Multi-time-scale optimal scheduling method for flexible resources at distribution stations： A case study of building microgrid considering virtual storage system

doi:10.19799/j.cnki.2095-4239.2023.0677

Abstract

Abstract:

With the proposal of the "dual carbon" goal, the distribution station area is connected to many new elements, such as distributed photovoltaics, electric vehicles, microgrids, and smart buildings, posing challenges to the safe operation of medium- and low-voltage distribution networks and offering considerably flexibility potential. Taking smart buildings connected to the distribution station area as an example, this study proposes a multi-time scale optimal scheduling method for a building microgrid considering a virtual storage system. Firstly, the thermal dynamics of the envelope of the building is modeled, and a virtual energy storage model that quantitatively describes the flexibility of buildings are constructed. The flexibility of buildings can be used by optimizing the charging and discharging powers of the virtual energy storage. Then, the adjustable virtual energy storage characteristics of buildings are considered in detail in the multi-time scale optimal scheduling model of flexible resources in distribution stations. The proposed multi-time scale optimal scheduling model can leverage the virtual energy storages at day-ahead and the intra-day stages. A day-ahead economic scheduling method aiming at the lowest operating cost and an intra-day correction method for tracking the setting value of the microgrid contact line is proposed. Finally, taking the cooling scenario in summer as an example, the effectiveness of the proposed multi-time-scale optimal scheduling method is verified using a typical building microgrid system. The results show that this method can effectively schedule the virtual energy storage capacity of the building, reduce the operating cost of the building microgrid in the day-ahead operation stage, and effectively mitigate the random fluctuation of renewable energy in the intra-day stage.

Key words: distribution station, flexible resources, microgrid, heat storage characteristics of building, virtual storage system, multi-time scale optimal scheduling

CLC Number:

TM 732

Xu HUANG, Guoqiang ZU, Wei SI, Qi DING, Mingyang LIU, Wanxin TANG, Xiaolong JIN. Multi-time-scale optimal scheduling method for flexible resources at distribution stations： A case study of building microgrid considering virtual storage system[J]. Energy Storage Science and Technology, 2024, 13(2): 568-577.

Figures/Tables 18

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Table 1

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Table 2

Parameters of the energy equipment"

参数	数值
电制冷机COP_EC	4
电制冷机功率上下限 $P E C ¯$ / $P E C ̲$	120 kW/0 kW
光伏使用维护成本C_{PV_om}	0.08元/kWh
电制冷机使用维护成本C_{EC_om}	0.01元/kWh

