activity
20162018
most citedPyPSA: Python for Power System Analysis

544 citations · 614 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2018★ 4 cited

Principal Flow Patterns across renewable electricity networks

Fabian Hofmann, Mirko Schäfer, Tom Brown +3

Using Principal Component Analysis (PCA), the nodal injection and line flow patterns in a network model of a future highly renewable European electricity system are investigated. I…

physics.soc-ph2018★ 18 cited

Flow-based nodal cost allocation in a heterogeneous highly renewable European electricity network

Bo Tranberg, Leon J. Schwenk-Nebbe, Mirko Schäfer +2

For a cost efficient design of a future renewable European electricity system, the placement of renewable generation capacity will seek to exploit locations with good resource qual…

physics.soc-ph2017★ 7 cited

PyPSA-ZA: Investment and operation co-optimization of integrating wind and solar in South Africa at high spatial and temporal detail

Jonas Hörsch, Joanne Calitz

South Africa is in the fortunate position of having access to high-quality wind and solar resources promising energy production costs well below the ones projected for future insta…

physics.soc-ph2017★ 544 cited

PyPSA: Python for Power System Analysis

Tom Brown, Jonas Hörsch, David Schlachtberger

Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods. PyPSA includes models for c…

physics.soc-ph2016★ 41 cited

Flow tracing as a tool set for the analysis of networked large-scale renewable electricity systems

Jonas Hörsch, Mirko Schäfer, Sarah Becker +2

The method of flow tracing follows the power flow from net-generating sources through the network to the net-consuming sinks, which allows to assign the usage of the underlying tra…