考虑机组优化选取的含风电电网滚动优化调度方法
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摘要
提出一种包含调整机组优化选取策略的滚动优化调度方法。根据负荷及风电超短期预测信息,首先考虑等效负荷(负荷+风电)预测偏差和机组调节容量匹配、机组爬坡量和等效负荷波动量匹配,以及减少外送断面越限原则进行调节机组优化选取。在此基础上,以机组调整成本及弃风量最小为目标,建立优化模型并采用改进粒子群优化算法求解。实际算例表明,考虑机组优化选取的滚动优化调度实现了超短期时间尺度上对负荷及风电预测偏差的自动跟踪调节,提高了发电计划的准确性,同时有效降低了综合发电成本和外送断面功率越限的风险,提高了电网运行的经济性和安全性。
A rolling optimization dispatching method is proposed including the optimal selection strategy of units to be adjusted.According to ultra-short-term predictive information of load and wind power,the units to be adjusted are optimized and selected first by considering the matching between the prediction deviation of equivalent load(load and wind)and units'regulation capacity,the matching between the units'climbing amount and the fluctuation quantity of equivalent load,as well as the principle of reducing risks of the interface power off-limits.On this basis,with minimum unit adjustment cost and abandoned wind power as the objective,an optimizing mathematical model is developed and solved by the improved particle swarm optimization(PSO)algorithm.A practical example shows that the rolling optimization dispatching method considering the optimal selection strategy of units can realize automatic tracking and regulation according to the prediction deviation of load and wind power on an ultra-short-term time scale,while improving the accuracy of generation schedule and reducing the comprehensive generating costs and the risks of the interface power getting off-limits to enhance the economy and security of power grid.
A rolling optimization dispatching method is proposed including the optimal selection strategy of units to be adjusted.According to ultra-short-term predictive information of load and wind power,the units to be adjusted are optimized and selected first by considering the matching between the prediction deviation of equivalent load(load and wind)and units'regulation capacity,the matching between the units'climbing amount and the fluctuation quantity of equivalent load,as well as the principle of reducing risks of the interface power off-limits.On this basis,with minimum unit adjustment cost and abandoned wind power as the objective,an optimizing mathematical model is developed and solved by the improved particle swarm optimization(PSO)algorithm.A practical example shows that the rolling optimization dispatching method considering the optimal selection strategy of units can realize automatic tracking and regulation according to the prediction deviation of load and wind power on an ultra-short-term time scale,while improving the accuracy of generation schedule and reducing the comprehensive generating costs and the risks of the interface power getting off-limits to enhance the economy and security of power grid.
引文
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[13]陈之栩,李丹,梁吉,等.华北电网日内发电计划及实时调度系统[J].电力系统自动化,2011,35(19):54-57.CHEN Zhixu,LI Dan,LIANG Ji,et al.Intraday generation plan and realtime dispatch system of North China grid[J].Automation of Electric Power Systems,2011,35(19):54-57.
[14]徐帆,丁恰,谢丽荣,等.实时发电计划闭环控制模型与应用[J].电网技术,2014,38(11):3187-3192.XU Fan,DING Qia,XIE Lirong,et al.A closed-loop control model for real-time generation scheduling and its application[J].Power System Technology,2014,38(11):3187-3192.
[15]查浩.电力系统概率优化调度理论研究[D].济南:山东大学,2009.
[16]吴杰康,龙军,何芬,等.电力市场中发电机组输出量的调节与优化[J].电网技术,2004,28(3):55-58.WU Jiekang,LONG Jun,HE Fen,et al.Output adjustment and optimization for generating sets in electricity markets[J].Power System Technology,2004,28(3):55-58.
[17]邓长虹,马庆,肖永,等.基于自学习迁移粒子群算法及高斯罚函数的无功优化方法[J].电网技术,2014,38(12):3341-3346.DENG Changhong,MA Qing,XIAO Yong,et al.Reactive power optimization based on self-learning migration particle swarm optimization and Gaussian penalty function[J].Power System Technology,2014,38(12):3341-3346.
[18]张俊,程春田,廖胜利,等.改进粒子群优化算法在水电站群优化调度中的应用研究[J].水利学报,2009,40(4):435-441.ZHANG Jun,CHENG Chuntian,LIAO Shengli,et al.Application of improved particle swarm optimization algorithm to operation of hydropower station group[J].Journal of Hydraulic Engineering,2009,40(4):435-441.
[2]朱永胜,王杰,瞿博阳,等.含风电场的多目标动态环境经济调度[J].电网技术,2015,39(5):1315-1322.ZHU Yongsheng,WANG Jie,QU Boyang,et al.Multiobjective dynamic economic emission dispatching of power grid containing wind farms[J].Power System Technology,2015,39(5):1315-1322.
