基于需求响应潜力时变性的风火荷协同控制方法
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摘要
随着可再生能源并网容量的不断提高,可利用智能需求响应技术提升电网消纳可再生能源的能力,但如何定量评估具有时变特性的需求响应潜力(DRP)是一个亟待解决的问题。该文提出一种考虑DRP时变性的风电、火电和负荷实时协同控制技术,以实现风电功率的最大消纳。首先,基于空调、热水器和电动汽车等智能家电的动态运行特性,建立智能家电的聚合响应数学模型;其次,在分析DRP时变特性的基础上提出DRP的定量计算方法;最后,综合考虑风电波动性、火电机组爬坡特性、DRP时变性以及电力网络潮流越限等约束,提出风火荷协同控制优化方法,以实现风电功率消纳的最大化。仿真算例结果表明该文所提方法的有效性和合理性。
With the increasing grid-connected capacity of renewable energy,the smart demand response(DR) is utilized to improve the capability of renewable energy accommodation.However,how to quantitatively evaluate the time-varying demand response potential(DRP) is an urgent problem to be solved.This paper proposes a coordinated real-time control method of wind farm,AGC units and loads considering the time-varying characteristics of DRP to realize the maximum of wind power utilization.Firstly,the aggregated mathematical models are built based on the dynamic operating characteristics of air conditioning,water heater and electric vehicle.Secondly,the quantitative method is presented on the basis of analyzing the time-varying characteristics of DRP.Finally,with the comprehensive consideration of wind fluctuation,AGC units ramp feature,time-varying DRP,and power flow transmission constraint,an optimal coordinated method of wind farm,AGC units and loads is proposed to maximize the wind accommodation.Simulation case studies show the effectiveness and rationality of the proposed method.
With the increasing grid-connected capacity of renewable energy,the smart demand response(DR) is utilized to improve the capability of renewable energy accommodation.However,how to quantitatively evaluate the time-varying demand response potential(DRP) is an urgent problem to be solved.This paper proposes a coordinated real-time control method of wind farm,AGC units and loads considering the time-varying characteristics of DRP to realize the maximum of wind power utilization.Firstly,the aggregated mathematical models are built based on the dynamic operating characteristics of air conditioning,water heater and electric vehicle.Secondly,the quantitative method is presented on the basis of analyzing the time-varying characteristics of DRP.Finally,with the comprehensive consideration of wind fluctuation,AGC units ramp feature,time-varying DRP,and power flow transmission constraint,an optimal coordinated method of wind farm,AGC units and loads is proposed to maximize the wind accommodation.Simulation case studies show the effectiveness and rationality of the proposed method.
引文
[1]薛禹胜,雷兴,薛峰,等.关于风电不确定性对电力系统影响的评述[J].中国电机工程学报,2014,34(29):5029-5040.Xue Yusheng,Lei Xing,Xue Feng,et al.A review on impacts of wind power uncertainties on power systems[J].Proceedings of the CSEE,2014,34(29):5029-5040.
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[7]王彩霞,乔颖,鲁宗相,等.低碳经济下风火互济系统日前发电计划模式分析[J].电力系统自动化,2011,35(22):111-118.Wang Caixia,Qiao Ying,Lu Zongxiang,et al.Day-ahead dispatch mode for wind-thermal power system in low-carbon economy[J].Automation of Electric Power Systems,2011,35(22):111-118.
[8]许汉平,李姚旺,苗世洪,等.考虑可再生能源消纳效益的电力系统“源-荷-储”协调互动优化调度策略[J].电力系统保护与控制,2017,45(17):18-25.Xu Hanping,Li Yaowang,Miao Shihong,et al.Optimization dispatch strategy considering renewable energy consumptive benefits based on“source-loadenergy storage”coordination in power system[J].Power System Protection and Control,2017,45(17):18-25.
[9]邹金,赖旭,汪宁渤.以减少电网弃风为目标的风电与抽水蓄能协调运行[J].电网技术,2015,39(9):2472-2477.Zou Jin,Lai Xu,Wang Ningbo.Mitigation of wind curtailment by coordinating with pumped storage[J].Power System Technology,2015,39(9):2472-2477.
[10]Greenblatt J B,Succar S,Denkenberger D C,et al.Baseload wind energy:modeling the competition between gas turbines and compressed air energy storage for supplemental generation[J].Energy Policy2007,35(3):1474-1492.
[11]孙建军,张世泽,曾梦迪,等.考虑分时电价的主动配电网柔性负荷多目标优化控制[J].电工技术学报,2018,33(2):401-412.Sun Jianjun,Zhang Shize,Zeng Mengdi,et al.Multi-objective optimal control for flexible load in active distribution network considering time-of-use tariff[J].Transactions of China Electrotechnical Society,2018,33(2):401-412.
