89 Liberty Street the Birthplace of Commercial Polyphase Power

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Joseph J. Cunningham history 89 Liberty Street the birthplace of commercial polyphase power

Today, 89 Liberty Street in New York City is part of the pedestrian In this issue’s “History” column, we examine the significance of 89 Liberty Street in plaza of the large office building known New York. This address was the 19th-century site of a small commercial structure as 1 Liberty Plaza. In 1887, it was the in which the first practical induction motor and polyphase system was constructed, site of a small commercial structure in patented, and demonstrated by inventor Nikola Tesla. The significance of this in- which the first practical induction motor novation was acknowledged over the subsequent half-century by leading electrical and polyphase system was constructed, engineers, culminating in this location being considered the birthplace of commer- patented, and demonstrated. Vital to cial polyphase power. Tlarge-scale commercial electrification, the polyphase system was declared by Joseph J. Cunningham returns for his ninth time to the pages of this “History” experts to be the most significant inven- column. He has researched and authored numerous works on topics such as indus- tion of the electrical age. It can be said trial electrification, electric utility power systems, and electric rail transportation. that the structure at 89 Liberty Street His book New York Power was published by the IEEE History Center Press in 2013. was truly the birthplace of modern ac We welcome him back as our guest history author for this issue of IEEE Power & technology in the United States. Energy Magazine. It was there that electrical engineer John Paserba and inventor Nikola Tesla (Figure 1) es- Associate Editor, History tablished his first laboratory in a small room on the second floor. With little more than a work bench, a few tools, and basic electrical instruments, he pro- electrical engineer and radio pioneer Dr. duced a demonstration of his induction E.F.W. Alexanderson said, “The induc- motor and the polyphase power system tion motor and our power system are it required. The significance of those in- enduring monuments to Nikola Tesla.” novations was noted over the next half- Dr. Charles Scott, past president of the century by leading electrical engineers. Bernard Behrend, vice president of the American Institute of Electrical Editor’s Note: In a previous article by Engineers (AIEE), declared during Joseph J. Cunningham, “Forgotten the May 1917 AIEE meeting at which Pioneer,” which appeared in the July/ the AIEE Edison medal was awarded August 2018 issue of IEEE Power & En- to Tesla, “Not since the appearance of ergy Magazine, the term Grand Cen- Faraday’s Experimental Researches in tral Station should be Grand Central Electricity has a great experimental truth been voiced so simply and so clearly. He Terminal, as the reference is to the left nothing to be done by those who present 1913 terminal, not the previ- followed him.” On another occasion, ous Grand Central Station of the 19th century that existed when the Daft/ figure 1. Nikola Tesla, circa 1890. Digital Object Identifier 10.1109/MPE.2018.2863618 Excelsior system was in operation. (Photo courtesy of Wikimedia Date of publication: 18 October 2018 Commons.)

88 ieee power & energy magazine 1540-7977/18©2018IEEE november/december 2018 AIEE and chair of the Electrical Engi- neering Department at Yale University, stated, “The evolution of electric power from the discovery of Faraday in 1831 to the great installation of the Tesla polyphase system at Niagara Falls in 1896 is undoubtedly the most tremendous event in all engineering history.” By 1882, Tesla had conceptualized an induction motor driven by the rotating magnetic field produced by ac that was out of phase. He conceived the principle in Europe, but his failure to elicit interest on the part of potential investors led to his emigration to America and a search for financial support in New York. After several false starts, he obtained financial backing from A.K. Brown of the Western Union Tele- graph Company and two of Brown’s colleagues. Together, they founded the figure 2. An 1899 map of the key locations in New York City, which are Tesla Electric Company in April 1887 discussed in this article. A: The Tesla lab at 89 Liberty Street; B: The Safety (later and leased a second-floor room at 89 United) Electric Light and Power Company offices; C: Edison’s 1889 Liberty Liberty Street. That location was propi- Street Annex Station; D: The Excelsior Power Company building, and E: Edison’s tious, for if any place could be claimed 1882 Pearl Street Station. (Image courtesy of Wikipedia Commons.) as the “cradle of commercial power,” the

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scce-2019-util-energy-IEEE-ad-nov-half-page.indd 1 9/18/18 2:34 PM The structure Stochastic Hydro: at 89 Liberty Uncertainty, solved. Street was truly Storage management and the birthplace dispatch decisions are always of modern ac hampered by uncertainty. technology Fortunately, a hydro tool exists to in the United guide optimal decision making. States. PLEXOS is the world’s leading energy simulation software that financial district area of lower Manhat- tan would certainly be it. allows you to customise and Edison’s legendary 1882 Pearl Street conﬁgure to deliver optimised power station at 255-57 Pearl Street was only a few blocks away. One block closer, models and effective forecasts. at 33-43 Gold Street, the Excelsior Pow- er Company’s new electric generating station was nearly complete; it supplied power for the first dc motors that elec- trified industries in lower Manhattan. Just across from Broadway, at 59 Lib- erty Street, the Safety Electric Light and Power Company established an office and was then renamed the United Elec- tric Light and Power Company, an electric utility pioneer destined to perfect efficient ac distribution systems ad apted to dense urban areas. Diagonally across from Broadway, at 60 Liberty Street, Edison’s second power station was constructed in 1886. Described as an “annex” station, it had no boilers but was powered by steam from the boilers of the building in which it was located. Numerous arc light, telegraph, and tele- phone facilities were also constructed in the area. In 1881, the New York Board of Fire Underwriters wrote the first known electrical safety code from its office in the Borrell Building at 115 Broadway, just one block south. That code is said to be the predecessor of the first National Electric Code, which appeared a decade later. Figure 2 shows a geographical perspective of these historic areas. Tesla’s initial goal was a practical induction motor and polyphase system that could be patented and then demonstrated to industrialists for commercial energyexemplar.com development. Thus, 89 Liberty Street november/december 2018 ieee power & energy magazine 91 was where the visions of experi- uivalent to that of the commer- mental devices that he had built cial dc systems of the day. in Europe were translated into His patent attorneys of the law practical hardware. His power firm Duncan, Curtis, and Page was provided by an alternator submitted the system to the pat- driven by a steam engine in the ent office on 12 October 1887. The stationery and printing company patent office responded that, as on the first floor. He could use the system was so complex, it the alternator only when the steam needed to be divided into smaller engine was not in use for print- units. New submissions followed ing, which limited his work hours on 30 November and 23 Decem- to times when the business was ber 1887. closed. Nonetheless, the facili- Seven patents were granted ties sufficiently met his needs, figure 3. Tesla’s model induction motor as on 1 May 1888 as follows: mo- and he produced working com- demonstrated before the AIEE in May 1888. (Image tors: 381968, 381969, and 382279; ponents within a few months. courtesy of the National Museum of American transmission: 382280 and 382281; By the summer of 1887, he had History, Smithsonian Institution.) distribution: 381970; and conver- produced a polyphase system and sion and distribution: 382282. induction motor that was to be analy University. Anthony’s results indicated Five more patents followed for varia- zed by electrical engineering expert that the motor and polyphase system tions of four- and three-wire systems: Prof. William A. Anthony of Cornell promised to achieve an efficiency eq 390413, 390414, and 390415, and 18

