Impact assessment of plug-in electric vehicles on distribution networks

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(1)Impact Assessment of Plug-in Electric Vehicles on 1 Distribution Networks. THESIS Presented to. UNIVERSITY OF THE ANDES ENGINEERING FACULTY DEPARTAMENT OF ELECTRICAL ENGINEERING AND ELECTRONICS. Submitted in partial fulfillment for the requirement of degree in. MASTER OF SCIENCE IN ELECTRICAL ENGINEERING By. Israel Roncancio Reyes. IMPACT ASSESSMENT OF PLUG-IN ELECTRIC VEHICLES ON DISTRIBUTION NETWORKS. Adviced by. Phd. Mario Alberto Ríos Macías. June 2011.

(2) Impact Assessment of Plug-in Electric Vehicles on 2 Distribution Networks. ACKNOWLEDGEMENTS I want to express my appreciation to all individuals who have in one way or the other contributed to the completion of this project. I would like to thank my advisor Dr. Mario Alberto Ríos for the guidance and support he has offered throughout the project development. I would also like to thank my wife and family for their undivided support during this time: to my wife for her increasing love along this journey, to my mother for her inspiring example, to Gabriel for his passion for life, to Rathziel for being my most beloved learner and last but not least to my father for keeping help me in such ways that just we know..

(3) Impact Assessment of Plug-in Electric Vehicles on 3 Distribution Networks Index 1 2. INTRODUCTION ...............................................................................................................4 OBJECTIVES .....................................................................................................................5 2.1 Main Objective.........................................................................................................5 2.2 Specific Objectives...................................................................................................5 3 PLUG-IN HYBRID ELECTRIC VEHICLES (PHEV) – BACKGROUND ......................................5 3.1 Electric Vehicles Overview......................................................................................5 3.2 Energy Storage Systems Overview ..........................................................................7 3.3 Nowadays Issues ......................................................................................................8 4 IMPACT ASSESSMENT METRICS ......................................................................................8 4.1 Steady State Metrics.................................................................................................8 4.2 Spectral Metrics .......................................................................................................9 5 GENERAL SCENARIOS FOR ASSESSMENT ......................................................................10 6 A MODEL OF LOAD CURVE ESTIMATION WITH PHEV...................................................10 6.1 Statistical Approach ...............................................................................................10 6.1.1 Stay time Ts .....................................................................................................11 6.1.2 Consumed energy Ec .......................................................................................11 6.1.3 Stored energy Eb .............................................................................................13 6.1.4 Maximum electrical energy recharged by onboard batteries Emax ..................13 6.1.5 Recharged energy Er .......................................................................................14 6.2 Construction of PHEV Low Voltage Load Model.................................................15 7 DISTRIBUTION SYSTEM FOR TESTING PURPOSES .........................................................17 8 IMPACT ASSESSMENT ...................................................................................................19 8.1 Low Voltage Networks Impact Assessment ..........................................................19 8.1.1 Node 652 – LV Network ................................................................................19 8.1.2 Summary of results .........................................................................................24 8.2 Medium Voltage Network Impact Assessment......................................................28 9 CONCLUSION .................................................................................................................29 9.1 Summary ................................................................................................................29 9.2 Further Work ..........................................................................................................30 10 REFERENCES ..................................................................................................................30 11 APENDIX ........................................................................................................................32 11.1 Nomenclature......................................................................................................32.

(4) Impact Assessment of Plug-in Electric Vehicles on 4 Distribution Networks 1. INTRODUCTION It is expected that electric vehicles market growth comes ahead due to ecological concern and energy efficiency maximization. As Plug-In Hybrid Electric Vehicles are by now the up to date development in the electric vehicles area and being aware of its plug to grid skills, it can be inferred that the new loads these represent could affect current electricity networks. This project presents an alternative method to determine this load based on a statistical approach using random variables but also assesses the impact of incorporating this new burden not only from a steady state perspective but also from a spectral viewpoint. Many studies have shown that climate change is a direct consequence of global temperature rise, being its cause the worldwide greenhouse gases emissions [16], [21], [30]. As an international issue, institutions around the world, including governments, corporations, agencies, etc. have become more interested in research around strategies to reduce those emissions. Then, an existing but underused technology: electric vehicle, have found a possible new golden era, because its spreading could solve various troubles of dealing with gases produced during fossil fuels combustion [25], [31]. The key point here is that, on one hand electric vehicles do not release gases to atmosphere and, in the other hand, greenhouse gases emanations are centralized to power plants, being easier to be treated. Nevertheless, electric vehicles (EV) are not completely developed since its driving autonomy is limited to batteries capacity. Automotive energy storage systems have been researched intensively during last decade [29] but economic viable solutions are still not achieved. In order to partially overcome this matter, hybrid electric vehicle (HEV) has been commercially produced since the end of the last century by Japanese manufacturers and recently by others from India, the U.S. and Europe. The scope of this hybridization is to use power from both electric motors and traditional engines for propelling the vehicle; nevertheless the vehicle is solely fed with fuel [5]. The idea succeeded and by now there are millions of these types of vehicles running in American and Japanese highways predominantly, and in some places of Europe (France, Germany), Asia (India) and Latin America (Chile). Due to lack of electric recharge ability HEV were improved to Plug-In hybrids (PHEV), which besides of HEV advantages were also capable of being directly plugged to electrical grids using the same connectors as home appliances. These products are now state of the art vehicles with high recharging flexibility (by fuel or by electricity); however it is still under revision the impact its connection will have on the existing low and medium voltage networks [23] because the massive utilization of these cars could led networks to troubles related to overloads, voltage regulation, phase unbalancing, harmonic distortion and losses [2]..

(5) Impact Assessment of Plug-in Electric Vehicles on 5 Distribution Networks 2. OBJECTIVES 2.1 Main Objective To assess the impact of Plug-In Electrical Vehicles (PHEV) utilization on distribution networks within technical and regulatory frameworks.. 2.2 Specific Objectives a. To develop a methodological approach for modeling the burden PHEV comprises to the network. b. To construct a demand model for PHEV taking into account load profiles, penetration rates of PHEV among residential users and recharging habits of vehicles owners. c. To define metrics that quantifies the impact of using PHEV on distribution networks following next parameters: chargeability, voltage profiles, power factor, and system losses. d. To determine impact of using PHEV on distribution networks based on metrics that measure harmonic distortion for current and voltage.. 3. PLUG-IN HYBRID ELECTRIC VEHICLES (PHEV) – BACKGROUND First of all, some clarifications regarding basics of classification should be done. To prevent possible confusions within the context of the present document, hereinafter the following nomenclature with initials will apply: E (electric), V (vehicle), B (battery), H (hybrid), P (Plug-In), FC (Fuel cell). Then, many combinations could be done but a common framework has been set. Since there are no standards which defines taxonomy for electric vehicle (EV) concepts, misunderstandings among different researches may arise if readers are not aware of the meaning of the words. Electric vehicle (EV) refers to all kind of vehicles that employs electrical means for propulsion; it is a general concept which comprises several quantity of technologies, not just battery electric vehicles (BEV).. 3.1 Electric Vehicles Overview There are many interpretations for beginnings of electric motion used for propels vehicles, however, following the scope of the present project it will be set the latter part of the 19th century as the origin of EVs, many ideas involving electric mobility had been developed by the middle of the century but it was in 1895 when H.Morris and P.Salom produced and patented the first electric automobile in Pennsylvania, the Electrobat [15]. It weighed 4400 pounds and was steered by their rear wheels equipped with two 1.5 horsepower motors; also, it was provided with a lead battery which supplies energy autonomy for 40 km traveling at 32 km/h. After success of this.

