This open-source code base aims to optimize grid planning by comparing different measures such as reactive power from fast charging stations, demand-side flexibility from local energy communities, and using line reinforcement to address undervoltage problems in radial distribution grids.
Work performed
The Python code base was developed 2022–2024 through work in CINELDI in collaboration with projects FINE and FuChar.
It includes a grid reinforcement optimization model for and functionality for socio-economic analysis and risk analysis of grid development plans.
Significant results
It has been used for economic assessment of integrating fast-charging stations in the distribution grid, evaluating grid development strategies considering real options and risks, and incentive allocation for energy communities.
Impact for distribution system innovation
It demonstrates how the CINELDI framework for planning of active distribution grids can be implemented as a software tool.
It illustrates how DSOs can use a data-based optimization approach to make better power grid development decisions.
A screen grab from the open-source code base PLAN_ADGrid
