| Summary: | 碩士 === 國立雲林科技大學 === 電機工程系 === 102 === This thesis investigates the problem that dispatches the power generation to achieve the lowest fuel cost, and the constraints must be satisfied. Because of the development and the improvement of industry in this century, the demand of electrical energy increases year by year. Most countries develop renewable energy because the source in the earth is limited. The renewable energy not only reduces the cost of thermal power generation but also reduces the pollution that exhaust gas causes. Thus, this thesis considers the hydropower, wind power and solar power in the power generation scheduling.
Different from the traditional problem of power generation scheduling, which only considers minimizing costs. This thesis considers the characteristic that how the change of atmospheric flows day and night affects the emission of exhaust gas. Due to the pollutants dispersed by the flowing of atmosphere more rapidly in the daytime and reduce the emission gas near the power plant. However, the serious pollution caused by atmosphere flow stagnating in the nighttime. This thesis studies on the best dispatch between economic and environment considering and combines two objective functions of generation cost and emission cost in daytime and nighttime.
Because there are uncertainties in the load demand, the available water in the reservoir, wind speed and radiation, this thesis builds uncertain models and a new fitness function to get the solution that use Differentiated-Particle Swarm Optimization of Virus-Evolutionary (D-VEPSO) method to meet the actual situation. Finally, 10 thermal power generating units, 7 equivalent hydroelectric power units (a pumped storage hydroelectric power units included), an equivalent wind turbine and an equivalent solar generator system are performed and the proposed method is compared with other intelligent algorithms. The result shows that the proposed method can get a better solution.
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