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In energy modelling, open data and open source code can help enhance traceability and reproducibility of model exercises which contribute to facilitate controversial debates and improve policy advice. While the availability of open power plant databases increased in recent years, they often differ considerably from each other and their data quality has not been systematically compared to proprietary sources yet. Here, we introduce the python-based ‘powerplantmatching’ (PPM), an open source toolset for cleaning, standardizing and combining multiple power plant databases. We apply it once only with open databases and once with an additional proprietary database in order to discuss and elaborate the issue of data quality, by analysing capacities, countries, fuel types, geographic coordinates and commissioning years for conventional power plants. We find that a derived dataset purely based on open data is not yet on a par with one in which a proprietary database has been added to the matching, even though the statistical values for capacity matched to a large degree with both datasets. When commissioning years are needed for modelling purposes in the final dataset, the proprietary database helps crucially to increase the quality of the derived dataset.
Defossiliation of the energy system is crucial in the face of the impending risks of climate change. Electricity generation by burning fossil fuels is being displaced by renewable energy sources like hydro, wind and solar, driven by support schemes and falling costs from technological advances as well as manufacturing scale effects. The unavoidable shift from flexibly dispatchable generation to weather-dependent spatio-temporally varying generators transforms the generation and distribution of electricity into highly interdependent complex systems in multiple dimensions and disciplines:
In time, different scales, stretching from intra-day, diurnal, synoptic to seasonal oscillations of the weather interact with years and decades of planning and construction of capacity. In space, long-range correlations and local variations of weather systems as well as local bottlenecks in transmission networks affect solutions. The investment decisions about technological mix and spatial distribution of capacity follow economic principles, within restrictions which adapt in social feedback loops to public opinion and lobbyist influences.
In this work, a family of self-consistent models is developed which map physical steady-state operation, capacity investments and exogeneous restrictions of a European electricity system, in higher simultaneous spatial and temporal detail as well as scope than has previously been computationally tractable. Increasing the spatial detail of the renewable resources and co-optimizing the expansion of only a few transmission lines, reveals solutions to serve the European electricity demand at about today’s electricity cost with only 5% of its carbon-dioxide emissions; and importantly their electricity mix differs from the findings at low spatial resolution.
As important intermediate steps,
• new algorithms for the convex optimization of electricity system infrastructure are derived from graph-theoretic decompositions of network flows. Only these enable the investigation of model detail beyond previous computational limitations.
• a comprehensive European electricity network model down to individual substations at the transmission voltage levels is built by combining and completing data from freely available sources.
• a network reduction technique is developed to approximate the detailed model at a sequence of spatial resolutions to investigate the role of spatial scale, and identify a level of spatial resolution which captures all relevant detail, but is still computationally tractable.
• a method to trace the flow of power through the network, which is related to a vector diffusion process on a directed flow graph embedded in a network, is used to analyse the resulting technology mix and its interactions with the power network
The open-source nature of the model and restriction to freely available data encourages an accessible and transparent discussion about the future European electricity system, primarily based on renewable wind and solar resources.
In the last decades, energy modelling has supported energy planning by offering insights into the dynamics between energy access, resource use, and sustainable development. Especially in recent years, there has been an attempt to strengthen the science-policy interface and increase the involvement of society in energy planning processes. This has, both in the EU and worldwide, led to the development of open-source and transparent energy modelling practices.This paper describes the role of an open-source energy modelling tool in the energy planning process and highlights its importance for society. Specifically, it describes the existence and characteristics of the relationship between developing an open-source, freely available tool and its application, dissemination and use for policy making. Using the example of the Open Source energy Modelling System (OSeMOSYS), this work focuses on practices that were established within the community and that made the framework's development and application both relevant and scientifically grounded. Keywords: Energy system modelling tool, Open-source software, Model-based public policy, Software development practice, Outreach practice
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 conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. It is designed to be easily extensible and to scale well with large networks and long time series. In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations. PyPSA is positioned in the existing free software landscape as a bridge between traditional power flow analysis tools for steady-state analysis and full multi-period energy system models. The functionality is demonstrated on two open datasets of the transmission system in Germany (based on SciGRID) and Europe (based on GridKit).