针对温控负载变化的虚拟电厂控制策略研究

doi:10.3969/j.issn.2097-0706.2024.01.004

摘要/Abstract

摘要：

温控负载是一种变化剧烈、波动频繁、高峰负荷比例较高的负载，对电网的安全产生了极大的影响。为了应对这一问题，系统的输出功率往往会超过90%负荷最大值的调峰限制。针对虚拟电厂（VPP）系统内的超限过程进行了研究，并提出了2种控制策略，以降低超限总电量或者利用超限电量的同时获取收益。在验证策略的控制效果时，选取了2种由温控负载引起的功率需求的典型变化。研究结果表明，温控负载引起的功率需求峰值的变化对策略具有重要影响。在安全模式下，超限比例与峰值的增加呈反比，而与平均功率的增加呈正比；在收益模式下，峰值变化会影响储能放电过程，平均功率变化则会影响储能充电过程。随着功率需求的增加，收益逐渐减小，收益范围为3.62万~3.25万元。

关键词: 虚拟电厂, 温控负载, 协调控制策略, 电价差收益, APROS, 电动汽车, 储能, 调峰

Abstract:

Thermostatically controlled load（TCL） is of frequent and drastic fluctuations and high- proportion peak load，which threats the safe operation of the power grid. To alleviate the fluctuation， a power system has to raise its load over the peak load regulation constraint of 90% maximum load. To study the over-limit process of a virtual power plant（VPP）system， two control strategies are proposed to reduce the total amount of over-limit electricity and gain profits from the process. Two types of variation laws of the power demand varying with TCLs are selected to study the effectiveness of the strategies. The results show that the peak value of the power demand varying with TCLs has a great impact on the strategies. Under the safe mode，the proportion of over-limit electricity is inversely proportional to the power demand peak value，while is proportional to the average value. Under the profiting mode，since the peak value and average value of power affects the charging and discharge processes，the profit decreases with the increase of power demand，ranging from 36 200 to 32 500 yuan.

Key words: virtual power plant, thermostatically controlled load, coordinated control strategy, price differential revenue, APROS, electric vehicle, stored energy, peak regulation

中图分类号:

TK01

田泽禹, 沙钊旸, 赵全斌, 严卉, 种道彤. 针对温控负载变化的虚拟电厂控制策略研究[J]. 综合智慧能源, 2024, 46(1): 28-37.

TIAN Zeyu, SHA Zhaoyang, ZHAO Quanbin, YAN Hui, CHONG Daotong. Research on control strategy for virtual power plants in response to thermostatically controlled loads[J]. Integrated Intelligent Energy, 2024, 46(1): 28-37.

