Stochastic Modeling of Electricity Market Prices in Europe ... .pdfStochastic Modeling of Electricity Market Prices in Europe with Large Shares of Renewable Generation Michael M. Belsnes

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


ContentsContents

The EMPS model

European model for scenario work

Example of achieved results- Prices- Energy balances

Summing up


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


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


Wind and solar in Norway 2030 (GWh/week)

StochasticStochasticrenewablerenewableresourcesresourcesin Europeanin Europeancountries...countries...


I.4I.4 Aggregation into sub-systemsAggregation into sub-systems

A node representation of loadcenters defined by variouscriteria:• Country borders

• Congestion

• Hydrological

• Markets


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


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))


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


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


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.


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


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


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


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


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


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


II.3|II.3| The European SUSPLAN modelThe European SUSPLAN model


...results in...results invariablevariablemarket pricesmarket prices

Norway 2030 (€cent/kWh)

Austria 2030 (€cent/kWh)


III.3III.3 Electricity flows stage 2030Electricity flows stage 2030


III.4III.4 Electricity flow Electricity flow –– example 2030 example 2030Average exchange from Norway to Sweden (2030)

MW

Hours


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


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]


ExtraExtra


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


MarketsMarketsandand

infrastructures (2)infrastructures (2)

The NordicEMPS model


ConsumptionGeneration

Consumption

and

generation

Scale:

French generation: 523 TWh

Available results (2)Available results (2)


Energy flow

in Europe

=10 TWh

Available results (3)Available results (3)


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


Hydropower


Thermalgeneration

P

Transmission capacityto other subsystems

Firm powerdemand

Spot powerdemand

HydropowerI.5I.5 SubsystemSubsystem

modelmodel

Solar

Wind


Hydro inflow in Norway 2030Hydro inflow in Norway 2030

GWh

Weeks


Wind and solar in Norway 2030Wind and solar in Norway 2030

0

100

200

300

1 10 19 28 37 46


GWh

Weeks


III.1III.1 European electricity prices European electricity prices –– Norway Norway20302030

€cent/KWh

Weeks


III.2III.2 European electricity prices European electricity prices –– Austria Austria20302030

€cent/KWh

Weeks

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Stochastic Modeling of Electricity Market Prices in Europe ... .pdfStochastic Modeling of Electricity Market Prices in Europe with Large Shares of Renewable Generation Michael M. Belsnes