IEEE Electrification - September 2021 - 28

x Should the off-peak/on-peak prices directly calculated
from the actual average prices of the off-peak/on-peak
hours?
x Should we artificially enlarge the price gap between
the peak and off-peak periods?
x What is a reasonable duration of the on-peak periods?
x Shall we define multiple on-peak periods in a day?
x How often the time-of-use rates should be updated?
For example, a small price gap may not create enough
incentive to shift a desired number of loads over to the
off-peak periods; however, a large price gap may force
customers to pay more than they should in the peak
periods. Note there are always some customer groups
who have no access to any LC technologies, so setting
the peak price to be higher-than-the-actual-cost is unfair
to those customers. If the duration of the on-peak period
is too long for most LC technologies to cope with, its efficacy
will be in question. If the time-of-use rates are
updated on a daily basis, it requires the LC technology to
have the corresponding communication capabilities as
well as be automated because a customer may not manually
change the parameter on a daily basis. Therefore,
balance between the considerations must be made. A
deeper understanding on customer behaviors and the
flexibility of affordable LC technologies will definitely
help in this domain.
How to determine the contribution of each LC resource
when providing an aggregated LC service is also a challenge.
For example, in a cold winter day, when a control
signal, " drop thermostat by 2 °F, " is sent out to an LC group
consisting of 1000 HVAC units. The question is: shall we
reward all 1000 HVAC the same?
Suppose there are two HVAC units: one originally set at
75 °F and the other one originally at 72 °F. Then, by dropping
2 °F, the HVAC set at 75 °F contributes more energy
reduction but the customer who drops temperature setting
from to 72 °F to 70 °F suffers more. So, which HVAC
unit shall we reward more: the one who suffers more or
the one provides more demand reduction?
Suppose that the signal is to set all HVACs at 68 °F
instead. The HVAC with 75 °F set point originally will provide
a large energy reduction than the HVAC with 72 °F
set point. So, shall we reward the household with the 75 °F
more because they provide more energy reduction with
the same amount of suffering?
Under the current practice, all HVACs are rewarded the
same, but in the future, with more precise smart meter
measurements, we can quantify the LC performance
much better. However, does that mean we can reward the
LC resources more fairly?
Although we may not know a crystal clear " right " or
" wrong " answer for a question like this, it is always helpful
to keep asking such questions. We may need to devise
reward mechanisms that are fair to all LC resources and
customer groups by taking into account of the LC service
availability, affordability, and quality.
28
IEEE Electrification Magazine / SEPTEMBER 2021
Conclusion
From investigating history, we can discover what motivated
each evolution, find out what the technical and
sociological enablers were for the success implementations,
and learn why some seemingly futuristic and
well-intentioned attempts failed. Nowadays, the operation
of many other critical infrastructures is more than
ever intertwined with the reliable, secure, and safe
power grid operation. To achieve 100% clean energy, the
grid generation fleet may evolve into a completely different
mix from that what we grew up with. The development
of a highly-flexible LC control framework for
coping with the large percentage of variable generation
resources may be the best solution for resolving many
emerging grid operational issues. After all, loads are the
origin of the power system balancing problems.
When designing futuristic LC schemes, we, power
engineers, may need to keep an open mind. Technology
breakthroughs are driving the transformation in our
sister domains such as social economics, behavior
analysis, information technology, AI, and cybersecurity.
In the past, reliability, safety, and security considerations
made power professionals more conservative;
therefore, we were considered to be reluctant to
embrace new technologies. Looking into the future, we
shall not let the fear-of-change block our vision to discover
new solutions to emerging problems. In conclusion,
LC is one of the two keys (the other key is, of
course, the GC) to open the door to a smooth transition
to a 100% green energy future without collapsing the
old grid control framework. A new era for intelligent
load control is coming and it is well worth the investment
of time and effort.
For Further Reading
D. Pengwei, N. Lu, and H. Zhong, Demand Response in Smart
Grids. New York: Springer-Verlag, 2019.
P. Du and N. Lu, Energy Storage for Smart Grids: Planning and
Operation for Renewable and Variable Energy Resources. New York:
Elsevier, 2014.
N. Lu, " An evaluation of the HVAC load potential for providing
load balancing service, " IEEE Trans. Smart Grid, vol. 3, no. 3,
pp. 1263-1270, Sept. 2012. doi: 10.1109/TSG.2012.2183649.
P. Du and N. Lu, " Appliance commitment for household
load scheduling, " IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 411-
419, June 2011. doi: 10.1109/TSG.2011.2140344.
G. Henri, N. Lu, and C. Carrejo, " Mode‐based energy storage
control approach for residential photovoltaic systems, " IEEE
Trans. Smart Grid, vol. 2, no. 1, pp. 69-76, 2019.
M. Vanouni and N. Lu, " A reward allocation mechanism for
thermostatically controlled loads participating in intra-hour
ancillary services, " IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 4209-
4219, 2017. doi: 10.1109/TSG.2017.2652981.
Biography
Ning Lu (nlu2@ncsu.edu) is with North Carolina State University,
Raleigh, North Carolina, 27695, USA.
IEEE Electrification - September 2021

Table of Contents for the Digital Edition of IEEE Electrification - September 2021