(6) Impact Assessment of Plug-in Electric Vehicles on 6 Distribution Networks model lots of vehicles were sold by the newly founded Morris & Salom Electric Carriage and Wagon Company, other models were launched and a new market emerged when other companies were founded in Britain, France, and the US. It seemed that fast expansion of this technology was unstoppable but in 1904 the main disadvantages of gasoline vehicles with internal combustion engine (ICE) were beat by Henry Ford. It started an assembly line of low-priced, lightweight and gaspowered vehicles with significant improvements in noise, vibration an odor issues [6]. Even thought electricity based technologies refused to disappear, Henry Ford´s progress in vehicles machinery forces the vanishing from the scene of electric ones by the end of 1930. Nonetheless by the beginning of the decade of 70s, many countries concerned about energy shortage and dependence on external sources, resumed their investigations on this matter and interest in EVs resurged. In fact, in 1976 it was launched the Electric and Hybrid Vehicle Research in the United States by means of Development and Demonstration Act, Public Law 94-413. Also, California set a command on the utilization of zero emission vehicles at the early 2000s. Since short distance applications were the main target for EVs due to the limitation of batteries, these driving autonomy limitations of EVs boosted hybrid electric vehicles HEV growth by the end of the last century in Japan, India, the U.S. and Europe. The aim of hybridization is to fulfill vehicle needs of mechanical energy from both electric motors and traditional engines [32]. Nonetheless, this kind of equipment still uses fossil fuel energy as main power supply. The most representative HEV is Toyota Prius (released in Japan in 1997, worldwide by the mid of 2000); more than 2 million units of this model have been sold globally. Regarding HEV lack of electric recharge capability, Plug-In hybrid electric vehicles PHEV came up around year 2000. These cars keep both fuel and electricity feeding as they are supposed to be directly connected to distribution networks via low voltage connectors (similar to home appliances), keeping the possibility of being supplied from conventional gas stations [1]. Up to now the most recent definition of PHEV has been done by IEEE-USA’s Energy Policy Committee in June 2007. It is known as a position statement in which it have been settled that: “A plug-in hybrid electric vehicle (PHEV) is defined as any hybrid electric vehicle which contains at least: (1) a battery storage system of 4 Kwh or more, used to power the motion of the vehicle; (2) a means of recharging that battery system from an external source of electricity; and (3) an ability to drive at least ten miles in all-electric mode, and consume no gasoline” [14]. This definition should be kept on mind for proper comprehension throughout this text. It is expected that the mixture philosophy of PHEV tends to enhance the electricity dependence and decreases use of petrol; resulting (in a distant future) in a complete fleet of full battery -or other energy storage means- reliant vehicles named Plug-In Electric Vehicles PEV (2011 Nissan Leaf is a product of such aim)..

(7) Impact Assessment of Plug-in Electric Vehicles on 7 Distribution Networks 3.2 Energy Storage Systems Overview Power train of EVs complements the output of an internal combustion engine (ICE) during acceleration with an electric motor and in most of cases recuperates some energy while braking, this is why it is called regenerating braking. The autonomy and efficiency of PHEVs relies on energy storage system (ESS) features to not only save high quantities of energy but also release it rapidly following load demands [33]. Thus, two important concepts for ESSs involve energy density and power density, the first refers to the amount of energy stored per unit of weight and the second to amount of power supplied per unit of weight. A great variety of strategies for storing energy in EVs exists, among these options there are: ultracapacitors, flywheels, batteries and fuel cells. Ultracapacitors stores energy by separating positive and negative charges in parallel plates (electrodes) using a physical layer in between (insulator). As long as there are no chemical reactions with the electrodes, a long cycle life is guaranteed for ultracapacitors but low energy density is also expected. On the other side, the power density is noticeably high since the charges are physically gathered from the electrodes [33]. In ultracapacitors electrical energy is kept by the device, but for the case of flywheels it is stored mechanically, the principle of functioning is based on mechanics basics: a wheel some inertia moment that rotates around an axle can stores an amount of energy proportional to rotational inertia and square value of angular speed. The flywheel is designed to have a high inertia moment in order to maximize the quantity of available energy to be released [15]. As known, batteries are electrochemical cells that convert stored chemical energy into electrical energy. They are featured by its great energy density, compact size, and high reliability [5]. In fuel cells energy is taken from a substance (fuel) placed on the anode that reacts in the electrolyte with another substance (oxidant) on the cathode. During energy generation process, fuel and oxidant flow into the cell at the same time as the products of reaction flow out. As long as the reactants availability is retained, electricity is produced. Many combinations of fuel and oxidant can be done but current investigations are focused on hydrogen as fuel and oxygen as oxidant because of highest energy density of hydrogen and nonpolluting products for reaction (just water) [33]. Since vehicles demands high power density for accelerating and maneuvering and also requires high energy density to keep overall weight low while enhancing autonomy, no single ESS could meet all requisites of EVs. Mixed energy sources complement drawbacks of each single device. At the present days, combinations of batteries and ultracapacitors are the most common options for vehicular ESSs. As mentioned, batteries typically exhibit high energy densities storing major part of onboard electric energy whilst the rest remains in ultracapacitors for instances when high power is required [19]..

(8) Impact Assessment of Plug-in Electric Vehicles on 8 Distribution Networks 3.3 Nowadays Issues Currently, environmental issues are one the biggest incentives for EVs massification instead of energy alarms as some years ago, but many question about this fact arises. Regarding these questions is concerns about affordability for common residential users and capability for distribution networks to stand the burden this loads comprises. In the first case, the main features that make EV affordable are the autonomy and cost. To undertake the autonomy, the development of advanced batteries is in progress as well as improvement of fuel cells (FCs) to deal with both specific energy and energy density that are much lower in batteries than in fossil fuels [6]. To struggle the cost, electric motors, power converters, electronic controllers, energy management units and other EV ancillary equipment have been improved in the last years. In the second case nevertheless, the impact of PHEV massive utilization is yet under study [2], [9], [20], [22] and [23]. This project is intended to provide a specific impact assessment from several perspectives (steady state, spectral, etc) on a test distribution system that involves medium voltage (MV) and low voltage (LV) networks, using more than one evaluation criteria.. 4. IMPACT ASSESSMENT METRICS Firstly, it is defined the rules followed to assess the behavior of the whole MV grid as an aggregate of more than one LV network when a given degree penetration of PHEV occurs. Such PHEV penetration ratios should be set up and hereinafter each of that ratio will defined a different operating scenario (being the base case a scenario with null penetration ratio of PHEV). Seeing that the main objective of the present work is to give not only a steady state but also a spectral perspective of the evaluation, many metrics have been settled to independently judge many aspects of systems performance. They have been, obviously, classified in two groups: steady state for power systems frequency analysis (60 Hz in this case) and spectral for analysis among frequencies multiple of power systems’ frequency. The main goal of these metrics is to measure the operating “distance” between the real performance of networks and ideal performance (benchmark) given by international standards such as IEC, ANSI or IEEE.. 4.1 Steady State Metrics Due to the fact that steady state evaluation of a power system involves too many aspects, the scope of impact assessment is delimited to four features: transformers loading (chargeability), standard voltage, power factor and losses. As many of the utilities use oil immersed transformers for primary (MV) and secondary (LV) distribution systems, IEC standard 60354 from 1991 was the guide employed to estimate capacity levels of transformer loading [17]. It establishes that transformer load follows a 24-hour period sequence that can be approximated to a 2-step load with a low value (valley time, majority of daytime) and a high value (peak time,.

