GE Energy

GE Energy

Asia Development BankWind Energy Grid Integration Workshop:

OPERATIONS and DISPATCHNicholas W. Miller

GE Energy Consulting

BeijingSeptember 22-23, 2013

Nicholas W. Miller, GE Energy Consulting

ADB Wind Integration WorkshopSeptember 23-24, 2013

2/

© 2013 General Electric Company

ADB topic list

Wind energy dispatching methodology ( International Expert)

• Wind farm as Capacity source or Energy source

• Policies for scheduling wind energy

• Policies for curtailing wind energy

• Comparison different methods

• Software and other tools, processes for scheduling wind energy

• Role of Wind energy forecasting

3

U n i t C o m m i t m e n ta n d

D a y - A h e a d S c h e d u l i n g

L o a d F o l l o w i n g( 5 M i n u t e D i s p a t c h )

F r e q u e n c y a n d T i e - L i n e R e g u l a t i o n

( A G C )

D a y - a h e a d a n d M u l t i - D a y

F o r e c a s t i n g

Fa

ste

r (s

ec

on

ds

)

Tim

e F

ram

e

S

low

er

(Ye

ars

)

P l a n n i n g a n d O p e r a t i o n P r o c e s s

T e c h n o l o g yI s s u e s

H o u r - A h e a d F o r e c a s t i n g

a n d P l a n t A c t i v e P o w e r

M a n e u v e r i n g a n d M a n a g e m e n t

R e s o u r c e a n dC a p a c i t y P l a n n i n g

( R e l i a b i l i t y )

U n it D is p a tc h

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

7 0 0

0 2 0 0 0 4 0 0 0 6 0 0 0 8 0 0 0

H o u r

MW

R e a l - T i m e a n d A u t o n o m o u s P r o t e c t i o n a n d C o n t r o l F u n c t i o n s

( A G C , L V R T , P S S , G o v e r n o r , V - R e g , e t c . )

C a p a c i t y V a l u a t i o n( U C A P , I C A P )

a n dL o n g - T e r m L o a d

G r o w t h F o r e c a s t i n g

2 0 0 1 A v e r a g e L o a d v s A v e r a g e W i n d

0

5 , 0 0 0

1 0 , 0 0 0

1 5 , 0 0 0

2 0 , 0 0 0

2 5 , 0 0 0

3 0 , 0 0 0

1 6 1 1 1 6 2 1

H o u r

NY

ISO

Lo

ad (

MW

)

0

2 0 0

4 0 0

6 0 0

8 0 0

1 , 0 0 0

1 , 2 0 0

1 , 4 0 0

1 , 6 0 0

Win

d O

utp

ut

(MW

)

J u ly lo a d A u g u s t lo a d S e p t e m b e r lo a d

J u ly w i n d A u g u s t w in d S e p t e m b e r w in d

0

5 0 0

1 0 0 0

1 5 0 0

2 0 0 0

2 5 0 0

3 0 0 0

1 6 1 1 2 1

M i n u t e s

MW

S e p t e m b e r M o r n i n g A u g u s t M o r n i n g M a y Ev e n in g O c t o b e r Ev e n in g A p r i l A f te r n o o n

1 Y e a r

1 D a y

3 H o u r s

1 0 M in u t e s

Time Scales for System Planningand Operation Processes

4Nicholas W. Miller, GE Energy Consulting


System Operation Process - OverviewDay Ahead• Prepare load forecast (Total MW load for each hour of the day)• Commit units that will run to serve the load (accounts for uncertainty)• Preliminary dispatch schedule for each unit (by hour)

Units with long startup times are “committed” for operation during the next day

Hour Ahead

• Perform hour-ahead load forecast• Adjust hourly dispatch for committed units as required to match

actual load

Real Time

• Load-following (typically, dispatch is adjusted at 5-minute intervals)• Adjustments based on “economic dispatch”, using marginal costs or

competitive bids• Regulation (fast adjustments of MW to regulate frequency and

intertie power flows)



For grid operations, wind is “similar” to load .

