Python for Power System Analysis (PyPSA) Version 0.10.0

Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern power systems that include features such as conventional generators with unit commitment, variable wind and solar generation, storage units, sector coupling and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series. Find out more at: https://pypsa.org/ and http://github.com/FRESNA/PyPSA This is release 0.10.0 of PyPSA. Hyperlinked release notes can be found here: https://pypsa.org/doc/release_notes.html#pypsa-0-10-0-7th-august-2017 This release contains some minor new features and a few minor but important API changes. * There is a new component Global Constraints for implementing constraints that effect many components at once (see also the LOPF subsection Global constraints). Currently only constraints related to primary energy (i.e. before conversion with losses by generators) are supported, the canonical example being CO2 emissions for an optimisation period. Other primary-energy-related gas emissions also fall into this framework. Other types of global constraints will be added in future, e.g. “final energy” (for limits on the share of renewable or nuclear electricity after conversion), “generation capacity” (for limits on total capacity expansion of given carriers) and “transmission capacity” (for limits on the total expansion of lines and links). This replaces the ad hoc network.co2_limit attribute. If you were using this, instead of network.co2_limit = my_cap do network.add("GlobalConstraint", "co2_limit", type="primary_energy", carrier_attribute="co2_emissions", sense="<=", constant="my_cap"). The shadow prices of the global constraints are automatically saved in network.global_constraints.mu. * The LOPF output network.buses_t.marginal_price is now defined differently if network.snapshot_weightings are not 1. Previously if the generator at the top of the merit order had marginal_cost c and the snapshot weighting was w, the marginal_price was cw. Now it is c, which is more standard. See also Nodal power balances. * network.pf() now returns a dictionary of pandas DataFrames, each indexed by snapshots and sub-networks. converged is a table of booleans indicating whether the power flow has converged; error gives the deviation of the non-linear solution; n_iter the number of iterations required to achieve the tolerance. * network.consistency_check() now includes checking for potentially infeasible values in generator.p_{min,max}_pu. * The PyPSA version number is now saved in network.pypsa_version. In future versions of PyPSA this information will be used to upgrade data to the latest version of PyPSA. * network.sclopf() has an extra_functionality argument that behaves like that for network.lopf(). * Component attributes which are strings are now better handled on import and in the consistency checking. * There is a new generation investment screening curve example showing the long-term equilibrium of generation investment for a given load profile and comparing it to a screening curve analysis. * There is a new logging example that demonstrates how to control the level of logging that PyPSA reports back, e.g. error/warning/info/debug messages. * Sundry other bug fixes and improvements. * All examples have been updated appropriately. Thanks to Nis Martensen for contributing the return values of network.pf() and Konstantinos Syranidis for contributing the improved network.consistency_check()