Electric Vehicles Home Charging Impact on a ... - IEEE Xplore

Electric Vehicles Home Charging Impact on a Distribution Transformer in a Portuguese Island R. Godina, N. G. Paterakis

O. Erdinç

E. M. G. Rodrigues, J. P. S. Catalão

Univ. Beira Interior, Covilhã, Portugal [email protected], [email protected]

Yildiz Tech. Univ., Istanbul, Turkey, and INESC-ID, IST, Univ. Lisbon, Lisbon, Portugal [email protected]

Univ. Beira Interior, Covilhã, Portugal, and INESC-ID, IST, Univ. Lisbon, Lisbon, Portugal [email protected], [email protected]

Use of smart grid technologies in insular areas is increasing with the installation of diverse test systems in distinctive islands around the world. Even if the interrelated power system structure is considered to be more stiff in terms of stability, insular areas that can also produce an essential basis for potential islanding operation requirements may be viewed as perfect test beds for the pre-evaluation of the smart grid concept before embracing it for wider areas such as large population cities, regions composed of several cities, or even to an entire country [3].

Abstract—This paper analyses the impact of the high penetration of electric vehicles (EVs) charging loads on the thermal ageing of distribution transformers of an isolated electric grid in a Portuguese Island. In this paper, a transformer thermal model is used to estimate the hot-spot temperature (θh) given the load ratio. Real data are used for the main inputs of the model, i.e. residential load, transformer parameters, time-of-use rates and electric vehicle parameters. Conclusions are duly drawn. Keywords—distribution transformer; EV charging; loss-of-life; battery; transformer ageing

Distribution networks are designed to deliver electricity to the final customers and their sizing is usually based on an estimated electricity demand. Therefore, there is a general need to develop modelling techniques to help quantify the effects that high penetration level of EV charging loads may have on distribution networks and thus ensure that this environmentally benign technology is not unnecessarily constrained. Distribution transformers are vital links in distribution networks which are to experience unprecedented loads from EV charging. Various studies have been carried out to evaluate whether the existing electricity network and mainly the transformer insulation temperature could withstand the widespread adoption of EVs [1] [4-7].

I. INTRODUCTION Purely electric vehicles (EVs) hold the promise, if widely adopted, of drastically reducing carbon emissions from surface transport and could, therefore, form a major thrust in the global efforts to meet the emission reduction targets. The use of EVs is more challenging than hybrid vehicles as they are only powered by electric energy. Although the EVs would be primarily used for transportation, from the point of view of a System Operator they could be practically viewed as a distributed storage resource. As a result, when they are not used to satisfy their primordial role, they could provide a range of ancillary services to the power system such as regulation, operating reserves, back-up power etc. Also the use of EVs could support the peak shifting [1].

Distribution transformers with oil-immersed core are one of the most common equipment that is found in distribution networks. The distribution system of Azores uses almost exclusively oil-immersed distribution transformers – some of them upgraded very recently [8]. Furthermore, transformers in their existing form are expected to continue to be in service for many years to come due to its widespread use and intrinsic high reliability in its simplicity. Therefore, the impacts of characteristic smart grid operations such as EV charging on transformer life and performance considerations must be accurately assessed.

With an increasing number of EVs connected to power systems for charging, there is a concern that existing distribution networks may become more heavily loaded than anticipated when they were designed. Low penetration levels may result in little impact but, as the number of EVs increases, there could be a real possibility of local distribution networks being congested. Simultaneous charging of a large number of vehicles can lead to grid inadequacy in terms of available capacity and security. This can be avoided, if the EVs are appropriately integrated within the grid. Integrating the EV within the grid is an important option, if they are controlled properly. For the EVs, simultaneous charging of a large number of vehicles becomes feasible [2]. Without the integration, the grid may face voltage sag, feeder congestions, line overloads, etc., especially for an isolated electrical grid such as São Miguel, Azores, where this integration is lacking.

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This paper presents a model that allows the evaluation of the effect of EVs charging loads on the thermal ageing of power distribution transformer, which in turn is a part of an isolated electrical grid of São Miguel Island, Azores, Portugal. The method takes into account the uncertainty of EV battery charging loads, i.e., the randomness of individual charger starttime, initial battery-state-of charge (SOC), and charging modes.

