Demand Response Framework for Indiarb/Professional Activities/DR16.pdf · Demand Response Framework...

Demand Response Framework for

India

Workshop on DSM & DR IIT Bombay - 4th March 2016

Rangan Banerjee

What is Demand Response?

• Changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity

• DR includes all intentional electricity consumption pattern modifications by end-use customers that are intended to alter the timing, level of instantaneous demand, or total electricity consumption

7

Albadi and EL-Saadany (2008)

DR Programme Classification

• Incentive Based Programmes

– Direct Load Control

– Interruptible Load Control

– Market Based – Demand Bidding, Emergency DR, Capacity Market, Ancillary Services Market

• Price Based Programmes

– Time of Use Pricing

– Critical Peak Pricing

– Real Time Pricing

Albadi and EL-Saadany (2008)

Role of DR in Power Planning

4USDOE, 2006

Schematic of DR framework

5

#1 Understand variations in demand

6

0

50

100

150

200

250

300

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

Lo

ad

(in

kW

)

Day 1 Day 2

Features ParametersEvening

setback

Evening

shoulderMorning

ramp-up

Morning

Start-up

High load

duration

Near-Peak LoadNear-Base Load

Rise time

Fall time

LBNL, 2011

Delhi Load Curve

7

Seasonal Variations - Delhi

Source: NRLDC 2006

Mumbai Electricity Load Profiles

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

21-May-09

26-May-11

21-May-13

IITB Hospital

-2000

0

2000

4000

6000

8000

10000

12000

12:0

0:0

0 A

M

12:2

6:0

0 A

M

12:5

2:0

0 A

M

1:1

8:0

0 A

M

1:4

4:0

0 A

M

2:1

0:0

0 A

M

2:3

6:0

0 A

M

3:0

2:0

0 A

M

3:2

8:0

0 A

M

3:5

4:0

0 A

M

4:2

0:0

0 A

M

4:4

6:0

0 A

M

5:1

2:0

0 A

M

5:3

8:0

0 A

M

6:0

4:0

0 A

M

6:3

0:0

0 A

M

6:5

6:0

0 A

M

7:2

2:0

0 A

M

7:4

8:0

0 A

M

8:1

4:0

0 A

M

8:4

0:0

0 A

M

9:0

6:0

0 A

M

9:3

2:0

0 A

M

9:5

8:0

0 A

M

10:2

4:0

0 A

M

10:5

0:0

0 A

M

11:1

6:0

0 A

M

11:4

2:0

0 A

M

12:0

8:0

0 P

M

12:3

4:0

0 P

M

1:0

0:0

0 P

M

1:2

6:0

0 P

M

1:5

2:0

0 P

M

2:1

8:0

0 P

M

2:4

4:0

0 P

M

3:1

0:0

0 P

M

3:3

6:0

0 P

M

4:0

2:0

0 P

M

4:2

8:0

0 P

M

4:5

4:0

0 P

M

5:2

0:0

0 P

M

5:4

6:0

0 P

M

6:1

2:0

0 P

M

6:3

8:0

0 P

M

7:0

4:0

0 P

M

7:3

0:0

0 P

M

7:5

6:0

0 P

M

8:2

2:0

0 P

M

8:4

8:0

0 P

M

9:1

4:0

0 P

M

9:4

0:0

0 P

M

10:0

6:0

0 P

M

10:3

2:0

0 P

M

10:5

8:0

0 P

M

11:2

4:0

0 P

M

11:5

0:0

0 P

M

Pow

er

(in W

att

s)

Time of Day

IIT Hospital ( Old Feeder )

Chart 3: IITB Hospital – (Logged Data)

#1 Demand Variability –World Cup

http://webarchive.nationalarchives.gov.uk/20121217150421/http://www.decc.gov.uk/assets/decc/statistics/publications/trends/articles_issue/560-trendssep10-electricity-demand-article.pdf

9

Gujarat Wind – March 3, 2016

12

PV Output Variability

Apt et al, 2011

Jan 1, 2015

June 2, 2015

Solar Output Variation

Gujarat Solar –March 3, 2016

15

Understanding variations in supply

16

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

180.00

6:3

0

7:0

0

7:3

0

8:0

0

8:3

0

9:0

0

9:3

0

10:0

0

10:3

0

11:0

0

11:3

0

12:0

0

12:3

0

13:0

0

13:3

0

14:0

0

14:3

0

15:0

0

15:3

0

16:0

0

16:3

0

17:0

0

17:3

0

18:0

0

18:3

0

19:0

0

MW

Time of the Day

Chiraka (Gujarat)Solar Generation

11/4/2012

13/4/2012

29/4/2012

#2 Determine Sector wise and End-Use

wise Load Patterns.