[3]朱建全,段翩,刘明波.计及风险与源-网-荷双层协调的电力实时平衡调度[J].中国电机工程学报,2015,35(13):3239-3247.ZHU Jianquan,DUAN Pian,LIU Mingbo.Electric real-time balance dispatch via bi-level coordination of source-grid-load of power system with risk[J].Proceedings of the CSEE,2015,35(13):3239-3247.
[4]徐立中,易永辉,朱承治,等.考虑风电随机性的微网多时间尺度能量优化调度[J].电力系统保护与控制,2014,42(23):1-8.XU Lizhong,YI Yonghui,ZHU Chengzhi,et al.Multi-time scale optimal energy dispatch of microgrid considering stochastic wind power[J].Power System Protection and Control,2014,42(23):1-8.
[5]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.
[6]陈建华,吴文传,张伯明,等.安全性与经济性协调的鲁棒区间风电调度方法[J].中国电机工程学报,2014,34(7):1033-1040.CHEN Jianhua,WU Wenchuan,ZHANG Boming,et al.Arobust interval wind power dispatch method considering the tradeoff between security and economy[J].Proceedings of the CSEE,2014,34(7):1033-1040.
[7]李志刚,吴文传,张伯明.消纳大规模风电的鲁棒区间经济调度:(一)调度模式与数学模型[J].电力系统自动化,2014,38(20):33-39.DOI:10.7500/AEPS20131123001.LI Zhigang,WU Wenchuan,ZHANG Boming.A robust interval economic dispatch method accommodating large-scale wind power generation:Part one dispatch scheme and mathematical model[J].Automation of Electric Power Systems,2014,38(20):33-39.DOI:10.7500/AEPS20131123001.
[8]查浩,韩学山,杨朋朋.电网运行状态下的概率优化调度[J].中国电机工程学报,2008,28(28):54-60.ZHA Hao,HAN Xueshan,YANG Pengpeng.Probabilistic optimal dispatching under operating condition of power network[J].Proceedings of the CSEE,2008,28(28):54-60.
[9]FEI X,JAMES D.Risk-based security and economy tradeoff analysis for real-time operation[J].IEEE Trans on Power Systems,2007,22(4):2287-2288.
[10]张国强,张伯明,吴文传.考虑风电接入的协调滚动发电计划[J].电力系统自动化,2011,35(19):18-22.ZHANG Guoqiang,ZHANG Boming,WU Wenchuan.Coordinated roll generation scheduling considering wind power integration[J].Automation of Electric Power Systems,2011,35(19):18-22.
[11]白杨,钟海旺,夏清,等.电量协调与成本控制的日内滚动发电计划[J].电网技术,2013,37(10):2965-2972.BAI Yang,ZHONG Haiwang,XIA Qing,et al.An intraday rolling scheduling with unit energy coordination and operating cost control[J].Power System Technology,2013,37(10):2965-2972.
[12]邓勇,朱泽磊,黄文英,等.日内及实时发电计划的递进协调方法[J].电力系统自动化,2015,39(1):141-146.DOI:10.7500/AEPS20140306013.DENG Yong,ZHU Zelei,HUANG Wenying,et al.Hierarchical coordinated method for intraday and real-time generation scheduling[J].Automation of Electric Power Systems,2015,39(1):141-146.DOI:10.7500/AEPS20140306013.
[13]陈之栩,李丹,梁吉,等.华北电网日内发电计划及实时调度系统[J].电力系统自动化,2011,35(19):54-57.CHEN Zhixu,LI Dan,LIANG Ji,et al.Intraday generation plan and realtime dispatch system of North China grid[J].Automation of Electric Power Systems,2011,35(19):54-57.
[14]徐帆,丁恰,谢丽荣,等.实时发电计划闭环控制模型与应用[J].电网技术,2014,38(11):3187-3192.XU Fan,DING Qia,XIE Lirong,et al.A closed-loop control model for real-time generation scheduling and its application[J].Power System Technology,2014,38(11):3187-3192.
[15]查浩.电力系统概率优化调度理论研究[D].济南:山东大学,2009.
[16]吴杰康,龙军,何芬,等.电力市场中发电机组输出量的调节与优化[J].电网技术,2004,28(3):55-58.WU Jiekang,LONG Jun,HE Fen,et al.Output adjustment and optimization for generating sets in electricity markets[J].Power System Technology,2004,28(3):55-58.
[17]邓长虹,马庆,肖永,等.基于自学习迁移粒子群算法及高斯罚函数的无功优化方法[J].电网技术,2014,38(12):3341-3346.DENG Changhong,MA Qing,XIAO Yong,et al.Reactive power optimization based on self-learning migration particle swarm optimization and Gaussian penalty function[J].Power System Technology,2014,38(12):3341-3346.
[18]张俊,程春田,廖胜利,等.改进粒子群优化算法在水电站群优化调度中的应用研究[J].水利学报,2009,40(4):435-441.ZHANG Jun,CHENG Chuntian,LIAO Shengli,et al.Application of improved particle swarm optimization algorithm to operation of hydropower station group[J].Journal of Hydraulic Engineering,2009,40(4):435-441.