计及不确定因素的需求侧灵活性资源优化调度
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摘要
充分把握需求侧资源灵活性可以更好地实现日前优化调度以及合理地向电网提供辅助服务。然而,需求侧资源种类丰富、数量大、容量小、随机性强且分布于系统结构底层的不同位置,因此需要对其进行整合与量化。以含有高比例分布式能源的节点为研究对象,采用鲁棒边界的虚拟电池模型量化描述了具有不确定因素的集群电动汽车的灵活性与光伏出力。在此基础上,由交互平台参与日前电力市场,并采用协调优化策略合理地分配日前发用电计划与备用容量。算例验证了提出方法的有效性,既解决了由于高比例分布式能源导致的计算复杂问题,又降低了用户用电隐私信息暴露的风险。
Sufficient grasp of flexibilities available from resources on demand side can better realize day-ahead optimal scheduling and reasonably provide ancillary services to power grids.However,the resources on demand side have abundant species,large quantities,small capacities and strong randomness.In addition,resources on demand side are usually distributed in different locations at the bottom of the system structure.Thus,these flexibilities need to be integrated and quantified.This paper takes the nodes with high-penetration of distributed energy as the research object.Virtual battery model with robust boundary is used to quantatively describe the flexibilities of aggregated electric vehicles and photovoltaic output considering their uncertainties.Based on the proposed virtual battery model,the transactive platform participates in the day-ahead electricity market and adopts a coordinated optimization strategy to reasonably allot energy schedule and reserve capacity.The case study verifies the effectiveness of the proposed method,which can reduce computational complexity caused by high-penetration distributed energy,and reduce the exposure risk of privacy electricity information for users.
Sufficient grasp of flexibilities available from resources on demand side can better realize day-ahead optimal scheduling and reasonably provide ancillary services to power grids.However,the resources on demand side have abundant species,large quantities,small capacities and strong randomness.In addition,resources on demand side are usually distributed in different locations at the bottom of the system structure.Thus,these flexibilities need to be integrated and quantified.This paper takes the nodes with high-penetration of distributed energy as the research object.Virtual battery model with robust boundary is used to quantatively describe the flexibilities of aggregated electric vehicles and photovoltaic output considering their uncertainties.Based on the proposed virtual battery model,the transactive platform participates in the day-ahead electricity market and adopts a coordinated optimization strategy to reasonably allot energy schedule and reserve capacity.The case study verifies the effectiveness of the proposed method,which can reduce computational complexity caused by high-penetration distributed energy,and reduce the exposure risk of privacy electricity information for users.
引文
[1] 肖云鹏,王锡凡,王秀丽,等.面向高比例可再生能源的电力市场研究综述[J].中国电机工程学报,2018,38(3):663-674.XIAO Yunpeng,WANG Xifan,WANG Xiuli,et al.Review on electricity market towards high proportion of renewable energy[J].Proceedings of the CSEE,2018,38(3):663-674.
[2] 吴巨爱,薛禹胜,谢东亮,等.电动汽车参与运行备用的能力评估及其仿真分析[J].电力系统自动化,2018,42(13):101-107.DOI:10.7500/AEPS20180130012.WU Juai,XUE Yusheng,XIE Dongliang,et al.Evaluation and simulation analysis of reserve capability for electric vehicles[J].Automation of Electric Power Systems,2018,42(13):101-107.DOI:10.7500/AEPS20180130012.
[3] HAO H,WU D,LIAN J M,et al.Optimal coordination of building loads and energy storage for power grid and end user services[J].IEEE Transactions on Smart Grid,2018,9(5):4335-4345.
[4] 刘静琨,张宁,康重庆.电力系统云储能研究框架与基础模型[J].中国电机工程学报,2017,37(12):3361-3371.LIU Jingkun,ZHANG Ning,KANG Chongqing.Research framework and basic models for cloud energy storage in power system[J].Proceedings of the CSEE,2017,37(12):3361-3371.
