Code covered by the BSD License
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Calibrating Simulating Natur...
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Modeling Simulating Hourly E...
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Modeling Simulating Hourly T...
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Simulation of Hybrid Electric...
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backtestPlantPortfolio(assets...
BACKTESTPLANTPORTFOLIO applies the simple dispatch algorithm on
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dispatch(capacity, heatRate, ...
DISPATCH computes optimal daily dispatch decisions for a gas-fired plant
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dynamicDateTicks(axH, link)
DYNAMICDATETICKS is a wrapper function around DATETICK which creates
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fetchDBElecData(startDate, en...
FETCHDBDATA is a modified auto-generated function to import electricity
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fetchNGData()
FETCHDBDATA is a modified auto-generated function to import natural gas
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fitPlot(dates, YMatrix1, res1...
FITPLOT is a modified auto-generated function to create a plot of the actual series,
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genPredictorsElec(dates, hour...
GENPREDICTORSELEC generates a set of predictors for modeling electricity
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simulateElecPrices(elecModel,...
SIMULATEELECPRICES simulates the electricity price model derived in script
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simulateNGPrices(NGModel, dat...
SIMULATENGPRICES simulates the natural gas price model derived in script
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simulatePlantPortfolio(assets...
SIMULATEPLANTPORTFOLIO jointly simulates natural gas prices, temperatures
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simulateTemperature(tempModel...
SIMULATETEMPERATURE simulates the temperature model derived in script
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View all files
Energy Trading & Risk Management with MATLAB Webinar Case Study
by Ameya Deoras
29 Jun 2010
(Updated 03 Jan 2011)
MATLAB code for the generation asset risk analysis case study
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Watch this File
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| File Information |
| Description |
* Update: To see the recorded webinar please visit, http://www.mathworks.com/company/events/webinars/wbnr50145.html
The case study presented here demonstrates using MATLAB to build an application for measuring the risks associated with a portfolio of gas-fired power plants operated in New England. The application has an interface implemented in Excel with all of the analytics performed by MATLAB. The application allows the user to specify the characteristics of the 7 plants including the capacity, heat rate, variable operation and maintenance costs and minimum run time. This portfolio can be backtested using a simple dispatch strategy on historical gas and electricity prices to compute historical profit and plant operation statistics. The risk measures are computed by simulating gas and electricity prices into the future using a hybrid model implemented in MATLAB, simulating the dispatch for each scenario of market prices and computing cash-flows arising from the operation of the plants. The distribution of the cash-flows is analyzed to produce a 90% and 95% cash-flow at risk measure for each plant as well as for the portfolio of generation assets. All of this functionality is presented with a succinct Excel front end.
The document titled "Introduction to ETRM Case Study" will guide you through the different components of the analysis.
Note: The data used in this application is not provided with the MATLAB Central File Exchange entry. The data can be obtained from the New England ISO at http://www.iso-ne.com/. The Natural Gas spot price data can be obtained from the Wall Street Journal at http://www.wsj.com. You can still view the results of running each script on the data by viewing the MATLAB-published reports included in the archive and also linked below. |
| Acknowledgements |
Intelligent Dynamic Date Ticks
inspired this file.
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| Required Products |
Curve Fitting Toolbox
Database Toolbox
Econometrics Toolbox
MATLAB Builder EX
MATLAB Compiler
Statistics Toolbox
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| MATLAB release |
MATLAB 7.10 (R2010a)
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| Updates |
| 01 Jul 2010 |
Added link to recorded webinar |
| 03 Jan 2011 |
Minor edit. Added MAT-file. No change to code. |
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