Open AccessReactive power optimisation considering wind farms based on an optimal scenario method
Author(s) -
Zhang Cong,
Chen Haoyong,
Ngan Honwing
Publication year - 2016
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.2016.0036
Subject(s) - monte carlo method , mathematical optimization , interior point method , computer science , wind power , ac power , electric power system , power (physics) , grid , control variable , point (geometry) , genetic algorithm , control (management) , voltage , control theory (sociology) , engineering , mathematics , statistics , physics , geometry , electrical engineering , quantum mechanics , machine learning , artificial intelligence
Uncertain reactive power optimisation incorporating wind power (RPOWP) is mainly solved by chance constrained programming (CCP) and robust programming (RP) at present. However, it is well‐known that CCP is a time‐consuming method due to usage of Monte Carlo simulation (MCS), and RP always gives conservative solutions with poor economy, for it conducts optimisation on the box sets without using the available distribution information. To overcome these merits, this study proposes a method called optimal scenario method (OSM), and it can deal with the uncertainties incorporated in RPOWP without using MCS, as well as utilise the distributions of wind power. It is implemented in two stages by using the interior point method (IPM) and genetic algorithm (GA). First, IPM is used to find the optimal wind power scenario by controlling continuous control variables, where the number of voltage overrun is the smallest in all scenarios and the average real power losses is also reduced. Second, GA is employed to reduce the times of constraints violation further by coordinating discrete control variables. Two test systems are analysed in case study, with results of IEEE14 system compared with CCP method, and IEEE57 system is tested to show the applicability of OSM to larger systems. It is concluded that the proposed OSM performs better than conventional method and holds ability to apply to larger grid.