A Case Study by Using Decision Suite 5.0 to
Analyze Price Settings for An Electricity
Distribution Company
William Zhu
WEL Networks 1 Limited
P.O. Box 925
Hamilton, New Zealand
1 An
electricity distribution company with 83,000
customers and assets of $400
William Zhu, WEL Networks, NZ
million.
October 2010
1
An asset based pricing model is constructed for an electricity company based on the
Decision Suite 5.0. Key risk associated factors are identified. @Risk 5.0 is used to
find the best distributions for the key factors, to compare the required with
expected revenues, as well as to test the sensitivities of those factors. RiskOptimizer
5.0 is employed to find the best new tariffs. Finally, the expected revenue under
different scenarios is summarised by utilising the Precision Tree 5.0.
Key Words:
Cost Recovery Model
Required versus Expected Revenue
Palisade Decision Suite V5.0
Stochastic Sensitivity Analysis, Optimization and Decision Tree
Visual Basic Application and Built-in @Risk Functions in Excel
William Zhu, WEL Networks, NZ
October 2010
2
 Brief Review of the Pricing Model
 Principles to Split Assets and Costs
 Introducing Stochastic Variables
 Sensitivity Analyses by Using Decision Suite
 Suggestions and Conclusions
William Zhu, WEL Networks, NZ
October 2010
3
Decision
Suite
Pricing
Settings
Sensitivity
Report
Tariff
Report
William Zhu, WEL Networks, NZ
October 2010
4
 Background
 Characteristics of Electricity Distribution Company
 Purpose of the Project
 Tariff Level versus Tariff Structure Changes
 Financial Analysis
 Chargeable Prices Based on A Variety of Energy Consumptions
 Likelihood of Expected Revenue Meeting Required Revenue
 Major Risks and Their Impacts
 Investment Optimization by Employing New Tariffs
 Model Description
 Methodology --- Top-down Asset Split + LRIC
 Framework of the Model
William Zhu, WEL Networks, NZ
October 2010
5
Aims --- Change/Remain Current
 Tariff Systems
 Tariff Levels
Y
Game Over
Key Question
Do customers pay
what they
consumed?
N
How do we know?
Current
Customer
Profile
Asset
Distribution
Profile
Cost Profile
 Capex
 Opex
PRICE
SETTING
Prospective Outlook
 Business Plan
 Targeted ROI
 Regulation Impacts
 Inflation Adjustment
Counterpart’s Comparison
 Domestic Companies
 Overseas Experiences
William Zhu, WEL Networks, NZ
October 2010
6
 Identification of Key factors
 Information Collection
 Split Assets and Costs into Different Groups
 Risk Specification
 Sensitivity Analysis
 Price Optimization
 Decision Tree Diagram
 Reports
William Zhu, WEL Networks, NZ
October 2010
7
 Existing Assets at:
 Very High Voltage Level
 High Voltage Level
 Low Voltage Level
 Cost
 Capital Expenditure on Replacement(Capex 1)
 Capital Expenditure on Future Peak Load Growth (Capex 2)
 Operating Expenditure on Peak Load Related (Opex 1)
 Operating Expenditure on Non Peak Load Related (Opex 2)
 Other Operating Costs
 Customer Profiles
 Type and Number of Customers
 A variety of Energy Usages (eg; kWh, AMD, CMD, RCPD)
 Temperature Information
William Zhu, WEL Networks, NZ
October 2010
8
Line Services ($K)
Value
A. Return
Network Assets Employed
Network Existing Assets
Network Invstmnt on RA
Network Invstmnt on LG
Network WACC (before tax)
Target Total Asset Return
160,890
143,950
10,070
6,870
21.4%
34,476
B. Expenses
Depreciation
Maintenance
Other Load Related Opex
Wages, Salaries and Other Opex
Total Distribution Costs
1 --- Required Lines Sales Revenue
Pass Through Services ($K)
Connection
Inter-connection
Other Charge
2 --- Total Pass Through Revenue
Total Required Distribution Revenue ($K)
William Zhu, WEL Networks, NZ
6,220
1,975
1,616
1,364
11,174
45,651
Value
904
3,768
4,672
50,323
October 2010
9
INPUT TABLE
No #
1
2
3
4
5
6
Item
Unit
Value
Price Reference Year
Year Start Apr-10
Asset Split for Vtg Grp
RC%
option
Asset Split for Tarif Grp
option Wgt A&C
Winter Weather
Avg
option
MASS Mthly ICP Growth
Avg
option
Fixed Tariff Capped?
option
Cap?