[5] 张利,杨建,菅学辉,等.考虑次小时尺度运行灵活性的含储能机组组合[J].电力系统自动化,2018,42(16):48-56.DOI:10.7500/AEPS20170930007.ZHANG Li,YANG Jian,JIAN Xuehui,et al.Unit commitment with energy storage considering operation flexibility at sub-hourly time-scales[J].Automation of Electric Power Systems,2018,42(16):48-56.DOI:10.7500/AEPS20170930007.
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[7] 王克道,陈启鑫,郭鸿业,等.面向可交易能源的储能容量合约机制设计与交易策略[J].电力系统自动化,2018,42(14):54-60.DOI:10.7500/AEPS20180110010.WANG Kedao,CHEN Qixin,GUO Hongye,et al.Mechanism design and trading strategy for capacity contract of energy storage towards transactive energy[J].Automation of Electric Power Systems,2018,42(14):54-60.DOI:10.7500/AEPS20180110010.
[8] HU J J,YANG G Y,KOK K,et al.Transactive control:a framework for operating power systems characterized by high penetration of distributed energy resources[J].Journal of Modern Power Systems and Clean Energy,2017,5(3):451-464.
[9] 胡俊杰,王坤宇,艾欣,等.交互能源:实现电力能源系统平衡的有效机制[J].中国电机工程学报,2019,39(4):953-966.HU Junjie,WANG Kunyu,AI Xin,et al.Transactive energy:an effective mechanism for balancing electric energy system[J].Proceedings of the CSEE,2019,39(4):953-966.
[10] 王成山,李鹏,于浩.智能配电网的新形态及其灵活性特征分析与应用[J].电力系统自动化,2018,42(10):13-21.DOI:10.7500/AEPS20171012002.WANG Chengshan,LI Peng,YU Hao.Development and characteristic analysis of flexibility in smart distribution network[J].Automation of Electric Power Systems,2018,42(10):13-21.DOI:10.7500/AEPS20171012002.
[11] ULBIG A,ANDERSSON G.Analyzing operational flexibility of electric power systems[J].International Journal of Electrical Power & Energy Systems,2015,72:155-164.
[12] MORSTYN T,FARRELL N,DARBY S J,et al.Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants[J].Nature Energy,2018,3(2):94-101.
[13] GONZALEZ VAYA M,ANDERSSON G.Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets under uncertainty[J].IEEE Transactions on Power Systems,2015,30(5):2375-2385.
[14] 吴界辰,艾欣,张艳,等.配售分离环境下高比例分布式能源园区电能日前优化调度[J].电网技术,2018,42(6):1709-1719.WU Jiechen,AI Xin,ZHANG Yan,et al.Day-ahead optimal scheduling for high penetration of distributed energy resources in community under separated distribution and retail operational environment[J].Power System Technology,2018,42(6):1709-1719.
[15] HU J,LAN T,KAI H,et al.Robust allocation of reserve policies for a multiple-cell based power system[J].Energies,2018,11(2):381.
[16] BAROT S,TAYLOR J A.A concise,approximate representation of a collection of loads described by polytopes[J].International Journal of Electrical Power & Energy Systems,2017,84:55-63.
[17] MULLER F L,SUNDSTROM O,SZABO J,et al.Aggregation of energetic flexibility using zonotopes[C]// 2015 54th IEEE Conference on Decision and Control (CDC),December 15-18,2015,Osaka,Japan.
[18] 魏韡,刘锋,梅生伟.电力系统鲁棒经济调度(一)理论基础[J].电力系统自动化,2013,37(17):37-43.WEI Wei,LIU Feng,MEI Shengwei.Robust and economical scheduling methodology for power systems:Part one theoretical foundations[J].Automation of Electric Power Systems,2013,37(17):37-43.
[19] ZHAO C Y,GUAN Y P.Unified stochastic and robust unit commitment[J].IEEE Transactions on Power Systems,2013,28(3):3353-3361.
