This repository contains a research code for islanded grid modeling. An astonishing works insired some ideas deserve to be mentioned.
- Bahta, S. T. “Design and Analyzing of an Off-Grid Hybrid Renewable Energy System to Supply Electricity for Rural Areas : Case Study: Atsbi District, North Ethiopia.” (2013).
- Aemro, Yohannes B.; Moura, Pedro; de Almeida, Aníbal T. 2020. "Design and Modeling of a Standalone DC-Microgrid for Off-Grid Schools in Rural Areas of Developing Countries" Energies 13, no. 23: 6379.
- Ibrahim, H. & Ilinca, Adrian & Perron, Jean. (2007). Comparison and Analysis of Different Energy Storage Techniques Based on their Performance Index. 393 - 398. 10.1109/EPC.2007.4520364.
- Urraca, R.; Huld, T.; Gracia Amillo, A.M.; Martinez-de-Pison, F.J.; Kaspar, F.; Sanz-Garcia, A. 2018. "Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data". Solar Energy, 164, 339-354.
And many more. All the research in this field is valueable and should be highly appreciated.
- models - a module contains model objects representing grid components. Currently load.py contains models.Consumer object representing a single household load, storage.py contains models.StorageModel object (Battery system model), pv_model.py contains models.PVModel object, a model of solar arrays system.
- radiance_analysis.ipynb - a jupyter notebook with exploratory analysis of solar irradience data for areas of interests (Kherson, Ukraine and Schipkau, Germany)
- load_analysis.ipynb - a jupyter notebook with exploratory analysis and grid modeling
- cost_calculation.ipynb - a jupyter notebook calculating economic indecies and values
- [research]solar_radiation_prediction.ipynb - a jupyter notebook with exploration of machine learning approaches for irradiance forecasting (reserved for future development)