Investments Portfolio Optimal Planning
Transcript of Investments Portfolio Optimal Planning
1 - May 2011 – K. Fessart & J. Lonchampt
Investments Portfolio
Optimal Planning
EDF R&D
Karine Fessart & Jérôme Lonchampt
Salt Lake City - PLIM 2012
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1. Context
2. The IPOP Method principles
3. Test case
4. Conclusion
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1. Context
Nuclear asset management issues for EDF : 58 NPPs to be operated beyond 40 years and therefore requiring a huge amount of investments to provide safe and reliable operation.
Necessity to define this portfolio by optimizing the profitability and measuring the associated risk.
Therefore decision-makers have to :
• Assess the benefits and the associated risks
• Define investments prioritization (postponement, cancellation)
• Plan on long term in order to provide the industry and financiers a view of technical and financial requirements
• Measure the robustness of decisions to control the consequence of a possible context change
Development of several methods and tools by EDF R&D among
which the IPOP® tool
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1. The scope
IPOP studies are required for high-stakes investments (preventive
maintenance tasks, asset enhancements or logistic investments such as
spare part purchases). They can be defined by :
• Very expensive investments
Or
• Investments leading to severe consequences in case of failure of the
associated components (mostly unavailability issues)
Or
• Major maintenance tasks (that is to say
performed once or twice over the plant lifetime)
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2. IPOP Goals
IPOP is dedicated to support the following issues :
• Compare various alternative investment scenarios thanks to several
indicators (NPV, cash flows, risk measures…) : partial or complete
refurbishment, replacement, technological enhancement, spare part
purchase…
• Optimizing investments decisions
• Sizing spare part stock
• Planning an investments portfolio taking into account constraints (budget,
logistical or technical constraints)
• Prioritizing investments (cancellation, postponement…)
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2. IPOP modules
IPOP allows three kinds of calculation :
1. Measure of the profitability of a portfolio of investments (Mean value
calculation module)
2. Selection and planning of an optimal set of investments taking into
account various constraints (Optimization algorithm module)
3. Measure of the risk associated to a portfolio of investments (Risk
indicators calculation module)
IPOP may be applied for single component issues as well as for fleet level
(with various components) study.
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2. IPOP description
Input Data:
Reliability
Costs
Logistic
Constraints
…
Company
Information
System
Optimization
Algorithm
Portfolio
Average
value
calculator
“Optimal”
Investments
planning
Portfolio
Risk
Indicators
calculator
Investments
Planning
Risk
Assessment
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2. IPOP : Profitability of a portfolio
Profitability is measured by the Net Present Value (NPV) :
CF = discounted cash flows (direct costs, outages, power generation...)
Valuation of the losses avoided and the profits created by the
investment
Cash flows are calculated through a PDMP
(Piecewise Deterministic Markov Process)
reliability model.
The supply chain (or spare part management)
is also modelled for valuation
)()()( StrategyCFØCFStrategyNPV
Situation
without
investment
Situation
with the
studied
investments
O
C
P
Component 1
O
C
P
Component 2
O
C
P
Component n
O
C
P
Component N
Spare Parts
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2. IPOP : Selection and planning of an optimal
set of investments
IPOP can take into account various constraints :
• Economical : global budget limit, annual budget limit…
• Logistic : a delay between two preventive replacements, replacement
of two particular components at the same outage…
• External : regulatory issues requiring replacement before a given
date,…
The optimization is based on genetic algorithm calculations (operational
research)
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2. IPOP : Measure of risk
Why a risk measure ?
Major maintenance tasks (including the spare part purchase) are related
to high impact (capital costs and unavailability) and low probability
events : residual risks
Average values for indicators supporting the decision process are not
informative enough
development in IPOP of a probabilistic module (Monte-Carlo
simulation algorithm) to provide probabilistic
density functions of indicators,
especially the NPV
The module allows detailed analysis of the risk sources in order to give
information for strategy improvements
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Test case:
Transformers lifetime
management
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3. Test case : description
• 31 transformers on 5 plants
• Grouped in 14 families : each family shares the same spare part stock
• Failure consequence : • Force outage to replace the transformer by a spare
• If non spare available, plant may operate with a reduced power level (from 0% to 100%)
• Transformer reliability : Weibull law scaled by expert judgment based
on industry experience
• Valuated investments : • Preventive replacement (date based)
• Purchase of one spare part for each family
• Constraint : limit of one investment per site each year
• Calculated cash flows : • Preventive and corrective maintenance cost
• Holding fees for spare parts
• Planned and unplanned transformer purchase
• Forced outage for failed transformer replacement
• Power loss awaiting a spare part
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3. Test case : Average results
Optimized strategy :
• Results highlight that the main priority is to purchase spare parts to cover the risk
of forced outage
• The preventive replacement program starts two years later
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032
$M
CCNPP Preventive Maintenance CCNPP Supply GINNA Preventive Maintenance
GINNA Supply NMP Preventive Maintenance NMP Supply
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3. Test case : Average results
Profits mainly come from avoided loss
of power (85%) thanks to spare part
purchase.
13% of profits are due to the
preventive replacement program
avoiding failures and so reducing
the time of forced outage.
Preventive Maintenance
Cost
52%
Holding Fees for spare parts
10%
Planned Transformer
Purchase
38%
Loss
Corrective Maintenance
Cost
1%
Unplanned Transformer
Purchase
(after failure)
1%
Forced Outage for Replacing
failed
transformer13%
Power Loss awaiting spare
part
85%
Profits
Losses are equally spread over
preventive maintenance cost and
spare parts management (purchases
and holding fees)
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3. Test case : Probabilistic results
0
0,005
0,01
0,015
0,02
0,025
0,03
0
NVP (M$)
Probability
Failure scenario 1 No long forced outage avoided by the strategy
Failure scenario 2 One long forced outage avoided by the strategy (Group 5)
Failure scenario 3 Two long forced outage avoided by the strategy (Group 5)
NPV probabilistic density function
IPOP provides detailed
analysis of the Monte Carlo
simulations providing the
decision-makers with
relevant risk analysis
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3. Test case : Sensitivity analysis
An important step of a study in order to take into account parameters
uncertainty in the decision making process.
For the test case : • Reliability of transformers : a less pessimistic Weibull law has been tested
• Supply lead time : consideration of a reduced value in case of an emergency (purchasing an
existing spare part from another plant)
IPOP provides a measure of the impact on the optimal investment planning :
the robustness of the solution
solution
Reference Reliability Purchase Lead
Time
Input data
universe
Reference -0,48% -11,74%
Reliability -0,17% -10,92%
Purchase Lead
Time -1,09% -1,97%
Measure the gap (in % of the NPV) between the chosen solution
and the optimal one for a given input data universe
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4. Conclusion
IPOP has prove its ability to deal with complex asset management issues.
In the actual context of power uprates and life extensions, such a tool is
particularly relevant for decision makers.
IPOP Test release has been delivered in April 2012.
A collaboration with EPRI has started en 2010 in order to integrate IPOP to
the future ILCM suite (Integrated Life Cycle Management) in which IPOP
will be connected to a failure curve database. (scheduled in 2013)
Further work is already identified to allow optimization on risk indicators and
not only on average ones (for example : looking for a solution minimizing
the probability of regret).
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Thank you
for your attention