Instruction
1) Use drop-down arrow to choose price area;
2) Select parameters (Cells with Lime Color)
3) Enter Reference Year (the Cell with Green Color)
4) Go to the "Intro" sheet for Detailed Instruction
OUTPUT TABLE 1 --- Electricity Distribution Tariffs
Tariff Grp
Tariff Type
UN Energy Usage
Distrib
CN Energy Usage
Tariff
Peak Load Usage
Fixed
UN Energy Usage
PassThgh CN Energy Usage
Tariff
Peak Load Usage
Fixed
UN Energy Usage
Total
CN Energy Usage
Tariff
Peak Load Usage
Fixed
Unit
VHVUser HVUser LVUser1 LVUser2
c/kWh
2.29
6.29
4.67
3.78
c/kWh
$/kW/mth
2.49
8.68
14.48
14.93
$/mth
23.33
23.33
23.33
23.33
c/kWh
0.16
0.16
0.16
0.16
c/kWh
$/kW/mth
1.62
1.84
2.09
2.02
$/mth
c/kWh
2.45
6.45
4.83
3.93
c/kWh
$/kW/mth
4.11
10.52
16.57
16.95
$/mth
23.33
23.33
23.33
23.33
William Zhu, WEL Networks, NZ
October 2010
MASS
8.03
3.09
StLgt
11.71
1.00
0.81
0.32
0.75
8.84
3.41
1.00
12.46
-
10
 Revenue Associated
 Per Customer Energy Usage in Winter Season
 Annual Increases of Number of Customer
 Pattern Changes of Customer Energy Usages
 Government Regulation --- Policy Related
 Cost Associated
 Capex --- Over/Under Investment
 Opex --- Inflation, Third Party Charges, etc.
William Zhu, WEL Networks, NZ
October 2010
11
 Best Fit techniques to find proper distributions for




core input factors
Correlation relationships among input factors
Stochastic sensitivity analysis
Advanced @Risk techniques
A Decision Tree Diagram
William Zhu, WEL Networks, NZ
October 2010
12
Fit Distributions
on Input Factors
Correlation
Coefficient
@Risk Output
Distribution
Sensitivity
Analysis (A)
CDF
Curve
Histogram
Plot
Price
Optimization
Goal
Seek
Stress
Analysis
Sensitivity
Analysis (O)
Sequential
Analysis
Decision
Tree
LeveL1
LeveL2
LeveL3
William Zhu, WEL Networks, NZ
LeveL4
October 2010
13
 Monthly Increases of Residential Customer #
 Monthly Temperature
 Monthly Energy Usages per Residential Customer
 Monthly Customer Growth
 Trend Stationary
 Normal distribution
 Different Patterns for Different Periods Due to Economic Boom/Recession
 Monthly Temperature
 More Stable in Non Winter Seasons Compared to Winter Seasons
 Focus on Winter Seasons – June to August
 Monthly Energy Usages per Customer
 Peaked in Winter season
 Correlated With Temperature (with Exception for August)
William Zhu, WEL Networks, NZ
October 2010
14
Fit Comparison for MICP Variation
RiskNormal(-4.42884e-012,46.222)
90.0%
86.2%
0
5.0%
6.6%
0
0.01
69.7
0.009
0.008
0.007
0.006
Input
0.005
Normal
0.004
0.003
0.002
0.001
William Zhu, WEL Networks, NZ
October 2010
150
100
50
0
-50
-100
-150
0
15
William Zhu, WEL Networks, NZ
October 2010
16
William Zhu, WEL Networks, NZ
October 2010
17
Category
Mthly T Avg
Mthly MICP Avg
Mthly kWh/ICP
Apr
14.4
50,058
649
May
11.8
50,115
753
Jun
9.7
50,173
817
Monthly Info for MASS Group ($K)
Jul
Aug
Sep
Oct
9.2
10.0
11.4
13.2
50,231
50,289
50,346
50,404
883
856
753
711
Category
UN Energy Rev
CN Energy Rev
Peak Load Rev
Fixed Rev
Sub Total Rev
Apr
2,241
244
May
2,602
283
Jun
2,826
307
ExpRev for MASS Group ($K)
Jul
Aug
Sep
Oct
3,056
2,968
2,614
2,470
332
323
284
269
50
2,535
50
2,935
50
3,184
50
2,789
Feb
18.9
50,635
570
Mar
17.0
50,693
647
Nov
2,255
245
Dec
2,177
237
Jan
2,075
226
Feb
1,988
216