[12]Hamilton K,Gulhar N.Taking demand response to the next level[J].IEEE Power Energy Magazine,2010,8(3):60-65.
[13]Erdin?O,Tasc?karaoglu A,Paterakis N,et al End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts[J].IEEE Transactions on Smart Grid,2017,8(1):362-372.
[14]姚建国,杨胜春,王珂,等.平衡风功率波动的需求响应调度框架与策略设计[J].电力系统自动化,2014,38(9):85-92.Yao Jianguo,Yang Shengchun,Wang Ke,et al.Framework and strategy design of demand response scheduling for balancing wind power fluctuation[J].Automation of Electric Power Systems,2014,38(9):85-92.
[15]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,38(13):115-120.Bie Zhaohong,Hu Guowei,Xie Haipeng,et al Optimal dispatch for wind power integrated systems considering demand response[J].Automation of Electric Power Systems,2014,38(13):115-120.
[16]卢锦玲,於慧敏.考虑风电相依结构的虚拟发电厂内部资源随机调度策略[J].电工技术学报,2017,32(17):67-74.Lu Jinling,Yu Huimin.Stochastic scheduling strategy of resources in virtual power plant considering wind power dependence structure[J].Transactions of China Electrotechnical Society,2017,32(17):67-74.
[17]鞠立伟,秦超,吴鸿亮,等.计及多类型需求响应的风电消纳随机优化调度模型[J].电网技术,201539(7):1839-1846.Ju Liwei,Qin Chao,Wu Hongliang,et al.Wind power accommodation stochastic optimization model with multi-type demand response[J].Power System Technology,2015,39(7):1839-1846.
[18]王蓓蓓,刘小聪,李扬.面向大容量风电接入考虑用户侧互动的系统日前调度和运行模拟研究[J].中国电机工程学报,2013,33(22):35-44.Wang Beibei,Liu Xiaocong,Li Yang.Day-ahead generation scheduling and operation simulation considering demand response in large-capacity wind power integrated systems[J].Proceedings of the CSEE,2013,33(22):35-44.
[19]何成明,王洪涛,韦仲康,等.风电场与AGC机组分布式协同实时控制[J].中国电机工程学报,2015,35(2):302-309.He Chengming,Wang Hongtao,Wei Zhongkang,et al.Distributed coordinated real-time control of wind farm and AGC units[J].Proceedings of the CSEE,2015,35(2):302-309.
[20]曾丹,姚建国,杨胜春,等.应对风电消纳中基于安全约束的价格型需求响应优化调度建模[J].中国电机工程学报,2014,34(31):5571-5578.Zeng Dan,Yao Jianguo,Yang Shengchun,et al.Optimization dispatch modeling for price-based demand response considering security constraints to accommodate the wind power[J].Proceedings of the CSEE,2014,34(31):5571-5578.
[21]刘小聪,王蓓蓓,李扬,等.智能电网下计及用户侧互动的发电日前调度计划模型[J].中国电机工程学报,2013,33(1):30-38.Liu Xiaocong,Wang Beibei,Li Yang,et al.Day-ahead generation scheduling model considering demand side interaction under smart grid paradigm[J].Proceedings of the CSEE,2013,33(1):30-38.
[22]Shao S.An approach to demand response for alleviating power system stress conditions due to electric vehicle penetration[D].Arlington,VA,USA:Virginia Polytechnic Institute and State University,2011.
[23]Leehter Yao,Wei Hong Lim,Teng Shih Tsai.Areal-time charging scheme for demand response in electric vehicle parking station[J].IEEE Transactions on Smart Grid,2017,8(1):52-62.
[24]Ning Jia,Tang Yi,Gao Wenzhong.A hierarchical charging strategy for electric vehicles considering the users’habits and intentions[C]//2015 IEEE Power&Energy Society General Meeting,Denver,CO,USA,2015,DOI:10.1109/PESGM.2015.7285726.
[25]宋梦,高赐威,苏卫华.面向需求响应应用的空调负荷建模及控制[J].电力系统自动化,2016,40(14):158-167.Song Meng,Gao Ciwei,Su Weihua.Modeling and controlling of air-conditioning load for demand response appliances[J].Automation of Electric Power Systems,2016,40(14):158-167.
[26]https://sourceforge.net/p/gridlab-d/code/HEAD/tree/tr unk/residential/autotest/water_schedule.glm.
[27]Santos,McGuckin N,Nakamoto H Y,et al.Summary of travel trends:2009 national household travel survey[R].U.S.Department of Transportation Federal Highway Administration,Washington,DC,USA,2011.
[28]田立婷,史双龙,贾卓.电动汽车充电功率需求的统计学建模方法[J].电网技术,2010,34(11):126-130Tian Liting,Shi Shuanglong,Jia Zhuo.A statistical model for charging power demand of electric vehicles[J].Power System Technology,2010,34(11):126-130.
