____   _____  ____   ____   _       _               
 |  _ \ | ____|/ ___| / ___| (_) ____(_) _ __    __ _ 
 | |_) ||  _| | |     \___ \ | ||_  /| || '_ \  / _` |
 |  _ < | |___| |___   ___) || | / / | || | | || (_| |
 |_| \_\|_____|\____| |____/ |_|/___||_||_| |_| \__, |
                                                |___/ 

Documentation

The REC Sizing use case aims to provide an optimal investment plan for Renewable Energy Communities (REC) and Citizen Energy Communities (CEC) under the Enershare project.

To that end, a library was implemented, named rec_sizing (Renewable Energy Communities Sizing), that provides the user with several options for running a MILP optimization aimed at minimizing the collective operation and investment costs of the whole community. The MILP formulation not only schedules all controllable assets (namely batteries) and the optimal internal exchanges that should be established within the REC (a thorough explanation of the algorithms implemented is published here), but also the optimal investments in new RES generation and/or storage capacities.

Notes:

• The optimization is perfomed over the individual meters in the REC, defined by the user in the request.
• In the current version, the only option available is to run a collective MILP, where the local energy market (LEM) established within the REC is organized as a pool (i.e., bilateral transactions are not considered). The pricing mechanism for the market is one of the suggested outputs and is calculated according to the dual approach presented here
• Any suggested investment is limited to the meters defined in the request. This means that the tool will not suggest the inclusion of additional meters. If that option is to be considered, one or more "empty" meters can be explicitly included in the request.
• Any investment is applicable to the whole optimization period. This means that if the optimal solution requires installing additional RES or storage capacity behind a given meter, that capacity is considered to be operational from the beginning of the optimization horizon (defined by the user).

Main optimization functions overview

Under rec_sizing_tools.optimization_functions the user can find:

run_pre_collective_pool_milp

• run a purely collective pre-delivery MILP, considering a pool LEM structure

Install guide: use it as a library

The tool is implemented as a Python library. To install the library in, for example, a virtual environment, one must:

• download this repository

• change the working directory to the root folder of the repository:

  % cd /path/to/root_folder

• create the wheel file that will allow you to install the repository as a Python library (make sure you have previously installed Python ~= 3.10 in your local computer / server);

  % python setup.py bdist_wheel

• after creating your virtual environment with and activating it (see this example for conda environments), enter the newly created dist folder, copy the name of the .whl file and install the library by using:

  % pip install wheel_file_name.whl

• (optional) return to the root folder path and run the tests provided to assert everything is working as it should:

  % python setup.py pytest

• to import the library use:

  from rec_sizing.optimization_functions import run_pre_collective_pool_milp as sizing