Analyse Results

When running a simulation (Simulation.run()), step-wise results are returned for the full time horizon.

This means every simulation time step is available for post-processing, so you can build any analysis you need (plots, custom computations, validation, etc.).

It is important to note that controllers can pass additional custom information: the second return value of Controller.get_action(state) is stored in the simulation results as additional_control_info. With this, arbitrary controller-side context can be logged.

The field grid_builder_usage in the results logs the power which was supplied or fed into the GridBuilder (e.g. a "normal" grid connection point Grid) in each time step (positive=getting power from the grid, negative=feeding in).

Economics

For economic post-processing, NRGISE Open provides the Economics API, which can be applied to simulation results.

In practice, economic evaluation is typically performed over multiple years of operation. To do so, there are two common options:

1. Simulate the full investment horizon (for example 10 years).

   This is typically the most accurate option, but it requires time-series data over the full horizon. If you want to simulate multiple years while only having a one-year data profile (for example, to include storage aging effects), you can use EnergySystem.stretch_time() before running the simulation.

   Note that this increases computational effort. This is especially relevant for large simulation studies, for example with Batch Run.

2. Simulate one representative year and stretch the results in post-processing.

   In this option, the actual simulation uses a shorter representative horizon (usually one year). The resulting time series is then stretched to match the investment horizon of interest.

   For this workflow, use the economics helper stretch_data_over_investment_horizon(...) to stretch the parts of the results that are relevant for your economic calculations.

In most cases, cash flow must be calculated for the energy system. For Commercial & Industry and residential use cases, cash flow is often primarily defined by the yearly electricity bill. For this, dedicated helper functions are available for cash-flow calculations with tariff-based grid-charge modeling.

For an example workflow, see the economics example.

In most cases, it is recommended to use the economic summary from the economics module, because it aggregates the cash-flow-based evaluation into useful KPIs (amortisation time, NPV, ...).

Also, in many cases a baseline simulation is helpful when computing the economic summary, because you may want to compare your setup against a reference setup. For example, if you want to evaluate whether storage makes sense (or which storage is best), a common baseline is the same energy system without storage.