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A modeler's guide to handle complexity in energy systems optimization

A modeler's guide to handle complexity in energy systems optimization
A modeler's guide to handle complexity in energy systems optimization

Determining environmentally- and economically-optimal energy systems designs and operations is complex. In particular, the integration of weather-dependent renewable energy technologies into energy system optimization models presents new challenges to computational tractability that cannot only be solved by advancements in computational resources. In consequence, energy system modelers must tackle the complexity of their models by applying various methods to manipulate the underlying data and model structure, with the ultimate goal of finding optimal solutions. As which complexity reduction method is suitable for which research question is often unclear, herein we review different approaches for handling complexity. We first analyze the determinants of complexity and note that many drivers of complexity could be avoided a priori with a tailored model design. Second, we conduct a review of systematic complexity reduction methods for energy system optimization models, which can range from simple linearization performed by modelers to sophisticated multi-level approaches combining aggregation and decomposition methods. Based on this overview, we develop a guide for energy system modelers who encounter computational limitations.

Aggregation, Capacity expansion, Decomposition, Energy system optimization, LP, MILP
Kotzur, Leander
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Nolting, Lars
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Hoffmann, Maximilian
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Groß, Theresa
862f262a-b2b0-4923-93f7-237b2977bb05
Smolenko, Andreas
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Priesmann, Jan
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Büsing, Henrik
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Beer, Robin
3aa86c19-e446-495a-af8e-96907e62ca31
Kullmann, Felix
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Singh, Bismark
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Praktiknjo, Aaron
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Stolten, Detlef
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Robinius, Martin
041d8cee-3f68-433e-bde7-fe7d5f6e4c06
Kotzur, Leander
85ff7749-b85f-423f-815f-41ca2680c449
Nolting, Lars
0c3e71f4-1a79-4f67-b40a-8f490726037f
Hoffmann, Maximilian
8ccc2f31-b1b7-4f9f-bc10-c84391ef6777
Groß, Theresa
862f262a-b2b0-4923-93f7-237b2977bb05
Smolenko, Andreas
7a18275b-5f8c-4716-ba18-a6855d43cb0b
Priesmann, Jan
9b913d18-cf76-437d-83cb-0a5196bee68b
Büsing, Henrik
a9e5f419-1525-4955-bbbc-24242869a16e
Beer, Robin
3aa86c19-e446-495a-af8e-96907e62ca31
Kullmann, Felix
8e47172a-43de-43bb-ae0b-0ea63830a3e8
Singh, Bismark
9d3fc6cb-f55e-4562-9d5f-42f9a3ddd9a1
Praktiknjo, Aaron
6f4d9bbb-d95b-4c85-8beb-ae6dd940c652
Stolten, Detlef
80f18944-f5fe-406d-afb9-982c2044db23
Robinius, Martin
041d8cee-3f68-433e-bde7-fe7d5f6e4c06

Kotzur, Leander, Nolting, Lars, Hoffmann, Maximilian, Groß, Theresa, Smolenko, Andreas, Priesmann, Jan, Büsing, Henrik, Beer, Robin, Kullmann, Felix, Singh, Bismark, Praktiknjo, Aaron, Stolten, Detlef and Robinius, Martin (2021) A modeler's guide to handle complexity in energy systems optimization. Advances in Applied Energy, 4, [100063]. (doi:10.1016/j.adapen.2021.100063).

Record type: Review

Abstract

Determining environmentally- and economically-optimal energy systems designs and operations is complex. In particular, the integration of weather-dependent renewable energy technologies into energy system optimization models presents new challenges to computational tractability that cannot only be solved by advancements in computational resources. In consequence, energy system modelers must tackle the complexity of their models by applying various methods to manipulate the underlying data and model structure, with the ultimate goal of finding optimal solutions. As which complexity reduction method is suitable for which research question is often unclear, herein we review different approaches for handling complexity. We first analyze the determinants of complexity and note that many drivers of complexity could be avoided a priori with a tailored model design. Second, we conduct a review of systematic complexity reduction methods for energy system optimization models, which can range from simple linearization performed by modelers to sophisticated multi-level approaches combining aggregation and decomposition methods. Based on this overview, we develop a guide for energy system modelers who encounter computational limitations.

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More information

Accepted/In Press date: 13 August 2021
e-pub ahead of print date: 18 August 2021
Published date: 19 November 2021
Additional Information: Funding Information: The authors acknowledge the financial support of the Federal Ministry for Economic Affairs and Energy of Germany for the project METIS (project number 03ET4064 ). Publisher Copyright: © 2021 The Author(s)
Keywords: Aggregation, Capacity expansion, Decomposition, Energy system optimization, LP, MILP

Identifiers

Local EPrints ID: 472282
URI: http://eprints.soton.ac.uk/id/eprint/472282
PURE UUID: 8af4dfdd-c17b-4ece-80ff-bdfbcbc794f1
ORCID for Bismark Singh: ORCID iD orcid.org/0000-0002-6943-657X

Catalogue record

Date deposited: 30 Nov 2022 17:45
Last modified: 18 Mar 2024 04:08

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Contributors

Author: Leander Kotzur
Author: Lars Nolting
Author: Maximilian Hoffmann
Author: Theresa Groß
Author: Andreas Smolenko
Author: Jan Priesmann
Author: Henrik Büsing
Author: Robin Beer
Author: Felix Kullmann
Author: Bismark Singh ORCID iD
Author: Aaron Praktiknjo
Author: Detlef Stolten
Author: Martin Robinius

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