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The process endures until thhe battery voltage reaches its gassing limit, at which the current drops as the charger preserves a constant voltage [7]].

The remainder of the paper is organizzed as follows: in Section II, the employed methodology is deeveloped. Then, in Section III, the distribution network of São Miguel, M Azores, as well as the simulation results, are presented and discussed. Finally, conclusions are drawn in Section IV.

The simplified model of EV V battery charging profile can be mathematically expressed as:

II. METHODOLOGY

⎧ PEV                  0 < t < t1 ⎪ P (t ) = ⎨ t2 − t ⎪ PEV t − t       t1 < t < t2 ⎩ 2 1

A. EV battery charging profiles Electric vehicle charging is an additionn to existing load. EVs are noticeably different from other elecctrical loads due to their highly mobile and unpredictable nature. There are mostly three key factors which could affect the effect of EVs on distribution networks, specifically the chargging characteristics of the EVs, the driving profile and electrrical energy tariff incentives. As the EV market grows, moore and more car manufacturers enter the competition. As a consequence, a growing supply of EVs with different characteristics is available today [9]. As a result, in order to be more realistic, five different types of EVs are used in thiss study. The latest models of real existing EVs where used and these t are BMW i3, Renault ZOE, Ford Focus Electric, Nissan Leaf L and Kia Soul. Data for the charging types and duration off the five EVs are presented in Table I.

where P(t) is the charging pow wer in kW and PEV is the rated charging power in kW, which suffers variation by depending on the charging mode [11]. o a Lithium-ion battery, even In practice, the charging of under simplified conditions, is described by a function that reflects the mutually dependennt occurrences of battery SOC and charger type. However, forr the sake of simplicity the effect of ambient temperature onn the EV battery charging characteristics is not taken into account in this study. Furthermore, the EV batteryy charging process is assumed to be continuous as soon as it staarts up until the battery reaches full capacity. The power demand d throughout the whole charging process is frequenttly presented by the charging profile, which may be differentt depending on battery type and charging mode.

The percentage of BMW i3 was chosenn in this model as high as 40% since it is the fastest sellingg EV in Portugal according to the ACAP – the Portugguese Automobile Association [10]. Renault ZOE and Ford where selected to have a 20% market penetration since thesse brands already appear to have a significant share in the connventional vehicle market [10]. In recent times, EVs are becomingg technologically appealing with the advancement of Litthium-ion battery technology which offers the advantage of higher power and energy density. Due to the fact that Lithhium-ion batteries dominate the most recent group of EVs in development [6], in this paper it is assumed that the case-sttudy EVs employ Lithium-ion batteries. Due to the potential of o obtaining higher specific energy and energy density, the adooption of LithiumIon batteries is expected to grow fast in EV Vs. Nearly all EVs available in the market today use Lithium-Ionn batteries because of its mature technology. The battery capacityy for light vehicles in EVs is in the range of 6 kWh to 35 kWh. The charging time varies from 14 hours for slow charging batterries to less than an hour for fast charging batteries [9]. All the EVs used in this case study have Lithium-Ion batteries and to understand better effect off charging on the daily baseline load profile the charging profille of a Lithium-Ion battery is briefly described. As it can be seenn in can be seen in Fig. 1 when the battery SOC is low, the charger operates at rated current, which enables a great percenttage of the battery charge being reestablished during the initiaal charging hours. TABLE I. EVs BMW i3 Renault ZOE Ford Focus Electric Nissan Leaf Kia Soul

B. Model of EV Chargingg Load In this study, to create thhe model, the typical charging profile of Lithium-ion EV battteries is taken into account, and the stochastic behavior of thhe initial EV battery SOC is assessed using a probability deensity function (PDF) associated to travel distances. The charginng demand of an EV is dictated by the initial battery SOC, thhe charging start time, and the charging characteristics. The initial SOC of an EV battery is determined by the travel usage of the EV before recharging and can be perceived as a random variable associated to the travel distance. Based on a Portugueese study of the general travel information regarding Portugguese drivers of conventional vehicles in 2011 near Lisbon [12], a probability distribution of daily travel distance can be recoonstructed as shown in Fig. 2. It is considered that, comm monly, the distribution of travel distance is of lognormal tyype, with zero probability of occurrence of the negative distaances, and a “tail” prolonging to infinitum for positive distancees. The PDF of the EV travel distance can be expressed as:

