大规模可再生能源接入下的电力系统充裕性优化 (三)多场景的备用优化
|
摘要
场景分析法在用于描述可再生能源出力不确定性时,不仅能表征预测风/光功率等随机变量在时间—功率空间上的概率分布,还能进一步反映这些随机波动在时序上的相关性。多场景分析中普遍存在场景维数灾问题,在备用优化中更遭遇控制措施组合爆炸问题,加剧了求解的困难。场景削减有利于场景分析法的实用化,然而现有场景削减方法不能确保小概率高风险场景不被剪除,继而引发风险泄露问题。文中提出一种计及场景集剩余风险下逐步筛选场景的多场景备用优化方法,将场景削减与优化过程融合于一体。相比于传统的"先场景削减、再优化"的思路,所提出的方案能自适应地选取待优化场景集,且能有效识别小概率高风险场景。通过与基于传统场景削减方法的混合整数线性规划方法的对比,所提出的多场景优化方法在平衡优化效果与计算效率上具有显著优势。
When the scenario analysis method is used to describe the uncertainty of renewable energy,it can not only characterize the probability distribution of random variables(i.e.predicted wind/solar power)in time-power space,but also can further reflect the temporal correlation of these random fluctuations.The scenario dimension disaster is a common problem in scenario analysis.As it comes to reserve optimization,the combination explosion of control measures is also encountered,which exacerbates the difficulty of the solution.Scenario reduction is beneficial to the practical application of scenario analysis.However,traditional scenario reduction methods cannot ensure that low-probability and high-risk scenarios would not be cut off,and thus lead to risk leakage.A multi-scenario reserve optimization method of gradually filtering scenarios is proposed considering the residual risk of scene sets,which integrates the scenario reduction and optimization process.Compared with the traditional"scenario reduction first,and optimization second"approach,the proposed scheme can adaptively select the scenarios to be optimized,and can effectively identify low-probability and high-risk scenarios.Simulation results indicate that the proposed multi-scenario optimization method has significant advantages in balancing optimization effect and computational efficiency compared with the mixed-integer linear programming optimization method based on the traditional scenario reduction.
When the scenario analysis method is used to describe the uncertainty of renewable energy,it can not only characterize the probability distribution of random variables(i.e.predicted wind/solar power)in time-power space,but also can further reflect the temporal correlation of these random fluctuations.The scenario dimension disaster is a common problem in scenario analysis.As it comes to reserve optimization,the combination explosion of control measures is also encountered,which exacerbates the difficulty of the solution.Scenario reduction is beneficial to the practical application of scenario analysis.However,traditional scenario reduction methods cannot ensure that low-probability and high-risk scenarios would not be cut off,and thus lead to risk leakage.A multi-scenario reserve optimization method of gradually filtering scenarios is proposed considering the residual risk of scene sets,which integrates the scenario reduction and optimization process.Compared with the traditional"scenario reduction first,and optimization second"approach,the proposed scheme can adaptively select the scenarios to be optimized,and can effectively identify low-probability and high-risk scenarios.Simulation results indicate that the proposed multi-scenario optimization method has significant advantages in balancing optimization effect and computational efficiency compared with the mixed-integer linear programming optimization method based on the traditional scenario reduction.
引文
[1] CHEN C L.Optimal wind-thermal generating unit commitment[J].IEEE Transactions on Energy Conversion,2008,23(1):273-280.
[2] 周玮,彭昱,孙辉,等.含风电场的电力系统动态经济调度[J].中国电机工程学报,2009,29(25):13-18.ZHOU Wei,PENG Yu,SUN Hui,et al.Dynamic economic dispatch in wind power integrated system[J].Proceedings of the CSEE,2009,29(25):13-18.
[3] 王彩霞,鲁宗相,乔颖,等.基于非参数回归模型的短期风电功率预测[J].电力系统自动化,2010,34(16):78-82.WANG Caixia,LU Zongxiang,QIAO Ying,et al.Short-term wind power forecast based on non-parametric regression model[J].Automation of Electric Power Systems,2010,34(16):78-82.
[4] 孙元章,吴俊,李国杰,等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29(4):41-47.SUN Yuanzhang,WU Jun,LI Guojie,et al.Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming[J].Proceedings of the CSEE,2009,29(4):41-47.
