Upload
benjamin-lee
View
33
Download
2
Tags:
Embed Size (px)
DESCRIPTION
SAAC Review. Michael Schilmoeller Thursday May 19, 2011 SAAC. Sources of Uncertainty. Fifth Power Plan Load requirements Gas price Hydrogeneration Electricity price Forced outage rates Aluminum price Carbon allowance cost Production tax credits - PowerPoint PPT Presentation
Citation preview
SAAC Review
Michael SchilmoellerThursday May 19, 2011
SAAC
2
Sources of Uncertainty
Scope of uncertainty
• Fifth Power Plan– Load requirements– Gas price– Hydrogeneration– Electricity price– Forced outage rates– Aluminum price– Carbon allowance cost– Production tax credits– Renewable Energy Credit
(Green tag value)
• Sixth Power Plan– aluminum price and
aluminum smelter loads were removed
– Power plant construction costs
– Technology availability– Conservation costs and
performance
3
CharacteristicsResource Planning?
Reduce size and likelihood of bad outcomes
✔ ✔
Cost – risk tradeoff: reducing risk is a money-losing proposition
✔ ✔
Imperfect Information ✔ ✔
Buying an automobile?
No "do-overs", irreversibility
✔ ✔
4
CharacteristicsResource Planning?
Use of scenarios ✔ ✔
Resource allocations reflect likelihood of scenarios
✔ ✔
Resource allocations reflect severity of scenarios
✔ ✔
… even if "we cannot assign probabilities"
✔ ✔
Buying an automobile?
Some resources in reserve, used only if necessary
✔ ✔
5
Identifying Long-Term Ratepayer Needs
• Why and for whom is a plant built?– For the market or the ratepayer?– Built for independent power producers (IPPs) for sales into the
market, with economic benefits to shareholders?
• How much of the plant is attributable to the ratepayer?– This is usually a capacity requirement consideration– To what extent does risk bear on the size of the plant’s share ?
6
How the NWPCCApproach Differs
• No perfect foresight, use of decision criteria for capacity additions
• Likelihood analysis of large sources of risk (“scenario analysis”)
• Adaptive plans that respond to futures
7
Excel Spinner Graph Model
• Represents one plan responding under each of 750 futures
• Illustrates “scenario analysis on steroids”
8
Modeling Process
The portfolio model
Like
lihoo
d (P
roba
bilit
y) Avg Cost
10000 12500 15000 17500 20000 22500 25000 27500 30000 32500
Power Cost (NPV 2004 $M)->
Risk = average ofcosts> 90% threshold
Like
lihoo
d (P
roba
bilit
y) Avg Cost
10000 12500 15000 17500 20000 22500 25000 27500 30000 32500
Power Cost (NPV 2004 $M)->
Risk = average ofcosts> 90% threshold
Like
lihoo
d (P
roba
bilit
y) Avg CostAvg Cost
10000 12500 15000 17500 20000 22500 25000 27500 30000 3250010000 12500 15000 17500 20000 22500 25000 27500 30000 32500
Power Cost (NPV 2004 $M)->
Risk = average ofcosts> 90% threshold
9
Space of feasible solutions
Finding Robust Plans
Relian
ce on th
e likeliest ou
tcome
Risk Aversion
Efficient Frontier
10
Impact on NPV Costs and Risk
0
10
20
30
40
50
60
70
80
9030
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
Freq
uenc
y
Billions of 2006 Constant Dollars
NPV 20-Year Study Costs
Scope of uncertainty
C:\Documents and Settings\Michael Schilmoeller\Desktop\NWPCC - Council\SAAC\Presentation materials\L813 NPV Costs.xlsm
11
Decision Trees
• Estimating the number of branches– Assume possible 3 values (high, medium, low) for each of 9
variables, 80 periods, with two subperiods each; plus 70 possible hydro years, one for each of 20 years, on- and off-peak energy determined by hydro year
– Number of estimates cases, assuming independence: 6,048,000
• Studies, given equal number k of possible values for n uncertainties:
• Impact of adding an uncertainty:
Decision trees & Monte Carlo simulation
iesuncertaint values, , nkkN n
kN
N
1
12
Monte Carlo Simulation
• MC represents the more likely values• The number of samples is determined by the
accuracy requirement for the statistics of interest• The number of samples mk necessary to obtain
a given level of precision in estimates of averages grows much more slowly than the number of variables k:
Decision trees & Monte Carlo simulation
k
k
m
m
k
k 11
13
Monte Carlo Samples
• How many samples are necessary to achieve reasonable cost and risk estimates?
