Commit bac18e93 authored by Markus Millinger's avatar Markus Millinger
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Added abstract

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\begin{abstract}
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Ca. 100 words
\textbf{Ca. 100 words}
%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 steadystate 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).
BENOPT, an optimal material and energy allocation model is presented, which is used to assess cost-optimal and/or greenhouse gas abatement optimal allocation of renewable energy carriers across power, heat and transport sectors. A high level of detail on the processes from source to end service enable detailed life-cycle and cost assessments, as well as a systems perspective. The model has an up to hourly resolution, which can be aggregated depending on the task. Pareto analyses can be performed, as well as thorough sensitivity analyses. The model has been developed in Matlab and GAMS and is designed to analyse sector coupling and biomass usage, as a more detailed complement to IAMs and power system models.
\end{abstract}
\begin{keyword}
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\textbf{Indicate in what way the software has contributed (or how it will contribute in the future) to the process of scientific discovery; if available, this is to be supported by citing a research paper using the software.}
The model is suited for policy support on optimal deployment of biomass and hydrogen based energy carriers across transport, heat and energy sectors. With the help of the model, analyses on renewable fuel policy analysis for the Federal Ministry of Food and Agriculture (BMEL) \citep{Meisel.2020,Meisel.2019} have been performed, as well as an analysis on biomass use across all energy sectors within long-term scenarios for the Federal Ministry for Economic Affairs and Energy (BMWi) \cite{Thran.2019b}.
The model is suited for policy support on optimal deployment of biomass and hydrogen based energy carriers across transport, heat and energy sectors. With the help of the model, analyses on renewable fuel policy analysis for the Federal Ministry of Food and Agriculture (BMEL) \cite{Meisel.2020,Meisel.2019} have been performed, as well as an analysis on biomass use across all energy sectors within long-term scenarios for the Federal Ministry for Economic Affairs and Energy (BMWi) \cite{Thran.2019b}.
\textbf{Provide a description of the experimental setting (how does the user use the software?).}
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\textit{Optimization module}. Input data for the optimization is formatted to suit the GAMS data format and transferred to the optimization module in GAMS as described in an earlier version in \cite{Millinger.2019}. The data is transferred back to Matlab for data handling and plotting.
\textit{Sensitivity analysis module}. Monte Carlo sensitivity analysis \citet{Millinger.2018d,Millinger.2018b} can be performed with parallel computing, enabling faster runs. Any parameter can be added for variation and plotting related to individual parameters can be performed (Fig. \ref{fig:monteCarloScatter}).
\textit{Sensitivity analysis module}. Monte Carlo sensitivity analysis \cite{Millinger.2018d,Millinger.2018b} can be performed with parallel computing, enabling faster runs. Any parameter can be added for variation and plotting related to individual parameters can be performed (Fig. \ref{fig:monteCarloScatter}).
\textit{Plotting}. Extensive plotting can be performed, with examples shown in Figures 1-3.
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\textbf{Indicate in what way, and to what extent, the pursuit of existing research questions is improved (if so).}
The model was developed to integrate the most important aspects of the complex biomass usage and PtX within a systems perspective. A systems perspective does not merely consider each usage option or pathway independently, but their development in a system and is in the theory of industrial ecology manifested through the following areas \citep{Lifset.2002,Erkman.1997}: (i) a life-cycle perspective, (ii) material and energy flow analyses, (iii) systems modelling and ideally (iv) interdisciplinary analyses. A further important aspect, (v) technological change, should also be mentioned in this context \citep{Lifset.2002,Grubler.1998}. Aspects i-iii and v are included directly in the model \cite{Millinger.2018}, which enables a more holistic analysis than for instance LCAs of singular pathways. The results can be embedded within broader, interdisciplinary analyses.
The model was developed to integrate the most important aspects of the complex biomass usage and PtX within a systems perspective. A systems perspective does not merely consider each usage option or pathway independently, but their development in a system and is in the theory of industrial ecology manifested through the following areas \cite{Lifset.2002,Erkman.1997}: (i) a life-cycle perspective, (ii) material and energy flow analyses, (iii) systems modelling and ideally (iv) interdisciplinary analyses. A further important aspect, (v) technological change, should also be mentioned in this context \cite{Lifset.2002,Grubler.1998}. Aspects i-iii and v are included directly in the model \cite{Millinger.2018}, which enables a more holistic analysis than for instance LCAs of singular pathways. The results can be embedded within broader, interdisciplinary analyses.
