Open AccessAlleviation of VAr Impact on Critical Loading Margin with Redispatch in Deregulated Power Systems
Author(s) -
A. N. Venkateswarlu,
T. Kalyan Ram,
Sangameswara Raju.P
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.8.11534
Subject(s) - electric power system , computer science , margin (machine learning) , schedule , generator (circuit theory) , reliability engineering , power flow , stability (learning theory) , operator (biology) , power (physics) , control theory (sociology) , term (time) , point (geometry) , task (project management) , control (management) , engineering , mathematics , biochemistry , physics , chemistry , geometry , repressor , quantum mechanics , systems engineering , artificial intelligence , transcription factor , gene , operating system , machine learning
The stability management under deregulated environment has become typical task to the system due to random nature of load pattern and generation schedules. In addition, the regular uncertainties in system operation like line outage, generator outage or change in loading level are also causing to change in stability as well as security margins significantly. In order to manage transmission system security, the system operator can go for redispatch as a short term solution. In this article, an attempt is made to clear reactive power loading (VAr) impact on voltage instability margin or Critical Loading Margin (CLM). An Interior Point –Optimal Power Flow (IP-OPF) is applied to make system secured under (N-1) line contingencies. Using this secured schedule, the CLM is computed using Continuous Power Flow (CPF) for the two operating scenarios i.e., without VAr and with VAr loading on the system. The case study is simulated on IEEE 14-bus test network and outcome is validating that, the redispatch can also be apt for CLM enhancement even under contingencies as short term solution for stability management in real time.