I wrote my Bsc thesis about "Electricity price curve scenario generation". I've written a program code which helped with the generating part. It is written in Python. The dataset is loaded from an Excel table, and the code can be started from the Excel as well.
Short description for the files:
Price Curve optimization
text files: Contain the values from global optimization for faster running
root:
-The location of the file.
optim:
-Loading the dataset
-variable declaration
-calling functions
-global optimization
-plotting
lib:
calc.py:
-parameter (S, Sx,...,Sxy, a, sde, Kappa, Sigma) calculation for calibration
eh.py:
-for writing the global optimization resulsts
kalibration.py:
-S stochastic process calibration
-calibration of logPrice and Price from S process and season
plothelper.py:
-helps plotting given Prices and calibrated prices
pred.py:
-S stochastic process prediction for 7 days
-Price prediction for 7 days
predplot.py:
-helps plotting given and predicted prices
randopt.py:
-generate random starting coordinates(x_i)
-optimization from the x_i starting points
readwrite.py:
-read values from the Adatok Excel file
-write results back in the Excel file
resources:
Adatok.xlms:
-contains the Prices and the [0,1] values for holiday
-the program can be started by "clicking" optimalizálás button
test:
allfunctiontest.py:
-testing all the callable functions in one module
-I generated random values and calculated with them in the test part. -Tested every function (from lib) one-by-one