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考虑大规模光伏电站接入的电力系统旋转备用需求评估
引用本文:胡斌,娄素华,李海英,吴耀武,卢斯煜,黄旭锐.考虑大规模光伏电站接入的电力系统旋转备用需求评估[J].电力系统自动化,2015,39(18):15-19.
作者姓名:胡斌  娄素华  李海英  吴耀武  卢斯煜  黄旭锐
作者单位:强电磁工程与新技术国家重点实验室(华中科技大学), 湖北省武汉市 430074; 上海理工大学电气工程学院, 上海市 200093,强电磁工程与新技术国家重点实验室(华中科技大学), 湖北省武汉市 430074; 上海理工大学电气工程学院, 上海市 200093,强电磁工程与新技术国家重点实验室(华中科技大学), 湖北省武汉市 430074; 上海理工大学电气工程学院, 上海市 200093,强电磁工程与新技术国家重点实验室(华中科技大学), 湖北省武汉市 430074; 上海理工大学电气工程学院, 上海市 200093,强电磁工程与新技术国家重点实验室(华中科技大学), 湖北省武汉市 430074; 上海理工大学电气工程学院, 上海市 200093,强电磁工程与新技术国家重点实验室(华中科技大学), 湖北省武汉市 430074; 上海理工大学电气工程学院, 上海市 200093
基金项目:国家重点基础研究发展计划(973计划)资助项目(2012CB215102);国家自然科学基金资助项目(51207062,51207092);华中科技大学自主创新基金资助项目(2013TS057)。
摘    要:光伏发电系统出力的随机性与间歇性,使得电力系统的运行风险在大规模光伏电站并网后迅速增加,传统的旋转备用需求评估方法已经不能满足含光伏电站的系统运行要求。文中建立了太阳辐照度和光伏发电系统出力的概率分布模型,并采用拉丁超立方采样模拟光伏发电系统的出力场景;利用基于Huffman树的改进K-means聚类算法对光伏发电系统的出力场景进行有效聚类,在保证光伏发电系统出力分布特性的前提下减少了场景数量;在此基础上,提出了考虑大规模光伏电站接入的电力系统旋转备用需求评估模型,以系统综合运行费用最低为目标,兼顾了运行的经济性和可靠性。基于改进的IEEE-RTS 96系统,对所提模型进行了仿真分析,算例结果验证了模型的合理性和有效性。

关 键 词:光伏发电    旋转备用    拉丁超立方采样    改进K-means聚类算法
收稿时间:2014/9/26 0:00:00
修稿时间:2015/5/12 0:00:00

Spinning Reserve Demand Estimation in Power Systems Integrated with Large-scale Photovoltaic Power Plants
HU Bin,LOU Suhu,LI Haiying,WU Yaowu,LU Siyu and HUANG Xurui.Spinning Reserve Demand Estimation in Power Systems Integrated with Large-scale Photovoltaic Power Plants[J].Automation of Electric Power Systems,2015,39(18):15-19.
Authors:HU Bin  LOU Suhu  LI Haiying  WU Yaowu  LU Siyu and HUANG Xurui
Affiliation:State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China,State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China,Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China,State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China,State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China and State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China
Abstract:Owing to the randomness and intermittence of photovoltaic (PV) generation, the risk of power system operation is rapidly increased after integration with large-scale photovoltaic power plant. Traditional spinning reserve estimating methods are no longer able to meet the requirements of power system operation integrated with PV power plant. The probability distribution model of solar irradiance and PV generation output are developed, and the Latin hypercube sampling method is used to simulate PV generation output scenarios. The improved K-means clustering algorithm based on Huffman tree is used to effectively cluster the PV generation output scenarios, which will greatly reduce the scenario number while ensuring the probability distribution characteristics of PV generation output. On this basis, a model for spinning reserve demand estimation in the power system integrated with large-scale PV power plant is proposed to minimize the system comprehensive operation cost, thus balancing the economy and reliability of power system operation. The improved IEEE-RTS 96 system is simulated via the proposed model to prove its rationality and effectiveness. This work is supported by National Basic Research Program of China (973 Program) (No. 2012CB215102), National Natural Science Foundation of China (No. 51207062, No. 51207092), and Self-independent Innovation Fund of Huazhong University of Science and Technology (No. 2013TS057).
Keywords:photovoltaic generation  spinning reserve  Latin hypercube sampling  improved K-means clustering algorithm
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