IEEE Electrification - December 2022 - 49

morning peaks that need to be mitigated. The variable
nature of renewable generation as well as significantly
lower power generation for solar in the winter
months means that additional measures must be
taken to mitigate these winter peaks with very high
heating demand. Applications such as ILC and TCC
can mitigate these effects, but they must be deployed
at scale.
x Transactive signals or dynamic rates can be used to
exercise demand flexibility in buildings; however,
most utilities do not offer such rates. Utilities and public
service commissions must introduce dynamic rates
to incentivize BTM DERs to support grid reliability.
If these challenges are overcome, which is possible,
BTM DERs can be reliable resources for providing grid services.
There are several lessons learned from testing and
validating grid services in commercial buildings:
x Deploying grid services in large commercial buildings
with BACnet-based BASs is possible. However, the
effort to coordinate different BTM DERs is still labor
intensive. The lack of a standard naming convention
for the various sensors and control points in a BAS
and lack of meta information (units, the association
of the sensor with a system, etc.) make the deployment
of grid service applications labor intensive.
Efforts such as the American Society of Heating,
Refrigeration and Air Conditioning's 223P Designation
and Classification of Semantic Tags for Building Data
should help.
x In small commercial buildings and homes, interoperability
among various connected devices, including
connected thermostats, is lacking, making it difficult
to deploy a single solution that works with all devices.
x Although building-grid integration has the potential
to benefit both the grid and building owners/managers,
many building owners/managers are not aware of
the benefits.
x As noted previously, many existing demand-response
programs directly control a DER, and the customer
generally does not have an easy way to opt out. To
make grid services ubiquitous in buildings, the
deployment must be usercentric.
x Deploying forward-looking TCC in buildings and
the distribution network will result in a more optimal
price of electricity. However, significant automation
within a building is required to make it a
scalable process.
Conclusions
Given the growing desire to mitigate climate change, utilities
are increasing generation from renewable sources,
and many cities and states are mandating all-electric
buildings. The electrification of the building sector-
which already consumes more than 75% of the total
electricity generated in the United States-will increase
electricity consumption. Because renewable generation
is variable and not dispatchable, it will create significant
supply-demand imbalance. Mitigating the imbalance
using generation reserves will be more expensive and
not efficient. It will be less expensive and more efficient
to use BTM DERs to mitigate some of the supply-
demand imbalance.
Buildings have more than 77 GWs of demand flexibility
potential. As we have shown in this article, the use of grid
service applications in commercial buildings will result in
peak electricity reduction between 10% and 20% for 4-6 h
without significantly compromising the service levels. A
deeper reduction in electricity consumption is possible,
but it will affect the service levels. ILC was developed to
support current utility demand-response programs, such
as PLM and capacity bidding, but it can also be used for
TOU and CPP. TCC is forward looking and needs markets
at each transactive node, including multiple commodity
markets within a building.
Acknowledgment
We would like to acknowledge the Building Technologies
Office of the U.S. Department of Energy's Office of Energy
Efficiency and Renewable Energy, the U.S. Department of
Energy's Office of Electricity, and the Washington State
Department of Commerce Clean Energy Fund for supporting
this research and development effort.
For Further Reading
K. Kalsi. (2017). " Virtual batteries. " Presented at the U.S. Dept.
Energy Building Technol. Office Peer Rev. [Online]. Available:
https://www.energy.gov/
S. Katipamula, R. G. Lutes, S. Huang, J. Lian, H. Ngo, and D.
Hammerstrom, " Coordination of behind-the-meter distributed
energy resources for transactive grid services: multibuilding, "
Pacific Northwest Nat. Lab., Richland, WA, USA,
PNNL-28430, 2019.
W. Kim and S. Katipamula, " Development and validation
of an intelligent load control algorithm, " Energy Buildings, vol.
135, pp. 62-73, Jan. 2017, doi: 10.1016/j.enbuild.2016.11.040.
" California Independent System Operator. " Accessed:
Jun. 1, 2021. [Online]. Available: http://www.caiso.com
" Annual Energy Outlook 2018, " U.S. Energy Information
Administration, Washington, DC, USA, Feb. 2018. [Online].
Available: https://www.eia.gov/pressroom/presentations/
Capuano_02052018.pdf
Biographies
Srinivas Katipamula (srinivas.Katipamula@pnnl.gov) is
with Pacific Northwest National Laboratory, Richland, WA
99352 USA.
Robert G. Lutes (robert.lutes@pnnl.gov) is with Pacific
Northwest National Laboratory, Richland, WA 99352 USA.
Sen Huang (huangs@ornl.gov) is with Pacific Northwest
National Laboratory, Richland, WA 99352 USA.
Roshan L. Kini (roshan.kini@pnnl.gov) is with
Pacific Northwest National Laboratory, Richland, WA
99352 USA.
IEEE Electrification Magazine / DECEMBER 2022
49
https://www.energy.gov/ http://www.caiso.com https://www.eia.gov/pressroom/presentations/Capuano_02052018.pdf https://www.eia.gov/pressroom/presentations/Capuano_02052018.pdf
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