[5] 张放,刘继春,高红均,等.基于风电不确定性的电力系统备用容量获取[J].电力系统保护与控制,2013,41(13):14-19.ZHANG Fang,LIU Jichun,GAO Hongjun,et al.Reserve capacity model based on the uncertainty of wind power in the power system[J].Power System Protection and Control,2013,41(13):14-19.
[6] BOUFFARD F,GALIANA F D.Stochastic security for operations planning with significant wind power generation[J].IEEE Transactions on Power Systems,2008,23(2):306-316.
[7] 薛禹胜,刘强,DONG Zhaoyang,等.关于暂态稳定不确定性分析的评述[J].电力系统自动化,2007,31(14):1-7.XUE Yusheng,LIU Qiang,DONG Zhaoyang,et al.A review of non-deterministic analysis for power system transient stability[J].Automation of Electric Power Systems,2007,31(14):1-7.
[8] 王彩霞,鲁宗相.风电功率预测信息在日前机组组合中的应用[J].电力系统自动化,2011,35(7):13-18.WANG Caixia,LU Zongxiang.Unit commitment based on wind power forecast[J].Automation of Electric Power Systems,2011,35(7):13-18.
[9] WANG Jianhui,SHAHIDEHPOUR M,LI Zuyi.Security-constrained unit commitment with volatile wind power generation[J].IEEE Transactions on Power Systems,2008,23(3):1319-1327.
[10] MEIBOM P,BARTH R,HASCHE B,et al.Stochastic optimization model to study the operational impacts of high wind penetrations in Ireland[J].IEEE Transactions on Power Systems,2011,26(3):1367-1379.
[11] HOYLAND K,KAUT M,WALLACE S W.A heuristic for moment-matching scenario generation[J].Computational Optimization and Applications,2003,24(2/3):169-185.
[12] HEITSCH H,ROMISCH W.Scenario reduction algorithms in stochastic program[J].Computational Optimization and Applications,2003,24(2/3):187-206.
[13] RAZALI N M,HASHIM A H.Backward reduction application for minimizing wind power scenarios in stochastic programming[C]// Power Engineering and Optimization Conference,June 23-24,2010,Shal Alam,Malaysia:430-434.
[14] 颜拥,文福拴,杨首晖,等.考虑风电出力波动性的发电调度[J].电力系统自动化,2010,34(6):79-88.YAN Yong,WEN Fushuan,YANG Shouhui,et al.Generation scheduling with fluctuating wind power[J].Automation of Electric Power Systems,2010,34(6):79-88.
[15] 吴俊,薛禹胜,舒印彪,等.大规模可再生能源接入下的充裕性优化:(一)旋转级备用的优化[J].电力系统自动化,2019,43(8):109-116.DOI:10.7500/AEPS20181023001.WU Jun,XUE Yusheng,SHU Yinbiao,et al.Adequacy optimization for a large-scale renewable energy integrated power system:Part one spinning-grade reserve optimization[J].Automation of Electric Power Systems,2019,43(8):109-116.DOI:10.7500/AEPS20181023001.
[16] 吴俊,薛禹胜,舒印彪,等.大规模可再生能源接入下的充裕性优化:(二)多等级备用的协调优化[J].电力系统自动化,2019,43(10):19-26.DOI:10.7500/AEPS20181023002.WU Jun,XUE Yusheng,SHU Yinbiao,et al.Adequacy optimization for a large-scale renewable energy integrated power system:Part two multi-grade reserve collaborative optimization[J].Automation of Electric Power Systems,2019,43(10):19-26.DOI:10.7500/AEPS20181023002.
[17] WU Jun,XUE Yusheng,CAI Bin,et al.Unified optimization of generation and demand side reserves[C]// International Conference on Power System Technology,October 20-22,2014,Chengdu,China.
[18] 高红均,刘俊勇,魏震波,等.考虑风储一体的多场景两阶段调度决策模型[J].电力自动化设备,2014,34(1):135-140.GAO Hongjun,LIU Junyong,WEI Zhenbo,et al.Multi-scenario two-stage dispatch decision-making model for wind farm with integrated energy storage[J].Electric Power Automation Equipment,2014,34(1):135-140.
