Multiaggregator Collaborative Electric Vehicle Charge Scheduling Under Variable Energy Purchase and EV Cancelation Events

Gupta, Vishu; K, Srikanth Reddy; Kumar, Rajesh; Panigrahi, Bijaya Ketan

doi:10.1109/tii.2017.2778762

Cited by 55 publications

(45 citation statements)

References 18 publications

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“…We also assume there would always be some available charger(s) ready for deployment. In reality, the recharging of the chargers themselves [27,28] (includes battery for charging sensor nodes and battery for their movement, and whether or not the base station is the only source for providing energy to the chargers) is another issue that deserves further exploration, which will also be our future research topic.…”

Section: Discussionmentioning

confidence: 99%

A Genetic Approach to Solve the Emergent Charging Scheduling Problem Using Multiple Charging Vehicles for Wireless Rechargeable Sensor Networks

Cheng

2019

Energies

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Wireless rechargeable sensor networks (WRSNs) have gained much attention in recent years due to the rapid progress that has occurred in wireless charging technology. The charging is usually done by one or multiple mobile vehicle(s) equipped with wireless chargers moving toward sensors demanding energy replenishing. Since the loading of each sensor in a WRSN can be different, their time to energy exhaustion may also be varied. Under some circumstances, sensors may deplete their energy quickly and need to be charged urgently. Appropriate scheduling of available mobile charger(s) so that all sensors in need of recharge can be served in time is thus essential to ensure sustainable operation of the entire network, which unfortunately has been proven to be an NP-hard problem (Non-deterministic Polynomial-time hard). Two essential criteria that need to be considered concurrently in such a problem are time (the sensor’s deadline for recharge) and distance (from charger to the sensor demands recharge). Previous works use a static combination of these two parameters in determining charging order, which may fail to meet all the sensors’ charging requirements in a dynamically changing network. Genetic algorithms, which have long been considered a powerful tool for solving the scheduling problems, have also been proposed to address the charging route scheduling issue. However, previous genetic-based approaches considered only one charging vehicle scenario that may be more suitable for a smaller WRSN. With the availability of multiple mobile chargers, not only may more areas be covered, but also the network lifetime can be sustained for longer. However, efficiently allocating charging tasks to multiple charging vehicles would be an even more complex problem. In this work, a genetic approach, which includes novel designs in chromosome structure, selection, cross-over and mutation operations, supporting multiple charging vehicles is proposed. Two unique features are incorporated into the proposed algorithm to improve its scheduling effectiveness and performance, which include (1) inclusion of EDF (Earliest Deadline First) and NJF (Nearest Job First) scheduling outcomes into the initial chromosomes, and (2) clustering neighboring sensors demand recharge and then assigning sensors in a group to the same mobile charger. By including EDF and NJF scheduling outcomes into the first genetic population, we guarantee both time and distance factors are taken into account, and the weightings of the two would be decided dynamically through the genetic process to reflect various network traffic conditions. In addition, with the extra clustering step, the movement of each charger may be confined to a more local area, which effectively reduces the travelling distance, and thus the energy consumption, of the chargers in a multiple-charger environment. Extensive simulations and results show that the proposed algorithm indeed derives feasible charge scheduling for multiple chargers to keep the sensors/network in operation, and at the same time minimize the overall moving distance of the mobile chargers.

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Section: Discussionmentioning

confidence: 99%

A Genetic Approach to Solve the Emergent Charging Scheduling Problem Using Multiple Charging Vehicles for Wireless Rechargeable Sensor Networks

Cheng

2019

Energies

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“…Meanwhile, detailed information such as on-road EV charging demand and vehicular traffic is stored and processed in edge nodes. As the cloud node has a global overview of the SG, it can undertake delay-tolerant computing tasks that require systematic information [122]- [124]. For example, the cloud node can allocate power to each AG in the day-ahead power market while the AG performs the computing task of charging scheduling within the allocated power range using its local computing resource.…”

Section: F Computation Infrastructurementioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

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“…The process of EV scheduling is carried out in distinct phases, where the first phase deals with the scheduling of EVs, followed by a collaboration between aggregators where needed, in the second phase. As a mobility aware system is considered, the EV user specifies the state-of-charge (SOC) at the time the request is made, the source and destination of the EV, the route to be taken, the desired SOC and the estimated time of departure from the source [5]. Based on these parameters, the aggregator attempts to schedule the EV first at its own charging station and then through collaboration if necessary.…”

Section: Pvcs System and Problem Formulationmentioning

confidence: 99%

“…In addition, the estimated time of charging T D and T M are calculated based on whether the EV is being charged to the desired SOC (SOC D ) or to the minimum SOC required to reach the destination (SOC M ). The respective energy requirements are calculated based on the charging rate and the values of T D and T M [5]…”

Section: Non-collaborative and Collaborative Scheduling Set-upmentioning

confidence: 99%

Collaborative multi‐aggregator electric vehicle charge scheduling with PV‐assisted charging stations under variable solar profiles

Gupta

Kumar

et al. 2020

IET Smart Grid

Self Cite

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Electric vehicles (EVs) are on the path to becoming a solution to the emissions released by the internal combustion engine vehicles that are on the road. EV charging management integration requires a smart grid platform that allows for communication and control between the aggregator, consumer and grid. This study presents an operational strategy for PVassisted charging stations (PVCSs) that allows the EV to be charged primarily by PV energy, followed by the EV station's battery storage (BS) and the grid. Multi-Aggregator collaborative scheduling is considered that includes a monetary penalty on the aggregator for any unscheduled EVs. The impact of the PVCS is compared to the case with no PV/BS is included. A variation in the PV profile is included in the evaluation to assess its impact on total profits. Profit results are compared in cases of minimum, average and maximum PV energy output. The results indicate that the inclusion of penalties due to unscheduled EVs resulted in lowered profits. Further, the profits experienced an increase as the number of EVs scheduled through PV/BS increased, implying that a lesser percentage of EVs are scheduled by the grid when a greater amount of PV and battery energy are available.

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Multiaggregator Collaborative Electric Vehicle Charge Scheduling Under Variable Energy Purchase and EV Cancelation Events

Cited by 55 publications

References 18 publications

A Genetic Approach to Solve the Emergent Charging Scheduling Problem Using Multiple Charging Vehicles for Wireless Rechargeable Sensor Networks

A Genetic Approach to Solve the Emergent Charging Scheduling Problem Using Multiple Charging Vehicles for Wireless Rechargeable Sensor Networks

Energy and Information Management of Electric Vehicular Network: A Survey

Collaborative multi‐aggregator electric vehicle charge scheduling with PV‐assisted charging stations under variable solar profiles

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