风光水互补发电系统与需求侧数据中心联动的优化调度方法
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摘要
风光水互补发电系统中,积极利用需求侧的数据中心参与响应,在平衡风光间歇性出力、减轻水电机组调节压力以及提高源端经济效益等方面能够产生明显效果。数据中心作为高能耗负荷类型,能耗峰谷差大,资源分配和数据处理方式灵活,减少一定的用户满意度将有效地实现电量需求转移。根据批处理负荷区间性处理要求,提出数据中心对工作负荷的并发处理模式,在此基础上,计及IT设备处理能耗、空调散热能耗以及网络通信能耗3类主要电力消耗,建立数据中心资源分配、延迟决策与能耗转移模型。针对梯级水电之间出力、流量、水头等相互约束关系,并计入风光的不确定性,制定以经济效益、负荷追踪系数及风光消纳程度的多重目标,建立含风、水、光的多能互补发电端与负荷端数据中心的优化调度模型。最后通过算例仿真得到在不同季节、天气和时段下风光水的最佳出力及数据中心的最小负荷转移量,验证了所提方法的有效性和正确性。
In hybrid wind-solar-hydro power generation system, actively using data centers to participate in demand response can produce positive effects in balancing intermittent output of wind and solar power, relieving stress of hydro turbine and enhancing economic interests of the source side. Of a high energy consumption load type, data centers have a big difference in electricity demand between peak and valley. Resource allocation and the way to deal with workloads for data centers are flexible. Reducing a certain degree of user satisfaction can effectively achieve transfer of electricity demand. According to requirement of batch load to finish in a period of time, a concurrent processing mode of data center to handle data is put forward in this paper. On this basis, three main types of power consumption, including IT equipment, air conditioning heat dissipation and network communication, are taken into account, and a model of data center for resource allocation, delaying decision and energy transfer is established.Considering the constraints of cascade hydropower, such as output, flow, water head, and so on, and uncertainty of wind and solar power, multiple objectives of economic efficiency, load tracking coefficient and accommodation degree of wind and solar power are formulated, and an optimal scheduling model for hybrid wind-solar-hydro power generation system and data center in load side is established. Finally, the optimal output of the source side and the minimum load transfer volume of the data center in different seasons, weather conditions and time intervals are obtained with simulation examples, and validity and correctness of the proposed method are verified.
In hybrid wind-solar-hydro power generation system, actively using data centers to participate in demand response can produce positive effects in balancing intermittent output of wind and solar power, relieving stress of hydro turbine and enhancing economic interests of the source side. Of a high energy consumption load type, data centers have a big difference in electricity demand between peak and valley. Resource allocation and the way to deal with workloads for data centers are flexible. Reducing a certain degree of user satisfaction can effectively achieve transfer of electricity demand. According to requirement of batch load to finish in a period of time, a concurrent processing mode of data center to handle data is put forward in this paper. On this basis, three main types of power consumption, including IT equipment, air conditioning heat dissipation and network communication, are taken into account, and a model of data center for resource allocation, delaying decision and energy transfer is established.Considering the constraints of cascade hydropower, such as output, flow, water head, and so on, and uncertainty of wind and solar power, multiple objectives of economic efficiency, load tracking coefficient and accommodation degree of wind and solar power are formulated, and an optimal scheduling model for hybrid wind-solar-hydro power generation system and data center in load side is established. Finally, the optimal output of the source side and the minimum load transfer volume of the data center in different seasons, weather conditions and time intervals are obtained with simulation examples, and validity and correctness of the proposed method are verified.
引文
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[15]高赐威,曹晓峻,闫华光,等.数据中心电能管理及参与需求侧资源调度的展望[J].电力系统自动化,2017,41(23):1-7.Gao Ciwei,Cao Xiaojun,Yan Huaguang,et al.Energy management of data center and prospect for participation in demand side resource scheduling[J].Automation of Electric Power Systems,2017,41(23):1-7(in chinese).
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[18]李翔,姜晓红,吴朝晖,等.绿色数据中心的热量管理方法研究[J].计算机学报,2015,38(10):1976-1996.Li Xiang,Jiang Xiaohong,Wu Zhaohui,et al.Research of thermal management methods for green data centers,Chinese Journal of Computers,2015,38(10):1976-1996(in Chinese).
[19]陈家伟,陈杰,龚春英.永磁直驱风力发电系统气动载荷抑制策略[J].中国电机工程学报,2013,33(21):99-108.Chen Jiawei,Chen Jie,Gong Chunying.An aerodynamic load reduction method for PMSG-based direct-drive WECS[J].Proceedings of the CSEE,2013,33(21):99-108(in Chinese).
[20]熊焰,吴杰康,王强,等.风光气储互补发电的冷热电联供优化协调模型及求解方法[J].中国电机工程学报,2015,35(14):3616-3625.Xiong Yan,Wu Jiekang,Wang Qiang,et al.An optimization coordination model and solution for combined cooling,heating and electric power systems with complimentary generation of wind,PV,gas and energy storage[J].Proceedings of the CSEE,2015,35(14):3616-3625(in Chinese).
