计及新能源出力不确定性的电气综合能源系统协同优化
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摘要
随着波动性强的新能源大规模接入配电网,主网下网功率波动增强,危及主网运行安全。为了有效降低新能源消纳对配电网的不利影响,建立了考虑新能源出力不确定性的电气综合能源系统协同优化模型。其中,动态场景方法被用来刻画新能源出力的不确定性,优化目标为最小化全系统运行成本和主网下网功率波动量。模型中考虑了温控负荷调节能力及配电网交流潮流和天然气网潮流等约束。利用分段线性化和二阶锥松弛方法,将模型转化为混合整数二阶锥规划问题。最后,在IEEE 33节点配电网和23节点气网构成的电气综合能源系统进行夏季和冬季算例仿真,验证了利用惯性更大的气网平抑主网下网功率波动的有效性。同时,仿真结果表明利用温控负荷调节能力可降低系统成本,且确保系统运行的安全。
As the large-scale renewable energy with high volatility accessing to the distribution network,the tie-line power fluctuation of main network increases,which endangers its operation safety.In order to reduce the adverse impact of the renewable energy utilization on the distribution network,a coordinated optimization model is established for the integrated electricity-gas energy system,which takes into account the uncertainty of renewable energy output.In the model,the dynamic scenarios method is adopted to describe the uncertainty of renewable energy output,and the objective is to minimize the whole system operation costs and the tie-line power fluctuation.The model considers the regulation capability of thermostatically controlled load and AC power flow of distribution network and gas flow of gas distribution network.The piecewise linearization and second-order cone relaxation methods are used to transform the model into a mixed-integer second-order cone programming problem.Finally,the summer and winter simulation is conducted on an IEEE 33-node distribution network and a 23-node gas network.It is verified that the gas network with large inertia can be used to smooth the tie-line power fluctuation.Meanwhile,the simulation results show that the thermostatically controlled load can reduce system costs and ensure system operation safety.
As the large-scale renewable energy with high volatility accessing to the distribution network,the tie-line power fluctuation of main network increases,which endangers its operation safety.In order to reduce the adverse impact of the renewable energy utilization on the distribution network,a coordinated optimization model is established for the integrated electricity-gas energy system,which takes into account the uncertainty of renewable energy output.In the model,the dynamic scenarios method is adopted to describe the uncertainty of renewable energy output,and the objective is to minimize the whole system operation costs and the tie-line power fluctuation.The model considers the regulation capability of thermostatically controlled load and AC power flow of distribution network and gas flow of gas distribution network.The piecewise linearization and second-order cone relaxation methods are used to transform the model into a mixed-integer second-order cone programming problem.Finally,the summer and winter simulation is conducted on an IEEE 33-node distribution network and a 23-node gas network.It is verified that the gas network with large inertia can be used to smooth the tie-line power fluctuation.Meanwhile,the simulation results show that the thermostatically controlled load can reduce system costs and ensure system operation safety.
引文
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[12]朱嘉远,刘洋,许立雄,等.考虑风电消纳的热电联供型微网日前鲁棒经济调度[J].电力系统自动化,2019,43(4):40-48.DOI:10.7500/AEPS20180214007.ZHU Jiayuan,LIU Yang,XU Lixiong,et al.Robust dayahead economic dispatch of microgrid with combined heat and power system considering wind power accommodation[J].Automation of Electric Power Systems,2019,43(4):40-48.DOI:10.7500/AEPS20180214007.
[13]白凯峰,顾洁,彭虹桥,等.融合风光出力场景生成的多能互补微网系统优化配置[J].电力系统自动化,2018,42(15):133-141.DOI:10.7500/AEPS20170913008.BAI Kaifeng,GU Jie,PENG Hongqiao,et al.Optimal allocation for multi-energy complementary microgrid based on scenario generation of wind power and photovoltaic output[J].Automation of Electric Power Systems,2018,42(15):133-141.DOI:10.7500/AEPS20170913008.
[14]王丹,兰宇,贾宏杰,等.基于弹性温度可调裕度的中央空调集群需求响应双层优化[J].电力系统自动化,2018,42(22):118-126.DOI:10.7500/AEPS20180225003.WANG Dan,LAN Yu,JIA Hongjie,et al.Double-layer optimization strategy of cluster central air-conditioning demand response based on elastic temperature adjustable margin[J].Automation of Electric Power Systems,2018,42(22):118-126.DOI:10.7500/AEPS20180225003.
[15]徐熙林,宋依群,姚良忠,等.基于多层电价响应机制的主动配电网源-网-荷协调方法[J].电力系统自动化,2018,42(5):9-17.DOI:10.7500/AEPS20170430002.XU Xilin,SONG Yiqun,YAO Liangzhong,et al.Source-gridload coordination method for active distribution network based on multi-level electricity price response mechanism[J].Automation of Electric Power Systems,2018,42(5):9-17.DOI:10.7500/AEPS20170430002.
[16]王守相,孙智卿,刘喆.面向智能用电的家庭能量协同调度策略[J].电力系统自动化,2015,39(17):108-113.WANG Shouxiang,SUN Zhiqing,LIU Zhe.Co-scheduling strategy of home energy for smart power utilization[J].Automation of Electric Power Systems,2015,39(17):108-113.
