| Summary: | 碩士 === 國立澎湖科技大學 === 電機工程系電資碩士班 === 105 === Abstract
In recent years, rising environmental awareness has contributed to the construction of renewable energy power generation facilities, the use of electric cars, and a gradual reduction in the use of petrochemical fuel. In this study, the bus transportation system of the Penghu Islands in Taiwan was selected as the study target; diesel buses were substituted with battery-powered hybrid buses to meet designed bus schedules and routes, and renewable energy (i.e., wind and solar energy) was incorporated. In addition, the main transformer feeder loads and related demands and responses were assessed to optimize battery charge and discharge schedules and minimize electric bus manufacturing and electricity costs. Furthermore, bus system operations were simulated by substituting existing buses with electric buses, where surplus power stored in batteries was sold back to power grids and combined with wind and solar energy-generated power to achieve peak-shaving and valley-filling. This study used a genetic algorithm to optimize battery charge and discharge schedules. “Remaining battery power when initiating charging during daytime” and “remaining battery power when initiating power sell-back” were selected as the optimization parameters. This study also considered various situations, including decreasing battery costs and the increasing sales price of sellback electricity.
Two cases were employed in this study, and the results are as follows: Case 1 considered operation constraints and bus schedules, ignored wind and solar energy and feeder loads. Under existing battery costs and sell-back electricity sales prices set at three times the electricity price, and when the battery cost is set at 80% of the original cost and the sell-back electricity sales price at 150% of the original sales price, system optimization of Case 1 resulted in an overall manufacturing and electricity cost that was only 56.41% of the original cost. Case 2 incorporated wind and solar energy and main feeder loads and set battery charge and discharge restrictions based on three different daily average loads (i.e., ±10%, 20%, and 30%), effectively influencing the overall construction and electricity cost. According to the results, the smaller the adjustment range was, the more favorable the load peak-shaving and valley-filling effects and the higher the manufacturing and electricity cost became. The problem became markedly more complex when battery cost and sell-back electricity sales price were considered. On the basis of the case results, this study showed that the proposed method can be used to effectively develop electric bus systems.
|
|---|
