Analyzing Distribution Transformers at City Scale and the Impact of Evs and Storage
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Analyzing Distribution Transformers at City Scale and the Impact of EVs and Storage John Wamburu, Stephen Lee, Prashant Shenoy and David Irwin University of Massachusetts Amherst ABSTRACT increase in electricity usage. At a micro scale, the residential dis- Electric vehicles (EV) are rapidly increasing in popularity, which is tribution grid was built in a pre-EVA era and was not designed to signicantly increasing demand on the distribution infrastructure account for EV loads. For example, a typical home in the U.S. has in the electric grid. This poses a serious problem for the grid, as an average load 1.2kW, while an electric car such as Nissan Leaf most distribution transformers were installed during the pre-EV era, adds an additional load of 6.6kW, eectively doubling or tripling and thus were not sized to handle large loads from EVs. In parallel, the peak electric demand of the home. As a result, distribution grid smart grid technologies have emerged that actively regulate demand transformers that were sized before EVs may become overloaded to prevent overloading the grid’s infrastructure, in particular by and not be able to reliably support high EV penetrations. optimizing the use of grid-scale energy storage. In this paper, we At the same time, the emergence of the smart electric grid has rst analyze the load on distribution transformers across a small resulted in new technologies for more exible demand-side load city and study the potential impact of EVs as their penetration levels management and load mitigation in the grid. In particular, grid- increase. Our real-world dataset includes the energy demand from level energy storage is emerging as a key technology for supporting 1,353 transformers and charging proles from 91 EVs over a 1 year future smart grids, since it can smooth out uctuations from inter- period, and thus provides an accurate snapshot of the grid’s current mittent renewable energy sources, such as solar and wind, as well as state, and allows us to examine the potential impact of increasing enable grid optimizations, such as shaving peak loads and serving as EV penetrations. We then evaluate the benets of using smart grid backup power to reduce outage durations [28, 30, 31]. Interestingly, technologies, such as smart EV charging and energy storage, to grid-level energy storage can also be used to mitigate the impact mitigate the eects of increasing the EV-based load. of EV loads on distribution transformers. If judiciously deployed adjacent to distribution transformers, energy storage batteries can ACM Reference Format: reduce or eliminate transformer overloads due to EV charging and John Wamburu, Stephen Lee, Prashant Shenoy and David Irwin. 2018. Ana- increase transformer lifetimes. A complementary smart grid tech- lyzing Distribution Transformers at City Scale and the Impact of EVs and nology is intelligent load management via load shifting [11, 32]. In Storage. In e-Energy ’18: The Ninth International Conference on Future Energy Systems, June 12–15, 2018, Karlsruhe, Germany. ACM, New York, NY, USA, the context of electric vehicles, this technique translates to smart 11 pages. https://doi.org/10.1145/3208903.3208925 charging where the EV intelligently coordinates its charging with the distribution grid often by deferring its charging from peak to 1 INTRODUCTION o-peak periods whenever necessary [39, 41]. Together, energy storage and smart charging have the potential to mitigate the im- Advancements in battery and electric vehicle (EV) technology, com- pact of EV loads on the distribution grid, but how much and to what bined with public policy initiatives, is rapidly accelerating the elec- extent is unclear based on actual transformer capacities, projected trication of transportation. Major car and truck manufacturers EV loads, and current demand proles. have all announced new EV products, making it likely that EVs will In this paper, we study the impact of residential EVs on the become mainstream in the coming years. Nearly 200,000 EVs were demand experienced by a city-wide distribution grid in the New sold in 2017 in the U.S. alone—a 25% increase in sales over 2016 [1]. England region of United States and then analyze whether and how Reports from Norway indicate that 70% of all new cars being sold much grid energy storage and smart charging technologies can are now EVs. Of course, EVs are powered by batteries that must be mitigate this increased demand. Our study is empirical in nature charged frequently, e.g., often daily, using electricity from the grid. and is based on analyzing real load data from i) 13,523 residential Consequently, as EVs become commonplace, their impact on the homes and 1,353 distribution