Monte-Carlo Optimization Framework for
Assessing Electricity Generation
Portfolios
Peerapat Vithayasrichareon, Iain MacGill and Fushuan Wen*
Australasian Universities Power Engineering Conference 2009
University of Adelaide, Adelaide
September 2009
* Queensland University of Technology
Presentation Outline
ƒ Context
ƒ Introduction
¾ Traditional Optimal Generation Mix
¾ Stochastic approaches to incorporate uncertainty
ƒ Methodology
¾ Model Framework
ƒ Case Study
¾ Description
p
¾ Results
ƒ Conclusion
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Context
ƒ Electricity industry faces growing uncertainties
¾ Fuel prices, climate change policy and energy demand.
ƒ Uncertainties have substantial impact on generation
investment decision making
¾ Capital
p
intensive,, long
g lead times,, irreversible.
ƒ Minimise exposure to economic risks (‘cost uncertainty’) is
as important as minimising cost
¾ Price stability has economic value for end-users
¾ Investors may put more weight on risk than on expected costs
ƒ Value to incorporate risk assessment into decision support
tools for generation investment
¾ Challenging as key drivers are uncertain and correlated
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Traditional Optimal Generation Mix
ƒ Method to solve for least-cost
least cost
generation mix given
deterministic assumptions.
ƒ Although simple and useful, it
does not incorporate a wide
range of relevant issues:
OCGT 13%
CCGT 23%
¾ ‘Sunk’ existing assets Vs new
plants reserve requirement
plants,
ƒ This method ignores
uncertainties such as fuel
prices, energy demand and
g measures.
climate change
¾ Main focus of this study
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Approaches to incorporate uncertainty
ƒ Several methods to implicitly address risks and uncertainty
uncertainty.
ƒ Stochastic approach based on Monte Carlo Simulation
(MCS) technique is a comprehensive and flexible method.
¾ Can analyse problems with many uncertain parameters
p
represented
p
by
yp
probability
y distribution
¾ Outputs
Identify
uncertain
variables
Assign probability
distributions to
uncertain variables
Method to g
generate
random samples
Simulation
runs
(i = n samples)
Define correlation
among uncertain
variables
MCS
process
Possible results which
represented by prob.
prob
distribution
ƒ Drawbacks of MCS – Prob. of variables can be difficult to
estimate, computation time (accuracy VS computation time)
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Model Framework
ƒ Extends deterministic method by incorporating uncertainty
into key cost assumptions using MCS technique.
¾ Assess cost and risk of different p
possible g
generation p
portfolios.
ƒ Calculate the expected generation cost of various generation
portfolios in a year.
ƒ Consider generation portfolios instead of single technology
or deterministically solving for a least-cost generation mix
¾ Contribution of each technology to the cost and risk of the
entire generation portfolio
ƒ Extension
E t
i and
d contribution
t ib ti tto currentt lit
literatures
t
¾ Standard DCF & probabilistic analysis1, MVP approach2
1
2
P. Spinney and G. Watkins, 1996., D. Feretic and Z. Tomsic, 2005., F. Roques, W. Nuttall, and D. Newbery , 2006.
S. Awerbuch, 2006., J. Jansen, L. Beurskens, and X. Tilburg, 2006. S. Awerbuch and M. Yang, 2008.
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Model Framework
Expected
LDC
Costs and technical
data of each technology
Stochastic model of
uncertain parameters
ƒ Total generation cost ($/yr)
= Fixed Cost + Variable Cost
ƒ FC = annualised fixed cost
($/MW/yr)
Generate random values of uncertain parameters from
pre-defined probability distribution
Economic Dispatch to determine generation output
(MW) of each technology in each period based on VC
¾ incur regardless
g
of energy
gy
produced
Calculate
• Total energy of each technology in the period.
• Generation cost ($/YR) and CO2 emissions of each
mix of generation technologies in the period.
ƒ VC = O&M cost + Fuel cost +
Carbon cost ($/MWh)
$
Monte Carlo Simulation
(i = n samples)
NO
YES
Determine probability distribution of total cost of each
mix of generation technologies.
Expected generation cost, risk and CO2 emissions
¾ depends on energy produced
ƒ Amount
A
t off energy generated
t d
by each tech. is determined
using economic dispatch
based on VC.
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Model Framework
ƒ Generation cost outputs
p
from MCS represent
p
a range
g of
possible generation costs which can be represented by a
probability distribution
¾ Assume normal distribution - Mean and SD are used to
measure cost-risk profile. SD is used to measure risk (‘cost
uncertainty’)) which is the spread of possible outcomes
uncertainty
outcomes.
Assumptions
ƒ Adopts a social welfare maximisation perspective
¾ Focus on overall industry electricity generation cost
ƒ Installed capacity is always sufficient to meet demand.
ƒ Only considers economic risk arising from fuel and carbon
prices uncertainty – ignore technical or operational risks.
