Download 2nd Intelligent Hedging & Portfolio Optimisation for the

Gas and Power Assets:
Management and Portfolio Optimisation
with PLEXOS®
Dr Christos Papadopoulos
Regional Manager Europe
Energy Exemplar (Europe) Ltd
18/04/2013
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
Content
1. Introducing Energy Exemplar and PLEXOS®
2. Integration of the physical and financial markets
in PLEXOS
3. Long term co-optimisation of power and gas
assets
4. Exploring different modelling approaches for
medium to long term hedging decisions
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2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
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Energy Exemplar®
 Commercial since 1999
 Focused on PLEXOS® for Power Systems software
 Global client base served from three locations:
Adelaide, Australia
London, UK
California, USA
 20% staff with Ph.D. level qualifications spanning
Operations Research, Electrical Engineering,
Economics, Mathematics and Statistics
 By the beginning of 2013, worldwide installations
of PLEXOS exceeded 700 at over 135 sites
worldwide, in 32 countries.
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What is PLEXOS®?
PLEXOS® is a MILP-based next-generation Energy Markets/Systems
simulation and optimization software.
 Proven power market simulation tool
 Uses mathematical programming,
optimisation and stochastic techniques
 Robust analytical framework, used by:
 Energy Producers, Traders and Retailers
 Transmission System /Market Operators
 Energy Regulators/Commissions
 Consultants, Analysts and Research
Institutions
 Power Plant Manufacturers and Construction
companies
 Power system model scalable to
thousands of generators and
transmission lines and nodes
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What can be achieved with PLEXOS® (1)
Power Market Modelling, Simulation and Analysis - Short & Long term:
 Price Forecasts based on power system operational constraints and
market fundamentals, at nodal and regional level.
 Detailed operational planning and dispatch optimization while
modelling complex renewable-hydro-thermal and transmission
 Renewable integration analysis
 Investment planning and analysis
 Valuate and Optimise new generation and transmission builds and
retirements – what, when, where?
 Assessing the effectiveness of investment decisions and policies
 Portfolio Optimization and Valuation
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What can be achieved with PLEXOS® (2)
 Risk management via scenario analysis, stochastic
modelling and optimization:
 Optimal resource allocation decisions (fuel, heat, capacity) over
the long or short term subject to uncertainty (e.g. volatility in
fuel prices, wind, hydro inflows, demand)
 Fuel, Emissions and hedge contract evaluation and analysis
 Transmission and Ancillary Services/Balancing Analysis
 Regional, Zonal or Nodal
 Congestion Forecast and Management
 Security Constrained Dispatch (N-x)
 Optimal power flow modelling
 Interconnector Modelling
 Co-optimization of Energy/Ancillary Services and Gas
Markets and Energy dispatch
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Content
1. Introducing Energy Exemplar and PLEXOS®
2. Integration of the physical and financial markets
in PLEXOS®
3. Long term co-optimisation of power and gas
assets
4. Exploring different modelling approaches for
medium to long term hedging decisions
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Optimisation for the Energy Markets
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What is an Energy Portfolio?
Within the energy industry a portfolio can be divided into two
distinct parts:
 Physical Portfolio: represented by physical assets - Power Plants,
Gas Fields, flow (power/gas) lines, Storages etc.
 Contracts Portfolio: consisting of contract assets - Financial and
Physical contracts such as futures, forwards, swaps, options and
FTRs but also PTRs contracts for electricity and FTS for gas.
While the optimisation of a contract portfolio in a traditional
financial market has been well debated, problems can arise
when optimising the entire power portfolio.
PLEXOS® “emulates” how the market operates but more
importantly the real electricity price formulation mechanism.
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Energy Assets Portfolio Optimisation
In PLEXOS®, Portfolio Optimisation accounts for both
Physical and Financial Assets.
