10 LSWI variability+uncertainty

Power Systems & Energy Course

Large-Scale Wind
and Solar Integration:
Dealing with Variability
and Uncertainty
Jason MacDowell


2

An Introduction to Today’s 2 Lectures…
Focus on utility-scale wind and solar
What’s different about wind and solar?
Mitigating operational impacts
Key lessons learned from studies and operational
experiences

2016International,
General Electric
All Rights
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Electric
Inc. AllCompany.
rights reserved.
Not forReserved
distribution without permission.

2

3

Successful integration of high penetrations of wind

IMPORTANCE OF BUILDING ON SUCCESSES IN OTHER REGIONS
AND LEARNING FROM THEIR MISTAKES

Source: DOE/LBNL, 2015 Wind Technologies Market Report
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3


Moderate annual
average penetration
means high
instantaneous
penetrations

Source: AWEA (2/22/16)
and Ventyx (top). S.
Beuning, Xcel, 2011
(bottom).

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© 2016 General Electric Company. All Rights Reserved

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5

We can successfully integrate high
penetrations of solar

Source: Rothleder, CAISO, UVIG 2016
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5


6

How do you effectively integrate
wind and solar?
Cost-effective, efficient integration of wind and solar
Can be done through markets (RTOs) or verticallyintegrated utilities
Actions that increase operational efficiency are
generally the same actions that help integrate
renewables
Systems approach is critical
Requires cooperation: utilities, RTOs,
developers/owners, regulators/policymakers

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6


7

What Makes Wind/Solar Different?
Zero marginal cost
• Cycling impacts on other generation
• Cost recovery impacts on other generation

System Balancing
• Variability and uncertainty
• Load is also variable and uncertain

Reliability and stability
• Inverter-based, non-synchronous generation
• Essential reliability services
• Weak grid

Location
• Remote, with long transmission
• Distributed energy resources, behind the meter

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7

Overview
• Temporal/Spatial Patterns

• Variability in Wind and Load MW
 Solar Variability

• Uncertainty
• Forecasting for Wind Power
 Value of Improved Forecasts in Grid Operations

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8


GE’s Integration of Renewables Experience
2010 New England

Studies commissioned by utilities, commissions, ISOs...
• Examine feasibility of 100+ GW of new renewables
• Consider operability, costs, emissions, transmission

12 GW Wind
39% Peak Load
24% Energy

2008 Maui
70 MW Wind
39% Peak Load
25% Energy

2010 Oahu
500 MW Wind
100 MW Solar
55% Peak Load
25% Energy

2012 NSPI Study
900MW Wind
25% Energy

PJM Study (underway)
96GW Wind
22GW Solar
30% Energy

Gradients indicate systems subject to individual studies and also included in larger regional studies

2004 New York
3 GW Wind
10% Peak Load
4% Energy

2005 Ontario
15 GW Wind
50% Peak Load
30% Energy

2006 California
13 GW Wind

3 GW Solar
26% Peak Load
15% Energy

2007 Texas
15 GW Wind
25% Peak Load
17% Energy

2009 Western U.S.
72 GW Wind
15 GW Solar
50% Peak Load
27% Energy

Pan-Canadian
~72GW Wind
30% Energy

Universal need for fleet flexibility, new operating strategies and
markets, transmission reinforcement, grid friendly renewables
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9


Planning and
Operation Process

1 Year

Unit Dispatch
700

Resource and
Capacity Planning
(Reliability)

Capacity Valuation
(UCAP, ICAP)
and
Long-Term Load
Growth Forecasting

600
500

MW

Slower (Years)

Time Scales for
System Planning
and Operation
Processes

Technology
Issues

400

300
200
100
0
0

2000

4000

6000

8000

Hour

2001 Average Load vs Average Wind

1 Day

30,000

1,600
1,400

1,000
15,000

800
600

10,000
400

Wind Output (MW)

1,200
20,000

5,000
July load

August load

July w ind

200

Septem ber load

August w ind

Septem ber w ind

0

0
1

6

11

16

21

Hour

3000

3 Hours

2500

Load Following
(5 Minute Dispatch)

Faster (seconds)

Day-ahead and
Multi-Day
Forecasting

NYISO Load (MW)

Unit Commitment
and
Day-Ahead
Scheduling

Hour-Ahead
Forecasting
and
Plant Active Power
Maneuvering and
Management

2000

MW

Time Frame

25,000

1500

1000

500

0
1

61

121
M inu te s

September Morning

Frequency and
Tie-Line Regulation
(AGC)

Real-Time and
Autonomous Protection
and Control Functions
(AGC, LVRT, PSS,
Governor, V-Reg, etc.)

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A ugus t Morning

May Ev ening

Oc tober Ev ening

April Af ternoon

10 Minutes

10


50000

5000

40000

4000

30000

3000

Average Load
20000

Average L-W-S

Wind & Solar tend to
be complementary.

2000

Average Wind

Wind & Solar (MW)

Load (MW)

Temporal Pattern: July 2003 Average Day
(California)

Average Solar

10000

1000

0

0
1

5

9

13

17

21

Hour
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11


Temporal Pattern: January 2002 Average Day
(California)
50000

5000

Average Load
Average L-W-S
Average Wind

4000

Average Solar

30000

3000

20000

2000

10000

1000

0

Wind & Solar (MW)

Load (MW)

40000

0
1

5

9

13

17

21

Hour
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12


Temporal Pattern: All Days of July 2003
55000

15000

10000

Load (MW)

40000

Significant day-to-day
variation

25000

5000

10000

0
1

5

9

13

Hour

17

Wind & Solar (MW)

Average Load
Average Wind
Average Solar

21

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13

Temporal Pattern: All Days of January 2003
50000

10000

8000

30000

6000

20000

4000

10000

2000

0

Wind & Solar (MW)

40000

Load (MW)

Day-to-day variation in Wind is

higher in winter than in summer

Average Load
Average Wind
Average Solar

0
1

5

9

13

Hour

17

21

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14


Variability and Uncertainty…Layperson’s terms
For Example…
Generator Owner… “I can guarantee 1000MW of hydro all day tomorrow.”
System Operator… “OK, I will turn off 1000MW of other generation.

