| 引用本文: | 张衡,程浩忠,曾平良,张建平,陆建忠.分位数拟合的点估计法随机潮流在输电网规划中的应用[J].电力自动化设备,2018,(11): |
| ZHANG Heng,CHENG Haozhong,ZENG Pingliang,ZHANG Jianping,LU Jianzhong.Application of stochastic power flow based on quantile fitting point estimation method in transmission network expansion planning[J].Electric Power Automation Equipment,2018,(11): |
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| 摘要: |
| 为了更全面地描述输电网规划中面临的负荷波动、风电出力这些不确定因素，以点估计法随机潮流为基础、年综合费用最小为目标函数，并计及网络损耗，建立输电网优化规划模型。引入多项式正态变换技术对非正态变量相关性进行处理。采用基于最小二乘的分位数拟合法求取多项式正态变换的系数，有效地避免了积分运算。以总体样本均值和样本方差均值为指标，量化相关性对风电出力的影响。采用改进的粒子群优化算法对修改的IEEE-RTS 24节点系统进行算例分析，结果表明，随着相关性的增强，负荷、风速出现极值的概率增加，电网需要投建更多线路以应对系统中的不确定因素。 |
| 关键词: 输电网规划 点估计法 相关性 随机最优潮流 粒子群优化算法 |
| DOI：10.16081/j.issn.1006-6047.2018.11.007 |
| 分类号:TM73 |
| 基金项目:国家重点基础研发项目(2016YFB0900102)；国家自然科学基金重点项目(51337005) |
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| Application of stochastic power flow based on quantile fitting point estimation method in transmission network expansion planning |
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ZHANG Heng1, CHENG Haozhong1, ZENG Pingliang2, ZHANG Jianping3, LU Jianzhong3
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1.Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China;2.China Electric Power Research Institute, Beijing 100192, China;3.East China Power Grid Company, Shanghai 200002, China
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| Abstract: |
| In order to describe the uncertain factors, i. e. load fluctuation and wind power, faced in TNEP(Transmission Network Expansion Planning) more thoroughly, a transmission network optimization planning model is built based on stochastic power flow using PEM(Point Estimation Method),which takes the minimum annual comprehensive cost as its objective function and considers the network loss. The polynomial normal transformation technology is introduced to deal with the correlation of non-normal variables. The quantile fitting method based on least square is adopted to obtain the coefficients of polynomial normal transformation, effectively avoiding the integral operation. The impact of the correlation on wind power is quantified by the indexes of PM(Population Mean) and MSV(Mean of Sample Variance). The improved PSO(Particle Swarm Optimization) algorithm is used to analyze the modified IEEE-RTS 24-bus system, and results show that the probability of extreme values of load and wind speed is rising along with the increase of correlation, and power grid needs to build more lines to deal with the uncertain factors. |
| Key words: transmission network expansion planning point estimation method correlation stochastic optimal power flow particle swarm optimization algorithm |
