Power Quality Assessment in Distribution Systems Embedded With Plug-In Hybrid and Battery Electric Vehicles

Gray, Matthew K.; Morsi, Walid G.

doi:10.1109/tpwrs.2014.2332058

Cited by 171 publications

(82 citation statements)

References 20 publications

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“…They discovered that PEV fast-charging stations may substantially aggravate steady-state voltage stability on some local nodes. Gray and Morsi [22] assessed the impact of electric vehicles on power quality in the distribution network. Under-and over-voltage deviations and transformer overloads were quantified using Monte Carlo simulation for different electric vehicle penetration scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Key Features of Electric Vehicle Diffusion and Its Impact on the Korean Power Market

et al. 2018

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Abstract:The market share of electric vehicles is growing and the interest in these vehicles is rapidly increasing in industrialized countries. In the light of these circumstances, this study provides an integrated policy-making package, which includes key features for electric vehicle diffusion and its impact on the Korean power market. This research is based on a quantitative analysis with the following steps: (1) it analyzes drivers' preferences for electric or traditional internal combustion engine (ICE) vehicles with respect to key automobile attributes and these key attributes indicate what policy makers should focus on; (2) it forecasts the achievable level of market share of electric vehicles in relation to improvements in their key attributes; and (3) it evaluates the impact of electric vehicle diffusion on the Korean power market based on an achievable level of market share with different charging demand profiles. Our results reveal the market share of electric vehicles can increase to around 40% of the total market share if the key features of electric vehicles reach a similar level to those of traditional vehicles. In this estimation, an increase in the power market's system generation costs will reach around 10% of the cost in the baseline scenario, which differs slightly depending on charging demand profiles.

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Section: Literature Reviewmentioning

confidence: 99%

Key Features of Electric Vehicle Diffusion and Its Impact on the Korean Power Market

et al. 2018

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“…[54,55,86,89] Line & substation loading [17,18,26,76,84,85,88,90,92] Transformer ageing [84,93] Loading margin [62] GHG emissions [6,78,86,94] Reliability [76] In order to do so, Monte Carlo simulation, high-performance computation, principal component analysis, scenario reduction algorithm, and statistical clustering to find representative feeders as shown in Table 2.12 have been most commonly employed techniques in the literature. High-performance computation [32] Clustering to find representative feeders [76] Principal component analysis [18] 22 Though these studies are comprehensive enough, quite a few numbers of additional aspects can be incorporated to advance the understanding of the impact of the EV charging. These aspects are,  The voltage and SOC dependency of the EV load as discussed in Section 2.2 must be taken into account.…”

Section: Step 2: Analyzing the Impact Of Ev Charging On The Gridmentioning

confidence: 99%

“…Therefore, there is a need for more extensive scheduling strategy more specifically designed for a large EV population in BC. and n D,k have been modeled by time-invariant Normal distributions in [138] and n k+ has been updated as per (18). In contrast, the uncertainties of n k+ for every timeslot has been modeled by time-variant Normal distribution with the help of central limit theorem in [85].…”

Section: Whether It Designed For Hc or Bc Or CCmentioning

confidence: 99%

“…For instance, referred to Table 1.1, assuming an average household owns 2.12 EV units [18], the hosting DNO would require to supply an additional 3 kW power for the Level 1 charging for at least 4.25 hours per day [6], which is almost 60% of the peak household load [18]. In terms of the supplied energy, it is equivalent to 12.75 kWh, which is 80% of the supplied 15.9 kWh household energy in Queensland [19].…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

“…Such a large EV load could impact the distribution networks adversely on the voltage deviation, voltage unbalance, line loading, power and energy losses, etc. [18]. Besides, the impact could be further aggravated if the peaks of EV load and regular load coincide with each other.…”

Section: Chapter 1 Introductionmentioning

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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Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO

Kasturi

Nayak

2019

Int Trans Electr Energ Syst

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Summary Charging electric vehicles (EVs) by the grid leads to unexpected spikes in load demand threatening the power system health. As EVs store energy, they can dispense power to meet peak load demands. This paper proposes a charging/discharging strategy to augment the utility of the EVs with a controlled schedule by considering an appropriate driving pattern. EVs are allowed to charge or discharge at the workplace or home which are connected to the same bus of a radial distribution system. Multiobjective multiverse optimization algorithm (MOMVO) is utilized to extract the best possible number of EVs and the bus at which they must be connected, not only to minimize the impact of EV charging/discharging on the grid but also to reduce the costs associated with the operation on behalf of both the EV owner and the utility company. The results obtained ensure both technical and economical appositeness of the proposed strategy.

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Power Quality Assessment in Distribution Systems Embedded With Plug-In Hybrid and Battery Electric Vehicles

Cited by 171 publications

References 20 publications

Key Features of Electric Vehicle Diffusion and Its Impact on the Korean Power Market

Key Features of Electric Vehicle Diffusion and Its Impact on the Korean Power Market

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

Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO

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