Merge branch 'iscience'

Author: fneum

paper/broad-ranges.tex

@@ -1,13 +1,14 @@
 \documentclass[1p,11pt]{elsarticle}
 
-\journal{Joule}
+\journal{iScience}
 
 \widowpenalty10000
 \clubpenalty10000
 
 %% `Elsevier LaTeX' style
 \bibliographystyle{elsarticle-num}
 \biboptions{numbers,sort&compress,super}
+
 \abstracttitle{Summary}
 
 % format hacks
@@ -117,13 +118,13 @@
 
 \newpage
 
-\newgeometry{top=2cm, bottom=3cm}
+% \newgeometry{top=2cm, bottom=3cm}
 
 \par\noindent\rule{\textwidth}{0.4pt}
 
 \section*{Graphical Abstract}
 
-\includegraphics[width=\textwidth]{graphics/graphical-abstract.png}
+\includegraphics[width=\textwidth]{graphics/graphical-abstract-v2.png}
 
 \par\noindent\rule{\textwidth}{0.4pt}
 
@@ -137,70 +138,49 @@ \section*{Highlights}
 	\item degrees of freedom can help policymakers to circumvent public acceptance issues
 \end{itemize}
 
-\par\noindent\rule{\textwidth}{0.4pt}
-
-\section*{Context \& Scale}
-
-We address the perils of narrowly following optimisation results in renewable
-electricity system planning by presenting a wide range of almost equally
-cost-effective system designs that are technologically diverse and robust to
-uncertain technology cost projections. Especially along dimensions that affect
-levels of social acceptance, like the development of new overhead power lines or
-onshore wind turbines, we want to improve our understanding of what actions are
-likely viable within given cost ranges. We present system designs with a high
-chance of involving a limited cost increase, just as we identify those that are
-unlikely to be cost-efficient. Knowledge of the vast degrees of freedom we find
-in our analysis is highly policy-relevant. Policymakers can leverage these weak
-trade-offs to circumvent building infrastructure for which there is limited
-public support.
-% \par\noindent\rule{\textwidth}{0.4pt}
-
-% \section*{Words}
-% 3278 (excl. preamble, captions, and \nameref{sec:methods})
-
-\restoregeometry
+% \restoregeometry
 
 \newpage
 \usection{Introduction}{sec:introduction}
 
 \input{sections/introduction.tex}
 
+\usection{Results}{sec:results}
+
 \input{sections/results.tex}
 
+\usection{Limitations of the Study}{sec:limitations}
+
+\input{sections/limitations.tex}
+
 \usection{Discussion and Conclusion}{sec:conclusion}
 
 \input{sections/conclusion.tex}
 
-\usection{Experimental Procedures}{sec:methods}
+\usection{Star$\star$ Methods}{sec:methods}
 
 \input{sections/methods.tex}
 
-\section*{Acknowledgement}
+\section*{Acknowledgements}
 
 F.N. and T.B. gratefully acknowledge funding from the Helmholtz Association
 under grant no. VH-NG-1352. The responsibility for the contents lies with the
 authors.
 
+
 \section*{License}
 
 \href{http://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0
 International License (CC-BY-4.0)}
 
-\section*{CRediT Author Statement}
+\section*{Author Contributions}
 
 \textbf{Fabian Neumann:} Conceptualization, Methodology, Investigation, Software, Validation, Formal analysis, Visualization, Writing -- Original Draft, Writing -- Review \& Editing
 \textbf{Tom Brown:} Conceptualization, Writing -- Review \& Editing, Supervision, Project administration, Funding acquisition
 
-\section*{Data and Code Availability}
+\section*{Declaration of Interests}
 
-The code to reproduce the experiments as well as results dat including selected
-networks and all graphics is available at
-\href{https://github.com/fneum/broad-ranges}{github.com/fneum/broad-ranges} and
-archived at
-\href{https://doi.org/10.5281/zenodo.6641551}{doi:10.5281/zenodo.6641551}. We
-also refer to the documentation of PyPSA
-(\href{https://pypsa.readthedocs.io}{pypsa.readthedocs.io}) and PyPSA-Eur
-(\href{https://pypsa-eur.readthedocs.io}{pypsa-eur.readthedocs.io}).
+The authors declare no competing interests.
 
