A tool in this repository solves the following problem.
Input
$G$ — a MV distribution grid that has a DG-kernel$D$ such that:
$\text{V}(D)$ are all primary substations of$G$ .- There is an edge replacement sequence that constructs
$G$ from$D$ with all replaced edges coming from$\text{E}(D)$ .- All radial subnetworks of
$G$ are degenerate.$p : \text{V}(G) \setminus \text{V}(D) \rightarrow \mathbb{Q}$ — active power at each secondary substation of$G$ .$q : \text{V}(G) \setminus \text{V}(D) \rightarrow \mathbb{Q}$ — reactive power at each secondary substation of$G$ .$r : \text{E}(G) \rightarrow \mathbb{Q}$ — resistance of each edge.$x : \text{E}(G) \rightarrow \mathbb{Q}$ — reactance of each edge.
Output
$v : \text{V}(D) \rightarrow { -10, \dots, 10 }$ — an optimal tap position for each primary substation.$S \subseteq \text{E}(G)$ — set of edges where switches should be opened.
For further detail and definitions see our paper.
The program expects a GNBS file as an input. The GNBS file must describe grid
| Vertex attribute name | Type | Meaning | Possible values |
|---|---|---|---|
is primary substation |
B |
Flag of primary substations | T for primary substations, F for secondary substations |
p |
F8 |
Active power | A 64-bit float if is primary substation == F, X otherwise |
q |
F8 |
Reactive power | A 64-bit float if is primary substation == F, X otherwise |
| Edge attribute name | Type | Meaning | Possible values |
|---|---|---|---|
r |
F8 |
Resistance | A 64-bit float |
x |
F8 |
Reactance | A 64-bit float |
The program produces a GNBS file as an output. This GNBS file is a full copy of the input file with the following additional attributes defined:
| Vertex attribute name | Type | Meaning | Possible values |
|---|---|---|---|
tap position |
I1 |
Tap position |
An integer from is primary substation == T, X otherwise |
| Edge attribute name | Type | Meaning | Possible values |
|---|---|---|---|
opened switch |
B |
Flag of an opened switch | T or F |
You can find a full specification of GNBS format here.
The following solvers are implemented in this tool:
TreeDecompositionSolver— a solver that solves the problem with dynamic programming using tree decompositions.CPLEXSolver— a solver that solves the problem formulated as a MILP with the help of CPLEX.
To use the CPLEXSolver or to run the benchmark, you must have a copy of CPLEX installed on your computer. CPLEX is proprietary software owned by IBM. If you don't own a licence of CPLEX, you can still use our TreeDecompositionSolver without any problems or restrictions.
Clone this repository and run build.py, which is located in the root folder. Note that in order for compilation to be successful, the following must be present on your system:
python— a Python interpreter, version 3, distributed either with pip or conda.rustc— a Rust compiler, version 1.76 or newer.cargo— a Rust build system and package manager, version 1.76 or newer.- Internet connection.
The compiled program will be saved in the Switch selection folder, which is created automatically.
The tool is supplied with a CLI. Run the following command to learn how to use it:
.\switch-selection.exe -h
on Windows or
switch-selection -h
on Linux.
To reproduce the results from our PSCC paper, run
.\switch-selection.exe -b
on Windows or
switch-selection -b
on Linux. You must have a copy of CPLEX installed on your computer to run the benchmark.
When launched in the benchmark mode, the program will print messages to the console. For each sample, the output will contain a line that looks something like PDXPDXPDXPD or, more generally, in the language of regular expressions, (PDX)*PD. Here is what these symbols mean:
P— primary substations have been successfully generated.D— the corresponding distribution grid has been successfully generated.X— the generated sample turned out infeasible.
If the sample is found to be infeasible, the whole process starts again and is repeated until a feasible sample is produced. The time taken to identify infeasibility is not recorded and doesn't affect the metrics.