Grid Topology and Time Series Data: Assessing Future Grid Reinforcement Needs in Rural Morocco: The Role of PV and Storage

This dataset contains the research data (grid topology, component parameters, and time-series load/generation data) of the journal article https://doi.org/10.1080/14786451.2026.2618349:  

 Abstract:

Rural grids in Morocco face increasing energy demand and infrastructure limitations, necessitating sustainable solutions to ensure reliable electricity supply. This study investigates the impact of integrating photovoltaic (PV) systems and energy storage on grid reinforcement requirements in a rural medium-voltage grid in Morocco, considering an annual energy demand increase of at least 5\%. Based on a current grid in 2023, future grid scenarios for  2030 and 2040 were simulated using the open-source Python library pandapower to assess grid performance under different expansion strategies. These scenarios include (1) load growth without PV and storage integration, (2) PV integration alone, and (3) combined PV and energy storage solutions.

The findings indicate that integrating PV and storage reduces the need for traditional grid reinforcements and improves voltage stability. In particular, storage integration significantly decreases transformer loading and mitigates line congestion, highlighting its role in voltage stabilization. By reducing dependence on fossil fuel-based generation and increasing local energy self-sufficiency, these solutions contribute to Morocco’s renewable energy transition. The study emphasizes the need for strategic investments in distributed energy resources to enhance the sustainability and reliability of rural electricity grids.

Data Sources & Attribution

• Grid topology & measurements:
  Original data provided by Green Energy Park (Benguerir, Morocco).
• Transformer parameters:
• Nexans, “Nexan Transformer Datasheets,” https://www.nexans.ma/en/products/Transformers.html.
• F. IEE and University of Kassel, “Pandapower 3.0.0 Documentation,” https://pandapower.readthedocs.io/en/latest/std types/basic.html#trafo
• Line parameters:
• F. IEE and University of Kassel, “Pandapower 3.0.0 Documentation,” https://pandapower.readthedocs.io/en/latest/std types/basic.html#line
• Solar potential data:
• RenewablesNinja, “Renewables Ninja,” https://www.renewables.ninja/, 2019.

File Structure & Description

1. Grid Topology (Pandapower Format):
   • buses.csv, lines.csv, trafos.csv, loads.csv, switches.csv
   • Network structure in standard pandapower format.
2. Component Parameters:
   • line_parameters.csv, trafo_parameters.csv
   • Technical specifications (Nexans datasheets + pandapower standards).
3. Time Series Data:
   • HV-MV Transformer (10-min resolution)
     • active_p_hv_mv_trafo_[08|09|12]_23.csv
     • reactive_q_hv_mv_trafo_[08|09|12]_23.csv
     • Original measurements from Green Energy Park.
   • Distributed MV-LV Transformer Loads
     • active_p_mv_lv_trafos_[08|09|12]_23.csv
     • reactive_q_mv_lv_trafos_[08|09|12]_23.csv
     • Scaled proportionally to transformer capacity (method detailed in [1]).
4. Solar Generation Potential:

• • solar_potential.csv
  • Simulated profiles (Renewables Ninja, 2019).

Usage Notes

• Time series: 10-minute resolution
• Load distribution: HV-MV measurements were downscaled to MV-LV transformers based on installed capacity (see [1] for methodology).

Copyright

[1] S. Wuest, M. Laamim, E. Kasper, M. Schmidt, "Assessing Future Grid Reinforcement Needs in Rural Morocco: The Role of PV and Storage", International Journal of Sustainable Energy  DOI: https://doi.org/10.1080/14786451.2026.2618349

Please cite this paper if you use the supplementary material.