Mobility-Aware Smart Charging of Electric Bus Fleets

Moradipari, Ahmadreza; Tucker, Nathaniel; Zhang, Tuo; Cezar, Gustavo; Alizadeh, Mahnoosh

doi:10.1109/pesgm41954.2020.9281897

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…Various IoT devices enable city managers to control the physical objects in real-time and provide intelligent information to citizens in terms of the traffic system, transportation, public safety, healthcare, smart parking, and smart agriculture, and so on. For example, some researchers propose a dynamic dispatcher for a fleet of electric buses based on the real-time traffic and weather data [23]. Due to the concerns of data privacy, smart infrastructures are moving to compute resources close to where data reside, which makes FL framework suitable for deployment.…”

Section: Smart Citymentioning

confidence: 99%

Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

Zhang¹,

Gao²,

He³

et al. 2021

Preprint

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Billions of IoT devices will be deployed in the near future, taking advantage of the faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the blooming of IoT devices, vast quantities of data that may contain private information of users will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly be challenging the traditional ecosystem of centralized over-the-cloud learning and processing for IoT platforms. Federated Learning (FL) has emerged as the most promising alternative approach to this problem. In FL, training of data-driven machine learning models is an act of collaboration between multiple clients without requiring the data to be brought to a central point, hence alleviating communication and storage costs and providing a great degree of user-level privacy. We discuss the opportunities and challenges of FL for IoT platforms, as well as how it can enable future IoT applications.

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Section: Smart Citymentioning

confidence: 99%

Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

Zhang¹,

Gao²,

He³

et al. 2021

Preprint

Self Cite

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“…Several works, such as Kontou et al [9], Moradipari et al [10], Chen et al [11,12], Raab et al [13], Qin et al [14] have been done on the optimal charging schedules for electric buses to reduce the charging costs or emissions. For example, in Moradipari et al [10] work a mixed-integer linear programming problem was formulated to find the optimal charging schedule for the EB fleet of Stanford University's Marguerite Shuttle, based on the electricity rates set by the local utility (PG&E) and given the preset physical infrastructure and timetables for the EB fleet. The authors also assume free on-site solar energy that the fleet dispatcher uses to charge/recharge the EB and is the first choice when solar energy is available.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Charging Schedule Planning for Electric Buses Using Aggregated Day-Ahead Auction Bids

Żółtowska

Lin

2021

Energies

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This study aims to plan a cost-minimizing charging schedule for electric buses with fast charging stations. The paper conceptualizes the problem as a three-stage procedure, which is oriented around the participation of an electric bus aggregator in a day-ahead energy auction. First, the aggregation stage determines the bid parameters of buses. With bid parameters, aggregated cost-minimizing charging plans are obtained in the second stage conceived as the hourly day-ahead auction. The disaggregation of hourly plans into feasible minutely charging schedules is the third stage. The main contribution is the formulation of mixed-integer linear programming aggregation models to determine charging availability expressed as minimum and maximum hourly energy requirements taking into account detailed, minutely characteristics and constraints of the charging equipment and the buses. No price forecasts are required, and the plans adjust to the wholesale prices of energy. Defining only a few aggregated bids parameters used in linear programming constraints and incorporating them into the auction model is another contributing factor of this paper, allowing the scheduling of storage-based participants economically. The proposed methodology has been verified on a recently published case study of a real-world bus service operated on the Ohio State University campus. We show that the auction-based charging of all 22 buses outperforms as-soon-as-possible schedules by 7% to even 28% of daily cost savings. Thanks to the aggregated bids, buses can flexibly shift charges between high- and low-price periods while preserving constraints of the charging equipment and timetables.

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“…Many of these electric bus fleet charging and operation coordination works, however, do not account for emissions, the addition of co-located storage, or electricity demand charges [16]. The methods in [17] and [18] minimize emissions but do not jointly optimize for electricity costs, vehicle operations, or integration of battery storage.…”

Section: Introductionmentioning

confidence: 99%

Towards a 24/7 Carbon-Free Electric Fleet: A Digital Twin Framework

Ribeiro,

Luke,

Martin

et al. 2024

Preprint

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This paper proposes a 24/7 Carbon-Free Electric Fleet digital twin framework for modeling, controlling, and analyzing an electric bus fleet, co-located solar photovoltaic arrays, and a battery energy storage system. The framework consists of forecasting modules for marginal grid emissions factors, solar generation, and bus energy consumption that are input to the optimization module, which determines bus and battery operations at minimal electricity and emissions costs. We present a digital platform based on this framework. For a case study of Stanford University's Marguerite Shuttle, the platform reduced peak charging demand by 99%, electric utility bill by $2779, and associated carbon emissions by 100% for one week of simulated operations for 38 buses. When accounting for operational uncertainty, the platform still reduced the utility bill by $784 and emissions by 63%.

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Mobility-Aware Smart Charging of Electric Bus Fleets

Cited by 12 publications

References 8 publications

Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

Optimal Charging Schedule Planning for Electric Buses Using Aggregated Day-Ahead Auction Bids

Towards a 24/7 Carbon-Free Electric Fleet: A Digital Twin Framework

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