Getting Started

Prerequisites

In order to use optihood, the following prerequisites are needed on your machine:

Python 3.9 is installed.
Git is installed.
An active Github account to clone the repo.
A solver is installed. Gurobi solver is recommended, although other solvers like CBC, GLPK, Cplex could also be used.

Installation

As of now, optihood is available as an open source code and needs to be installed from source. Please follow the instructions mentioned below to complete the installation. The commands given below are suited for the Windows platform and should be run from within the optihood directory in a Command Prompt. For other platforms, similar alternative commands could be used.

Clone the optihood repo to a folder called ‘optihood’ on your local machine:
```
git clone https://github.com/SPF-OST/OptiHood.git
```
All the next commands should be run from within the optihood folder. Create a virtual environment and activate it:
```
py -3.9 -m venv venv
venv\Scripts\activate
```
Install the requirements into the created virtual environment:
```
pip install wheel
pip install -r requirements.txt
```
It might be required to install C++ build tools. To do that, click on the link that appears with the error message and follow the instructions (it is the lapack package that is missing). In order to be able to install the missing package, it is required to have a complete Visual Studio instance and installing it with the “Desktop development with C++” workload.

Verify the installation of the oemof package and the solver by running the installation test in your virtual environment:

oemof_installation_test

If the installation is successful the following message would be displayed (the installed solver would be shown as working):

*****************************
Solver installed with oemof:

cbc: not working
glpk: not working
gurobi: working
cplex: not working

*****************************
oemof successfully installed.
*****************************

To test whether the installation worked well, you could run a basic example.

Setting up your optimization model

Optihood offers several functionalities to define an energy network, optimize it and visualize the results, which provides the user with a complete framework for optimization without the need to code by hand. You could learn more about using optihood by walking through the remaining sections of the documentation:

Defining an energy network