Optimal Scheduling for Electric Vehicle Charging With Discrete Charging Levels in Distribution Grid

Sun, Bo; Huang, Zhe; Tan, Xiaoqi; Tsang, D.H.K.

doi:10.1109/tsg.2016.2558585

Cited by 150 publications

(57 citation statements)

References 24 publications

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“…For instance, a well-coordinated charging control of EVs can guarantee that the total power consumption is within the capacity limit of power distribution network transformers [26], which thus positively contribute to the planning of infrastructures in energy systems.…”

Section: Key Decisions To Coordinate Afod and Amodmentioning

confidence: 99%

Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

Tan

Leon-Garcia

2018

2018 4th International Conference on Universal Village (UV)

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With the rise of transportation electrification, autonomous driving and shared mobility in urban mobility systems, and increasing penetrations of distributed energy resources and autonomous demandside management techniques in energy systems, tremendous opportunities, as well as challenges, are emerging in the forging of a sustainable and converged urban mobility and energy future. This paper is motivated by these disruptive transformations and gives an overview of managing autonomous mobility and energy services in future smart cities. First, we propose a three-layer architecture for the convergence of future mobility and energy systems. For each layer, we give a brief overview of the disruptive transformations that directly contribute to the rise of autonomous mobility-on-demand (AMoD) systems.Second, we propose the concept of autonomous flexibility-on-demand (AFoD), as an energy service platform built directly on existing infrastructures of AMoD systems. In the vision of AFoD, autonomous electric vehicles provide charging flexibilities as a service on demand in energy systems. Third, we analyze and compare AMoD and AFoD, and we identify four key decisions that, if appropriately coordinated, will create a synergy between AMoD and AFoD. Finally, we discuss key challenges towards the success of AMoD and AFoD in future smart cities and present some key research directions regarding the system-wide coordination between AMoD and AFoD.

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Section: Key Decisions To Coordinate Afod and Amodmentioning

confidence: 99%

Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

Tan

Leon-Garcia

2018

2018 4th International Conference on Universal Village (UV)

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“…Distinct from conventional loads, PEV loads are stochastic and impulsive, which means they are of high power density and vary in a fast and discrete manner. Prior works have shown that these load characteristics will result in negative impacts on the power grid, including disruptively varying voltage profiles along the feeder and overloading of service transformers [4,6,7,8,9,10]. This will consequently affect the operating state of grid asset and induce asset depreciation over the long term.…”

Section: Introductionmentioning

confidence: 99%

An integrated algorithm for evaluating plug-in electric vehicle’s impact on the state of power grid assets

Mao

Gao

Wang

2019

International Journal of Electrical Power & Energy Systems

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Plug-in Electric Vehicles (PEV) exert an increasingly disruptive influence on power delivery systems with penetration surge in the past decade. Therefore, accurately assessing their impact plays a crucial role in managing grid assets and maintaining power grids' reliability. However, PEV loads are stochastic and impulsive, which means they are of high power density and vary in a fast and discrete manner. These load characteristics make conventional assessment methods unsuitable. This paper proposes an algorithm, which captures the intertemporal response of grid assets and allows fast assessment through an integrated interface. To realize these advantageous features, we establish analytical models for two generic classes of grid assets (continuous and discrete operating assets) and recast their cost functions in the statistical settings of PEV charging. Distinct from simulation-based methods, the proposed method is analytical, and thus greatly reduce the computation resources and data required for accurate assessment. The effectiveness of the proposed algorithm has been demonstrated on a set of power distribution networks in Columbus metropolitan area, in comparison with the conventional assessment methods.

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“…Every EV treated as a separate entity [53,93,129,144,146,[152][153][154][155] Probability density function (PDF) of battery charging load [53] n k+ approximated as a Gaussian distribution [85] 38…”

Section: Methods Of Treatment Of Individual Ev Referencesmentioning

confidence: 99%

“…In contrast, the least laxity first (LLF) algorithm calculates the laxity instead of the urgency factor for finding the appropriate timeslots. The major drawback of these algorithms is that every EV needs to be treated separately, which results in a large runtime and large CPU memory requirements due to a large number of variables involved [31,152,155]. Table 2.33 Existing charging strategies in the literature…”

Section: Methods Of Treatment Of Individual Ev Referencesmentioning

confidence: 99%

“…Finds the time-slots in which each of the n k+ EVs need to be charged [31,93,155] Calculates the urgency factor of charging [31,67,155] The least laxity first (LLF) algorithm calculates the laxity [34,157,158] A SOC and laxity-based charging/discharging [34] To overcome the drawback, the authors in [34] have proposed a SOC and laxity-based charging/discharging. It divides the entire SOC-laxity plane into several groups as in Fig.…”

Section: Charging Strategies Referencesmentioning

confidence: 99%

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PV-based EV charging station with vehicle-to-grid services for business premises

Islam¹

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The transportation sector is the second major contributor to the global greenhouse gas (GHG) emissions, next to electricity generation sector. GHG emissions are projected to grow in the future due to the over-reliance on fossil fuels for both electricity generation and transportation sectors.However, the growth can be stalled by gradually introducing electric vehicles (EVs) as they produce less GHG compared to their conventional counterparts. Despite that, their proliferation has been stunted by a completely new set of challenges in addition to a very high capital cost. These challenges include low driving ranges, scarcity of electric vehicle charging stations (EVCS), long charging time and inability to provide charging service readily. Nevertheless, some of these challenges can be addressed by installing EVCS at business premises (i.e., offices, universities, etc.) and augmenting EVCS with onsite PV and battery energy storage (BES). In that case, the impacts of EV charging on the grid will be less significant; charging services will be available readily, and GHG emissions growth can be reduced significantly. Moreover, EVCS, under suitable conditions, would be able to provide vehicle-to-grid (V2G) services, which makes EVCS more attractive.Despite such prospective opportunities, offhand planning and operational planning of EVCS would deem them unachievable. In addition, the inherent intermittency of PV, and EV and grids loads will pose considerable hindrance on availing those benefits.Hence, this research focusses on an EVCS with onsite PV and BES, in the perspective of planning and operational planning including V2G services. The cornerstone of the planning and operational planning is an appropriate EV load model that captures the uncertainties of EV charging. In addition, the EV load model reflects the grid voltage and EV battery's state of charge (SOC)-dependency of EV charging. Moreover, it accounts for the diversity of the EV population embodied by market shares, battery sizes, charging levels, and charging voltages, currents, and power factors. Furthermore, it is scalable to facilitate seamless assimilation of a large EV population. Therefore, a new EV load model is first proposed with MATLAB/Simulink validation reflecting the factors above along with the recommendations by the standard BS EN 61851-23:2014. This EV model is then incorporated into the planning activities, which include four chronological exercises. Firstly, the impact (i.e., grid voltage, current, losses, etc.) of the EV charging on the grid at different locations, namely home, office, public charging, is investigated considering the uncertainties. Secondly, the optimal location of the PV and BES based EVCS is divulged regarding the reduction of the impact and costs involved while enhancing the charging quality of service (QoS). Thirdly, a suitability analysis is performed for the obtained optimal location to find the most suitable combination of the grid upgrading, PV deployment, and BES iii deployment to satisfy the QoS, ...

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Optimal Scheduling for Electric Vehicle Charging With Discrete Charging Levels in Distribution Grid

Cited by 150 publications

References 24 publications

Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

An integrated algorithm for evaluating plug-in electric vehicle’s impact on the state of power grid assets

PV-based EV charging station with vehicle-to-grid services for business premises

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