The Impact of Electric Vehicle Density on Local Grid Costs: Empirical Evidence from Norway.

AuthorWangsness, Paal Brevik

    Do electric vehicle (EV) owners impose a negative externality on other electricity consumers when they plug in their cars at home during peak hours for electricity? In the absence of any peak pricing scheme, if the high power consumption of EVs leads to higher local grid costs, the resulting increase in uniform grid tariffs will be shared among all customers. Simulation exercises suggest that uncoordinated EV charging might have an impact on the local grid (see e.g., De Hoog, Alpcan, Brazil, Thomas, & Mareels, 2015; Masoum, Deilami, Moses, Masoum, & Abu-Siada, 2011), but the empirical evidence is scarce. What can we learn from actual data in the country with the highest EV share, namely Norway?

    The high EV share must be viewed as a result of national climate policy, which aims to fulfll Norway's part of the Paris agreement. Norway has a goal of ensuring that all new passenger cars are zero emission vehicles by 2025. Incentives like low vehicle taxes, toll road exemptions, and access to bus lanes has resulted in the highest penetration of EVs worldwide. By January 2020, there were about 260 000 battery electric vehicles (BEVs) and 115 000 plug-in hybrids (PHEVs) in Norway, a country with only 5.3 million inhabitants. In 2019, BEVs accounted for 42 percent and PHEVs for 14 percent of all new vehicles (Norwegian Electric Vehicle Association, 2020).

    The Norwegian Water Resources and Energy Directorate (NVE) presents a scenario where the growth in BEVs in Norway continues and reaches 100 % of the new car sales after 2025. This implies 1.5 million BEVs in Norway in 2030, resulting in a 3 % increase in domestic electricity consumption (Skotland, Eggum, & Spilde, 2016). So even with rapid electrification of passenger transport, we can expect aggregate electricity generation to cope without major challenges.

    However, while a BEV's energy consumption may be modest, its power usage could be quite high. Currently, power demand per electricity consuming unit in a household usually varies from 2.3 to 7.3 kW. Skotland et al. (2016) find through a survey that most BEV owners do their daily charging at home (almost 90 %). Charging at work or at public charging stations seems at this point to be mainly supplemental. NVE's review indicates that most BEV owners start their charging late in the evening and cover most of their charging needs during night hours, while some start charging their vehicle immediately after work, which is a peak period for electricity consumption.

    Uncoordinated charging (or "dumb charging") will increase electricity consumption during the morning and evening peaks (Graabak, Wu, Warland, & Liu, 2016). De Hoog et al. (2015) point out that if EV charging is not controlled, adverse impacts on the distribution network are expected: power demand may exceed distribution transformer ratings; line current may exceed line ratings; phase unbalance may lead to excessive current in the neutral line; and voltages at customers' points of connection may fall outside required levels. A similar point is made by Neaimeh et al. (2015). Skotland et al. (2016) develop a stress-test for neighborhoods with high BEV density. If 70 % of the residents charge their BEVs simultaneously during peak hours, they find that power demand can increase by up to 5 kW per household. This results in overload for more than 30 % of the transformer stations currently servicing the distribution network.

    Our motivation for this paper is as follows: The number of BEVs is growing fast, and there exists a literature that warns that BEV charging will cause substantial future costs to the local grid unless measures are put in place. If indeed the aggregate uncoordinated charging from BEV owners does induce higher costs to local grid companies (Distribution System Operators--DSOs), then Norwegian data would be the first place to investigate. Detailed data of all Norwegian DSOs and all registered BEVs during the last ten years gives a unique opportunity to analyze this relationship. To our knowledge, such an empirical analysis has not been done before on real data in a country-wide analysis. It will therefore push the knowledge frontier on a debated, but relatively unexplored topic empirically. Findings may have implications for how to regulate DSOs, how to price household power usage and how to assess the net social cost of achieving emission reduction targets through promoting EVs.