92 ieee power & energy magazine november/december 2018 cient and excessively expensive due to Barrington, Massachusetts, which was The polyphase the small areas they supplied. based on the transformer invented by concept also Westinghouse had been on a two- William Stanley. In an effort to develop year quest to develop ac systems that a system of his own, Westinghouse li- offered a could transmit power over a large area. censed the Stanley patent and also those In March of 1886, he financed an ex- of a variety of European inventors. All substantial perimental ac lighting system in Great of those efforts employed single-phase increase in efficiency when compared to the single-phase system.

followed. Ultimately, 40 patents were granted for the inventions developed at 89 Liberty Street. STRENGTH Market Search The development of patentable devices was merely the beginning for Tesla, as it was still necessary to attract the interest of a company with the resources to produce and market the system. A series of demonstrations followed until the cli- max on 16 May 1888, when Tesla deliv- ered a presentation on the polyphase system and induction motor to the AIEE at Columbia University. The initial Tesla motor is shown in Figure 3. As word of that lecture spread, Tesla and his polyphase system concept became the lead- RESILIENCE ing subject of the electrical engineering publications of the day. One man who learned of the lecture was George Westinghouse, a Pittsburgh industrialist with substantial experience in both system development and the manufacturing of components. Af- ter inventing the railroad air brake in 1869 and expanding into railroad sig- naling in 1882, he then ventured into commercial electric power. Westing- house’s vision for commercial power development focused on systems that could encompass entire geographic re- gions rather than limited urban areas. He was dissatisfied with the restric- tions of the dc lighting systems that WE ARE HB were being rapidly installed in all urban areas; he thought they were ineffi- november/december 2018 ieee power & energy magazine 93 Flexible. Accurate. Smart.

(a)

(b)

figure 4. Engineers working for George Westing- house refined Tesla’s concept and introduced this two-phase motor in 1888. (a) A front view and (b) ART Series side view. (Photos courtesy of the National Muse- Current Sensors um of American History, Smithsonian Institution.)

ART Series sensors are the first ac circuit designs that were derived from their dc pre- to combine easy-to-use flexible decessors. Westinghouse understood that lighting Rogowski coil technology with would never fully amortize an installation; it was Class 0.5s accuracy. When it comes the provision of power for motors that made utility to measuring energy production, systems profitable. However, there was not, as yet, an monitoring substation transformer ac motor suited to the variety of tasks that dc motors health or real-time allocation of energy performed well. costs and usage in smart buildings, Upon learning of Tesla’s new system and being in- ART Series sensors set the standard. trigued by his use of the ac motor, Westinghouse went to 89 Liberty Street to see the new concept in action. There, he found not just an ac motor but a complete At the heart of power electronics. polyphase system of alternators, transformers, and other components. The polyphase concept also offered a substantial increase in efficiency when compared www.lem.com to the single-phase system. Stories, some more likely myth than fact, abounded about that meeting at 89 Liberty Street. There is no doubt that, on 7 July 1888,

LEMA12618_PrintAd_ART_SmartIndustry_IEEE.indd 1 5/9/18 2:38 PM Oerlikon Works in Switzerland (and a Wenstrom in Sweden, and Galileo Fer- Upon his founder of the Brown Boveri Company raris in Italy. Ferraris would appear to discovery of in 1891) demonstrated a similar rotat- have been primary among all the claim- ing field. European claimants to the ants, having demonstrated the concept the rotating idea of the rotating magnetic field and in the early 1880s. Others explored the polyphase system included Michael concept but did not make claims to prima- magnetic field Dolivo-Dobrovolsky in Germany, Jonas cy, including Walter Bailey in London, concept, Tesla had devised the polyphase making life visibly safer circuit required to create that rotating field.

TM Westinghouse, impressed with what he SpanLite had seen, closed a deal with Tesla to li- Self-Illuminated cense all of the patents for manufacturing Power Line Marker and marketing. Financial details vary by accounts, but the deal was done. Figure 4 shows a refined Tesla induction motor. Meets FAA Advisory Tesla continued his work at 89 Lib- Circular 70/7460-1L erty Street. By the end of the year, (Dec. 2015) Westinghouse persuaded him to relo- Installs directly on cate to Pittsburgh to consult directly live lines up to 500 kV on the commercial development of the polyphase system. After only 20 months, the saga of 89 Liberty Street as the site of the most significant development in the history of electric power was over. Though short in duration, Tesla’s lab in that small loft space produced developments that ultimately determined the future course of commercial electric power. The events at 89 Liberty Street were to have a significant, though unforeseen, impact on the electric industry 17 years later.

Primacy Disputes The issue of primacy of the rotating magnetic field and the polyphase sys- Bird LED Obstruction Low Line tem is complex because it involves a Diverters Lights Flags & Markers number of players and systems. Elihu Thomson, of the Thomson-Houston Electric Company, claimed primacy in the development of a single-phase induction motor. Oliver Schallenberger of 800-722-8078 • pr-tech.com Westinghouse claimed a similar deve making life visibly safer 6709 lopment from his work on ac watthour meters, while C.E.L. Brown of the november/december 2018 ieee power & energy magazine 95 Louis Duncan at Johns Tesla system and practi- work indicates that Tesla had long sought Hopkins University, cal induction motor. to build a motor without mechanical and Frank J. Sprague developed American electrical ex- contacts for the transfer of power to the in New York. pert Carl Hering sought armature. This goal originated for Tesla A flurry of claims the split-phase to resolve the issue du at the age of 17, when he observed the filled technical jour- ring a tour of Europe sparks in a Gramme dynamo and en- nals in the 1889–1892 motor, which in the early 1890s. He gaged in a caustic exchange of opinion period, but there could could be reported that Ferraris with the professor of his physics class. be no denying that Tesla did not believe that Tes- Upon his discovery of the rotating mag- had built the first patent- started on a la could have known netic field concept, Tesla had devised able components at 89 of Ferraris’s experi- the polyphase circuit required to create Liberty Street in 1887. single-phase ments nor where they that rotating field. Some reports tried to re- would lead. solve the issue by giving circuit without Ferraris’s estimation 89 Liberty Street Ferraris credit for the any additional would appear to be cor- Cited in Litigation rotating field and Tesla rect, since virtually all Polyphase systems were vital to large- credit for the polyphase components. research into Tesla’s scale ac electrification, but simple,

figure 6. Sheet 3 of Tesla Patent 381968. This three-phase concept was a harbinger of the future trend and eventual dominance of three-phase systems. “The peculiar advan- figure 5. Sheet 2 of Tesla Patent 381968, “Electromagnetic tage of this disposition is in obtaining a more concentrated Motor,” dated 1 May 1888, which shows Tesla’s initial two- and powerful field,” as stated by Tesla on page 3, line 70 of phase generator and induction motor. the patent text.