Table 2

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Table 3

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References 23

1	朱张涛, 陈豪杰, 戴俊杰, 等. 基于空间变换的含风电场和电动汽车配电网概率潮流计算[J]. 储能科学与技术, 2017, 6(1): 127-134.
	ZHU Z T, CHEN H J, DAI J J, et al. Probabilistic load flow calculation of distribution network with wind power and electric vehicles based on space transform[J]. Energy Storage Science and Technology, 2017, 6(1): 127-134.
2	清华大学建筑节能中心. 中国建筑节能年度发展研究报告2021[EB/OL].[2022-03-01]. https://berc.bestchina.org/?ky/Article250/96.html.
3	靳小龙. 集成智能楼宇的电/气/热区域综合能源系统建模及运行优化研究[D]. 天津: 天津大学, 2018.
	JIN X L. Research on modeling and operation optimization of integrated energy system in electric/gas/thermal area of integrated intelligent building[D]. Tianjin: Tianjin University, 2018.
4	王雷, 王鑫, 张蓉蓉, 等. 碳中和目标下的微网运行经济性分析[J]. 储能科学与技术, 2023, 12(1): 319-328.
	WANG L, WANG X, ZHANG R R, et al. Economic analysis of microgrid operation based on carbon neutrality[J]. Energy Storage Science and Technology, 2023, 12(1): 319-328.
5	卢一涵. 考虑用户用能灵活性的社区综合能源系统双层优化研究[D]. 天津: 天津大学, 2020.
	LU Y H. Research on bi-level optimization of community comprehensive energy system considering user's flexibility in energy consumption[D]. Tianjin: Tianjin University, 2020.
6	徐琪森, 陈炯, 徐广鹦. 含分布式电源及电动汽车的园区微网优化运行方法[J]. 电测与仪表, 2023, 60(4): 27-33.
	XU Q S, CHEN J, XU G Y. Optimized operation method of park micro-grid containing distributed power and electric vehicles[J]. Electrical Measurement & Instrumentation, 2023, 60(4): 27-33.
7	ZHAO Y, LU Y H, YAN C C, et al. MPC-based optimal scheduling of grid-connected low energy buildings with thermal energy storages[J]. Energy and Buildings, 2015, 86: 415-426.
8	VAN ROY J, LEEMPUT N, GETH F, et al. Electric vehicle charging in an office building microgrid with distributed energy resources[J]. IEEE Transactions on Sustainable Energy, 2014, 5(4): 1389-1396.
9	赵立夏, 武志刚. 社区型独立微网的日前优化调度[J]. 电气自动化, 2020, 42(2): 19-22, 48.
	ZHAO L X, WU Z G. Day-ahead optimized scheduling for community-based independent micro-grids[J]. Electrical Automation, 2020, 42(2): 19-22, 48.
10	陈曦, 徐青山, 杨永标. 考虑风电不确定性的CCHP型微网日前优化经济调度[J]. 电力建设, 2020, 41(6): 107-113.
	CHEN X, XU Q S, YANG Y B. Day-ahead optimized economic dispatch of CCHP microgrid considering wind power uncertainty[J]. Electric Power Construction, 2020, 41(6): 107-113.
11	VERBEKE S, AUDENAERT A. Thermal inertia in buildings: A review of impacts across climate and building use[J]. Renewable and Sustainable Energy Reviews, 2018, 82: 2300-2318.
12	JIN X L, MU Y F, JIA H J, et al. Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system[J]. Applied Energy, 2017, 194: 386-398.
13	李艳, 胡志豪, 随权, 等. 考虑风光热不确定性和相变储能系统的城市建筑微网电-热联合调度[J]. 电网技术, 2019, 43(10): 3687-3697.
	LI Y, HU Z H, SUI Q, et al. Microgrid joint electro-thermal scheduling for phase change energy storage system of urban buildings considering uncertainty of wind, light and heat[J]. Power System Technology, 2019, 43(10): 3687-3697.
14	朱晓荣, 谢婉莹, 鹿国微. 采用区间多目标线性规划法的热电联供型微网日前调度[J]. 高电压技术, 2021, 47(8): 2668-2677.
	ZHU X R, XIE W Y, LU G W. Day-ahead scheduling of combined heating and power microgrid with the interval multi-objective linear programming[J]. High Voltage Engineering, 2021, 47(8): 2668-2677.
15	刘辉, 刘强, 张立, 等. 考虑需求侧协同响应的热电联供微网多目标规划[J]. 电力系统保护与控制, 2019, 47(5): 43-51.
	LIU H, LIU Q, ZHANG L, et al. Multi-objective planning for combined heat and power microgrid considering demand side cooperative response[J]. Power System Protection and Control, 2019, 47(5): 43-51.
16	李孝均, 朱兰, 翁秀良. 含建筑相变储能系统的微网日前调度[J]. 太阳能学报, 2023, 44(6): 84-90.
	LI X J, ZHU L, WENG X L. Day-ahead scheduling of micro-grid with building phase change energy storage system[J]. Acta Energiae Solaris Sinica, 2023, 44(6): 84-90.
17	LIU W L, LIU C L, LIN Y J, et al. Optimal scheduling of residential microgrids considering virtual energy storage system[J]. Energies, 2018, 11(4): 942.
18	靳小龙, 穆云飞, 贾宏杰, 等. 融合需求侧虚拟储能系统的冷热电联供楼宇微网优化调度方法[J]. 中国电机工程学报, 2017, 37(2): 581-591.
	JIN X L, MU Y F, JIA H J, et al. Optimal scheduling method for a combined cooling, heating and power building microgrid considering virtual storage system at demand side[J]. Proceedings of the CSEE, 2017, 37(2): 581-591.
19	GLORIANT F, TITTELEIN P, JOULIN A, et al. Modeling a triple-glazed supply-air window[J]. Building and Environment, 2015, 84: 1-9.
20	YANG I H, YEO M S, KIM K W. Application of artificial neural network to predict the optimal start time for heating system in building[J]. Energy Conversion and Management, 2003, 44(17): 2791-2809.
21	JIN X L, WU J Z, MU Y F, et al. Hierarchical microgrid energy management in an office building[J]. Applied Energy, 2017, 208: 480-494.
22	董文哲, 杨斯泐, 梁宗佑, 等. 集成混合储能及RPC的牵引供电系统优化运行[J]. 储能科学与技术, 2023, 12(4): 1185-1193.
	DONG W Z, YANG S L, LIANG Z Y, et al. Research on optimal operation of traction power supply system with integrated hybrid energy storage and RPC[J]. Energy Storage Science and Technology, 2023, 12(4): 1185-1193.
23	徐青山, 曾艾东, 王凯, 等. 基于Hessian内点法的微型能源网日前冷热电联供经济优化调度[J]. 电网技术, 2016, 40(6): 1657-1665.
	XU Q S, ZENG A D, WANG K, et al. Day-ahead optimized economic dispatching for combined cooling, heating and power in micro energy-grid based on hessian interior point method[J]. Power System Technology, 2016, 40(6): 1657-1665.

