基于负荷重要性和源-荷互补性的离网系统终端电/热/冷负荷投切策略

doi:10.19595/j.cnki.1000-6753.tces.191676

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2625 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to reduce the imbalance between supply and demand of off grid system and improve the efficiency of energy utilization, this paper proposes a strategy based on the importance of load and the complementarity between source and load. Firstly, the importance index of load and the complementarity index of source and load are defined, and the comprehensive index is determined by entropy weight method. On this basis, the load switching strategy based on single time scale energy supply and comprehensive index and the load switching strategy based on multi time scale energy supply and comprehensive index are proposed successively. Considering that the prediction deviation will lead to state of charge(SOC) deviation from the planned value, and then affect the load switching scheme, this paper uses the rolling optimization scheduling method based on model predictive control(MPC) to reduce the uncertainty factors. Finally, this paper takes a typical summer day of an off-grid system as an example to verify the effectiveness of the proposed strategy.

Key words： Load importance source load complementarity power/heating/cooling load switching entropy weight method energy storage

Received: 02 December 2019

PACS:

TM73

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Liu Xiaolong
	Li Xinran
	Liu Zhipu
	Lu Yinghua
	Luo Zhen

Cite this article:

Liu Xiaolong,Li Xinran,Liu Zhipu等. Research on Power/Heating/Cooling Load Switching Strategy of Off-Grid System Based on Load Importance and Source Load Complementarity[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 552-564.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.191676 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I3/552