[29]徐智威,胡泽春,宋永华,等.充电站内电动汽车有序充电策略[J].电力系统自动化,2012,36(11):38-43.Xu Zhiwei,Hu Zechun,Song Yonghua,et al.Coordinated charging of plug-in electric vehicles in charging stations[J].Automation of Electric Power Systems,2012,36(11):38-43.
[30]Ning Jia,Tang Yi.A bi-level coordinated optimization strategy for smart appliances considering online demand response potential[J].Energies,201710:525.
[31]Conejo A J,Carrion M,Morales J M.Decision making under uncertainty in electricity markets[M].New York,USA:Springer,2010.
[2]郭鹏,文晶,朱丹丹,等.基于源-荷互动的大规模风电消纳协调控制策略[J].电工技术学报,2017,32(3):1-9.Guo Peng,Wen Jing,Zhu Dandan,et al.The coordination control strategy for large-scale wind power consumption based on source-load interactive[J].Transactions of China Electrotechnical Society,2017,32(3):1-9.
[3]胡源,别朝红,宁光涛,等.计及风电不确定性的多目标电网规划模型与算法研究[J].电工技术学报,2016,31(10):168-175.Hu Yuan,Bie Zhaohong,Ning Guangtao,et al.The expected model and algorithm of multi-objective transmission network planning considering the uncertainty of wind power[J].Transactions of China Electrotechnical Society,2016,31(10):168-175.
[4]Zhang Ning,Kang Chongqing,Xia Qing,et al.Modeling conditional forecast error for wind power in generation scheduling[J].IEEE Transactions on Power Systems,2014,29(3):1316-1324.
[5]张磊,罗毅,肖雅元,等.大规模风电并网条件下AGC机组跨区分布式最优协调控制[J].电工技术学报,2016,31(9):42-49.Zhang Lei,Luo Yi,Xiao Yayuan,et al.Transregional and distributed optimal coordinated control of AGC units under large-scale wind power grid[J].Transactions of China Electrotechnical Society,2016,31(9):42-49.
[6]许昌,魏媛,李涛,等.大型风电机组机组层AGC控制策略研究[J].电力系统保护与控制,2017,45(2):69-74.Xu Chang,Wei Yuan,Li Tao,et al.Research on automatic generation turbine control strategy of large wind turbine[J].Power System Protection and Control,2017,45(2):69-74.
[7]王彩霞,乔颖,鲁宗相,等.低碳经济下风火互济系统日前发电计划模式分析[J].电力系统自动化,2011,35(22):111-118.Wang Caixia,Qiao Ying,Lu Zongxiang,et al.Day-ahead dispatch mode for wind-thermal power system in low-carbon economy[J].Automation of Electric Power Systems,2011,35(22):111-118.
[8]许汉平,李姚旺,苗世洪,等.考虑可再生能源消纳效益的电力系统“源-荷-储”协调互动优化调度策略[J].电力系统保护与控制,2017,45(17):18-25.Xu Hanping,Li Yaowang,Miao Shihong,et al.Optimization dispatch strategy considering renewable energy consumptive benefits based on“source-loadenergy storage”coordination in power system[J].Power System Protection and Control,2017,45(17):18-25.
[9]邹金,赖旭,汪宁渤.以减少电网弃风为目标的风电与抽水蓄能协调运行[J].电网技术,2015,39(9):2472-2477.Zou Jin,Lai Xu,Wang Ningbo.Mitigation of wind curtailment by coordinating with pumped storage[J].Power System Technology,2015,39(9):2472-2477.
[10]Greenblatt J B,Succar S,Denkenberger D C,et al.Baseload wind energy:modeling the competition between gas turbines and compressed air energy storage for supplemental generation[J].Energy Policy2007,35(3):1474-1492.
[11]孙建军,张世泽,曾梦迪,等.考虑分时电价的主动配电网柔性负荷多目标优化控制[J].电工技术学报,2018,33(2):401-412.Sun Jianjun,Zhang Shize,Zeng Mengdi,et al.Multi-objective optimal control for flexible load in active distribution network considering time-of-use tariff[J].Transactions of China Electrotechnical Society,2018,33(2):401-412.
[12]Hamilton K,Gulhar N.Taking demand response to the next level[J].IEEE Power Energy Magazine,2010,8(3):60-65.
[13]Erdin?O,Tasc?karaoglu A,Paterakis N,et al End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts[J].IEEE Transactions on Smart Grid,2017,8(1):362-372.
[14]姚建国,杨胜春,王珂,等.平衡风功率波动的需求响应调度框架与策略设计[J].电力系统自动化,2014,38(9):85-92.Yao Jianguo,Yang Shengchun,Wang Ke,et al.Framework and strategy design of demand response scheduling for balancing wind power fluctuation[J].Automation of Electric Power Systems,2014,38(9):85-92.