[17]王成山,刘梦璇,陆宁.采用居民温控负荷控制的微网联络线功率波动平滑方法[J].中国电机工程学报,2012,32(25):36-43.WANG Chengshan,LIU Mengxuan,LU Ning.A tie-line power smoothing method for microgrid using residential thermostatically-controlled loads[J].Proceedings of the CSEE,2012,32(25):36-43.
[18]PINSON P,GIRARD R.Evaluating the quality of scenarios of short-term wind power generation[J].Applied Energy,2012,96(8):12-20.
[19]VAPNIK V N.The nature of statistical learning theory[M].Berlin,Germany:Springer-Verlag,1995.
[20]DUPACOVA J,GROWE-KUSKA N.Scenario reduction in stochastic programming:an approach using probability metrics[J].Mathematical Programming,2003,95(3):493-511.
[21]DING T,LIU S,YUAN W,et al.A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks[J].IEEETransactions on Sustainable Energy,2016,7(1):301-311.
[22]PINSON P,MADSEN H,NIELSEN H A,et al.From probabilistic forecasts to statistical scenarios of short-term wind power production[J].Wind Energy,2010,12(1):51-62.
[23]CARRION M,ARROYO J M.A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem[J].IEEE Transactions on Power Systems,2006,21(3):1371-1378.
[24]FARIVAR M,LOW S H.Branch flow model:relaxations and convexification-PartⅠ[J].IEEE Transactions on Power Systems,2013,28(3):2554-2564.
[25]SALDARRIAGA C,HINCAPIE R A,SALAZAR H.Aholistic approach for planning natural gas and electricity distribution networks[J].IEEE Transactions on Power Systems,2013,28(4):4052-4063.
[26]WANG J,BOTTERUD A,BESSA R,et al.Wind power forecasting uncertainty and unit commitment[J].Applied Energy,2011,88(11):4014-4023.
[27]Solar-PV power generation data[EB/OL].[2018-05-30].http://www.elia.be/en/grid-data/power-generation/Solarpowergeneration-data/Graph.
[2]姚良忠,朱凌志,周明,等.高比例可再生能源电力系统的协同优化运行技术展望[J].电力系统自动化,2017,41(9):36-43.DOI:10.7500/AEPS20170112013.YAO Liangzhong,ZHU Lingzhi,ZHOU Ming,et al.Prospects of coordination and optimization for power systems with high proportion of renewable energy[J].Automation of Electric Power Systems,2017,41(9):36-43.DOI:10.7500/AEPS20170112013.
[3]ARCOS-AVILES D,PASCUAL J,GUINJOAN F,et al.Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting[J].Applied Energy,2017,205:69-84.
[4]赵波,肖传亮,徐琛,等.基于渗透率的区域配电网分布式光伏并网消纳能力分析[J].电力系统自动化,2017,41(21):111-117.DOI:10.7500/AEPS20170306016.ZHAO Bo,XIAO Chuanliang,XU Chen,et al.Penetration based accommodation capacity analysis on distributed photovoltaic connection in regional distribution network[J].Automation of Electric Power Systems,2017,41(21):111-117.DOI:10.7500/AEPS20170306016.
[5]别朝红,王旭,胡源.能源互联网规划研究综述及展望[J].中国电机工程学报,2017,37(22):6445-6462.BIE Zhaohong,WANG Xu,HU Yuan.Review and prospect of planning of energy internet[J].Proceedings of the CSEE,2017,37(22):6445-6462.
[6]唐早,刘俊勇,刘友波,等.空调聚合商参与下的负荷控制与配电网重构[J].电力系统自动化,2018,42(2):42-49.DOI:10.7500/AEPS20170614023.TANG Zao,LIU Junyong,LIU Youbo,et al.Load control and distribution network reconfiguration with participation of airconditioning load aggregators[J].Automation of Electric Power Systems,2018,42(2):42-49.DOI:10.7500/AEPS20170614023.
[7]LIN W,JIN X,MU Y,et al.A two-stage multi-objective scheduling method for integrated community energy system[J].Applied Energy,2018,216:428-441.
[8]宋晨辉,冯健,杨东升,等.考虑系统耦合性的综合能源协同优化[J].电力系统自动化,2018,42(10):38-45.DOI:10.7500/AEPS20170914008.SONG Chenhui,FENG Jian,YANG Dongsheng,et al.Collaborative optimization of integrated energy considering system coupling[J].Automation of Electric Power Systems,2018,42(10):38-45.DOI:10.7500/AEPS20170914008.
[9]徐航,董树锋,何仲潇,等.考虑能量梯级利用的工厂综合能源系统多能协同优化[J].电力系统自动化,2018,42(14):123-130.DOI:10.7500/AEPS20170825006.XU Hang,DONG Shufeng,HE Zhongxiao,et al.Multi-energy cooperative optimization of integrated energy system in plant considering stepped utilization of energy[J].Automation of Electric Power Systems,2018,42(14):123-130.DOI:10.7500/AEPS20170825006.