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How can the physical market be represented in PLEXOS? (1)
 Generator Class
 Thermal generators
 polynomial heat rate modelling
 Start costs vary by fuel state
 Energy Ramp constraints
 CCGT operation
 GT & steam turbine optimisation
 Combined heat and power Plants
 Heat production from a boiler
 Heat Storage option
 Hydro & pumped storages
 allow long-term decomposition via
targets or water values to shorter more
detailed phases
 Wind, Solar, Geo-thermal & others
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How can the physical market be represented in PLEXOS? (2)
 Fuel
Ability to model price to be a variable
Min and Max off-take quantities
 Transmission Lines
Net Transfer Capacities
Wheeling Charges
Line Ramp Rates
 Nodes, Regions & Zones
Demand Scenarios
Demand Side Participation
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Integration of the Contracts Portfolio (1)
• Physical Contracts
• Multi-step Price/Quantity
• Min/Max Generation
• Financial Contracts
•
•
•
•
CFDs
Caps
Floors
Collars
• Fuel Contracts
• Multi-step Price/Quantity
• Take-or-Pay
• Transmission Rights Contracts
• Financial Transmission Rights (FTRs)
• Settlements Residue Auctions (SRAs)
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Integration of the Contract Portfolio (2)
 Markets Class
 Can be used to model not only forward & futures
(financial) markets but also capacity, fuel, ancillary
services (physical) markets.
 Optimisation of sales and/or purchases to/from
Companies
 Forward Markets can be optimised first in a twostage optimisation process ,then the physical market
is solved
 Company Class
 Allows the ‘bundling’ of physical and financial assets
 Various company wide reporting options available
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How can PLEXOS® be used in Portfolio
Optimisation & Hedging?
• Setting up the forward market in PLEXOS
• A market must be linked to a node.
• Companies can be linked to report revenue/costs back to each
portfolio owner.
– Forward markets are solved
first to determine
sales/purchases then these
levels are fixed while the
physical markets are solved.
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Content
1. Introducing Energy Exemplar and PLEXOS®
2. Integration of the physical and financial markets
in PLEXOS
3. Long term co-optimisation of power and gas
assets
4. Exploring different modelling approaches for
medium to long term hedging decisions
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Optimisation for the Energy Markets
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PLEXOS® Gas Modelling (2013)
Icon
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Class
Gas Field
Description
field from which gas is extracted
Gas Storage
storage where gas can be injected and extracted
Gas Pipeline
pipeline for transporting gas
Gas Node
connection point in gas network
Gas Demand
Demand for gas covering one or more nodes
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
PLEXOS® - Co-optimisation of both Power and
Gas Portfolio Assets.
• Goal is to provide modellers an integrated gas and electricity
model that is straight-forward for power market modellers to
understand and use.
• Short and long term simulations
• Both system-planner (cost minimisation) and strategic
(maximise profit) solutions
• Investment planning: Gas field, storage, and pipeline potential
candidates defined with:
• Capital cost of construction (builds cost, WACC, economic
life, project start date, min/max build constraints)
• Operating costs (fixed and variable)
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Simulation Phases in PLEXOS®
LT Plan – Optimal investment
New Builds/retirements
PASA – Optimal reserve share
Maintenance Schedule
MT – Resource Allocation
Operating Policies
ST – Chronological
Unit Commitment
Detailed by-period results
1 year
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30 years
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LT Plan - Long Term Capacity Expansion
Planning
Finds the optimal combination of generation and transmission
new builds and retirements that minimizes the net present value
of the total costs (incl. fixed and variable operating costs) of the
system over a long-term planning horizon.
The following types of expansion/retirements and features are
supported:
• Building and retiring generating plants and transmission lines
• Multi-stage build projects
• Expanding the capacity on existing transmission interfaces
• Taking up new physical load /generation contracts
• Deterministic or stochastic optimization
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PASA - Projected Assessment of System
Adequacy
PASA is a simulation that focuses on the balance of supply and
demand in the medium term.
• When used in combination with MT Schedule and/or ST
Schedule, the primary purpose of the PASA is to determine,
where and when maintenance outages should occur.
• It can model planned and random outages of generation
plants and transmission lines, and its severity
• In multi-region models PASA calculates the optimal amount of
reserve that should be shared between regions using the
transmission network. (Equalizing regional capacity reserves
done using quadratic programming formulation.)
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2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
MT Schedule - Medium Term Scheduling
and Simulation
MT Schedule is used to give fast results for medium to long-term studies. The
MT Schedule handles all user-defined constraints including those that span
several weeks, months, or years. This might include:
• Fuel off-take commitments e.g. gas take-or-pay contracts
• Energy limits, Emission quotas
• Long-term storage management taking into account inflow uncertainty
MT Schedule:
• Gives the option of Load Duration Curves or Chronological modelling
approach, similar to that in LT Plan. Each constraint is optimised over its
original timeframe and the MT Schedule to ST Schedule Bridge algorithm
converts the solution obtained, e.g. a storage trajectory, to targets or
allocations for use in the shorter step of ST Schedule
• Can model competitive behaviour of portfolios over the medium term.
