IEEE Power & Energy Magazine - July/August 2021 - 38

The impact of not achieving the assumed load flexibility in
the demand forecast on the resource portfolio is illustrated in
Figure 11. Higher and more pronounced load peaks occur in
the evening if the anticipated flexibility associated with lightduty
EV charging is reduced. The resulting less flexible load
profile entails additional renewable generation and storage
capacity for both the solar-heavy and balanced scenarios. The
solar-heavy scenario requires more than 17 GW of additional
resources, an increase of more than 13% in the total resource
portfolio, and it is driven primarily by an increment in both
solar and battery resources. The balanced portfolio requires
more than 5 GW of additional resources, an increase of less
than 5% in the total resource portfolio, and it is driven primarily
by an increment in battery storage.
The results of Figure 11 illustrate that being able to manage
the load from such a large EV fleet in 2045 could have a substantial
impact on resource needs. Furthermore, faster charging rates
and higher concentrations of vehicles will require grid upgrades.
To minimize these upgrades, vehicles will need to charge in locations
and at times that reduce stress on the grid.
Making Sure EV Charging Is Smart
One of the key factors to accelerate the adoption of electric
transportation to meet the 2045 climate goals is charging
infrastructure availability. To this end, electric car charging
infrastructure programs developed by electric utilities, such
as SCE's Charge Ready program, should support the expanGW
160
140
120
100
80
60
40
20
129.3
117.9
112.7
10.0
18.7
1.7
1.5
51.5
29.3
Balanced
Scenario-
Flexible
Load
Battery
1.7
19.1
10.0
51.4
0.0
35.7
Balanced
Scenario-
Inflexible
Load
1.7
9.5
10.0
85.6
72.6
4.0
31.5
Solar-Heavy
Scenario-
Flexible
Load
Pump
Storage
4.0
44.3
Solar-Heavy
Scenario-
Inflexible
Load
Solar
In-State Wind Out-of-State Wind Geothermal
figure 11. The impact of load flexibility on 2,045 comparative
resource portfolios.
38
ieee power & energy magazine
sion of EV charging infrastructure at homes, workplaces,
and destination centers for public and private fleets. The
deployment of make-ready infrastructure to support EV supply
equipment should also be leveraged to design and implement
pilots aimed at evaluating programs and strategies for
effective EV load management.
A pioneering plug-in EV (PEV) smart charging pilot evaluated
residential-based smart charging technologies through lab
testing and field testing. From the middle of 2013 until the end
of 2014, this pilot evaluated a variety of technologies, both at
SCE's labs and at employees' homes in southern California.
Two communication paths for residential EV load management
were tested, namely direct to device and business to business.
Direct to device indicates an architecture by which SCE communicates
directly to customer devices using the Smart Energy
Profile 2.0 (SEP2 or IEEE 2030.5). Business to business represents
an architecture whereby a third party, like an aggregator,
enrolls in a demand response program and manages loads based
on SCE-called events utilizing the OpenADR 2.0 protocol. Figure
12 depicts the communication paths, protocols, and technologies
deployed and evaluated in the EV smart charging pilot.
Key findings from the pilot have been leveraged for developing
146.5
1.7
5.5
5.4
future charging strategies, programs, and infrastructure advisory
protocols. SEP2, a new standard that had been recently released
at the time of the smart charging pilot, was effectively demonstrated
in the pilot's use cases (enrollment, demand response,
load control, and pricing, among others), proving to be suitable
as a head-end communication protocol. Also, it has been verified
that customers enrolled in EV load management programs need
flexibility, e.g., through automation or pricing, to address concerns
about external entities interrupting their EV charge.
In a subsequent EV workplace charging pilot involving 80
intelligent charging stations installed at nine SCE facilities, SCE
gained insight into the correlation between charging time and
price and the demand response potential from EV charging. For
instance, most of the pilot participants were charging their EV
between 5 a.m. and noon, indicating that drivers are influenced
by factors such as establishing a regular charging routine at
work and opting for charging at lower off-peak prices.
Moreover, 76% of EVs participated in the demand
response test events scheduled during morning off-peak hours
(7:30-10 a.m.), while 72% of EVs participated during afternoon
on-peak events (1-2 p.m.), which suggests that the
pilot user population was generally satisfied with the demand
response strategies tested. The load curtailment measured for
a sample of demand response events is displayed in Figure 13.
The average load curtailment for the off-peak events was
67 kW, and it was 23 kW for the on-peak events, indicating that
the level of EV load flexibility could be almost three times
higher during off-peak hours than during peak hours.
Further insight on EV load management strategies and metrics
was gained through a mandatory pilot for customers participating
in SCE's electric car charging infrastructure program who
installed level 2 (208-240 V) charging stations. The pilot study
was based on a day-ahead program that provided advanced notice
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