[10]潘振宁,王克英,瞿凯平,等.考虑大量EV接入的电-气-热多能耦合系统协同优化调度[J].电力系统自动化,2018,42(4):104-112.DOI:10.7500/AEPS20170527007.PAN Zhenning,WANG Keying,QU Kaiping,et al.Coordinated optimization dispatch of electricity-gas-heat multienergy system considering high penetration of electric vehicles[J].Automation of Electric Power Systems,2018,42(4):104-112.DOI:10.7500/AEPS20170527007.
[11]ZHANG L,TANG W,LIANG J,et al.Coordinated dayahead reactive power dispatch in distribution network based on real power forecast errors[J].IEEE Transactions on Power Systems,2016,31(3):2472-2480.
[12]朱嘉远,刘洋,许立雄,等.考虑风电消纳的热电联供型微网日前鲁棒经济调度[J].电力系统自动化,2019,43(4):40-48.DOI:10.7500/AEPS20180214007.ZHU Jiayuan,LIU Yang,XU Lixiong,et al.Robust dayahead economic dispatch of microgrid with combined heat and power system considering wind power accommodation[J].Automation of Electric Power Systems,2019,43(4):40-48.DOI:10.7500/AEPS20180214007.
[13]白凯峰,顾洁,彭虹桥,等.融合风光出力场景生成的多能互补微网系统优化配置[J].电力系统自动化,2018,42(15):133-141.DOI:10.7500/AEPS20170913008.BAI Kaifeng,GU Jie,PENG Hongqiao,et al.Optimal allocation for multi-energy complementary microgrid based on scenario generation of wind power and photovoltaic output[J].Automation of Electric Power Systems,2018,42(15):133-141.DOI:10.7500/AEPS20170913008.
[14]王丹,兰宇,贾宏杰,等.基于弹性温度可调裕度的中央空调集群需求响应双层优化[J].电力系统自动化,2018,42(22):118-126.DOI:10.7500/AEPS20180225003.WANG Dan,LAN Yu,JIA Hongjie,et al.Double-layer optimization strategy of cluster central air-conditioning demand response based on elastic temperature adjustable margin[J].Automation of Electric Power Systems,2018,42(22):118-126.DOI:10.7500/AEPS20180225003.
[15]徐熙林,宋依群,姚良忠,等.基于多层电价响应机制的主动配电网源-网-荷协调方法[J].电力系统自动化,2018,42(5):9-17.DOI:10.7500/AEPS20170430002.XU Xilin,SONG Yiqun,YAO Liangzhong,et al.Source-gridload coordination method for active distribution network based on multi-level electricity price response mechanism[J].Automation of Electric Power Systems,2018,42(5):9-17.DOI:10.7500/AEPS20170430002.
[16]王守相,孙智卿,刘喆.面向智能用电的家庭能量协同调度策略[J].电力系统自动化,2015,39(17):108-113.WANG Shouxiang,SUN Zhiqing,LIU Zhe.Co-scheduling strategy of home energy for smart power utilization[J].Automation of Electric Power Systems,2015,39(17):108-113.
[17]王成山,刘梦璇,陆宁.采用居民温控负荷控制的微网联络线功率波动平滑方法[J].中国电机工程学报,2012,32(25):36-43.WANG Chengshan,LIU Mengxuan,LU Ning.A tie-line power smoothing method for microgrid using residential thermostatically-controlled loads[J].Proceedings of the CSEE,2012,32(25):36-43.
[18]PINSON P,GIRARD R.Evaluating the quality of scenarios of short-term wind power generation[J].Applied Energy,2012,96(8):12-20.
[19]VAPNIK V N.The nature of statistical learning theory[M].Berlin,Germany:Springer-Verlag,1995.
[20]DUPACOVA J,GROWE-KUSKA N.Scenario reduction in stochastic programming:an approach using probability metrics[J].Mathematical Programming,2003,95(3):493-511.
[21]DING T,LIU S,YUAN W,et al.A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks[J].IEEETransactions on Sustainable Energy,2016,7(1):301-311.
[22]PINSON P,MADSEN H,NIELSEN H A,et al.From probabilistic forecasts to statistical scenarios of short-term wind power production[J].Wind Energy,2010,12(1):51-62.
[23]CARRION M,ARROYO J M.A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem[J].IEEE Transactions on Power Systems,2006,21(3):1371-1378.
[24]FARIVAR M,LOW S H.Branch flow model:relaxations and convexification-PartⅠ[J].IEEE Transactions on Power Systems,2013,28(3):2554-2564.
[25]SALDARRIAGA C,HINCAPIE R A,SALAZAR H.Aholistic approach for planning natural gas and electricity distribution networks[J].IEEE Transactions on Power Systems,2013,28(4):4052-4063.
[26]WANG J,BOTTERUD A,BESSA R,et al.Wind power forecasting uncertainty and unit commitment[J].Applied Energy,2011,88(11):4014-4023.
[27]Solar-PV power generation data[EB/OL].[2018-05-30].http://www.elia.be/en/grid-data/power-generation/Solarpowergeneration-data/Graph.