Mar
2,262
246
50
2,551
51
2,464
51
2,351
51
2,255
51
2,559
ExpMASSRev
Regression Coefficients
Mthly kWh/ICP / Jun
Mthly kWh/ICP / Jul
Mthly kWh/ICP / Aug
Mthly kWh/ICP / May
Mthly kWh/ICP / Oct
Mthly kWh/ICP / Sep
Mthly kWh/ICP / Apr
Mthly kWh/ICP / Feb
Mthly kWh/ICP / Jan
Mthly kWh/ICP / Nov
Mthly kWh/ICP / Mar
Mthly kWh/ICP / Dec
Mthly MICP Avg / Apr
Mthly MICP Avg / May
Mthly MICP Avg / Jun
Mthly MICP Avg / Jul
0.51
0.48
0.40
0.34
0.6
0.5
0.4
0.3
0.2
0.23
0.22
0.20
0.16
0.16
0.15
0.13
0.13
0.06
0.05
0.05
0.04
0.1
Tariff Group
MASS
ReqRev ($)
33,114
ExpRev ($K)
33,351
Target Rev
35,000
Req ROI (%)
16.3%
Iterat #
2,000
Mean
33,351
Mimimum
31,928
Maximum
36,088
Stdev
530
Prob < ReqRev (%)
34.3%
Most Sensible Factors
June
kWh/ICP Usage
July
in Winter Months
August
May
50
2,949
Jan
18.4
50,577
595
0
Target
ROI
50
3,341
Dec
17.0
50,519
625
-0.1
Start
Simulation
50
3,439
Nov
14.8
50,462
648
Coefficient Value
William Zhu, WEL Networks, NZ
October 2010
18
Using @Risk Advanced Techniques to do the following
three Important Analyses
 Goal Seek
 Stress Analysis
 Sequential Analysis
William Zhu, WEL Networks, NZ
October 2010
19
 Goal Seek
 Purpose --- same as in the deterministic case, but
under stochastic environment
 Results Compared to the deterministic case
Goal Seek Result
Item
St Case Dt Case
Target Revenue ($K)
33,351
33,114
Adjust ROI (%) to
16.1%
16.3%
William Zhu, WEL Networks, NZ
October 2010
20
 Purpose of Stress Analysis
 Intuitive Explanation --- optimistic vs.
pessimistic Views on input factors
 Statistical Explanation --- constrain
distribution by “stressing” it to a pre-defined
range so that Monte Carlo simulations only
sample data from that specified range
 Stress Analysis on 12 kWh/ICP Factors
Case
Base Case
Range: 0% ~ 25%
Range: 75% ~ 100%
Stress Results on kWh/ICP
Mean
Min
Max
StdDev
33,351
31,928
36,088
530
31,483
30,618
32,036
228
35,517
34,572
37,901
402
William Zhu, WEL Networks, NZ
5%
32,498
31,049
34,945
95%
34,282
31,810
36,249
October 2010
21
 Purpose of Sequential Analysis
 Intuitive Explanation --- testing different scenarios as
input factors change steadily
 Statistical Explanation --- Sampling input distributions
(rather than “stressing” them) sequentially to run multiple Monte
Carlo simulations through each case (comparable to what
RiskSimTable() function does but with more flexibility)
 Sequential Analysis on Winter kWh/ICP Factors
 Sequential Results
William Zhu, WEL Networks, NZ
October 2010
22
Mean of ExpMASSRev V64 vs Input Distribution Percentile
34400
34200
34000
33800
33600
Mthly kWh/ICP / Jun X49
33400
Mthly kWh/ICP / Jul Y49
Mthly kWh/ICP / Aug Z49
33200
33000
32800
William Zhu, WEL Networks, NZ
120%
100%
80%
60%
40%
20%
32600
0%
ExpMASSRev
Sequential Sensitivty Analysis
Input
Output: ExpMASSRev
Name
Analysis
Value Mean StdDev
5%
95%
Mthly kWh/ICP / Jun Perc%: 1%
702 32,910
461 32,166 33,721
Mthly kWh/ICP / Jun Perc%: 5%
726 33,000
461 32,257 33,811
Mthly kWh/ICP / Jun Perc%: 25%
768 33,162
461 32,418 33,973
Mthly kWh/ICP / Jun Perc%: 50%
806 33,307
461 32,564 34,118
Mthly kWh/ICP / Jun Perc%: 75%
854 33,491
461 32,749 34,303
Mthly kWh/ICP / Jun Perc%: 95%
948 33,852
461 33,111 34,665
Mthly kWh/ICP / Jun Perc%: 99%
1,037 34,194