    This paper complements previous studies that look at the effects that BEVS and PHEVs can have on the electricity market. Our analysis covers a relatively long time-period of real experiences with increasing BEV density (over 10 % of the car feet in some areas), while most of the relevant literature up until now have been simulation exercises in numerical models of local grids. Hattam and Greetham (2017) analyze how EVs affect load profiles on neighborhood level in low voltage networks. Azadfar, Sreeram, and Harries (2015) look at charging behavior in terms of time of day, duration, frequency and electricity consumption in light of its implication for electricity network management. Barton et al. (2013) look at the challenges for grid balancing when EV charging becomes more prominent, and stress the importance of demand side management with time-shifting of electricity loads from periods of peak demand to of-peak, and from periods of low renewable energy supply to periods of high supply.

    Other studies also argue for demand side management (see e.g., Haidar, Muttaqi, & Sutanto, 2014; Masoum et al., 2011) as an alternative to costly upgrades of distribution transformer stations. Some of these studies also argue for pricing schemes that disincentivize charging during peak hours (see e.g., Barton et al., 2013; Clement-Nyns, Haesen, & Driesen, 2011; Masoum et al., 2011; O'Connell et al., 2012). In the future, smart-charging technology and vehicle-to-grid (1) (V2G) and vehicle-to-building (V2B) solutions may also provide a means to mitigate capacity problems in both electricity generation and distribution (Barton et al., 2013; Clement-Nyns et al., 2011; Mwasilu, Justo, Kim, Do, & Jung, 2014; Sioshansi & Denholm, 2010), but bidirectional EV charging is in its infancy (Haidar et al., 2014), and seems to come at a relatively high cost due to increased battery degradation, energy losses, changes in infrastructure, and extra communication between EVs and the grid (Habib, Kamran, & Rashid, 2015).

    Exploiting local differences in the growth of the BEV feet over time, we investigate how an increase in the number of BEVs affects the costs of the local DSO. We look at both total costs and individual cost components. We analyze data on 107 DSOs over the period 2008-2017 using fixed-effects estimation that account for time-invariant characteristics of the DSO. We also control for growth in output indicators that could be correlated with growth in the BEV feet.

    The main finding is that increases in the BEV feet are associated with positive and statistically significant increases in costs when controlling for other DSO outputs and year dummies. The point estimates also imply that the effect is economically significant. However, there is a lot of heterogeneity in these results, where the marginal cost estimates are a lot higher for small DSOs in rural areas, and a lot lower for larger DSOs in urban areas.

    Section 2 presents the regulatory setting for local grid operators in Norway, and why the growth in BEVs may exacerbate existing market failures. Section 3 presents the methods and data. In section 4 we present the results from our empirical analysis. Section 5 discusses the results and concludes.


    Norwegian DSOs are regulated under a revenue cap model with benchmark (or yardstick) competition against other DSOs (see e.g., Decker, 2014, pp. 103-140), where they set their tariffs based on this revenue cap. The revenue cap is composed of 40 % cost recovery and 60 % cost norm based on benchmark modeling using data envelopment analysis (DEA) (NVE, 2015). This means that an increase in costs increases the revenue cap, which allows the DSO to raise its tariffs. However, the revenue cap, and therefore the tariffs, are constrained by the cost development of the other DSOs that comprise the benchmark competition.

    Still, at least some of the increase in capital cost will eventually lead to higher tariffs, and these will have to be paid by all consumers connected to the local grid, and not just the households demanding more capacity. It can be viewed as a pecuniary external cost in an incomplete market (Greenwald & Stiglitz, 1986). That is, the households demanding more capacity do not face the full cost of the capacity expansion, and indirectly impose costs on other consumers.

    We describe the mechanisms for how an increased number of BEVs may lead to higher costs to DSOs and subsequently to higher grid tariffs through the following steps:

  3. The BEV share increases in a neighborhood.

  4. Households will charge their BEVs at 3.6-7.2 kW, and the demand for power capacity will increase.

  5. With a certain size of the BEV share and a certain share of the owners charging simultaneously, the existing distribution transformer and/or the cables between the transformer and the household will not be able to handle the power capacity demand at certain times of day, certain times of year. This may lead to more inspection and maintenance before new investments need to be made.

  6. The DSO invests in capacity expansion in the local grid. The cost of such capacity expansion will depend on whether enhancements need to be done for the transformer and/or the cables, the amount of transformer capacity that needs to be installed, whether the new transformer fts in the old box that contained the old transformer, and the costs of digging.

    * The new investment increases the capital stock for the DSO.

  7. Regulation then says that the DSO can charge higher grid tariffs to...

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