[19] 黄杰,薛禹胜,许剑冰,等.电力市场与电力系统的动态交互仿真平台:(一)功能设计[J].电力系统自动化,2011,35(10):16-22.HUANG Jie,XUE Yusheng,XU Jianbing,et al.Dynamic simulation platform for power market and power system:Part one function design[J].Automation of Electric Power Systems,2011,35(10):16-22.
[20] 谢东亮,薛禹胜,薛峰,等.电力市场与电力系统的动态交互仿真平台:(二)支撑层设计[J].电力系统自动化,2011,35(11):1-7.XIE Dongliang,XUE Yusheng,XUE Feng,et al.Dynamic simulation platform for power market and power system:Part two support layer design[J].Automation of Electric Power Systems,2011,35(11):1-7.
[21] 谢东亮,薛禹胜,薛峰,等.电力市场与电力系统的动态交互仿真平台:(三)应用层设计[J].电力系统自动化,2011,35(12):7-14.XIE Dongliang,XUE Yusheng,XUE Feng,et al.Dynamic simulation platform for power market and power system:Part three application layer design[J].Automation of Electric Power Systems,2011,35(12):7-14.
[22] 娄素华,胡斌,吴耀武,等.碳交易环境下含大规模光伏电源的电力系统优化调度[J].电力系统自动化,2014,38(17):91-97.LOU Suhua,HU Bin,WU Yaowu,et al.Optimal dispatch of power system integrated with large scale photovoltaic generation under carbon trading environment[J].Automation of Electric Power Systems,2014,38(17):91-97.
[23] DUPACOVA J,GROWE-KUSKA N,ROMISCH W.Scenario reduction in stochastic programming:an approach using probability metrics[J].Mathematical Programming:Series A,2003,95(3):493-511.
[2] 周玮,彭昱,孙辉,等.含风电场的电力系统动态经济调度[J].中国电机工程学报,2009,29(25):13-18.ZHOU Wei,PENG Yu,SUN Hui,et al.Dynamic economic dispatch in wind power integrated system[J].Proceedings of the CSEE,2009,29(25):13-18.
[3] 王彩霞,鲁宗相,乔颖,等.基于非参数回归模型的短期风电功率预测[J].电力系统自动化,2010,34(16):78-82.WANG Caixia,LU Zongxiang,QIAO Ying,et al.Short-term wind power forecast based on non-parametric regression model[J].Automation of Electric Power Systems,2010,34(16):78-82.
[4] 孙元章,吴俊,李国杰,等.基于风速预测和随机规划的含风电场电力系统动态经济调度[J].中国电机工程学报,2009,29(4):41-47.SUN Yuanzhang,WU Jun,LI Guojie,et al.Dynamic economic dispatch considering wind power penetration based on wind speed forecasting and stochastic programming[J].Proceedings of the CSEE,2009,29(4):41-47.
[5] 张放,刘继春,高红均,等.基于风电不确定性的电力系统备用容量获取[J].电力系统保护与控制,2013,41(13):14-19.ZHANG Fang,LIU Jichun,GAO Hongjun,et al.Reserve capacity model based on the uncertainty of wind power in the power system[J].Power System Protection and Control,2013,41(13):14-19.
[6] BOUFFARD F,GALIANA F D.Stochastic security for operations planning with significant wind power generation[J].IEEE Transactions on Power Systems,2008,23(2):306-316.
[7] 薛禹胜,刘强,DONG Zhaoyang,等.关于暂态稳定不确定性分析的评述[J].电力系统自动化,2007,31(14):1-7.XUE Yusheng,LIU Qiang,DONG Zhaoyang,et al.A review of non-deterministic analysis for power system transient stability[J].Automation of Electric Power Systems,2007,31(14):1-7.
[8] 王彩霞,鲁宗相.风电功率预测信息在日前机组组合中的应用[J].电力系统自动化,2011,35(7):13-18.WANG Caixia,LU Zongxiang.Unit commitment based on wind power forecast[J].Automation of Electric Power Systems,2011,35(7):13-18.
[9] WANG Jianhui,SHAHIDEHPOUR M,LI Zuyi.Security-constrained unit commitment with volatile wind power generation[J].IEEE Transactions on Power Systems,2008,23(3):1319-1327.
[10] MEIBOM P,BARTH R,HASCHE B,et al.Stochastic optimization model to study the operational impacts of high wind penetrations in Ireland[J].IEEE Transactions on Power Systems,2011,26(3):1367-1379.