Compared to IAMs, an increased level of detail regarding VRE, sector coupling and across the more diverse supply chain options from source to end service is given. Compared to power system models, biomass and other sectors are depicted in more detail. The model is also well suited to investigate the sensitivity of developments, on which a large number of parameters have an influence, especially in the complex area of biomass use. Thus, a model gap is bridged with BENOPT.
\textbf{Indicate how widespread the use of the software is within and outside the intended user group.}
The model has been used for numerous analyses. Assessments of biofuels regarding costs \cite{Millinger.2017b,Millinger.2018d} and greenhouse gas emissions \cite{Millinger.2018b}, as well as optimal biofuel deployment \cite{Millinger.2019} and renewable fuel policy analysis for the BMEL \cite{Meisel.2020,Meisel.2019} have been published, as well as analyses on biochemicals \cite{Musonda.2020}. An analysis on biomass use across transport, heat and power sectors within long-term scenarios for the BMWi has been performed \cite{Thran.2019b}. A coupling with a general equilibrium agricultural model and a land use model \cite{Thran.2016,Thran.2017} have been performed, as well as an analysis of electrofuels/power-to-X \cite{Millinger.2020}.
The model has been used for numerous analyses. Assessments of biofuels regarding costs \cite{Millinger.2017b,Millinger.2018d} and greenhouse gas emissions \cite{Millinger.2018b}, as well as optimal biofuel deployment \cite{Millinger.2019} and renewable fuel policy analysis for the BMEL \cite{Meisel.2020,Meisel.2019} have been published, as well as analyses on biochemicals \cite{Musonda.2020}. An analysis on biomass use across transport, heat and power sectors within long-term scenarios for the BMWi has been performed \cite{Thran.2019b}. Coupling with a general equilibrium agricultural model and a land use model \cite{Thran.2016,Thran.2017} have been performed, as well as an analysis of electrofuels/power-to-X \cite{Millinger.2020}.
\textit{Future Work.} Stand-alone versions focusing on chemical products \cite{Musonda.2020} as well as on the heat sector \cite{Jordan.2020, Jordan.2019} have been developed, with details being planned for integration into the main model. Aspects concerning sustainable agriculture, nutrition and industry are underway. Sector coupling and (renewable) power based options as well as carbon capture are increasingly included in the modelling, which enables a holistic analysis of renewable futures in the sectors considered. Increasing the temporal resolution in the heat and transport sectors to better analyse sector coupling is planned.
\textit{Future Work.} Stand-alone versions focusing on chemical products \cite{Musonda.2020} as well as on the heat sector \cite{Jordan.2020, Jordan.2019} have been developed, with details being planned for integration into the main model. Aspects concerning sustainable agriculture, nutrition and industry are underway. Sector coupling and (renewable) power based options as well as carbon capture are increasingly included in the modelling, which enables a holistic analysis of renewable futures in the sectors considered. Increasing the temporal resolution in the heat and transport sectors to better analyse sector coupling is planned. An increased geographical scope can be undertaken, depending on available data.
%Indicate in what way the software is used in commercial settings and/or how it led to the creation of spin-off companies (if so).
\section{Conclusions}
\label{sec:conclusion}
\textbf{Set out the conclusion of this original software publication.}
%\textbf{Set out the conclusion of this original software publication.}
%A multi-objective Bayesian optimization software was outlined. The software is able to calculate the Pareto front of optimization problems with fewer objective functions evaluations than most currently available optimization algorithms. The implementation is made in Python, so that it is easy of use and can be interfaced with many other programming languages. A series of benchmark functions were tested and the software was able to find reliable Pareto front approximations with only a few evaluations of the objectives.
%In this paper a new toolbox has been presented for simulating and optimising power systems. Python for Power System Analysis (PyPSA) provides components to model variable renewable generation, conventional power plants, storage units, coupling to other energy sectors and multiply connected AC and DC networks over multiple periods for the optimisation of both operation and investment. Tools are also provided for steady-state analysis with the full load flow equations. PyPSA’s performance for large datasets, comparisons with other software packages and several example applications are demonstrated.
%As free software, the code of PyPSA can easily be inspected and extended by users, thereby contributing to further research and also transparency in power system modelling. Given that public acceptance of new infrastructure is often low, it is hoped that transparent modelling can contribute to public understanding of the various options we face when designing a sustainable energy system.
\section{Conflict of Interest}
%Please select the appropriate text:
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