(9) Impact Assessment of Plug-in Electric Vehicles on 9 Distribution Networks associated with energy consumption rush hour) depending on ambient temperature which is agree to be 20°C for this case [17]. Besides, it also sets up an emergency loading capacity that can be held for short times intervals in transformer lifetime. As the proposed assessment analysis is done for day periods that are supposed to occurs indefinitely in time, emergency issues related to this characteristic have been neglected. So that for each of the defined scenarios the daily load profile suggested by IEC standard is found out. Afterward, standard’s suggestion is compared against network profile: if power drawn out from transformers is less than IEC recommendation it is seen that current network components complies with IEC requirements under scenario conditions and it is deduced that scenario is supported, otherwise is concluded that scenario is not supported. According to Chapman [27] -and as widely accepted- voltage regulation it is a dimensionless quantity defined at the receiving end of a transmission line as the ratio of voltage drop (difference between no-load voltage and full-load voltage) and full load voltage. Despite this measurement could be a good indicator of quality of voltage level, there are many different criteria (between engineers and institutions) about optimum values for this quantity that difficult to find a neutral point view for judgment. Hence, it has been decided to follow IEC standard 60038 from 2002 to evaluate voltage profiles of networks for each phase among all nodes [18]. It should noted that IEC standard establishes for 120/208 V systems that recommended voltage range should lie between +10% and -10% of this value and also that for 13.8 kV systems the highest and lowest voltages are permit to remain at +5% and -10%, correspondingly. For all scenario conditions, if at least voltage value of one phase of one of the nodes it is out limits comprised in the mentioned standard tables, it is said that network supports the scenario. Even thought analysis is mainly done accordingly to IEC parameters; local utility regulation regarding standard voltages varies depending on countries internal legal framework, special attention should be paid to this fact when local specific studies are being executed. A comparative study for the power factor and losses metrics must be executed. It means that scenarios with PHEV under use are quantitatively compared against base case scenario (0% penetration rate). Regarding power factor, it is calculated in the upstream node of the transformer feeding the network; losses will also include transformer losses and lines losses up to the same node.. 4.2 Spectral Metrics It also can be declared that several aspects could also be evaluated within spectral context, it follows that the assessment is delimited to review boundaries violations of current and voltage distortion for individual harmonic according to ANSI-IEEE standard 519 [3]. Harmonic distortion for case base scenario loads is negligible because original components of the network are linear..

(10) Impact Assessment of Plug-in Electric Vehicles on 10 Distribution Networks Despite this project has been IEC standard oriented until now, IEEE 519 was selected. Such decision has been taken because IEC is focused on voltage quality and the conditions the whole network and power systems should fulfill to guarantee that no issue relating harmonic distortion will arise. In the other hand, IEEE considers different stakeholders involving in the energy exchange and separate responsibilities regarding that fact. So, recommend practices are indicated for both parts: in the case of end users it states the limits of current source distortion based on electrical characteristics of connection point (short circuit level and nominal feeding current) and for utilities perspective designates the margins for voltage harmonic distortion. At the end, distortion issues will be resolved by each side accordingly to determined responsibilities or shared duties will be settled down.. 5. GENERAL SCENARIOS FOR ASSESSMENT Once all networks for analysis are completely outlined, the following step is to determine the loading arrangement under impact assessment will be carried on. Following similar studies developed worldwide it was set that PHEV penetration ratios will be fixed to 25%, 50% and 100%. It means that it will be analyze what happens to distribution systems (primary and secondary) during an entire day following steps of one hour, when one-fourth, one-half and each of the current combustion motor vehicles is replaced by a PHEV. Amid all data collected from existing LV secondary distribution systems reports of local utilities it is selected the one that covers population with one car per home (per user) in average, then it is assumed that all users will have just one car despites statistics indicates that some could have a little bit more or less. All LV networks pointed out in the previous section takes into consideration this fact. In order to properly understand this document hereinafter, it will be agreed that Scenarios I, II and III will referred respectively to penetration ratios cited above (25%, 50%, and 100%) and correspondingly Scenario 0 will be base case, with no PHEV under use.. 6. A MODEL OF LOAD CURVE ESTIMATION WITH PHEV It will be supposed that there is always any way of getting (of at least reasonably assume) reliable information describing the whole mobility model involved in low voltage impact assessment. This means that statistical data about daily vehicle routine, driven distance and PHEV’s market share exist, so, some random variables related to this data can be raised. Thus, based on that info the complete low voltage load model for PHEV can be developed.. 6.1 Statistical Approach Daily vehicle routine information is required to create a variable named stay time Ts which illustrates the uncertainty around the time a vehicle stays at home so it can be.

(11) Impact Assessment of Plug-in Electric Vehicles on 11 Distribution Networks plugged in to the low voltage network and consequently be charged. Driven distance is related to the amount of consumed energy Ec a vehicle is called for to perform all daily routines its owner needs to fulfill. Finally, PHEV’s market share characteristics indicate the vehicles penetration ratio and the technical issues of these cars. Energy stored Eb by onboard batteries can be obtained from the latter data. Taking into account that hybrid vehicles consume not only electrical energy but also petrochemical energy (fuel), and based on the three variables mentioned above, probabilistic analysis can now be performed: maximum electrical energy recharged by onboard batteries Emax could be found based on the stay time Ts and stored energy Eb and actual recharged energy Er is then estimated by comparison of Emax and Ec. This energy is the one that is pulled out from the low voltage grid daily by each PHEV at local outlets (home, office, shopping center, parking slots).. 6.1.1 Stay time Ts To begin with analysis regarding the time a vehicle could be charged, it might be considered the places where electrical outlets will be accessible; since electrical energy is a vital commodity, almost all the time a car is not being driven a connection to low voltage grid can be found: at home, at the office, at the mall, etc. Despite this fact, not all the places with available connections are willing to plug the vehicles because of the price of the energy it consumes. Therefore, just home recharging is a reasonable assumption: such connection cannot be restricted although physical obstacles may be defeated for some cases (i.e. power outlet availability in underground parking slots for buildings). As we supposed previously, statistical information describing people daily driving routine exists as in some recently published documents [9], [20] and [22]. So, it is possible to have detailed data specifying hour by hour –or step by step if narrower time resolution is possible- probability of being at home for a vehicle so that let us denote pi this probability of a PHEV being at home at time i. Then, the whole set of pi values conforms a random variable P (due to nature of available data this random variable is discrete but is bounded within the daily 24-hour period). The probability distribution function of variable Ts will depend on pi values because each event of Ts is composed by a subset of events of P, hence, the probabilities that defines this distribution function will be determined by combinations of pi values. Since these probability values for random variable Ts are not easy to describe recursively by means of a formula, computational tools are used to asses these values.. 6.1.2 Consumed energy Ec As a result of all vehicles carrying out energy transformation during locomotion, energy consumption features are directly related to driven distances ones: the longer the distance, the higher the energy consumed is. The connection is then evident but the big issue around this item is the mathematical relationship between this two variables. Since statistical data about driven distance patterns is usually available, it.

(12) Impact Assessment of Plug-in Electric Vehicles on 12 Distribution Networks can be represented by a random variable and it is assumed that energy consumed is a function of this random variable. Some studies done by EPRI [7]-[8] and DOE [23] (by means of its many institutions across the country) in the United States have modeled the Americans daily driving behavior. The less complex relationship found, settles that energy consumed by vehicles and driven distance are linearly related by a constant rate that is then referred as specific energy requirement [kWh/mile] and varies among PHEV’s sizes: compact sedan (0.26 kWh/mile), mid-size sedan (0.30 kWh/mile), mid-size sport utility vehicle-SUV (0.38 kWh/mile) and full-size SUV (0.46 kWh/mile) [23]. Now, it seems that energy consumed will depend also on PHEV size, so that random variable Ec must include a relationship between random variable D –driven distance by end users- and market penetration of reviewed size of PHEV. Let j=1, 2, 3, 4 denotes the type of a PHEV: compact sedan, mid-size sedan, mid-size SUV and full-size SUV respectively, it follows that we can call Vj as the participation of type j vehicles among total number of PHEV’s of the whole market. Accordingly Vj is the ratio between quantity of type j vehicles and the sum of all vehicles, it depicts the probability for a PHEV to belong to type j set. These values will be different for each study case as it refers to different situations, but ∑Vj=1 always has to be accomplished. Let us note Etj the random variable describing the energy consumed by type j vehicles so linear relationship between these variables and Ts becomes obvious, the linear operator was already mentioned (specific energy requirement) and it is different for each type of vehicle. Total consumed energy Ec is computed as a weighted sum of all Eti variables. All facts and data explained above are summarized in Table I. TABLE I Specifications for PHEV types Vehicle class. Specific energy requirements [kWh/mile]*. Specific energy requirements [kWh/km]. Random variable Etj. Compact sedan. 0.26. 0.16. Et1= 0.16Ts. Mid-size sedan. 0.30. 0.19. Et2= 0.19Ts. Mid-size SUV. 0.38. 0.24. Et3= 0.24Ts. Full-size SUV. 0.46. 0.29. Et4= 0.29Ts. *Column information from [23].. Other (more complex) results for specific energy requirements can be reviewed but the numbers pointed out above are enough for the fixed scope as linear relationships comprises less computational effort, and consequently will be use hereinafter..