[21]曲直,李海峰,柳进,等.面向波动平滑、负荷跟踪和功率平衡的风电功率优化调控策略[J].中国电机工程学报,2015,33(16):47-55.Qu Zhi,Li Haifeng,Liu Jin,et al.Wind power optimal regulation strategies for fluctuation smoothing,load following and power balancing[J].Proceedings of the CSEE,2015,33(16):47-55(in Chinese).
[22]叶林,屈晓旭,么艳香,等.风光水多能互补发电系统日内时间尺度运行特性分析[J].电力系统自动化,2018,42(4):158-164.Ye Lin,Qu Xiaoxu,Yao Yanxiang,et al.Analysis on intraday operation characteristics of hybrid wind-solar-hydro power generation system[J].Automation of Electric Power Systems,2018,42(4):158-164(in Chinese).
[23]朱兰,周雪莹,唐陇军,等.计及可中断负荷的微电网多目标优化运行[J].电网技术,2017,41(6):1847-1854.Zhu Lan,Zhou Xueying,Tang Longjun,et al.Multi-objective optimal operation for microgrid considering interruptible loads[J].Power System Technology,2017,41(6):1847-1854(in Chinese).
[24]程剑南,黄天喜.迪庆州电网负荷特性研究[J].广东科技,2013,6(22):20-26.Cheng Jiannan,Huang Tianxi.Study on load characteristics of Diqing[J].Guangdong Science and Technology,2013,6(22):20-26(in Chinese).
[25]宋曙光.风电功率波动特性及其与储能协调策略的研究[D].济南:山东大学,2011.
[26]孟涛.多能互补独立电力系统控制策略及动态仿真分析[D],北京:华北电力大学,2012.
[27]静铁岩,吕泉,郭琳,等.水电-风电系统日间联合调峰运行策略[J].电力系统自动化,2011,35(22):97-104.Jing Tieyan,Lu Quan,Guo Lin,et al.An inter-day combined operation strategy of hydro and wind power system for regulating peak load[J].Automation of Electric Power Systems,2011,35(22):97-104(in Chinese).
[2]董文略,王群,杨莉.含风光水的虚拟电厂与配电公司协调调度模型[J].电力系统自动化,2015,39(9):75-81.Dong Wenlue,Wang Qun,Yang Li.A coordinated dispatching model for a distribution utility and virtual power plants with wind/photovoltaic/hydro generators[J].Automation of Electric Power Systems,2015,39(9):75-81(in Chinese).
[3]邹云阳,杨莉.基于经典场景集的风光水虚拟电厂协同调度模型[J].电网技术,2015,39(7):1855-1859.Zou Yunyan,Yang Li.Synergetic dispatch models of a wind/PV/hydro virtual power plant based on representative scenario set[J].Power System Technology,2015,39(7):1855-1859(in Chinese).
[4]夏永洪,吴虹剑,辛建波,等.考虑风/光/水/储多源互补特性的微网经济运行评价方法[J].电力自动化设备,2017,37(7):63-69.Xia Yonghong,Wu Hongjian,Xin Jianbo,et al.Evaluation of economic operation for microgrid with complementary DGs and energy storage[J].Electric Power Automation Equipment,2017,37(7):63-69(in Chinese).
[5]王雅慧,彭寒梅.基于动态电价引导的风光水储多源能量管理[J].计算技术与自动化,2015,34(2):60-65.Wang Yahui,Peng Hanmei.Research of comprehensive energy management for multi-resource in wind-PV-water storage power system based on dynamic price guide[J].Computing Technology and Automation,2015,34(2):60-65(in chinese).
[6]Hao Wang,Jianwei Huang,Xiaojun Li,et al.Proactive demand response for data centers:a win-win solution[J].IEEE Transactions on Smart Grid,2016,7(3):1584-1596
[7]余晓,吴剑章,王巍.面向能耗优化的数据中心服务定价策略[J].东南大学学报(自然科学版),2017,37(7):63-69.Yu Xiao,Wu Jianzhang,Wang Wei.Data center service pricing strategies for energy consumption optimization[J].Journal of Southeast University(Natural Science Edition),2017,37(7):63-69(in Chinese).
[8]敬超.面向云数据中心的高效能调度及资源管理研究[D].上海:上海交通大学,2014.
[9]Li Jie,Qi Wenbo.Towards optimal operation of internet data center microgrid[J].IEEE Transactions on Smart Grid,2018,9(2):971-979.
[10]Guido Carpinelli,Fabio Mottola,Daniela Proto,et al.Amulti-objective approach for microgrid scheduling[J].IEEETransactions on Smart Grid,2017,8(5):2109-2118.
[11]曹晓峻,高赐威,李德智,等.数据网络与电力网络混合运行建模及其参与系统经济运行[J].中国电机工程学报,2018,38(5):1448-1456.Cao Xiaojun,Gao Ciwei,Li Dezhi,et al.Mixed operation model of data network and power network and its participation in the economic operation of power system[J].Proceedings of the CSEE,2018,38(5):1448-1456(in Chinese).