96 ieee power & energy magazine november/december 2018 89 Liberty Street and demonstrated to application for the split-phase motor Clearly, there other visitors at that location in Sep- had also preceded the 22 April 1888 could be no tember of 1887. Judge Hazel further publication by Ferraris of an article on found that all such demonstrations a similar concept. dispute that and disclosures had occurred well That was significant, for it was still prior to the spring of 1888 and that claimed by some Tesla critics and also by Tesla held Tesla’s submission of the patent business opponents of Westinghouse that primacy in regard to the split-phase motor patent XGSLab - the Easy to Use, dispute. Full-Featured Grounding Solution

single-phase circuits were enough for XGSLab is the full featured grounding solution that can lighting in residential and small com- take you from a basic single-soil-layer step and touch mercial applications. Single-phase mo- analysis to the most advanced multilayer/zone soil tors were also needed for those appli- models. All this comes at a fraction of the base price cations, but these motors could not be started without specialized switches or and cost of ownership of comparable packages. capacitors. To address that deficiency, · Ground grid design and grounding system analysis Tesla developed the split-phase motor, · Uniform, multilayer and multi-zone soil models which could be started on a single-phase Step and touch potential analysis circuit without any additional compo- · Below and above ground systems nents. It was patented and became part · Lightning effects and electro- of the Tesla/Westinghouse system patent · contracts, a fact which proved pivotal in magnetic interferences future patent litigation. · Fault current distribution AC watthour meters used a simi- · Time and frequency domain lar principle, and some manufacturers sought to bypass the Tesla/Westing- house patents. A number of lawsuits were pursued by the Westinghouse Company Global Solution against the infringers, although virtual- The only software in the ly all of the litigations were resolved in favor of the Tesla patent. One exception, market that takes into account in 1904, led to an appeal in which Judge International (IEC), Hazel of the Circuit Court of Western European (EN) and New York sustained Tesla’s patents USA (IEEE) 511559 and 511560 against a manufacturer of watthour meters (Westinghouse standards vs. Catskills Lighting Company). That declaration reversed the previous decision, as it gave different weight to the Explore more online and statements of witnesses. The decision request a free demo copy at: found that Tesla had made disclosures to several investors (Brown and Nel- www.EasyPower.com/grounding lis) and also to his patent solicitor Page, all of which had occurred in the ® EasyPower is the exclusive representative fall of 1887. Power made easy. of XGSLab software in the USA and Canada. The decision found that the split- phase motor had been conceived at november/december 2018 ieee power & energy magazine 97 Prof. Ferraris of the University of Turin had discovered the rotating field concept prior to Tesla. Clearly, there could be no dispute that Tesla held primacy in regard to the split-phase motor patent dispute. A particular vociferous criticism had been voiced by William Stanley, who had been granted the primary U.S. transformer patent and subsequently licensed it to Westinghouse. Stanley’s complaint was focused primarily against Westinghouse, since he had severed relations with Westinghouse after a dispute and formed his own company. He particularly disputed the terms of the license of his transformer patent to Westinghouse. Stanley’s criticism was aimed at Westinghouse and his ac system, which, of course, included the Tesla patents. Judge Hazel’s ruling was subsequently confirmed by Judge Kohlsatt in a similar case in the Northern District of Il- linois. Both decisions upheld the split-phase Tesla patent as licensed by the Westinghouse Company. Those decisions effectively ended the debate in favor of Tesla and the West- inghouse Company as the U.S. Supreme Court refused to hear an appeal. Subsequent rulings declared that West- inghouse’s license of the Tesla patents also applied to polyphase synchronous motors and rotary converters. Although the patents expired in 1905 (at that time, patents expired after

figure 7. Sheet 1 of Tesla Patent 390413, “System of Electrical Distribution,” dated 2 October 1888, which shows a simplified two-phase three-wire system with a common return.

17 years), Westinghouse continued to win patent litiga t ion through 1906. Figures 5–8 show key sheets of two of Tesla’s patents. Thus, 89 Liberty Street was pivotal in the commercializa- tion of Tesla’s ac system concept and the patents that were vital to large-scale commercial electrification. While General Electric had installed polyphase transmission lines of a different concept in the early 1890s, Westinghouse and General Electric cross-licensed each other’s patents in 1896. Gen- eral Electric clearly deemed the value of the Tesla patents held by Westinghouse to be so important to large-scale commercial electrification that, in the exchange, it gave Westinghouse access to more than a decade of patents held by General Electric and its predecessor companies. As the 20th century dawned, those two leading companies, plus a few smaller firms that held licenses or valid patents of their own, launched the modern electrical age. The 89 Liberty Street location did not last much longer. It was demolished in the early 1900s to make way for one of

98 ieee power & energy magazine november/december 2018 the most spectacular buildings of the time, the 1908 Singer Building. Ornate in the best style of the day, the great marble lobby resembled a cathedral in detail and scale. It became an icon of the new urban image; variations on the theme appeared in print and film illustrations of the future as imagined by the artists of the day. It also inspired a similar depiction in the legendary figure 8. Sheet 2 of Tesla Fritz Lang film Metropolis. In point of Patent 390413, “System fact, the future as envisioned by those of Electrical Distribution,” dated 2 October 1888, artists was completely dependent on the which shows a simplified very devices that had been created in system applied to trans- that small, short-lived laboratory at 89 formers and single-phase Liberty Street. incandescent light circuits. The magnificent Singer Build- ing was acquired by U.S. Steel and demolished in 1968 for the new Liberty Plaza structure, which is one of the largest office structures in New York City. Although the footprint of 89 Liberty Street today is a pedestrian plaza, the progenies of those primitive polyphase machines of 130 years ago

GWE-18042_SustainBus_HP-Ad_186x127_noBleed-FINAL_180905.indd 1 9/5/18 2:33 PM are manifested in the dense urban de For Further Reading Company,” IEEE Power Energy Mag., velopment around it. That development L. I. Anderson, Bibliography Dr. Niko- vol. 11, no. 3, pp. 84–98, 2013. depends on light, power, transporta- la Tesla (1856–1943), 2nd ed. Minne- “The Brooklyn Edison system: Gen- tion, climate control, communications, apolis, MN: Tesla Society, 1956. erating stations and character of load in and commercial activities, all of which H. J. Sexton and H. S. Orcutt, Ori- City of Churches,” Elect. World, vol. are, in some manner, driven by the vast gin and Growth of Alternating Current 83, no. 21, pp. 1305–1321, 1911. polyphase system installed in both un- Networks. New York: New York Edi- “Ferraris vs. Tesla,” Elect. World, derground distribution networks and son Co., 1936. vol. 21, no. 15, p. 273, 1893. also in the “vertical” or “spot” power net- C. Hering, “Mr. Tesla and the Dreis- “Tesla split-phase motor patent de- works within the tall structures. trom patent,” Elect. World, vol. 19, no. 6, cision,” Elect. World, vol. 43, no. 12, pp. 84–85, 1892. p. 548, 1904. Acknowledgment F. W. Smith, “Development of an al- “Tesla split-phase motor patent de- The author would like to thank Robert ternating current system,” Elect. World, cision,” Elect. World, vol. 43, no. 14, Colburn of the IEEE History Center vol. 80, no. 11, pp. 555–557, 1922. p. 634, 1904. for locating a map of the area shown J. J. Cunningham, “An ac pioneer: in Figure 2. The United Electric Light & Power p&e

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november/december 2018 ieee power & energy magazine 101 How Electric Vehicles and the Grid Work Together

