Weather Shape Impact on Resource Adequacy Analysis

114 Pages·2017·5.84 MB·English

Weather Shape Impact on Resource Adequacy Analysis
Kevin Carden
Astrapé Consulting
1
Astrapé Consulting
MISO
CPUC
Astrapé Clients –
Economic/Physical
PG&E
SPP NCEMC Astrapé Clients –Physical
Reliability
Duke
TVA
PNM
Santee
Southern
Entergy
Cooper
Company
ERCOT
CLECO
22
Resource Adequacy Framework
 Capture Uncertainty in the Following Variables
 Weather (35 years of weather history)
 Impact on Load and Resources (hydro, wind, PV, temp derates on thermal resources)
 Economic Load Forecast Error (distribution of 5 points)
 Unit Outage Modeling (100+ iterations)
 Multi-Area Modeling – Pipe and Bubble Representation
 Adjust reserve margin levels using either load or generation
 Total Base Case Scenario Breakdown
35 5 175
x =
Weather Years LFE Points Load Scenarios
(Equal Probability) (Associated Probabilities) (Associated Probabilities)
175 x 100 = 17,500
Load Scenarios Unit Outage Draws 8760 Hour Simulations
33
Incorporating Weather Uncertainty for Load
• Collect Recent Hourly Loads
1. Develop
Load/Weather • Collect Recent Weather Data
Relationship • Normalize to Single Base Year
• Train using Neural Network Software
• Collect 1980-2016 Temperature
2. Apply
Relationship to
Create Synthetic
Shapes
3. Scale Loads
4. Simulate Study
from Base Year
Year with Each
to Future Study
Shape
Year
44
Demo Study Setup
 2 Zone System (Zone A & B)
 Each Zone ~1800 MW peak load
 Summer Peaking with 15% Renewable Penetration
 Economically Committed and Dispatched
 1000 MW Transfer Capability
55
Load Modeling: Summer Peak Weather Variability
8.0%
6.0%
r
e
h 4.0%
t
a
e
W
2.0%
l
a
m
r
o
0.0%
N

m
o
r
-2.0%
f

%
-4.0%
System Average Peak Temperature: 98.2 ◦F
System Max Peak Temperature: 104.4 ◦F
-6.0%
2 1 2 2 1 1 1 1 1 2 2 1 2 1 1 1 2 2 2 1 2 1 2 1 2 2 2 1 2 1 1 1 1 1 2 1
0 9 0 0 9 9 9 9 9 0 0 9 0 9 9 9 0 0 0 9 0 9 0 9 0 0 0 9 0 9 9 9 9 9 0 9
0 8 0 0 8 9 9 8 8 0 0 8 0 9 8 8 0 1 1 9 0 9 1 9 0 0 1 9 1 9 8 9 8 8 1 9
4 7 1 0 4 7 9 8 6 8 9 2 5 2 5 3 6 2 1 6 2 3 4 1 7 3 0 8 5 5 1 0 9 0 3 4
Year
66
Zone A Weather Duration Curve
Top 200 Hours
105
1994
103
1980
101
1998
1999
99
)
F 1992
°
( 97

e 1993
r
u
t 95 2000
a
r
e
2009
p
93
m
1985
e
T
91
1996
2001
89
1988
87
1984
2004
85
0 50 100 150 200
Hour
77
Zone A Load Duration Curve (Original Unscaled)
Top 200 Hours
1800
1994
1980
1750
1998
1700
1999
1992
1650
)
W
1993
M
( 1600 2000
d
a
o 2009
L
1550
1985
1500 1996
2001
1450
1988
1984
1400
0 50 100 150 200 2004
Hour
88
Time of 10 Highest Loads in Each Weather Year
11
1980 1981
10
1982 1983
9
1984 1985
8 1986 1987
s
d
1988 1989
a
o 7
L 1990 1991

t
s 6 1992 1993
e
h
g 1994 1995
i 5
H
1996 1997

0
1 4 1998 1999

p
o 2000 2001
T 3
2002 2003
2
2004 2005
2006 2007
1
2008 2009
0
2010 2011
1 1 2 3 4 5 5 6 7 8 8 9 1 1 1 1
/ / / / / / / / / / / / 0 1 2 2
1 2 1 1 7 1 2 1 1 5 2 2 2012 2013
/ / / /
5 8 4 5 8 2 9 2 1 9 3 2
6 7
2014 2015
Date
99
Seasonal Load Variability
 Multiple-Shape approach captures off-peak risk better than single
shape
 May and September show highest variance in load over 34 year
period
Max September peak could be >20%
170,000 25%
above normal September peak
160,000
20% r
e
150,000 h
ta Normal Weather
e
140,000 W
) 15%
W
l
a
M
m
130,000
(
r
d o
a 10% N Max Across 33
o 120,000
L m Weather Years
o
r
110,000 F

5% %
100,000 Max % Away from
Normal Weather
90,000 0% (Secondary Axis)
1 2 3 4 5 6 7 8 9 10 11 12
Month
1100