This project implements an Optimized Power Flow (OPF) model focusing on maximizing photovoltaic (PV) generation while managing overvoltage issues within a power distribution network. The model employs Pyomo, a Python-based, open-source optimization modeling language, to solve the OPF problem, ensuring that PV generation is maximized under the constraints of network voltage limits and other operational conditions.
- PV Generation Maximization: Enhances renewable energy utilization by optimizing the output of PV units within network constraints.
- Voltage Regulation: Incorporates voltage constraints to prevent overvoltages, ensuring that the voltage at every node in the distribution network remains within safe and predefined limits.
- Configuration Parameters: Allows users to easily adjust key parameters such as PV generation limits, voltage thresholds, and network topology through a simple configuration file.
Ensure you have Python 3.6 or later installed on your system. The project depends on several external libraries, which can be installed using pip: Install Dependencies
```bash
pip install -r requirements.txt
```
Execute the OPF model from the project root directory:
python src/main.pyThe optimization model is highly configurable to adapt to different network scenarios and requirements. Configuration is managed through the config/settings.json file, where you can specify:
scaling_factor,size: Load and generation scaling.Sbase,V_base: Base power and voltage for per-unit calculations.PV_threshold,PV_pu: PV generation curtailment limits.- Additional customizable parameters.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details here
This work was supported by the project consortiums of the research projects Fine ("Flexible Integration of Local Energy Communities into the Norwegian Electricity Distribution System") funded by the Research Council of Norway under Grant 308833. This work is also funded by CINELDI - Centre for intelligent electricity distribution, an 8 year Research Centre under the FME-scheme (Centre for Environment-friendly Energy Research, 257626/E20).