Stochastic Modeling of Electricity MarketStochastic Modeling of Electricity MarketPrices in Europe with Large Shares ofPrices in Europe with Large Shares of
Renewable GenerationRenewable Generation
Michael M. Belsnes
2009-09-08 2TREN/FP7/EN/218960/SUSPLAN
IntroductionIntroduction
Why consider stochastic modeling in an Europeancontext?
Better decisions regarding:• Information about what prices to expect• RES and required infrastructure for RES integration• Energy storage, where and how much
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ContentsContents
The EMPS model
European model for scenario work
Example of achieved results- Prices- Energy balances
Summing up
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I.1I.1 The EMPS modelThe EMPS model
A fundamental model for analyzing geographical distributedhydrothermal energy systems• Model objective: Maximization of socio economic surplus• The different elements of the electrical power system are
modeled with their capacities and costs.
Stochastic features:• inflow• temperature-related uncertainty in demand and CHP capacity• wind speed related uncertainty in wind power generation• Large scale PV delivery of electricity
Build for hydropower but can model any energy systems withstorage and stochastic inputs
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Hydro inflow in Norway 2030 (GWh/week)
-2000
0
2000
4000
6000
8000
10000
1 10 19 28 37 46
0 percentile 25 percentile 50 percentile 75 percentile 100 percentile
0
100
200
300
1 10 19 28 37 46
0 percentile 25 percentile 50 percentile 75 percentile 100 percentile
Wind and solar in Norway 2030 (GWh/week)
StochasticStochasticrenewablerenewableresourcesresourcesin Europeanin Europeancountries...countries...
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I.4I.4 Aggregation into sub-systemsAggregation into sub-systems
A node representation of loadcenters defined by variouscriteria:• Country borders
• Congestion
• Hydrological
• Markets
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Thermalgeneration
P
Transmission capacityto other subsystems
Firm powerdemand
Spot powerdemand
I.5I.5 SubsystemSubsystemmodelmodel
Solar
Wind
Hydropower
Storable inflow
Non-storable inflow
Overflow Bypass Plant
Hydropower
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Bulk power transport betweennodes with:• direction dependent capacity
• direction dependent costs
• linear losses
Possible to use the same structurefor the main gas infrastructure
I.6I.6 Infrastructure(sInfrastructure(s))
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Modeling (1) - Thermal powerModeling (1) - Thermal power
Two main modeling types: Fuel available upon demand
• capacity a function of price• market price decides generation• availability of units may be treated by stochastic costing
algorithm (Expected Incremental Cost curve)
Fixed fuel volumes• generation fixed weekly or for longer periods (e.g. annually)• local fuel storage• solved as hydro equivalents
Simplified modeling of thermal start-up costs
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Modeling (2) - DemandModeling (2) - Demand
Firm demand• Weekly• Duration curve within week• Temperature dependency• Price elasticity• Curtailment costs
Spot-type demand• Price and quantity
Dynamic price elasticity
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Calculating a strategy for hydroCalculating a strategy for hydro
The problem cannot be solved directly:• Simplified aggregated representation of hydro (single module equivalent
for each area)• Even this simplified problem not solved perfectly
Practical solution• Using stochastic dynamic programming (SDP) to solve for each sub
system• Heuristics used to handle interconnection between subsystems:
– Feedback from simulation to strategy calculation– An automatic calibration process has been implemented successfully
(maximum socioeconomic surplus)– Manual control and modification of strategy by calibration
Resulting strategy: water values as function of time and reservoir levelfor each aggregate sub system.