 % tidy with https://flamingtempura.github.io/bibtex-tidy/
 \addcontentsline{toc}{section}{References}

paper/graphics/broad-ranges-graphical-abstract.drawio

@@ -1003,4 +1003,15 @@ @article{trutnevyte_retrospective
 issn = {1364-0321},
 doi = {10/gh276b},
 author = {Evelina Trutnevyte and Will McDowall and Julia Tomei and Ilkka Keppo},
+}
+@article{pickeringDiversityOptions2022,
+  title = {Diversity of Options to Eliminate Fossil Fuels and Reach Carbon Neutrality across the Entire {{European}} Energy System},
+  author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},
+  year = {2022},
+  month = jun,
+  journal = {Joule},
+  volume = {6},
+  number = {6},
+  pages = {1253--1276},
+  doi = {10.1016/j.joule.2022.05.009},
 }

paper/library.bib

@@ -1,20 +1,20 @@
 To achieve ambitious CO$_2$ emission reduction targets quickly, the planning of
-energy systems must accommodate societal preferences, e.g.~regarding
+energy systems must accommodate societal preferences, such as those regarding
 transmission reinforcements or onshore wind parks, and must also acknowledge
-uncertainties of technology cost projections. To date, however, many models lean
-towards only minimising system cost and only using a single set of cost
-projections. Here, we address both criticisms in unison. While taking account of
-cost uncertainties, we apply multi-objective optimisation techniques to explore
-trade-offs in a fully renewable European electricity system between rising
-system cost and the deployment of individual technologies for generating,
-storing and transporting electricity. We identify boundary conditions that must
-be met for cost-efficiency that are robust to how cost developments will unfold;
-for instance, we find that some grid reinforcement and long-term storage
-alongside large wind capacities appear essential. We reveal that near the
-cost-optimum a broad spectrum of technologically diverse options exist, which
-allows policymakers to make trade-offs regarding unpopular infrastructure.
-
-% The analysis requires to manage many computationally demanding scenario runs
-% efficiently, for which we leverage multi-fidelity surrogate modelling
-% techniques using sparse polynomial chaos expansions and low-discrepancy
-% sampling.
+uncertainties of technology cost projections among many other uncertainties. To
+date, however, many models lean towards only minimising system cost and only
+using a single set of cost projections. Here, we address both criticisms in
+unison. While taking account of cost uncertainties, we apply multi-objective
+optimisation techniques in a fully renewable European electricity system to
+explore trade-offs between rising system cost and the deployment of individual
+technologies for generating, storing and transporting electricity. We identify
+ranges of capacity expansion plans that are cost-efficient and incorporate
+uncertainty about future technology cost developments; for instance, we find
+that some grid reinforcement and long-term storage alongside large wind
+capacities are important to keep costs within 8\% of the least-cost solutions.
+We reveal that near the cost-optimum a broad spectrum of technologically diverse
+options exist, which allows policymakers to make trade-offs regarding unpopular
+infrastructure. For the analysis we carried out more than 50,000 optimisation runs. To
+manage many computationally demanding scenario runs efficiently, we leverage
+multi-fidelity surrogate modelling techniques using sparse polynomial chaos
+expansions and low-discrepancy sampling.

paper/sections/abstract.tex

@@ -1,48 +1,60 @@
 % summary
 In this work, we systematically explore a space of alternatives beyond
-least-cost solutions for society and politics to work with. We show how narrowly
-following cost-optimal results underplays an immense degree of freedom in
-designing future renewable power systems. To make our finding that there is no
-unique path to cost-efficiency more robust, we account for the inherent uncertainties
-regarding technology cost projections, and draw conclusions about
-the range of options, boundary conditions and cost sensitivities:
+least-cost solutions for society and politics to work with subject to uncertain
+technology cost projections. We show how narrowly following cost-optimal results
+underplays an immense degree of freedom in designing future renewable power
+systems. To make our finding that there is no unique path to cost-efficiency
+more robust, we account for technology cost uncertainties as one example of the
+many unknowns faced in the energy transition, and draw the following conclusions:
 
