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Study and development of models and resolution approaches for security-constrained optimal power flow problems

Grant number: 13/11726-2
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): September 01, 2013
Effective date (End): February 28, 2014
Field of knowledge:Engineering - Electrical Engineering - Power Systems
Principal researcher:Geraldo Roberto Martins da Costa
Grantee:Guilherme Guimarães Lage
Home Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

The object of this research proposal is the study and development of models and resolution approaches for Security-Constrained Optimal Power Flow (SC-OPF) problems. In the existing literature, many of the operational security representation approaches are too conservative, with high computational costs and/or even ineffective. In addition, the discrete nature of some controls is ignored. This research is methodologically founded on the analysis of power system operational security representation approaches and on the formulation and resolution of Nonlinear Programming (NLP) problems with discrete variables. To accomplish this, the main concepts on power system operational security and the resolution on NLP problems with discrete decision variables will be outlined by means of a background review on these subjects. Tests with the developed models and resolution approaches will be carried out with the IEEE power systems, with up to 300 buses. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FERNANDES, RICARDO A. S.; LAGE, GUILHERME G.; DA COSTA, GERALDO R. M. A Decision-making Framework Based on Artificial Neural Networks and Intelligent Agents for Transmission Grid Operation. ELECTRIC POWER COMPONENTS AND SYSTEMS, v. 44, n. 8, p. 883-893, 2016. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.