(13) Impact Assessment of Plug-in Electric Vehicles on 13 Distribution Networks 6.1.3 Stored energy Eb It has been stated that PHEV’s can be used as active components of distribution grids besides its transportation role because of the great amount of traveling energy a PHEV can save: Tesla Motors 2010 Roadster battery pack stores around 75 kWh [11], which were formerly extracted from the distribution grid. Knowing the quantity of PHEV’s a low voltage distribution network will serve and the capacity of the batteries of each one, the burden this new load (PHEV) comprises to the system can be determined. Nowadays the range of all electric range offered by suppliers is very wide fluctuating from a few kWh of first generation Toyota Prius to 60-100 kWh of target vehicles the DOE and EPRI studies pursuits [29]. This energy features might define market share distribution among available PHEV options since by the moment customers start to think about miles driven in all-electric mode; by now, fuel autonomy is not a critical issue when buying a car- because of short time refueling and high availability of gas stations- but in the case of PHEV it is. At this point let Eb be a new random variable illustrating the behavior of market share. It will start with statistics about sold PHEV and one can depict basic descriptive statistical figures such as the one that shows energy battery range divided between littler classes, which in turn will have an absolute frequency associated. In view of the fact that this kind of this bar graphics are basic discrete distribution functions, it can be inferred that assuming its shape as new random variable probability distribution is not out of common sense. This probability distribution may have almost infinite different shapes as cases studies differ.. 6.1.4 Maximum electrical energy recharged by onboard batteries Emax The time a car is plugged-in to the low voltage grid defines the maximum electrical energy it can recharge on its onboard batteries. Within the scope of this work it is assumed that PHEV users connect the car to the grid as soon as they arrive home. Thus, it is taken for granted that as long as a car is parked at home, it is plugged-in to the distribution network. This explanation reveals the reason why random variable Emax is a function of stay time Ts but relationship is not yet defined. Based on information provided by many manufacturers and researches presented in more than a few papers [5], [24] it can be concluded that power drawn out by batteries is not constant along the recharging period. According to data sheets provided by manufactures [11]-[12] it takes approximately between 2 to 4 hours for batteries to reach around 80% of their peak recharge and correspondingly it would take around 8-10 additional hours to completely be full recharged, then the amount of energy a PHEV will be able to save relies not also in the time it stays at home (Ts) but also on the size of the battery pack it owns or alternatively the energy it can store (Eb). Therefore, random variable Emax -consumed energy by PHEV- can be modeled as a product of Eb and a factor that is strictly function of Ts, as shown in (1). The value of.

(14) Impact Assessment of Plug-in Electric Vehicles on 14 Distribution Networks f(Ts) will be referred as load percentage factor as it must fluctuate between 0 to 1 indicating 0 to 100% of full battery pack capacity: E max = E b ⋅ f (T s ). (1). A new issue arises: which shape should f take. Recalling qualitative information explained above it becomes apparent that a two-line piecewise function must be designed so load percentage factor rises from 0 to 0.8 in the first 2 to 4 hours and then reach the maximum value 1 at around 10 to 14 hours. Another choice is an upside down exponential function as shown by (2), being k a constant banking on the time the battery performs a “fast” initial recharge of around 60% of full battery capacity (this period is around 1-4 hours as mentioned before, so charging time constant k could a value between ¼ and 1). f (T s ) = 1 − e − kTs. (2). Many similar (or not) functional relationships could be established, however just the one reviewed by (2) will be used within the scope of this work.. 6.1.5 Recharged energy Er Once consumed energy and maximum electrical energy recharged by onboard batteries are defined, it is needed to determine the amount of (electrical) energy a car really recharges: on one hand, if the distance driven by a car and its battery pack supposes an specific maximum energy to be recharge but the energy consumed during the whole day is beneath this quantity, the latter energy is the threshold of recharged energy (it makes no sense to recharge more energy than the spent during the daily routine); on the other hand if maximum energy to be recharge is way lower than energy consumed, the former is now the limiting quantity. Let Er be the random variable describing daily energy recharged by a car, then previous statements are mathematically summarized as: E Er =  c  E max. if E c < E max if E max < E c. (3). The latter equation may be rewritten as: E r = min (E max , E c ). (4). In view of the fact that Emax and Ec are independent random variables, a mathematical expression is able to define for Er. From [4], let fX(x), fY(y) be the probability density.

(15) Impact Assessment of Plug-in Electric Vehicles on 15 Distribution Networks function of two independent random variables X and Y, similarly let FX(x), FY(y) be the respectively cumulative distribution function. If a third random variable is defined as: Z = min ( X , Y ). (5). f Z ( z ) = f X ( z )[1 − FY ( z )] + f Y ( z )[1 − F X ( z )]. (6). The following statement is true:. Replacing (6) in the case depicted by (4), lead us to the following relationship:. [. ]. [. f Er (e) = f Emax (e) 1 − FEc (e) + f Ec (e) 1 − FEmax (e). ]. (7). The probability density function of random variable under discussion is now defined; cumulative distribution function can also be found by direct integration (summation for discrete cases) over (7).. 6.2 Construction of PHEV Low Voltage Load Model As a result from the previous analysis the random variable recharged energy Er has been found. Subsequent procedure is straight forward: some Montecarlo simulations let creates independent samples of energy recharged by some cars and then power curves for each car can be found as loading shape formulation has been mentioned before in (2) during maximum energy to be recharged discussion. Firstly, Er is reproduced via its cumulative distribution function Montecarlo simulations: a random number generator for a uniform random variable defined within [0, 1] is required. So, once a random number is generated it can be mapped into random variable Er. Consequently, for some x numbers randomly generated, there will be up to erx (x an integer) values of energy recharged associated. Let be er1 a value obtained with the explained method, this value for recharged energy (in kWh) can be temporarily expanded as a power curve since it is known that power integration over time equals energy. If this power curve is noted as p(t) and instantaneous energy as e(t), from basics of circuits the next equation is fulfilled: de(t ) = p (t ) dt. (8). Some curve shapes for maximum recharged energy were explained, since actual recharged energy will follow the same trend, the selected functional form in (2) is still valid. Therefore, for a PHEV with all-day-long energy demand er1 the following equations apply:. (. e(t ) = e r1 1 − e − kt. ). (9).

(16) Impact Assessment of Plug-in Electric Vehicles on 16 Distribution Networks p(t ) = ker1e − kt. (10). In (9) and (10) k represents a constant between ¼ - ½ hours, е is the Euler number (not to be confused with values for energy) and time t is measured in hours. A direct consequence of previous argumentation is that a daily sequence of power drawn out from the grid has been found for a vehicle; so, lots of similar computations can be done to recreate the whole vehicles set. Fig. 1 provides a graphical approximation (via flowchart) to the whole process of getting a complete daily load profile for the entire PHEV fleet. At the beginning it is known information about charging time constant k, the number of vehicles x and the recharged energy Er, these are the input values. k, x, Er. for i=1,x. 1 PMC. 1 PMC. 0. 0. eri. ti. pi(t) ei(t) No. i=x? Yes. ∑pi(t). Fig. 1. Model Overview.

