Development of Energy Demand Model for Power System Analysis

Yohei Yamaguchi and Yoshiyuki ShimodaGraduate School of Engineering, Osaka University

yohei@see.eng.osaka-u.ac.jp

Modeling of Residential Energy Demand

2

Time use

Operation of home appliances

Status and specEnergy

consumption

3

Housewife

Chile 1

Chile 2

TV/Media Cleaning Bathing Others

0：00 6：00 12：00 18：00 24：00

Workingmale

Sleeping Work/School Meal Cooking Laundry

Child1

Child2

Refrigerator

IH stoveRice cookerMicrowave

0：00 6：00 12：00 18：00 24：00

Washing machine

TV

Cleaner

0.0

1.0

2.0

3.0

4.0

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00Elec

trici

ty d

eman

d [k

W]

Appliance Lighting KitchenAC Water heating PV generation

7

ApplianceOwnership

Large

Spec/efficiency

House

Occupants’ time use &appliance use

Energy demand

End use energy model Appliance Hot water use, etc.

Indoor environment model Lighting, AC

Home equipment model Water heater Battery, EV/PHV

Family agents

Time use Appliance

use

House objects

(1) Occupants’ time use

(2) Operation of home appliances

(3) Specification of apps

(4) Ownership of apps

(5) Specification of house

(6) Meteorological conditions

…

…

…

…

…

…

Stock Modelling Demand modeling

Time use

Operation of home appliances

Status and specEnergy

consumption

Measurement Electricity, water, gas 5 min resolution

227 Households No information

available except floor plan

Result Good agreement

in the mean & underestimation in deviation

Time variation

8

MeasurementModel

May 13th (sun) and 14th (mon)

0

500

1000

0:00 4:00 8:00 12:0016:0020:00 0:00 4:00 8:00 12:0016:0020:00 0:00

Predicted electricity consumption

9

0

50

100

150

0:00 6:00 12:00 18:00

Ele

ctri

city

co

nsum

ptio

n [W

] MeasuredSimulation

Kitchen plug

0

100

200

0:00 6:00 12:00 18:00

Ele

ctri

city

co

nsum

ptio

n [W

] MeasuredSimulation

Refrigerator

0

10

20

30

0:00 6:00 12:00 18:00E

lect

rici

ty

cons

umpt

ion

[W] Measured

SimulationWashing machine

Appliances for housework Daily time-varying characteristic is well captured Morning peak is too high while underestimated after

evening time

0

20

40

60

0:00 6:00 12:00 18:00

Ele

ctri

city

co

nsum

ptio

n [W

] Dish Wahser

0

4

8

12

16

20

24

0~1

500

1500~2

000

2000~2

500

2500~3

000

3000~3

500

3500~4

000

4000~4

500

4500~5

000

5000~5

500

5500~6

000

6000~6

500

6500~7

000

7000~7

500

7500~8

000

8000~

割合

[%]

アンケート 新サンプリング

Annual electricity consumption

10

SimulationMeasured

Three potential causes of underestimation in deviation Common parameters Ignorance of households with extremely high/low consumption Interrelationship among parameters

Mean [kWh] SD [kWh]Simulation 3,889 1,023Measured 4,040 1,662

11

0.00.10.20.30.40.50.60.70.80.91.0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

Ele

ctri

city

dem

and

[kW

/hou

seho

ld]

CoolingHeatingHot waterLightingKitchenAppliancesTVRefrigiratorAdditionalActual data

10/01 Mon. 10/02 Tue. 10/03 Wed. 10/05 Fri. 10/06 Sat. 10/07 Sun.10/04 Tur.

0.00.10.20.30.40.50.60.70.80.91.0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

0:00

6:00

12:0

0

18:0

0

Ele

ctri

city

dem

and

[kW

/hou

seho

ld]

CoolingHeatingHot waterLightingKitchenAppliancesTVRefrigiratorAdditionalActual data

08/06 Mon. 08/07 Tue. 08/08 Wed. 08/09 Tur. 08/10 Fri. 08/11 Sat. 08/12 Sun.

Seasonal variation (Summer)

12

0

2

4

6

8

10

12

14

16

18

06/0

1

06/0

8

06/1

5

06/2

2

06/2

9

07/0

6

07/1

3

07/2

0

07/2

7

08/0

3

08/1

0

08/1

7

08/2

4

08/3

1

09/0

7

09/1

4

09/2

1

09/2

8

10/0

5

10/1

2

10/1

9

10/2

6

Ele

ctri

city

dem

and

[kW

h/da

y/ho

useh

old]

Measured dataSimulation

ChallengesTime variation: Behavior and appliance operation Consideration of interaction among household members (having meals and

taking bath/shower) Classification by behavioral characteristics Understanding relationship between action and appliance use

Household variation Time use and appliance operation

• Underestimated due to mixing sample data with different characteristics• Classification enables to generate individual-specific behavioral characteristics (Aerts et

al., 2014)• Data scarcity issue (Wilke et al., 2013)

Appliance ownership• Database considering the variation among family composition

Establishment of sampling method

Price and climate/weather effect Understanding price effect to consider demand response Understanding climate/weather effect by extracting occupants presence and

action from measured energy consumption data

13

14

Work Self caring Meal CommutingWork School/University Housework Caring family

Child care Rest Transportation Media useShopping Study/research Hobby Sports

Communication Social activity Medical care Others

Existing approachesNon-homogeneous Markov-chain (Widen et al, Richardson et al) Behavior transition probability

Tanimoto’s roulette sélection Model (2008) Percentage of people conducting

behaviors

Wilke’s model (2013) based on Survival Analysis Starting probability

(Multi-nominal logit model)