时刻	光伏额定容量/kW	偏差
13:45	100	0.69 kW
	150	1.32 kW
	200	1.94 kW
	250	2.57 kW
	300	3.19 kW

16:00	100	0 kW
	150	-1.90 kW
	200	-8.65 kW
	250	-15.40 kW

[1]	Xiang ZHANG, Jundong DUAN, Boyang KANG. Dual incentive optimal scheduling of microgrid considering flexible energy storage of electric vehicles [J]. Energy Storage Science and Technology, 2023, 12(8): 2556-2564.
[2]	Bin LI, Jilei YE, Yu ZHANG, Shanshan SHI, Haojing WANG, Lili LIU, Mingzhe LI. Microgrid-coordinated control strategy with distributed new energy and electro-mechanical hybrid energy storage [J]. Energy Storage Science and Technology, 2023, 12(5): 1510-1515.
[3]	Lei WANG, Xin WANG, Rongrong ZHANG, Zonghao LIU, Shenglong MU, Gang CHEN. Economic analysis of microgrid operation based on carbon neutrality [J]. Energy Storage Science and Technology, 2023, 12(1): 319-328.
[4]	Jingchao SHEN, Jian HU, Jingliang HU, Ticao JIAO, Xiaomei QI, Yunpeng WANG, Di YU, Shangqi LIU. Parameter adaptive backstepping control of bidirectional DC-DC converter for DC microgrid energy storage device [J]. Energy Storage Science and Technology, 2022, 11(5): 1512-1522.
[5]	Yu LI, Zhanqiang ZHANG, Keqilao MENG, Haotian WEI. Optimal control strategy for energy storage in island microgrid based on hierarchical control [J]. Energy Storage Science and Technology, 2022, 11(1): 176-184.
[6]	Yanping WEI, Jun WANG, Nanfan LI, Changli SHI. Grid-connected switch control strategy suitable for energy storage converter in microgrid [J]. Energy Storage Science and Technology, 2022, 11(1): 156-163.
[7]	Biao CHEN, Wei YU, Xufeng FANG, Haoxin LI, Lei CAO, Jun PAN. A cooperative economic dispatch of active distribution network and island microgrid with intra area battery sharing [J]. Energy Storage Science and Technology, 2021, 10(6): 2181-2190.
[8]	ZHANG Wenjian, CUI Qingru, LI Zhiqiang, YU Kang. Application of electrochemical energy storage in power generation [J]. Energy Storage Science and Technology, 2020, 9(1): 287-295.
[9]	HAO Wenbo, ZHENG Changbao, HU Cungang, RUI Tao, ZHANG Jin. A distributed optimization strategy for an island microgrid with price incentives [J]. Energy Storage Science and Technology, 2019, 8(4): 659-664.
[10]	WU Ming, XIONG Xiong, JI Yu, DING Baodi, ZHANG Ying. Overview on multi-microgrid technologies [J]. Energy Storage Science and Technology, 2019, 8(4): 621-628.
[11]	KOU Lingfeng, XIONG Xiong, HOU Xiaogang, NIU Geng, QU Xiaoyun, CHEN Fan. Microgrid technology for low voltage distribution transformer station area [J]. Energy Storage Science and Technology, 2019, 8(4): 665-670.
[12]	ZHANG Jin, HU Cungang, RUI Tao. Nash bargaining model for direct electricity trading on distribution side with multi-microgrids participation [J]. Energy Storage Science and Technology, 2019, 8(4): 645-653.
[13]	ZHUO Jing, WANGXingxing, JIA Tianyi. Simplified design of independent hydropower-PV-storage micro-grid system [J]. Energy Storage Science and Technology, 2018, 7(S1): 34-38.
[14]	ZHAO Qilong, HE Bowei, MENG Ling. Energy management system for microgrid based on energy storage technology [J]. Energy Storage Science and Technology, 2018, 7(S1): 108-111.
[15]	GUO Wei, ZHANG Jiancheng, LI Chong, SU Hao. Control method of flywheel energy storage array for grid-connected wind-storage microgrid [J]. Energy Storage Science and Technology, 2018, 7(5): 810-814.