TY - JOUR
T1 - Estimation of Distribution Systems Expected Energy Not Supplied Index by Multi-level Monte Carlo Method
AU - Nazmul Huda, A. S.
AU - Živanovic, Rastko
PY - 2019/6/15
Y1 - 2019/6/15
N2 - ABSTRACT—: The article presents the estimation of power distribution systems Expected Energy Not Supplied (EENS) index by incorporating time-varying load models of different customer sectors. An application of a new and efficient advanced Monte Carlo simulation (MCS) with controllable accuracy called Multilevel Monte Carlo (MLMC) method is proposed for this estimation. The purpose of the proposed method is to increase the simulation speed of EENS estimation. The method could be easily replaced by the traditional MCS-based estimation which requires huge computational effort for achieving high level of simulation accuracy. Five distribution networks with seven different load models of Roy Billinton Test System are chosen as test distribution systems. To verify the effectiveness of the proposed method, computational time and estimated values of EENS using MLMC method are compared to the results from MCS. The computation performance of EENS estimation can be influenced by different factors and criteria which are also explored in this study. The results presented in the article show that acceptable results can be obtained using the proposed method while substantial reduction of computational effort is also achieved.
AB - ABSTRACT—: The article presents the estimation of power distribution systems Expected Energy Not Supplied (EENS) index by incorporating time-varying load models of different customer sectors. An application of a new and efficient advanced Monte Carlo simulation (MCS) with controllable accuracy called Multilevel Monte Carlo (MLMC) method is proposed for this estimation. The purpose of the proposed method is to increase the simulation speed of EENS estimation. The method could be easily replaced by the traditional MCS-based estimation which requires huge computational effort for achieving high level of simulation accuracy. Five distribution networks with seven different load models of Roy Billinton Test System are chosen as test distribution systems. To verify the effectiveness of the proposed method, computational time and estimated values of EENS using MLMC method are compared to the results from MCS. The computation performance of EENS estimation can be influenced by different factors and criteria which are also explored in this study. The results presented in the article show that acceptable results can be obtained using the proposed method while substantial reduction of computational effort is also achieved.
KW - Euler-Maruyama method
KW - expected energy not supplied
KW - multilevel Monte Carlo
KW - power distribution systems
KW - time-varying load models
UR - http://www.scopus.com/inward/record.url?scp=85073665364&partnerID=8YFLogxK
U2 - 10.1080/15325008.2019.1628120
DO - 10.1080/15325008.2019.1628120
M3 - Article
AN - SCOPUS:85073665364
SN - 1532-5008
VL - 47
SP - 810
EP - 822
JO - Electric Power Components and Systems
JF - Electric Power Components and Systems
IS - 9-10
ER -