(17) Impact Assessment of Plug-in Electric Vehicles on 17 Distribution Networks After that, the process explained in previous section is executed for all PHEV determining eri energy amounts by Montecarlo simulations; at the same time other simulations are similarly carried out to find out the exact moment of the day the power sequence starts to run (ti), a typical daily load profile is used: the probability for vehicle owner to reach home is approximate to this profile. The next step is to generate daily load profiles for each car taking into account not only the amount of energy eri and initiating time ti but also the charging constant time k, for both electric power p(t) and energy e(t) curves. The procedure is performed until the total number of vehicles x is reached. Finally, all curves all summed up together to obtain the complete daily load profile.. 7. DISTRIBUTION SYSTEM FOR TESTING PURPOSES Based on 13 Node Test Feeder released by the Distribution System Analysis Subcommittee of IEEE [13], a grid was designed for testing purposes. Because of such system power consumption was too high for our purposes, a one-tenth scaling was done over the whole load profile and so changes in conductors, capacitors and other equipment were applied when required. Other modifications that one shall be aware of are: Nodes 633 and 634 were merged into one (634) hence all original IEEE Test Feeder nodes were Medium Voltage (MV) ones; for each MV node with available spot load data a step-down transformer (DYn type, 5 step no-load tap changer) with its own Low Voltage (LV) node was incorporated, single phase loads were split to the three phases in such a way the MV grid perceives three phase loads and; spot load data were connected to LV side of the transformers in order to create an end-user secondary system for each of the LV nodes. Also, voltage levels were adjusted from 115 kV, 4.16 kV and 0.48 kV to 34.5 kV, 13.8 kV and 0.208 kV, respectively, to match standard voltage levels in most of Latin American countries. The main aim of these variations was to perform impact assessment at two different voltages levels: LV and MV. Thus, there is one MV primary distribution system and eight LV secondary distribution systems feeding all end-users while being fed from MV nodes: 611, 634, 645, 646, 652, 671, 675 and 692. All networks indicated above were then modeled in ETAP© software (product developed by Operating Technologies Inc) as shown in Fig. 2 for MV grid. Since no LV network information is presented in IEEE system, it was taken from typical secondary distribution network standardized by local utilities. One topology was chosen and then scaled and replicated to the eight networks considering specific load values indicated in the IEEE document. These loads values were the basis from which conductors, step-down transformers and general grid equipment sizes were determined. Fig. 3 depicts Node 634 secondary distribution grid. In LV grid, end-users (residential) and public lighting loads were differentiated therefore when including PHEVs no one were connected to nodes where loads were there for lightning purposes exclusively..

(18) Impact Assessment of Plug-in Electric Vehicles on 18 Distribution Networks. Fig. 2. MV Distribution Network. Fig. 3. Typical LV Distribution Network.

(19) Impact Assessment of Plug-in Electric Vehicles on 19 Distribution Networks 8. IMPACT ASSESSMENT In order to have a better overview of system behavior with PHEV loads inclusion, assessment has been divided accordingly to network topology. Initially, it is focused on LV results for each of the grids and then they are transfer to MV network model data for obtaining final results.. 8.1 Low Voltage Networks Impact Assessment There are eight LV grids under study, denoted as: 611, 634, 645, 646, 652, 671, 675 and 692 respecting to feeding node. Thus, more than a few figures, tables and charts could be presented as results; nonetheless, just some remarkable matters will be detailed, letting the other less notable to be mentioned briefly. Before explanations began, it should be commented that all loads in the LV grids are single phase evenly distributed along the three phases, that is the reason why it has been said that from MV point of view the load looks like three phase but strictly speaking it is not true.. 8.1.1 Node 652 – LV Network This network is supplied by a 13.8/0.208 kV transformer with 45 kVA power capacity. As Fig. 2 illustrates, it is composed by two main lines feeding from a distribution node with some sub-branches, with 22 nodes total. The average power drawn out from the network during 20 valley hours is 14.85 kVA but power peak is nearby 25 kVA. According IEC standard such load behavior allows transformer peak power value to be 61 kVA during the 4 rush hours. In fact, just 72% of maximum energy that network can deliver is being expended (390 kWh vs. 572 kWh), Fig. 4 displays that issue. 70.000. 70.000 Total KVA. Total KVA 60.000. 60.000. Users Scenario I. Users Scenario 0 50.000. PHEVs 25. 50.000. IEC 60354 @20°C IEC 60354 @20°C. 40.000. Fig. 4. Node 652 network. Scenario 0 (without PHEV) loading profile.. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H00. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. 0.000. H06. 0.000 H05. 10.000. H04. 10.000. H03. 20.000. H02. 20.000. H01. 30.000. H00. 30.000. H01. kVA. kVA. 40.000. Fig. 5. Node 652 network. Scenario I loading profile.. Once PHEV runs, Scenario I, II and III results shows that transformer is better loaded if valley hour’s power gets closer to rated power: maximum energy utilized from transformer grows correspondingly to 82.2% (531 kWh vs. 645 kWh), 88.2% (634 kWh vs. 719 kWh) and 100.2% (940 kWh vs. 939 kWh, overload is insignificant so it is.

(20) Impact Assessment of Plug-in Electric Vehicles on 20 Distribution Networks ignored), Fig. 5 to 7 highlights such issue: according to IEC guidelines, transformer can stand all Scenarios conditions. It must be observed that loading profile that maximizes energy availability from transformer is a 45 kVA constant value throughout day –an ideal case- to obtain 1080 kWh. It is known that many utility companies uses to oversized rated power for MV-LV transformer to retain some future expansion capacity, this feature is quite useful for PHEV penetration since expected reserve power by utility is then consumed by vehicles. 70.000. 70.000. Total KVA. Total KVA 60.000. 60.000. Users Scenario II PHEVs 50. 50.000. Users Scenario III PHEVs 100. 50.000. IEC 60354 @20°C. IEC 60354 @20°C 40.000. Fig. 6. Node 652 network. Scenario II loading profile.. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H00. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. 0.000 H06. 0.000 H05. 10.000. H04. 10.000. H03. 20.000. H02. 20.000. H01. 30.000. H00. 30.000. H01. kVA. kVA. 40.000. Fig. 7. Node 652 network. Scenario III loading profile.. Regarding voltage profile of all nodes, panorama is not as great as up to now. Tap changer position is selected in order to fit minimum and maximum voltage of all nodes within the gap defined by standards, Fig. 8 depicts the situation for Scenario 0: minimum voltage is recorded for Phase B of Node 217 at hour 20 (93.7% of rated voltage) and maximum voltage goes for Phase C of Node LV at hour 3 (104.2% of rated voltage, this is the node where LV transformers bushings are connected). In the picture the minimum and maximum voltage hour by hour is recorded for the whole set of nodes comprising 652 network. As soon as PHEV are plugged to network voltage profile is affected by the new loads, so again, tap changer is fixed to try to bring all node voltages into the windows stated by standards. The goal is accomplished for Scenarios I (Fig. 9) and II (Fig. 10), but for Scenario III (Fig. 11) it is not possible to adjust tap changer to achieve the target: voltage drop in the end-line nodes is too high at peak hours (Phase B of Node 217 voltage is 82.6% of rated voltage at hour 20)..

(21) 110.0. 110.0. 108.0. 108.0. 106.0. 106.0. 104.0. 104.0. 102.0. 102.0 V (%). 100.0 98.0. 100.0 98.0 MIN. MIN 96.0. 96.0 MAX. MAX 94.0. 94.0 IEC 60038. IEC 60038 92.0. 92.0. IEC 60038. 90.0. IEC 60038. Fig. 8. Node 652 network. Scenario 0 minimum and maximum voltage profile.. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H02. H01. H00. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H01. H00. 90.0 H03. V (%). Impact Assessment of Plug-in Electric Vehicles on 21 Distribution Networks. Fig. 9. Node 652 network. Scenario I minimum and maximum voltage profile.. 110.0. 110.0. 108.0 108.0. 106.0 104.0. 106.0. 102.0 104.0. 100.0 98.0. V (%). V (%). 102.0 100.0. 96.0 94.0 92.0. 98.0 MIN. 90.0. MAX. 88.0. MIN. 96.0 94.0. MAX. 86.0. IEC 60038. IEC 60038. 84.0 92.0. IEC 60038. IEC 60038. 82.0. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H01. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H01. H00. Fig. 10. Node 652 network. Scenario II minimum and maximum voltage profile.. H00. 80.0. 90.0. Fig. 11. Node 652 network. Scenario III minimum and maximum voltage profile.. Basics of the evaluation regarding voltage profile is that international standards states a window within which voltage cans fluctuate the entire day, then accordingly to circuit loads, utilities adjusts tap changers to match nodes voltage to such window. The problem is when the window is narrower than voltage fluctuation: In 652 network the voltage gap (difference between maximum and minimum node voltage during day) increases from 10.7% in Scenario 0 to 13.5%, 15.1% and 22.5% in Scenarios I, II and III correspondingly. But IEC 60038 establishes that window wide should not be wider than 20% (fluctuation is allow between +/-10%). It is appreciated that tap changer will not be able to fix voltages within desired constraints in all cases because of limited number of steps for these kind of transformers..