Lessons Learned from One of the Largest Electric Vehicle Trials in the World

In the coming years, hundreds of thousands of new electric vehicles (EVs), from plug-in hybrids to fully electric, will hit the roads around the world, adding to the current EV fleet of more than 2 million, according to the Global EV Outlook 2017. The electrification of transportation can bring environmental, health, and economic benefits when coupled with a low-carbon electricity generation portfolio; however, ensuring that this transition goes smoothly requires addressing several grid-integration challenges. To understand the challenges and opportunities that come with the widespread adoption of EVs, particularly passenger light-duty vehicles, many distribu- Ition network operators (DNOs) and stakeholders in various countries have carried out EV trials. One of the largest EV trials in the world was My Electric Avenue (MEA) (www.myelectricavenue.info) in the United Kingdom. Led by EA Technology, the trial ran from

Digital Object Identifier 10.1109/MPE.2018.2863060 ©istockphoto.com/3alexd Date of publication: 18 October 2018

January 2013 to December 2015 and was subsidized by the Low Carbon Networks Fund along with partners from industry, DNOs, and academia. The MEA project deployed more than 200 Nissan LEAFs to customers in the United Kingdom to study the driving and charging habits of a Vehicles and geographically and socioeconomically diverse population. This industrial project also investigated the technical effects of EVs on European-style low-voltage networks and trialed the direct control of EV charging points to increase hosting capacity. In this article, we provide details about the MEA trials, including the main infrastructure adopted. Based on the data analysis and network studies carried out, we present key findings in terms of 1) the the Grid Work charging habits of EV users, 2) the impact of EVs on low- voltage networks, and 3) the effectiveness of the proposed strategy to increase hosting capacity. Using what was learned from this large-scale project, we then show the additional results that aid in understanding the extent to Together which EVs could provide services to the electric grid. Finally, we sum- marize the key lessons learned from MEA.

The My Electric Avenue Project The MEA project deployed more than 200 Nissan LEAFs with a battery size of 24 kWh across the Unit- ed Kingdom (Figure 1), making it one of the largest (if not the largest) EV trials in the world to date that examines the challenges and benefits arising from the use of this technology at home (slow-charging mode at approximately 3.6 kW). The project’s main objective was to trial a solution (known as Esprit) to mitigate the impacts that EVs may pose on European-style low-voltage networks (i.e., multiple low-voltage feeders connected to the same distribution transformer supplying dozens or hundreds of customers). To achieve this, the project performed EV data analysis, modeling, impacts, and management studies. MEA was the first project to focus on how to best manage the local electricity network when a large number of EVs charge on the same street at the same time.

november/december 2018 ieee power & energy magazine 65 The MEA project had several commercial, social, and technical aims (see the EA Technology project close-down report in the “For Further Reading” section for full details). The key technical objectives were to ✔✔ learn customer driving and charging habits ✔✔ develop and test the equipment to ascertain its ease of installation ✔✔ evaluate the range of networks that will experience technical problems (voltages lower than statutory limits and overloads) through the creation of EV clusters ✔✔ investigate the types of networks in which the Esprit technology can operate successfully. To accomplish these objectives, MEA carried out two trials: one technical and one social. The technical trial (illustrated by the red pins in Figure 1) created ten clusters with a total of 101 EVs (7–13 EVs per low-voltage network) to study the performance of the EV management solution. For this trial, the project primarily used existing infrastructure, as it only needed to install sensors at the head of the feeders, a controllable charging point, and a programmable logic controller (PLC) at the substation to host the EV management solution (Figure 2). The social trial (shown by the blue pins in Figure 1), on the other hand, involved 118 participants with the purpose of studying the charging behavior of EV users (such as range anxi- ety and typical charging patterns). In this trial, the EV users did not have a “smart” charging point because they were not managed at all. The project studied the technical aspects associated with figure 1. The distribution of EVs during the MEA project the adoption of EVs and also the social, environmental, (technical: red; social: blue). and economic implications, resulting in a comprehensive

Three-Phase Cable Charging Point One-Phase Cable Sensor Data and Control Flow

Substation 11/0.4 kV

PLC

figure 2. The infrastructure used for deploying the EV management solution.

66 ieee power & energy magazine november/december 2018 The electrification of transportation can bring environmental, health, and economic benefits when coupled with a low-carbon electricity generation portfolio.

understanding that DNOs and regulators will find useful. ing event. However, the study methodology can be used to Table 1 details the key aspects of the project. The corre- consider as many charging events as necessary. sponding reports, findings, and key data are publicly available on the project’s website. Charging Early or at Night? It was found that the first charge might occur any time during Understanding How People the day, but the second charge was more likely to occur after Charge Their EVs midday. Figure 4 presents the start-charging time for week- Understanding when and for how long EV users will charge days separated into three periods of the day (morning peak their vehicles is one of the most critical aspects to realisti- from 6 to 10 a.m., evening peak from 3 to 9 p.m., and the rest cally studying EV interactions with the grid; however, EV of the day). The most common first charge time started at data are scarce, which highlights the need for more trials approximately 8 a.m. (before work) or at 6 p.m. (after work); that make key data publicly available. The MEA project recorded more than 85,000 nonmanaged charging events. table 1. The MEA project fact sheet. Every time an EV user charged a vehicle, the onboard monitoring system recorded the start charging time and ✔ Total cost: US$13 million* the initial/final state of charge (SOC). The project used ✔ Project length: three years the information from the 219 EVs involved in the project ✔ Project website: www.myelectricavenue.info to understand the drivers’ charging habits. Although the ✔ Scale knowledge gained from the MEA project may be specific to n Total number of EV users: 219 (101 technical and 118 EV drivers in the United Kingdom (and similar countries), social) the methodology used is generic and can be adopted by any n Low-voltage networks in the technical trials: ten (nine country or region. residential and one commercial) One interesting finding from the MEA project is that the ✔ Infrastructure variance in the charging behavior across seasons is limited, n Measurements i.e., there is no seasonality. This allows for grouping all charg- • Network: Phases V and I (10-min resolution) ing events and corresponding metrics into two categories: • EVs: start/end charging time, initial/final SOCs, start/ weekdays and weekends. Another interesting finding is the end trip time, and initial/final odometer highly erratic charging pattern shown by EV users during n Communications roughly the first seven days of use. This was considered to be • EV charging point  substation: PLC a familiarization phase, and the final database excluded the ✔ Project partners events during the first week (i.e., <1% of the total charging n Leader: EA Technology events). The following sections discuss, via histograms, the n DNOs: project’s key findings in terms of charging habits, including • Scottish and Southern Energy Network (lead DNO) the number of charging events per day, start charging time, • Northern Powergrid (participating DNO) initial/final SOC, and percentage of EVs charging on the n EV supplier: Nissan same day. n Academia: • The University of Manchester, United Kindgom Charging More Than Once Per Day? (technical analysis) Whether on a weekday or during the weekend, approxi- • De Montfort University, Leicester, United Kingdom mately 30% of EV users charged their vehicles more than (socioeconomic analysis) once per day, as shown in Figure 3, which also shows, how- n Others: Fleetdrive Electric, Zero Carbon Futures, and ever, that the majority of EV users (70%) charged their vehi- Ricardo cle only once per day. This finding is unique because most ✔ Areas of study: engineering, social sciences, environment, EV studies do not explore multiple charging events. Finally, and economics because of the low occurrence of three or more charging *All dollar values in this article are reported adopting the following events (<8%), they were considered part of the second charg- conversion rate: GBP £1 ➝ US$1.431. november/december 2018 ieee power & energy magazine 67 80

