Optimal dispatch of electrified autonomous mobility on demand vehicles during power outages

Abstract

The era of fully autonomous, electrified taxi fleets is rapidly approaching, and with it the opportunity to innovate myriad on-demand services that extend beyond the realm of human mobility. This project envisions a future where autonomous plug-in electric vehicle (PEV) fleets can be dispatched as both a taxi service and a source of on-demand power serving customers during power outages. We develop a PDE-based scheme to manage the optimal dispatch of an autonomous fleet to serve passengers and electric power demand during outages as an additional stream of revenue. We use real world power outage and taxi data from San Francisco for our case study, modeling the optimal dispatch of several fleet sizes over the course of one day; we examine both moderate and extreme outage scenarios. In the moderate scenario, the revenue earned serving power demand is negligible compared with revenue earned serving passenger trips. In the extreme scenario, supplying power accounts for between $1 and $2 million, amounting to between 32% and 40% more revenue than is earned serving mobility only, depending on fleet size. While the overall value of providing on-demand power depends on the frequency and severity of power outages, our results show that serving power demand during large-scale outages can provide a substantial value stream, comparable to the value to be earned providing grid services.

An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

“…In [34], an optimal SAEV dispatch method during power outages was presented. They found that grid services can become a large source of revenue for a SAEV system during extreme grid outage events.…”

Computation and implementation of an optimal mean field control for smart charging

“…It has been successfully applied in [18], where the aggregate charging power of PEVs is adapted to highly intermittent renewable power, and in [19] to perform renewable power tracking. The optimal control of PDEs for smart charging has been studied in a linear optimization framework in [20], where PEV fleets provide regulation and vehicle-to-grid (V2G) services, and in [21] where the optimal dispatch of an autonomous fleet of taxis is analyzed to serve passengers and electric power demand during outages. The optimal control of PDEs is adopted in a linear quadratic framework in [22], combined with Model Predictive Control (MPC) techniques, to solve the optimization problem of smart charging in real time.…”

An Artificial-Neural-Network-Based Model for Real-Time Dispatching of Electric Autonomous Taxis

“…(2) Dispatching based on first-come-first-served queueing theory was adopted in Zhang and Pavone [18] and Jäger et al [12]. (3) The dispatch problem has been formulated and solved as optimization problems with various objectives, such as maximizing profit [19]- [20]; minimizing total operating cost [21]; minimizing total idle travel distance [22]- [23]; minimizing the total pickup distance [17], [24]; and maximizing the number of customers served [25]- [26]. Major ride-hailing platforms, such as Uber, Lyft and DiDi, have collected requests within a short time window and solved the optimization problem at the end of each time window, called ride request batching [24].…”

“…First, we propose an optimization-based dispatch model that considers the trade-off between taxi system efficiency and customer equity. Previous studies have considered multiple objectives associated with taxi operators and customers either in the objective function (e.g., [29]- [30]) or as a constraint [20]. However, the trade-off between taxi travel time for pickup and request waiting time has not been explicitly examined.…”