1. 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

2. Content Introducing Energy Exemplar and PLEXOS®
Integration of the physical and financial markets in PLEXOS
Long term co-optimisation of power and gas assets
Exploring different modelling approaches for medium to long term hedging decisions
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3. 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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4. 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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5. 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
SCUC - optimize generation dispatch subject to load (possibly price sensitive), transmission constraints, reserve requirements, generation operating limits (min up/down time, ramp rates, etc).
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6. 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
SCUC - optimize generation dispatch subject to load (possibly price sensitive), transmission constraints, reserve requirements, generation operating limits (min up/down time, ramp rates, etc).
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7. Content Introducing Energy Exemplar and PLEXOS®
Integration of the physical and financial markets in PLEXOS®
Long term co-optimisation of power and gas assets
Exploring different modelling approaches for medium to long term hedging decisions
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8. 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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9. Energy Assets Portfolio Optimisation
In PLEXOS®, Portfolio Optimisation accounts for both Physical and Financial Assets.
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10. 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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11. 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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12. Integration of the Contracts Portfolio (1)
Physical Contracts
Multi-step Price/Quantity
Min/Max Generation
Financial Contracts
CFDs
Caps
Floors
Collars
Fuel Contracts
Take-or-Pay
Transmission Rights Contracts
Financial Transmission Rights (FTRs)
Settlements Residue Auctions (SRAs)
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13. 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 two-stage 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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14. 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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15. Content Introducing Energy Exemplar and PLEXOS®
Integration of the physical and financial markets in PLEXOS
Long term co-optimisation of power and gas assets
Exploring different modelling approaches for medium to long term hedging decisions
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16. PLEXOS® Gas Modelling (2013)
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17. 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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18. Simulation Phases in PLEXOS®
LT Plan – Optimal investment
PASA – Optimal reserve share
MT – Resource Allocation
ST – Chronological Unit Commitment
New Builds/retirements
Maintenance Schedule
Operating Policies
Detailed by-period results
1 year
4 years
30 years
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19. 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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20. 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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21. 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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22. ST Schedule
ST Schedule is mixed-integer programming (MIP) based chronological optimization. It can emulate the dispatch and pricing of real market-clearing 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.
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23. Content Introducing Energy Exemplar and PLEXOS®
Integration of the physical and financial markets in PLEXOS
Long term co-optimisation of power and gas assets
Exploring different modelling approaches for medium to long term hedging decisions
18/04/2013
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24. 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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25. 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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26. 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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27. 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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28. 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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29. How can PLEXOS® be used in Portfolio Optimisation & Hedging?
COMPANY A
COMPANY B
COMPANY C
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30. 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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31. How can PLEXOS® be used in Portfolio Optimisation & Hedging?
CfD Example:
Financial Contract
Property
Value
Units
Timeslice
RETAIL #1
Quantity
700 MW
PEAK
550
OFF-PEAK
Floor Price
$55
$/MWh
Cap Price
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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32. 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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33. How can PLEXOS® be used in Portfolio Optimisation & Hedging?
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34. How can PLEXOS® be used in Portfolio Optimisation & Hedging?
Case 2: Physical market and allow hedging with Financial Contracts
Endogenous Profile
CFD Strike price modelled with samples using base profile with 20% error Std Dev and 50% Auto Correlation
Contract Quantity modelled using set profile in bands of 100MW blocks representing each sample
Exogenous Profile
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35. How can PLEXOS® be used in Portfolio Optimisation & Hedging?
Case 2: Physical market and allow hedging with Financial Contracts – COMPANY A
Sample
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
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
70,029
12,415
82,444
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36. How can PLEXOS® be used in Portfolio Optimisation & Hedging?
Case 2: Physical market and allow hedging with Financial Contracts – COMPANY B
Sample
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
21,455
1,636
23,091
Sample 3
300
835
21,397
2,930
24,327
Sample 4
400
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
21,237
9,165
30,402
Sample 10
1000
21,482
12,415
33,898
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37. How can PLEXOS® be used in Portfolio Optimisation & Hedging?
Case 2: Physical market and allow hedging with Financial Contracts – COMPANY C
Sample
Contract Amount (MW)
Generation (GWh)
Pool Revenue ($000)
Net Contract Settlement ($000)
Net Pool Revenue ($000)
Sample 1
100
2,981
71,836
906
72,742
Sample 2
200
2,982
71,844
1,636
73,480
Sample 3
300
71,832
2,930
74,762
Sample 4
400
71,824
2,630
74,454
Sample 5
500
2,980
71,794
4,816
76,610
Sample 6
600
71,817
4,080
75,898
Sample 7
700
71,831
6,196
78,027
Sample 8
800
71,845
7,052
78,897
Sample 9
900
71,827
9,165
80,991
Sample 10
1000
71,811
12,415
84,226
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38. How can PLEXOS® be used in Portfolio Optimisation & Hedging?
Physical market and allow hedging with Financial Contracts
(Mean of 10 samples)
Pool Revenue ($)
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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39. 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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40. EE European Datasets (CWE Market)
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41. EE European Datasets (CWE Market)
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42. EE European Datasets (CWE Market)
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43. EE European Datasets (CWE Market)
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44. Thank you for your time, attention opportunity.
and the
opportunity.
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