Day-ahead optimal scheduling of smart integrated energy communities considering demand-side resources

被引：0
|
作者：
Cai Z. [1 ]
Peng M. [1 ]
Shen M. [2 ]
机构：
[1] School of Electrical and Information Engineering, Hunan University, Changsha
[2] School of Computer Science, Beijing Information Science & Technology University, Beijing
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2021年 / 41卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Combined cooling; heating and power; Day-ahead scheduling; Demand response; Integrated energy; Multi-energy complementary; Smart communities;
D O I：
10.16081/j.epae.202101027
中图分类号：
学科分类号：
摘要：
In view of the excessive consumption of fossil energy and the increasingly severe environmental pollution, the day-ahead optimal scheduling of smart integrated energy communities combined energy supply side and demand side is studied, in which the utilization of demand-side resources are considered. On the supply side, a combined cooling, heating and power system with photovoltaic and wind power generation is demonstrated. A control strategy is proposed to increase the accommodation of local renewable energy considering the multi-energy complementary way. On the demand side, a refined load classification method is proposed, which considers the charging and discharging functions of household energy storages and electric vehicles, the frequent starting and stopping of electric equipment, travel scheme of electric vehicles, and operating time constraints of related equipment. Moreover, the operation decisions are analyzed for cooling and heating demands with uncontrollable running time. By introducing the unit price of energy supply, the supply side and demand side are combined to carry out the day-ahead optimal scheduling. Simulative results demonstrate that the proposed method can effectively reduce the cost of both supply and demand sides, the environmental pollution and the peak-valley difference of electric demands. © 2021, Electric Power Automation Equipment Press. All right reserved.
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页码：18 / 24and32
页数：2414
相关论文
共 19 条
  • [1] GUO Tongli, Brief analysis of the world energy supply and demand pattern and China's energy cleaning strategy, Ener-gy and Energy Conservation, 11, pp. 25-28, (2019)
  • [2] WANG Yanan, WU Jiekang, MAO Xiaoming, Economic distribution model of cooling, heating and power energy in multi-energy supply system based on stochastic dynamic programming, Electric Power Automation Equipment, 39, 6, pp. 21-26, (2019)
  • [3] HU Rong, MA Jie, LI Zhenkun, Et al., Optimal allocation and applicability analysis of distributed combined cooling-heatingpower system, Power System Technology, 41, 2, pp. 418-425, (2017)
  • [4] ZHU Guangya, CHOW T., Design optimization and two-stage control strategy on combined cooling, heating and power sys-tem, Energy Conversion and Management, 199, (2019)
  • [5] WANG Qinghua, LIU Jizhen, HU Yang, Et al., Optimal opera-tion strategy of multi-energy complementary distributed CCHP system and its application on commercial building, IEEE Access, 7, pp. 127839-127849, (2019)
  • [6] YANG Yongbiao, YU Jiancheng, LI Yijie, Et al., Optimal load leveling dispatch of CCHP incorporating photovoltaic and sto-rage, Automation of Electric Power Systems, 41, 6, pp. 6-12, (2017)
  • [7] YAN Yi, ZHANG Chenghui, LI Ke, Et al., The optimal operation strategy for hybrid combined cooling, heating and power microgrid with compressed air energy storage, Proceedings of the CSEE, 38, 23, pp. 6924-6936, (2018)
  • [8] FU Zhixin, LI Xiaoyi, ZHU Junpeng, Et al., Intelligent optimization strategy of home energy management based on Markov decision process, Electric Power Automation Equipment, 40, 7, pp. 141-152, (2020)
  • [9] CHEN Zhong, LI Yunqian, LENG Zhaoying, Et al., Refined mo-deling and energy management strategy of typical household high-power loads, Automation of Electric Power Systems, 42, 22, pp. 135-147, (2018)
  • [10] ZENG Bo, JIANG Wenqian, YANG Zhou, Et al., Optimal load dispatching method based on chance-constrained programming for household electrical equipment, Electric Power Automation Equipment, 38, 9, pp. 27-33, (2018)
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