Optimal scheduling of electric vehicles car-sharing service with multi-temporal and multi-task operation

Lai, Kexing; Chen, Tao; Natarajan, Balasubramaniam

doi:10.1016/j.energy.2020.117929

Cited by 28 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They solve it by applying mixed integer linear programming (MILP). Lai et al [51] propose an optimization model based on MILP for optimal routing and charging scheduling of electric vehicles in a carsharing service, considering a multi-temporal and multi-task operation. The proposed model formulates the decision-making process for various electric vehicle states (i.e., charging, parking, transporting), as well as additional constraints representing state transitions, working hour requirements, and neutral energy position of batteries.…”

Section: Sustainability and Smart Urban Mobilitymentioning

confidence: 99%

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Ghorbani

Herrera

Ammouriova

et al. 2022

Future Transportation

View full text Add to dashboard Cite

Urban logistics consumes a large portion of energy resources worldwide. Thus, optimization algorithms are used to define mobility modes, vehicle fleets, routing plans, and last-mile delivery operations to reduce energy consumption such as metaheuristics. With the emergence of smart cities, new opportunities were defined, such as carsharing and ridesharing. In addition to last-mile delivery, these opportunities form a challenging problem because of the dynamism they possess. New orders or ride requests could be placed or canceled at any time. Further, transportation times might evolve due to traffic conditions. These dynamic changes challenge traditional optimization methods to propose solutions in real-time to large-scale energy-optimization problems. Thus, a more `agile optimization’ approach is required to provide fast solutions to optimization problems when these changes occur. Agile optimization combines biased randomization and parallelism. It provides `good’ solutions compared to solutions found by traditional optimization methods, such as in-team orienteering problems. Additionally, these solutions are found in short wall clock, real-time.

show abstract

Section: Sustainability and Smart Urban Mobilitymentioning

confidence: 99%

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Ghorbani

Herrera

Ammouriova

et al. 2022

Future Transportation

View full text Add to dashboard Cite

show abstract

“…Multiple if‐else statements need to be formulated and configured into the optimization model. This subsection provides a summary of formulations of involved if‐else statements in this model 19 …”

Section: Optimization Modelmentioning

confidence: 99%

“…For instance, Chen et al develop an optimization model to determine the routing selection and charging management of an EV fleet in car‐sharing business to improve EV energy efficiency and riders' welfare simultaneously 18 . Multitemporal and multitask operations are further incorporated into the EV‐based car‐sharing scheduling problem in Reference 19. Iacobucci et al discuss the effects of V2G technology on the performance of the EV‐based car‐sharing business model in Reference 20.…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

Wang

Tang

Lai

et al. 2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Summary With the increasing market share of electric vehicles (EVs), optimizing the planning decisions of charging stations becomes critical to support long‐distance traveling. Besides, the booming car‐sharing industry provides an alternative business model to enhance the utilization of vehicle resources. Recently, several car‐sharing enterprises have launched the collaborations with charging station construction companies to construct their own charging facilities, which calls for the co‐optimization of charging station planning and scheduling of EVs in car‐sharing business. Therefore, this paper develops an optimization model for electric vehicle charging stations placements and type selections, considering routing selection and charging management of an electric vehicle fleet in car‐sharing business. Specifically, a stochastic programming framework is exploited to incorporate a variety of operating scenarios, which reflect the uncertainties of riders' requirements, drivers' itineraries, traffic flow information, and electricity prices. The proposed model aims to improve the welfare of all riders and reduce total cost of fulfilling riders' pickup and delivery requirements simultaneously, in which determining the optimal locations and types of charging facilities is required. Moreover, portfolio management is integrated into the optimization model to mitigate investment risk. After a series of reformulation, the proposed optimization model can be recast into a mixed‐integer linear programming problem. Using two transportation networks, the effectiveness of the proposed model is validated in numerical studies. It finds that the optimal electric vehicle charging station planning strategy is determined according to the operator's preferences, electricity prices, and installation budgets. Moreover, diversity within operating scenarios yields the positive value‐of‐stochastic‐solution that demonstrates the necessity of implementing stochastic programming.

show abstract

“…Aside from power lines and pipelines that interconnect electricity, heating, gas and water facilities, road transportation systems act as an indispensable sector that delivers a variety of materials, services and merchandise to those infrastructures. Incorporating the geographical information and traffic conditions into energy system operation has been discussed in [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Robustness Assessment of Hetero-Functional Graph Theory Based Model of Interdependent Urban Utility Networks

Munikoti,

Lai,

Natarajan

2020

Preprint

Self Cite

View full text Add to dashboard Cite

The increasing urban population imposes a substantial and growing burden on the supporting infrastructure, such as electricity, water, heating, natural gas, road transportation, etc. This paper presents a Hetero-functional graph theory (HFGT) based modeling framework for these integrated infrastructures followed by an analysis of network robustness. The supporting infrastructures along with the infrastructure repair facilities are considered. In contrast to conventional graph representations, a weighted HFGT model is used to capture the system processes and mutual dependencies among resources. To assess robustness of the inter-dependent networks, impacts of complete/partial and random/targeted attacks are quantified. Specifically, various contingency scenarios are simulated and the vulnerability of the network is evaluated. Additionally, several robustness metrics are proposed to provide a comprehensive evaluation of system robustness. The proposed weighted HFGT modeling and robustness assessment approach is tested using a synthetic interdependent network, comprising of an electrical power system, a water network, a district heating network, a natural gas system and a road transportation network. Results demonstrate that system robustness can be enhanced via securing system information and mitigating attack strength.

show abstract

Optimal scheduling of electric vehicles car-sharing service with multi-temporal and multi-task operation

Cited by 28 publications

References 17 publications

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

Robustness Assessment of Hetero-Functional Graph Theory Based Model of Interdependent Urban Utility Networks

Contact Info

Product

Resources

About