Extreme Quantiles Dynamic Line Rating Forecasts and Application on Network Operation

被引:5
作者
Dupin, Romain [1 ]
Cavalcante, Laura [2 ]
Bessa, Ricardo J. [2 ]
Kariniotakis, Georges [1 ]
Michiorri, Andrea [1 ]
机构
[1] PSL Univ, MINES ParisTech, Ctr Proc Renewable Energies & Energy Syst PERSEE, CS 10207 Rue Claude Daunesse, F-06904 Sophia Antipolis, France
[2] INESC TEC, Ctr Power & Energy Syst, Campus FEUP,Rua Dr Roberto Frias, P-4200465 Porto, Portugal
基金
欧盟第七框架计划;
关键词
forecasting; ampacity; overhead lines; electric power systems; OVERHEAD LINES; SYSTEM; REGRESSION;
D O I
10.3390/en13123090
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper presents a study on dynamic line rating (DLR) forecasting procedure aimed at developing a new methodology able to forecast future ampacity values for rare and extreme events. This is motivated by the belief that to apply DLR network operators must be able to forecast their values and this must be based on conservative approaches able to guarantee the safe operation of the network. The proposed methodology can be summarised as follows: firstly, probabilistic forecasts of conductors' ampacity are calculated with a non-parametric model, secondly, the lower part of the distribution is replaced with a new distribution calculated with a parametric model. The paper presents also an evaluation of the proposed methodology in network operation, suggesting an application method and highlighting the advantages. The proposed forecasting methodology delivers a high improvement of the lowest quantiles' reliability, allowing perfect reliability for the 1% quantile and a reduction of roughly 75% in overconfidence for the 0.1% quantile.
引用
收藏
页数:19
相关论文
共 32 条
[1]  
[Anonymous], INN SMART GRID TECHN
[2]   Dynamic Line Rating Using Numerical Weather Predictions and Machine Learning: A Case Study [J].
Aznarte, Jose L. ;
Siebert, Nils .
IEEE TRANSACTIONS ON POWER DELIVERY, 2017, 32 (01) :335-343
[3]   Risk constrained short-term scheduling with dynamic line ratings for increased penetration of wind power [J].
Banerjee, Binayak ;
Jayaweera, Dilan ;
Islam, Syed .
RENEWABLE ENERGY, 2015, 83 :1139-1146
[4]   Nonparametric estimation of extreme conditional quantiles [J].
Beirlant, J ;
De Wet, T ;
Goegebeur, Y .
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2004, 74 (08) :567-580
[5]  
Chaiwongven A., 2018, P 2018 IEEE INT C EN, P1, DOI DOI 10.1109/EEEIC.2018.8493657
[6]  
Cigre W., 2002, THERMAL BEHAV OVERHE
[7]   NEW THERMAL RATING APPROACH - REAL-TIME THERMAL RATING SYSTEM FOR STRATEGIC OVERHEAD CONDUCTOR TRANSMISSION-LINES .1. GENERAL DESCRIPTION AND JUSTIFICATION OF REAL-TIME THERMAL RATING SYSTEM [J].
DAVIS, MW .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1977, 96 (03) :803-809
[8]   Optimal Dynamic Line Rating Forecasts Selection Based on Ampacity Probabilistic Forecasting and Network Operators' Risk Aversion [J].
Dupin, Romain ;
Michiorri, Andrea ;
Kariniotakis, George .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2019, 34 (04) :2836-2845
[9]   Probabilistic Real-Time Thermal Rating Forecasting for Overhead Lines by Conditionally Heteroscedastic Auto-Regressive Models [J].
Fan, Fulin ;
Bell, Keith ;
Infield, David .
IEEE TRANSACTIONS ON POWER DELIVERY, 2017, 32 (04) :1881-1890
[10]   On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power [J].
Gallego-Castillo, Cristobal ;
Bessa, Ricardo ;
Cavalcante, Laura ;
Lopez-Garcia, Oscar .
ENERGY, 2016, 113 :355-365