[11] HOYLAND K,KAUT M,WALLACE S W.A heuristic for moment-matching scenario generation[J].Computational Optimization and Applications,2003,24(2/3):169-185.
[12] HEITSCH H,ROMISCH W.Scenario reduction algorithms in stochastic program[J].Computational Optimization and Applications,2003,24(2/3):187-206.
[13] RAZALI N M,HASHIM A H.Backward reduction application for minimizing wind power scenarios in stochastic programming[C]// Power Engineering and Optimization Conference,June 23-24,2010,Shal Alam,Malaysia:430-434.
[14] 颜拥,文福拴,杨首晖,等.考虑风电出力波动性的发电调度[J].电力系统自动化,2010,34(6):79-88.YAN Yong,WEN Fushuan,YANG Shouhui,et al.Generation scheduling with fluctuating wind power[J].Automation of Electric Power Systems,2010,34(6):79-88.
[15] 吴俊,薛禹胜,舒印彪,等.大规模可再生能源接入下的充裕性优化:(一)旋转级备用的优化[J].电力系统自动化,2019,43(8):109-116.DOI:10.7500/AEPS20181023001.WU Jun,XUE Yusheng,SHU Yinbiao,et al.Adequacy optimization for a large-scale renewable energy integrated power system:Part one spinning-grade reserve optimization[J].Automation of Electric Power Systems,2019,43(8):109-116.DOI:10.7500/AEPS20181023001.
[16] 吴俊,薛禹胜,舒印彪,等.大规模可再生能源接入下的充裕性优化:(二)多等级备用的协调优化[J].电力系统自动化,2019,43(10):19-26.DOI:10.7500/AEPS20181023002.WU Jun,XUE Yusheng,SHU Yinbiao,et al.Adequacy optimization for a large-scale renewable energy integrated power system:Part two multi-grade reserve collaborative optimization[J].Automation of Electric Power Systems,2019,43(10):19-26.DOI:10.7500/AEPS20181023002.
[17] WU Jun,XUE Yusheng,CAI Bin,et al.Unified optimization of generation and demand side reserves[C]// International Conference on Power System Technology,October 20-22,2014,Chengdu,China.
[18] 高红均,刘俊勇,魏震波,等.考虑风储一体的多场景两阶段调度决策模型[J].电力自动化设备,2014,34(1):135-140.GAO Hongjun,LIU Junyong,WEI Zhenbo,et al.Multi-scenario two-stage dispatch decision-making model for wind farm with integrated energy storage[J].Electric Power Automation Equipment,2014,34(1):135-140.
[19] 黄杰,薛禹胜,许剑冰,等.电力市场与电力系统的动态交互仿真平台:(一)功能设计[J].电力系统自动化,2011,35(10):16-22.HUANG Jie,XUE Yusheng,XU Jianbing,et al.Dynamic simulation platform for power market and power system:Part one function design[J].Automation of Electric Power Systems,2011,35(10):16-22.
[20] 谢东亮,薛禹胜,薛峰,等.电力市场与电力系统的动态交互仿真平台:(二)支撑层设计[J].电力系统自动化,2011,35(11):1-7.XIE Dongliang,XUE Yusheng,XUE Feng,et al.Dynamic simulation platform for power market and power system:Part two support layer design[J].Automation of Electric Power Systems,2011,35(11):1-7.
[21] 谢东亮,薛禹胜,薛峰,等.电力市场与电力系统的动态交互仿真平台:(三)应用层设计[J].电力系统自动化,2011,35(12):7-14.XIE Dongliang,XUE Yusheng,XUE Feng,et al.Dynamic simulation platform for power market and power system:Part three application layer design[J].Automation of Electric Power Systems,2011,35(12):7-14.
[22] 娄素华,胡斌,吴耀武,等.碳交易环境下含大规模光伏电源的电力系统优化调度[J].电力系统自动化,2014,38(17):91-97.LOU Suhua,HU Bin,WU Yaowu,et al.Optimal dispatch of power system integrated with large scale photovoltaic generation under carbon trading environment[J].Automation of Electric Power Systems,2014,38(17):91-97.
[23] DUPACOVA J,GROWE-KUSKA N,ROMISCH W.Scenario reduction in stochastic programming:an approach using probability metrics[J].Mathematical Programming:Series A,2003,95(3):493-511.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |