IEEE Electrification - June 2019 - 48

In terms of
geographical
distribution and
density, EV adoption
is not expected to
be evenly distributed
across all
distribution grids.

density of EVs in San Francisco is 20
times greater than that in Sacramento. As most EVs are purchased by
higher-income households, disparities might also appear between
neighborhoods, which means that
higher-income neighborhoods will
likely reach an EV penetration of
100% or higher before other neighborhoods. Note, however, that EV
adoption could become more homogenous as supplies increase and prices
fall. As California ramps up to its goal
of 5 million EVs by 2030, a new charging infrastructure is already being
installed at workplaces, residential
areas, parking lots, and other public places. In fact, various databases, such as Plugshare and OpenChargeMap,
are now available to track the charging infrastructure and
keep EV drivers informed. In this new situation, utilities
need to take into account plans for charging infrastructures and analyze the expected impact of uncontrolled EV
charging on the distribution grid.

Characteristics of the Distribution Grid
Distribution grid planning is needed to determine the
investments required to ensure a reliable power supply
within network constraints. EVs have the potential to
disrupt the grid. To prevent that from happening, plans
need to be made now for widespread EV integration. As
more DERs are added on the network, distribution grid
planning becomes more complex. With weather conditions affecting renewable energy sources, daily weather
forecasts need to be considered. Also, the state of charge
for batteries and sudden load variations from EVs have
to be incorporated into plans. While tools exist to simulate individual components of the power system [transmission grids, distribution grids, photovoltaics (PVs),
buildings, communication infrastructure, and EVs], few
frameworks are available to enable holistic power system
cosimulation. Therefore, improved tools for distribution
grid planning could help to better measure the required
capacity margin for feeders in the presence of significant

Feeders (%)

20

Median Capacity
Margin: 3.1 MW

15

Distribution Grid Reinforcement
and Associated Costs

10
5
0

0

1

2
3
4
5
6
7
8
Feeder Capacity margins (MW)

9

10

Figure 1. The capacity margin in megawatts for more than 3,000
feeders in PG&E's territory in 2017.

48

variable generation and load units,
such as EVs.
To allow for traditional load
growth, distribution grids are built
with some capacity margin. In this
study, we had access to a database of
3,000 distribution feeders in PG&E's
service area and estimated the available capacity margin for each feeder
(Figure 1). Overall, the available capacity margin for 50% of the 3,000 feeders
is less than 3.1 MW. While this margin
is large enough for traditional peakload growth, it might not be sufficient
to accommodate the higher power
consumption of EV chargers. For
instance, if all stations are in use, 50% of the feeders could
host, at most, only 25 dc fast chargers (rated at 120 kW) or
470 smaller level 2 chargers (rated at 6.6 kW).
As the energy demand from charging stations follows
the geographically uneven adoption of EVs, some feeders
might have high EV penetrations, while others could
experience no or negligible additional power demand
from EVs. The higher power requirement of fast-charging
EVs, the potential clustering of EVs on the same feeder,
and their distance from the feeder head all affect the
grid. As a result, medium- and low-voltage networks in
their current state might not reliably support high EV
penetration. In general, distribution grids could encounter undervoltages and currents exceeding transformer
and line power ratings, harmonics, and phase imbalances. These effects could reduce reliability, increase power
losses, and lower margins for future load growth, which
will result in a cost for both utilities and customers. Fortunately, utilities regularly perform long-term load forecasting, taking into account the adoption of EVs and the
resulting impact on the system, while prioritizing capital
investments to maintain adequate capacity. To ensure
grid reliability, simulation tools need to adequately represent the stochasticity of EV demand and offer potential
control strategies. Addressing this rapid load growth, utilities can take a number of actions, including reinforcing
the grid with additional lines and transformers, adding
local stationary storage, incentivizing off-peak consumption with time-of-use (TOU) rates, and developing ecosystems for grid assets (such as EVs) to provide
distribution grid services.

I E E E E l e c t r i f i cati o n M agaz ine / J UN E 2019

Utilities are regulated monopolies with a strong incentive
to promote grid reinforcement, as they are directly remunerated for their investments. In this context, the grid
could be reinforced for the theoretical worst-case scenario
where all EVs charge at the same time during peak hours,
therefore justifying expenditures for additional lines,



IEEE Electrification - June 2019

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