[1] 赵霞, 杨仑, 瞿小斌, 等. 电-气综合能源系统能流计算的改进方法[J]. 电工技术学报, 2018, 33(3): 5-15.
Zhao Xia, Yang Lun, Qu Xiaobin, et al.An improved method for energy flow calculation of electric gas integrated energy system[J]. Transactions of China Electrotechnical Society, 2018, 33(3): 5-15.
[2] 韩佶, 苗世洪, 李超, 等. 计及相关性的电-气-热综合能源系统概率最优能量流[J]. 电工技术学报, 2019, 34(5): 169-181.
Han Ji, Miao Shihong, Li Chao, et al.The probability optimal energy flow of electricity gas heat integrated energy system considering correlation[J]. Transactions of China Electrotechnical Society, 2019, 34(5): 169-181.
[3] 张义志, 王小君, 和敬涵, 等. 考虑供热系统建模的综合能源系统最优能流计算方法[J]. 电工技术学报, 2019, 34(3): 562-570.
Zhang Yizhi, Wang Xiaojun, He Jinghan, et al.The optimal energy flow calculation method of comprehensive energy system considering heating system modeling[J]. Transactions of China Electrotechnical Society, 2019, 34(3): 562-570.
[4] 王莉, 吴任博, 潘凯岩, 等. 园区智能综合能源管理运营中心建设探究[J]. 电气技术, 2019, 20(8): 110-114, 130.
Wang Li, Wu Renbo, Pan Kaiyan, et al.Research on the construction of intelligent comprehensive energy management and operation center in the park[J] Electrical Engineering, 2019, 20(8): 110-114, 130.
[5] Bahrami S, Sheikhi A.From demand response in smart grid toward integrated demand response in smart energy hub[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 650-658.
[6] 薛开阳, 楚瀛, 凌梓, 等. 考虑柔性负荷的综合能源系统低碳经济优化调度[J]. 可再生能源, 2019(8): 1206-1213.
Xue Kaiyang, Chu Ying, Ling Zi, et al.Low carbon economy optimal scheduling of integrated energy system considering flexible load[J]. Renewable Energy, 2019(8): 1206-1213.
[7] El-Sharafy M Z, Farag H E. Automatic restoration in distribution systems considering DG transfer and islanded microgrids[C]//IEEE Electrical Power & Energy Conference, Ottawa, ON, 2016: 1-6.
[8] Mohammad H H, Mohsen M, Ali A G.Optimum islanded microgrid reconfiguration based on maximization of system loadability and minimization of power losses[J]. International Journal of Electrical Power & Energy Systems, 2016, 78: 343-355.
[9] Xu Xiaoyuan, Yan Zheng, Mohammad S, et al.Maximum loadability of islanded microgrids with renewable energy generation[J]. IEEE Transactions on Smart Grid, 2019, 5(10): 4696-4705.
[10] Jithendranath J, Debapriya D.Scenario-based multi-objective optimization with loadability in islanded microgrids considering load and renewable generation uncertainties[J]. IET Renewable Power Generation, 2019, 5(13): 785-800.
[11] Abdelaziz M M A, Farag H E, El-Saadany E F. Optimum reconfiguration of droop-controlled islanded microgrids[J]. IEEE Transactions on Power Systems, 2016, 31(3): 2144-2153.
[12] 曾鸣, 徐志勇, 刘宝华, 等. 基于VOLL定价思想的用户重要性分类研究[J]. 电力自动化设备, 2008, 28(8): 31-35.
Zeng Ming, Xu Zhiyong, Liu Baohua, et al.Research on user importance classification based on VOLL pricing idea[J] Electric Power Automation Equipment, 2008, 28(8): 31-35.
[13] 赵会茹, 欧大昌, 张奇, 等. 基于ANP灰色关联的电力用户重要性评估[J]. 能源技术经济, 2012, 24(7): 38-43.
Zhao Huiru, Ou Dachang, Zhang Qi, et al.Importance assessment of power users based on ANP grey relation[J]. Energy Technology and Economy, 2012, 24(7): 38-43.
[14] 付菊霞, 陈洁, 滕扬新, 等. 基于集合经验模态分解的风电混合储能系统能量管理协调控制策略[J]. 电工技术学报, 2019, 34(10):58-66.
Fu Juxia, Chen Jie, Teng Yangxin, et al.Energy management coordination control strategy of wind power hybrid energy storage system based on set empirical mode decomposition[J]. Transactions of China Electrotechnical Society, 2019, 34(10): 58-66.
[15] 孙玉树, 张国伟, 唐西胜, 等. 风电功率波动平抑下的MPC双储能控制策略研究[J]. 电工技术学报, 2019, 34(3): 571-578.
Sun Yushu, Zhang Guowei, Tang Xisheng, et al.Research on MPC dual energy storage control strategy under stable wind power fluctuation[J]. Transactions of China Electrotechnical Society, 2019, 34(3): 571-578.
[16] Rafael Sebastián.Application of a battery energy storage for frequency regulation and peak shaving in a wind diesel power system[J]. IET Generation Transmission & Distribution, 2015, 10(3): 764-770.
[17] Yang Jingjie, Guo Bingqing, Bo Qu.Economic optimization on two time scales for a hybrid energy system based on virtual storage[J]. Journal of Modern Power Systems and Clean Energy, 2018, 6(5): 164-174.
[18] Jin Xiaolong, Mu Yunfei, Jia Hongjie, et al.An optimal scheduling model for a hybrid energy microgrid considering building based virtual energy storage system[J]. Energy Procedia, 2016, 88: 375-381.
[19] 鞠平, 姜巍, 赵夏阳, 等. 96点短期负荷预测方法及其应用[J]. 电力系统自动化, 2001, 25(22): 32-36.
Ju Ping, Jiang Wei, Zhao Xiayang, et al.96 point short-term load forecasting method and its application[J]. Power System Automation, 2001, 25(22): 32-36.
[20] Gao Chenglu, Li Shucai, Wang Jing, et al.The risk assessment of tunnels based on grey correlation and entropy weight method[J]. Geotechnical and Geological Engineering, 2018, 36: 1621-1631.
[21] Parisio A, Wiezorek C, Kyntaja T, et al.Cooperative MPC-based energy management for networked microgrids[J]. IEEE Transactions on Smart Grid, 2017, 6(8): 3066-3074.
[22] Afram A, Janabi-Sharifi F, Fung A S, et al.Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: a state of the art review and case study of a residential HVAC system[J]. Energy and Buildings, 2017, 141: 96-113.