gas and power assets: management and portfolio optimisation with plexos® dr christos papadopoulos...

2nd Intelligent Hedging & Portfolio Optimisation for the Energy Markets

Gas and Power Assets:Management and Portfolio Optimisation with PLEXOS®

Dr Christos PapadopoulosRegional Manager Europe

Energy Exemplar (Europe) Ltd

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1. Introducing Energy Exemplar and PLEXOS®2. Integration of the physical and financial markets

in PLEXOS3. Long term co-optimisation of power and gas

assets4. Exploring different modelling approaches for

medium to long term hedging decisions

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Content

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Energy Exemplar®Commercial since 1999Focused on PLEXOS® for Power Systems softwareGlobal 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®?

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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PLEXOS® is a MILP-based next-generation Energy Markets/Systems simulation and optimization software.


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 518/04/2013

What can be achieved with PLEXOS® (1)


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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What can be achieved with PLEXOS® (2)



in PLEXOS®3. Long term co-optimisation of power and gas



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Content


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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

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In PLEXOS®, Portfolio Optimisation accounts for both Physical and Financial Assets.


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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

How can the physical market be represented in PLEXOS? (1)

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FuelAbility to model price to be a variableMin and Max off-take quantities

Transmission LinesNet Transfer CapacitiesWheeling ChargesLine Ramp Rates

Nodes, Regions & ZonesDemand ScenariosDemand Side Participation

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How can the physical market be represented in PLEXOS? (2)


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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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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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Integration of the Contract Portfolio (2)


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.

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– 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


PLEXOS® Gas Modelling (2013)

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Icon Class Description

Gas Field 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


PLEXOS® - Co-optimisation of both Power and Gas Portfolio Assets.

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• 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)


Simulation Phases in PLEXOS®

LT Plan – Optimal investmentLT Plan – Optimal investment

PASA – Optimal reserve sharePASA – Optimal reserve share

MT – Resource AllocationMT – Resource Allocation

ST – Chronological Unit CommitmentST – Chronological Unit Commitment

New Builds/retirements

Maintenance Schedule

Operating Policies

Detailed by-period results

1 year 4 years 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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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 uncertaintyMT 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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ST ScheduleST 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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Content

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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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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

Hedge Planning


Spot Prices Use the Scenario Class in PLEXOS to adjust out input

parametersDemand, 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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Example of a user-defined endogenous profile entered in PLEXOS® to model the forward power price stochastically.

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Demand Profile for fictional region ‘R1’ for 2011

Generators sorted by technology

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COMPANY A COMPANY B

COMPANY C

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• 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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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

CfD Example:

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.



• 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 PoolIf 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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CFD Strike price modelled with samples using base profile with 20% error Std Dev and 50% Auto Correlation

Case 2: Physical market and allow hedging with Financial Contracts

Contract Quantity modelled using set profile in bands of 100MW blocks representing each sample

Endogenous Profile

Exogenous Profile


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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 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


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Case 2: Physical market and allow hedging with Financial Contracts – COMPANY B




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


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Case 2: Physical market and allow hedging with Financial Contracts – COMPANY C




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


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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


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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Including the Competition Object in PLEXOS®


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EE European Datasets (CWE Market)


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Thank you for your time,

attention and the

opportunity.

gas and power assets: management and portfolio optimisation with plexos® dr christos papadopoulos...

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