Title page for etd-0605118-192051


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URN etd-0605118-192051
Author Yi-Ting Chen
Author's Email Address No Public.
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Department Electrical Engineering
Year 2017
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Application of Artificial Intelligence to Unit Commitment Problem Considering Priority List and Renewable Energy
Date of Defense 2018-06-21
Page Count 76
Keyword
  • Monte Carlo method
  • Genetic Algorithm(GA)
  • Historical Simulation Method
  • Unit Commitment(UC)
  • Artificial Bee Colony(ABC) Algorithm
  • Abstract Due to the global resources depletion and conception of Environmental awareness, application related to renewable energy were gradually being valued. The researches considering the renewable energy have increased in the recent year. Unit Commitment (UC) is a very important issue in the power system, which is the key to deciding electric price in the electric power market. Traditional generators in Unit Commitment included thermal units, nuclear units, or hydraulic power plants, excluding the renewable energy. However, according to the developed technique and development, the power generation would eventually be enough to consider it. Moreover, the abolition of the nuclear unit increased the demand for renewable energy. But the unstable power generation of renewable energy made the UC problem more complicated and would increase the difficulty computing solution in UC problem. Therefore, researches were working on enumerating the instability of renewable energy in recent years.
    In this thesis, some recent methods of numerical analysis were adopted to analyze the instability of renewable energy, in order to apply for UC. Because of the more constraints and condition determination, in this thesis we use the pure Artificial Intelligence Algorithm to solve the UC problem more efficiently by separating the problem into two parts, one is the on/off the unit matrix and the other is 24-hour power generation. At the end of case discussions, we simulate the improper power generation forecasting, end in the similar solution after the situation happened, proving the stability of the UC combining numerical renewable energy in.
    Advisory Committee
  • Wen-Kung Chang - chair
  • Yun-Hung liu - co-chair
  • Shih-Chieh huang - co-chair
  • Whei-Min Lin - advisor
  • Files
  • etd-0605118-192051.pdf
  • Indicate in-campus at 3 year and off-campus access at 5 year.
    Date of Submission 2018-08-01

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