Volt-Var Optimization (WSU)

This repository contains the restoration application for IEEE 9500-node model (https://github.com/GRIDAPPSD/Powergrid-Models/tree/develop/blazegraph/test/dss/WSU). The application is hosted on GridAPPS-D platform.

Layout

Please clone the repository https://github.com/GRIDAPPSD/gridappsd-docker (refered to as gridappsd-docker repository) next to this repository (they should both have the same parent folder)

Creating the restoration application container

From the command line execute the following commands to build the wsu-restoration container

osboxes@osboxes> cd WSU-VVO-app
osboxes@osboxes> docker build --network=host -t wsu-vvo-app .

Add the following to the gridappsd-docker/docker-compose.yml file

wsu_vvo:
  image: wsu-vvo-app
  depends_on: 
    gridappsd    

Run the docker application

osboxes@osboxes> cd gridappsd-docker
osboxes@osboxes> ./run.sh

# you will now be inside the container, the following starts gridappsd

gridappsd@f4ede7dacb7d:/gridappsd$ ./run-gridappsd.sh

Run the application container

osboxes@osboxes> cd WSU-VVO-app
osboxes@osboxes> docker exec -it gridappsddocker_wsu_vvo_1 bash

# you will now be inside the application container, the following runs the application from terminal

root@1b762c641f24:/usr/src/gridappsd-vvo# cd wsu_vvo
root@1b762c641f24:/usr/src/gridappsd-vvo/wsu_vvo# python main.py 1234 '{"power_system_config":  {"Line_name":"_AAE94E4A-2465-6F5E-37B1-3E72183A4E44"}}'

Next to start the application through the viz follow the directions here: https://gridappsd.readthedocs.io/en/latest/using_gridappsd/index.html#start-gridapps-d-platform

Forming optimization problem

In the optimization problem, test feeders are modeled as graph; G (V, E), where V is set of nodes and E is set of edges. The graph and line parameters of the test feeder can be extracted from (https://github.com/shpoudel/D-Net).

Get real-time topology and load data of the test case

First, the real time topology of the test case is extracted from the operating feeder in platform (top_identify.py). Then, load data for each node is obtained (get_Load.py) and stored in json format to be used in linear power flow while writing constraints for optimization problem.