考虑DG无功支撑和开关重构的主动配电网分布鲁棒无功优化模型
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摘要
近年来,风光等分布式电源(distributed generation, DG)的大力发展给主动配电网无功优化带来了新的挑战。传统随机优化和鲁棒优化方法在处理清洁能源不确定性方面存在片面性或保守性等问题。基于此,该文构建以系统网损为优化目标,并考虑DG无功支撑和开关重构的主动配电网分布鲁棒无功优化模型。该模型除了包括传统无功优化中的开关、变电站有载调压变压器、离散与连续无功补偿装置等元素,重点对双馈风机和微型燃气轮机的容量曲线进行详细建模,从而融入DG的无功支撑能力,并设计有功无功出力耦合特性约束的线性化方法。结合风电和光伏的典型场景数据,以及决策变量的调节特性,构建基于数据驱动的分布鲁棒两阶段无功优化模型,其中不确定性概率分布置信集合同时受到1-范数和∞-范数约束。模型采用列与约束生成(columns and constraints generation,CCG)算法进行求解,并在IEEE33节点算例上验证模型对系统无功支撑能力的提升和分布鲁棒方法的有效性。
In recent years, the rapid development of the distributed generation(DG) has brought new challenges to the reactive power optimization in the active distribution network. The traditional stochastic optimization and robust optimization methods may result in over-conservative or over-risky decisions when dealing with the uncertainty of renewable energies. Therefore, this paper proposed a distributionally robust reactive power optimization model for active distribution network, which regarded the network loss as its optimization target, and considerd the reactive power support and the switch reconfiguration. This model included not only switching, on load tap changer(OLTC), discrete and continuous reactive power compensation devices usually contained in traditional reactive power optimization, but also the detailed model of capacity curve of double-fed induction generator and micro-gas turbine. After the reactive power support was integrated, the linearization method of active and reactive power output coupling characteristics constraint was specially designed. Then, based on the typical scenario data of wind and photovoltaic power, and the regulation characteristics for different decision variables, a data-driven based two-stage distributionally robust reactive power optimization model was set up, in which the uncertainty probability distribution confidence set was simultaneously constrained by 1-norm and ∞-norm. Finally, by the columns and constraints generation algorithm, the IEEE 33-bus system was applied to verify the effectiveness of proposed.
In recent years, the rapid development of the distributed generation(DG) has brought new challenges to the reactive power optimization in the active distribution network. The traditional stochastic optimization and robust optimization methods may result in over-conservative or over-risky decisions when dealing with the uncertainty of renewable energies. Therefore, this paper proposed a distributionally robust reactive power optimization model for active distribution network, which regarded the network loss as its optimization target, and considerd the reactive power support and the switch reconfiguration. This model included not only switching, on load tap changer(OLTC), discrete and continuous reactive power compensation devices usually contained in traditional reactive power optimization, but also the detailed model of capacity curve of double-fed induction generator and micro-gas turbine. After the reactive power support was integrated, the linearization method of active and reactive power output coupling characteristics constraint was specially designed. Then, based on the typical scenario data of wind and photovoltaic power, and the regulation characteristics for different decision variables, a data-driven based two-stage distributionally robust reactive power optimization model was set up, in which the uncertainty probability distribution confidence set was simultaneously constrained by 1-norm and ∞-norm. Finally, by the columns and constraints generation algorithm, the IEEE 33-bus system was applied to verify the effectiveness of proposed.
引文
[1]Zheng Weiye,Wu Wenchuan,Zhang Boming,et al.Afully distributed reactive power optimization and control method for active distribution networks[J].IEEETransactions on Smart Grid,2016,7(2):1021-1033.
[2]Lin Chenhui,Wu Wenchuan,Zhang Boming,et al.Decentralized reactive power optimization method for transmission and distribution networks accommodating large-scale DG integration[J].IEEE Transactions on Sustainable Energy,2017,8(1):363-373.
