电网与清洁能源
電網與清潔能源
전망여청길능원
ADVANCES OF POWER SYSTEM & HYDROELECTRIC ENGINEERING
2014年
12期
126-131,138
,共7页
统计数据%规划研究%随机过程%风电功率曲线%随机微分方程
統計數據%規劃研究%隨機過程%風電功率麯線%隨機微分方程
통계수거%규화연구%수궤과정%풍전공솔곡선%수궤미분방정
statistic data%planning studies%stochastic pro-cess%wind power curves%stochastic differential equation
近年来，电力系统进行中的显著变革之一是其对风能的扩大利用。系统运营者在以往的统计数据基础上对风电功率进行长期的规划研究。该研究的主要内容是人工建立风电曲线来模拟风电厂的实际运转状态。文中研究的核心问题是如何通过随机过程模拟生成风电功率曲线，首先建立风电功率模拟生成模型，再考虑到季节性和一天内变动等实际问题对模型进行修正。其次，建立适合模型的随机微分方程，并结合分布函数和自相关函数设置边界条件。再次，通过设计算法模拟随机微分方程中包含的布朗运动分量来简化方程形式，并利用Matlab软件进行求解。最后以中国和西班牙两个国家的数据做了算例分析，主要从各月和全年风电功率平均值和方差，以及各月和全年概率密度分布等方面来验证模型的可行性和解法的合理性。
近年來，電力繫統進行中的顯著變革之一是其對風能的擴大利用。繫統運營者在以往的統計數據基礎上對風電功率進行長期的規劃研究。該研究的主要內容是人工建立風電麯線來模擬風電廠的實際運轉狀態。文中研究的覈心問題是如何通過隨機過程模擬生成風電功率麯線，首先建立風電功率模擬生成模型，再攷慮到季節性和一天內變動等實際問題對模型進行脩正。其次，建立適閤模型的隨機微分方程，併結閤分佈函數和自相關函數設置邊界條件。再次，通過設計算法模擬隨機微分方程中包含的佈朗運動分量來簡化方程形式，併利用Matlab軟件進行求解。最後以中國和西班牙兩箇國傢的數據做瞭算例分析，主要從各月和全年風電功率平均值和方差，以及各月和全年概率密度分佈等方麵來驗證模型的可行性和解法的閤理性。
근년래，전력계통진행중적현저변혁지일시기대풍능적확대이용。계통운영자재이왕적통계수거기출상대풍전공솔진행장기적규화연구。해연구적주요내용시인공건립풍전곡선래모의풍전엄적실제운전상태。문중연구적핵심문제시여하통과수궤과정모의생성풍전공솔곡선，수선건립풍전공솔모의생성모형，재고필도계절성화일천내변동등실제문제대모형진행수정。기차，건립괄합모형적수궤미분방정，병결합분포함수화자상관함수설치변계조건。재차，통과설계산법모의수궤미분방정중포함적포랑운동분량래간화방정형식，병이용Matlab연건진행구해。최후이중국화서반아량개국가적수거주료산례분석，주요종각월화전년풍전공솔평균치화방차，이급각월화전년개솔밀도분포등방면래험증모형적가행성화해법적합이성。
Wind energy expansion is one of the prominent ongoing evolutions of power systems in recent years. Normally power system operators carry out long-term planning studies for wind power with the previous statistic data. The study presented in this paper focuses on the artificially built wind power curves simulating the actual behavior of wind farms. The core of the studies is how to sample wind power curves via stochastic process. Firstly,the initial sampling model for power curves is built and then gradually revised considering seasonal and diurnal changes. Secondly,a stochastic differential equation fitting the model is built,and the boundary conditions are set up in view of distribution functions as well as auto correlation functions. Thirdly,the form of stochastic differential equation is simplified by means of designing algorithm simulating Brownian movement component included in the SDE. Finally, both China’s and Spain’s data are modeled to verify the feasibility and rationality of the model mainly through simulating monthly and yearly power level expectation,standard deviation as well as monthly and yearly probability density distribution.