70 Weekday Weekend

Probability (%) 30

10 1.53 1.92 0.46 0.63 0.15 0.24 0.09 0.10 0

1234567+

Number of Connections Per Day

figure 3. A normalized histogram of the number of EV connections per day.

if a second charge happened, this 3 typically started after 6 p.m. This 2.5 demonstrates that a few EVs were charged at home before and after 2 working hours, a finding that 1.5 aligns with what was reported in 1 the social trials. During week-

Probability (%) ends, the first charge most likely 0.5 started between 9 a.m. and 6 p.m., 0 and the second charge occurred 12 246810 12 246810 (a.m.) (p.m.) later in the evening. In the MEA project, we found no significant Time of Day (15-min Resolution) (a) differences in the start-charging times among weekdays (Monday– 3 Friday) or between Saturday and 2.5 Sunday. Charging events occur- 2 ring on holidays were treated as weekdays or weekends, depend- 1.5 ing on the day of the week the 1 holiday occurred. Probability (%) 0.5

0 Always Expecting 12 246810 12 246810 a Full Battery? (a.m.) (p.m.) EV drivers are likely to plug their Time of Day (15-min Resolution) vehicle into the grid when their (b) battery SOC is relatively low, and 6–10 a.m. 3–9 p.m. they will probably leave it plugged 9 p.m.–6 a.m., 10 a.m.–3 p.m. in until the battery is full. The MEA project found that the ini- figure 4. Normalized histograms of the start-charging time per EV connection tial and final SOCs depend on the (weekday). The (a) first and (b) second charging events. number of charging events and

68 ieee power & energy magazine november/december 2018 One interesting finding from the MEA project is that the variance in the charging behavior across seasons is limited, i.e., there is no seasonality.

the start-charging time. For instance, an EV charged over- (approximately 71%) that the EV will have an SOC ranging night is likely to reach full charge. If it is used during the from three to nine units. This behavior indicates that EVs morning for a short trip, then for the next charge, this EV are likely to be charged shortly after the drivers return home will have a relatively high initial SOC. To characterize the from work (weekdays) or leisure activities (weekends). The initial and final SOCs, it is critical to consider not only the MEA project also found that, regardless of the type of day, number of charging events but also the start-charging time, the probability of a first charge with an initial SOC more particularly for the three main periods of the day (morning than nine is higher during the morning (Figure 5). This sug- peak, evening peak, and the rest of the day). In the project, gests that some EVs are charged overnight, used briefly in three periods were considered: two for the charging peaks the morning, and then charged again (counting as the first during the morning and afternoon/evening (Figure 4) and event in that day). one for the rest of the day. To highlight the effects of time In terms of the final SOC, Figure 6 highlights that the dependency, the normalized histograms when ignoring the probability of reaching 12 units (full charge) in a first charg- relationship between the start-charging time and the SOC ing event is more than 65% (on weekend mornings, it is are also given. This is referred to as the whole day. Note approximately 52%), and the likelihood of reaching 11 units that the SOC recorded by the Nis- san LEAFs range from zero to 12 units, i.e., one unit equals 2 kWh 14 (8.33% of the 24-kWh battery). 12 Figure 5 shows the normalized 10 histograms per charging event of 8 the initial SOC during weekdays 6 for three selected periods during 4 Probability (%) the day as well as for the whole 2 day. Regardless of the time, the 0 likelihood of an EV being charged 0 (0) when its initial SOC is two units 1 (8.3) 3 (25) 6 (50) 9 (75) 2 (16.6) 4 (33.3) 5 (41.6) 7 (58.3) 8 (66.6) or fewer is lower than 15%. This 10 (83.3)11 (91.6)12 (100) Number of Units (% of Battery SOC) suggests that most EV users prefer to maintain the battery SOC (a) above this relatively low level 14 (16.6%). Additionally, the prob- 12 ability of an EV being charged 10 when its initial SOC is between 8 three and nine units is more than 6 65%. This implies that most EV 4 Probability (%) users charge their vehicle when 2 the SOC is between 25% and 0 75%, a finding aligned with the 0 (0) results of social surveys. From a 1 (8.3) 3 (25) 6 (50) 9 (75) 2 (16.6) 4 (33.3) 5 (41.6) 7 (58.3) 8 (66.6) time perspective, a first charging 10 (83.3)11 (91.6)12 (100) event during weekdays between Number of Units (% of Battery SOC) 3 and 9 p.m. is most likely (approxi- (b) mately 78%) to start with an ini- 6–10 a.m. 3–9 p.m. tial SOC between three and nine 9 p.m.–6 a.m., 10 a.m.–3 p.m. Whole Day units; however, if this first charge occurs on weekends between figure 5. Normalized histograms of the initial SOC per charging event considering 12 and 6 p.m., it is very likely time dependency (weekday). The (a) first and (b) second charging events. november/december 2018 ieee power & energy magazine 69 or more is higher than 70% (approximately 63% on week- new charging event early in the morning. During weekdays, end mornings). On the other hand, second charging events, the highest probability (approximately 75%) of reaching a full which occurred mostly at night and represented fewer than charge occurred between 3 and 9 p.m. On weekends, this one-third of all events, are less likely to reach full charge happened mostly (approximately 80%) between 6 p.m. and because using these EVs at night results in some requiring a 7 a.m. Overall, the probability of finishing an EV charging event with eight units or fewer is lower than 20%, suggesting 80 that users prefer to end charging 70 events with a high final SOC. 60 50 Charging on the Same 40 Day as Your Neighbor? 30 EV users have different charging 20 Probability (%) needs; some are likely to charge 10 0 their EVs every day, but others might not. This is shown in Figure 7, 0 (0) 3 (25) 6 (50) 9 (75) which provides the probabilities 1 (8.33) 2 (16.66) 4 (33.33)5 (41.66) 7 (58.33)8 (66.66) 12 (100) 10 (83.33)11 (91.66) of charging on the same day for Number of Units (% of Battery SOC) different percentages of EVs on (a) weekdays and weekends. Regard- 70 less of the type of day, the prob- 60 ability of all EVs being charged 50 at least once on the same day is 40 approximately 7%, i.e., twice per month. In addition, the probabil- 30 ity of the majority of EVs (half 20 Probability (%) or more) charging at least once 10 on the same weekday is more 0 than 75%, i.e., more than three-

0 (0) quarters of a month. Interestingly, 3 (25) 6 (50) 9 (75) 1 (8.33) 2 (16.66) 4 (33.33)5 (41.66) 7 (58.33)8 (66.66) 12 (100) 10 (83.33)11 (91.66) for approximately 6% of the days Number of Units (% of Battery SOC) (nearly twice per month), no EVs (b) were charged at all.

