Network Impacts and Cost Savings of Controlled EV Charging

Verzijlbergh, Remco; Grond, Marinus O.W.; Lukszo, Zofia; Slootweg, J.G.; Ilić, Marija

doi:10.1109/tsg.2012.2190307

Cited by 181 publications

(98 citation statements)

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“…While the vehicle is plugged, battery charging (and, possibly, discharging) must be handled such that vehicle's battery is charged over a given threshold (90% in our tests) before the due date. Smart control of EV charging is subject to significant attention by researchers [13], due to its easy controllability and expected increase in EV penetration rates.…”

Section: Appliance Classesmentioning

confidence: 99%

Restricted Neighborhood Communication Improves Decentralized Demand-Side Load Management

Giusti

Salani

et al. 2014

IEEE Trans. Smart Grid

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Abstract-We address demand-side management of dispatchable loads in a residential microgrid by means of decentralized controllers deployed in each household. Controllers simultaneously optimize two possibly conflicting objectives: minimization of energy costs for the end user (considering a known, timedependent tariff) and stabilization of the aggregate load profile (load flattening). The former objective can be optimized independently by each controller. On the other hand, the latter could benefit from a communication infrastructure that allows the controllers to explicitly exchange information and coordinate.To study how different levels of communication pervasiveness affect system performance, we developed a realistic microsimulation environment accounting for the behavior of residents, dispatchable and non-dispatchable household loads, and the effects on the distribution network. We considered a generic model of communication among household controllers, not tied to any specific technology, and based on the partitioning of the households in a number of groups (neighborhoods). Controllers within the same neighborhood enjoy full connectivity, but cannot interact with controllers outside of their neighborhood. Through extensive simulation experiments, we observed that even communication neighborhoods constituted by as few as 3-4 households are sufficient to effectively stabilize the aggregate network load profile, with minimal bandwidth consumption. Increasing the neighborhood size leads to comparatively negligible performance improvements. We conclude that effective load flattening can be achieved with minimal requirements of communication infrastructure and transmitted information.

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Section: Appliance Classesmentioning

confidence: 99%

Restricted Neighborhood Communication Improves Decentralized Demand-Side Load Management

Giusti

Salani

et al. 2014

IEEE Trans. Smart Grid

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“…Although EV advocates have spent decades on tackling all kinds of challenges, EV charging control strategies which can affect the impacts and benefits significantly are still under discussion [24,25]. Yilmaz et al [26] reviewed the current status and implementation of battery chargers, charging power levels, and infrastructure for plug-in EVs.…”

Section: Introductionmentioning

confidence: 99%

The Concept of EV’s Intelligent Integrated Station and Its Energy Flow

et al. 2015

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Abstract:The increasing number of electric vehicles (EVs) connected to existing distribution networks as time-variant loads cause significant distortions in line current and voltage. A novel EV's intelligent integrated station (IIS) making full use of retired batteries is introduced in this paper to offer a potential solution for accommodating the charging demand of EVs. It proposes the concept of generalized energy in IIS, based on the energy/power flow between IIS and EVs, and between IIS and the power grid, to systematically evaluate the energy capacity of IIS. In order to derive a unique and satisfactory operation mode, information from both the grid (in terms of load level) and IIS (in terms of its energy capacity and EVs battery charging/exchanging requests) is merged. Then, based on the generalized energy of different systems, a novel charging/discharging control strategy is presented and whereby the operating status of the grid and energy capacity of IIS are monitored to make reasonable operation plans for IIS. Simulation results suggest that the proposed IIS offers peak load shifting when EV battery charging/exchanging requests are satisfied compared to existing charging stations. OPEN ACCESSEnergies 2015, 8 4189

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“…However, neither PV nor BES has been considered in these papers. Deterministic grid load or grid load ignored [16,17,47,53,57,75,[80][81][82][83][84][85][86][87] Recorded grid load data; probabilistic EV load [88] Constructed worst case scenario with deterministic EV load [89] Probabilistic EV and grid loads [32,90,91] Deterministic PV output [17,83,84] Probabilistic PV output [47,75,86] For probabilistic modelling, the combined EV load is a function of the number of EVs [82,85], their charging characteristics with respect to SOC as discussed in Section 2.2 and various other parameters. Referred to Table 2.7, among these parameters, daily travel distance, arrival time and departure time are frequently used.…”

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

confidence: 99%

“…Then, as illustrated in Table 2.11, the impact of the EV charging is analysed in terms of overall load profile, losses, voltage, line and substation loading, loading margin, reliability, power quality, unbalance, charging costs including the investment, transformer ageing, and GHG emissions. [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.…”

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

confidence: 99%

“…Similar applications of the impact indices in EV charging can be observed in the literature too. Among them, the proportions of Transformers, substations, and feeders overload for the uncontrolled and controlled charging for various scenarios have been discussed in [89]. A new index -average daily power transaction variation (ADPTV) -has been defined in [80], for one EV.…”

Section: How the Grid Is Incorporated In The Scheduling?mentioning

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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Network Impacts and Cost Savings of Controlled EV Charging

Cited by 181 publications

References 4 publications

Restricted Neighborhood Communication Improves Decentralized Demand-Side Load Management

Restricted Neighborhood Communication Improves Decentralized Demand-Side Load Management

The Concept of EV’s Intelligent Integrated Station and Its Energy Flow

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

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