461 33,451 35,007
Mthly kWh/ICP / Jul Perc%: 1%
715 32,709
472 31,966 33,547
Mthly kWh/ICP / Jul Perc%: 5%
776 32,940
472 32,197 33,777
Mthly kWh/ICP / Jul Perc%: 25%
843 33,198
472 32,455 34,035
Mthly kWh/ICP / Jul Perc%: 50%
883 33,351
473 32,609 34,189
Mthly kWh/ICP / Jul Perc%: 75%
923 33,505
473 32,762 34,343
Mthly kWh/ICP / Jul Perc%: 95%
990 33,763
473 33,021 34,601
Mthly kWh/ICP / Jul Perc%: 99%
1,050 33,994
473 33,252 34,831
Mthly kWh/ICP / Aug Perc%: 1%
801 33,140
483 32,363 33,925
Mthly kWh/ICP / Aug Perc%: 5%
804 33,148
483 32,372 33,934
Mthly kWh/ICP / Aug Perc%: 25%
817 33,199
483 32,422 33,984
Mthly kWh/ICP / Aug Perc%: 50%
839 33,286
483 32,509 34,071
Mthly kWh/ICP / Aug Perc%: 75%
878 33,434
483 32,656 34,219
Mthly kWh/ICP / Aug Perc%: 95%
967 33,778
483 33,001 34,563
Mthly kWh/ICP / Aug Perc%: 99%
1,057 34,122
483 33,347 34,907
Percentile
October 2010
23
 Aim to postpone capex and reduce TP charges
 Introduce a kW based tariff on MASS market
 Only record kW in winter but charge it annually
 Give customers incentives to shift kWh usages
 Survey customer response rate
 Randomize response rates at different levels
 Add constraints to key variables
 Maximize cost savings by using Optimizer 5.0 to
find the best price
William Zhu, WEL Networks, NZ
October 2010
24
Input Table
Item
Max Load Shed
Load Drop for individual ICP
Invstmnt Cost
Max Avg Tariff Change %
New Tariff Threshold Level 1
New Tariff Threshold Level 2
New Tariff Threshold Level 3
Unit
kW
kW/ICP
$/kW
%
$/kW
$/kW
$/kW
Linked Parameters (to PM Model)
Item
Unit
Diversified Pk Load
kW/ICP
ROI
%/yr
Total ICP
#
Deterministic
ICP Growth
No/yr
Optimisation
Annual Total Pk Load
kW
Orig Total Req Revenue
$
PassThrgh Cost Savings
$/kW
Stochastic Analysis
Item
Unit
ICP Slope Under $50 Svg
ICP/$
ICP Slope Betwn $50 and $100 Svg
ICP/$
ICP Slope Betwn $100 and $400 Svg
ICP/$
ICP Slope > $400 Svg
ICP/$
New Tariff Threshold Level 1 Mean (Best
$/kW
New Tariff Threshold Level 2 Simulation)
$/kW
=
New Tariff Threshold Level 3 310073.66
$/kW
Solver Parameters
Target --- Best New Tariff
$/kW/yr
Maximum Cost Savings
$
Value
36,000
1.2
17.3
10.0%
50
100
400
Value
2.0
15%
50,000
693
169,259
33,113,919
24.4
New Required Revenue
Category
Unit
New Total Req Revenue
$
UN kWh Rev
$
Peak Load kW Rev
$
Fixed Rev
$
Value
32,803,844
21,798,833
10,396,695
608,316
New Tariffs
Category
Unit
Overall Distribution Charge
c/kWh
kWh Usage Charge
c/kWh
Peak Load Charge in Winter Only
$/kW/yr
Fixed Charge Per Month
$/mth
Value
7.71
5.13
118.33
1.00
ICP Response
50,000
45,000
Value
60
157
50
100
400
40,000
35,000
30,000
25,000
20,000
15,000
118
310,075
10,000
5,000
Solver Constraints
# of ICP Response (No)
kW savings (kW)
Avg Tariff Change % (%)
-
9,579
11,495
6.3%
<= 50,000
<= 36,000
<= 10.0%
-
100
William Zhu, WEL Networks, NZ
200
300
400
October 2010
500
600
25
 Reasons to Use Decision Tree
 Many cases/scenarios exist
 Easy to get lost without intuitive diagram
 Try to find the overall expected Revenue
 Factors/Cases for Building Decision Tree
 Winter Energy Usages --- 3 chance nodes;
 Customer Number Increases --- 3 chance nodes;
 Third Party Price Increases --- 2 chance nodes;
 Employ New Tariff System --- 2 Decision nodes.