[3]Ding Tao,Liu Shiyu,Yuan Wei,et al.A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks[J].IEEETransactions on Sustainable Energy,2016,7(1):301-311.
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[5]刘公博,颜文涛,张文斌,等.含分布式电源的配电网动态无功优化调度方法[J].电力系统自动化,2015,39(15):49-54.Liu Gongbo,Yan Wentao,Zhang Wenbin,et al.Optimization and dispatching method of dynamic reactive power in distribution network with distributed generators[J].Automation of Electric Power Systems,2015,39(15):49-54(in Chinese).
[6]邢海军,程浩忠,张逸.基于多种主动管理策略的配电网综合无功优化[J].电网技术,2015,39(6):1504-1510.Xing Haijun,Cheng Haozhong,Zhang Yi.Reactive power comprehensive optimization in distribution network based on multiple active management schemes[J].Power System Technology,2015,39(6):1504-1510(in Chinese).
[7]陈继明,祁丽志,孙名妤,等.多场景下含风电机组的配电网无功优化的研究[J].电力系统保护与控制,2016,44(9):129-134.Chen Jiming,Qi Lizhi,Sun Mingyu,et al.Reactive power optimization for distribution network with multi-scenario wind power generator[J].Power System Protection and Control,2016,44(9):129-134(in Chinese).
[8]季玉琦,耿光飞,温渤婴,等.基于最优分割法的含DG配电网动态无功优化[J].电网技术,2017,41(8):2585-2592.Ji Yuqi,Geng Guangfei,Wen Boying,et al.Dynamic reactive power optimization in distribution network with DG based on optimal partition[J].Power System Technology,2017,41(8):2585-2592(in Chinese).
[9]Papavasiliou A,Oren S S,O’Neill R P.Reserve requirements for wind power integration:a scenario-based stochastic programming framework[J].IEEE Transactions on Power Systems,2011,255(4):2197-2206.
[10]Sahin,Shahidehpour M,Erkmen I.Allocation of hourly reserve versus demand response for security-constrained scheduling of stochastic wind energy[J].IEEETransactions on Sustainable Energy,2013,4(1):219-228.
[11]Lopez J,Pozo D,Contreras J,et al.A multiobjective minimax regret robust VAr planning model[J].IEEETransactions on Power Systems,2017,32(3):1761-1771.
[12]Zhihuan L,Linhong L,Xianzhong D.Non-dominated sorting genetic algorithm-II for robust multi-objective optimal reactive power dispatch[J].IET Generation,Transmission&Distribution,2010,4(9):1000-1008.
[13]Jabr R A.Adjustable robust OPF with renewable energy sources[J].IEEE Transactions on Power Systems,2013,28(4):4742-4751.
[14]张海鹏,林舜江,刘明波.考虑光伏不确定性的低压配电网分散无功补偿鲁棒优化配置[J].电网技术,2016,40(12):3880-3887.Zhang Haipeng,Lin Shunpeng,Liu Mingbo.Robust optimal allocation of reactive power compensation in low voltage distribution networks considering uncertainty of photovoltaic generation[J].Power System Technology,2016,40(12):3880-3887(in Chinese).
[15]周任军,闵雄帮,童小娇,等.电力环保经济调度矩不确定分布鲁棒优化方法[J].中国电机工程学报,2015,35(13):3248-3256.Zhou Renjun,Min Xiongbang,Tong Xiaojiao,et al.Distributional robust optimization under moment uncertainty of environmental and economic dispatch for power system[J].Proceedings of the CSEE,2015,35(13):3248-3256(in Chinese).
[16]Chen Yue,Wei Wei,Liu Feng,et al.Distributionally robust hydro-thermal-wind economic dispatch[J].Applied Energy,2016,173:511-519.
[17]Peng Xiong,Jirutitijaroen P,Singh C.A distributionally robust optimization model for unit commitment considering uncertain wind power generation[J].IEEETransactions on Power Systems,2017,32(1):39-49.
