计及线路随机故障的机会约束最优风电消纳模型
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摘要
在大规模风电并网的背景下,为提升电网运行的经济性与安全性,基于最优风电消纳比的概念提出一种计及输电线路随机故障的两阶段机会约束机组组合模型。一方面,采用机会约束量化风电出力的不确定性,确定最优的机组组合方式与风电消纳比;另一方面,综合考虑电网基态与故障态的运行方式,在故障发生后引入弃风、切负荷等控制措施,保证电网的稳定运行。为应对机会约束模型带来的求解难题,采用采样平均近似算法处理机会约束,并通过双线性Benders分解法将原问题分解为预测风电出力下的预测主问题和风电出力波动条件下的场景子问题,主子问题迭代求解,得到系统优化运行策略。6节点系统与IEEE RTS-79测试系统算例验证了所提模型与算法的有效性。
With high proportion of wind power penetration, to enhance economy and security of power grids, based on the concept of optimal wind accommodation point, a chance-constrained two-stage unit commitment(UC) model considering transmission line contingencies is proposed. On one hand, uncertainty of wind power is described with chance constraint to determine optimal UC scheduling and wind accommodation point. On the other hand, normal states and the states after contingencies are also considered at the same time. By introducing load and wind power curtailment into contingency control, the stability of power grids is guaranteed. To achieve computational efficiency, the chance constraint is dealt with sample average approximation(SAA) method, and the primal proposed model is converted into main problem and subproblems with bilinear Benders decomposition method. The decomposed main problem optimizes operational cost of power grids based on prediction of wind power generation, while the subproblems focus on re-dispatching cost under fluctuation of wind power. Validity and effectiveness of the proposed model are illustrated with simulation on 6-bus system and modified IEEE RTS-79 system.
With high proportion of wind power penetration, to enhance economy and security of power grids, based on the concept of optimal wind accommodation point, a chance-constrained two-stage unit commitment(UC) model considering transmission line contingencies is proposed. On one hand, uncertainty of wind power is described with chance constraint to determine optimal UC scheduling and wind accommodation point. On the other hand, normal states and the states after contingencies are also considered at the same time. By introducing load and wind power curtailment into contingency control, the stability of power grids is guaranteed. To achieve computational efficiency, the chance constraint is dealt with sample average approximation(SAA) method, and the primal proposed model is converted into main problem and subproblems with bilinear Benders decomposition method. The decomposed main problem optimizes operational cost of power grids based on prediction of wind power generation, while the subproblems focus on re-dispatching cost under fluctuation of wind power. Validity and effectiveness of the proposed model are illustrated with simulation on 6-bus system and modified IEEE RTS-79 system.
引文
[1]康重庆,姚良忠.高比例可再生能源电力系统的关键科学问题与理论研究框架[J].电力系统自动化,2017,41(9):2-11.Kang Chongqing,Yao Liangzhong. Key Scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J].Automation of Electric Power Systems,2017,41(9):2-11(in Chinese).
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[17]马燕峰,陈磊,李鑫,等.基于机会约束混合整数规划的风火协调滚动调度[J].电力系统自动化,2018,42(5):127-132.Ma Yanfeng,Chen Lei,Li Xin,et al.Rolling dispatch of wind-coal coordinated system based on chance-constrained mixed integerprogramming[J].Automation of Electric Power Systems,2018,42(5):127 -132(in Chinese).
[18] James L, Shabbir A. A sample approximation approach for optimization with probabilistic constraints[J]. SIAM Journal on Optimization.2008,19(2):674-699.
[19] Zeng B,An Y,Kuznia L.Chance constrained mixed integer program:bilinear and linear formulations,and benders decomposition[J].Mathematics,2014.
[20] Wang Y,Li Z,Shahidehpour M,et al.Stochastic co-optimization of midterm and short-term maintenance outage scheduling considering covariates in power systems[J].IEEE Transactions on Power Systems,2016,31(6):4795-4805.
[21]张伯明,陈寿孙,严正.高等电力网络分析[M].北京:清华大学出版社,2007:212-214.
[22] Mccormick G P.Computability of global solutions to factorable nonconvex programs:part i—convex underestimating problems[J].Mathematical Programming,1976,10(1):147-175.
[23] Wen Y,Guo C,Kirschen D S,et al.Enhanced security-constrained OPF with distributed battery energy storage[J].IEEE Transactions on Power Systems,2015,30(1):98-108.
[24] FORCE TRTST.IEEE Committee Report.IEEE reliability test system[J].IEEE Transactions on Power Apparatus&Systems,1979,98(6):2047-2054.
[2]孙荣富,王东升,丁华杰,等.风电消纳全生产过程评价方法[J].电网技术,2017,41(9):2777-2783.Sun Rongfu,Wang Dongsheng,Ding Huajie,et al.Whole-process evaluation methods of wind power accommodation[J].Power System Technology,2017,41(9):2777-2783(in Chinese).
[3]卢锦玲,张津,丁茂生.含风电的电力系统调度经济性评价[J].电网技术,2016,40(8):2258-2264.Lu Jinling,Zhang Jin,Ding Maosheng.Economic evaluation of power system dispatch integrated with wind power[J]. Power System Technology,2016,40(8):2258-2264(in Chinese).
