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NRGISE Open is a Python framework for simulating local energy systems over time.

It uses sequential time-step simulation to model the interaction between electrical components of the energy system and control strategies under realistic operating conditions. NRGISE provides ready-to-use components and controllers while remaining extensible, allowing researchers and engineers to implement custom models and controllers. Unlike optimization-focused frameworks, NRGISE is built around explicit controllers and rolling-horizon simulation, making it well suited for techno-economic assessments under realistic operating conditions.

What you can do with NRGISE Open

  • Simulate common use cases out of the box: Plug your PV and load data into our predefined examples to simulate use cases such as self-consumption maximization and peak shaving without writing custom code.
  • Techno-economic assessments: Evaluate how storage sizing, control strategies and system configurations affect technical and economic performance.
  • Develop and benchmark controllers: Use NRGISE as a simulation environment for developing, testing, and benchmarking custom controllers, from simple rule-based strategies to model predictive control, reinforcement learning, and other advanced approaches.
  • Study impact of forecasting: Integrate custom forecasting models into rolling-horizon simulations and investigate how forecast errors affect system operation and economic performance.
  • Run simulation studies at scale: Execute parameter sweeps and batch simulations to compare hundreds or thousands of scenarios for sizing studies, sensitivity analyses, or scientific experiments.
  • Extend the framework: Implement your own components, storage models, controllers, forecasters, aging models, or economic models using our well-defined interfaces.

When another tool may be a better fit

  • When you just want to see the mathematically optimal system layout without explicitly considering control strategies: Use an optimization-based tool like oemof, ETHOS.FINE or PyPSA
  • When you want to model sector coupling (electricity, heat and mobility): Use oemof or ETHOS.FINE
  • When you need to model a large power grid: Use a spatially resolved tool like PyPSA
  • When you want a deep dive into battery aging: Use SimSES
  • When you want to deploy your controller in real-time: Use a deployment tool like OpenEMS (however, NRGISE controllers can be coupled with it)

If none of these tools fit your list, see OpenMod for a broader tool comparison