0

5

10

15

20

25

30

35

40

45

50

0 6 12 18 24Hour

GW

LoadWindLoad - Wind

• Like load, wind can be forecast a day ahead

• Grid operators can plan day-ahead operations base on a load forecast and a wind generation forecast

• Dispatchable generation is allocated to serve the net of the forecast load minus the forecast wind

• Uncertainty in the wind forecast adds to the uncertainty in the load forecast

• Adjustments are made using hour-ahead forecasts and real-time data

Dispatchable Generation Serves “Net Load”

Net Load= Load Minus Wind(This is what must be

served by other types of generation)



Overview

• Temporal/Spatial Patterns

• Variability in Wind and Load MW

• Uncertainty

• Forecasting for Wind Power



Study Area Total Monthly Wind and Solar Energy for 2004 - 2006

0

2000

4000

6000

8000

10000

12000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

To

tal

En

erg

y (G

Wh

)Monthly Energy GWh from Wind & Solar for Years 2004–2006(30% Wind Energy - In Area Scenario)

‘04

‘05

‘06

Notable difference in Wind & Solar energy across the months and over

the years

S t u d y A r e a T o t a l M o n t h l y W i n d a n d S o la r E n e r g y ( 2 0 0 6 , 3 0 % )

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c

To

tal

En

erg

y (G

Wh

)

P V

C S P w s

W in d



Study Area Percent Monthly Wind and Solar Energy for 2004 - 2006

0%

10%

20%

30%

40%

50%

60%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month of Year

% o

f L

oad

En

erg

y

06

S t u d y A r e a T o t a l M o n t h l y W i n d a n d S o la r E n e r g y ( 2 0 0 6 , 3 0 % )

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c

To

tal

En

erg

y (G

Wh

)

P V

C S P w s

W in d

‘04

‘05

‘06

2006 percent monthly energy ranges from 18% (July) to 55% (April) in study

footprint

30% is not always 30%

Monthly Energy % from Wind & Solar for Years 2004–2006(30% Wind Energy - In Area Scenario)

55% of energy from wind and

solar



Study Footprint Total Load, Wind and Solar Variation Over Month of July(30% Wind Energy in Footprint)

0

10000

20000

30000

40000

50000

60000

1-Jul 8-Jul 15-Jul 22-Jul 29-Jul

MW

Ld(Base)

Wd(30%)

PV(30%)

CSP(30%)

LP Scenario



LP Scenario

-5000

0

5000

10000

15000

20000

25000

30000

35000

1-Apr 8-Apr 15-Apr 22-Apr 29-Apr

MW

Ld(Base)

Wd(30%)

PV(30%)

CSP(30%)

Study Footprint Total Load, Wind and Solar Variation Over Month of April(30% Wind Energy in Footprint)



Study Footprint 2006 Net Load Duration – In Area Scenario

-10000

0

10000

20000

30000

40000

50000

60000

0 583 1166 1749 2332 2915 3498 4081 4664 5247 5830

Net

Loa

d Le

vel (

MW

)

Study_Area Baseline

Study_Area L-W-S (10%)



0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Deciles of Year

Min load 22169 MW

Below existing min load ~57% of year, for 30% scenario

GE Energy 12

0%

20%

40%

60%

80%

100%

120%

0 1000 2000 3000 4000 5000 6000 7000 8000

Hours

Win

d P

enet

rati

on

as

% o

f L

oad

(%

)Hourly Wind Penetration

Daily Wind Penetration

Weelky Wind Penetration

Monthly Wind Penetration

Annual Wind Penetration

What Does 30% Penetration Mean?



Overview



• Uncertainty




Variability Analysis - Deltas

68%

99.7%

95%

μ – 3σ μ – 2σ μ – σ μ μ + σ μ + 2σ μ + 3σ

68%

99.7%

95%

μ – 3σ μ – 2σ μ – σ μ μ + σ μ + 2σ μ + 3σ

34%34%

13.5% 13.5%2.35% 2.35%

68%

99.7%

95%

μ – 3σ μ – 2σ μ – σ μ μ + σ μ + 2σ μ + 3σ

68%

99.7%

95%

μ – 3σ μ – 2σ μ – σ μ μ + σ μ + 2σ μ + 3σ

34%34%

13.5% 13.5%2.35% 2.35%

Statistics used to characterize variability:• Delta (∆) – The difference between successive data points in a series,

or period-to-period ramp rate. – Positive delta is a rise or up-ramp– Negative delta is a drop or down-ramp

• Mean () – The average of the deltas (typically zero within a diurnal cycle)

• Sigma (σ) – The standard deviation of the deltas; measures spread about the meanFor a normal distribution of deltas, σ is related to the percentage of deltas within a certain distance of the mean



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

0

5000

10000

15000

20000

25000

30000

35000

40000

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

Lo

ad

an

d N

et

Lo

ad

De

lta

(M

W)

Average Daily Profile of Deltas Over Year 2006 (30% Wind Energy in Footprint – LP Scenario)(Avg. +/- sigma, Minimum, Maximum)

Hour of Day

5644 MW(Nov 14)

-4931 MW(Jun 7)

Load DeltasNet Load DeltasTotal LoadTotal Net Load

Load DeltasNet Load DeltasTotal LoadTotal Net Load

To

tal

Lo

ad

an

d N

et

Lo

ad

(M

W)



