考虑风电功率预测不确定性的日前发电计划鲁棒优化方法
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摘要
随着风能发电电源并网装机容量的不断升高,风电的强随机性迫使日前发电计划需要购买大量的发电备用,造成全系统发电经济性恶化。在分析风电功率时变预测不确定性的基础上,提出了计及日内现货市场滚动交易的两阶段带补偿鲁棒优化方法。通过在第一阶段日前优化过程中计及日内滚动交易偏差电力的预期,降低了日前发电计划中的备用总需量;对于第二阶段日内调整电力大小的求解,采用了高精度的预测模型,以提升系统发电经济性。最后通过对比分析算例验证了所提方法的正确性和有效性。
With the increase of wind power installed capacity,the stochastic behaviours of wind energy pose a great challenge to the day-ahead generation scheduling.Thus a large amount of reserve capacity is needed,which causes economic deterioration of the power system.An improved two-stage robust optimization with recourse method considering the rolling trading of inner-day electricity market is proposed based on the analysis of time-varying prediction uncertainty of wind power.In the first stage of day-ahead optimization process,reserve capacity of day-ahead generation scheduling is reduced considering expected power deviation of inner-day rolling trading.In the second stage,prediction model with high precision is used to calculate the inner-day regulated power to improve the economy of the power system.Correctness and effectiveness of the proposed method are verified by contrastive analysis cases.
With the increase of wind power installed capacity,the stochastic behaviours of wind energy pose a great challenge to the day-ahead generation scheduling.Thus a large amount of reserve capacity is needed,which causes economic deterioration of the power system.An improved two-stage robust optimization with recourse method considering the rolling trading of inner-day electricity market is proposed based on the analysis of time-varying prediction uncertainty of wind power.In the first stage of day-ahead optimization process,reserve capacity of day-ahead generation scheduling is reduced considering expected power deviation of inner-day rolling trading.In the second stage,prediction model with high precision is used to calculate the inner-day regulated power to improve the economy of the power system.Correctness and effectiveness of the proposed method are verified by contrastive analysis cases.
引文
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[2]GIEBEL G,BROWNSWORD R,KARINIOTAKIS G,et al.The state-of-the-art in short-term prediction of wind power:a literature overview[EB/OL].[2013-01-01].http://citeseerx.ist.psu.edu/showciting?cid=1163167.
[3]JIANG Yu,CHEN Xingying,YU Kun,et al.Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm[J].Journal of Modern Power Systems and Clean Energy,2017,5(1):126-133.
[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]中共中央、国务院.关于进一步深化电力体制改革的若干意见[EB/OL].[2017-10-21].http://tgs.ndr c.gov.cn/zywj/201601/t20160129_773852.html.Central Committee of the Communist Party of China,State Council.Several opinions on further deepening the reform of electric power system[EB/OL].[2017-10-21].http://tgs.ndrc.gov.cn/zywj/201601/t20160129_773852.html.
[6]南方电网公司.南方区域电力现货市场建设工作方案[EB/OL].[2017-10-21].http://www.energynews.com.cn/show-9-10027-1.html.China Southern Power Grid.Work plan for the construction of electric power spot market in the southern regional grid[EB/OL].[2017-10-21].http://www.energynews.com.cn/show-9-10027-1.html.
[7]丁一,惠红勋,林振智,等.面向电力需求侧主动响应的商业模式及市场框架设计[J].电力系统自动化,2017,41(14):2-9.DOI:10.7500/AEPS20170218001.DING Yi,XI Hongxun,LIN Zhenzhi,et al.Design of business model and market framework oriented to active demand response of power demand side[J].Automation of Electric Power Systems,2017,41(14):2-9.DOI:10.7500/AEPS20170218001.
[8]杨明,韩学山,王士柏,等.不确定运行条件下电力系统鲁棒调度的基础研究[J].中国电机工程学报,2011,31(增刊1):100-107.YANG Ming,HAN Xueshan,WANG Shibai,et al.Fundamental research for power system robust dispatch under uncertain operating condition[J].Proceedings of the CSEE,2011,31(Supplement 1):100-107.