17

Determine Sector wise Load Patterns.

18

#2 Determine Sector wise and End-Use

wise Load Patterns.

19

Estimation of load profiles IITB MB

0

10

20

30

40

50

60

70

80

12:0

0 A

M

1:0

0 A

M

2:0

0 A

M

3:0

0 A

M

4:0

0 A

M

5:0

0 A

M

6:0

0 A

M

7:0

0 A

M

8:0

0 A

M

9:0

0 A

M

10:0

0 A

M

11:0

0 A

M

12:0

0 P

M

1:0

0 P

M

2:0

0 P

M

3:0

0 P

M

4:0

0 P

M

5:0

0 P

M

6:0

0 P

M

7:0

0 P

M

8:0

0 P

M

9:0

0 P

M

10:0

0 P

M

11:0

0 P

M

Energy savings from

DSM

New Load curve

Old Load curve(kW)

Total peak demand savings = 20.5 kW = 20.7 kVA (@0.99 pf lag)

Energy savings = 161 kWh/day

20

0

10

20

30

40

50

60

70

80

12:00…

2:00…

4:00…

6:00…

8:00…

10:00…

12:00…

2:00…

4:00…

6:00…

8:00…

10:00…

MB TotalLoad

MB totallighting

Total fans

Totalcomputers

Total AC


21

Determining hardware control strategies.

22

DR strategies for Air Conditioning (AC) loads:


23


24

30 T MeltingArc furnace

Bar mill

Wire mill

40 T Melting Arc

furnace

St. steel Scrap mix or

Alloy steel scrap mix

Alloy steel

scrap mix

Convertor (only for

St Steel)

Ladle Arc

furnace

VD or VOD

station

Bloom caster

Billet caster

Bloom mill

ooo

ooo

Reheat furnace

Reheat furnace

Reheat

furnace

Wire products for

final finish

Rods, Bars for final

finish

Open store

Open store

Open store

Open store

Steel Plant Flow Diagram

25

0

10

20

30

40

50

60

Time hours

Lo

ad

MW

Optimal with TOU tariff

Optimal with flat tariff

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

Steel Plant Optimal Response to TOU tariff

26


27

Load Forecasting

• Black Box methods – Time series forecasting, Day matching, Artificial Neural Network Models

• Weather dependent models – Forecast Temperature

• Forecast variability in Supply

• Uncertainty in forecasts

• Improve suplly, demand forecasting methods

28

Need for DR

• Supply – Demand Matching

• Potential Shortfall based on forecasts

• Sudden tripping, outage

• Sudden load increases

• Storage, Pumped Hydro etc..

• Notification Period, Amount Needed

29


30

Example Of DR Baseline

31https://www.naesb.org//pdf4/dsmee100608enernoc_2.pdf

Estimating DR Technical & Economic potential

32

0

50

100

150

200

250

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Ele

ctr

ical L

oad

(in

kV

A)

Co

oli

ng

Lo

ad

(in

TR

)

Time of the Day

Cooling Load (in TR)

Electrical load (in kVA) without Thermal storage

Electrical load (in kVA) with Thermal storage (Under Flat tariff)

Estimating DR Technical & Economic potential

33

0

500

1000

1500

2000

2500

3000

3500

0:1

5

1:1

5

2:1

5

3:1

5

4:1

5

5:1

5

6:1

5

7:1

5

8:1

5

9:1

5

10:1

5

11:1

5

12:1

5

13:1

5

14:1

5

15:1

5

16:1

5

17:1

5

18:1

5

19:1

5

20:1

5

21:1

5

22:1

5

23:1

5

Lo

ad

(in

kW

)

Time of the day

Use of thermal storage during DR call: Tata power-Mumbai

Customer Baseline (in kW)

Event Day customer meter data (in kW)


34

Deciding Policies, Regulations & incentives for DR and identifying

various responses to DR Call/Trigger.

35

Deciding Policies, Regulations & incentives for DR and identifying

various responses to DR Call/Trigger.

36

A.Dave, T.Kanitkar and R.Banerjee Analysing Implications of India's Renewable Energy Targets, 2016 (under review)

Summing Up

• Need to have an overall framework

• Need to document transaction costs, participation rates

• Need to improve forecasting and analytical tools

• Assess effectiveness and viability of DR by scaling pilots to utility wide deployment

• Public domain information, analysis

38

End Note

39

https://www.cartoonstock.com/directory/p/power_saving.asp

Thank You

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