• How precise is the sample mean of the tail, that is, TailVaR90?
Implication to Number of Futures
14
Assumed Distribution
0123456789
10111213141516
109
115
121
127
133
139
145
151
157
163
169
175
181
187
193
199
205
211
217
223
Freq
uenc
y
Billions of 2006 Constant Dollars
Tail Risk
Implication to Number of Futures
C:\Documents and Settings\Michael Schilmoeller\Desktop\NWPCC - Council\SAAC\Presentation materials\L813 NPV Costs 02.xlsm
15Implication to Number of Futures
Dependence of Tail Average on Sample Size
0
10
20
30
40
50
60
70
11
6
11
6.7
5
11
7.5
11
8.2
5
11
9
11
9.7
5
12
0.5
12
1.2
5
12
2
12
2.7
5
12
3.5
12
4.2
5
12
5
12
5.7
5
12
6.5
12
7.2
5
12
8
12
8.7
5
12
9.5
13
0.2
5
13
1
13
1.7
5
75 samples per average
C:\Documents and Settings\Michael Schilmoeller\Desktop\NWPCC - Council\SAAC\Presentation materials\L813 NPV Costs 02.xlsm, worksheet “Samples_75”
σ=1.677
0
10
20
30
40
50
60
70
80
90
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
Freq
uenc
y
Billions of 2006 Constant Dollars
NPV 20-Year Study Costs
16
Accuracy and Sample Size• Estimated accuracy of TailVaR90 statistic is
still only ± $3.3 B (2σ)!*
0
10
20
30
40
50
60
70
80
90
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
Freq
uenc
y
Billions of 2006 Constant Dollars
NPV 20-Year Study Costs
Implication to Number of Futures
0
10
20
30
40
50
60
70
116
116.
7511
7.5
118.
25 119
119.
7512
0.5
121.
25 122
122.
7512
3.5
124.
25 125
125.
7512
6.5
127.
25 128
128.
7512
9.5
130.
25 131
131.
75
75 samples per average
*Stay tuned to see why the precision is actually 1000x better than this!
17
Accuracy Relative to the Efficient Frontier
123200
124200
125200
126200
127200
128200
129200
77000 78000 79000 80000 81000 82000 83000
Ris
k (N
PV
$2
00
6 M
)
Cost (NPV $2006 M)
L813
L813 L813 Frontier
C:\Backups\Plan 6\Studies\L813\Analysis of Optimization Run_L813vL811.xls
Implication to Number of Futures
18
Finding the Best Plan
• Each plan is exposed to exactly the same set of futures, except for electricity price
• Look for the plan that minimizes cost and risk
• Challenge: there may be many plans (Sixth Plan possible resource portfolios:1.3 x 1031)
Implication to Number of Plans
19
Space of feasible solutions
The Set of Plans Precedes the Efficient Frontier
Relian
ce on th
e likeliest ou
tcome
Risk Aversion
Efficient Frontier
Implication to Number of Plans
20
Finding the “Best” Plan
155600
155800
156000
156200
156400
156600
156800
157000
0 500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
7500
8000Ta
ilVar
90 ($
M N
PV)
simulation number
Reduction in TailVar90with increasing
simulations (plans)
C:\Documents and Settings\Michael Schilmoeller\Desktop\NWPCC - Council\SAAC\Presentation materials\Asymptotic reduction in risk with increasing plans.xlsm
Implication to Number of Plans
21
How Many 20-Year Studies?
