Characterizing the Response of Commercial and Industrial Facilities to Dynamic Pricing Signals from the Utility Page: 4 of 15
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There are many ways to achieve DR. For instance, in Interruptible Load Management (ILM) and Direct Load
Control (DLC) programs customers allow the program sponsor (e.g., utility, aggregator, etc.) to control their loads
within some prior agreed-upon constraints in exchange for credits and/or incentive payments. Much work as been
done to develop strategies to control loads through ILM and DLC for peak load management [8-10].
DR can also be achieved through dynamic electricity pricing such as RTP and CPP. Many economists advocate
RTP tied directly to wholesale prices [1,4,11]. Dynamic prices could also be a function of the "value and cost of
electricity in different time periods" , which implies that they could encapsulate information other than wholesale
prices. For instance, retail electricity prices could be higher during peak times (e.g., two times the wholesale price)
to encourage power use reductions.
Another way to achieve DR is to allow loads to participate in wholesale electricity markets. For example, in
the Participating Load Pilot (PLP) program conducted by PG&E in California, C&I facilities submit offers (their
expected electricity load and a possible load reduction) and bids (price asked for achieving their offer) to the day-
ahead non-spinning reserve A/S market . These bids/offers are optimized by the California ISO (CAISO)
together with supply-side bids/offers, and if a facility's bid is accepted and they are called to act they must deliver
their offered load reduction within minutes. While this particular program is not the subject of this paper, some data
from this program are analyzed here.
Baseline model + Residual model = Facility model
time time time
Figure 1. Illustration of how a baseline model and a residual model add to create a model of a facility's
If a C&I facility chooses to participate in a DR program it must develop price response strategies. Motegi et al.
 present a number of commercial building control strategies for DR including HVAC strategies (global
temperature adjustment of zones; adjustments to the air distribution and/or cooling systems), lighting strategies
(switching off selected lighting zones, fixtures, and/or lamps; stepped dimming and/or continuous dimming), and
turning off miscellaneous equipment. Other strategies for C&I facilities could include shifting energy use to other
times of the day (e.g., pre-cooling building thermal mass, shifting industrial processes), energy storage other than in
the building thermal mass, and on-site generation.
A C&I facility's DR strategies are limited by the facility's available resources and its control system's
capabilities. Therefore, devices that are already automated such as HVAC components are commonly used in DR
A simple way to model a facility capable of responding to dynamic prices is to break the model into two parts:
(1) a baseline model that predicts facility electricity demand on 'normal' days (i.e., days on which there is no
dynamic pricing), and (2) a dynamic pricing response model that predicts changes in electricity demand (from the
baseline) that result from dynamic pricing signals. This second model, referred to as a DR residual model in this
paper, predicts both sheds in demand and shifts in energy use. Figure 1 illustrates how a baseline model and a
residual model sum to create a model of a facility's electricity demand on a day in which there is one high price DR
event that is known day-ahead (hence the ability of the facility to shift energy consumption to the period directly
before the event).
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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/m1/4/: accessed January 16, 2019), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.