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Designing a Model to Obtain Residents’
Response for the Financial Incentives
in a Demand Response Program
Abigail C. Teron1, Qinran Hu2, Hantao Cui2, Dr. Fangxing Li2
Universidad del Turabo1, University of Tennessee2
Abstract
Demand Response
•The utility companies are in the search of different ideas Changes
and alternatives in order to decrease the use of power
through the customers’ motivation of financial incentives in
a Demand Response Program.
•Design a model that will predict how much utility needs to
pay in incentives to the people in order to get a response in
the DR.
•The model focuses on the peoples’ information related to
their activities, attitudes and the use of different appliances
collected from BLS, EIA and CURENT survey.
in electric usage by customers in response of
acknowledging electricity price over time or additional
financial incentives designed to lower electricity usage
during peak hours.
Why use
Demand Response?
Because
the promotion of demand response is an
important way to make Power System more efficient,
which has become more popular in the last decades.
Who wants to do
How is going to be implement the
Demand Response?
Demand Response?
In
$
regulated power industry utility wants to do demand
response.
Problem
Electricity Market
to lower the electricity usage during peak hours.
• They can not pay more than it should in
incentives because the curve can go lower.
Generator
Buy electricity
Utility is willing to pay reasonable financial incentives
• They can not pay less that it should because
the curve will increase more than it should.
ISO
Houses
Sale
Utilities
Solution
Design a model that find the optimal amount to achieve the DR.
Artificial Neural Network
Hidden
Input
Output
Model
neural network?
•Is an interconnected group of nodes, similar to the
•With the use of neurons is a simpler
neuron in the brain.
•Presented as system of interconnected neurons that way to solve problems.
•They read an input, process it, and
compute values from inputs.
generate an output.
Represents an artificial neuron
Circular node
•Key element of the neural network is
Connection from the output of one
arrow
neuron to the input of another
their ability to learn characteristics.
neuron
•It classifies information obtained.
People
information
How much
utility want
to reduce &
When they
want to
reduce it
Why use
Peoples’ Information
People
information
MODEL
How
much
they
need to
pay to
the
people
BLS
EIA
CURENT
survey
Activities
ATUS
Appliances
Peoples’
attitude
EIA
BLS
CURENT
Survey
y1
.
.
yk.
yk+
1
yk+
2
P
e
o
p
l
e
function
.
.
ym
.
ym
+1
ym
+2
.
.
yn.
Small case study
I
n
f
o
r
m
a
t
i
o
n
How much
utility need
to pay to
the people
in
incentives
y2
Functions
H
i
s
t
o
r
i
c
a
l
D
a
t
a
Conclusion
•Propose
a model to get residence response for the
financial incentives in demand response.
•Under this model utility is able to predict how much they
can pay in incentive to the clients’ in order to decrease
the demand to improve the system.
This work made use of Engineering Research Center shared facilities supported by the Engineering Research
Center Program of the National Science Foundation and the Department of Energy under NSF Award Number
EEC-1041877 and the CURENT Industry Partnership Program.