Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution
- Local and regional energy systems are becoming increasingly entangled. Therefore, models for optimizing these energy systems are becoming more and more complex and the required computing resources (run-time and random access memory usage) are increasing rapidly. The computational requirements can basically be reduced solver-based (mathematical optimization of the solving process) or model-based (simplification of the real-world problem in the model). This paper deals with identifying how the required computational requirements for solving optimization models of multi-energy systems with high spatial resolution change with increasing model complexity and which model-based approaches enable to reduce the requirements with the lowest possible model deviations. A total of 12 temporal model reductions (reduction of the number of modeled time steps), nine techno-spatial model reductions (reduction of possible solutions), and five combined reduction schemes were theoretically analyzed and practically applied to a test case. The improvement in reducing the usage of computational resources and the impact on the quality of the results were quantified by comparing the results with a non-simplified reference case. The results show, that the run-time to solve a model increases quadratically and memory usage increases linearly with increasing model complexity. The application of various model adaption methods have enabled a reduction of the run-time by over 99% and the memory usage by up to 88%. At the same time, however, some of the methods led to significant deviations of the model results. Other methods require a profound prior knowledge and understanding of the investigated energy systems to be applied. In order to reduce the run-time and memory requirements for investment optimization, while maintaining good quality results, we recommend the application of (1) a pre-model that is used to (1a) perform technological pre-selection and (1b) define reasonable technological boundaries, (2) spatial sub-modeling along network nodes, and 3) temporal simplification by only modeling every nth day (temporal slicing), where at least 20% of the original time steps are modeled. Further simplifications such as spatial clustering or larger temporal simplification can further reduce the computational effort, but also result in significant model deviations.
Author: | Christian KlemmORCiD, Frauke WieseORCiD, Peter VennemannORCiD |
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DOI: | https://doi.org/10.1016/j.apenergy.2022.120574 |
ISSN: | 0306-2619 |
Parent Title (English): | Applied Energy |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2023/01/30 |
Year of first Publication: | 2023 |
Provider of the Publication Server: | FH Münster - University of Applied Sciences |
Release Date: | 2023/01/30 |
Tag: | energy system model; memory usage; model-based; multi-energy system; run-time |
Volume: | 334 |
First Page: | 120574 |
Faculties: | Energie · Gebäude · Umwelt (EGU) |
open_access (DINI-Set): | open_access |
Publication list: | Vennemann, Peter |
Klemm, Christian | |
Licence (German): | Creative Commons - Namensnennung (CC BY 4.0) |