(Sophisticated game-theoretic behaviours like Nash-Cournot competition
or ‘simply’ recovery of fixed costs.)
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2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
ST Schedule
ST Schedule is mixed-integer programming (MIP) based chronological
optimization. It can emulate the dispatch and pricing of real marketclearing engines, but it provides a wealth of additional functionality to
deal with:
• unit commitment;
• constraint modelling;
• financial/portfolio optimization; and
• Monte Carlo simulation.
ST Schedule provides two methods for modelling the chronology:
• Full Chronology Every trading period (interval) inside the ST
Schedule horizon is modelled explicitly. (Interval can be 1min to
24hrs in length.)
• Typical Week One week is modelled each per month in the horizon
and results are applied to the other weeks.
18/04/2013
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
Content
1. Introducing Energy Exemplar and PLEXOS®
2. Integration of the physical and financial markets
in PLEXOS
3. Long term co-optimisation of power and gas
assets
4. Exploring different modelling approaches for
medium to long term hedging decisions
18/04/2013
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
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Energy Portfolio Optimisation with PLEXOS®
• The return of an energy portfolio is affected by four
major sources of uncertainty;
• Power Spot Prices
• Demand (Power/Fuel)
• Inflows
• Fuel Prices
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Hedge Planning
A hedge is a fixed price instrument (physical or financial)
whose value moves opposite the market movement and
thereby mitigates the risk of the market moving against you,
at the cost of the lost benefit when the market moves to you.
Hedges can include e.g.:
•
•
•
•
Financial gas contracts
Fixed price coal contracts
Generator tolling agreements
etc.
• Finding the appropriate hedge strategy is a process of
measuring the “unhedged” risk, and finding a portfolio of
hedges that constraints the risk to a desired level, i.e.:
• 5% of total generation cost
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2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
 Spot Prices
Use the Scenario Class in PLEXOS to adjust out input
parameters
 Demand, Fuels & Inflows
Represent inputs using stochastic modelling via the
Variable Class
Provided with user-defined samples with an assigned
probability for each sample (exogenous) or we can
create a endogenous sample using an expected profile
that can be scaled up or down a specified distribution
(endogenous).
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
Example of a user-defined endogenous profile entered in PLEXOS® to
model the forward power price stochastically.
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
Generators sorted
by technology
Demand Profile for fictional
region ‘R1’ for 2011
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
COMPANY A
COMPANY B
COMPANY C
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
• Including the Financial Contract Class
• Used to represent CfDs in a Gross Pool or Forward Contracts in
a Net Pool
• CfDs can be either one-way (e.g Company and exchange) or
two-way contracts (Between two participating companies –
Generator and Retailer)
• A financial contract can be linked to generators so that contract
quantity is dependent on one or more generators generation
levels.
• PLEXOS® can stochastically model either contract quantity
and/or price as a variable so we can model different mix of
contract quantities and prices to determine the most profitable
result for our 3 companies.
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
CfD Example:
Financial
Contract
Property
Value
Units
Timeslice
RETAIL #1
Quantity
700 MW
PEAK
RETAIL #1
Quantity
550 MW
OFF-PEAK
RETAIL #1
Floor Price
$55 $/MWh
RETAIL #1
Cap Price
$55 $/MWh
In a Gross Pool this example defines a contract-for-difference (CfD), or
two-way contract, with a strike (floor=cap) price of $55/MWh. The
contract guarantees the generator receives at least this price for
generation, and at the same time it guarantees that the load will pay
no more than this price for the quantities shown.
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
• Lets assume each company has 10 different contracting
quantity levels available to them.
• Starting from 0MW up to 1,000MW increasing in
100MW increments. Each 100MW increment will be a
separate Profile in PLEXOS.
