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

2 – PLIM Salt Lake City - May 2012– K. Fessart & J. Lonchampt

1. Context

2. The IPOP Method principles

3. Test case

4. Conclusion

3 - PLIM Salt Lake City - May 2012– K. Fessart & J. Lonchampt

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


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)


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…)


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.


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


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


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)


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


Test case:

Transformers lifetime

management


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


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


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)


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


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


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).


Thank you

for your attention

investments portfolio optimal planning

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