Two-stage optimal scheduling of community integrated energy system considering demand response

被引：0
|
作者：
Liu R. [1 ]
Li Y. [1 ]
Yang X. [1 ]
Li Y. [1 ]
Sun G. [1 ]
Shi S. [3 ]
机构：
[1] College of Electric Engineering, Shanghai University of Electric Power, Shanghai
[2] State Grid Materials Company, SMEPC, Shanghai
[3] Electric Power Research Institute of SG Shanghai Electric Power Company, Shanghai
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2021年 / 42卷 / 09期
关键词：
Controllable load; Energy storage; Integrated energy system; Mathematical model; Optimization;
D O I：
10.19912/j.0254-0096.tynxb.2019-0974
中图分类号：
学科分类号：
摘要：
From the perspective of community energy operators, by integrating information such as controllable loads and wind turbine output in the community integrated energy system (IES), a two-stage optimal scheduling model of community IES considering controllable loads is proposed. The day-ahead scheduling analyzes the impact of various types of controllable loads on the operational costs of the community IES before and after the response. The intra-day scheduling is based on the day-ahead scheduling scheme, considering the fluctuation of wind turbine output and load in a short period of time and according to the different response rates of cooling, heating and power, a two-stage rolling optimization stabilization model is proposed to solve the adjustment output of each controllable equipment. The simulation results show that the optimal scheduling of controllable loads can effectively reduce the comprehensive operating cost of community IES; The two-stage optimal scheduling model can meet the user's energy demand and timely stabilize the random fluctuations on both sides of the source and load during the day, realizing the IES economy and smooth operation. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：46 / 54
页数：8
相关论文
共 15 条
  • [1] SUN H B, PAN Z G, GUO Q L., Energy management for multi-energy flow: Challenges and prospects, Automation of electric power systems, 40, 15, pp. 1-8, (2016)
  • [2] ZHANG Y C, LIU K P, LIAO X B, Et al., Multi-time scale source-load coordination dispatch model for power system with large-scale wind power, High voltage technology, 45, 2, pp. 600-608, (2019)
  • [3] XIAO F, AI Q., Multiple time-scale optimal dispatch of demand response resource for microgrid based on model predictive control, Electric power automation equipment, 38, 5, pp. 184-190, (2018)
  • [4] GUO S Q, YUAN Y, ZHANG X S, Et al., Energy management strategy of isolated microgrid based on multi-time scale coordinated control, Transactions of China Electrotechnical Society, 29, 2, pp. 122-129, (2014)
  • [5] HU R, ZHANG M L, LI Z K, Et al., Optimal operation for CCHP system considering shiftable loads, Power system technology, 42, 3, pp. 715-721, (2018)
  • [6] FU Y, JIANG Y L, LI Z K, Et al., Optimal economic dispatch for microgrid considering shiftable loads, Proceedings of the CSEE, 34, 16, pp. 2612-2620, (2014)
  • [7] YANG X., Optimal scheduling of active distribution networks based on flexible loads on multiple time scales, (2018)
  • [8] XU L Z, YI Y H, ZHU C Z, 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)
  • [9] BAO Z J, ZHOU Q, YANG Z H, Et al., A multi time-scale and multi energy-type coordinated microgrid scheduling solution: Part II optimization algorithm and case studies, IEEE transactions on power systems, 30, 5, pp. 2267-2277, (2015)
  • [10] WU M, LUO Z, JI Y, 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)
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