 \paragraph{Wide Range of Trade-Offs}
-We find that there is a substantial range of options
-within 8\% of the least-cost renewable electricity system
-regardless of how cost developments will unfold.
-This holds across all technologies individually
-and even when considering dependencies between
-wind and solar, offshore and onshore wind, as well as hydrogen and battery storage.
-
-\paragraph{Must-Avoid Boundary Conditions}
-We also carve out a few boundary conditions which
-must be met to keep costs low and are not affected
-by the prevailing cost uncertainty.
-For a fully renewable electricity system,
-either offshore or onshore wind capacities
-of the order of 600 GW
-along with some long-term storage technology and
-transmission network reinforcement appears essential.
+We find that there is a substantial range of options within 8\% of the
+least-cost fully renewable electricity system regardless of how cost
+developments will unfold. This holds across all technologies individually and
+even when considering dependencies between wind and solar, offshore and onshore
+wind, as well as hydrogen and battery storage as examples of flexibility options.
+
+\paragraph{Solutions to Avoid}
+We also carve out parts of the solution space which are unlikely to keep costs
+within given cost ranges given the considered range of technology cost futures.
+For a fully renewable electricity system, either offshore or onshore wind
+capacities of the order of 600 GW along with some long-term storage technology
+and transmission network reinforcement appears essential in the scenarios we
+analyse. Less wind capacity leads to high cost solutions in our model.
+
+
 
 % \paragraph{Robustness to Cost and Near-Optimal Perturbations}
 
-\paragraph{Technology Cost Sensitivities}
+\paragraph{Key Technology Cost Sensitivities}
 We identify onshore wind cost as the apparent main determinant of system cost,
 though it can often be substituted with offshore wind for a small additional
 cost. This aligns with the finding that the near-optimal feasible space is flat.
 Moreover, the deployment of batteries is the most sensitive to its cost, whereas
 required levels of transmission expansion are least affected since transmission
-cost were not considered to be uncertain. \\
+cost were not considered to be uncertain.
+
+\paragraph{Benefits of Combining MGA and Global Sensitivity Analysis}
+The combination of modelling-to-generate-alternatives (MGA) to explore the
+near-optimal solution space and global sensitivity analysis to account for an
+uncertain input parameter space unifies two approaches to uncertainty
+quantification. The presented methodology is helpful to show that near-optimal
+insights are robust to some uncertainty (in our case technology cost). Likewise,
+it can show whether some parts of the near-optimal solution space are more or
+less affected by uncertainty.
 
 % concluding remark
-The robust investment flexibility in shaping a fully renewable power system
-opens the floor to discussions about social trade-offs and navigating around
-issues, such as public opposition towards wind turbines or transmission lines.
-Rather than modellers making normative choices about how the energy system
-should be optimised, we offer methods that present a wide spectrum of options
-and trade-offs that are feasible and within a reasonable cost range, to help
-society decide how to shape the future of the energy system.
+The robust finding of our study is that there is consistent investment flexibility in
+shaping fully renewable power systems, even without availing of the myriad
+flexibility options offered through sector-coupling. This opens the floor to
+discussions about social trade-offs and navigating around issues, such as public
+opposition towards wind turbines or transmission lines. Rather than modellers
+making normative choices about how the energy system should be optimised, by
+applying multi-fidelity surrogate modelling techniques and the
+modelling-to-generate-alternatives methodology we offer a methodology to present
+a wide spectrum of options and trade-offs that are feasible and within a
+reasonable cost range, to help society decide how to shape the future of the
+energy system.
 
 % ------------------- not used ----------------------------