transformers gathered at 5 minute electric grid will be profound. At a macro scale, all of the energy granularity over a 2-year period and ii) real charging data from over used to power automobiles, currently supplied by gasoline, will 91 EVs in use over a one year period. While there has been prior need to be provided by the electric grid, resulting in a manifold work on analyzing the impact of EV loads [9, 35, 43], our study diers from prior work in several key aspects. For example, Clement- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed Nyns et al. [9] largely focuses on characterizing the aggregate load for prot or commercial advantage and that copies bear this notice and the full citation impact from EVs, and does not consider the issue of mitigating the on the rst page. Copyrights for components of this work owned by others than the load impact using grid storage, while Verzijlbergh et al. [43] focuses author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specic permission on peak load analysis and thus only considers mitigating the one and/or a fee. Request permissions from [email protected]. day that experiences the peak annual load. e-Energy ’18, June 12–15, 2018, Karlsruhe, Germany In contrast, we analyze the impact of EVs on transformer loads © 2018 Copyright held by the owner/author(s). Publication rights licensed to Associa- tion for Computing Machinery. throughout the grid over a 2-year period and specically study ACM ISBN 978-1-4503-5767-8/18/06...$15.00 how the distribution of loads changes as the penetration of EVs https://doi.org/10.1145/3208903.3208925 e-Energy ’18, June 12–15, 2018, Karlsruhe, Germany John Wamburu, Stephen Lee, Prashant Shenoy and David Irwin increases. As we show later, understanding the impact on the proba- In our work, the exact topology of the distribution grid is not bility distribution of loads is as important as analyzing the peak load important since we focus specically on distribution edge trans- alone. While Ramanujam et al. [35] examines a similar problem, it formers — transformers at the edge of the distribution network that drives its simulations using synthetic estimates of existing loads, are directly connected to the end users. Furthermore, since we are rather than real-world empirical data, and is thus not an accurate specically interested in EV charging, we consider the portion of characterization of real-world conditions. We analyze long-term the distribution grid that serves residential and commercial/oce ne-grained transformer load data across an entire city to char- customers and ignore industrial users (since EVs are unlikely to acterize the real-world implications of increasing EV penetration, be connected to transformers serving an industrial user, such as a and examine ways to mitigate problems using grid-scale energy manufacturing plant). We assume that distribution edge transform- storage. In conducting our empirical analysis, this paper makes the ers serving homes or those serving business users are likely to see following contributions: increasing EV loads — resulting from users charging electric cars Transformer Distribution Analysis. We use a city-scale dataset at home or in oce parking lots with EV chargers. to conduct an in-depth analysis of the existing transformers and Such distribution edge transformers come in a range of capac- quantify their dierent load proles. Surprisingly, we observe that ities, varying from small 5-10 kilo-Volt-Ampere (kVA) pole-top most transformers are not over provisioned in the network and all transformers to larger 500, 1000 and 1500 kVA transformers. Note transformers are already designed to gracefully handle temporary that transformer capacity is rated in kVA, which is the unit used overloads. Moreover, we nd that 19.2% of transformers are heavily for apparent power, i.e., the product of the root mean square (rms) overloaded, having a utilization of over 100%. of voltage and current in an AC power system. Small transformers Impact of Electric Vehicles. We analyze the eect of increas- may serve a small number of homes (e.g., 2 to 4 homes), while the ing penetrations of EVs and the eect on the load experienced by larger ones serve apartment complexes or oce buildings. transformers in the grid and their lifetime under multiple dierent Electric utilities size edge transformers based on their expected scenarios, e.g., uniform and skewed distributions of EVs. Our results load. However, typical capacity planning for transformers in the indicate that the percentage of critically overloaded transformers is grid works dierently from capacity planning in server farms and low for small levels of EV penetration (1-5% of homes), but increases data centers, which is a well-studied problem [8, 27]. In particular, signicantly at higher penetrations levels (20-40% of homes).