PLEXOS® Integrated Energy Model: Modelling the Impact of LNG Demand

on the East Coast Gas Market

Olumide Adisa (Ph.D)Gas Market Modeller

March 2014

Energy Exemplar®• Commercial since 1999

• Focused on PLEXOS® for Energy Systems Software

• Global client base served from four locations:

– Adelaide, Australia

– London, United Kingdom

– Johannesburg, South Africa

– Sacramento, CA, USA West Coast

– Hartford, CT, USA East Coast

• 20% staff with Ph.D. level qualifications spanning Operations Research,Electrical Engineering, Economics, Mathematics and Statistics

• Client base growing 30% p.a.2

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Client Map• Worldwide installations of PLEXOS exceed 1060 at more than 175 sites worldwide in 37 countries.

• Users: Power Generation Companies, Transmission System Operators (TSOs), Independent System Operators (ISOs), Electricity and Gas Market Operators, Energy Commission and Regulators, Price Forecasting Agencies, Power Plant Manufacturers, Consultants, Analysts, Academics & Research Institutions

Analysis tools in PLEXOS®

What makes PLEXOS® engine so powerful?

Power market design: Ireland, Australia, New Zealand, UK, CAISO, MISO, Singapore, Chile

Cutting edge technology: ✓ .Net Framework 4.0 and XML✓ Built-in functions and solving methods.✓ Optimization: Xpress, CPLEX, Gurobi, Mosek

Simulator Features:✓ Long, medium and short term✓ Stochastic and deterministic optimization

Analysis Capabilities:✓ Cost-based analysis✓ Simulate competitive behaviour✓ Co-optimization of resources and services✓ Profit Maximization✓ Integrated Transmission and Hydro

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Features: Overview

Generation

Portfolio Optimization

Customizations

TimeframesGas Modelling

DSM

Financials

Stochastic

Renewables

VisualizationTransmissionMarkets

TransmissionRadial and meshed networksRegional pricingNodal pricingLarge-scale networks AC & DCInterface limitsLosses (regional & zonal)LMP decompositionWheeling chargesPricing methodsContingencies and SC-OPFSCUC (Contingency)ISO level outputsTransformersPhase shiftersInterfaces

GenerationOptimal capacity expansionUnit commitmentHeat rate modelMaintenance optimizationMonte Carlo simulationFuel constraintsEmission constraintsTechnical limitsAuxiliary useAncillary servicesCCGT & CHPSCUC (Contingency)

RenewablesAll typesEnergy constraintsMust-run limitsExternal profilesCascading HydroPumped StorageUncertainty

Timeframes1-minute to 10’s of yearsConstraint decompositionLDC modelChronological modelTime Slices

StochasticVariable inputsCorrelationsStochastic optimizationMonte CarloBox-Jenkins methodsBrownian with mean reversion

FinancialsFinancial contractsCfD & FTRGenerator bid formationGaming modelsPricing and UpliftEscalators

CustomizationsGeneric constraintsOpen PLEXOSAutomationData retrieval

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Markets & Portfolio Optimisation

EnergyAncillary ServicesHeatFuelCapacity

Demand Bidding & Participation

EnergyAncillary ServicesInterruptible loads

Integrated Gas ModellingBasinsFieldsStoragePipelineExternal gas marketsOptimal capacity expansionTransmission constraintsPeak shaving servicesLinepack optimizationDemand forecastingProduction constraintsNodal processingDemand bids and quantityProduction cost tranchesGas contractsPipeline de-rating

VisualisationGeospatialPre-set results graphsGoogle Earth

PLEXOS Features

Gas Model Planned FeaturesLNG shipment optimization modelling

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PLEXOS Scope

PLEXOS Simulator Structure

Long Term Optimal Investment

Optimal Maintenance Scheduling

Short Term Chronological

Medium Term Decomposition

10 + year studies

Build & Retire Production & Transmission

Captures Production Contracts

PLEXOS Simulator Structure

Long Term Optimal Investment

Optimal Maintenance Scheduling

Short Term Chronological

Medium Term Decomposition

Schedules maintenance

Models forced outages

Computes LOLP, optimal reserve

levels etc

PLEXOS Simulator Structure

Long Term Optimal Investment

Optimal Maintenance Scheduling

Short Term Chronological

Medium Term Decomposition Breaks down constraints

Optimises constraints in

storages, fields etc

Fast results for MT studies

PLEXOS Simulator Structure

Long Term Optimal Investment

Optimal Maintenance Scheduling

Short Term Chronological

Medium Term Decomposition

Chronological optimisation in each ST period

Emulates market clearing engines

Captures competitive

behaviour eg Nash-Cournot

MODELLING THE IMPACT OF LNG DEMAND ON THE EAST COAST GAS MARKET

CASE STUDY: NSW

PLEXOS® Integrated Energy Model

Key PLEXOS® Gas Module Interface Classes

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Icon Class Description

Gas Basin Basins from which gas is produced

Gas Field Field from which gas is extracted

Gas Storage Storage where gas is injected/extracted

Gas Pipeline Pipeline for transporting gas

Gas Node Connection point in gas network

Gas Demand Demand for gas covering nodes

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SA

QLD

NSW

VIC

TAS

SEA gas Pipeline

MSP Pipeline

Eastern gas pipeline

Tasmanian gas pipeline

QSN Link

• Maximization of social welfare bytaking a least-cost modelling approach.

• Perfectly competitive market – nomarket power of participants

• LNG netback price of $13/GJ in QLD

• LNG demand - 6 trains totalling1518PJ/Year by 2023

• New build variables for CSGdevelopment in NSW

• Production and transmissionconstraints from AEMO GSOO 2013

• Published AEMO natural gas load used

Study topology and assumptions

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Results

5.2

5.3

5.4

5.5

5.6

5.7

5.8

5.9

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Ave

rage

Gas

Pri

ce (

$/G

J)

Year

Scenario 1 - No Netback Production

No Storage With Storage With CSG Development

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Results

0

1

2

3

4

5

6

7

8

9

10

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Ave

rage

Gas

Pri

ce (

$/G

J)

Year

Scenario 2 - Netback Production

No Storage With Storage With CSG Development

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Conclusion – Where/how does optimization fit in our market?

• Demand Side Management

• Peak Demand Shaving

• Shortage Curtailment

• Maintenance Planning

• Optimal Out-of-Service Units Management

• Resource Management for gas prices

• Resource Allocation and Planning

• Contract Portfolio and Swing Optimization

LT ModellingMT Modelling

ST ModellingMaintenance

• Optimal investment planning

• Cost savings on portfolioin the long term

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