Modelling European Power Supply and demand with MATLAB … · Modelling European Power Supply and...
Transcript of Modelling European Power Supply and demand with MATLAB … · Modelling European Power Supply and...
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Modelling European Power Supply and demand with
MATLAB and Parallel Computing
Lea Bloechlinger
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Power Trading and Analysis
Trading
• Commodities
– Power (DE, FR, UK, BE, NL,..) Base, Peak, Offpeak
– Coal, Gas, Emissions
• Futures and Forwards (Financial and Physical), Options
• Intraday, Day-Ahead, Weeks, Months, Quarter, Year
Analysis
• Fundamental approach
• Model the whole power system, estimate the marginal costs, compare with
market prices, derive trade ideas
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Fundamental Analysis
• What are the costs of meeting all power demand in CWE
– using the available generation capacity
– taking into account the different characteristics of units (efficiency,
ramping flexibility, etc.)
– taking into account the expected renewable production
– assuming optimal border flows within CWE
– assuming border flows from external countries according to price
differentials in the forward curves
Stack model which provides an hourly cost curve for the BoY and YA
Compare with market prices and follow day on day changes
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Stack Model
1. Input Data
2. Optimization Model
3. Results
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Generation Units
1700 Units wo wind/solar/hydro
• Characterised by
– Fuel Type: Nuke, Lignite, Coal, Gas, Fuel Oil, Diesel Oil, Biomass
– Technology: ST, GT, CCGT, CHP
– Capacity
– Efficiency / Age
– Operating Modus
• Which implies different
– Variable Costs
– Startup Costs
– Ramping Flexibility
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Generation Units – Daily Changes/Uncertainty
• Variable costs depend on fuel and emission price which permanently
changes
• Available capacity
– Planned Maintenance
• Daily unit-specific information provided by some utilities
• Daily fuel-specific information provided by TSO covering majority of
plants
• Own assumptions, seasonal shapes for not-covered unit and the
longer end of the curve
– Unplanned Outages
• Realtime UMM (urgent market messages)
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Abstraction in Model
30
35
40
45
50
55
60
65
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
22000
Capacity (MW)
Net E
ffic
iency (
%)
Gas 2:
Gas 1:
Gas 3:
Gas 4:
Gas 5:
Gas 6:
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Renewable Generation
• Wind, Solar, Hydro • Zero Variable Costs
Germany Wind Forecasts
0
2,000
4,000
6,000
8,000
10,000
12,000
15/9
Hr
1
15/9
Hr
13
16/9
Hr
1
16/9
Hr
13
17/9
Hr
1
17/9
Hr
13
18/9
Hr
1
18/9
Hr
13
19/9
Hr
1
19/9
Hr
13
20/9
Hr
1
20/9
Hr
13
21/9
Hr
1
21/9
Hr
13
22/9
Hr
1
22/9
Hr
13
23/9
Hr
1
23/9
Hr
13
24/9
Hr
1
24/9
Hr
13
Max w eekly historic
Min w eekly historic
Historic average
Meteopow er 15/09/12
04:00
Point Cabon 13/09/12
Point Cabon 14/09/12
Meteologica 15/09/12
06:00
Meteologica 14/09/12
23:00
Previento 14/09 08:25
Previento 15/09 06:00
TSO
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European Power Grid
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Crossborder Transmission Lines
• Crossborder Transmission Lines
– 11 external borders
NO, SE, DK1, DK2, PL, CZ, HU, SI, IT, ES, IE
– 9 internal borders
• Characterised by
– Installed Capacity
– Available Capacity - Daily updates published by TSO
– Price Differential - Permanently changing
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• Short-/Mid-term Demand
– Weather data (temperature, cloud cover,..)