CHARGING TYPES AND DURATIO ON OF THE 5 EVS % of EV 40% 20% 20% 10% 10%

Slow Charge

Fast Charge

Power kW

Time h

Power kW

Time h

2,4 3,7 3,68 3,6 2,3

8-10 6 6-7 8 11-14

7,4 7,4 7,36 6,6 6,6

3 3 3-4 5 4-5

(1)

Fig. 1. Charging Profile of a Lithium--Ion Battery [9].

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( d ; μ , σ ) =

1 d 2πσ 2

×e

−

( ln d − μ ) 2σ

2

Since the temperature disstribution is not uniform, the hottest section of the transformeer will consequently be the most damaged. Thus, the hot-spot temperature t directly affects the life duration of transformers [6]] [17].

2

(2)

,  d > 0

where d is the daily distance covered by a vehicle, v μ is the ln mean and σ is the standard deviation of the t corresponding normal distribution. For the case of vehicle travel distance in Portugal in 2011 as shown in Fig. 3, μ = 2,995 and σ = 0,768.

The rate at which the ageinng of paper insulation for a hotspot temperature is increased or o decreased compared with the ageing rate at a reference hot-sspot temperature (110ºC) [14] is the relative ageing rate V [15].

Set the average daily travel distance, the SOC at the beginning of a recharge cycle that is thee residual battery capacity can be estimated using (2) by assum ming that the SOC of an EV descents linearly with the distance of o the journey: ⎛ d ⎞ Ei = ⎜ 1 − ⎟ × 100 % d R ⎠ ⎝

The relative ageing rate for the thermally upgraded paper is above one for hot-spot temperratures greater than 110 ºC and means that the insulation agess faster compared to the ageing rate at a reference hot-spot tem mperature, and it is lower than one for hot-spot temperatures leess than 110 ºC [4].

(3)

For thermally upgraded papper, that is chemically modified to improve the stability of the cellulose structure, the relative ageing rate V is [15]:

where Ei represents the initial SOC of an EV V battery, d is the daily distance covered by a vehicle, which is a random variable conditioned to a lognormal distribution and dR is the maximum range of the EV and by assuming that eacch trip starts with 100% SOC. A typical average value for travvel distance is 100 km [13].

⎛ 150000 15000 ⎞ − ⎜ ⎟ 110 + 273 θ h + 273 ⎠

V = e⎝

Over a certain period of tim me, the loss of life L during the time interval tn is as follows:

Substituting (3) into (2) and switching thhe variable from d to E, the PDF of the battery SOC after the jouurney of one day is obtained as follows: h ( E ; μ , σ ) =

1 dR ( 1 − E ) 2πσ

2

×e

l dR ) ⎤⎦ ⎡ ln ( 1 − E ) − ( μ − ln −⎣ 2σ 2

(5)

t2

N

t1

n=1

L = ∫Vdt or  L ≈ ∑Vn × tn  

2

(6)

By definition, the hot-sppot temperature is the hottest temperature of any spot in the transformer winding. By experiencing elevated electricaal loads it originates high corewinding temperatures which in turn cause chemical breakdown p of insulating oil and insulating paper.

,  0 < E < 1 (4)

and shown in Fig. 3. Based on the information withdrawn froom both PDF, it is feasible to assess the residual battery capacitty at the beginning of a recharge cycle. The initial time of battery charging is influenced by the electricity tariff rate structuure and the purpose of the use of the EVs by the users which is uncertain u factor as seen in Fig.2.

The simple idea behind thee top-oil temperature rise model is that an increase in the lossses is a consequence from an increase in the loading of the transformer t and subsequently of the global temperature in thee transformer. The temperature fluctuations are dependent on thhe overall thermal time constant of the transformer which in tuurn depends on the rate of heat transfer to the environment annd the thermal capacity of the transformer.

