Modeling Residents’ Response to the Financial Incentives in Demand Response Programs Abigail C....

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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•Introduction•Background•Demand Response

•Problem formulation•Simulation results•Problem•Solution of the problem•Artificial neural network•Model

•Conclusion•Future work

Outline

Introduction

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•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.

Generator and demand curve

4

Generator curve

=

Demand curve

Background

•Peak hour causes instability in power system•Reduce the gap between both graph.

Demand Response?

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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.

Who wants to do DR?

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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.

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

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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.

http://en.wikipedia.org/wiki/File:Colored_neural_network.svg

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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

Small case study

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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

Flowchart

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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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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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StartATUSstart

p, t1

size(p)size(t1)


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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StartATUSstart

p, t1

size(p)size(t1)



Set the number of hidden neurons.

net = patternnet(34)

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StartATUSstart

p, t1

size(p)size(t1)




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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StartATUSstart

p, t1

size(p)size(t1)





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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StartATUSstart

p, t1

size(p)size(t1)





Test neural network

Convert output back to decimal number

Convert the 24 rows and 20 columns back into decimal number.

26

StartATUSstart

p, t1

size(p)size(t1)





Test neural network

Convert output back to decimal number Output

idx of test Y

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StartATUSstart

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSstart

ATUSstart

ATUSend

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StartATUSstart

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSstart

StartATUSend

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSend

CURENT30min

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StartATUSstart

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSstart

StartATUSend

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSend

StartCURENT30min

p, t3





Test neural network


idx of test Y

size(p)size(t3)

EndCURENT30min

Convert the value in binary

number

CURENT2hr

30

StartATUSstart

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSstart

StartATUSend

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSend

StartCURENT30min

p, t3





Test neural network


idx of test Y

size(p)size(t3)

EndCURENT30min

StartCURENT2hr

p, t4





Test neural network


idx of test Y

size(p)size(t4)

EndCURENT2hr

CURENTutility

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StartATUSstart

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSstart

StartATUSend

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSend

StartCURENT30min

p, t3





Test neural network


idx of test Y

size(p)size(t3)

EndCURENT30min

StartCURENTUtility

p, t5





Test neural network


idx of test Y

size(p)size(t5)

EndCURENTUtility

StartCURENT2hr

p, t4





Test neural network


idx of test Y

size(p)size(t4)

EndCURENT2hr

EIAac

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StartATUSstart

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSstart

StartATUSend

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSend

StartCURENT30min

p, t3





Test neural network


idx of test Y

size(p)size(t3)

EndCURENT30min

StartCURENTUtility

p, t5





Test neural network


idx of test Y

size(p)size(t5)

EndCURENTUtility

StartCURENT2hr

p, t4





Test neural network


idx of test Y

size(p)size(t4)

EndCURENT2hr

StartEIAac

p, t1

size(p)size(t1)




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

Merge models

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StartATUSstart

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSstart

StartATUSend

p, t1





Test neural network


idx of test Y

size(p)size(t1)

EndATUSend

StartCURENT30min

p, t3





Test neural network


idx of test Y

size(p)size(t3)

EndCURENT30min

StartCURENTUtility

p, t5





Test neural network


idx of test Y

size(p)size(t5)

EndCURENTUtility

StartCURENT2hr

p, t4





Test neural network


idx of test Y

size(p)size(t4)

EndCURENT2hr

StartEIAac

p, t1

size(p)size(t1)




Test neural network

idx of test Y

StartEIAac

StartEIAewh

p, t2

size(p)size(t2)




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

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

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.

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.

Modeling Residents’ Response to the Financial Incentives in Demand Response Programs Abigail C....

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