[12]张娉,杨婷.龙羊峡水光互补运行机制的研究[J].华北水利水电大学学报(自然科学版),2015,36(3):76-81.Zhang Ping,Yang Ting.Research on Longyangxia hydro-photovoltaic complementary operation mechanism[J].Journal of North China University of Water Resources and Electric Power(Natural Science Edition),2015,36(3):76-81(in Chinese).
[13]吴丹.贵州省政府培育大数据产业案例研究[D].成都:电子科技大学,2017.
[14]谷立静,周伏秋,孟辉.我国数据中心能耗及能效水平研究[J].中国能源,2010,32(11):42-45.Gu Lijing,Zhou Fuqiu,Meng Hui.Study on energy consumption and energy efficiency level of data centers in China[J].China Energy,2010,32(11):42-45(in Chinese).
[15]高赐威,曹晓峻,闫华光,等.数据中心电能管理及参与需求侧资源调度的展望[J].电力系统自动化,2017,41(23):1-7.Gao Ciwei,Cao Xiaojun,Yan Huaguang,et al.Energy management of data center and prospect for participation in demand side resource scheduling[J].Automation of Electric Power Systems,2017,41(23):1-7(in chinese).
[16]Lee Y,Kulkarni I,Pompili D,Parashar M.Proactive thermal management in green data centers[J].The Journal of Supercomputing,2012,60(2):165-195.
[17]Pakbaznia E,Pedram M.Minimizing data center cooling and server power costs[C]//Proceedings of the 2009 ACM/IEEE International Symposium on Low Power Electronics and Design.San Fancisco,CA,USA:ACM,2009:145-150.
[18]李翔,姜晓红,吴朝晖,等.绿色数据中心的热量管理方法研究[J].计算机学报,2015,38(10):1976-1996.Li Xiang,Jiang Xiaohong,Wu Zhaohui,et al.Research of thermal management methods for green data centers,Chinese Journal of Computers,2015,38(10):1976-1996(in Chinese).
[19]陈家伟,陈杰,龚春英.永磁直驱风力发电系统气动载荷抑制策略[J].中国电机工程学报,2013,33(21):99-108.Chen Jiawei,Chen Jie,Gong Chunying.An aerodynamic load reduction method for PMSG-based direct-drive WECS[J].Proceedings of the CSEE,2013,33(21):99-108(in Chinese).
[20]熊焰,吴杰康,王强,等.风光气储互补发电的冷热电联供优化协调模型及求解方法[J].中国电机工程学报,2015,35(14):3616-3625.Xiong Yan,Wu Jiekang,Wang Qiang,et al.An optimization coordination model and solution for combined cooling,heating and electric power systems with complimentary generation of wind,PV,gas and energy storage[J].Proceedings of the CSEE,2015,35(14):3616-3625(in Chinese).
[21]曲直,李海峰,柳进,等.面向波动平滑、负荷跟踪和功率平衡的风电功率优化调控策略[J].中国电机工程学报,2015,33(16):47-55.Qu Zhi,Li Haifeng,Liu Jin,et al.Wind power optimal regulation strategies for fluctuation smoothing,load following and power balancing[J].Proceedings of the CSEE,2015,33(16):47-55(in Chinese).
[22]叶林,屈晓旭,么艳香,等.风光水多能互补发电系统日内时间尺度运行特性分析[J].电力系统自动化,2018,42(4):158-164.Ye Lin,Qu Xiaoxu,Yao Yanxiang,et al.Analysis on intraday operation characteristics of hybrid wind-solar-hydro power generation system[J].Automation of Electric Power Systems,2018,42(4):158-164(in Chinese).
[23]朱兰,周雪莹,唐陇军,等.计及可中断负荷的微电网多目标优化运行[J].电网技术,2017,41(6):1847-1854.Zhu Lan,Zhou Xueying,Tang Longjun,et al.Multi-objective optimal operation for microgrid considering interruptible loads[J].Power System Technology,2017,41(6):1847-1854(in Chinese).
[24]程剑南,黄天喜.迪庆州电网负荷特性研究[J].广东科技,2013,6(22):20-26.Cheng Jiannan,Huang Tianxi.Study on load characteristics of Diqing[J].Guangdong Science and Technology,2013,6(22):20-26(in Chinese).
[25]宋曙光.风电功率波动特性及其与储能协调策略的研究[D].济南:山东大学,2011.
[26]孟涛.多能互补独立电力系统控制策略及动态仿真分析[D],北京:华北电力大学,2012.
[27]静铁岩,吕泉,郭琳,等.水电-风电系统日间联合调峰运行策略[J].电力系统自动化,2011,35(22):97-104.Jing Tieyan,Lu Quan,Guo Lin,et al.An inter-day combined operation strategy of hydro and wind power system for regulating peak load[J].Automation of Electric Power Systems,2011,35(22):97-104(in Chinese).
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