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Simulating the strategySimulating the strategy Incremental water values are used as decision tables for
simulating weekly operational decisions for record of weatheryears
Time resolution: Reservoir balances: weeklyPower balances : load periods within the
week Planning horizon: typically 5 - 10 years (maximum 25)
Weekly operational decisions are simulated using acombination of:• network flow for optimal regional decision• heuristics for detailed hydro operation
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I.7I.7 Calculation of market pricesCalculation of market prices
Generation from non-storable RES
Regulated hydro power
Thermal power
SupplyDemand
Cost [cent/kWh]
Capacity [GWh]Optimal decision for each interval in week n
Curtailment
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0
5
10
15
20
25
30
35
40
45
50
0 50 100 150 200 250 300 350 400 450 500 550Possible Yearly Power (TWh/year)
(Cost/kWh)
Hydro +RES Power
Normal Demand Curve Normal Year
High RES Year
Low RES Year
Warm winter
Cold winter
I.8I.8 Variation in market pricesVariation in market prices
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Available resultsAvailable results System operation
• Generation per unit / type• reservoir storage, water flow• supply, consumption, trade• exchange between areas
Marginal value of electrical energy (may represent forecast ofmarket price)
Economical results• Socio economic surplus• Curtailment• Quota prices
Emissions
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II.1II.1 The Scenario frameworkThe Scenario framework
Storylines WP1Based on existing studies
‘Today’
2010 2020 20402030 2050
Shar
e of
RES
Time (Year)
Min. RES levels
Scenario analysesWP2 / WP3
Assumethat 2020goals arereached
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II.2II.2 Organization of EMPS in SUSPLANOrganization of EMPS in SUSPLAN
GUI EU27+Access DB
Simulationresults
Run scripts +
input files
Optimize strategy
SimulateEnergy system
EMPS
Result scripts
Excel tables
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II.3|II.3| The European SUSPLAN modelThe European SUSPLAN model
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...results in...results invariablevariablemarket pricesmarket prices
Norway 2030 (€cent/kWh)
Austria 2030 (€cent/kWh)
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III.3III.3 Electricity flows stage 2030Electricity flows stage 2030
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III.4III.4 Electricity flow Electricity flow –– example 2030 example 2030Average exchange from Norway to Sweden (2030)
MW
Hours
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IV.1IV.1 Summing upSumming up
The presentation points to that infrastructure andRES integration cannot be separated as they arepart of the interconnected energy/power system.• Only by seeing them together we can account for the
interconnectivity in the system, and provide decisionsupport “for saving the planet” – a sustainable energysystem.
The implemented scenario interfaces makes itpossible to establish data for different scenarios in aconsistent manner
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Contact informationContact information
Presenter:• Michael M. Belsnes, email: [email protected]
SUSPLAN coordinator:• Bjørn Harald Bakken, email: [email protected]
GreenNet contact:• Hans Auer, email: [email protected]
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ExtraExtra
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Start cost applied in special runStart cost applied in special run
Start cost in €/MWFuel type
29Lignite
44Oil
18Gas
44Bio
29Coal
50Nuclear
• Cost for “Warm” start is used
• Cost is multiplied with MW installation per area
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MarketsMarketsandand
infrastructures (2)infrastructures (2)
The NordicEMPS model
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ConsumptionGeneration
Consumption
and
generation
Scale:
French generation: 523 TWh
Available results (2)Available results (2)
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Energy flow
in Europe
=10 TWh
Available results (3)Available results (3)
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II.2II.2 Organization in modulesOrganization in modules
Optimize strategy
SimulateEnergy system
Thermal data &
Market data
Hydro data
Simulatewatercourses
Hydro results Economic tables Emissions
Grid dataInflow etc.Interface
Calculations
Input
Log files ASCII files Binary files
Resultinterface
Input files
Output
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Hydropower
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Thermalgeneration
P
Transmission capacityto other subsystems
Firm powerdemand
Spot powerdemand
HydropowerI.5I.5 SubsystemSubsystem
modelmodel
Solar
Wind
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Hydro inflow in Norway 2030Hydro inflow in Norway 2030
GWh
Weeks
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Wind and solar in Norway 2030Wind and solar in Norway 2030
0
100
200
300
1 10 19 28 37 46
0 percentile 25 percentile 50 percentile 75 percentile 100 percentile
GWh
Weeks
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III.1III.1 European electricity prices European electricity prices –– Norway Norway20302030
€cent/KWh
Weeks
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III.2III.2 European electricity prices European electricity prices –– Austria Austria20302030
€cent/KWh
Weeks