图/表 14

图1

表1

图2

图3

表2

图4

图5

图6

图7

图8

图9

图10

图11

图12

参考文献 26

[1]	艾芊. 双碳目标下虚拟电厂资源聚合与协同调控研究[J]. 电力工程技术, 2022, 41(6): 1.
	AI Qian. Research on resource aggregation and cooperative regulation of virtual power plant under dual carbon target[J]. Electric Power Engineering Technology, 2022, 41(6): 1.
[2]	方燕琼, 艾芊, 范松丽. 虚拟电厂研究综述[J]. 供用电, 2016, 33(4): 8-13.
	FANG Yanqiong, AI Qian, FAN Songli. A review on virtual power plant[J]. Distribution & Utilization, 2016, 33(4): 8-13.
[3]	NAVAL N, YUSTA J M. Virtual power plant models and electricity markets—A review[J]. Renewable and Sustainable Energy Reviews, 2021, 149: 111393. doi: 10.1016/j.rser.2021.111393
[4]	GOIA B, CIOARA T, ANGHEL I. Virtual power plant optimization in smart grids: A narrative review[J]. Future Internet, 2022, 14(5): 128. doi: 10.3390/fi14050128
[5]	KALAF A A, ALYOZBAKY O S, ALGHANNAM A I. A modern technique to manage energy profile in Iraq: virtual power plant (VPP)[C]// Journal of Physics: Conference Series. IOP Publishing, 2021, 1973(1): 012078.
[6]	FAN S, LIU J, WU Q, et al. Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm[J]. Applied Energy, 2020, 277: 115583. doi: 10.1016/j.apenergy.2020.115583
[7]	OSHNOEI A, KHERADMANDI M, BLAABJERG F, et al. Coordinated control scheme for provision of frequency regulation service by virtual power plants[J]. Applied Energy, 2022, 325: 119734. doi: 10.1016/j.apenergy.2022.119734
[8]	张伟, 罗世刚, 滕婕, 等. 考虑温控负荷聚合调控的新能源-储能联合规划[J]. 储能科学与技术, 2023, 12(6):1901-1912. doi: 10.19799/j.cnki.2095-4239.2023.0054
	ZHANG Wei, LUO Shigang, TENG Jie, et al. Joint planning of renewable energy and storage considering thermostatically controlled loads aggregation regulation[J]. Energy Storage Science and Technology, 2023, 12(6):1901-1912. doi: 10.19799/j.cnki.2095-4239.2023.0054
[9]	郭旭歆, 高赐威, 王朝亮, 等. 基于中央空调虚拟储能模型的调峰策略研究[J]. 电力需求侧管理, 2022, 24(4): 42-46.
	GUO Xuxing, GAO Ciwei, WANG Chaoliang, et al. Peak adjustment strategy based on central air conditioning virtual energy storage model[J]. Power Demand Side Management, 2022, 24(4): 42-46.
[10]	KIM J, MULJADI E, GEVORGIAN V, et al. Capability‐coordinated frequency control scheme of a virtual power plant with renewable energy sources[J]. IET Generation, Transmission & Distribution, 2019, 13(16): 3642-3648. doi: 10.1049/gtd2.v13.16
[11]	CHAPALOGLOU S, NESIADIS A, ILIADIS P, et al. Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island's power system[J]. Applied Energy, 2019, 238: 627-642. doi: 10.1016/j.apenergy.2019.01.102
[12]	马学礼, 王笑飞, 孙希进, 等. 燃煤发电机组碳排放强度影响因素研究[J]. 热力发电, 2022, 51(1): 190-195.
	MA Xueli, WANG Xiaofei, SUN Xijin, et al. Influence factors of carbon emission intensity of coal-fired power units[J]. Thermal Power Generation, 2022, 51(1): 190-195.
[13]	TIAN Z Y, WANG Z, CHONG D J, et al. Coordinated control strategy assessment of a virtual power plant based on electric public transportation[J]. Journal of Energy Storage, 2023, 59: 106380. doi: 10.1016/j.est.2022.106380
[14]	JU L W, YIN Z, ZHOU Q Q, et al. Nearly-zero carbon optimal operation model and benefit allocation strategy for a novel virtual power plant using carbon capture, power-to-gas, and waste incineration power in rural areas[J]. Applied Energy, 2022, 310: 118618. doi: 10.1016/j.apenergy.2022.118618
[15]	孙惠, 翟海保, 吴鑫. 源网荷储多元协调控制系统的研究及应用[J]. 电工技术学报, 2021, 36(15):3264-3271.
	SUN Hui, ZHAI Haibao, WU Xin. Research and application of multi-energy coordinated control generation, network, load and storage[J]. Transactions of China Electrotechnical Society, 2021, 36(15): 3264-3271.
[16]	ROUZBAHANI H M, KARIMIPOUR H, LEI L. A review on virtual power plant for energy management[J]. Sustainable energy technologies and assessments, 2021, 47: 101370. doi: 10.1016/j.seta.2021.101370
[17]	GOIA B, CIOARA T, ANGHEL I. Virtual power plant optimization in smart grids: A narrative review[J]. Future Internet, 2022, 14(5): 128. doi: 10.3390/fi14050128
[18]	POURGHADERI N, FOTUHI‐FIRUZABAD M, MOEINI‐AGHTAIE M, et al. A local flexibility market framework for exploiting DERs' flexibility capabilities by a technical virtual power plant[J]. IET Renewable Power Generation, 2023, 17(3): 681-695. doi: 10.1049/rpg2.v17.3
[19]	ZHANG L, LIU D Y, CAI G W, et al. An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading[J]. International Journal of Electrical Power & Energy Systems, 2023, 144: 108558. doi: 10.1016/j.ijepes.2022.108558
[20]	MICHAEL N E, HASAN S, BISWAL B, et al. Reactive power compensation using electric vehicle and data center by integrating virtual power plant[J]. Electric Power Components and Systems, 2022, 50(4-5): 245-255. doi: 10.1080/15325008.2022.2136288
[21]	AHMADIAN A, PONNAMBALAM K, ALMANSOORI A, et al. Optimal management of a virtual power plant consisting of renewable energy resources and electric vehicles using mixed-integer linear programming and deep learning[J]. Energies, 2023, 16(2): 1000. doi: 10.3390/en16021000
[22]	薛露露, 韦围, 刘鹏, 等. 中国纯电动公交车运营现状分析与改善对策[R/OL]. 北京: 世界资源研究所, 2019. https://www.wri.org.cn/publications.
	XUE Lulu, WEI Wei, LIU Peng, et al. Overcoming the operational challenges of electric buses:Lessons learnt from China[R/OL]. Beijing: World Resources Institute, 2019. https://www.wri.org.cn/publications.
[23]	ULLAH Z, HASSANIN H, CUGLEY J, et al. Planning, operation, and design of market-based virtual power plant considering uncertainty[J]. Energies, 2022, 15(19): 7290. doi: 10.3390/en15197290
[24]	王学科, 陈北洋, 周保卫, 等. 基于分离预处理的城镇污泥原位资源化技术分析[J]. 华电技术, 2021, 43(12): 23-28.
	WANG Xueke, CHEN Beiyang, ZHOU Baowei, et al. Analysis of insitu municipal sludge resource utilization technology based on separation pretreatment[J]. Huadian Technology, 2021, 43(12): 23-28.
[25]	侯鲁洋, 葛磊蛟, 王飚, 等. 面向新型产消者的综合能源系统和电力市场研究[J]. 综合智慧能源, 2022, 44(12): 40-48. doi: 10.3969/j.issn.2097-0706.2022.12.006
	HOU Luyang, GE Leijiao, WANG Biao, et al. Research on the integrated energy system and the electricity market towards new prosumers[J]. Integrated Intelligent Energy, 2022, 44(12): 40-48. doi: 10.3969/j.issn.2097-0706.2022.12.006
[26]	葛磊蛟, 崔庆雪, 李明玮, 等. 面向低碳经济运行的新型电力系统态势感知技术综述[J]. 综合智慧能源, 2023, 45(1): 1-13. doi: 10.3969/j.issn.2097-0706.2023.01.001
	GE Leijiao, CUI Qingxue, LI Mingwei, et al. Review on situational awareness technology in a low-carbon oriented new power system[J]. Integrated Intelligent Energy, 2023, 45(1): 1-13. doi: 10.3969/j.issn.2097-0706.2023.01.001