In total, there are 3 × 3 × 2 × 2 = 36 cases.
 Results
William Zhu, WEL Networks, NZ
October 2010
26
Expected Revenue Under Different Scenarios ($K)
Tariff Structure
New Tariff
Current Tariff
Case
Pgh P↑
Pgh P- Pgh P↑ Pgh PICP Low and kWh/ICP Wint Low
32,173
32,373
31,973
32,173
ICP Low and kWh/ICP Wint Avg
32,946
33,146
32,746
32,946
ICP Low and kWh/ICP Wint High
33,719
33,919
33,519
33,719
ICP Avg and kWh/ICP Wint Low
32,309
32,509
32,109
32,309
ICP Avg and kWh/ICP Wint Avg
33,085
33,285
32,885
33,085
ICP Avg and kWh/ICP Wint High
33,860
34,060
33,660
33,860
ICP High and kWh/ICP Wint Low
32,421
32,621
32,221
32,421
ICP High and kWh/ICP Wint Avg
33,200
33,400
33,000
33,200
ICP High and kWh/ICP Wint High
33,978
34,178
33,778
33,978
William Zhu, WEL Networks, NZ
October 2010
27
New Tarif s
TRUE
0
Expected Revenue
TP Raise Price?
+
33,293
Decision
33,293
Current Tarif s
FALSE
0
William Zhu, WEL Networks, NZ
TP Raise Price?
+
33,155
October 2010
28
YES
90.0%
0
New Tariffs
TRUE
0
+
33,279
TP Raise Price?
33,293
NO
10.0%
0
Expected Revenue
ICP Grow th
ICP Grow th
+
33,415
Decision
33,293
YES
30.0%
0
Current Tariffs
FALSE
0
ICP Grow th
+
33,015
TP Raise Price?
33,155
NO
70.0%
0
William Zhu, WEL Networks, NZ
ICP Grow th
+
33,215
October 2010
29
Low
15.0%
0
YES
90.0%
33,279
Avg
55.0%
0
High
0
33,318
Winter Usages
+
33,316
TP Raise Price?
33,293
Low
15.0%
0
NO
10.0%
Winter Usages
+
33,262
ICP Grow th
0
33,415
Avg
55.0%
0
High
Winter Usages
+
30.0%
0
Expected Revenue
Winter Usages
+
30.0%
0
New Tariffs
33,062
ICP Grow th
0
TRUE
Winter Usages
+
33,401
Winter Usages
+
33,516
Decision
33,293
YES
30.0%
0
Current Tariffs
FALSE
0
ICP Grow th
+
33,015
TP Raise Price?
33,155
NO
70.0%
0
ICP Grow th
+
33,215
William Zhu, WEL Networks, NZ
October 2010
30
15.0%
Low
2.025%
32,173
Low
15.0%
32,173
Winter Usages
0
33,062
55.0%
Avg
7.425%
32,946
30.0%
High
32,946
4.05%
33,719
YES
90.0%
33,719
ICP Grow th
0
33,279
15.0%
Low
7.425%
33,085
Avg
55.0%
33,085
Winter Usages
0
33,318
55.0%
Avg
27.225%
33,085
30.0%
High
33,085
14.85%
33,860
15.0%
Low
33,860
4.05%
32,421
High
30.0%
32,421
Winter Usages
0
33,316
Avg
55.0%
14.85%
33,200
High
30.0%
33,978
New Tariffs
TRUE
0
33,978
TP Raise Price?
33,293
NO
10.0%
0
Expected Revenue
33,200
8.1%
ICP Grow th
+
33,415
Decision
33,293
YES
30.0%
0
Current Tariffs
FALSE
0
ICP Grow th
+
33,015
TP Raise Price?
33,155
NO
70.0%
0
ICP Grow th
+
33,215
William Zhu, WEL Networks, NZ
October 2010
31
 An asset based pricing model is constructed for an
electricity distribution company
 Risk factors are identified and fit with distributions by
using Decision Suite software
 Required versus expected revenue are compared based
on the embedded @Risk distribution functions
 Key factors are analysed via the Advanced Stochastic
Sensitivity Techniques
 Optimal New Price is found via RiskOptimizer 5.0
programme
 A simple decision tree is provided for summarizing
different scenario results
 Excel macros are built into the model to allow users to
use @Risk software easily
William Zhu, WEL Networks, NZ
October 2010
32