CPS: Small: Collaborative Research: Models and System-Level Coordination Algorithms for Power-in-the-Loop Autonomous Mobility-on-Demand Systems

CPS：小型：协作研究：功率在环自主按需移动系统的模型和系统级协调算法

基本信息

批准号：
1837125
负责人：
Mahnoosh Alizadeh
金额：
$ 20万
依托单位：
University of California-Santa Barbara
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837125&HistoricalAwards=false
关键词：
CPS Small Collaborative Research Models

项目摘要

The goal of this project is to investigate how self-driving, electric vehicles transporting passengers on demand (a system referred to as autonomous mobility-on-demand, or AMoD) can enable optimized, coupled control of the power and transportation networks. The key observation is that the AMoD technology will give rise to complex couplings between the power and transportation networks, namely couplings between charging demand and electricity prices as people move around a city. The hypothesis is that by exploiting such couplings through control and optimization, AMoD systems will lead to lower electricity generation costs and higher integration levels of intermittent renewable energy resources such as wind and solar, while providing more convenient transportation. The results of this project will provide guidelines to transportation stakeholders and policy-makers regarding the deployment of autonomous vehicles on a societal scale, benefitting the U.S. economy by fostering clean and efficient future transportation systems. This project will devise theoretical models and optimization tools for the characterization of the aforementioned couplings and for the system-level control of AMoD with the power network in the loop. The key technical idea is to cast the coupled power and transportation networks in the formal framework of flow optimization, whereby city districts, charging stations, and roads are abstracted as nodes and edges of a graph, and the movements of customers, vehicles, and energy are abstracted as flows over such a graph. This project will then devise a control framework to optimize over the decision variables, e.g., vehicles' routes, charging decisions, and power generation schedules.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目标是研究按需运输乘客的自动驾驶电动汽车（称为自主按需出行或 AMoD 的系统）如何实现电力和交通网络的优化、耦合控制。关键的观察结果是，AMoD 技术将引起电力和交通网络之间的复杂耦合，即人们在城市中移动时充电需求和电价之间的耦合。假设通过控制和优化利用这种耦合，AMoD 系统将降低发电成本，提高风能和太阳能等间歇性可再生能源的集成水平，同时提供更便利的交通。该项目的结果将为交通利益相关者和政策制定者提供有关在社会范围内部署自动驾驶汽车的指南，通过培育清洁高效的未来交通系统来造福美国经济。该项目将设计理论模型和优化工具，用于表征上述耦合以及环路电力网络 AMoD 的系统级控制。关键技术思想是将耦合的电力和交通网络构建在流优化的形式化框架中，将城区、充电站和道路抽象为图的节点和边，以及客户、车辆和能源的流动被抽象为这样一个图上的流。然后，该项目将设计一个控制框架来优化决策变量，例如车辆路线、充电决策和发电计划。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和技术进行评估，被认为值得支持。更广泛的影响审查标准。