[18]Yang I.A convex optimization approach to distributionally robust Markov decision processes with Wasserstein distance[J].IEEE Control Systems Letters,2017,1(1):164-169.
[19]Delage E,Ye Yinyu.Distributionally robust optimization under moment uncertainty with application to data-driven problems[J].Operations Research,2010,58(3):595-612.
[20]Ding Tao,Yang Qingrun,Yang Yongheng,et al.Adata-driven stochastic reactive power optimization considering uncertainties in active distribution networks and decomposition method[J].IEEE Transactions on Smart Grid,2017,doi:10.1109/TSG.2017.2677481.
[21]Darivianakis G,Eichler A,Smith R S,et al.A data-driven stochastic optimization approach to the seasonal storage energy management[J].IEEE Control Systems Letters,2017,1(2):394-399.
[22]Bagheri A,Wang Jianhui,Zhao Chaoyue.Data-driven stochastic transmission expansion planning[J].IEEETransactions on Power Systems,2017,32(5):3461-3470.
[23]Pan Kai,Guan Yongpei.Data-driven risk-averse stochastic self-scheduling for combined-cycle units[J].IEEETransactions on Industrial Informatics,2017,13(6):3058-3069.
[24]Rueda-Medina A C,Franco J F,Rider M J,et al.Amixed-integer linear programming approach for optimal type,size and allocation of distributed generation in radial distribution systems[J].Electric Power Systems Research,2013,97:133-143.
[25]Gao Hongjun,Liu Junyong,Wang Lingfeng.Robust coordinated optimization of active and reactive power in active distribution systems[J].IEEE Transactions on Smart Grid,2017,doi:10.1109/TSG.2017.2657782.
[2]Lin Chenhui,Wu Wenchuan,Zhang Boming,et al.Decentralized reactive power optimization method for transmission and distribution networks accommodating large-scale DG integration[J].IEEE Transactions on Sustainable Energy,2017,8(1):363-373.
[3]Ding Tao,Liu Shiyu,Yuan Wei,et al.A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks[J].IEEETransactions on Sustainable Energy,2016,7(1):301-311.
[4]王玲玲,王昕,郑益慧,等.计及多个风电机组出力相关性的配电网无功优化[J].电网技术,2017,41(11):3463-3469.Wang Lingling,Wang Xin,Zheng Yihui,et al.Reactive power optimization of distribution network considering output correlation of multiple wind turbines[J].Power System Technology,2017,41(11):3463-3469(in Chinese).
[5]刘公博,颜文涛,张文斌,等.含分布式电源的配电网动态无功优化调度方法[J].电力系统自动化,2015,39(15):49-54.Liu Gongbo,Yan Wentao,Zhang Wenbin,et al.Optimization and dispatching method of dynamic reactive power in distribution network with distributed generators[J].Automation of Electric Power Systems,2015,39(15):49-54(in Chinese).
[6]邢海军,程浩忠,张逸.基于多种主动管理策略的配电网综合无功优化[J].电网技术,2015,39(6):1504-1510.Xing Haijun,Cheng Haozhong,Zhang Yi.Reactive power comprehensive optimization in distribution network based on multiple active management schemes[J].Power System Technology,2015,39(6):1504-1510(in Chinese).
[7]陈继明,祁丽志,孙名妤,等.多场景下含风电机组的配电网无功优化的研究[J].电力系统保护与控制,2016,44(9):129-134.Chen Jiming,Qi Lizhi,Sun Mingyu,et al.Reactive power optimization for distribution network with multi-scenario wind power generator[J].Power System Protection and Control,2016,44(9):129-134(in Chinese).
[8]季玉琦,耿光飞,温渤婴,等.基于最优分割法的含DG配电网动态无功优化[J].电网技术,2017,41(8):2585-2592.Ji Yuqi,Geng Guangfei,Wen Boying,et al.Dynamic reactive power optimization in distribution network with DG based on optimal partition[J].Power System Technology,2017,41(8):2585-2592(in Chinese).