C. Distribution Transformer Loss of Lif ife A proper preservation of mineral-oil--tilled distribution transformers is of a very important in power systems, therefore a need is created to adopt a caring appproach concerning transformer loading, in order to benefit as much as possible from their availability and long term service. The insulation system of a power transfoormer is essentially made of paper and oil which suffers from aggeing. Unexpected rise of the load results in a rise of the hot-sppot temperature θh and subsequently affects the thermal decoomposition of the paper [14-16].

In steady state, the total trannsformer losses are proportional to the top-oil temperature rise. In transient conditions, the hota a function of time, for varying spot temperature is described as load current and ambient tempperature [14]. The oil insulation system of a transformer under working w conditions is exposed to several types of stress, such as thermal, electrical, environmental and mechanicaal. The outcome of each stress factors or the interaction effectss of them affect the ageing of the insulating system.

Fig. 2. Probability distribution of daily vehicle travel distance. d

Fig. 3. Probability density of battery SOC S after one day of driving.

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For increasing step of loads, the top-oil and winding hotspot temperatures rise to a level correspondinng to a load factor of K. The top-oil θo(t) temperature is as follow ws:

To find transient solutioons for top-oil and hot-spot temperatures – a thermal modeel is developed and proposed for the distribution transformer. The properties of the distriibution transformer used in this paper are extracted from Ravetta et al. [18] that presented the o transformer with Oil Natural data of a real 630 kVA (Pr) oil Air Natural (ONAN) cooling where w a natural convectional flow of hot oil is utilized for coolinng. The properties are shown in Table II.

θ o ( t ) = Δθ o , i + x −t ⎡1 + R × K2 ⎤ ⎪⎧ ⎪⎫ ⎛ ( k11 ×τ o ) ⎞ + ⎨Δθ o , r × ⎢ ⎟ ⎥ − Δθ o , i ⎬ × ⎜ 1 − e ⎠ ⎣ 1+R ⎦ ⎪⎩ ⎪⎭ ⎝

(7)

where ∆θo,i is top-oil (in tank) temperature rise r at start in °K, ∆θo,r is top-oil temperature rise at rated curreent in °K, R is the ratio of load loss to no-load loss at rated currrent, K is the load factor (load current/rated current), x is the oill exponent, k11 is a thermal model constant and τ0 is average oil time t constant.

III. SIMULATION RESULTS A. Structural componentss of the insular grid The Azores, a Portuguese autonomous a region, is a 9 islands archipelago located in the Norrth Atlantic circa 3900 km from the east coast of North Americaa. São Miguel Island is the main and most populated island.

The hot-spot temperature rise ∆θh(t) is as follows:

{

}

Δθ h ( t ) = Δθ h ,i + H × gr × K y − Δθ h , i × −t ⎡ ⎛ ⎛ ⎞ × ⎢ k21 × ⎜ 1 − e ( k22 ×τ w ) ⎟ − ( k21 − 1 ) × ⎜ 1 − e ⎜ ⎝ ⎠ ⎝ ⎣⎢

− ( t × k22 )

τo )

⎞⎤ ⎟⎥ ⎟⎥ ⎠⎦

In this paper, a part of São Miguel medium voltage a example. A transformer that distribution network is used as supplies a residential area is chhosen. Fig. 4 shows a part of the medium voltage distribution network n and an identification of several outputs. For this case study s the transformer substation PT80 is used which suppliees 292 households through a 630kVA, 10kV/0.4kV oil-immeersed transformer. During the summer of 2014 several measurements were performed at the transformer substation s PT80 and the energy consumption of the 292 househholds was recorded, thus a daily baseline load profile was createed as shown in Fig.5. It may be observed that a 630 kVA transfformer is oversized for a 140 kW of peak in daily baseline loaad profile, considering that in Azores higher consumption iss witnessed during the summer [8].

