georghinkel/ttc2017smartGrids

The TTC 2017 Smart Grid Case

Case description

The docs/2017_TTC_SmartGrid.pdf file contains the case description.

Prerequisites

• 64-bit operating system
• Python 2.7 or higher
• R

Solution Prerequisites

• ModelJoin: Java 1.8
• NMF: .NET Framework 4.5.1 and MSBuild on the path

Using the framework

The scripts directory contains the run.py script which is used for the following purposes:

• run.py -b -- builds the projects
• run.py -b -s -- builds the projects without testing
• run.py -g -- generates the instance models
• run.py -m -- runs the benchmark
• run.py -v -- visualizes the results of the latest benchmark

The config directory contains the configuration for the scripts:

• config.json -- configuration for the model generation and the benchmark
• reporting.json -- configuration for the visualization

Running the benchmark

The script runs the benchmark for the given number of runs, for the specified tools and change sequences.

The benchmark results are stored in a CSV file. The header for the CSV file is stored in the output/header.csv file.

Reporting and visualization

Make sure you read the README.md file in the reporting directory and install all the requirements for R.

Running the example solution

1. Start with Eclipse Modeling or Eclipse for DSL Developers.
2. Add the http://download.eclipse.org/mmt/qvto/updates/releases update site and install the QVT Operational SDK, version 3.6.0.v20160606-1156.
3. Import the solutions/ModelJoin project.
4. If you get compile errors related to character encoding, change the encoding of the project to ISO-8859-1.

Run the de.fzi.ttc2017.smartGrids.App class as a Java Application with the required settings. For example:

• Arguments
  • -view outageDetection
• Environment
  • ChangePath: ../../changes/changeSequence1
  • Sequences: 1

Implementing the benchmark for a new tool

To implement a tool, you need to create a new directory in the solutions directory and give it a suitable name.