基于随机模型预测控制的含大规模风电接入的电力系统优化调度
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摘要
风电是重要的清洁可再生能源,将其引入智能电网中对节能减排有着重要的意义.为降低大规模风电不确定性给电网调度带来的影响,提出一种基于随机模型预测控制的风电与传统机组协调调度方法.考虑了部分传统机组需要人工调度而无法频繁、连续操作的情况,并引入可调负荷以增加系统可调度能力.构建基于混合整数二次规划(MIQP)的风电调度目标函数,以及包括机组最大可调节次数、最小运行/停机时间、可调度负荷总能量需求一致性、风电切负荷比例等约束.提出两阶段场景缩减方法以实现典型场景的快速筛选.通过与传统开环调度方法的性能对比表明所提出方法的可行性与有效性,并在此基础上进一步分析机组启停次数和可调度负荷对系统运行的影响.
Wind power is an important clean and renewable energy. Integrating it into smart grid is significant to the energy conversion and emission reduction. In order to reduce the negative impact introduced by uncertainties and randomness of large scale wind power integration, a stochastic model predictive control(SMPC) based optimization and scheduling approach is proposed to coordinate to the wind power and traditional fossil generators. The discrete generation regulation constraints of some traditional generators without the automatic generation control(AGC) function are considered, and schedulable loads are introduced to make the system more flexible. A mixed integer quadratic programming(MIQP) based energy management model is constructed, and the regulation frequency constraints, minimum up/down time constraints and discrete output constraints are all considered. A two-stage scenario cutting method is proposed to efficiently choose typical scenarios. Experimental results show that the approach proposed is flexible and efficient by comparing with the traditional scheduling approach. Furthermore, the impact of start-up/shut-down times and schedulable loads is discussed.
Wind power is an important clean and renewable energy. Integrating it into smart grid is significant to the energy conversion and emission reduction. In order to reduce the negative impact introduced by uncertainties and randomness of large scale wind power integration, a stochastic model predictive control(SMPC) based optimization and scheduling approach is proposed to coordinate to the wind power and traditional fossil generators. The discrete generation regulation constraints of some traditional generators without the automatic generation control(AGC) function are considered, and schedulable loads are introduced to make the system more flexible. A mixed integer quadratic programming(MIQP) based energy management model is constructed, and the regulation frequency constraints, minimum up/down time constraints and discrete output constraints are all considered. A two-stage scenario cutting method is proposed to efficiently choose typical scenarios. Experimental results show that the approach proposed is flexible and efficient by comparing with the traditional scheduling approach. Furthermore, the impact of start-up/shut-down times and schedulable loads is discussed.
引文
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[22]张彦,张涛,刘亚杰,等.基于随机模型预测控制的能源局域网优化调度研究[J].中国电机工程学报,2016,36(13):3451-3462.(Zhang Y,Zhang T,Liu Y J,et al.Stochastic model predictive control for energy management optimization of an energy local network[J].Proc of the CSEE,2016,36(13):3451-3462.)
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[24]Alessandra Parisio,Evangelos Rikos,Luigi Glielmo.A model predictive control approach to microgrid operation optimization[J].IEEE Trans on Control Systems Technology,2014,22(5):1813-1827.
[2]徐青山,丁一帆,郑爱霞.计及需求响应的电网安全优化调度模型[J].控制与决策,2018,33(3):549-556.(Xu Q S,Ding Y F,Zheng A X.Safe and optimized scheduling of power system considering demand response[J].Control and Decision,2018,33(3):549-556.)
[3]江琦,路改香,唐昊,等.智能电网弹性响应时间业务需求的接入控制[J].控制与决策,2014,29(7):1311-1315.(Jiang Q,Lu G X,Tang H,et al.Access control of demand requests with response time flexibility in smart grids[J].Control and Decision,2014,29(7):1311-1315.)
[4]Zhang Y,Wang R,Zhang T,et al.Stochastic model predictive control based economic dispatch for hybrid energy system including wind and energy storage devices[C].IEEE Symposium Series on Computational Intelligence.Cape Town:IEEE,2015:1267-1271.
[5]Zhang Y,Meng F L,Wang R,et al.A stochastic MPCbased approach to integrated energy management in microgrids[J].Sustainable Cities and Society,2018,41:349-362.