(8)

where ∆θh,i is hot-spot-to-top-oil (in tank) gradient g at start in °K, H is the hot-spot factor, gr is the avverage winding to average oil (in tank), y is the winding exponent, k21 and k22 are thermal model constants and τw is a winding time t constant. For decreasing step of loads, the top-oil and winding hotspot temperatures decrease to a level correspoonding to a K [14]. The top-oil temperature θo(t) can be calculateed as follows: x

⎡1 + R × K2 ⎤ θ o ( t ) = Δθ o ,r × ⎢ ⎥ + ⎣ 1+ R ⎦ x ⎧⎪ ⎡ 1 + R × K 2 ⎤ ⎫⎪ ⎛ − t ( k11 ×τ o ) ⎞ + ⎨Δθ o , i − Δθ o , r × ⎢ ⎟ ⎥ ⎬×⎜e ⎠ ⎣ 1 + R ⎦ ⎪⎭ ⎝ ⎪⎩

(9)

The hot-spot temperature rise is as follow ws: Δθ h ( t ) = H × gr × K y

(10)

In conclusion, with θo(t) and Δθh(t) from Eq. (7) and (8) for increasing load steps, and Eq. (9) and (10) for f decreasing load steps and plus the ambient temperature θa thhe overall hot-spot temperature θh(t) equation is as follows: θ h ( t ) = θ a + θ o ( t ) + Δθ h ( t ) TABLE II. Symbol gr H k11 k21 k22 Pr R x y ∆θo,r τ0 τw

Fig. 4. A part of São Miguel medium m voltage distribution network and the identification of PT80 output [8].

(11)

USED TRANSFORMER PAR RAMETERS Value 15.9 1.25 0.5 2 2 630 5.957 0.8 1.3 41.5 210 10

Units Ws/K

kVA

ºK Minutes Minutes

Fig. 5. The daily baseline load profilee of the transformer substation PT80.

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The EV load demand can be dictated to some s extent by the electricity tariff structure. For this model thee current electricity tariff of Azores Islands that entered into force f in 2015 was taken into account. Although a three rate tariff t for domestic consumers currently also exists in Azores, for this study the two rate tariff was used. The off-peak tariff iss 190% lower than the peak tariff and it is activated immediatelyy after 22:00 [19].

2 of each day, as for the available immediately after 22:00 remaining 45% of EVs, it is assumed a that these users are not very much concerned with offf-peak tariffs and that the fast charging it is used at 07:00, when w users wake up and go to work and the slow charging mode m is used right after home arrival, usually after 18:00. The second scenario explorees only for comparison purposes a case where all the EVs are 100% 1 discharged and with 75% EV penetration and is assumed as in the first scenario that 55% of EVs begin charging at 22::00 and 45% of EVs start fast charging at 07:00, and the sloow charging mode is used right after home arrival, after 18:00. The impact on the dailyy baseline load profile of the transformer substation PT80 made m by the energy consumption of the EVs at several penetration ratios from both scenarios is shown in Fig. 6. The starting times of chaarging for both scenarios were chosen due to the fact that coonsumers do not usually require fast recharging because they have h adequate time - 3 to 8 h (depending on charge level) duuring the non-working period of the day or after 22:00 at home in order to avoid the inconvenience of visiting a puublic charging station. Also, for this paper, the charging demandd of multiple EVs is a direct sum of the charging demand of disscrete EVs to the daily baseline load profile. By analyzing Fig. 6 we cann extract that for a penetration of EVs of more than 75% thhe distribution transformer is overloaded. Also, from the information i obtained from the model and presented in Fig. 6, it is possible to assess the transformer insulation ageinng affected by the hot-spot temperature and the loss of liffe L of the transformer which is presented in Fig. 7. Using the ageing equations (5) and (6), transformer loss of life can now be determined. Thhe loss of life of the transformer is presented in hours for each day d of EV charging which means that from the transformer exppected life at 0% penetration is withdrawn a number of hours for each day of charging. The results can be seen the Table IIII. From both Fig. 6 and 7 and a from the Table III we can conclude that the off peak tarifff will encourage users to prefer a certain hour of charging, in thiis case, 22:00, that will cause a concentration of EVs charging at the same time which in turn o the distribution transformer, a will provoke an overcharging of sudden increase of the hot-spoot temperature and consequently will affect the transformer lifettime. ______________________