[15]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,38(13):115-120.Bie Zhaohong,Hu Guowei,Xie Haipeng,et al Optimal dispatch for wind power integrated systems considering demand response[J].Automation of Electric Power Systems,2014,38(13):115-120.
[16]卢锦玲,於慧敏.考虑风电相依结构的虚拟发电厂内部资源随机调度策略[J].电工技术学报,2017,32(17):67-74.Lu Jinling,Yu Huimin.Stochastic scheduling strategy of resources in virtual power plant considering wind power dependence structure[J].Transactions of China Electrotechnical Society,2017,32(17):67-74.
[17]鞠立伟,秦超,吴鸿亮,等.计及多类型需求响应的风电消纳随机优化调度模型[J].电网技术,201539(7):1839-1846.Ju Liwei,Qin Chao,Wu Hongliang,et al.Wind power accommodation stochastic optimization model with multi-type demand response[J].Power System Technology,2015,39(7):1839-1846.
[18]王蓓蓓,刘小聪,李扬.面向大容量风电接入考虑用户侧互动的系统日前调度和运行模拟研究[J].中国电机工程学报,2013,33(22):35-44.Wang Beibei,Liu Xiaocong,Li Yang.Day-ahead generation scheduling and operation simulation considering demand response in large-capacity wind power integrated systems[J].Proceedings of the CSEE,2013,33(22):35-44.
[19]何成明,王洪涛,韦仲康,等.风电场与AGC机组分布式协同实时控制[J].中国电机工程学报,2015,35(2):302-309.He Chengming,Wang Hongtao,Wei Zhongkang,et al.Distributed coordinated real-time control of wind farm and AGC units[J].Proceedings of the CSEE,2015,35(2):302-309.
[20]曾丹,姚建国,杨胜春,等.应对风电消纳中基于安全约束的价格型需求响应优化调度建模[J].中国电机工程学报,2014,34(31):5571-5578.Zeng Dan,Yao Jianguo,Yang Shengchun,et al.Optimization dispatch modeling for price-based demand response considering security constraints to accommodate the wind power[J].Proceedings of the CSEE,2014,34(31):5571-5578.
[21]刘小聪,王蓓蓓,李扬,等.智能电网下计及用户侧互动的发电日前调度计划模型[J].中国电机工程学报,2013,33(1):30-38.Liu Xiaocong,Wang Beibei,Li Yang,et al.Day-ahead generation scheduling model considering demand side interaction under smart grid paradigm[J].Proceedings of the CSEE,2013,33(1):30-38.
[22]Shao S.An approach to demand response for alleviating power system stress conditions due to electric vehicle penetration[D].Arlington,VA,USA:Virginia Polytechnic Institute and State University,2011.
[23]Leehter Yao,Wei Hong Lim,Teng Shih Tsai.Areal-time charging scheme for demand response in electric vehicle parking station[J].IEEE Transactions on Smart Grid,2017,8(1):52-62.
[24]Ning Jia,Tang Yi,Gao Wenzhong.A hierarchical charging strategy for electric vehicles considering the users’habits and intentions[C]//2015 IEEE Power&Energy Society General Meeting,Denver,CO,USA,2015,DOI:10.1109/PESGM.2015.7285726.
[25]宋梦,高赐威,苏卫华.面向需求响应应用的空调负荷建模及控制[J].电力系统自动化,2016,40(14):158-167.Song Meng,Gao Ciwei,Su Weihua.Modeling and controlling of air-conditioning load for demand response appliances[J].Automation of Electric Power Systems,2016,40(14):158-167.
[26]https://sourceforge.net/p/gridlab-d/code/HEAD/tree/tr unk/residential/autotest/water_schedule.glm.
[27]Santos,McGuckin N,Nakamoto H Y,et al.Summary of travel trends:2009 national household travel survey[R].U.S.Department of Transportation Federal Highway Administration,Washington,DC,USA,2011.
[28]田立婷,史双龙,贾卓.电动汽车充电功率需求的统计学建模方法[J].电网技术,2010,34(11):126-130Tian Liting,Shi Shuanglong,Jia Zhuo.A statistical model for charging power demand of electric vehicles[J].Power System Technology,2010,34(11):126-130.
[29]徐智威,胡泽春,宋永华,等.充电站内电动汽车有序充电策略[J].电力系统自动化,2012,36(11):38-43.Xu Zhiwei,Hu Zechun,Song Yonghua,et al.Coordinated charging of plug-in electric vehicles in charging stations[J].Automation of Electric Power Systems,2012,36(11):38-43.
[30]Ning Jia,Tang Yi.A bi-level coordinated optimization strategy for smart appliances considering online demand response potential[J].Energies,201710:525.
[31]Conejo A J,Carrion M,Morales J M.Decision making under uncertainty in electricity markets[M].New York,USA:Springer,2010.