Optimal choice and allocation of distributed generations using evolutionary programming

In this paper, evolutionary programming (EP) is proposed to determine the optimal choice and allocation of multi-type distributed generations (DG) to enhance power transfer capability and minimize system power losses of power transactions between source and sink areas in power systems. The optimal a...

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Main Authors: Jomthong R., Jirapong P.
Format: Conference Proceeding
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79955959855&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/43166
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-431662017-09-28T06:51:18Z Optimal choice and allocation of distributed generations using evolutionary programming Jomthong R. Jirapong P. In this paper, evolutionary programming (EP) is proposed to determine the optimal choice and allocation of multi-type distributed generations (DG) to enhance power transfer capability and minimize system power losses of power transactions between source and sink areas in power systems. The optimal allocation includes the optimal type, size, and location. Two types of DG including photovoltaic (PV) and wind turbine (WT) are used in this study. The objective function is formulated as maximizing the benefit to cost ratio. The benefit means increasing in total transfer capability (TTC) with deducting system losses while the costs are the investment and operating costs of the selected DG units. Power transfer capability determinations are calculated based on the optimal power flow (OPF) technique. Test results on the modified IEEE 30-bus system show that the proposed EP can determine the optimal choice and allocation of DG to achieve the best TTC in the power system with the highest benefit to cost ratio. 2017-09-28T06:51:18Z 2017-09-28T06:51:18Z 2010-12-01 Conference Proceeding 2-s2.0-79955959855 10.2316/P.2010.701-067 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79955959855&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43166
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description In this paper, evolutionary programming (EP) is proposed to determine the optimal choice and allocation of multi-type distributed generations (DG) to enhance power transfer capability and minimize system power losses of power transactions between source and sink areas in power systems. The optimal allocation includes the optimal type, size, and location. Two types of DG including photovoltaic (PV) and wind turbine (WT) are used in this study. The objective function is formulated as maximizing the benefit to cost ratio. The benefit means increasing in total transfer capability (TTC) with deducting system losses while the costs are the investment and operating costs of the selected DG units. Power transfer capability determinations are calculated based on the optimal power flow (OPF) technique. Test results on the modified IEEE 30-bus system show that the proposed EP can determine the optimal choice and allocation of DG to achieve the best TTC in the power system with the highest benefit to cost ratio.
format Conference Proceeding
author Jomthong R.
Jirapong P.
spellingShingle Jomthong R.
Jirapong P.
Optimal choice and allocation of distributed generations using evolutionary programming
author_facet Jomthong R.
Jirapong P.
author_sort Jomthong R.
title Optimal choice and allocation of distributed generations using evolutionary programming
title_short Optimal choice and allocation of distributed generations using evolutionary programming
title_full Optimal choice and allocation of distributed generations using evolutionary programming
title_fullStr Optimal choice and allocation of distributed generations using evolutionary programming
title_full_unstemmed Optimal choice and allocation of distributed generations using evolutionary programming
title_sort optimal choice and allocation of distributed generations using evolutionary programming
publishDate 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79955959855&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/43166
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