B. Discussion of Results The current status of EV market sharee globally can be considered low, not exceeding 7% in leadingg countries such as Norway [6]. However, in this study, veryy high penetration levels are examined. Especially for an insularr area, such as Sao Miguel, the relatively high transportation coost of fossil fuels, the presence of rich potential of renewable ennergy sources, and the opportunities that emerge from the efficieent management of an EV fleet [3], leads the authors to believe thhat the penetration levels that are likely to be met in such areass in the future will be significantly higher than in continental arreas. Furthermore, governmental incentive initiatives usually teend to target more areas such as islands and as a result, potentiall subside programs or tax reduction schemes to promote the puurchase and use of EVs are very likely to massively motivate users to replace their conventional car with an EV. From the data collected from the PDF it is possible to apply the transformer thermal model, using the loaad ratio as an input to obtain the hot-spot and top oil temperatuures. For this case study one day and a half of the baseline load profile of the summer period of the transformer substationn PT80 is used. An appropriate algorithm is implemented to asssess the impact of EVs charging loads on the thermal ageinng of distribution transformer based on the methodology prevviously presented. Battery charging of the electric vehicles impposes an extra load on the distribution transformer. Assuming that t a distribution transformer supplies several EVs in a neighhborhood, different charging time and load profiles are obtained o for the transformer. The algorithm integrates data obtained from the PDF and calculates the hot-spot tempperature and the transformer’s loss of life due to EVs chargingg loads. Two different scenarios are examined, thhe first being with different initial SOC of the EVs based on the PDF function, plus different penetration ratios of EVs are considered in this study for the household neighborhood, begginning with 75% penetration and then with 80%, 85%, 90% %, 95% and 100%. Also, it is considered that 50% of the EV ow wners charge their cars in slow charging mode and the other 50% % in domestic fast charging mode. Finally, it is assumed that 55% 5 of EVs begin charging at 22:00 since as seen before, for Azores A the off-peak tariff is 190% lower than the peak tarifff and it becomes TABLE III.

LOSS OF LIFE IN HOURS OF THE TRANSFORMER Scenarios Looss of Life in Hours

Scenario 1 0% EV Penetration 75% EV Penetration 80% EV Penetration 85% EV Penetration 90% EV Penetration 95% EV Penetration 100% EV Penetration Scenario 2 75% EV Penetration

0.00 0.54 0.72 0.98 1.34 1.87 2.63 Fig. 6. The daily baseline load profilee with the two studied scenarios.

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REFERENCES

Fig. 7.