Overview



• Uncertainty




Standard Deviations of Day-Ahead Forecast Errors

2001-2003 11 Months DAH Scatter Plot of Sigma

0

200

400

600

800

1000

15000 20000 25000 30000 35000

Peak Load for Corresponding Month (MW)

Sig

ma

(MW

)

Load Wind Load - Wind January 2001

800 MW Without Wind

950 MWWith Wind

33,000 MW Peak Annual Load3,300 MW Total Wind Plant Rating



Overview



• Uncertainty




Forecasting • Wind forecasting is absolutely essential

– Forecasting increases economic value of wind power by >25% or more

– Wide-spread extreme wind events are predictable (e.g. widely publicized Texas events were predicted)

Texas February 24, 2007 event

Arrival of such fronts is generally forecastable, several hours ahead within a 30-minute window

Thirty-Minute Extreme Wind Drops

0

10

20

30

40

50

60

70

80

90

100

-2600 -2200 -1800 -1400 -1000 -600 -200

Wind Delta (MW)

Nu

mb

er

of

30

-Min

ute

Pe

rio

ds

5000 MW

10000 MW(1)

10000 MW(2)

15000 MW

Extreme Thirty-Minute Wind Drops

~1.5hours

~1600 MW

Reserve Requirements



1 2 3 4 5 6 7 8 9 10 #N/A (blank)

1

2

3

4

5

6

7

8

9

10

(blank)

Large System Net Load Variability : Separating Wind and Load Effects (30% case)

Load Level

Win

d L

evel

10-minute

• Net load variability increases with wind

• Implied reserve requirement is 3 x

• Requirement is a function of both load level and wind level

1 3 5 7 9

#N/A

1

3

5

7

9

(blank)450-500

400-450

350-400

300-350

250-300

200-250

150-200

100-150

50-100

0-50

Distillation of 3 to Simple Rule: X% of Load plus Y% of Wind Production with a max



Wind Power Data: Extrema More Important in Small SystemsProvided by AWS Truewind (2 years of 10-min for each plant)

500 MW Wind case (inc 2 x 200MW remote island plantsCan’t lean on the Neighbors

Wind power production (MW)10

-min

Win

d Pow

er C

hang

e (M

W)

Wind power production (MW)

10-m

in W

ind

Pow

er C

hang

e (M

W)

New Reserve = Spin + Up Regulation = 185MW + f (Wind)

Flexible Generation



Dealing with Variability

• Balance of generation portfolio (dispatchable generation) must have the capability to respond to variations in net load– Net load = (Load MW) – (Wind MW)

• Generators must have room to maneuver up or down– Ramp RANGE up and down

• Generators must be capable to maneuver fast enough to follow changes in net load– Ramp RATE (MW/minute)The following slides show how Ramp Range and Ramp Rate for an operating area are affected by

increasing penetration of wind generation



I30R

-25,000

-20,000

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

4/10 4/11 4/12 4/13 4/14 4/15 4/16

MW

Ra

ng

e o

r M

W/m

in. R

ate

Ramp Up (MW/min.)

Ramp Down (MW/min.)

Range Up (MW)

Range Down (MW)

I10R

-25,000

-20,000

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

4/10 4/11 4/12 4/13 4/14 4/15 4/16

MW

Ra

ng

e o

r M

W/m

in. R

ate

Ramp Up (MW/min.)

Ramp Down (MW/min.)

Range Up (MW)

Range Down (MW)

I20R

-25,000

-20,000

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

4/10 4/11 4/12 4/13 4/14 4/15 4/16

MW

Ra

ng

e o

r M

W/m

in. R

ate

Ramp Up (MW/min.)

Ramp Down (MW/min.)

Range Up (MW)

Range Down (MW)

Grid maneuverability decreases as wind penetration increases

10% Wind Energy 20% Wind Energy

30% Wind EnergyWeek of April 10, Spring Season

• Load levels are typically low

• Wind generation is typically higher in spring than other seasons

• Wind plant output is typically greater at night

• Grid has difficulty operating at “minimum load”



Subhourly Time Simulations

QSS (Quasi Steady-State) Simulations

vs.

