########### Statistics ########### .. seealso:: See :doc:`/api/statistics` for the API reference. Overview ========= The ``pypsa.statistics`` module provides an accessor to the ``pypsa.Network`` object via ``n.statistics``, enabling users to efficiently calculate key network metrics. This module is designed to simplify the analysis of network data by abstracting complex calculations into more accessible methods. It is particularly useful for users who prefer to avoid manual calculations and directly obtain relevant statistical data about the network. Available Metrics ================== .. autosummary:: ~pypsa.statistics.StatisticsAccessor.system_cost ~pypsa.statistics.StatisticsAccessor.capex ~pypsa.statistics.StatisticsAccessor.installed_capex ~pypsa.statistics.StatisticsAccessor.expanded_capex ~pypsa.statistics.StatisticsAccessor.optimal_capacity ~pypsa.statistics.StatisticsAccessor.installed_capacity ~pypsa.statistics.StatisticsAccessor.expanded_capacity ~pypsa.statistics.StatisticsAccessor.opex ~pypsa.statistics.StatisticsAccessor.supply ~pypsa.statistics.StatisticsAccessor.withdrawal ~pypsa.statistics.StatisticsAccessor.transmission ~pypsa.statistics.StatisticsAccessor.energy_balance ~pypsa.statistics.StatisticsAccessor.curtailment ~pypsa.statistics.StatisticsAccessor.capacity_factor ~pypsa.statistics.StatisticsAccessor.revenue ~pypsa.statistics.StatisticsAccessor.market_value Usage ----------------- To utilize the statistics module, instantiate the accessor from a PyPSA network object as follows: .. doctest:: >>> n = pypsa.Network() You can then call specific methods from the ``statistics`` property to calculate various metrics, such as installed capacity or operational expenditure. For example: .. doctest:: >>> installed_capacity = n.statistics.installed_capacity() >>> installed_capacity Component carrier Generator gas 100.0 wind 50.0 solar 30.0 Name: optimal_capacity, dtype: float64 >>> opex = n.statistics.opex() >>> opex Component carrier Generator gas 1000.0 wind 0.0 solar 0.0 Name: opex, dtype: float64 Have a look at the method's docstring for more details on the arguments. For example, to calculate the capital expenditure (capex) as a sum for all components, you can use: .. doctest:: >>> n.statistics.capex(aggregate_groups='sum') Similarly, to calculate the operational expenditure for all Link components, which connect to hydrogen (H2) buses: .. doctest:: >>> n.statistics.opex(comps=["Link"], bus_carrier="H2") Statistic groupers =================== Groupers can be used via the ``groupby`` argument in the statistic methods. All default groupers are defined in the :class:`pypsa.statistics.grouping.Groupers` class and currently included are, grouping by .. * :meth:`carrier ` * :meth:`bus_carrier ` * :meth:`name ` * :meth:`bus ` * :meth:`country ` * :meth:`location ` * :meth:`unit ` * A list of registered groupers can be accessed via :meth:`pypsa.statistics.groupers.list_groupers ` Custom groupers can be registered on module level via :meth:`pypsa.statistics.groupers.add_grouper `. The key will be used as identifier in the ``groupby`` argument. Usage ----------------- .. doctest:: >>> groupers = n.statistics.groupers >>> n.statistics.capex(groupby=groupers.carrier) carrier gas 10000.0 wind 5000.0 solar 3000.0 Name: capex, dtype: float64 >>> # or simply >>> n.statistics.capex(groupby='carrier') carrier gas 10000.0 wind 5000.0 solar 3000.0 Name: capex, dtype: float64 Groupers can also be used to create multiindexed groupers. For example, to group by bus and carrier: .. code-block:: python groupers = n.statistics.groupers n.statistics.capex(groupby=groupers['bus', 'carrier']) # or simply n.statistics.capex(groupby=['bus', 'carrier']) .. autosummary:: ~pypsa.statistics.grouping.Groupers.add_grouper ~pypsa.statistics.grouping.Groupers.list_groupers ~pypsa.statistics.grouping.Groupers.carrier ~pypsa.statistics.grouping.Groupers.bus_carrier ~pypsa.statistics.grouping.Groupers.name ~pypsa.statistics.grouping.Groupers.bus ~pypsa.statistics.grouping.Groupers.country ~pypsa.statistics.grouping.Groupers.location ~pypsa.statistics.grouping.Groupers.unit Advanced Examples and Visualization ======================================= In addition to basic usage, the statistics module offers advanced functionality for in-depth analysis and visualization of network metrics. Here are some advanced examples and visualization techniques: 1. **Comparative Analysis**: Users can compare different scenarios or network configurations by calculating metrics for each scenario and visualizing the results side by side. For example, compare the installed capacity of renewable energy sources in two different network models. 2. **Temporal Analysis**: Utilize the aggregate_time parameter to analyze temporal variations in network metrics. Plotting time series data can reveal patterns and trends over time, such as seasonal variations in energy supply or demand. 3. **Geospatial Visualization**: If the network includes geospatial data, users can create maps to visualize the distribution of network components and metrics geographically. This can be particularly useful for understanding spatial dependencies and identifying areas with high or low capacity utilization. 4. **Scenario Planning**: Explore different scenarios or what-if analyses by adjusting input parameters and observing the impact on network metrics. For example, simulate the effect of increasing renewable energy penetration on curtailment and market value. 5. **Interactive Dashboards**: Develop interactive dashboards using visualization libraries like Plotly or Bokeh to allow users to dynamically explore network metrics and drill down into specific details. Dashboards can provide a user-friendly interface for exploring complex network data. Example Code Snippet: .. doctest:: >>> import matplotlib.pyplot as plt >>> # Calculate installed capacity >>> installed_capacity = n.statistics.installed_capacity().droplevel(0) >>> # Plot installed capacity by component type >>> installed_capacity.plot(kind='bar', figsize=(10, 6)) >>> plt.title('Installed Capacity by Component Type') >>> plt.xlabel('Component Type') >>> plt.ylabel('Installed Capacity (MW)') >>> plt.xticks(rotation=45) >>> plt.grid(axis='y') >>> plt.tight_layout() >>> plt.savefig('statistics_advanced_usage.png') >>> plt.close() .. figure:: ../img/statistics_advanced_usage.png :alt: Installed Capacity by Component Type :width: 100% Installed capacity by component type. This code snippet calculates the installed capacity for each component type in the network and visualizes the results using a bar plot. Similar visualizations can be created for other metrics, providing valuable insights into the network's composition and characteristics.