Application of Quantum Genetic Algorithm for Thermal Generation Unit Commitment
| Autor: | Peng-Hsiang Wang, 王鵬翔 |
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| Rok vydání: | 2011 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 99 Nowadays, Taiwan electric power energy is mainly generated by thermal units. For example, the energy production and purchased for Taiwan Power Company in 2009, the total electric power production by thermal unit is about 75.3% in which 50% is generated by Taiwan Power Company itself and 25.3% by others. For this large number of thermal units currently operating in the power system, optimal unit and commitment schedules to save the total cost is significant importance.The unit commitment is involving different constraints, for example the unit start-up and shut-down schedules to meet the power demand at minimum cost. The other necessary constraints to satisfy the commitment schedules are also required. This thesis combines Quantum Algorithm and Genetic Algorithm to present a new algorithm, called Quantum Genetic Algorithm (QGA). The Quantum Genetic Algorithm uses the coding method of quantum probability vector, and also use the quantum bit and quantum superposition at the same time. The superposition can let it express more state. The probability expression characteristic can be expresses the solution state by certain probability. It can raise the ability of optimal solution.Three research cases have been studied and analyzed in the thesis. The optimal commitment for these three cases which involves six, ten and twenty thermal units in the system have been carried out to find the best operation cost over 24-hour period. The result which compare with other methods, i.e. Dynamic Programming and Genetic Algorithm show Quantum Genetic Algorithm more useful and efficient in short-term unit commitment. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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