Gross Pool
If Region Price is below the Floor Price then:
• Settlement = (Floor Price – Region Price) x Settlement
Quantity
If Region Price is above the Cap Price then:
• Settlement = (Region Price - Cap Price) x Settlement
Quantity
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
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Optimisation for the Energy Markets
How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
Case 2: Physical market and allow hedging with Financial Contracts
Endogenous Profile
Contract Quantity modelled using
set profile in bands of 100MW
blocks representing each sample
CFD Strike price modelled with samples
using base profile with 20% error Std Dev
and 50% Auto Correlation
Exogenous Profile
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2nd Intelligent Hedging & Portfolio
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
Case 2: Physical market and allow hedging with Financial Contracts – COMPANY A
Sample
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Contract
Amount (MW)
Generation (GWh)
Pool Revenue
($000)
Net Contract
Settlement
($000)
Net Pool
Revenue ($000)
Sample 1
100
2,847
70,049
906
70,955
Sample 2
200
2,846
70,023
1,636
71,659
Sample 3
300
2,849
70,093
2,930
73,023
Sample 4
400
2,848
70,060
2,630
72,690
Sample 5
500
2,857
70,292
4,816
75,108
Sample 6
600
2,842
69,917
4,080
73,998
Sample 7
700
2,847
70,027
6,196
76,223
Sample 8
800
2,852
70,155
7,052
77,207
Sample 9
900
2,856
70,256
9,165
79,420
Sample 10
1000
2,847
70,029
12,415
82,444
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
Case 2: Physical market and allow hedging with Financial Contracts – COMPANY B
Sample
17/04/2013
Contract
Amount (MW)
Generation (GWh)
Pool Revenue
($000)
Net Contract
Settlement
($000)
Net Pool
Revenue ($000)
Sample 1
100
837
21,438
906
22,344
Sample 2
200
837
21,455
1,636
23,091
Sample 3
300
835
21,397
2,930
24,327
Sample 4
400
837
21,438
2,630
24,068
Sample 5
500
828
21,234
4,816
26,050
Sample 6
600
843
21,588
4,080
25,668
Sample 7
700
838
21,464
6,196
27,660
Sample 8
800
832
21,322
7,052
28,374
Sample 9
900
828
21,237
9,165
30,402
Sample 10
1000
838
21,482
12,415
33,898
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
Case 2: Physical market and allow hedging with Financial Contracts – COMPANY C
Sample
17/04/2013
Contract
Amount (MW)
Generation (GWh)
Pool Revenue
($000)
Net Contract
Settlement
($000)
Net Pool
Revenue ($000)
Sample 1
100
2,981
71,836
906
72,742
Sample 2
200
2,982
71,844
1,636
73,480
Sample 3
300
2,981
71,832
2,930
74,762
Sample 4
400
2,981
71,824
2,630
74,454
Sample 5
500
2,980
71,794
4,816
76,610
Sample 6
600
2,981
71,817
4,080
75,898
Sample 7
700
2,981
71,831
6,196
78,027
Sample 8
800
2,982
71,845
7,052
78,897
Sample 9
900
2,981
71,827
9,165
80,991
Sample 10
1000
2,980
71,811
12,415
84,226
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
Physical market and allow hedging with Financial Contracts
(Mean of 10 samples)
Pool Revenue
($)
Generation
(GWh)
Net Contract
Settlement($)
Net Pool
Revenue ($)
Company A
69,494,000
2,789
25,034,000
94,528,000
Company B
22,542,000
883
3,959,000
26,501,000
Company C
71,218,000
2,953
18,032,000
84,250,000
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Optimisation for the Energy Markets
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How can PLEXOS® be used in Portfolio Optimisation &
Hedging?
Including the Competition Object in PLEXOS®
Recognises that Financial Contracts
can effect the bidding behaviour of
generators e.g. the higher the
generating company’s contract
cover the more likely they are to bid
close to marginal cost.
When the property is enabled
additional constraints are added to
ensure that the company generates
to meet contract level if economic
to do so.
This in turn has the effect of moving
the pool price back towards the
contract prices over the duration of
the simulation.
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Optimisation for the Energy Markets
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EE European Datasets (CWE Market)
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2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
EE European Datasets (CWE Market)
17/04/2013
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
EE European Datasets (CWE Market)
17/04/2013
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
EE European Datasets (CWE Market)
17/04/2013
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets
Thank you for your
time,
attention
and the
opportunity.
18/04/2013
2nd Intelligent Hedging & Portfolio
Optimisation for the Energy Markets