6–10 a.m. 3–9 p.m. Modeling EV 9 p.m.–6 a.m., 10 a.m.–3 p.m. Whole Day Charging Demand The MEA project developed a figure 6. Normalized histograms of the final SOC per charging event considering methodology that combines the time dependency (weekday). The (a) first and (b) second charging events. normalized histograms presented here as well as the typical EV demand (approximately 3.6 kW) 40 and its power factor (0.98 induc- 35 Weekday tive) to produce daily time-series 30 Weekend EV profiles. Following a random 25 selection approach, the methodol- 20 ogy defines the key parameters of 15 an EV profile: the number of connections a day, the start-charging Probability (%) 10 time of each connection, and the 5 initial and final SOC of each con- 0 0102030405060708090 100 nection [see Quirós-Tortós et al. EV Penetration Level (%) (2018) in the “For Further Read- ing” section]. figure 7. The normalized histogram for different percentages of EVs (same-day Figure 8 shows examples of charging). three individual EV profiles for

70 ieee power & energy magazine november/december 2018 weekdays as well as the average profile of 1,000 EVs. It highlights 4 1.6 different start-charging times, 3.5 EV Load 1 1.4 charging durations (based on the 3 EV Load 2 1.2 initial and final SOCs), and an EV EV Load 3 Model 2.5 1 with two daily charges (EV Load 1). 2 0.8 The EV profiles are validated Whole Day comparing the average demand 1.5 0.6 of the created pool (“Model” 1 0.4 in Figure 8) with the average 0.5 0.2 Average EV Demand (kW) Individual EV Demand (kW) Monitored EV charging behavior monitored 0 0 during the MEA trial. Clearly, 12 246810 12 24 6810 the average charging behavior of (a.m.) (p.m.) the created profiles matches that Time of Day (1-min Resolution) of the monitored EVs. The average peak demand using the pro- figure 8. EV profiles and average (1,000 EVs) demand (weekday). files (1.09 kW) occurred at nearly the same time (between 8 and 9 p.m.) as the average peak d e m a n d monitored during the trial 2 (1.08 kW). Moreover, it was found 1.8 Households that the average energy consump- 1.6 EVs 1.4 tion using the created EV pro- Households and EVs 1.2 files (12.33 kWh) differed by less 1 than 3% from the average energy 0.8 consumption shown by the EVs 0.6 monitored in the trial (12.63 kWh). 0.4 To highlight the effects of Average Demand (kW) 0.2 ignoring the dependency of the 0 initial and final SOCs on the start- 12 246810 12 246810 (a.m.) (p.m.) charging time, a set of 1,000 EV profiles was also created using the Time of Day (1-min Resolution) whole-day normalized histogram shown in Figures 5 and 6. The figure 9. The average demand of 1,000 households, EVs, and households plus EVs average EV demand shown in Fig- (weekday). ure 8 demonstrates that, although the evening peak is similar to the whole-day approach, the vious model) coincided with that of nonelectrically heated morning peak is 0.5 kW as compared to 0.8 kW (EVs con- households. (The 1,000 households are assumed to have an nected in the morning are likely to charge for shorter peri- EV.) From the perspective of DNOs, this coincident demand ods than those connected overnight). Finally, it was found can translate to technical problems on their networks that because the dependency of the initial and final SOCs on because it can significantly exceed the values used for the the start-charging time is being ignored, EV charging mod- original distribution network design. For instance, if a resi- eled with this whole-day alternative can (mistakenly) show dential low-voltage network has been designed considering approximately 13% more energy consumption (14.24 kWh) an average peak demand (also known in the United King- compared to the monitored charging behavior. dom as after-diversity maximum demand) of 1–1.5 kW per house, then adding an average of 1 kW per house resulting EVs and the Grid from the widespread adoption of EVs will likely lead to volt- It is generally assumed that some people charge their EVs ages below the statutory limits and/or to asset thermal over- at home shortly after returning from work. This means that loads. It is, therefore, in the interest of DNOs to evaluate how the corresponding EV demand could be coincident with that these networks will be affected by different numbers of EVs. from the use of multiple appliances, such as lights. Although this will depend on weather and behavioral aspects (which Impacts on Low-Voltage Networks vary from one country to another), in the MEA project this The MEA network studies first investigated the hosting assumption holds true. As shown in Figure 9, the maximum capacity of three-phase low-voltage networks [modeled in average charging demand from 1,000 EVs (using the pre- OpenDSS (distribution system simulator)] considering EV november/december 2018 ieee power & energy magazine 71 Transformer Feeder 1 Feeder 2 Feeder 3 Feeder 4 Feeder 5 Feeder 6 700 160 (m) 600 Feeder 1 Feeder 2 Feeder 3 140 120 500 Thermal Limit 100 400 80 300 60 200 40

Feeder 4 Utilization Factor (%) 100 Feeder 5 Feeder 6 20 (m) 0 0 0 100 200 300 400 500 600 700 0102030405060708090 100 EV Penetration Level (%) (a) (b)

figure 10. An example of a (a) real low-voltage network and (b) its corresponding thermal impacts.

penetration levels (i.e., the number of houses that are all the capacity of the transformer can be exceeded by roughly single-phase connected with a single EV) from 0% to 100% 50% for high penetrations. Consequently, it is possible to in increments of 10%. The assessment, which was differ- conclude that the hosting capacity of this example low-volt- ent from many other impact studies, followed a stochastic age network is 40% and the bottleneck in the first instance approach to consider the uncertainties associated with the is created by the transformer. demand of households as well as the location and demand The EV impact study was extended to nine low-voltage of EVs. networks as part of the MEA project for a total of 31 low- Figure 10(a) shows the topology (i.e., length, density voltage feeders. Figure 11 shows that more than 20% of the of the circuits, and so on) of a real European low-voltage transformers (two of nine) can have thermal problems for a network as an example to discuss the potential EV impacts 40% penetration. For the same number of EVs, only one feeder and the resulting hosting capacity. Figure 10(b) presents the (~3%) may experience thermal overloads. This means that the corresponding average utilization factor (considering the hosting capacity of similar low-voltage networks might also asset’s nominal capacity) and one standard deviation of the be constrained, in the first instance, by the transformer. As transformer and the six feeders. Voltages below the statu- the penetration of EVs increases above 70%, four of the nine tory limit (0.94 per unit) were negligible in this low-voltage transformers will operate beyond their nominal capacity, and network. This figure highlights that the transformer is the more than 15% of the feeders will require intervention because bottleneck on this example low-voltage network; its capac- of thermal issues. The latter can increase to 20% for 100% ity is likely to be exceeded above 40% EV penetration. EV penetration (all houses with an EV). For 90 and 100% EV Overloads of the underground cables occur only for high penetration levels, only two (long) feeders with voltages lower EV penetrations (90% or more). The figure also shows that than the statutory limits were found. This means that some low-voltage networks may be constrained by voltages when very high EV penetrations occur.

