Comments (4)
Hi Fred,
I was writing some of this one today. I've been looking at a data sources and some of them have a "shunt" key in the dict, and was using the "gs" and "bs" values in that part of the dict as the b and g. Then added noise to them, and storing the perturbed values back into the dict.
I need to add this as a variable as well. Haven't done anything on this yet.
from powermodelsprivacypreserving.jl.
shunt
objects at the top level in the PowerModels data model don't leak such information, they are abstractions of capacitor banks, and their value is independent of branch length. However branch shunts do leak length-related information. The branch shunts are the br_b
values in the matpower format (col 5), and are mapped to b_fr
and b_to
(each of them assigned br_b/2
). There is no way to supply branch shunt conductance data through the matpower format, but PowerModels' internal data format does support it, as g_fr
and g_to
. In normal transmission lines, g_fr
and g_to
are negligibly small relative to the other branch parameters, so assuming they're 0 is appropriate. Nevertheless, in super-high voltage transmission (e.g. >750kV) it may be significant. Furthermore, branches are often used in the loss model for transformers. In that case, shunt conductance is actually very significant. So, we can also consider anonymising transformer parameters in a similar fashion.
from powermodelsprivacypreserving.jl.
Both series reactance and shunt susceptance are nonlinear functions of the length and the geometric mean distance (GMD) between the conductors. Branch shunt capacitance varies a lot between underground cables and overhead lines (because of the huge difference in GMD).
If you know the voltage level, you can infer length based on current/power rating and then infer GMD and category (overhead/underground).
Therefore, I think we better obfuscate series and shunt values independently, this gives less risk that we leak information.
from powermodelsprivacypreserving.jl.
this is on master, so I can close it
from powermodelsprivacypreserving.jl.
Related Issues (20)
- collect list of relevant papers
- transformer tap/shift anonymisation HOT 1
- make code more amenable to running on different data sets
- add unit tests for the Milestone V 0.2 prototype
- write up a report detailing the implementation
- analyse Dvorkin et al paper, present short analysis/comparison HOT 3
- Agenda for workshop 30 April 2020
- Chance constraints over Guassian and other distributions
- make sure package can be installed using the package manager HOT 5
- Agenda for meeting 7 May 2020 HOT 1
- Agenda for meeting 14 May 2020
- Write out obfuscate impedance values in mfile
- due to the noise addition, sometimes, b_shunt is negative
- housekeeping todos HOT 1
- extend obfuscation to more components
- extend obfuscation to unbalanced grid models
- scalability analysis, using PG Lib
- define branching strategy, policies for pull request, code review HOT 1
- Agenda for workshop 23 April 2020 HOT 1
- implementation todos towards v0.2 HOT 1
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from powermodelsprivacypreserving.jl.