– Calendar (hour, weekday, month)
• Long-term Demand
– Economy, IP Numbers
– Efficiency measures
Demand
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Stack Model
1. Input Data
2. Optimization Model
3. Results
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Optimization Problem - Linear Program
min f‘x minimize costs
s.t. A x < = b e.g. Production < Available Capacity
A_eq x = b_eq e.g. Demand = Supply
x < = ub e.g. Available Cap < Installed Cap
-x < = lb e.g. Production > 0
Some numbers for an optimization period of 1 month (DE, FR, NL, BE, UK, CH)
• No decision variables x: 262’080
• No of <= restrictions: 253’440
• No of = restrictions: 48’960
• A: [253’440x262’080 double] A_eq: [48’960x262’080 double]
• b: [253’440x1 double] b_eq: [48’960x1 double]
• f: [262’080x1 double] lb, ub, x: [262’080x1 double]
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Implementation Details
• Object-Oriented Approach
– OptProg
• Contains all matrices going into the Linear Program
• Use of dependent properties (easily link parts of matrices)
– Stack
• Contains all input and output
• Intuitively organized
• Advantages of Object-Orient Approach
– Maintainability
– Easily extentable
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Use of LinProg
• options =
optimset('LargeScale', 'on','Algorithm', 'interior-point', 'TolFun', 0.00001);
• [x,fval,exitflag,output,lambda] =
linprog( OptProg.d_f.Value, f
OptProg.A, A
OptProg.d_b.Value, b
OptProg.A_eq Aeq
OptProg.d_b_eq.Value, beq
OptProg.d_lb.lb, LB
OptProg.d_ub.ub, UB
[],
options);
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Implementation Details
• Parallel Computing
– Linprog not possible to run in parallel but model can be cut along time-
dimension
– Depending on ratio of time window and number of scenarios parfor loop
either over time or scenarios
– Easy implementation, see next slide
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Easy Implementation of Parallel Computing
if nR < 3 % if less then three time periods
for i = 1:nR % normal loop over time but
[obj, optp] = runBase(countries, datestr(sdts(i)), datestr(edts(i)),
publishDate, scenID, ExtractID);
obj = addDummies(obj);
parfor j = 1:length(scenIds) % but parfor-loop over scenarios
runDem(obj, optp, scenIds(j), fullDataSet, modelIds(j), series)
end
end
else % if more then three time periods
parfor i=1:nR % parfor-loop over time periods
[obj, optp] = runBase(countries, datestr(sdts(i)), datestr(edts(i)),
publishDate, scenID, ExtractID);
obj = addDummies(obj);
for j = 1:length(scenIds) % and normal loop over dem scenarios
runDem(obj, optp, scenIds(j), fullDataSet, modelIds(j), series)
end
end
end
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Agenda
1. Input Data
2. Optimization Model
3. Results
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Stack
CountryStack DE CountryStackFR ... CountryStack UK
Demand Supply Borders
Demand Solar Wind RoR CHP ImCap ExCap ImFlow NetFlow ImPrice ImFee ...
Fcst Seasonal
FuelUnits HydroUnits
Fuel Efficiency VarCost ProdCost StartupCost Cap Avail Production Reserve
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Results
• Due to the object-oriented approach easy but still flexible way to illustrate
results also for unexperienced Matlab users
>> obj = stacks(countries, startDate, endDate, publishDate, scenarioID)
>> obj = create_opt_stack(obj)
>> help stack/plot_generation
out = plot_generation(obj, Country, varargin)
Plots the generation by unit together with the netdemand for the chosen country.
Returns the plotted data as a dataset.
Variable Description Default/List of Choice
obj stack mandatory
Country Country mandatory
frequency Time Frequency *Hourly, Daily, Weekly, Monthly
quality Quality *Base, Peak, OffPeak, OffPeak5D, WE
gen Switch Gen/Avail/Cap *Gen/Avail/Cap
startDate Start of plot *obj.StartDate
endDate End of plot *obj.EndDate
fuellevel fuel or unit level *false/true
addprice add prices *false/true
See also plot_demand, plot_stack, plot_border, plot_allborders, plot_price
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Results
13-Sep-12 14-Sep-12 15-Sep-12 16-Sep-12 17-Sep-12 18-Sep-120
10
20
30
40
50
60
0
10
GW
DE - Gen and NetDemand - Base
ML DE Lignite 3 - 12.69
ML DE Nuclear 1 - 15.14
ML DE Lignite 1 - 15.74
ML DE Lignite 2 - 16.13
ML DE Coal 4 - 35.62
ML DE Coal 3 - 36.74
ML DE Coal 2 - 38.02
ML DE Coal 1 - 40.02
ML DE Gas 5 - 44.45
ML DE Gas 4 - 54.39
ML DE Gas 3 - 60.38
ML DE Gas 2 - 67.08
ML DE Gas 1 - 82.51
ML DE Oil 2 - 90.8
ML DE Gas 6 - 102.26
ML DE Oil 1 - 164.38
NetDemand
Demand
NetNetDemand
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13-Sep-12 14-Sep-12 15-Sep-12 16-Sep-12 17-Sep-12 18-Sep-120
10
20
30
40
50
60
0
GW
FR - Gen and NetDemand - Base
ML FR Nuclear 1 - 13
ML FR Nuclear 2 - 24
ML FR Coal 3 - 40.68
ML FR Coal 2 - 41.12
ML FR Coal 1 - 43.48
ML FR Coal 4 - 45.39
ML FR Gas 1 - 51.27
ML FR Hydro Res 1 - 65.13
ML FR Hydro Res 2 - 72.32
ML FR Gas 2 - 79.13
ML FR Hydro Res 3 - 80.36
ML FR Hydro Res 4 - 88.4
ML FR Oil 1 - 135.17
ML FR Oil 2 - 135.17
NetDemand
Demand
NetNetDemand
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Benefits of Matlab
• Model moved from xls/vba to Matlab/Database
– Much more flexibility regarding
• time aggregation
• geographic coverage
• scenario analysis
– Increase in performance
• Much easier handling of large dataset