考虑负荷随机性的风电消纳能力概率评估
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摘要
针对目前风电消纳能力的评估研究方法不能全面考虑电力系统随机性因素对风电消纳能力影响的不足,从电力系统中的不确定因素出发,考虑系统负荷的随机性,提出一种风电消纳能力的概率评估方法。首先采用加权高斯混合分布对负荷进行概率建模,然后采用切片采样算法对负荷的概率模型进行采样并获得其样本空间,最后将样本值依次代入潮流方程进行潮流计算,确定每组样本值在满足系统安全运行前提下所对应的最大风电接入功率,并统计其概率指标。对含有风电接入的IEEE 39节点系统进行仿真计算,当采样规模为3000次时,基于切片采样算法的马尔科夫链蒙特卡洛模拟法(S-MCMC)的标准差仅为0.08%,由此验证了该研究方法相比于传统研究方法的准确性和有效性。
According to the insufficient of the wind power accommodation assessment,this paper proposed a new probabilistic assessment method. In the method,the randomness of the system load is considered. Firstly,the Gaussian mixture model of the power load is constructed. Secondly,the sample space of the load power is obtained by slice sampling from the probability model of load power. Finally,the sample values are substituted into the power flow equation to calculate the power flow and the value of the maximum wind power is determined under the condition of system safety operation constraints. The results of traditional research methods and the proposed method are compared in the reconstructive IEEE 39-bus system. When the sample size is 3000,the standard deviation of S-MCMC(Markov chain Monte Carlo with slice sampling)is only 0.08%,which shows that the proposed method has high accuracy and effectiveness.
According to the insufficient of the wind power accommodation assessment,this paper proposed a new probabilistic assessment method. In the method,the randomness of the system load is considered. Firstly,the Gaussian mixture model of the power load is constructed. Secondly,the sample space of the load power is obtained by slice sampling from the probability model of load power. Finally,the sample values are substituted into the power flow equation to calculate the power flow and the value of the maximum wind power is determined under the condition of system safety operation constraints. The results of traditional research methods and the proposed method are compared in the reconstructive IEEE 39-bus system. When the sample size is 3000,the standard deviation of S-MCMC(Markov chain Monte Carlo with slice sampling)is only 0.08%,which shows that the proposed method has high accuracy and effectiveness.
引文
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[18]Load Data[EB/OL].http://www.nysio.com,2015-01-01.
[2]吕泉,王伟,韩水,等.基于调峰能力分析的电网弃风情况评估方法[J].电网技术,2013,37(7):1887-1894.[2]Lyu Quan,Wang Wei,Han Shui,et al.A new evaluation method for wind power curtailment based on analysis of system regulation capability[J].Power System Technology,2013,37(7):1887-1894.
[3]黎静华,龙裕芳,文劲宇,等.满足充裕性指标的电力系统可接纳风电容量评估[J].电网技术,2014,38(12):3396-3404.[3]Li Jinghua,Long Yufang,Wen Jinyu,et al.Assessment of wind power capacity in power systems to meet the adequacy indexes[J].Power System Technology,2014,38(12):3396-3404.
[4]贾文昭,杨彬,杨志刚,等.计及调峰调频能力的风电消纳能力评估[J].华北电力技术,2014,(5):10-16.[4]Jia Wenzhao,Yang Bin,Yang Zhigang,et al.Assessment of wind power accommodation and ability of peak regulation and frequency modulation[J].North China Electric Power,2014,(5):10-16.
[5]Liew S N,Strbac G.Maximising penetration of wind generation in existing distribution networks[J].IETGeneration Transmission and Distribution,2002,149(3):256-262.
[6]王锐,顾伟,孙蓉,等.基于概率最优潮流的风电接入能力分析[J].电网技术,2011,35(12):214-220.[6]Wang Rui,Gu Wei,Sun Rong,et al.Analysis on wind power penetration limit based on probabilistically optimal power flow[J].Power System Technology,2011,35(12):214-220.
[7]贾文昭,康重庆,李丹,等.基于日前风功率预测的风电消纳能力评估方法[J].电网技术,2012,36(8):69-75.[7]Jia Wenzhao,Kang Chongqing,Li Dan,et al.Evaluation on capability of wind power accommodation based on its day-ahead forecasting[J].Power System Technology,2012,36(8):69-75.
[8]康重庆,贾文昭,徐亁耀,等.考虑网络安全约束的实时实时风电消纳能力评估[J].中国电机工程学报,2013,33(16):69-75.[8]Kang Chongqing,Jia Wenzhao,Xu Qianyao,et al.Capability valuation of wind power accommodation considering security constraints of power grid in realtime dispatch[J].Proceedings of the CSEE,2013,33(16):23-29.
[9]田建芳,毛亚珊,翟桥柱,等.基于风电消纳能力评估的安全约束经济调度方法[J].电网技术,2015,39(9):2398-2403.[9]Tian Jianfang,Mao Yashan,Zhai Qiaozhu,et al.Security constrained unit commitment with wind power based on evaluation of wind power penetration capacity[J].Power System Technology,2015,39(9):2398-2403.
[10]林小雨,江岳文,温步瀛.基于逼近与牛顿插值法的最佳风电接纳水平确定[J].电力系统保护与控制,2015,43(18):12-17.[10]Lin Xiaoyu,Jiang Yuewen,Wen Buying.Determination of optimal wind power acceptance level based approaching and Newton interpolation[J].Power System Protection and Control,2015,43(18):12-17.
[11]王湘明,高杨,刘丽钧.风电场最大注入容量的研究[J].太阳能学报,2012,33(9):1510-1515.[11]Wang Xiangming,Gao Yang,Liu Lijun.Research on capacity of wind farm maximum power integration[J].Acta Energiae Solaris Sinica,2012,33(9):1510-1515.
[12]Shu Zhen,Jirutitijaroen P,Leite da Silvc A M,et al.Accelerated state evaluation and latin hypercube sequential sampling for composite system reliability assessment[J].IEEE Transactions on Power Systems,2014,29(4):1692-1700.
[13]James C S.Estimation via Markov chain Monte Carlo[J].IEEE Control System Magazine,2003,23(2):35-45.
[14]Papaefthymiou G,Klockl B.MCMC for wind power simulation[J].IEEE Transactions on Energy Conversion,2008,23(1):234-240.
[15]Sirisena H R,Brown E P M.Representation of nonGaussian probability distributions in stochastic load-flow studies by the method of Gaussian sum approximations[J].IEE Proceedings,1983,130(4):165-171.
[16]Ueda N,Nakano R.Deterministic annealing EMalgorithm[J].Neural Networks,1998,11(2):271-282.
[17]Neal R M.Slice sampling[J].Annals of Statistics,2003,31(3):705-767.
[18]Load Data[EB/OL].http://www.nysio.com,2015-01-01.