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PSSE with dask/Python for large datasets in contingency results

asked 2020-09-09 21:11:28 -0500

Div_1991 gravatar image


I am trying to extract the results of acc files using pssarrays.acccsummary and pssarrays.acccsolution. I have more than 80,000 contingencies. When I try to read the result of each contingency using for loop in python, I get MemoryError after reading 8,000 contingencies. The size of my .acc file is 2GB

Since I am working with large datasets, will Dask be helpful for this issue? Did anyone face a similar issue?

Any suggestion is greatly appreciated. Thanks in advance

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answered 2020-09-10 09:10:45 -0500

jconto gravatar image

Divide an Conquer.

Contingencies have specific features for testing the electric network. Select contingencies are defined per geographical regions (sometimes bus numbering can be used as proxy for geographical location), or per areas, zones in network model, per type (P1, P2, .. P7 in NERC TPL definition or equivalent definitions), per structure (single element in contingency, double elements,...) .

It would be better to create the contingency sets definition before running ACCC. Multiple ACCC would be created but much smaller in size, and processing them may be faster if done in parallel.

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Asked: 2020-09-09 21:11:28 -0500

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Last updated: Sep 10