modeling residents’ response to the financial incentives in demand response programs abigail c....
TRANSCRIPT
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Modeling Residents’ Response to the Financial Incentives in Demand
Response Programs
Abigail C. TeronQinran Hu, Hantao Cui, Dr. Fangxing Li
Universidad del Turabo
Final PresentationJuly 17, 2014
Knoxville, Tennessee
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2
•Introduction•Background•Demand Response
•Problem formulation•Simulation results•Problem•Solution of the problem•Artificial neural network•Model
•Conclusion•Future work
Outline
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Introduction
3
•The utility companies are in the search of different ideas 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.
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Generator and demand curve
4
Generator curve
=
Demand curve
Background
•Peak hour causes instability in power system•Reduce the gap between both graph.
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Demand Response?
5
What is
•Promoting demand response is an important way to make Power System more efficient, which has become more popular nowadays.
Demand Response?Why use
•Changes 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.
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Who wants to do DR?
6
Generator
Utilities
Houses
ISO
Generator
Houses
ISO
Utilities
Electricity Market
• Regulated power industryObligation to serve with guaranteed rate of return. A monopoly system and integrated vertically
Buy electricity
SaleUtilities
Electric utility engages in the generation, transmission, and distribution of electricity for sale generally in a regulated market.
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•Utility is willing to pay reasonable financial incentives to lower the electricity usage during peak hours.
•Utility provides financial incentives to organizations that implement efficiency projects or initiatives.
How?
Why?
•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.
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0.5 1 1.5 2 2.5 30
0.5
1
1.5
2
2.5
3
3.5
Y-Values
Y-Values
0.5 1 1.5 2 2.5 30
0.51
1.52
2.53
3.5
Y-Values
Y-Values
Problem
8
1. They cannot pay more than it should in incentives
2. They cannot pay less than it should
Solution
Design a model that find the optimal amount to achieve the
DR.
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• It is an interconnected group of nodes, similar to the neuron in the brain.• It is presented as system of interconnected neurons that compute values from inputs.
Represents an artificial neuron Circular node
arrow Connection from the output of one neuron to the input of another neuron
Why neural network?
Artificial Neural Network
• With the use of neurons is a simpler way to solve problems.
• They read an input, process it, and generate an output.
• Key element of the neural network is their ability to learn characteristics.
• It classifies information obtained.
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MODEL
How much utility want to reduce & When they want
to reduce it
People information
How much they need to pay to
the people
Model
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People information
Appliances
Peoples’ attitude
ActivitiesATUS
EIA
CURENT survey
BLS
People information
Inputs of the model
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EIAWhat is EIA?It is the U.S. Energy Information Administration that collects, analyzes, and disseminates independent and impartial energy information to promote sound policymaking, efficient markets, and public understanding of energy and its interaction with the economy and the environment.
EIA: Energy Information Administration
CURENT Survey
What is CURENT Survey?It is based on self-reported data collected in 2013 from 711 US residents across 48 states. Relies on understanding customers’ reaction to financial incentives
CURENT: Center for ultra-wide-area resilient electric energy transmission network
BLSATUS: American time use surveyWhat is a time-use survey?Time-use surveys measure the amount of time people spend doing various activities, such as work, childcare, housework, watching television, volunteering, and socializing.
Inputs of the model
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.
.
.
.
.
.
EIA
BLS
CURENT Survey
y1
.
.
.
y2
yk
yk+1
yk+2
ym
ym+1
ym+2
yn
function
How much utility need to
pay to the people in incentives
Output of the model
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MATLAB® is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. You can use MATLAB for a range of applications, including signal processing and communications, image and video processing, control systems, test and measurement, computational finance, and computational biology. More than a million engineers and scientists in industry and academia use MATLAB, the language of technical computing.
Pattern Recognition Neural Network: helps classify peoples information with BLS, CURENT Survey and EIA information as a Target.
Construction of the model
Artificial neural network
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Small case study
15
Test peoples’ information
Teached neural network informationInputs:
Peoples characteristics
Inputs:Peoples characteristics
Historical data
Historical data
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Test peoples’ information
Teached neural network informationAttitude towards
financial incentives the DR
Appliances: ac and electric water heather
Activities: hours working
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Flowchart
17
StartATUSstart
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StartATUSstart
p, t1
Inputs: peoples’ information and historical data
[p , t1] = peoples_dataset2
BLS
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StartATUSstart
p, t1
size(p)size(t1)
Size of triangulation connectivity list
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20
StartATUSstart
p, t1
size(p)size(t1)
24 rows 20 columns
Convert t1 in binary number saving it in to a new variable: temp
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21
StartATUSstart
p, t1
size(p)size(t1)
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
setdemorandstream(391418381)Since the neural network is initialized with random initial weights, the results after training vary slightly every time the example is run. To avoid this randomness, the random seed is set to reproduce the same results every time.
