Home Energy Management System (HEMS) is a device which is developed to fulfill the demands of the industry and the prosumers. The project contains codes for 2 different optimization techniques such as MILP and Stochastic Optimization as well as the scenario generation for the SO and a base scenario which simulates the household behaviour without a HEMS. The implementation is achieved with Python.

Each algorithm is created to have different objective functions in order to satisfy the different demands of the users such as energy minimization, cost minimization and profit maximization.

In order to run the scripts please make sure Pyomo, Gurobi solver and Anaconda are installed properly on your machine.

You can refer to following manuals for installation:

• Pyomo:

http://www.pyomo.org/installation

• Gurobi:

http://www.gurobi.com/registration/download-reg

• Anaconda:

https://www.anaconda.com/distribution/

The repository is organized as 4 different sub-projects.

To run, simply open the sub-project using Spyder and click Run.

The project requires four input datasets: availability of electric vehicle, solar power generation, power demand of the household, and the electricity price. The full versions of these datasets are not included in this repository due to licensing issues.

However, these 4 datasets can be replaced with sets containing 15 minutes of resolution for a year.

Feel free to contact the author for further instructions.

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

E-mail: [email protected]