[9]Papavasiliou A,Oren S S,O’Neill R P.Reserve requirements for wind power integration:a scenario-based stochastic programming framework[J].IEEE Transactions on Power Systems,2011,255(4):2197-2206.
[10]Sahin,Shahidehpour M,Erkmen I.Allocation of hourly reserve versus demand response for security-constrained scheduling of stochastic wind energy[J].IEEETransactions on Sustainable Energy,2013,4(1):219-228.
[11]Lopez J,Pozo D,Contreras J,et al.A multiobjective minimax regret robust VAr planning model[J].IEEETransactions on Power Systems,2017,32(3):1761-1771.
[12]Zhihuan L,Linhong L,Xianzhong D.Non-dominated sorting genetic algorithm-II for robust multi-objective optimal reactive power dispatch[J].IET Generation,Transmission&Distribution,2010,4(9):1000-1008.
[13]Jabr R A.Adjustable robust OPF with renewable energy sources[J].IEEE Transactions on Power Systems,2013,28(4):4742-4751.
[14]张海鹏,林舜江,刘明波.考虑光伏不确定性的低压配电网分散无功补偿鲁棒优化配置[J].电网技术,2016,40(12):3880-3887.Zhang Haipeng,Lin Shunpeng,Liu Mingbo.Robust optimal allocation of reactive power compensation in low voltage distribution networks considering uncertainty of photovoltaic generation[J].Power System Technology,2016,40(12):3880-3887(in Chinese).
[15]周任军,闵雄帮,童小娇,等.电力环保经济调度矩不确定分布鲁棒优化方法[J].中国电机工程学报,2015,35(13):3248-3256.Zhou Renjun,Min Xiongbang,Tong Xiaojiao,et al.Distributional robust optimization under moment uncertainty of environmental and economic dispatch for power system[J].Proceedings of the CSEE,2015,35(13):3248-3256(in Chinese).
[16]Chen Yue,Wei Wei,Liu Feng,et al.Distributionally robust hydro-thermal-wind economic dispatch[J].Applied Energy,2016,173:511-519.
[17]Peng Xiong,Jirutitijaroen P,Singh C.A distributionally robust optimization model for unit commitment considering uncertain wind power generation[J].IEEETransactions on Power Systems,2017,32(1):39-49.
[18]Yang I.A convex optimization approach to distributionally robust Markov decision processes with Wasserstein distance[J].IEEE Control Systems Letters,2017,1(1):164-169.
[19]Delage E,Ye Yinyu.Distributionally robust optimization under moment uncertainty with application to data-driven problems[J].Operations Research,2010,58(3):595-612.
[20]Ding Tao,Yang Qingrun,Yang Yongheng,et al.Adata-driven stochastic reactive power optimization considering uncertainties in active distribution networks and decomposition method[J].IEEE Transactions on Smart Grid,2017,doi:10.1109/TSG.2017.2677481.
[21]Darivianakis G,Eichler A,Smith R S,et al.A data-driven stochastic optimization approach to the seasonal storage energy management[J].IEEE Control Systems Letters,2017,1(2):394-399.
[22]Bagheri A,Wang Jianhui,Zhao Chaoyue.Data-driven stochastic transmission expansion planning[J].IEEETransactions on Power Systems,2017,32(5):3461-3470.
[23]Pan Kai,Guan Yongpei.Data-driven risk-averse stochastic self-scheduling for combined-cycle units[J].IEEETransactions on Industrial Informatics,2017,13(6):3058-3069.
[24]Rueda-Medina A C,Franco J F,Rider M J,et al.Amixed-integer linear programming approach for optimal type,size and allocation of distributed generation in radial distribution systems[J].Electric Power Systems Research,2013,97:133-143.
[25]Gao Hongjun,Liu Junyong,Wang Lingfeng.Robust coordinated optimization of active and reactive power in active distribution systems[J].IEEE Transactions on Smart Grid,2017,doi:10.1109/TSG.2017.2657782.