7 Increasing the Hosting Capacity 6 Transformer To mitigate the impacts resulting from the adoption of EVs 5 Feeder–Thermal 4 Feeder–Voltage and, thus, increase the ability of low-voltage networks to 3 host more EVs, the MEA project tested a solution to manage 2 EV charging points. Known as Esprit, this solution discon- 1 nects EV charging points when a technical issue occurs in Number of Feeders 0 the low-voltage network. When there is spare headroom (i.e., Number of Transformers/ 0102030405060 70 80 90 100 EV Penetration Level (%) no more problems), it then reconnects them. Deploying this solution requires the following infrastruc- figure 11. The impact results on the nine low-voltage ture (Figure 2): voltage sensors and actuators at the charging networks (nine transformers and 31 feeders) involved in the points, communications links, voltage and current sen- MEA project. sors at the head of the low-voltage feeders, and a PLC at the

72 ieee power & energy magazine november/december 2018 substation to host the solution. When a thermal or voltage Figure 13(d) shows that, for a particular EV charging point, problem is detected, the controller follows a hierarchical the disconnection happens near the time of high aggregated corrective approach (from feeder to transformer) to discon- EV demand. It also shows that when the overall network nect the EV charging points (Figure 12). On the other hand, demand is much lower, the control strategy reconnects this the reconnection adopts a hierarchical preventive approach EV, resuming the charging process until the expected SOC (from transformer to feeder). Because the infrastructure is reached (i.e., 3 h and 40 min). does not involve direct inputs from the EV or the user, the In general, the EV management solution was effective EV management solution uses the EV charging time as a not only in mitigating network issues throughout all of the proxy of the unknown SOC to determine the most suitable simulations carried out for the nine low-voltage networks EVs to be managed. This means that disconnections occur but also throughout the trials (when communications were first on customers with higher charging times because it is effective). This is because of the very nature of the solution: assumed that their EVs have reached a higher SOC. Recon- the elements causing the issues (the EVs) are disconnected. nections, on the other hand, occur first on EVs that have been This means that the larger the EV penetration, the more (and disconnected for longer times. longer) disconnections are likely to happen. Inevitably, cus- Because the trials were limited to clusters of no more than tomers will be affected. 13 EVs, simulations were carried out to investigate the per- Effort was made to understand the extent of the delays formance of the EV management solution with different pen- resulting from the direct management of EV charging etration levels. For consistency between simulations with and points. To estimate this extent, which is related to cus- without EV management, the final SOC used to define each tomer satisfaction, the project introduced an index called EV profile is considered to be the expected SOC that the EV the customer impact level (CIL). The CIL, which is clas- user should eventually reach, i.e., in both cases, the same EV sified in ten groups from zero to nine, is applied to each profile consumes the same amount of energy. EV and indicates the extra charging time needed to reach For EV management, the project investigated different con- the expected SOC when the EV management is applied trol cycle lengths (from 1 to 30 min). Figure 13 illustrates the (this value is given in percentage of the originally expected network performance and operation of EV charging points dur- charging time.) The first group corresponds to a CIL equal ing a full day considering the cases without and with control to zero. From one to nine, each group corresponds to CIL (1-min cycles) for a 100% EV penetration level in the exam- values within ranges increasing incrementally by 25%. To ple low-voltage network. Without managing the EV charging points, such a penetration would result in a transformer loading of 136% as well as multiple customer voltages Inputs Internal Processes significantly less than the statutory Charging Point Status (On/Off) Update Each EV Charging Time limit. On the other hand, the proposed EV management solution is able to keep the utilization factor of Hierarchical Corrective Hierarchical Preventive Disconnection Reconnection the transformer below its nominal capacity (other values can also be used depending on loading cycles Feeder Level Transformer Level and other factors) and voltages (per Phase per Feeder) within statutory limits. This means that the adopted EV management solution is able to increase the host- Selection of EVs ing capacity of this example low- Transformer Level Select Disconnected EVs with the Shortest Charging Time voltage network from 40 to 100%. To achieve 100% EV penetration in this low-voltage network, Selection of EVs Feeder Level (per EV in Ranking) the control managed a total of Select EVs with the Longest Select EV for Reconnection if Feeder 190 EV charging points (54.1%), Charging Time Constraints Are Satisfied mostly between 4 p.m. and 12 a.m. Figure 13(c) shows the Control Action Control Action Disconnect EVs per Phase per Feeder effect of this charging point man- Reconnect EVs agement on the aggregated EV Disconnect EVs at the Network Level demand: the peak without control is shifted to later hours. Finally, figure 12. The flowchart of the EV control solution. november/december 2018 ieee power & energy magazine 73 illustrate this, Figure 14 indicates the percentage level of take longer. In general, the MEA project did not find charg- impact that an EV user might experience in the example ing delays to be of significant concern for penetration levels low-voltage network. For low penetrations (up to 40%, up to 60% due to the low probabilities for CILs larger than which is the hosting capacity of the CIL network), EVs will zero. Beyond this, half or more of the EV users could expe- not be affected, i.e., their CIL is zero. However, the higher rience delays, but, at most, they will be double that of what the penetration, the more likely an EV user will have a CIL would otherwise be needed (i.e., a CIL <5). larger than zero, i.e., reaching the expected SOC will likely The studies also found that longer control cycles (5 and 10 min) increase the number of EV users without delays; however, 30-min control cycles were found to be too long to adequately miti- 800 Without Control 520 kVA gate network impacts. On the other 600 1-min Control Cycle 10:38 p.m. hand, from the perspective of the EV battery, using information pro- 400 Thermal Limit (kVA) vided by the EV manufacturer, the 200 MEA project was able to recom- 0 mend a minimum on-time period 4 6810 12 24 6810 12 24 (a.m.) (p.m.) (a.m.) of 15 min (uninterrupted charg- Time of Day ing). This means that if control (a) cycles are to be adopted, an effective deployment should consider 1.05 the tradeoff between the benefits 1 from multiminute control cycles, the capabilities of the EV batteries, 0.95 and the potential technical issues (per unit) 0.90 Voltage Limit on the networks. 0.85 4 6810 12 24 6810 12 24 Provision of Services: (a.m.) (p.m.) (a.m.) To What Extent? Time of Day EVs, like other vehicles, are and (b) will be primarily used as a means 500 of transportation; however, with 400 the widespread adoption of EVs, 300 it is possible to think of them in

(kVA) 200 terms of not only disconnections 100 caused by distribution network issues (as investigated in the MEA 0 4 6810 12 24 6810 12 24 project) but also for their provision (a.m.) (p.m.) (a.m.) of grid services to help maintain Time of Day balance between demand and gen- (c) eration. But if EVs are indeed to be 4 seriously considered for such purposes, we need to first understand 3 4:44 p.m. 11:13 p.m. the extent to which the provision of 2 6:53 p.m. grid services is possible. (kVA) 12:44 a.m. 1 8:24 p.m. Clearly, the disconnection of a single EV will provide negligible 0 4 6810 12 24 6810 12 24 grid services, but hundreds of (a.m.) (p.m.) (a.m.) thousands or even millions of EVs Time of Day working together can cause a sig- (d) nificant aggregated reduction in electricity demand, requiring less figure 13. An example of a 24-h-period of operation and performance of the con- generation and potentially reduc- trol. Results for the (a) transmitter loading, (b) minimum voltage, (c) aggregated EV ing costs. However, to quantify the demand, and (d) individual EV demand. potential aggregated demand that