[1] Q. Gong, S. Midlam-Mohler, V. Marano and G. Rizzoni, "Study of PEV Charging on Residential Distribution Transformer Life," IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 404-412, 2012. [2] R. Das, K. Thirugnanam, P. Kumar, R. Lavudiya and M. Singh, "Mathematical Modeling for Economic Evaluation of Electric Vehicle to Smart Grid Interaction," IEEE Transactions on Smart Grid, vol. 5, no. 2, pp. 712-721, 2014. [3] O. Erdinc and N. G. Paterakis, "Chapter 1 - Overview of Insular Power Systems: Challenges and Opportunities," in Smart and Sustainable Power Systems: Operations, Planning and Economics of Insular Electricity Grids, Boca Raton, Florida, CRC Press (TAYLOR & FRANCIS Group), 2015. [4] A. Hilshey, P. Hines, P. Rezaei and J. Dowds, "Estimating the Impact of Electric Vehicle Smart Charging on Distribution Transformer Aging," IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 905-913, 2013. [5] R. Vicini, O. Micheloud, H. Kumar and A. Kwasinski, "Transformer and home energy management systems to lessen electrical vehicle impact on the grid," IET Generation, Transmission & Distribution, vol. 6, no. 12, pp. 1202-1208, 2012. [6] K. Qian, C. Zhou and Y. Yuan, "Impacts of high penetration level of fully electric vehicles charging loads on the thermal ageing of power transformers," International Journal of Electrical Power & Energy Systems, vol. 65, pp. 102-112, 2015. [7] M. J. Rutherford and V. Yousefzadeh, "The Impact of Electric Vehicle Battery Charging on Distribution Transformers," in 2011 Twenty-Sixth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Fort Worth, TX, USA, 2011. [8] EDA S.A. - Electricidade dos Açores, "Caracterização Das Redes De Transporte E Distribuição De Energia Eléctrica Da Região Autónoma Dos Açores," Ponta Delgada, 2014. [in Portuguese] [9] K. Young, C. Wang, L. Y. Wang and K. Strunz, "Chapter 2 - Electric Vehicle Battery Technologies," in Electric Vehicle Integration into Modern Power Networks, New York, Springer New York, 2013, pp. 1556. [10] ACAP, "Associação Automóvel de Portugal," [Online]. Available: http://www.acap.pt/pt/home. [Accessed 20 02 2015]. [in Portuguese] [11] P. Zhang, K. Qian, C. Zhou, B. Stewart and D. Hepburn, "A Methodology for Optimization of Power Systems Demand Due to Electric Vehicle Charging Load," IEEE Transactions on Power Systems, vol. 27, no. 3, pp. 1628-1636, 2012. [12] N. B. R. d. C. Pereira, MSc Thesis - Eficiência energética no sector dos transportes rodoviários: metodologia para quantificação do excesso de energia consumida devido ao factor comportamental na condução de veículos automóveis ligeiros, Lisbon: Departamento de Ciências e Tecnologia da Biomassa - Universidade Nova de Lisboa, 2011. [in Portuguese] [13] K. Qian, C. Zhou, M. Allan and Y. Yuan, "Modeling of Load Demand Due to EV Battery Charging in Distribution Systems," IEEE Transactions on Power Systems, vol. 26, no. 2, pp. 802-810, 2011. [14] IEC 60076-7, "Loading Guide for Oil-immersed Power Transformers," 2005. [15] C57.91-1995, "Guide for Loading Mineral-Oil-Immersed," IEEE Standard, 1995. [16] H. Pezeshki, P. Wolfs and G. Ledwich, "Impact of High PV Penetration on Distribution Transformer Insulation Life," IEEE Transactions on Power Delivery, vol. 29, no. 3, pp. 1212-1220, 2014. [17] H. Turker, S. Bacha, D. Chatroux and A. Hably, "Low-Voltage Transformer Loss-of-Life Assessments for a High Penetration of Plug-In Hybrid Electric Vehicles (PHEVs)," IEEE Transactions on Power Delivery, vol. 27, no. 3, pp. 1323-1331, 2012. [18] C. Ravetta, M. Samanna’, A. Stucchi and A. Bossi, "Thermal behavior of distribution transformers in summertime and severe loading conditions," in 19th International Conference on Electricity Distribution, Vienna, 2007. [19] EDA S.A. - Electricidade dos Açores , "Preçário 2015 das Tarifas da Região Autónoma dos Açores," EDA - Electricidade dos Açores, Ponta Delgada, 2015. [in Portuguese]

The Hot-Spot Temperature of the distribution transformer.

By applying the scenario 1 it can be concluded that for more than 75% of EV penetration the transformer will overloaded resulting in an increase of the hot-spot temperature of the distribution transformer. The loss of life increases with the increase of EV penetration in this scenario. In an unlikely event, by applying the scenario 2 consisting of 75% of the EV penetration, the loss of life of the distribution transformer is significantly higher. By analyzing the results obtained from Table III it can be concluded that the transformer life loss is very sensitive to hotspot temperature variation because the ageing factor follows an exponential function. Hence, EV charging indeed significantly affects transformer lifetime. IV. CONCLUSIONS In this paper a model to evaluate the effect of EVs charging loads on the thermal ageing of power distribution transformer was applied and described. Since transformer insulation ageing is mainly affected by the hot-spot temperature, a transformer thermal model was used to estimate the hot-spot temperature given the knowledge of load ratio. The main inputs to the model, including residential load, transformer parameters, offpeak tariff and five different vehicle parameters were taken from real data. Given that the transformer used was oversized, as occurs with the majority of them on São Miguel, Azores – this study showed that even the most oversized transformers can be overloaded after a certain increase of EV penetration. It also showed that off peak tariff can have an influence over EV users and affect the distribution transformer lifetime. The developed methodology can be applied to assess the impact of multiple EVs charging in the same residential area, or in other circumstances such as those in which a public charging station is used instead of home EV charging. ACKNOWLEDGMENT The authors thank FEDER through COMPETE and FCT, under FCOMP-01-0124-FEDER-020282 (Ref. PTDC/EEAEEL/118519/2010), UID/CEC/50021/2013 and SFRH/BPD/103744/2014, and also the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048. ________

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