智能电网
智能電網
지능전망
Smart Grid
2015年
1期
43-47
,共5页
杨星宇%邹达%孙义豪%赵霞%任洲洋%曾执丰
楊星宇%鄒達%孫義豪%趙霞%任洲洋%曾執豐
양성우%추체%손의호%조하%임주양%증집봉
光伏发电%Monte Carlo%概率潮流%非参数核密度估计
光伏髮電%Monte Carlo%概率潮流%非參數覈密度估計
광복발전%Monte Carlo%개솔조류%비삼수핵밀도고계
photovoltaic%Monte Carlo%probabilistic load flow%non-parametric kernel density estimation
针对现有含光伏概率潮流分析中未考虑光伏电站夜间无功输出的不足,基于Monte Carlo模拟提出一种计及光伏电站昼夜功率输出差异的概率潮流计算方法。首先,根据光伏电站昼夜有功/无功功率的输出特征,建立其潮流计算模型;接着,利用非参数核密度估计理论建立光伏白天有功输出的概率模型,采用正态分布模拟负荷的随机波动,基于Monte Carlo模拟建立考虑光伏电站昼夜功率输出差异的概率潮流分析方法;最后,用甘肃敦煌某光伏电站的实测数据、IEEE14节点测试系统进行仿真分析,验证该方法的有效性及正确性。仿真发现,忽略光伏电站夜间的无功输出,将无法准确估计潮流结果的概率分布信息。
針對現有含光伏概率潮流分析中未攷慮光伏電站夜間無功輸齣的不足,基于Monte Carlo模擬提齣一種計及光伏電站晝夜功率輸齣差異的概率潮流計算方法。首先,根據光伏電站晝夜有功/無功功率的輸齣特徵,建立其潮流計算模型;接著,利用非參數覈密度估計理論建立光伏白天有功輸齣的概率模型,採用正態分佈模擬負荷的隨機波動,基于Monte Carlo模擬建立攷慮光伏電站晝夜功率輸齣差異的概率潮流分析方法;最後,用甘肅敦煌某光伏電站的實測數據、IEEE14節點測試繫統進行倣真分析,驗證該方法的有效性及正確性。倣真髮現,忽略光伏電站夜間的無功輸齣,將無法準確估計潮流結果的概率分佈信息。
침대현유함광복개솔조류분석중미고필광복전참야간무공수출적불족,기우Monte Carlo모의제출일충계급광복전참주야공솔수출차이적개솔조류계산방법。수선,근거광복전참주야유공/무공공솔적수출특정,건립기조류계산모형;접착,이용비삼수핵밀도고계이론건립광복백천유공수출적개솔모형,채용정태분포모의부하적수궤파동,기우Monte Carlo모의건립고필광복전참주야공솔수출차이적개솔조류분석방법;최후,용감숙돈황모광복전참적실측수거、IEEE14절점측시계통진행방진분석,험증해방법적유효성급정학성。방진발현,홀략광복전참야간적무공수출,장무법준학고계조류결과적개솔분포신식。
The reactive power output of photovoltaic power plant at nighttime is not considered in probabilistic load flow calculation in current literature. Based on Monte Carlo technique, a probabilistic load flow calculation method is proposed, which takes into account the circadian differences of photovoltaic power plant output. Firstly, according to the circadian characteristics of the photovoltaic power plant, its power flow calculation model is established, which generates active power in daylight and reactive power at nighttime. Secondly, the randomness of photovoltaic power plant’s active power output during the daytime is described by using non-parametric kernel density estimation theory. And the normal distribution is applied to simulate the random fluctuations of bus loads. Then, a method based on Monte Carlo of probabilistic load flow calculation, considering the circadian differences of photovoltaic power plant output, is established. At last, the correctness and effectiveness of the proposed method are verified by using the measured data from a photovoltaic power plant in Dunhuang, Gansu province and IEEE 14 standard test system. Simulation shows that neglect of reactive power output of the photovoltaic power plant at nighttime will lead to the fact that the probability distribution cannot be estimated accurately.