74 ieee power & energy magazine november/december 2018 can be reduced at any given time, it is crucial to understand how many 100 90 EVs are charging simultaneously 40% 60% 80% 100% 80 so that they can be disconnected. 70 Given that the EV models based 60 on the MEA data include the 50 40 start-charging time and duration 30 of each charging event, it is possi- Probability (%) 20 ble to know when power (3.6 kW) 10 0 is withdrawn by each EV. Conse- quently, these models can also be 0 (0%) used to determine the amount of 1 (1–25%) 9 (>200%) 2 (26–50%)3 (51–75%) 4 (76–100%) power (and corresponding number 5 (101–125%)6 (126–150%)7 (151–175%)8 (176–200%) of EVs) that could, in theory, CIL be disconnected. The number of EVs simulta- figure 14. The probability of CILs for control actions every minute. neously charging will depend on multiple factors, including the time of day (morning/after- degradation) must be measured against the potential ben- noon/evening), type of day (weekday or weekend), weather efits that they may receive. conditions, and so on. For illustration purposes, Figure 15 The widespread adoption of EVs and their evolving tech- presents—for weekdays only—the results of 1,000 EV- nologies also allows for envisioning a future in which EVs availability curves resulting from a Monte Carlo analysis can feed electricity back into the grid, known as vehicle- using 1,000 EV profiles (each with the same probability) to-grid (V2G) integration. V2G refers to the partial use of and considers the MEA process in terms of the number of the energy in the batteries while EVs are plugged into the EVs charging on the same day (Figure 7). This availability is grid. V2G generation could also be used to help balance given for each minute and represents the percentage of EVs demand and generation in the regional or national system that are being simultaneously charged. or even locally (at home or within microgrids), potentially As shown, this availability varies significantly throughout reducing costs and increasing reliability. From the perspec- the day, from negligible during early morning hours (roughly tive of the provision of services, however, an estimation of 1% at 5 a.m.) to significant at night (30% at 8:30 p.m.); on V2G availability is also necessary. Although the charging average (as shown by the thick black line), 20% of the EVs patterns of these more advanced EVs will depend on many would be available during peak time, and approximately 3% other (future) factors, data similar to those used in the MEA would be available early in the morning, resulting in a daily project will make it possible to estimate EV availability. In average of 9.83%. In the United Kingdom, this value trans- particular, this quantification needs to capture theSOC of lated to a population of 10 million EVs (expected by 2030) EVs to determine the amount of energy that could, in theory, that could result in an average availability of approximately be discharged from the EV while keeping the autonomy of 3.54 GW. This means that, in 12 years, the system operator the vehicle acceptable for the user and reducing the degrada- might be able to use these services when trying to maintain tion of its battery. balance in the electricity system. It can be concluded that the deployment of these services can provide a significant range of benefits to the electricity system, particularly at night when the United Kingdom might need it the most. 35 The provision of grid services from EVs appears to be 30 a very attractive alternative for our electricity systems. To 25 truly assess this potential, it is critical to capture the interac- 20 tions among EV management schemes required to mitigate 15 problems at the distribution level (such as the one pro- 10 posed in the MEA project) and the availability of EVs to 5 provide the services. The availability shown in Figure 15 0 12 246810 12 246810 12

would significantly change if the EV management solution Aggregated EV Availability (%) (a.m.) (p.m.) (a.m.) deployed in the MEA project had been considered when creating the models. We must quantify the cost-effective- Time of Day ness of these services, which are likely to require invest- ments in communications and infrastructure. Finally, figure 15. The aggregated EV availability for the provision the effects on customer satisfaction (delays and battery of reserves. november/december 2018 ieee power & energy magazine 75 Key Lessons Learned on the networks. The EV management solution tested by the This article presented the main outcomes from one of the MEA project is considered practical and scalable enough to largest (if not the largest) EV trials in the world, MEA. The be deployed by other DNOs. key lessons learned in terms of the charging habits of EV users, how EVs impact low-voltage networks, the effective- Provision of Services ness of the proposed EV management solution to increase The produced EV models also allow for quantifying the corresponding hosting capacity, and the potential provision potential provision of grid services to help maintain the bal- of services to the grid are summarized. ance between demand and generation. While the availability changes from weekdays to weekends and throughout the EV Charging Behavior day, it was found that, in approximately 12 years, the aver- The MEA project recorded the charging behavior of nonman- age weekday availability can be equivalent to approximately aged EV users in the United Kingdom for nearly two years. 4.5 million U.K. households. Although there is a great po Among all key outcomes presented in this article, the data tential for EVs to provide services, their deployment will analysis demonstrated that approximately one-third of the fully depend on the benefits that the EV customers receive. EVs were charged more than once per day (a unique finding Hence, the scale and range of these services will eventually not previously explored) and that the charging behavior from require understanding not only the technical challenges but one season to another is similar. The MEA project also found also the social, financial, and environmental ones. that not all EVs are charged on the same day; the results show The outcomes from the MEA project can help integrate that, on average, there were two days each month in which EVs into most electricity systems around the world. In the no EV was charged (from the 219 EVs involved in the MEA United Kingdom, a new project called Electric Nation (www project, there were two days per month, on average, in which .electricnation.org.uk) is already building on the understand- no EV was plugged in at all). The results of the data analysis ing from MEA and plans to deploy more than 500 EVs to are expected to be used in different EV studies ranging from study different cost-effective solutions to manage networks customer demand to system-level management schemes. ranging from demand response to V2G applications. The deployment of this project, and many others that will take EV Impact on Low-Voltage Networks place in the next decade, demonstrates the value of EV tri- The MEA network studies first investigated the hosting capac- als in providing outcomes that will facilitate the transition ity of different low-voltage distribution networks. The proba- toward the electrification of the transport sector. bilistic analysis highlighted that the peak demand of the EVs is likely to coincide with the existing evening peak. From the For Further Reading perspective of DNOs, the maximum demand of households International Energy Agency. (2017, June). Global EV outlook with an EV charging in slow mode will grow, on average, to 2017: Two million and counting. IEA. Paris, France. [Online]. roughly 2 kW per household (i.e., DNOs in the United King- Available: https://www.iea.org/publications/freepublications/ dom will need to plan for 2 kW per house), double that of the publication/GlobalEVOutlook2017.pdf conventional demand. Studies on nine different low-voltage EA Technology. (2016, Mar.). My Electric Avenue— networks found that, for some of them, problems start at 40% Project closedown report. EA Tech. Chester, U.K. [Online]. penetration (i.e., a hosting capacity of 40%). This was mainly Available: http://myelectricavenue.info/sites/default/files/ due to the transformer located at the substation, followed by documents/Close%20down%20report.pdf thermal problems at the low-voltage feeders; only long feeders J. Quirós-Tortós, A. Navarro-Espinosa, L. F. Ochoa, and T. may face voltage issues for very high EV penetration levels. Butler, “Statistical representation of EV charging: Real data The proposed methodology to assess this hosting capacity can analysis and applications,” in Proc. PSCC, 2018, pp. 1–6. be adapted by other countries in the process of deploying EVs. J. Quirós-Tortós, L. F. Ochoa, S. W. Alnaser, and T. But- ler, “Control of EV charging points for thermal and voltage EV Management Solution management of LV networks,” IEEE Trans. Power Syst., vol. The MEA project also demonstrated that deploying the EV 31, no. 4, pp. 3028–3039, 2016. management solution can increase the hosting capacity of low-voltage networks to 100% in all of the simulations and Biographies actual trials. Although the technical impacts could be fully Jairo Quirós-Tortós is with the University of Costa Rica, mitigated since the EVs are switched off when a problem San Jose. arises, the studies showed that charging delays and battery Luis (Nando) Ochoa is with the University of Mel- degradation can occur because of the repeated management bourne, Australia, and the University of Manchester, United of the EV. This means that if EV management solutions are Kingdom. to be truly adopted, an effective deployment should consider Timothy Butler is with EA Technology, Chester, United the tradeoff among the benefits from the control, the capa- Kingdom. bilities of the EV batteries, and the potential technical issues p&e

76 ieee power & energy magazine november/december 2018