(22) Impact Assessment of Plug-in Electric Vehicles on 22 Distribution Networks Next analysis will check overall network losses and power factor in the MV node. In the first case it was calculated the percentage of total apparent power drawn out from the transformer that is lost by means of lines and the transformer itself, in the Scenario 0 the percentage varies throughout day between 4% to 7% reaching 7% to 15% in the Scenario III. The upsurge in losses is proportional to PHEV penetration as depicted by Fig. 12. For power factor there were no input data available so it was assumed constant at a value given by the IEEE model documents. As soon as vehicle loading is added it is seen a non linear upgrading of power factor which become proportional to penetration rate for the subsequent Scenarios. It ought to be mentioned that power factor for battery charger related to PHEV is selected as 0.95 for all vehicles following National Electric Vehicle Infrastructure Working Council (IWC) Record of Consensus (ROC) recommendations as in [26], so, for networks with initial power factor below 0.95 (all cases) it will be seen an improvement that is more significant for the lowest initial values. 0.920. 16.00% Losses Scenario 0 14.00%. 0.900. Losses Scenario I Losses Scenario II. 12.00%. 0.880. Losses Scenario III 10.00%. 0.860 8.00% 0.840 6.00% 0.820. Total Scenario 0. 4.00%. Total Scenario I 2.00%. 0.800. 0.00%. 0.780. Total Scenario II. Fig. 12. Power losses for Scenarios 0, I, II and III.. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H01. H00. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H01. H00. Total Scenario III. Fig. 13. Power factor for Scenarios 0, I, II and III.. Concerning spectral assessment, guidelines defined by IEEE 519 were used. In the case of voltage it indicates a value for top individual harmonic distortion referred to fundamental frequency component; this value depends of system voltage: for 69 kV and below is 3%. Due to harmonic components were found for all nodes during 24 hour periods, results can be obtained in two ways: it can be seen how many nodes infringes IEEE guidelines hour by hour, or, it can be observed how much time do a node exhibits violations of IEEE recommendations in the full day. Percentage referred to total number of hours or nodes are found in both cases and depicted in Fig. 14 and 15 respectively. It is obvious that Scenario I and II are last by the network but not so Scenario III. In the latter, Fig. 14 shows that during peak hours (19 to 22) more than 80% of nodes exhibit.

(23) Impact Assessment of Plug-in Electric Vehicles on 23 Distribution Networks individual harmonic distortion above permitted, in fact most of the day there at least 40% of nodes over recommended values. From Fig. 15 can be inferred that nodes faraway from feeding transformer are beyond IEEE recommendations more hours in the day: in one of the main lines, from node 103 to 106 the peak distortion values are overcome more than 12 hours a day (>50%); on the other main branch, from node 204 to 217 happens the same, except for node 213 (maybe because is located at the end of a sub-branch with low single phase loads and few users). 100.0%. 100.0% Scenario I. 90.0%. 90.0% 80.0%. Scenario I Scenario II. Scenario II 80.0%. Scenario III. Scenario III. 70.0%. 70.0%. 60.0%. 60.0%. 50.0%. 50.0%. 40.0% 40.0% 30.0% 30.0% 20.0% 20.0% 10.0% 10.0% 0.0%. Fig. 14. Individual voltage distortion violations by hours.. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H01. H00. 0.0%. Fig. 15. Individual voltage distortion violations by nodes.. In the current distortion case calculations are not as easy as in voltage; the highest allowed values for distortion of odd order harmonics varies among the order and depend on relationship between maximum short-circuit current, Isc, and maximum load demand current (fundamental frequency component), IL. So, for a certain branch feeding a node, the abovementioned currents are estimated then the ratio between both are found and finally a table in the cited standard indicates the maximum harmonic current distortion in percent of IL. With this in mind, the procedure is applied to all nodes and a similar analysis as for voltage distortion could be done, Fig 16 and 17 displays such results. In the case of current, it seems that distortion limits are more demanding due to the fact that no Scenarios appear to completely fulfill standard requirements: in Scenario I there is one node with issues, node 213 –end of a small sub branch- violates current distortion constraints during most of the day (but besides that fact everything within Scenario I conditions is OK). For Scenario III situation is critical because there is no hour in which network complies with requisites, on the contrary Fig. 16 depicts that at least 60% of the nodes are out of the boundaries the entire day. Just nodes 105, 106, 111 and 217 do not exhibit such behavior (see Fig. 17) but these items correspond to the end termination of main lines or sub branches..

(24) Impact Assessment of Plug-in Electric Vehicles on 24 Distribution Networks 100.0%. 100.0% Scenario I. 90.0%. 90.0% 80.0%. Scenario I Scenario II. Scenario II 80.0%. Scenario III. Scenario III. 70.0%. 70.0%. 60.0%. 60.0%. 50.0%. 50.0%. 40.0% 40.0% 30.0% 30.0% 20.0% 20.0% 10.0% 10.0% 0.0%. Fig. 16. Individual current distortion violations by hours.. H23. H22. H21. H20. H19. H18. H17. H16. H15. H14. H13. H12. H11. H10. H09. H08. H07. H06. H05. H04. H03. H02. H01. H00. 0.0%. Fig. 17. Individual current distortion violations by nodes.. Examining Scenario II results is seen that corresponds to an intermediate case between I and III, it is not quite good but neither critical. There are some hours in service with no violations but also there are some nodes (101, 102 and 213) that exceed limits of operation during more than 30% of the day. Up to now, a complete assessment has been performed for a LV network. It is out of the scope of the current work the give a detailed analysis of the rest of the networks but to present a complete summary of the results is needed. Therefore, the next sub section is intended to show that information.. 8.1.2 Summary of results As explained, all the networks have the same topology but differ in the total power of the circuit. Table I sums up the rated power of transformers for the different LV networks.. LV Network 611 634 645 646. TABLE I Transformers Power for LV networks Transformer Transformer LV Network Power [kVA] Power [kVA] 45 652 45 112.5 671 225 45 675 225 75 692 45. To recall, there are four items that counts for the steady state analysis: a) Chargeability: It represents the percentage of power drawn out from transformer referring to IEC guidelines..

(25) Impact Assessment of Plug-in Electric Vehicles on 25 Distribution Networks b) Standard Voltage: It denotes compliance to IEC standards for voltages, so if any node voltage in the Scenario under inspection violates the recommendations it is noted as “” (non conformance), otherwise it is stated that evaluation of this item is OK. c) Average Losses: It counts average percentage losses during entire day, of overall network respect to total apparent power drawn out from transformer. d) Average Power Factor: It indicates the average percentage power factor during entire day measured at MV node of the networks. All mentioned results are systematically summarized in Table II. From its results for LV networks impact assessment of steady state metrics can be inferred for each of the discussed Scenarios: a) All LV networks stand the main condition of Scenario I (25% penetration rate of PHEVs). Thus, Scenario I complies with international standard requirements in question. b) Some LV networks do not endure the main condition of Scenario II (50% penetration rate of PHEVs). From a strict point of view it could be said that Scenario II do not meet the terms set by international standards, nonetheless, being a little bit less demanding (but more flexible) it can be stated that Scenario II almost fulfills the conditions and so, some modifications in two networks (645 and 675) should arise to correct undesired behavior. c) None of LV networks supports the electrical efforts demanded by the main condition of Scenario III (50% penetration rate of PHEVs). Scenario III cannot be considered as a real operating state of the networks.. LV Network (Node) 611. 634. 645. TABLE II LV Networks steady state assessment. Summary of results Charge Average Std Voltage Average Scenario ability Power [IEC] Losses [%] [%] Factor [%] 0 78.3 OK 4.8 89.9 I 89.7 OK 6.3 91.1 II 96.7 OK 7.4 91.6 110.2 10.0 91.9 III 0 79.8 OK 5.3 79.0 I 88.0 OK 6.4 82.6 II 95.0 OK 7.4 84.6 III 106.3 OK 9.7 86.4 0 82.6 OK 5.2 80.3 I 92.2 OK 6.5 84.5 7.8 86.5 II 98.7 III 111.7 10.9 88.4.

