Artificial intelligence based algorithm for customers to take advantage of the real time pricing systems
Real Time Pricing (RTP) system is becoming more and more important for both electric utility industries and their customers. Many electric utilities and their customers have replaced the traditional Time of Use (TOU) system with the RTP system. This is a reality as well as a trend since the benefits...
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sg-ntu-dr.10356-44082023-07-04T15:09:48Z Artificial intelligence based algorithm for customers to take advantage of the real time pricing systems Hu, Anshuang. Lie, Tek Tjing School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Real Time Pricing (RTP) system is becoming more and more important for both electric utility industries and their customers. Many electric utilities and their customers have replaced the traditional Time of Use (TOU) system with the RTP system. This is a reality as well as a trend since the benefits under the RTP system are quite substantial economically. This thesis proposes a method of using the medium concept for the customers to take advantage of the RTP system. It uses the artificial intelligent techniques to realize the proposed idea. Fuzzy logic is utilized to set up the load forecast model. Genetic algorithm is utilized to determine the coefficients of the energy state equation, and a non-simplex method is utilized to fulfill the optimal distribution of the supply energy. Finally, the effect of the thermal storage energy loss (TSEL) to the optimization scheduling under the RTP system is investigated. Some simulation results are presented to illustrate the proposed method and some analyses of the related contents are put forward. Master of Engineering 2008-09-17T09:50:55Z 2008-09-17T09:50:55Z 2000 2000 Thesis http://hdl.handle.net/10356/4408 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Hu, Anshuang. Artificial intelligence based algorithm for customers to take advantage of the real time pricing systems |
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Real Time Pricing (RTP) system is becoming more and more important for both electric utility industries and their customers. Many electric utilities and their customers have replaced the traditional Time of Use (TOU) system with the RTP system. This is a reality as well as a trend since the benefits under the RTP system are quite substantial economically. This thesis proposes a method of using the medium concept for the customers to take advantage of the RTP system. It uses the artificial intelligent techniques to realize the proposed idea. Fuzzy logic is utilized to set up the load forecast model. Genetic algorithm is utilized to determine the coefficients of the energy state equation, and a non-simplex method is utilized to fulfill the optimal distribution of the supply energy. Finally, the effect of the thermal storage energy loss (TSEL) to the optimization scheduling under the RTP system is investigated. Some simulation results are presented to illustrate the proposed method and some analyses of the related contents are put forward. |
author2 |
Lie, Tek Tjing |
author_facet |
Lie, Tek Tjing Hu, Anshuang. |
format |
Theses and Dissertations |
author |
Hu, Anshuang. |
author_sort |
Hu, Anshuang. |
title |
Artificial intelligence based algorithm for customers to take advantage of the real time pricing systems |
title_short |
Artificial intelligence based algorithm for customers to take advantage of the real time pricing systems |
title_full |
Artificial intelligence based algorithm for customers to take advantage of the real time pricing systems |
title_fullStr |
Artificial intelligence based algorithm for customers to take advantage of the real time pricing systems |
title_full_unstemmed |
Artificial intelligence based algorithm for customers to take advantage of the real time pricing systems |
title_sort |
artificial intelligence based algorithm for customers to take advantage of the real time pricing systems |
publishDate |
2008 |
url |
http://hdl.handle.net/10356/4408 |
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1772826578856706048 |