Open AccessNovel fast forecasting method for nodal voltage violations
Author(s) -
Ye Rui,
Huang Xueliang,
Chen Zhong,
Ji Zhenya,
Tan Linlin
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0363
Subject(s) - voltage , computer science , reliability (semiconductor) , probabilistic logic , monte carlo method , reliability engineering , electric power system , probabilistic forecasting , ac power , probabilistic method , key (lock) , control theory (sociology) , power (physics) , engineering , control (management) , artificial intelligence , mathematics , statistics , physics , computer security , quantum mechanics , electrical engineering
With the increase penetration of new energy and electric vehicles (EVs), the nodal voltage violations (NVVs) problem is widely concerned. Voltage predictive regulation methods can prevent NVVs and improve the performance of traditional voltage control. A fast and reliable forecasting method for NVVs is a key requirement to support this application. However, it is time‐consuming to forecast NVVs using a Monte Carlo simulation‐based probabilistic load flow method and the traditional deterministic method based on load forecasting has low reliability due to it not considering the uncertainty of load forecasting errors. A novel fast forecasting method for NVVs with shorter computational time and higher reliability is proposed. In the proposed method, the uncertainty of the load forecasting is taken into account and the impact of active/reactive power change on NVVs can be quantified like the traditional deterministic method. Thus, this method can assist in correcting voltage regulation plans so that voltage constraints can be satisfied as much as possible. Finally, the IEEE 69‐bus test system and real historical load series are used to validate the effectiveness of the proposed method.