Characterizing the Response of Commercial and Industrial Facilities to Dynamic Pricing Signals from the Utility Page: 3 of 15
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critical lighting. Examples of load shifting strategies include pre-cooling and water heating during times of the day
when energy costs are low.
From the perspective of the ISO and the utility there is a strong need to accurately predict characteristics of
C&I facilities' demand sheds and shifts (including their variability) resulting from dynamic pricing signals. Better
shed/shift forecasts are important to the ISO for scheduling electricity supply to meet demand. Predictions also
allow the utility to understand how characteristics of dynamic pricing signal responses vary across facilities and
which facilities are best suited to different DR programs. In addition, understanding facilities' responses to
dynamic prices allows the utility to model, analyze, and compare new dynamic pricing programs and their ability to
achieve the utility's goals (e.g., peak demand reductions, highly-predictable demand reductions, energy savings,
It is also advantageous for C&I facilities to be able to predict characteristics of their own demand sheds/shifts.
Facility managers would like to minimize energy costs subject to available resources, constraints (e.g., occupant
comfort), and dynamic pricing information. Understanding how a facility responds to pre-programmed dynamic
pricing response strategies is essential for minimizing energy costs. For instance, data presented in this paper show
that the time interval between receipt of a demand response signal and complete execution of a demand reduction
(referred to as 'ramp time' in this paper), using an automated demand response system, varies from less than three
minutes to more than two hours. Facility managers should consider the ramp time of their facility when devising
optimal price response strategies.
Another important reason to understand facility sheds/shifts resulting from dynamic pricing signals is to
determine the optimal lead-time (defined here as the time between when prices are published and when they take
effect) at which dynamic prices should be announced to the customer. It is advantageous to the facility to receive
pricing information with large lead-times so that the facility can plan its response, leveraging all of its DR resources,
to minimize total energy costs. However, to participate in real-time electricity markets, facilities would receive
pricing information at short lead times (e.g., one hour), which would not allow them to leverage all of their DR
resources (e.g., pre-cooling) resulting in smaller demand sheds. There are clearly tradeoffs in facility response to
dynamic pricing as pricing information lead-time changes. Though this is not the subject of this paper, we are
currently researching these tradeoffs.
One way to predict demand sheds and shifts, and their variability, is by modeling C&I facilities. Facility
models can be built with physical equations or historical electricity demand data. Existing physical equation-based
facility models can accurately predict certain facility loads (e.g., HVAC loads, some lighting loads) if the
characteristics of the facility are known and the model is calibrated with actual demand data. However, physical
equation-based models seldom accurately predict human-controlled loads (e.g., some lighting loads, human-
controlled process loads, plug loads, etc.) and their variability. Alternatively, statistical models (which we employ in
this paper) built with historical electricity demand data capture all facility loads and do not require calibration or
knowledge of facility characteristics. However, historical electricity demand data are not generally sub-metered so
different types of loads cannot be disaggregated. Also, statistical models capture only historical facility behavior
and cannot be used to predict future facility behavior (though statistical models can be updated as more data become
In this paper, we describe a method to generate statistical models of C&I facilities including their response to
dynamic pricing signals. Facility models comprise a baseline demand model and a residual demand model that
predicts deviations from the baseline model due to dynamic pricing signals. To understand the diversity of facility
responses to dynamic pricing signals, we have characterized the response of 44 C&I facilities participating in
Pacific Gas and Electric's (PG&E's) CPP Program.
We begin with a brief review of relevant research on DR. We then detail our approach and describe the data
used in our analysis. We present our preliminary results and conclude.
DR is defined by DOE as "a tariff or program established to motivate changes in electric use by end-use
customers in response to changes in the price of electricity over time, or to give incentive payments designed to
induce lower electricity use at times of high market prices or when grid reliability is jeopardized" .
DR has many benefits. It can be used to reduce price volatility during peak periods . It also contributes to
system reliability . In addition, DR could be used to provide ancillary services (A/S) such as spinning reserve 
and regulation/load following .
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Mathieu, Johanna L.; Gadgil, Ashok J.; Callaway, Duncan S.; Price, Phillip N. & Kiliccote, Sila. Characterizing the Response of Commercial and Industrial Facilities to Dynamic Pricing Signals from the Utility, article, July 1, 2010; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc1013123/m1/3/: accessed December 14, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.