Capacity Expansion Planning
Transcript of Capacity Expansion Planning
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
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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.
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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 4
MODELING FLEXIBILITY
EFFICIENCY
SIMULATION ALTERNATIVES
ADVANCED CAPABILITIES
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 maintenanc
e
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
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Key PLEXOS® Gas Module Interface Classes
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 by taking a least-cost modelling approach.
• Perfectly competitive market – no market power of participants
• LNG netback price of $13/GJ in QLD
• LNG demand - 6 trains totalling 1518PJ/Year by 2023
• New build variables for CSG development in NSW
• Production and transmission constraints from AEMO GSOO 2013
• Published AEMO natural gas load used
Study topology and assumptions
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Results
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 20244.9
55.15.25.35.45.55.65.75.85.9
Scenario 1 - No Netback Production
No Storage With Storage With CSG Development
Year
Aver
age
Gas P
rice
($/G
J)
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Results
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 20240123456789
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Scenario 2 - Netback Production
No Storage With Storage With CSG Development
Year
Aver
age
Gas P
rice
($/G
J)
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Conclusion – Where/how does optimization fit in our market?• Resource
Management for gas prices
• Resource Allocation and Planning
• Contract Portfolio and Swing Optimization
• Demand Side Management
• Peak Demand Shaving
• Shortage Curtailment
• Maintenance Planning
• Optimal Out-of-Service Units Management
LT Modelling
MT Modelling
ST Modelling
Maintenance
• Optimal investment planning
• Cost savings on portfolioin the long term
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