• How long would this take on the Council’s Aurora2 server?
studiesyear -20 10 2.625
750 3500
futures plans
6
n
Implication to Computational Burden
22
• Assume a benchmark machine can process 20-year studies as fast:– Xeon 5365, 3.0 MHz, L2 Cache 2x4, 4 cores/4
threads per core– 38 GFLOPS on the LinPack standard– To the extent this machine underperforms the Council
server, the time estimate would be longer
• Total time requirement for one study on the Tianhe-1A: 3.54 days (3 days, 12 hours, 51 minutes) and estimated cost $37,318
On the World’s Fastest Machine
Implication to Computational Burden
23
How the RPM Satisfies the Requirements of a Risk Model• Statistical distributions of hourly data
– Estimating hourly cost and generation– Application to limited-energy resources– The price duration curve and the revenue curve
• Valuation costing• An open-system models• Unit aggregation• Performance and precision
24
Estimating Energy Generation
Price duration curve (PDC)
Statistical distributions
25
Gross Value of Resources Using Statistical Parameters of
Distributions
e
ee
ge
ee
g
e
ge
dd
ppd
(h))(p
p
p
NN
dNpdNpc
12
1
21
2/)/ln(
ln ofdeviation standard is
price gas theis
pricey electricit average theis
variablerandom )1,0( afor CDF theis
where
(4) )()( Assumes:
1) prices are lognormally distributed
2) 1MW capacity
3) No outages
V
Statistical distributions
26
Estimating Energy Generation
*
*
1)(CDFcf
)(CDF
Calculus) of Thm (Fund
)(CDF
*
*
gg
gg
g
ppgHg
gH
ppg
e
P
eH
p
V
NCp
pNCp
V
dppNCV
Applied to equation (4), this gives us a closed-form evaluation of the capacity factor and energy.
Statistical distributions
27
Implementation in the RPM
• Distributions represent hourly prices for electricity and fuel over hydro year quarters, on- and off-peak– Sept-Nov, Dec-Feb, Mar-May, June-Aug– Conventional 6x16 definition– Use of “standard months”
• Easily verified with chronological model• Execution time <30µsecs• 56 plants x 80 periods x 2 subperiods
Statistical distributions
28
Energy-Limited Dispatch
Statistical distributions
29
Application of Revenue Curve Equilibrium Prices
Statistical distributions
Cu
mu
lati
ve M
ark
et
Pri
ce
(mil
ls/k
W)
Time (hours) 8760
Diesel ECC
SCCT ECC
CCCT ECC
Net revenue for the diesel (negative)
h* for diesel
Source: page 5, Figure 3, Q:\MS\Markets and Prices\Market Price Theory MJS\Price Relationships in Equilibrium2.doc
30
“Valuation” CostingComplications from correlation of fuel price, energy, market prices
priceLoads (solid) & resources (grayed)
Valuation Costing
)( imi
im ppqQpc --= åOnly correlations are now those with the market
31
Open-System Models
?
Open-System Models
32
Modeling Evolution
• Problems with open-system production cost models– valuing imports and exports– desire to understand the implications of events
outside the “bubble”
• As computers became more powerful and less expensive, closed-system hourly models became more popular– better representation of operational costs and
constraints (start-up, ramps, etc.)– more intuitive
Open-System Models
33
Open Systems Models• The treatment of the Region as an island seems
like a throw-back– We give up insight into how events and
circumstances outside the region affect us– We give up some dynamic feedback
• Open systems models, however, assist us to isolate the costs and risks of participant we call the “regional ratepayer”
• Any risk model must be an open-system model
Open-System Models
34
The Closed- Electricity System Model
fuel price+εi
dispatchprice
energygeneration
energyrequire-ments
market price +εi for electricity
Only one electricity price balances requirements and generation
• If fuel price is the only “independent” variable, the assumed source of uncertainty, electricity price will move in perfect correlation
• That is, outside influences drive the results• We are back to an open system
Open-System Models
35
The RPM Convention
• Respect the first law of thermodynamics: energy generated and used must balance
• The link to the outside world is import and export to areas outside the region
• Import (export) is the “free variable” that permits the system to balance generation and accommodate all sources of uncertainty
• We assure balance by controlling generation through electricity price. The model finds a suitable price by iteration.
