Determining the Location of Charging Station for a One-Way Electric Car-Sharing System Under Demand Uncertainty
YAN (TU Delft - Civil Engineering & Geosciences)
O. Cats – Graduation committee member (TU Delft - Transport and Planning)
J. Gao – Mentor (TU Delft - Transport, Mobility and Logistics)
M. Y. Maknoon – Mentor (TU Delft - Transport and Logistics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This thesis explores the optimization of charging station location within a one-way electric car-sharing system, addressing the inherent challenges of demand uncertainty. We introduce a novel deep learning-based stochastic programming framework (LMSP Framework) to tackle this issue. This framework integrates two key components:
1. A deep learning model (LSTM-MLP-MDN), composed of Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and Mixture Density Network (MDN) architectures, which predicts the probability distribution of traffic demand using historical data.
2. A two-stage stochastic programming model, designed to strategically optimize the locations of charging stations and the initial number of vehicles at each station under demand uncertainty.
The primary goal of this framework is to improve the profitability and operational efficiency of the car-sharing system by optimizing both the location and capacity of charging stations, effectively solving the Charging Station Location Problem (CSLP).
We validate the effectiveness, adaptability, and feasibility of our framework through a comprehensive case study in Manhattan, utilizing historical traffic data to ensure the reliability of the deployment plan. Our results demonstrate that integrating deep learning techniques with stochastic programming significantly enhances both the accuracy of demand forecasting and the consistency of resource allocation in the optimization process for charging station locations. Specifically, the LMSP Framework achieves higher operational efficiency in the short term, as evidenced by superior metrics like Demand Satisfaction Ratio (DSR) and Charging Station Utilization Rate (CSU) compared to traditional methods. This ensures more balanced and efficient resource allocation across different demand scenarios. However, traditional approaches tend to perform better in short-term financial indicators, such as profit and Return on Investment (ROI), as they employ more aggressive resource allocation strategies based on higher demand forecasts. Despite this, the LMSP Framework's focus on operational efficiency positions it as a more viable option for long-term profitability and user satisfaction, offering a sustainable solution for urban mobility.
Furthermore, our analysis provides valuable recommendations for future charging station deployments. These findings have important implications for the planning and operation of electric car-sharing systems, potentially contributing to more sustainable and efficient urban mobility solutions for all stakeholders involved.