充電站營運商之最佳價格策略

Abstract

隨著氣候變遷造成的環境影響，環保意識抬頭，各國紛紛制定能源轉型政策，降低溫室氣體排放的發展策略。以電動車取代燃油車降低溫室氣體排放，是能源轉型極為重要的一環。加速電動車之發展，首要之務即是布建友善之電動車使用環境，例如促進住宅、商業與公共停車空間廣布充電樁，或是設置電動車充電格位等。因此，城市的充電站規劃十分複雜，不僅牽涉到電力配置、電網容量、停車位等問題，還需考慮充電站的設置位置、充電樁的類型、數量及充電價格等因子。而充電需求預測、充電價格制訂及充電樁數量分配會互相影響，例如，充電需求隨充電價格變動，同時充電之需求預測則影響充電站的設置位置及充電樁的設置數量。然而，充電樁供給量及充電需求也會造成充電價格改變，因此，本研究目的為提出針對停車場之充電設施的最佳規劃，並透過需求預測與價格制定相互影響的關係找出讓充電站營運商可以獲得最大利潤之最佳價格策略。本研究以時空高斯過程 (Spatial-temporal Gaussian process) 建立的電動汽車充電需求預測模型來預測需求，模型中考慮人口數、興趣點個數及停車價格等因子，並建立混整數非線性規劃 (Mixed Integer Nonlinear Programing, MINLP) 的最佳化模型以求各充電站之最適充電價格。

With increasing of environmental awareness for dramatic climate changes, energy policy for cutting greenhouse gas emissions is urgent and critical for every country in the world. Using electric vehicles (EVs) to replace conventional gasoline cars is one of critical aspects to reduce the gas emissions to enhance the energy transition. To increase the use of EVs, an EV-user friendly charging infrastructure and attractive charging price are two important factors. With the consideration of the capacity of electrical power, the sites of charging points, and the numbers of AC/DC chargers, this study determines the pricing strategy and the amount of charging points based on the demand forecasts for EV charging infrastructure. The proposed model is to develop an optimal pricing strategy for charge point operators (CPOs) with the maximization of total profits. In this thesis, a spatiotemporal model is used to forecast the EV charging demands for parking lots. A Mixed Integer Nonlinear Programing (MINLP) model is constructed and formulated to calculate the best charging price of different types of EV charging in each charging station. A case study is presented to demonstrate the effectiveness of the proposed model.