参数	值	参数	值
额定容量/（A·h）	540	额定开路电压/V	540.0
电芯容量/（A·h）	270	电芯开路电压/V	3.2
SOC的可用范围/%	10~100	电压变化范围/V	420.0~613.0
储能的额定功率/(kW·h)	291.6	最大放电电流/A	-540
串联电芯数	169	最大充电电流/A	1 080
并联电芯数	2	额定放电功率/kW	291.6

时间段	连入电网的储能组	电费	调度目的
11:30—16:30	C	平值	电动公交车储能在谷电价时最大化充电，充电结束后储能组C剩余储能S_OC=1
16:30—18:30	A	平值	储能车组A工作结束接入电网，同时此时段光伏机组输出功率下降，负载开始受到温控负载的影响，需求增大。为获得最大的收益，在此时段调度燃煤机组输出功率为额定功率，P=350 MW，储能组A充电，充电结束S_OC，_t_=18:30
18:30—23:00	A，C	峰值	为获得最大收益，储能在峰值电价时段完全放电，此时段燃煤机组的输出功率信号为P_peak。时段结束时，储能的设计容量剩余均为S_OC=0.1，电池放电下限值
23:00—调峰时段结束	C	谷值	储能放电结束，但调峰时段仍在继续，需要满足系统功率需求，设定P_valley= P_demand_,t_=23：00为输出功率设定值