A Python toolbox for simulating and optimizing modern energy systems — renewables, storage, sector coupling across electricity, heat, and hydrogen — for national-scale capacity planning.
GridOptima is a Python toolbox for simulating and optimizing modern energy systems — from conventional generators and wind/solar farms to battery storage, sector-coupled heat networks, green hydrogen pipelines, and national capacity expansion planning. Developed in partnership with TU Berlin’s Department of Digital Transformation in Energy Systems and funded by the German Research Foundation, it is the standard tool for academic and institutional energy transition research across Europe.
Energy companies, grid operators, and governments making billion-dollar investment decisions in renewable capacity, transmission infrastructure, and hydrogen networks need rigorous scenario modeling tools. The energy transition is not just an electricity problem — heat networks, hydrogen, and transport electrification all interact. Legacy planning tools either model a single sector in isolation or require expensive commercial licenses (PLEXOS, GAMS) that limit research transparency and reproducibility. National energy transition plans that can’t be audited by independent researchers are a governance problem.
GridOptima provides least-cost optimization across entire multi-sector energy systems in a single framework. The linopy optimization layer supports linear programming (LP) and mixed-integer programming (MIP) using HiGHS, CPLEX, or Gurobi solvers. NetworkX handles energy network topology for optimal power flow calculations. Multi-period investment optimization models capacity expansion over decades, not just dispatch in a single year. Sector coupling — connecting electricity, heat, and hydrogen networks — enables analysis of the full energy transition rather than isolated subsystems. Its peer-reviewed publication in the Journal of Open Research Software gives energy planners confidence in methodological rigor.
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