Probabilistic Optimal Power Flow in Correlated Hybrid Wind-Photovoltaic Power Systems

被引:173
作者
Aien, Morteza [1 ]
Fotuhi-Firuzabad, Mahmud [2 ]
Rashidinejad, Masoud [3 ]
机构
[1] Grad Univ Adv Technol, Dept Elect Engn, Kerman, Iran
[2] Sharif Univ Technol, Dept Elect Engn, Ctr Excellence Power Syst Management & Control, Tehran, Iran
[3] Shahid Bahonar Univ Kerman, Dept Elect Engn, Kerman, Iran
基金
美国国家科学基金会;
关键词
Correlation; probabilistic optimal power flow; two point estimation method; uncertainty modeling; wind turbine generator (WTG); POINT ESTIMATE METHOD;
D O I
10.1109/TSG.2013.2293352
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As a matter of course, the unprecedented ascending penetration of distributed energy resources (DERs), mainly harvesting renewable energies (REs), is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system, consequently, necessitates probabilistic analyses of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognized as an appropriate probabilistic method in small scale or even medium scale problems. This paper develops a new methodology for probabilistic optimal power flow (P-OPF) studies for such problems by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables but the proposed method has been equipped with this ability. In order to justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the Mathpower 30-bus test systems are examined using the proposed method, then, the obtained results are compared against the Monte Carlo simulation (MCS) results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.
引用
收藏
页码:130 / 138
页数:9
相关论文
共 31 条
[1]   Unscented transformation-based probabilistic optimal power flow for modeling the effect of wind power generation [J].
Aien, Morteza ;
Fotuhi-Firuzabad, Mahmood ;
Aminifar, Farrokh .
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2013, 21 (05) :1284-1301
[2]  
[Anonymous], 2011, IEEE PRESS SERIES PO
[3]  
Basil, 1998, P 16 N SEA FLOW MEAS, P1
[4]  
Billinton R, 2013, Reliability assessment of electrical power systems using Monte Carlo methods
[5]   Effects of load forecast uncertainty on bulk electric system reliability evaluation [J].
Billinton, Roy ;
Huang, Dange .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2008, 23 (02) :418-425
[6]   Probabilistic LMP Forecasting Considering Load Uncertainty [J].
Bo, Rui ;
Li, Fangxing .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2009, 24 (03) :1279-1289
[7]  
Boot P, 2010, ECNE10041
[8]   PROBABILISTIC LOAD FLOW [J].
BORKOWSKA, B .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1974, PA93 (03) :752-759
[9]   Scenario reduction in stochastic programming -: An approach using probability metrics [J].
Dupacová, J ;
Gröwe-Kuska, N ;
Römisch, W .
MATHEMATICAL PROGRAMMING, 2003, 95 (03) :493-511
[10]   AN OPTIMAL POINT ESTIMATE METHOD FOR UNCERTAINTY STUDIES [J].
HE, J ;
SALLFORS, G .
APPLIED MATHEMATICAL MODELLING, 1994, 18 (09) :494-499