Open-System Models
36
Equilibrium search
Open-System Models
37
Unit Aggregation
0.00
2.00
4.00
6.00
8.00
10.00
12.00
4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000
VO
M ($
/MW
h)
Heat Rate (BTU/kWh)
West 1 West 2 West 3
West 4 Beaver East 4
East 5 East 7 East 8
Hermiston Ignore East 1
• Forty-three dispatchable regional gas-fired generation units are aggregated by heat rate and variable operation cost
• The following illustration assumes $4.00/MMBTU gas price for scaling
Source: C:\Backups\Plan 6\Studies\Data Development\Resources\Existing Non-Hydro\100526 Update\Cluster_Chart_100528_183006.xls
Unit Aggregation
38
Cluster Analysis
11
30
12
19
13
05
12
90
11
31
12
46
12
47 1
24
81
02
11
04
10
20
14
67
14
68
16
50
16
51
11
98
11
99
12
01
12
02
10
23
11
36
10
28
14
75
14
43
13
68
12
00
12
28
10
89 15
71
14
11
10
00
12
04
12
03
10
01
05
41
79
71
29
11
29
21
40
21
40
3
01
23
45
Dendrogram of agnes(x = Both_Units, diss = FALSE, metric = "manhattan", stand = TRUE)
Agglomerative Coefficient = 0.98Both_Units
He
igh
t
Source: C:\Backups\Plan 6\Studies\Data Development\Resources\Existing Non-Hydro\100526 Update\R Agnes cluster analysis\Cluster Analysis on units.doc
Unit Aggregation
39
Performance
• The RPM performs a 20-year simulation of one plan under one future in 0.4 seconds
• A server and nine worker computers provide “trivially parallel” processing on bundles of futures. A master unit summarizes and hosts the optimizer.
• The distributed computation system completes simulations for one plan under the 750 futures in 30 seconds
• Results for 3500 plans (2.6 million 20-year studies) require about 29 hours
Performance and Precision
40
Precision
Source: email from Schilmoeller, Michael, Monday, December 14, 2009 12:01 PM, to Power Planning Division, based on Q:\SixthPlan\AdminRecord\t6 Regional Portfolio Model\L812\Analysis of Optimization Run_L812.xls
Performance and Precision
41
Choice of Excel as a Platform• The importance of transparency and
accessibility, availability of diagnostics• Olivia• The ability of Olivia to write VBA code for
the model• RPM’s layout of data and formulas • High-performance Excel
– XLLs– Carefully controlled calculations
• System requirements• Crystal Ball and CB Turbo
42
The Efficient Frontier
123
124
125
126
127
128
129
77 78 79 80 81 82 83
Th
ou
sa
nd
s
Thousands
Side Effects
Inef
fect
ive
source: \EUCI 100323 Presentation\Efficient Frontier\EUCI 100323 01.xls
123
124
125
126
127
128
129
77 78 79 80 81 82 83
Th
ou
sa
nd
s
Thousands
Side Effects
Inef
fect
ive
source: \EUCI 100323 Presentation\Efficient Frontier\EUCI 100323 01.xls
43
What does the Efficient Frontier Tell Us?• The Efficient Frontier does not
tell us what to do• The Efficient Frontier tells us
what not to do• Most useful if there are a large
number of choices
44
End