中国电机工程学报
中國電機工程學報
중국전궤공정학보
Proceedings of the CSEE
2015年
18期
4581-4590
,共10页
多风电场%风电场出力场景%时空相关性%风速联合概率分布%大气动力模型%扩展卡尔曼滤波
多風電場%風電場齣力場景%時空相關性%風速聯閤概率分佈%大氣動力模型%擴展卡爾曼濾波
다풍전장%풍전장출력장경%시공상관성%풍속연합개솔분포%대기동력모형%확전잡이만려파
multiple wind farms%wind power scenario%temporal and spatial correlations%joint probability distribution of wind speeds%atmospheric dynamic model%extended Kalman filter
具有时空相关性的多风电场未来出力的场景，在大规模风电接入下的日前、日内滚动经济调度问题中有着重要的应用。文章基于物理机理的解析模型提出一种生成多风电场未来时段出力场景的方法。该方法以大气运动方程和风速降尺度方程为基础，建立了描述风电场群高空大气与地表风运动关系的随机动态系统。结合地面实时量测风速信息，采用扩展卡尔曼滤波算法对系统状态进行估计，并对各风电场未来风速的联合概率分布进行预测。再利用蒙特卡洛仿真、风电功率曲线和场景约简技术生成具有时空相关性的各风电场未来时段的出力场景。算例中以美国密苏里州四个风电场为例进行仿真测试，并将该方法与高斯连接函数法进行对比和评价，验证了所提方法的有效性。
具有時空相關性的多風電場未來齣力的場景，在大規模風電接入下的日前、日內滾動經濟調度問題中有著重要的應用。文章基于物理機理的解析模型提齣一種生成多風電場未來時段齣力場景的方法。該方法以大氣運動方程和風速降呎度方程為基礎，建立瞭描述風電場群高空大氣與地錶風運動關繫的隨機動態繫統。結閤地麵實時量測風速信息，採用擴展卡爾曼濾波算法對繫統狀態進行估計，併對各風電場未來風速的聯閤概率分佈進行預測。再利用矇特卡洛倣真、風電功率麯線和場景約簡技術生成具有時空相關性的各風電場未來時段的齣力場景。算例中以美國密囌裏州四箇風電場為例進行倣真測試，併將該方法與高斯連接函數法進行對比和評價，驗證瞭所提方法的有效性。
구유시공상관성적다풍전장미래출력적장경，재대규모풍전접입하적일전、일내곤동경제조도문제중유착중요적응용。문장기우물리궤리적해석모형제출일충생성다풍전장미래시단출력장경적방법。해방법이대기운동방정화풍속강척도방정위기출，건립료묘술풍전장군고공대기여지표풍운동관계적수궤동태계통。결합지면실시량측풍속신식，채용확전잡이만려파산법대계통상태진행고계，병대각풍전장미래풍속적연합개솔분포진행예측。재이용몽특잡락방진、풍전공솔곡선화장경약간기술생성구유시공상관성적각풍전장미래시단적출력장경。산례중이미국밀소리주사개풍전장위례진행방진측시，병장해방법여고사련접함수법진행대비화평개，험증료소제방법적유효성。
The wind power scenarios of multiple wind farms with temporal and spatial correlations play an important role for day-ahead or hours-ahead stochastic generation scheduling problems with significant wind power penetration. In this paper, we proposed a physical mechanism based analytical model to generate temporal and spatial correlated scenarios of wind power generation for multiple wind farms. A stochastic dynamic system was established on top of the atmospheric dynamic equations and wind speed downscaling equations to describe the relationship between the atmospheric and near-surface wind field for all wind farms. Combing with wind speed measurements, the extended Kalman filter was applied to estimate the system state and to forecast the joint probability density function (PDF) of wind speeds of multiple wind farms. Based on this joint PDF, the spatial and temporal correlated wind power scenarios of the wind farms were then generated through the procedures of Monte Carlo simulation, wind power conversion and scenario reduction. In case studies, the proposed method were tested and compared with Gaussian Copula based methods based on data of 4 wind farms in the State of Missouri in USA. The evaluation results verify the effectiveness of the proposed method.