(26) Impact Assessment of Plug-in Electric Vehicles on 26 Distribution Networks LV Network (Node) 646. 652. 671. 675. 692. Scenario 0 I II III 0 I II III 0 I II III 0 I II III 0 I II III. Charge ability [%] 73.1 82.1 89.8 102.6 72.0 82.2 88.2 100.2 75.9 85.6 92.6 103.8 79.2 88.7 98.1 109.9 85.0 93.9 100.0 112.2. Std Voltage [IEC]. Average Losses [%]. OK OK OK OK OK OK OK OK OK OK OK OK OK OK -. 5.1 6.3 7.4 10.7 4.9 5.9 7.0 9.6 6.8 8.1 9.7 12.7 7.4 8.9 10.4 14.3 5.6 6.9 8.0 11.0. Average Power Factor [%] 86.1 88.2 89.5 90.5 83.0 86.6 88.0 90.0 82.9 85.3 86.2 86.7 84.8 86.4 87.1 86.9 74.7 80.5 83.3 86.3. Other meaningful issues can be addressed from information presented in Table II are the following: -. -. -. Node 652 is the unique network that does not overload the transformer in Scenario III. It can be deduced that around 70% or less chargeability in Scenario 0 lead the network to stand Scenario III PHEVs extra load. Power factor increases as PHEV penetration ratio grows, however it looks that there is a saturation point over augmentation is no admitted. In Node 675 network average power factor increases up to 87.1% in Scenario II and then decreases to 86.9% in Scenario III, it is the only case in which such behavior is witnessed. In most cases IEC voltage standard is not followed by Scenario III, in spite of this there are three exceptions: Node 634 has no problems regarding this issue and Node 645 and 675 do not even stands Scenario II. It can be hypothesized that conductors’ selection may affect the network behavior. Up to now first part of complete LV assessment have been done, now reminding spectral metrics, Table III summarizes results concerning this subject. It is noted that results are not included for Scenario 0 due to loads are assumed to be harmonic less in.

(27) Impact Assessment of Plug-in Electric Vehicles on 27 Distribution Networks the base case, then no results are able to be obtained. Also, one should be aware that hour by hour and node by node data is brought together via percentage average values. Thus, “Average Voltage Issues [% hrs]” refers to the average number of hours (in percentage) that nodes voltage harmonic distortion requisites are violated, as well as “Average Voltage Issues [% nds]” points out the average number of nodes by hour that voltage harmonic distortion requisites are infringed.. LV Network (Node) 611. 634. 645. 646. 652. 671. 675. 692. TABLE III LV Networks spectral assessment. Summary of results Average Average Average Average Voltage Voltage Current Current Scenario Issues Issues Issues Issues [% hrs] [% nds] [% hrs] [% nds] I 0.0 0.0 1.0 0.6 II 4.8 5.2 23.2 22.1 III 72.2 72.1 68.3 67.7 0.0 0.0 0.0 0.0 I II 9.5 10.4 20.6 19.5 III 81.9 82.3 71.2 70.8 0.0 0.0 1.0 0.6 I II 4.8 5.2 23.2 22.1 III 72.2 72.1 68.3 67.7 I 0.0 0.0 0.0 0.0 II 1.0 1.1 20.0 19.3 III 71.2 71.2 72.4 72.1 0.0 0.0 3.2 3.0 I II 0.0 0.0 8.7 8.2 III 42.9 43.3 69.4 69.0 0.0 0.0 1.0 0.6 I II 48.2 48.5 43.1 42.0 III 86.1 86.1 79.2 79.0 0.0 0.0 0.2 0.2 I II 59.3 59.7 48.8 47.6 III 88.9 89.0 81.7 81.8 0.0 0.0 0.8 0.4 I II 4.4 4.8 21.4 20.6 III 73.0 72.9 68.1 67.5. The context changes dramatically for spectral assessment: strictly speaking NO Scenarios are allowed by the networks, but, it can be figured out from shading data that Scenario I almost done it (highest average for current distortion is below 3.5%)..

(28) Impact Assessment of Plug-in Electric Vehicles on 28 Distribution Networks To finish with spectral evaluation two premises are affirmed: a) Scenario I can be practically supported by LV grids if some actions are taken in Node 652 network to struggle with current harmonic distortion. It could be done via some filters or capacitors. Other networks exhibit equal or less than 1.0% average harmonic current distortion which can be dismissed. b) Scenarios II and III cannot be considered as operating possibilities. In most of the cases there are a huge number of nodes during long time periods violating standard requirements for voltage and current harmonics distortion. Too many arrangements must be done in LV networks for properly operation with more than 50% PHEV penetration ratio. Collecting argumentation from steady state and spectral assessment it is obviously stated that Scenario I comprises unique operating circumstances for LV networks under international standards guidelines. Scenarios II and III required some (or lots of) investment in networks changes to reach technical conditions to fulfills the mentioned requirements.. 8.2 Medium Voltage Network Impact Assessment Last but not least, MV network assessment is performed based on data gathered from previous results; this info is uploaded to MV grid model in order to execute a similar analysis for 13.8kV voltage level, in this case the whole circuitry in fed by a 34.5/13.8 kV transformer with 630 kVA rated power. As in previous case, Table IV and V summarize evaluation results for steady state and spectral metrics. It should be taken into account that detailed explanation of Node 652 analysis have been done for guidance purposes, it shows the procedure followed during the entire research that gives rise to this document.. Network (Node) MV. TABLE IV MV Network steady state assessment. Summary of results Charge Average Std Voltage Average Scenario ability Power [IEC] Losses [%] [%] Factor [%] 0 92.9 OK 4.5 83.9 I 95.3 OK 5.1 90.8 II 103.3 OK 6.7 89.8 III 116.1 OK 9.9 88.1.

(29) Impact Assessment of Plug-in Electric Vehicles on 29 Distribution Networks. Network (Node) MV. TABLE V MV Network spectral assessment. Summary of results Average Average Average Average Voltage Voltage Current Current Scenario Issues Issues Issues Issues [% hrs] [% nds] [% hrs] [% nds] I 0.0 0.0 1.0 0.6 II 0.0 0.0 37.5 22.7 III 27.9 28.0 46.5 28.8. The hypothesis managed until now is strengthened by data recorded in the tables: Scenario I is completely stood by all the components of the whole distribution system since LV as long as MV networks exhibits great response to penetration rate of 25% for PHEVs. In the case of Scenario II things are not that easy: steady state assessment does not comply slightly, but spectral results are big deal. There are many problems with current and voltage harmonic distortion (it is more notorious in LV grids but that feature remains once the MV situation) as well as voltage drops in some LV networks. Supposing that voltage problem can be solved by low efforts, great modifications still need to be implemented to avoid distortion matters.. 9. CONCLUSION 9.1 Summary As shown, it has been offered an alternative stochastic approach for determining a load model of PHEV connected to a low voltage grid. It was exposed that data required to run this procedure is regularly available from papers, research institutes or national statistics department, such feature make it easier to develop the load model: it is just needed a computer and some programming software. It must be quoted that it was found highly probable (>95%) that a car spends at least 10 hours at home (fast chargers are not strictly required from automakers), thus it explains why Emax seems to be alike a uniform distribution variable: almost all vehicles will be able to recharge a huge amount of their energy storage capacity. One last remarkable issue has to be pointed out: average daily consumption tends to be 5 kWh per PHEV. If these values are compared against United States, Germany, Japan and Colombia daily electricity consumption, which are respectively: 39.01 kWh, 20.39 kWh, 23.18 kWh and 2.94 kWh [10], [28], it is immediately noticed that an extra load of around 13% to 170% is added to the existing low voltage network varying geographically. Special attention should be paid by Colombian networks operators (in general, operators from Less Developed Countries) since high extra efforts from MV and LV systems will be demanded in the future..

