Reducing the Impact of EV Charging Operations on the Distribution Network

Beaude, Olivier; Lasaulce, Samson; Hennebel, Martin; Mohand-Kaci, Ibrahim

doi:10.1109/tsg.2015.2489564

Cited by 78 publications

(51 citation statements)

References 44 publications

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“…Incentive-based demand response provide refunds to the consumers to schedule their loads at times of lower consumption [8]. There exists a vast literature dealing with the problem of minimal cost load scheduling under individual preferences [1,18]. A model of block load scheduling for price-based demand response is implemented under two different real-time pricing schemes in [23]: linear pricing and threshold pricing.…”

Section: Introductionmentioning

confidence: 99%

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Cadre¹,

Pagnoncelli

Homem-de-Mello

et al. 2019

European Journal of Operational Research

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We model the relation between an aggregator and consumers joining a coalition to reduce the risk resulting from the unpredictability of their base load demand, as a Stackelberg game formulated as a mathematical bilevel program with private information on the consumers' reservation prices. At the upper-level of the Stackelberg game, the aggregator optimizes his daily price profile so as to reach a net targeted profit which is the maximum value guaranteeing that no consumer will leave the coalition -to contract with a conventional retailer considered here as a fixed alternative -while meeting fairness criterion imposed by the cost-sharing mechanism. At the lower-level, the consumers are asked to provide in day ahead an estimate of their base load hourly demand profile and to schedule their shiftable loads depending on the price signal sent by the aggregator. We provide algorithms that determine the unique price profile and consumer shiftable load schedules as functions of the reservation price estimates. The Stackelberg game between the aggregator and the consumers being repeated for a period of time, the aggregator has the possibility to update his estimates of the reservation prices relying on a feedback function which depends on the percentage of activated loads. A randomized algorithm for consumers' reservation price learning based on regret minimization is provided. For four cost-sharing mechanisms such as uniform allocation, stand-alone cost, Shapley value, separable and non-separable costs, we determine the closed form of the aggregator's optimal net targeted profit guaranteeing the stability of the coalition. We also determine conditions guaranteeing the core non-emptiness and prove that for a profit-maximizing aggregator, the stand-alone cost is always preferable to the Shapley value, which coincides with the uniform allocation. Furthermore, the optimal size of the coalition -in terms of the aggregator's profit -can be determined analytically when the Shapley value is implemented as cost-sharing mechanism. The results are illustrated on a case study where we show that there exists an optimal net targeted profit below which the consumers energy bill is lower when joining the aggregator than with the conventional retailer. Coalition dynamics is also analyzed numerically depending on the consumer inertia in their energy supplier choice process, for each cost-sharing mechanism.

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Section: Introductionmentioning

confidence: 99%

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Cadre¹,

Pagnoncelli

Homem-de-Mello

et al. 2019

European Journal of Operational Research

Self Cite

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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%

“…A developed tool to generate the probabilistic EV loads or the parameters above can also be observed in [83]. Daily travel distance [16,17,53,57,75,80,81,84,87,91,92] Arrival time [16,53,57,81,87,90,92] Departure time [57,80,[86][87][88] Parking duration [80] Arrival SOC [53,86,87,90,93] Dynamic of number of EVs arriving and departing [86] Number of EVs simultaneously parked [47,53,82,85,87,91] Charging characteristics of EV battery [53,82] Discharging characteristics of EV battery [17] Dynamics of the SOC of EV battery [16,17,26,47,53,80,83,86,…”

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

confidence: 99%

“…Therefore, (2.12) can be scaled up for a large EV population as presented in (2.13), 100 . [57,75,80] Lognormal distribution [16,17,53,81,84,87,91] Empirical distribution [92] Arrival SOC Normal distribution [90] Lognormal distribution [53,87] Calculated using equation (2.12) [86,93] Number of EVs arriving and departing Normal distribution [86] Number of EVs parked An average model [87] Arrival time Normal distribution [57,81] Lognormal distribution [16,53,87,90] Empirical distribution [92] Departure time Lognormal distribution [57,87] Raleigh distribution [80] Parking duration 3 point estimation [75]  A method to solve (2.13) for a large EV population.…”

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%

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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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Two‐layer optimal charging strategy for electric vehicles in old residential areas

Xia

Liao

Chen

2021

Int Trans Electr Energ Syst

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Reducing the Impact of EV Charging Operations on the Distribution Network

Cited by 78 publications

References 44 publications

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

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

Two‐layer optimal charging strategy for electric vehicles in old residential areas

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