This paper proposes a methodology to utilize support vector machines (SVM) as a regressor tool for predicting market power. Both the companies, i.e., Generation (Gencos) and the Distribution (Discos), can utilize this tool to forecast market power on their perspective. Attributes and criterion are to be chosen properly to classify market power. In this paper, the effectiveness of SVM technique in predicting market power is formulated. Independent system operator (ISO) can also use this tool as regressor and it is discussed elaborately. Both linear and nonlinear kernels are compared. Nodal must run share (NMRS) is used as an index for predicting market power. A sample of three-bus system consisting of two generators and one load/two loads is used to illustrate the study. © Springer Science+Business Media Singapore 2016.

Prabhakar Karthikeyan S

Department of Electrical Engineering

School of Electrical Engineering

Vellore Campus

Shafeeque Ahmed K

Fathima F

Ananthavijayan R

Sahoo S.K

Naidu R.C.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Prediction of Market Power Using SVM as Regressor Under Deregulated Electricity Market

Advances in Intelligent Systems and Computing Proceedings of Fifth International Conference on Soft Computing for Problem Solving

This paper presents an approach to the application of Support Vector Machines (SVM) in estimating market power with the perspective of generation companies (Gencos). By choosing proper attributes and criterion for classification, SVM can be effectively used as a tool for market power estimation in the deregulated electricity market. In this paper, an analysis has been made to infer how effective the SVM technique is in predicting whether market power can be exercised by an entity or not. The person who is handling the technique i.e. Gencos decides the attributes chosen and the classifying criterion. Nodal Must Run Share Index is used in estimating market power. A simple three bus system with two generators and one load/two loads is chosen for the study. Performance of both linear and non-linear kernels is also compared. © Springer India 2015.

Lecture Notes in Electrical Engineering Power Electronics and Renewable Energy Systems

Application of SVM as Classifier in Estimating Market Power Under Deregulated Electricity Market

The concept of market power has gained its importance after the electric power industry started a process of transition and restructuring since early 1990s or so. In this competitive electricity market, the operational and the physical constraints of the network possess major threat to the market by the generation companies in exercising its market power. This paper presents a comprehensive review on market power with various indices which were used in market power analysis and the evolution of research and development in the field of market power. The literature work presented in this paper has been divided into various sections to facilitate the upcoming researchers who carry out their research in the area of market power under various environments. © 2012 Elsevier Ltd. All rights reserved.

International Journal of Electrical Power & Energy Systems

A review on market power in deregulated electricity market

Genetic algorithm based support vector machine for on-line voltage stability monitoring

Expert Systems with Applications

An effective Power Quality classifier using Wavelet Transform and Support Vector Machines

2014 IEEE National Conference on Emerging Trends In New & Renewable Energy Sources And Energy Management (NCET NRES EM)

Comparison of SVPWM and SVM controlled smart VSI fed PMSG for wind power generation system

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Proceedings of Fifth International Conference on Soft Computing for Problem Solving
Publisher	Data powered by TypesetSpringer Singapore
ISSN	2194-5357
Open Access	0