LTDS (Long-term Dynamic Simulations)• Provide Validation and Context for Operational

and Statistical Analysis– Cases Selected from Statistical Analysis– Boundary Conditions Set by Operational Analysis

• Evaluate Impact of Significant Wind Generation– Load Following & Ramp Rate Requirements– Regulation/AGC Requirements

• Illustrate Performance Issues• Illustrate Mitigation Measures



Unit-Type Dispatch

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

6:00 PM 10:00 PM 2:00 AM 6:00 AM 10:00 AM 2:00 PM

Pow

er (

MW

)

Combined Cycle: #1Steam: #2Hydro: #3Gas Turbine: #4

10% Wind

20% Wind

30% Wind

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

6:00 PM 10:00 PM 2:00 AM 6:00 AM 10:00 AM 2:00 PM

Pow

er (

MW

)

Combined Cycle: #1Steam: #2Hydro: #3Gas Turbine: #4

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

6:00 PM 10:00 PM 2:00 AM 6:00 AM 10:00 AM 2:00 PM

Pow

er (

MW

)

Steam: #1Gas Turbine: #2Hydro: #3Combined Cycle: #4

min

max



Dealing with Uncertainty

• Basic options are increased reserves or demand response

• Increasing reserves– Commit additional generation so that load will never be

interrupted

– Need to do it 100% of the time, because you do not know the reserves will be required

• Demand response– Interrupt or reduce load occasionally, as need arises

– A paid ancillary service



Distribution of Unserved Energy versus Discounting of Wind Forecast

0

200

400

600

800

1000

1200

1400

0 20 40 60 80 100

Hours

Ho

url

y U

ns

erv

ed

En

erg

y (

MW

h)

I30R

I30R05

I30R10

I30R15

I30R20

I30R25

Using Load to Meet Occasional Extremes

Load Energy

PAID

to be interupted

Load Interruption



Unserved Energy Value ($/MWh)

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

I 30R .05 I 30R .10 I 30R .15 I 30R .20 I 30R .25

Co

sts

($

/MW

h)

Average cost of reducing unservedenergy ($/MWh)

Incremental cost of reducingunserved energy ($/MWh)

Cost of reducing Unserved Energy by discounting wind generation forecasts. (i.e., adding reserves in proportion to

forecasted wind generation)

Costs are per MWh of energy reduced.

Interruptible loads are easily cost

justified

Impact on the Existing Generation Fleet?

• Lower capacity factors for base and mid-merit generation

• Use of “peakers” at “unusual” times

• Pressure to increase hydro maneuverability

• Increased combined cycle cycling (today and growing rapidly)

• Increased coal cycling (growing rapidly in some places)

• Increased O&M, higher outage rates, environmental

performance impacts

• Credible quantitative data is limited; sensitive

• Claims of costs, loss of life, and physical capability are variable

Severity of impacts and the allocation of costs is a topic of intense debate

Capacity Value of Wind Generation



Effective Capacityor Effective Load Carrying Capability (ELCC)ELCC is a measure of long-term adequacy

• Ability of a plant to serve load • Avoid loss of load by the power grid

Example of a 100 MW thermal plant• If forced outage rate is 10%, and• If forced outages are equally probable at any time,

then• ELCC is 90%

How does this measure apply to wind power?• Output of a wind plant is not dispatchable• Wind plant output is a function of available wind, and

it is time-dependent

Source: WindLogics 33



2001 Average Load versus Average Wind

0

5,000

10,000

15,000

20,000

25,000

30,000

1 6 11 16 21

Hour of Day

NY

ISO

Lo

ad

(M

W)

0

200

400

600

800

1,000

1,200

1,400

1,600

Win

d O

utp

ut

(MW

)

July load August load September load

July w ind August w ind September w ind

August

July

September



684

358

570

322

400261

105

600

Effective Capacity

Based on rigorous LOLP calculations using 2001 - 2003 load and wind profiles for NY State

Inland Wind Sites:• Capacity factors ~ 30%

• Effective capacity, UCAP ~ 10%

Offshore Wind Site:• Capacity factors ~ 40%

• Effective capacity, UCAP ~ 39%

Developed approximate calculation method:• UCAP ~ On-Peak Capacity Factor for 1:00-5:00pm, June-August

GEEnergy

Experience and Lessons Learned

GEEnergy



Major Study Results :•Large interconnected power systems can

accommodate variable generation (Wind + Solar) penetration levels exceeding 30% of peak loads

•But not by doing more of the same…..

To reach higher levels of wind generation and other renewables:

•Get the infrastructure right

•And use it betterThe debate has changed: No longer: “Is it possible?”

Now: “How do we get there?”



Renewables (%)

EnablersS

yste

m C

ost

Impediments Enablers• Wind Forecasting• Flexible Thermal fleet

– Faster quick starts– Deeper turn-down– Faster ramps

• More spatial diversity of wind/solar• Grid-friendly wind and solar• Demand response ancillary services

Impediments• Lack of transmission• Lack of control area cooperation• Market rules / contracts constraints• Unobservable DG – “behind the

fence”• Inflexible operation strategies during

light load & high risk periods

System CostUnserved Energy

Missing Wind/Solar Target

Higher Cost of Electricity

All grid can accommodate substantial levels of wind and solar power … There is

never a hard limit

Lessons Learned

GE Energy

Documents

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monthly energy gwh

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