[9]鞠立伟,于超,谭忠富.计及需求响应的风电储能两阶段调度优化模型及求解算法[J].电网技术,2015,39(5):1287-1293.JU Liwei,YU Chao,TAN Zhongfu.Two-stage scheduling optimization model and corresponding solving algorithm for power grid containing wind farm and energy storage system considering demand response[J].Power System Technology,2015,39(5):1287-1293.
[10]于丹文,杨明,翟鹤峰,等.鲁棒优化在电力系统调度决策中的应用研究综述[J].电力系统自动化,2016,40(7):134-143.DOI:10.7500/AEPS20150728008.YU Danwen,YANG Ming,ZHAI Hefeng,et al.An overview of robust optimization used for power system dispatch and decision-making[J].Automation of Electric Power Systems,2016,40(7):134-143.DOI:10.7500/AEPS20150728008.
[11]MORALES J M,CONEJO A J,PEREZ-RUIZ J.Economic valuation of reserves in power systems with high penetration of wind power[J].IEEE Transactions on Power Systems,2009,24(2):900-910.
[12]CONEJO A J,CARRIN M,MORALES J M.Decision making under uncertainty in electricity markets[M].USA:Springer,2010.
[13]李利利,丁恰,涂孟夫,等.机组组合问题的仿射可调整鲁棒优化模型与算法[J].电力工程技术,2017,36(3):33-37.LI Lili,DING Qia,TU Mengfu,et al.Affinely adjustable robust optimization model and algorithm for unit commitment problem[J].Electric Power Engineering Technology,2017,36(3):33-37.
[14]高红均,刘俊勇,魏震波,等.基于极限场景集的风电机组安全调度决策模型[J].电网技术,2013,37(6):1590-1595.GAO Hongjun,LIU Junyong,WEI Zhenbo,et al.A securityconstrainted dispatching model for wind generation units based on extreme scenario set optimization[J].Power System Technology,2013,37(6):1590-1595.
[15]谢鹏,彭春华,于蓉.大规模间歇式电源接入电网多目标鲁棒优化调度[J].电网技术,2014,38(6):1479-1484.XIE Peng,PENG Chunhua,YU Rong.Multi-objective robust optimized scheduling of power grid connected with large-scale intermittent power sources[J].Power System Technology,2014,38(6):1479-1484.
[16]叶荣,陈皓勇,王钢,等.多风电场并网时安全约束机组组合的混合整数规划解法[J].电力系统自动化,2010,34(5):29-33.YE Rong,CHEN Haoyong,WANG Gang,et al.A mixed integer programming method for security-constrained unit commitment with multiple wind farms[J].Automation of Electric Power Systems,2010,34(5):29-33.
[17]王勇超,陈皓勇,禤培正,等.基于最优场景集的含多类型电源鲁棒调度[J].电网技术,2016,40(2):354-362.WANG Yongchao,CHEN Haoyong,XUAN Peizheng,et al.A robust scheduling with multiple types of power sources based on optimal scenario set[J].Power System Technology,2016,40(2):354-362.
[18]CHEN X,JIANG Y,YU K,et al.Combined time-varying forecast based on the proper scoring approach for wind power generation[J].The Journal of Engineering,2017(14):2655-2659.
[19]MASSERAN N,RAZALI A M,IBRAHIM K,et al.Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia[J].Energy,2012,37(1):649-656.
[20]ZIMMERMAN R D,MURILLO-SANCHEZ C E,THOMASR J.MATPOWER:steady-state operations,planning,and analysis tools for power systems research and education[J].IEEE Transactions on Power Systems,2011,26(1):12-19.
[21]JIANG Y,CHEN X,YU K,et al.Determining reserve requirements in systems with significant stochastic Generation capacity using copulas[C]//2015 IEEE Eindhoven PowerTech,June 29-July 2,2015,Eindhoven,Netherlands:1-6.
[22]SUBCOMMITTEE P M.IEEE reliability test system[J].IEEE Transactions on Power Apparatus&Systems,1979,98(6):2047-2054.
[23]ERCOT.Zone locational marginal price[EB/OL].[2018-05-25].http://www.ercot.com.
[24]JIANG Y,CHEN X,YU K,et al.Stochastic joint optimization using energy storage system and demand response in an electricity market[C]//Tsinghua University-IETElectrical Engineering Academic Forum,May 13-15,2016,Beijing,China:1-6.