[20] 税月,刘俊勇,高红均,等.考虑风电不确定性的电气能源系统两阶段分布鲁棒协同调度[J].电力系统自动化,2018,42(13):43-50.DOI:10.7500/AEPS20171218008.SHUI Yue,LIU Junyong,GAO Hongjun,et al.Two-stage distributed robust cooperative dispatch for integrated electricity and natural gas energy systems considering uncertainty of wind power[J].Automation of Electric Power Systems,2018,42(13):43-50.DOI:10.7500/AEPS20171218008.
[21] LIN X X,JANAK S L,FLOUDAS C A.A new robust optimization approach for scheduling under uncertainty[J].Computers & Chemical Engineering,2004,28(6/7):1069-1085.
[22] MARGELLOS K,GOULART P,LYGEROS J.On the road between robust optimization and the scenario approach for chance constrained optimization problems[J].IEEE Transactions on Automatic Control,2014,59(8):2258-2263.
[23] ALTHOFF M,STURSBERG O,BUSS M.Computing reachable sets of hybrid systems using a combination of zonotopes and polytopes[J].Nonlinear Analysis:Hybrid Systems,2010,4(2):233-249.
[24] MATHIEU J L,VAYá M G,ANDERSSON G.Uncertainty in the flexibility of aggregations of demand response resources[C]// Conference of the IEEE Industrial Electronics Society,2013,Vienna,Austria:8052-8057.
[2] 吴巨爱,薛禹胜,谢东亮,等.电动汽车参与运行备用的能力评估及其仿真分析[J].电力系统自动化,2018,42(13):101-107.DOI:10.7500/AEPS20180130012.WU Juai,XUE Yusheng,XIE Dongliang,et al.Evaluation and simulation analysis of reserve capability for electric vehicles[J].Automation of Electric Power Systems,2018,42(13):101-107.DOI:10.7500/AEPS20180130012.
[3] HAO H,WU D,LIAN J M,et al.Optimal coordination of building loads and energy storage for power grid and end user services[J].IEEE Transactions on Smart Grid,2018,9(5):4335-4345.
[4] 刘静琨,张宁,康重庆.电力系统云储能研究框架与基础模型[J].中国电机工程学报,2017,37(12):3361-3371.LIU Jingkun,ZHANG Ning,KANG Chongqing.Research framework and basic models for cloud energy storage in power system[J].Proceedings of the CSEE,2017,37(12):3361-3371.
[5] 张利,杨建,菅学辉,等.考虑次小时尺度运行灵活性的含储能机组组合[J].电力系统自动化,2018,42(16):48-56.DOI:10.7500/AEPS20170930007.ZHANG Li,YANG Jian,JIAN Xuehui,et al.Unit commitment with energy storage considering operation flexibility at sub-hourly time-scales[J].Automation of Electric Power Systems,2018,42(16):48-56.DOI:10.7500/AEPS20170930007.
[6] LI Y,WU J,LIU B Z,et al.Integration of prosumers’ flexibilities in distribution network operation[C]//2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2),October 20-22,2018,Beijing,China.
[7] 王克道,陈启鑫,郭鸿业,等.面向可交易能源的储能容量合约机制设计与交易策略[J].电力系统自动化,2018,42(14):54-60.DOI:10.7500/AEPS20180110010.WANG Kedao,CHEN Qixin,GUO Hongye,et al.Mechanism design and trading strategy for capacity contract of energy storage towards transactive energy[J].Automation of Electric Power Systems,2018,42(14):54-60.DOI:10.7500/AEPS20180110010.
[8] HU J J,YANG G Y,KOK K,et al.Transactive control:a framework for operating power systems characterized by high penetration of distributed energy resources[J].Journal of Modern Power Systems and Clean Energy,2017,5(3):451-464.
[9] 胡俊杰,王坤宇,艾欣,等.交互能源:实现电力能源系统平衡的有效机制[J].中国电机工程学报,2019,39(4):953-966.HU Junjie,WANG Kunyu,AI Xin,et al.Transactive energy:an effective mechanism for balancing electric energy system[J].Proceedings of the CSEE,2019,39(4):953-966.