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22
StartATUSstart
p, t1
size(p)size(t1)
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
net = patternnet(34)
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23
StartATUSstart
p, t1
size(p)size(t1)
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
[net,tr] = train(net,p,temp)The training set is used to teach the network. Training continues as long as the network continues improving on the validation set.
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24
StartATUSstart
p, t1
size(p)size(t1)
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Test , Training Tool shows the network being
trained and the algorithms used to train it. It also displays the
training state during its revision and the criteria
which stopped the training will be
highlighted in green.
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25
StartATUSstart
p, t1
size(p)size(t1)
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
Convert the 24 rows and 20 columns back into decimal number.
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26
StartATUSstart
p, t1
size(p)size(t1)
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number Output
idx of test Y
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27
StartATUSstart
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSstart
ATUSstart
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ATUSend
28
StartATUSstart
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSstart
StartATUSend
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSend
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CURENT30min
29
StartATUSstart
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSstart
StartATUSend
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSend
StartCURENT30min
p, t3
Convert t3 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t3)
EndCURENT30min
Convert the value in binary
number
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CURENT2hr
30
StartATUSstart
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSstart
StartATUSend
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSend
StartCURENT30min
p, t3
Convert t3 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t3)
EndCURENT30min
StartCURENT2hr
p, t4
Convert t4 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t4)
EndCURENT2hr
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CURENTutility
31
StartATUSstart
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSstart
StartATUSend
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSend
StartCURENT30min
p, t3
Convert t3 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t3)
EndCURENT30min
StartCURENTUtility
p, t5
Convert t5 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t5)
EndCURENTUtility
StartCURENT2hr
p, t4
Convert t4 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t4)
EndCURENT2hr
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EIAac
32
StartATUSstart
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSstart
StartATUSend
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSend
StartCURENT30min
p, t3
Convert t3 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t3)
EndCURENT30min
StartCURENTUtility
p, t5
Convert t5 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t5)
EndCURENTUtility
StartCURENT2hr
p, t4
Convert t4 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t4)
EndCURENT2hr
StartEIAac
p, t1
size(p)size(t1)
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
idx of test Y
StartEIAac
Test , the output is in decimal. It was used the round function to return it
to 1 or 0
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Merge models
33
StartATUSstart
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSstart
StartATUSend
p, t1
Convert t1 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t1)
EndATUSend
StartCURENT30min
p, t3
Convert t3 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t3)
EndCURENT30min
StartCURENTUtility
p, t5
Convert t5 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t5)
EndCURENTUtility
StartCURENT2hr
p, t4
Convert t4 in binary number saving it in to a new variable: temp
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
Convert output back to decimal number
idx of test Y
size(p)size(t4)
EndCURENT2hr
StartEIAac
p, t1
size(p)size(t1)
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
idx of test Y
StartEIAac
StartEIAewh
p, t2
size(p)size(t2)
Apply weight to the neural network
Set the number of hidden neurons.
Train the artificial neural network
Test neural network
idx of test Y
StartEIAewh
ATUSstart(j)<=0 & ATUSend(j)>10e
Sort value of incentive from minimum value to maximum value
Counter to add the total kwKw = kw +ac*2 + ewh*2
Total $ incentivesI1 = I1 + m(i) + (ac + ewh)
I1 incentives
EndModel
kw = 0, I1 = 0, ac = 0, ewh = 0
EIAewh = 1
ewh = 1
EIAac = 1
ac = 1
Output: Incentives they have to pay to obtain the electric demand needed
Sort the value from the minimum to maximum value
Checks the hour of each person
Counts the electric water heather
Counts the air continue
EquationCounter of kW
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Results
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Information that utility can provide (assumed)
Money utility needs to pay in
incentives
Percentage of air conditioner and electric water
heather available and not available
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Conclusion
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This project proposed a model of residents’ response to the financial incentives in demand response programs.
•With this model, utility will be able to predict how much they need to pay in incentives in order to realize the expected demand reduction according to the residents’ characteristics, attitudes towards DR, daily activities and appliances.
•This model helps promote residential demand response to achieve load shifting during peak hours. Also, it brings a new idea for the electric utilities to design more efficient incentive based DR programs.
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Future work
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• As a future work a touch of sensibility is proposed in the model. It is very important to have both the company and the customer satisfied when working with a power demand required this is why is good to consider the peoples’ need related to tolerance to the heat and cold or social activity.
• First we will consider the fact whether the household contains an elderly person or a disabled gold who require all equipment at all times.
• The model will work omitting these households, taking into account the rest of the people. This utility will have a better proposal in suggesting incentives and demand response, besides having a better attitude results in relation to people.
• Second, be added to all persons who conducted the survey since in this case is proposed and tested with only 20 people in it. By doing this you will get a more concise Utility result of what just pay in order to get the response for the resident financial incentives person in DR.
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