[6]Soares J,Fotouhi Ghazvini M A,Borges N,et al.A stochastic model for energy resources management considering demand response in smart grids[J].Electric Power Systems Research,2017,143:599-610.
[7]Uckun C,Botterud A,Birge J R.An improved stochastic unit commitment formulation to accommodate wind uncertainty[J].IEEE Trans on Power Systems,2016,31(4):2507-2517.
[8]Wang Q,Guan Y,Wang J.A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output[J].IEEE Trans on Power Systems,2012,27(1):206-215.
[9]沈伟,吴文传,张伯明,等.消纳大规模风电的在线滚动调度策略与模型[J].电力系统自动化,2011,35(22):136-140.(Shen W,Wu W C,Zhang B M,et al.An on-line rolling generation dispatch method and model for accommodating large-scale wind power[J].Automation of Electric Power Systems,2011,35(22):136-140.)
[10]Lu P M,Wen B Y,Jiang Y W.Study on optimization of spinning reserve in wind power integrated power system based on multiple timescale and unit commitment coordination[J].Power System Protection and Control,2015,43(5):94-100.
[11]Zhao J Q,Tang J,Luo W H,et al.Day-ahead generation scheduling and spinning reserve decision-making model for power grid containing wind power[J].Electric Power Automation Equipment,2014,34(5):21-26.
[12]Ai X M,Tayierjiang B,Yang L B,et al.Optimizing the spinning reserve in wind power system using scenario method[J].Power System Technology,2018,42(3):835-841.
[13]Lei Y,Yang M,Han X S.A two-stage stochastic optimization of unit commitment considering wind power based on scenario analysis[J].Power System Protection and Control,2012(23):58-67.
[14]Sirus Mohammadi,Soodabeh Soleymani,Babak Mozafari.Scenario-based stochastic operation management of microgrid including wind,photovoltaic,micro-turbine,fuel cell and energy storage devices[J].Electrical Power and Energy Systems,2014,54:525-535.
[15]Meibom P,Barth R,Hasche B,et al.Stochastic optimization model to study the operational impacts of high wind penetrations in Ireland[J].IEEE Trans on Power Systems,2011,26(3):1367-1379.
[16]Wang H B,Qi Y Z,Wang C M,et al.Two-stage stochastic optimal scheduling model considering flexible load[J].Power System Technology,2018,42(11):3670-3676.
[17]Schulze,T,Grothey A,McKinnon K.A stabilised scenario decomposition algorithm applied to stochastic unit commitment problems[J].European J of Operational Research,2017,261(1):247-259.
[18]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.(Li Z G,Wu W C,Zhang B M.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.)
[19]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 J H,Wu W C,Zhang B M,et al.A robust interval wind power dispatch method considering the tradeoff between security and economy[J].Proc of the CSEE,2014,34(7):1033-1040.)
[20]李志刚,吴文传,张伯明,等.计及风电考虑离散化发电调节约束的在线滚动调度方法[J].电力系统自动化,2014,38(10):36-42.(Li Z G,Wu W C,Zhang B M,et al.A lookahead generation dispatch method considering discrete generation regulation constraints with large-scale wind power integration[J].Automation of Electric Power Systems,2014,38(10):36-42.)
[21]张伯明,陈建华,吴文传.大规模风电接入电网的有功分层模型预测控制方法[J].电力系统自动化,2014,38(9):6-14.(Zhang B M,Chen J H,Hu W C.A hierarchical model predictive control method of active power for accommodating large-scale wind power integration[J].Automation of Electric Power Systems,2014,38(9):6-14.)
[22]张彦,张涛,刘亚杰,等.基于随机模型预测控制的能源局域网优化调度研究[J].中国电机工程学报,2016,36(13):3451-3462.(Zhang Y,Zhang T,Liu Y J,et al.Stochastic model predictive control for energy management optimization of an energy local network[J].Proc of the CSEE,2016,36(13):3451-3462.)
[23]Ripaccioli G,Bernardini D,Di Cairano S,et al.Astochastic model predictive control approach for series hybrid electric vehicle power management[C].Proc of the2010 American Control Conf.Baltimore:IEEE,2010:5844-5849.
[24]Alessandra Parisio,Evangelos Rikos,Luigi Glielmo.A model predictive control approach to microgrid operation optimization[J].IEEE Trans on Control Systems Technology,2014,22(5):1813-1827.