Variability:
Generator Owner...“I can guarantee 1000MW of hydro from 2PM to 4PM
tomorrow.”
System Operator… “OK, I may turn down 1000MW of other generation,
rather then shutting it off.”

Uncertainty:
Generator Owner… “I think I will have 1000MW of hydro sometime
tomorrow.”
System Operator… “OK, I may turn off only 600MW of other generation
and I will keep 400MW spinning and have quick start capacity ready to
fire.”
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15


Western Wind and Solar Integration Study
“To help multiple utilities in the western U.S. understand the
costs and operating impacts of the variability and uncertainty
of wind and solar power on their grids and potential mitigation
options for these impacts.”
Wind and Solar Combinations (% Energy)
Baseline:

Existing Wind and Solar Generation

10% In-Area: 10% Wind, 1% Solar In Footprint
10% Wind, 1% Solar Out of Footprint

20% In-Area: 20% Wind, 3% Solar In Footprint
10% Wind, 1% Solar Out of Footprint
30% In-Area: 30% Wind, 5% Solar In Footprint
20% Wind, 3% Solar Out of Footprint
Solar Mix:
• 70% Concentrating Solar Plant with Storage

• 30% Photo-voltaic
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16


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

LP Scenario
60000

50000

Load

MW

40000

30000

Ld(Base)

Wd(30%)

20000

PV(30%)

Wind

CSP(30%)

10000

0
1-Jul

8-Jul

15-Jul

22-Jul

29-Jul

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17


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

35000

Load

LP Scenario

30000
25000

Ld(Base)
Wd(30%)
PV(30%)

20000
MW

CSP(30%)

15000
10000

Wind

5000
0
-5000
1-Apr

8-Apr

15-Apr

22-Apr

29-Apr

Day
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18


Monthly Energy GWh from Wind & Solar for Years 2004–2006
4000

(30% Wind Energy - In Area Scenario)
Study Area Total Monthly Wind and Solar Energy for 2004 - 2006
2000
12000
‘06

PV
CSPws
Wind

0

10000

Jan

Feb

Total Energy (GWh)

‘05

8000
‘04

6000

4000

2000

0
Jan

Feb

Mar

Apr

May

Jun
Jul
Month of Year

Aug

Sep

Oct

Nov

Dec

Notable difference in Wind & Solar energy
across the months and over the years
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19


Monthly Energy % from Wind & Solar for Years 2004–2006
4000
(30% Wind Energy - In Area Scenario)
Study Area Percent Monthly Wind and Solar Energy for 2004 - 2006
06

60%

% of Load Energy

0
Jan

Feb

‘05

40%

30%

CSPws
Wind

‘06

50%

PV

2000

55% of energy
from wind and
solar

‘04

20%

10%

0%
Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Month of Year

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

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20


Study Footprint 2006 Net Load Duration – In Area Scenario
Study Area Net Load Duration Curves
60000
Study_Area Baseline
Study_Area L-W-S (10%)
50000

Study_Area L-W-S (20%)
Study_Area L-W-S (30%)

Net Load Level (MW)

40000

30000
Min load 22169 MW

20000
Below existing min
load ~57% of year,
for 30% scenario

10000

0

-10000
00

10%
583

20%
1166

30%
1749

40%
50%
60%
2332
2915
3498
Deciles of Year
Hour of Year

70%
4081

80%
4664

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90%
5247

100%
5830

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Operational Impact of Wind & Solar:
What does 30% Penetration Mean?
Nova Scotia: Base Case

Nova Scotia: High Wind Case

30% Target
Curtailment

Source: Nova Scotia Renewable Energy Integration Study

It is critical to look at all time-frames of grid operation
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22


Overview
• Temporal/Spatial Patterns

• Variability in Wind and Load MW

 Solar Variability

• Uncertainty
• Forecasting for Wind Power
 Value of Improved Forecasts in Grid Operations

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23


Variability Analysis – “Deltas”
• “Delta” (Δ) is the changed from one time period to the next
– Several time periods are important for operations
– Examine hourly time periods first (covers typical period for inter-area
scheduling of power transfers

• If load increases, output of dispatchable generation must
increase
• If wind generation decreases, output of dispatchable
generation must increase
• Most challenging situation for grid operations:
– Wind (and solar) generation declines during the same period when load
increases

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24

Wind Deltas vs. Load Deltas for Summer 2006
(Base Wind Energy in Footprint – LP Scenario)
5000

Load and wind
deltas offset

Load Decreases
Wind Increases

4000

Summer

Wind Delta (MW) (30% Scenario)

3000
2000
1000
(-4250,-203)

0
(3674,-44)

-1000

Load Increases
Wind Decreases

-2000

-3000
-4000

MOST-CHALLENGING
REGION FOR GRID
OPERATORS

Load and Wind
deltas offset

-5000
-5000

-4000

-3000

-2000

-1000

0

1000

Load Delta (MW)

2000

3000

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4000

5000

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