[4]张倩文,王秀丽,杨廷天,等.含风电场电力系统的鲁棒优化调度[J].电网技术,2017,41(5):1451-1459.Zhang Qianwen,Wang Xiuli,Yang Tingtian,et al.A robust dispatch method for power grid with wind farms[J].Power System Technology,2017,41(5):1451-1459(in Chinese).
[5]朱丹丹,刘文颖,蔡万通,等.风电消纳目标下基于电量与功率滚动优化的荷源控制方法[J].电力系统自动化,2018,42(5):80-85.Zhu Dandan,Liu Wenying,Cai Wantong,et al.Load-source coordinated dispatch method for promoting wind power accommodation based on rolling optimization of energy and power[J].Automation of Electric Power Systems,2018,42(5):80-85(in Chinese).
[6]李亚龙,刘文颖,谢昶,等.高载能负荷消纳受阻风电的供应链博弈决策方法探究[J].电力系统自动化,2017,41(7):135-143.Li Yalong,Liu Wenying,Xie Chang,et al.Supply chains game based decision-making method of congested wind power consumption for high-energy load[J].Automation of Electric Power Systems,2017,41(7):135-143(in Chinese).
[7] Qiu F,Wang J.Chance-constrained transmission switching with guaranteed wind power utilization[J].IEEE Transactions on Power Systems,2015,30(3):1270-1278
[8] Hu B, Wu L. Robust SCUC considering continuous/discrete uncertainties and quick-start units:a two-stage robust optimization with mixed-integer recourse[J].IEEE Transactions on Power Systems,2016,31(2):1407-1419.
[9]周明,翟俊义,任建文,等.含风电并网的直流互联电网分散协调调度方法[J].电网技术,2017,41(5):1428-1434.Zhou Ming,Zhai Junyi,Ren Jianwen,et al.A decentralized coordinated dispatch approach for interconnected power grid with wind power via HVDC tie-line[J].Power System Technology,2017,41(5):1428-1434(in Chinese).
[10] Bobo D R,Mauzy D M,Trefny F J.Economic generation dispatch with responsive spinning reserve constraints[J].IEEE Transactions on Power Systems,1994,9(1):555-559.
[11] Wang J,Botterud A,Bess R,et al.Wind power forecasting uncertainty and unit commitment[J].Applied Energy,2011,88(11):4014-4023.
[12] Wu L,Shahidehpour M,Li Z.Comparison of scenario-based and interval optimization approaches to stochastic SCUC[J]. IEEE Transactions on Power Systems,2012,27(2):913-921.
[13] Wen Y,Guo C,Pandzic H,et al.Enhanced security-constrained unit commitment with emerging utility-scale energy storage[J].IEEE Transactions on Power Systems,2015,31(1):652-662.
[14] Wang C,Liu F,Wang J,et al.Robust risk-constrained unit commitment with large scale wind generation:An adjustable uncertainty set approach[J].IEEE Transactions on Power Systems,2017,32(1):723-733.
[15] Yu Y, Luh P B, Litvinov E, et al. Transmission contingency-constrained unit commitment with high penetration of renewables via interval optimization[J].IEEE Transactions on Power Systems,2017,32(2):1410-1421.
[16] Zhang Y,Wang J,Zeng B,et al.Chance-constrained two-stage unit commitment under uncertain load and wind power output using bilinear benders decomposition[J].IEEE Transactions on Power Systems,2017,32(5):3637-3647.
[17]马燕峰,陈磊,李鑫,等.基于机会约束混合整数规划的风火协调滚动调度[J].电力系统自动化,2018,42(5):127-132.Ma Yanfeng,Chen Lei,Li Xin,et al.Rolling dispatch of wind-coal coordinated system based on chance-constrained mixed integerprogramming[J].Automation of Electric Power Systems,2018,42(5):127 -132(in Chinese).
[18] James L, Shabbir A. A sample approximation approach for optimization with probabilistic constraints[J]. SIAM Journal on Optimization.2008,19(2):674-699.
[19] Zeng B,An Y,Kuznia L.Chance constrained mixed integer program:bilinear and linear formulations,and benders decomposition[J].Mathematics,2014.
[20] Wang Y,Li Z,Shahidehpour M,et al.Stochastic co-optimization of midterm and short-term maintenance outage scheduling considering covariates in power systems[J].IEEE Transactions on Power Systems,2016,31(6):4795-4805.
[21]张伯明,陈寿孙,严正.高等电力网络分析[M].北京:清华大学出版社,2007:212-214.
[22] Mccormick G P.Computability of global solutions to factorable nonconvex programs:part i—convex underestimating problems[J].Mathematical Programming,1976,10(1):147-175.
[23] Wen Y,Guo C,Kirschen D S,et al.Enhanced security-constrained OPF with distributed battery energy storage[J].IEEE Transactions on Power Systems,2015,30(1):98-108.
[24] FORCE TRTST.IEEE Committee Report.IEEE reliability test system[J].IEEE Transactions on Power Apparatus&Systems,1979,98(6):2047-2054.