Long term profit maximization strategy for charging scheduling of electric vehicle charging station

Rabiee, Abdorreza; Ghiasian, Ali; Chermahini, Moslem Amiri

doi:10.1049/iet-gtd.2018.5592

Cited by 31 publications

(5 citation statements)

References 33 publications

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“…First, we constructed a model to predict the average queuing time before charging at each charging location for 30 minutes into the future. Several studies have proposed methods for optimal scheduling of charging to maximize charging operator profit while minimizing wait time and power grid impacts [33], [34]. However, these studies do not consider how drivers acting independently without the ability to reserve a timeslot for charging might use a forecast of queue times to identify the best station for their needs.…”

Section: Predictive Modelsmentioning

confidence: 99%

Leveraging Big Data and Coordinated Charging for Effective Taxi Fleet Electrification: The 100% EV Conversion of Shenzhen, China

Bauer

Zheng

Shaheen

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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vehicles (EVs), but long charging durations and limited charger availability have prevented rapid adoption. Leveraging over two weeks of high-resolution GPS and battery data from almost 20,000 EVs in the all-electric Shenzhen taxi fleet, we analyze the potential to improve fleet-wide operations by optimizing both the location and timing of vehicle charging. We construct machine learning models to predict travel time, queuing time at charging stations, and charge consumption by time of day. Contrary to the emphasis on charging station siting in the literature, we find that optimizing charging locations would have a relatively limited impact. Instead, providing drivers with better real-time information about queuing times at charging stations, and enabling flexibility in battery charge during shift changes could reduce down-time per vehicle by over 30 minutes per day, while increasing the number of economically viable charging stations by over 50%. Moreover, taking full advantage of break periods and nighttime to charge could reduce downtime per vehicle by over one hour per day, reducing revenue losses due to charging by roughly 90%. These results are verified with evidence from real-time charging station data and driver shift-change data. Policy recommendations from this study include establishing citywide open data platforms to integrate real-time data on vehicle trajectory, battery charge, and charger availability, and providing drivers and companies with training on best charging practices. As a number of cities worldwide move toward fully electrified taxi fleets, this analysis has large-scale implications for decarbonized, cleaner urban areas.

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Section: Predictive Modelsmentioning

confidence: 99%

Leveraging Big Data and Coordinated Charging for Effective Taxi Fleet Electrification: The 100% EV Conversion of Shenzhen, China

Bauer

Zheng

Shaheen

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…In (Zhang, You, and Cai 2018) the number of EVs that leave the EVS without charging has been minimized, with an optimal pricing approach has been applied in the station. Long term profit of EVS owner and minimizing delay time of EVs have been discussed in (Rabiee, Ghiasian, and Chermahini 2018). EVS owner has the control to set the charging process of queues.…”

Section: Introductionmentioning

confidence: 99%

Smart green charging scheme of centralized electric vehicle stations

Makeen

Memon

Elkasrawy

et al. 2021

International Journal of Green Energy

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This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process.The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively.

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“…The literature on EV charge control addresses a variety of topics such as smart charging for cost minimisation [16], ancillary service provision [17], and admission control/queuing [18]. To solve these problems, a large number of distributed and centralised control schemes were proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimal load management strategy for large electric vehicle charging stations with undersized charger clusters

Gümrükcü

Ponci

Monti

et al. 2021

IET Electrical Syst in Trans

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This study proposes a load management strategy for parking and charging facilities with the capacity to serve several hundreds of electric vehicles. The strategy is built upon two assumptions on power distribution systems of large charging stations: i) they are configured as clusters, each comprising a number of charging units for reduced cabling complexity, ii) the power delivery components (such as feeders and circuit breakers) of individual clusters are sized for load factors smaller than 100% to reduce the capital costs. Unless controlled, the load demand can concentrate into particular cluster(s) whereas other clusters can still tolerate additional demand. This may lead to avoidable load interruptions and, thus, reduced energy provision. To address this issue, a load management strategy that optimises the distribution of vehicles across the clusters and their charging profiles is proposed. The strategy is compared in simulation with a benchmark strategy in different commercial parking lot scenarios. The results demonstrate that the optimal management achieves identical demand fulfilment rates despite more pronounced load factor limitations as compared to the benchmark strategy. This can enable further reduction in system component sizing. In the tested scenarios, the proposed strategy leads to increased long term profits ranging between 12% and 43%.

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Long term profit maximization strategy for charging scheduling of electric vehicle charging station

Cited by 31 publications

References 33 publications

Leveraging Big Data and Coordinated Charging for Effective Taxi Fleet Electrification: The 100% EV Conversion of Shenzhen, China

Leveraging Big Data and Coordinated Charging for Effective Taxi Fleet Electrification: The 100% EV Conversion of Shenzhen, China

Smart green charging scheme of centralized electric vehicle stations

Optimal load management strategy for large electric vehicle charging stations with undersized charger clusters

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