Two-layer Dispatch Model of Integrated Energy System Considering Dynamic Time-interval

被引：0
|
作者：
Ma Z. [1 ]
Jia Y. [1 ]
Han X. [1 ]
Kang L. [1 ]
Ren H. [1 ]
机构：
[1] Shanxi Key Lab of Power System Operation and Control (Taiyuan University of Technology), Taiyuan, 030024, Shanxi Province
来源：
Dianwang Jishu/Power System Technology | 2022年 / 46卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Dynamic time-interval; Energy characteristics; Integrated energy system; Multi-time scale;
D O I：
10.13335/j.1000-3673.pst.2021.1203
中图分类号：
学科分类号：
摘要：
In integrated energy systems with multiple energy sources such as electricity, cold/heat and natural gas, due to the differences in the dynamic characteristics of the energy sources, the scheduling instruction cycle is one of the key factors affecting the scheduling performances. Given the different characteristics of energy sources, a two-layer rolling optimization scheduling model considering the dynamic time-intervals is proposed. Firstly, with the goal of minimizing the total costs of system operation, the electricity, cooling/heat and gas energy are dispatched at the same time-intervals in the day-ahead scheduling. Then, for the slow and fast dynamic characteristics of the cooling/heating/natural gas load and the electrical load, a fast/slow dynamic two-layer rolling optimization scheduling model based on the model predictive control is proposed in the intraday scheduling. Different time-intervals are applied in the fast/slow layers, and the time-interval decision index is established in the slow layer, thus realizing the dynamic correction of the cycle time. The example analysis shows the feasibility and efficiency of the two-layer optimal scheduling model. Considering the dynamic time-intervals, the characteristics of energy network are fully explored, the optimal scheduling instruction intervals determined, the adjustment costs reduced and the system operating performances improved. Thus the coordinated operation of IES network and the multi-energy equipment are realized. © 2022, Power System Technology Press. All right reserved.
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页码：1721 / 1730
页数：9
相关论文
共 21 条
  • [1] DENG Jie, JIANG Fei, WANG Wenye, Et al., Study on cascade optimization operation of park-level integrated energy system considering dynamic energy efficiency model, Power System Technology, 46, 3, pp. 1027-1039, (2022)
  • [2] GE Xiaolin, WANG Yunpeng, ZHU Xiaohe, Et al., Day-ahead optimal scheduling for integrated power, heat and gas energy system considering differentiation energy inertia, Power System Technology, 45, 12, pp. 4630-4640, (2021)
  • [3] LI Jinghua, ZHU Mengshu, LU Yuejiang, Et al., Review on optimal scheduling of integrated energy systems, Power System Technology, 45, 6, pp. 2256-2269, (2021)
  • [4] ZHAO Dongmei, SONG Yuan, WANG Yunlong, Et al., Coordinated scheduling model with multiple time scales considering response uncertainty of flexible load, Automation of Electric Power Systems, 43, 22, pp. 21-30, (2019)
  • [5] BAO Zhejing, ZHOU Qin, YANG Zhihui, Et al., A multi time-scale and multi energy-type coordinated microgrid scheduling solution-part I: model and methodology, IEEE Transactions on Power Systems, 30, 5, pp. 2257-2266, (2015)
  • [6] LIU Fang, YANG Xiu, SHI Shanshan, Et al., Hybrid energy storage scheduling based microgrid energy optimization under different time scales, Power System Technology, 38, 11, pp. 3079-3087, (2014)
  • [7] XU Lizhong, YI Yonghui, ZHU Chengzhi, Et al., Multi-time scale optimal energy dispatch of microgrid considering stochastic wind power, Power System Protection and Control, 42, 23, pp. 1-8, (2014)
  • [8] WU Ming, LUO Zhao, JI Yu, Et al., Optimal dynamic dispatch for combined cooling heating and power microgrid based on model predictive control, Proceedings of the CSEE, 37, 24, pp. 7174-7184, (2017)
  • [9] XIAO Hao, PEI Wei, KONG Li, Multi-time scale coordinated optimal dispatch of microgrid based on model predictive control, Automation of Electric Power Systems, 40, 18, pp. 7-14, (2016)
  • [10] WANG Hao, AI Qian, GAN Lin, Et al., Collaborative optimization of combined cooling heating and power system based on multi-scenario stochastic programming and model predictive control, Automation of Electric Power Systems, 42, 13, pp. 51-58, (2018)
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