrical grid," Energy, Elsevier, vol. 141(C), pages 1072-1083.
    7. Liu, Fan & Bie, Zhaohong & Liu, Shiyu & Ding, Tao, 2017. "Day-ahead optimal dispatch for wind integrated power system considering zonal reserve requirements," Applied Energy, Elsevier, vol. 188(C), pages 399-408.
    8. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    9. Abujarad, Saleh Y. & Mustafa, M.W. & Jamian, J.J., 2017. "Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 215-223.
    10. Yunlong Gao & Feng Gao & Qiaozhu Zhai & Xiaohong Guan, 2013. "Self-balancing dynamic scheduling of electrical energy for energy-intensive enterprises," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(6), pages 1006-1025.
    11. Bahramara, Salah & Sheikhahmadi, Pouria & Golpîra, Hêmin, 2019. "Co-optimization of energy and reserve in standalone micro-grid considering uncertainties," Energy, Elsevier, vol. 176(C), pages 792-804.
    12. Azizipanah-Abarghooee, Rasoul & Niknam, Taher & Bina, Mohammad Amin & Zare, Mohsen, 2015. "Coordination of combined heat and power-thermal-wind-photovoltaic units in economic load dispatch using chance-constrained and jointly distributed random variables methods," Energy, Elsevier, vol. 79(C), pages 50-67.
    13. Daneshvar, Mohammadreza & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Asadi, Somayeh, 2020. "Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment," Energy, Elsevier, vol. 193(C).
    14. Jin, Ming & Feng, Wei & Marnay, Chris & Spanos, Costas, 2018. "Microgrid to enable optimal distributed energy retail and end-user demand response," Applied Energy, Elsevier, vol. 210(C), pages 1321-1335.
    15. Turk, Ana & Wu, Qiuwei & Zhang, Menglin & Østergaard, Jacob, 2020. "Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing," Energy, Elsevier, vol. 196(C).
    16. Jin, Ming & Feng, Wei & Liu, Ping & Marnay, Chris & Spanos, Costas, 2017. "MOD-DR: Microgrid optimal dispatch with demand response," Applied Energy, Elsevier, vol. 187(C), pages 758-776.
    17. 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.
    18. Nie, S. & Huang, Z.C. & Huang, G.H. & Yu, L. & Liu, J., 2018. "Optimization of electric power systems with cost minimization and environmental-impact mitigation under multiple uncertainties," Applied Energy, Elsevier, vol. 221(C), pages 249-267.
    19. Lin, Zhenjia & Chen, Haoyong & Wu, Qiuwei & Li, Weiwei & Li, Mengshi & Ji, Tianyao, 2020. "Mean-tracking model based stochastic economic dispatch for power systems with high penetration of wind power," Energy, Elsevier, vol. 193(C).
    20. Li, Zhengmao & Xu, Yan, 2019. "Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties," Applied Energy, Elsevier, vol. 240(C), pages 719-729.
    21. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "Economic dispatch of multiple energy carriers," Energy, Elsevier, vol. 138(C), pages 861-872.
    22. Lund, Henrik & Østergaard, Poul Alberg & Connolly, David & Mathiesen, Brian Vad, 2017. "Smart energy and smart energy systems," Energy, Elsevier, vol. 137(C), pages 556-565.
    23. Zhang, Yan & Meng, Fanlin & Wang, Rui & Kazemtabrizi, Behzad & Shi, Jianmai, 2019. "Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid," Energy, Elsevier, vol. 179(C), pages 1265-1278.
    24. Chen, J.J. & Qi, B.X. & Peng, K. & Li, Y. & Zhao, Y.L., 2020. "Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch," Applied Energy, Elsevier, vol. 261(C).
    25. Ji, Ling & Huang, Guo-He & Huang, Lu-Cheng & Xie, Yu-Lei & Niu, Dong-Xiao, 2016. "Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty," Energy, Elsevier, vol. 109(C), pages 920-932.
    26. Wang, Yuwei & Tang, Liu & Yang, Yuanjuan & Sun, Wei & Zhao, Huiru, 2020. "A stochastic-robust coordinated optimization model for CCHP micro-grid considering multi-energy operation and power trading with electricity markets under uncertainties," Energy, Elsevier, vol. 198(C).
    27. Zafarani, Hamidreza & Taher, Seyed Abbas & Shahidehpour, Mohammad, 2020. "Robust operation of a multicarrier energy system considering EVs and CHP units," Energy, Elsevier, vol. 192(C).
    28. Lv, Chaoxian & Yu, Hao & Li, Peng & Wang, Chengshan & Xu, Xiandong & Li, Shuquan & Wu, Jianzhong, 2019. "Model predictive control based robust scheduling of community integrated energy system with operational flexibility," Applied Energy, Elsevier, vol. 243(C), pages 250-265.
    29. Lu, W.T. & Dai, C. & Fu, Z.H. & Liang, Z.Y. & Guo, H.C., 2018. "An interval-fuzzy possibilistic programming model to optimize China energy management system with CO2 emission constraint," Energy, Elsevier, vol. 142(C), pages 1023-1039.
    30. 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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