What changed between these two PSS/E RAW files? We set out to answer that question once and for all, and in the process wrote some software that can match buses between two networks even if their bus numbers and names are different.

A blog reader wrote in to present me with a devious problem. the mdlnam function returns dynamic model name for generators, excitation systems and others. Can you spot the problem with the code below?

# get the generator dynamic model name for machine at bus 100.
ierr, genmodel = psspy.mdlnam(100, '1', 'GEN')
print genmodel
 
if genmodel == 'GENROE':
    print 'bus', 100, 'uses a GENROE model'
elif genmodel == 'GENROU':
    print 'bus', 100, 'uses a GENROU model'
elif genmodel == 'GENSAE':
    print 'bus', 100, 'uses a GENSAE model'
elif genmodel == 'GENSAL':
    print 'bus', 100, 'uses a GENSAL model'
else:
    print 'No match'


# >>>
# GENROE
# No match

In case it isn’t clear. The print statement prints GENROE, so we’re expecting the next output to be bus 100 uses a GENROE model. However, and this is where it becomes mysterious, the first if statement doesn’t match, nothing matches and instead the else clause No match is printed.

When running a load flow, checking for convergence is simple. There is a dedicated function in the PSS/E API called psspy.solved. How would you do the same thing during a dynamic study?

Here’s a reminder of how to use the AEMO Settlement Date.

The settlement date is a day and time used in many of AEMO’s database tables. It’s a fundamental marker of time because it marks one of the 48 trading intervals.

Every half hour, AEMO generates a new electricity price that is used to bill customers and pay generators. There are 48 half hours in a day, each is called a trading interval.

Following on from the last video “How to solve a loadflow with Python”

This video explains exactly how and why I used keyword arguments in my Python functions. If you want to learn how to make your scripts easier to read, this 9 minute video is for you.

I’ve been asked many times: “How do I solve a loadflow with Python?” It’s a great question because once you know how to solve a loadflow - you can begin to automate your PSSE work.

This video will show you exactly how to solve a PSSE loadflow using the full newton raphson solution.

Here is a short video based on the tutorial in running PSSE from Python

Last week, we investigated how a pool pass through contract could save you money. We concluded that when market conditions are favourable, your electricity bill will be lower if you take the pool price. That post raised some discussion about what if hedge contracts had been purchased instead.

In this post we investigate how hedging your electricity contract can insure you against the volatility of the market.

We investigate the raw cost of electricity. How much profit does a retailer make in selling us electricity?

Not everyone will find this article interesting. Because not everyone has a huge electricity bill or works in the electricity industry. If your electricity usage is greater than 50000 kWh / year (the equivalent usage of ten small families) then this is for you.

For those not reading on: Conclusion, A pool pass through contract could save 24% on your annual electricity bill. But by doing so you take on wholesale market risk instead of your retailer, and we note that in recent months it may have been more profitable to be on a fixed contract due to the large spike in volatility after the carbon price introduction.

Have you ever wanted to export data from PSSE into Microsoft Excel? In today’s post we’ll show you the exact steps that you need to export your data from PSSE directly into Microsoft Excel using Python.