Open AccessEquivalent model considering frequency characteristics and renewable uncertainties for probabilistic power flow
Author(s) -
Yu Juan,
Lin Wei,
Kamel Salah,
Li Wenyuan,
Zhang Lin
Publication year - 2018
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.2018.5730
Subject(s) - cholesky decomposition , probabilistic logic , renewable energy , equivalent circuit , computer science , monte carlo method , electric power system , power flow , power (physics) , mathematical optimization , engineering , mathematics , voltage , eigenvalues and eigenvectors , electrical engineering , statistics , physics , quantum mechanics , artificial intelligence
The data is not always shared among sub‐networks due to concerns about information privacy or difficulties in synchronous information exchange. It is difficult or even impossible to obtain the probabilistic power flow (PPF) by the centralised analysis. In this paper, an equivalent model considering static power–frequency characteristics (SPFCs) and renewable uncertainties is proposed. In this model, SPFCs of equivalent loads and generators are derived to retain SPFCs of original external loads and generators, respectively. In addition, probabilistic characteristics and correlations of equivalent loads are formulated based on Cholesky decomposition to preserve original external renewable uncertainties. The proposed model is further applied to PPF and then PPF can be solved via Monte Carlo simulation method, which makes that the PPF results can be obtained when the detailed data of the original external network cannot be shared. Owing to efficiently preserving the external SPFCs and renewable uncertainties in the proposed equivalent model, the accuracy of PPF results can be guaranteed. Simulation results of IEEE 14‐bus and IEEE‐118 bus systems demonstrate the effectiveness and superiority of the proposed equivalent model and its applications to PPF, compared with existing well known equivalent models and their applications to PPF.