[10] 王成山,李鹏,于浩.智能配电网的新形态及其灵活性特征分析与应用[J].电力系统自动化,2018,42(10):13-21.DOI:10.7500/AEPS20171012002.WANG Chengshan,LI Peng,YU Hao.Development and characteristic analysis of flexibility in smart distribution network[J].Automation of Electric Power Systems,2018,42(10):13-21.DOI:10.7500/AEPS20171012002.
[11] ULBIG A,ANDERSSON G.Analyzing operational flexibility of electric power systems[J].International Journal of Electrical Power & Energy Systems,2015,72:155-164.
[12] MORSTYN T,FARRELL N,DARBY S J,et al.Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants[J].Nature Energy,2018,3(2):94-101.
[13] GONZALEZ VAYA M,ANDERSSON G.Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets under uncertainty[J].IEEE Transactions on Power Systems,2015,30(5):2375-2385.
[14] 吴界辰,艾欣,张艳,等.配售分离环境下高比例分布式能源园区电能日前优化调度[J].电网技术,2018,42(6):1709-1719.WU Jiechen,AI Xin,ZHANG Yan,et al.Day-ahead optimal scheduling for high penetration of distributed energy resources in community under separated distribution and retail operational environment[J].Power System Technology,2018,42(6):1709-1719.
[15] HU J,LAN T,KAI H,et al.Robust allocation of reserve policies for a multiple-cell based power system[J].Energies,2018,11(2):381.
[16] BAROT S,TAYLOR J A.A concise,approximate representation of a collection of loads described by polytopes[J].International Journal of Electrical Power & Energy Systems,2017,84:55-63.
[17] MULLER F L,SUNDSTROM O,SZABO J,et al.Aggregation of energetic flexibility using zonotopes[C]// 2015 54th IEEE Conference on Decision and Control (CDC),December 15-18,2015,Osaka,Japan.
[18] 魏韡,刘锋,梅生伟.电力系统鲁棒经济调度(一)理论基础[J].电力系统自动化,2013,37(17):37-43.WEI Wei,LIU Feng,MEI Shengwei.Robust and economical scheduling methodology for power systems:Part one theoretical foundations[J].Automation of Electric Power Systems,2013,37(17):37-43.
[19] ZHAO C Y,GUAN Y P.Unified stochastic and robust unit commitment[J].IEEE Transactions on Power Systems,2013,28(3):3353-3361.
[20] 税月,刘俊勇,高红均,等.考虑风电不确定性的电气能源系统两阶段分布鲁棒协同调度[J].电力系统自动化,2018,42(13):43-50.DOI:10.7500/AEPS20171218008.SHUI Yue,LIU Junyong,GAO Hongjun,et al.Two-stage distributed robust cooperative dispatch for integrated electricity and natural gas energy systems considering uncertainty of wind power[J].Automation of Electric Power Systems,2018,42(13):43-50.DOI:10.7500/AEPS20171218008.
[21] LIN X X,JANAK S L,FLOUDAS C A.A new robust optimization approach for scheduling under uncertainty[J].Computers & Chemical Engineering,2004,28(6/7):1069-1085.
[22] MARGELLOS K,GOULART P,LYGEROS J.On the road between robust optimization and the scenario approach for chance constrained optimization problems[J].IEEE Transactions on Automatic Control,2014,59(8):2258-2263.
[23] ALTHOFF M,STURSBERG O,BUSS M.Computing reachable sets of hybrid systems using a combination of zonotopes and polytopes[J].Nonlinear Analysis:Hybrid Systems,2010,4(2):233-249.
[24] MATHIEU J L,VAYá M G,ANDERSSON G.Uncertainty in the flexibility of aggregations of demand response resources[C]// Conference of the IEEE Industrial Electronics Society,2013,Vienna,Austria:8052-8057.
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