(30) Impact Assessment of Plug-in Electric Vehicles on 30 Distribution Networks Scenarios for Plug-In hybrid electric vehicle PHEV penetration ratio over or equal to 50% are completely forbidden inasmuch as results proved that distribution system would operate outside recommendations globally accepted by means of international standards. In fact, a certainty arises: 100% penetration cannot be support by existing electrical infrastructure. There are not only troubles with steady state metrics but also with spectral performance. Also, impact assessment depends on standard used to determine the metrics which will serve as benchmark to appraise network behavior. In cases where local norms could be more demanding than international requirements suggested by IEC or IEEE, evaluation under such patterns would have thrown different statements. In this case, international worldwide accepted (or considered) standards have been utilized; however, special attention should be paid when regional assessment is being carried out. It was explained that normative framework is a vital component of such studies.. 9.2 Further Work Further studies should include medium and low voltage impact assessment based on the method explained in this paper for other topologies and specific data provided by utilities operators. An assessment example was shown to prove that impacts might be expected so special careful should be taken in order to correctly evaluate overload, voltage regulation, phases unbalancing, harmonic distortion and losses issues. Other implications regarding rearrangement of protection coordination studies might arise. It was also shown that there are practically no issues for penetration rates below 25% and therefore it is suggested that further research involves the unexplored gap between 25% and 50%, steps around 5% are proposed.. 10 REFERENCES [1] A. D. Karlis, R. Gborbani, E. Bibeau, P. Zatietel, Simulation and Control Aspects of a Plug in Hybrid Electric Vehicle, International Review of Electrical Engineering (IREE), vol. 4 n. 4, July-August 2009, pp. 557 – 563. [2] A. Maitra, K.S. Kook, J. Taylor, A. Giumento, Grid Impacts of Plug-in Electric Vehicles on Hydro Quebec’s Distribution System. IEEE PES Transmission and Distribution Conference and Exposition. 2010. [3] ANSI Standard IEEE 519: 1992. IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems. 1992. [4] A. Papoulis, S. Pillai, Probability, Random Variables and Stochastic Processes (4th edition, McGraw Hill, 2002). [5] C.C. Chan, K.T. Chau, Modern Electric Vehicle Technology (Oxford University Press. New York, U.S. 2001). [6] C. Chan, The State of the Art of Electric, Hybrid, and Fuel Cell Vehicles. Proceedings of the IEEE, vol. 95 n. 5. 2007, pp. 704 – 718. [7] Duvall, M, Advanced Batteries for Electric Drive Vehicles. A Technology and CostEffectiveness Assessment for Battery Electric Vehicles, Power Assist Hybrid Electric.

(31) Impact Assessment of Plug-in Electric Vehicles on 31 Distribution Networks Vehicles, and Plug-in Hybrid Electric Vehicles. Final Report. 1009299. Electric Power Research Institute, Palo Alto, CA. 2004. [8] Duvall, M, Comparing the Benefits and Impacts of Hybrid Electric Vehicle Options for Compact Sedan and Sport Utility Vehicles. Final Report 1006892. Electric Power Research Institute. Palo Alto, CA. 2002. [9] Environmental Assessment of Plug-In Hybrid Electric Vehicles - Volumen 1. Electric Power Research Institute, Natural Resources Defense Council, Charles Clark Group, California, U.S. 2007. [10] http://www.dane.gov.co/daneweb_V09/. Retrieved 09/12/2010. [11] http://www.motortrend.com/roadtests/alternative/112_0912_2010_tesla_roadst er_sport_test/specs.html#ixzz0b8yiYKO0. Retrieved 06/06/2011. [12] http://www.revaindia.com/specifications.html. Retrieved 06/06/2011. [13] IEEE Distribution Planning Working Group Report. Radial distribution test feeders 13 Node Test Feeder. IEEE Transactions on Power Systems, August 1991, Volume 6, Number 3, pp 975-985. [14] IEEE-USA. Board of Directors. Plug-In Electric Hybrid Vehicles. Position Statement, June 2007, Washington D.C., U.S. [15] I. Husain, Electric and hybrid vehicles: design fundamentals (CRC Press. Boca Raton, FL, U.S. 2003). [16] International Energy Outlook 2010. U.S. Energy Information Administration. Washington, DC. U.S. 2010. [17] International Standard IEC 354: 1991. Loading Guide for oil-immersed power transformers. 2nd Edition. 1992. [18] International Standard IEC 60038: 2002. IEC Standard Voltages. 2002. [19] J. Larminie, Electric vehicle technology explained (John Wiley. West Sussex, England. 2003). [20] K. Schneider, C. Gerkensmeyer, M. Kintner-Meyer, R. Fletcher. Impact Assessment of Plug-In Hybrid Vehicles on Pacific Northwest Distribution Systems. Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century. 2008. [21] K. S. Lackner, J. D. Sachs, a Robust Strategy for Sustainable Energy. The Brookings Institution. 2005. [22] L. Zhao, S. Prousch, M. Hübner and A. Moser, Simulation Methods for Assessing Electric Vehicle Impact on Distribution Grids, IEEE PES Transmission and Distribution Conference and Exposition. 2010. [23] M. Kintner-Meyer K. Schneider R. Pratt, Impacts Assessment of Plug-in Hybrid Vehicles on Electric Utilities and Regional US Power Grids_Part 1 Technical Analysis. Pacific Northwest National Laboratory Report. U.S., 2007. [24] P. Bauer, Yi Zhou, J. Doppler, N. Stembridge, Charging of Electric Vehicles and Impact on the Grid. 13th International Symposium MECHATRONIKA. 2010..

(32) Impact Assessment of Plug-in Electric Vehicles on 32 Distribution Networks [25] P. Naderi, S. M. T. Bathaee, A. Farhadi, Driving/Regeneration and Stability Enhancement for a Four-Wheel-Drive Hybrid Vehicle, International Review of Electrical Engineering (IREE), vol. 4 n. 4, July - August 2009, pp. 547 – 556. [26] Secondary Distribution Impacts of Residential Electric Vehicle Charging – Public Interest Energy Research. California Energy Commission. 2000. [27] S.J. Chapman, Electrical Machinery Fundamentals (Mc Graw Hill International Edition. 2004). [28] United Nations Development Programme. Human Development Report 2007/2008 (Palgrave Macmillan, New York. U.S. 2007). [29] V. Isastia, S. Meo, Overview on Automotive Energy Storage Systems, International Review of Electrical Engineering (IREE), vol. 4 n. 6, November-December 2009, pp. 1122 – 1144. [30] World Energy Outlook 2010, Executive Summary. International Energy Agency. Paris, France. 2010. [31] Y. Cheng, J. Van Mierlo, P. Lataire, Test Platform for Hybrid Electric Vehicle with the Super Capacitor Based Energy Storage, International Review of Electrical Engineering (IREE), vol. 3 n.3, May-June 2008, pp. 466 – 478. [32] Y. Gao, M. Ehsani, J.M. Miller, Hybrid Electric Vehicle: Overview and the State of the Art. IEEE ISIE 2005. Croatia, 2005. [33] Z. Li, A. Khaligh, Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art. IEEE Transactions on vehicular technology, vol. 59, n.6, pp. 2806 – 2814.. 11 APENDIX 11.1 Nomenclature PHEV: HEV: EV: P: xi: Ts: Vi: Eti: D: E c: Eb: Emax: k: e:. Plug-In Hybrid Electric Vehicle. Hybrid Electric Vehicle. Electric Vehicle. Probability of a PHEV being at home. Sample from X random variable. Stay time of one PHEV at home. Probability of being a type i vehicle. Consumed energy by type i vehicle. Driven distance. Consumed energy by the PHEV in one day. Energy stored by onboard batteries. Maximum electrical energy recharged by onboard batteries. Charging time constant. Euler number (2.7172…)..

(33) Impact Assessment of Plug-in Electric Vehicles on 33 Distribution Networks f x: F x: E r: p(t): e(t): Isc: IL:. Probability distribution function for X random variable. Cumulative distribution function for X random variable. Recharged energy. Instantaneous electric power. Instantaneous electric energy. Maximum short circuit current. Maximum load demand current (fundamental frequency component)..

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