15

Sleep to sleep

Sleep to cookingSleep to face washing

0%

20%

40%

60%

80%

100%

TUD 1

TUD 2

TUD 3

TUD S

… …

8 am 9 am

,exp ,

∑ exp ,

ln( )

Proposed model

16

0%

20%

40%

60%

80%

100%

0%

20%

40%

60%

80%

100%

0 2 4 6 8 10 12 14 16 18 20 22 24

Sleeping time

Awaking time

Work beginning time

Work ending time

Dinner beginning time

Routine behaviors Sleeping, Going work/school,

Having meals, Bathing One member decides meal

time Bathing time is adjusted to

use bathroom sequentially

Place routines first, Then non-routines Select a non-routine behavior Decide duration

0%10%20%30%40%50%60%70%80%90%

100%

0:00 0:30 1:00 1:30 2:00

N

iit

ntnt

APB

APBSP

,

,,

Proposed model

17

0%

20%

40%

60%

80%

100%

0:00 0:30 1:00 1:30 2:00

psNumbeOfSte

DrPBAPB

dun

VacantStep

dundut

nt

,,

,

Behavior with short duration Probability is almost equal to PB at t Behavior with long duration Probability is reduced by duration information

Cumulative frequency of Duration

Vacant Stepor

30 mins after Dr reached to 90%

Probability of behavior selection at each time of day

18

0%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:00

Sleeping Work/Study Outing Meal Houseworks TV, media Others

0%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:00

Sleeping Work/Study Outing Meal Houseworks TV, media Others

Working male

Housewife

Original TUD Simulation

Original TUD Simulation

0%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:00

Sleeping Work/Study Outing Meal Houseworks TV, media Others

0%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:00

Sleeping Work/Study Outing Meal Houseworks TV, media Others

20

Influence of probability to choose non-routine behaviors

Percentage of simulated days with the activities

Using percentage of people delays occurrence

Adjusted probability makes error smaller

Adjustment has the same effect as stating probability

0%

10%

20%

30%

40%

0 2 4 6 8 10 12 14 16 18 20 22

Pro

b

Original TUD PBAjusted PB Starting Prob

Meal Preparation

0%

10%

20%

30%

0 2 4 6 8 10 12 14 16 18 20 22

Pro

b

House care

0%

10%

20%

30%

0 2 4 6 8 10 12 14 16 18 20 22

Pro

b

Clothes care

Behavior duration

Mean duration of all considered behaviors observed within time regions.

Most of the result agreed well but some behaviors were underestimated due to the modelling methodology prioritizing routine behaviors.

21

0

30

60

90

120

150

0 30 60 90 120 150

Sim

ulat

ion

[min

utes

]

Original [minutes]

MorningAfternoon

0

30

60

90

120

150

0 30 60 90 120 150

Sim

ulat

ion

[min

utes

]

Original [minutes]

TimeRegion1TimeRegion2TimeRegion3TimeRegion4

Number of transitions per day

22

0

5

10

15

20

25

30

Working male Housewife

Num

ber o

f beh

avio

r tra

nsiti

ons

per d

ay

[Tim

es] OriginalTUD

Simulation

0%10%20%30%40%50%60%70%80%90%

100%

0:00 4:00 8:00 12:00 16:00 20:00

Cum

ulat

ive

frequ

ency

Sleeping Working Breakfast Dinner Bathing

Influence of interaction in household

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0%10%20%30%40%50%60%70%80%90%

100%

0:00 4:00 8:00 12:00 16:00 20:00

Cu

mu

lati

ve f

req

uen

cyAwaking Working Breakfast Dinner Bathing

Single working male

4 member family

Classification by key attributes (age, occupation, gender) and routine behaviors Working male/female by working time

Distinction between routine and non-routine behaviors Enables to use different data source

Data scarcity issue: N becomes small with increasing classifications N=81 for the housewife category. Non-routine behaviors database is

developed for time regions made by routine behaviors.24

0%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:000%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:00

0%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:000%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:000%

20%

40%

60%

80%

100%

0:00 4:00 8:00 12:00 16:00 20:00

Working female CL1

CL2

CL3

CL4

CL5

Classification of time use sample

Classification by key attributes (age, occupation, gender) and routine behaviors Working male/female by working time

Distinction between routine and non-routine behaviors Enables to use different data source

Data scarcity issue: N becomes small with increasing classifications N=81 for the housewife category. Non-routine behaviors database is

developed for time regions made by routine behaviors.25

0%

20%

40%

60%

80%

100%

0:00 0:30 1:00 1:30 2:00

Before breakfast Before lunch

Before dinner After dinner

0%

20%

40%

60%

80%

100%

0:00 0:30 1:00 1:30 2:00

Before work After work

Working maleDuration of

meal preparation Housewife

DiscussionBehavior modeling Distinguishes routine and non-routine behaviors

Probability used for behavior selection Percentage of people Delay PB weighted by duration Same effect as Wilke’s starting probability No transition property and overestimated number of transitions

Interaction among household members Calibration needed so that statistical characteristic in original TUD is

replicated (e.g. delay in bathing time) Family composition must be considered in development of database

for routine behaviors

Classification of TUD and data scarcity issue Classification by time use characteristics enhances individuality. To overcome the data scarcity issue, the time regions made by the

routine behaviors are considered within which statistical information is developed

26

Concluding remarksTime variation: Behavior and appliance operation A methodology has been established for behavior simulation

Next step is appliance use simulation

Household variation More understanding needed on the structure creating variety

Social science approach: “theory of practice”

Establishment of sampling method

Price and climate/weather effect Energy data analysis

Commercial sector model Archetype Engineering Model

27