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Case Study
Attributes**
Attributes
Technology
Coal
CCGT
OCGT
Capital cost ($/MW/yr)*
160,500 86,000 56,200
Fixed O&M ($/MW/yr)
43,000
25,000 14,000
Efficiency (%)
42
58
43
Variable O&M ($/MWh)
3.3
1.5
6.5
Emission factor (tCO2/MWh)
0.8
0.35
0.47
*Calculated from overnight cost using CRF with 8% discount rate
** Sources: IEA, NEA/IEA (2005), PBPower, Doherty et al. (2006)
NSW Load Duration Curve (LDC) 2007
ƒ This model is applied to a
case study
ƒ Generation mix of 3 tech:
Coal, CCGT & OCGT
ƒ Scenario of fuel and carbon
prices uncertainty.
ƒ Consider various generation
mix scenarios
¾ Share of each tech ranges
from 0-100%
0 100% of total capacity
in 20% increments
Data source: AEMO (http://www.aemo.com.au)
(http://www aemo com au)
¾ hence 21 p
possible
generation portfolios
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Case Study
ƒ Fuel and carbon p
prices is assumed to be normally
y distributed
ƒ Mean and SD of fuel prices based on historical data1,2
¾ Gas p
price: Mean = $
$6.45/GJ,, SD = 30% of mean ($
($1.935/GJ))
¾ Coal price: Mean =$2.85/GJ, SD = 10% of mean ($0.285/GJ)
ƒ Mean and SD of carbon p
price based on the assumption
p
¾ Mean = $20/tCO2,SD = 50% of mean ($10/tCO2)
g SD to allow for the p
possibility
y of significant
g
p
price
¾ Assume high
variation -- reflects current climate change policy uncertainties
ƒ Assume no correlation among fuel and carbon prices
¾ Straightforward to incorporate with this technique
¾ Currently under investigation
1IEA,
2IEA,
“Coal Information 2008” OECD/IEA, Paris, 2008
“Gas Information 2008” OECD/IEA, Paris, 2008
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Case study results
ƒ Results from the model consist of
¾ Expected generation cost ($/yr)
generation cost, which represents
p
the volatility
y or risk.
¾ SD of g
¾ Expected CO2 emissions of each generation portfolio (tCO2/yr)
ƒ For each g
generation mix – the calculation of cost is repeated
p
for 500 simulated years of uncertain fuel & carbon prices
Mean $6.45/GJ
SD $1.935/GJ
Mean $2.85/GJ
SD $0.285/GJ
Mean $20/tCO2
SD $10/tCO2
Distributions of 500 samples of gas
gas, coal and carbon prices for given Mean and SD
* Note the constrained normal distribution – minimum threshold for fuel and carbon price
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Efficient Frontier (EF) – the generation cost cannot be reduced without
increasing ‘cost uncertainty’ (risk).
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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ƒ Portfolio of 0%Coal, 80%CCGT, 20%OCGT has the lowest
expected cost, but exhibits a relatively high cost uncertainty.
ƒ If increase the share of coal to 20%, replacing the share of CCGT
- the portfolio is now 20%Coal, 60%CCGT, 20%OCGT (on EF)
¾ Cost uncertainty is reduced by 15% while gen.
gen cost increases by 1%.
1%
¾ CO2 emissions also increases by 30%
ƒ Comparisons among
portfolios can be made
by analysing tradeoffs
ff
between the cost, risk
and CO2 emissions
among generation
portfolios
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Case study results (Cont’)
ƒ Simulation results highlight
g g some significant
g
aspects
p
ƒ Trade-off between cost and risk among different portfolios
ƒ Contribution of each technology to cost and risk of the entire
portfolio
¾ Adding a less (or more) costly technology does not necessarily
reduce (or increase) the expected portfolio cost.
¾ Since the load is not uniform throughout the year - Trade-off
b t
between
fixed
fi d and
d variable
i bl costs
t enables
bl each
h ttechnology
h l
tto
play a valuable role in generation portfolio in meeting demand
in different periods.
p
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Case study results (Cont’)
ƒ Impact of gas price uncertainty on CCGT and OCGT is more
influential than the impact of carbon price uncertainty
¾ Although the volatility of carbon price is greater than that of
fuel price, CCGT & OCGT face a much higher fuel price
uncertainty since fuel cost is by far the largest cost component
(
(given
assumed expected carbon price))
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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Conclusions
ƒ The model is p
powerful yyet flexible
¾ Accommodate various uncertainties, varied type of generation
technology, load profile, any form of probability distribution for
stochastic parameters
parameters.
¾ Analyse various generation portfolios - highlight and identify cost-risk
tradeoffs between different g
generation p
portfolios
ƒ Incorporation of load profile
¾ Generation output is determined in a more practical way
¾ Capture economic merits of each technology
¾ Tradeoffs between fixed and variable cost of each technology
ƒ The model has a significant potential to support decision
making in generation investment under various uncertainties
ƒ The
Th results
lt are sensitive
iti tto iinputs
t and
d price
i assumptions.
ti
Vithayasrichareon, MacGill, Wen, "Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios"
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ƒ Thank you
ƒ Questions?
Many of our publications are available at:
www.ceem.unsw.edu.au
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