Resolution difference-based coupling

20 November 2023

Introduction

Energy infrastructure is expected to be an essential component of future energy systems within the Netherlands. Models form the basis of understanding complex interactions of energy systems. Many models that analyze energy infrastructure are available. However, their capabilities and scopes are scattered. Policymakers are interested in a comprehensive oversight of information to provide informed decision-making on integrated energy systems. Here, a multi-modeling concept with the scope of scaling comes into the picture. In addition, models used for energy-related decision support are either available at a higher abstract level or do not contain the entire energy system. Separate modeling of the system is insufficient when studying complex socio-technical systems, as the behavior of the whole system is more than the sum of the behavior of individual parts due to possible interactions between system components (`Vangheluwe et al., 2002`_). One of the main challenges of creating coupling infrastructure is addressing issues related to bridging different resolution levels between models (`Nikolic et al., 2019`_). ‘Resolution’ in this context corresponds to the level of detail on which the models operate, related to space, time, or modeled object (`Rabelo et al., 2016`_). The aim was to bridge the gaps between models regarding the abovementioned resolution gaps by applying the scaling method. Accordingly, the research question is formulated as follows:

How can issues arising when coupling multiple energy models with different resolutions be resolved effectively?

This thesis is part of a larger project aiming to create a multi-model infrastructure to couple existing models to understand better complex energy systems at different geographical scopes within the Netherlands (`Nikolic, 2023`_). Bram focused on detecting and alleviating coupling-related issues related to combining models with different resolutions.

State-of-the-art

Brandmeyer and Karimi (`Brandmeyer & Karimi, 2000`_) suggested five levels of coupling: one-way data transfer, loose coupling, shared coupling, joined coupling, and tool coupling. Aggregation and Disaggregation (A/D) of objects is complicated because this requires changing functions and writing an adjacent model at a different resolution (`Salome, 2021`_). The proper choice of resolution is decided by the purpose of a model (`Jie Chen & Xiaoyu Li, 2021`_) and data availability (`Degbelo & Kuhn, 2018`_). It is sometimes advantageous to use multiple low-resolution models instead of a singular high-resolution model, such as for military simulation (`Xuefei et al., 2017`_). Within a multi-model structure, all models can contribute meaningfully to each other to create a more integral view of a complex system (`Nikolic, 2023`_; `Seck & Honig, 2012`_).

Method, results, and findings

The method involved creating a model audit for each model. During this process, the auditing method was standardized to facilitate its reuse. The audit enabled better model understanding and identified parameters or variables to be considered when determining A/D functions and consistency checks.

Criteria for shortlisting models

  • The models must vary in resolution sufficiently to provide a challenging gap to bridge

  • The models must be energy modeling-related

  • There must be a feasible case for why one would want to couple these models

  • The models must be readily available to research

Models shortlisted:

Two coupling cases were finalized:

  • ETM-HWP coupling: ETM provided electricity price, power production, and wind speed. HWP model derived the profitability of a wind farm. This is a one-way connection from ETM to HWP. The coupling is static. The resolution difference is related to wind farm modeling details.

  • ETM-EVM coupling: ETM provided electricity price and population information (along with growth) to EVM. EVM calculated electric vehicle (EV) electricity demand and storage supply curves. ETM has a national approach, while EVM distributes agents across space at a municipality level. This coupling effort tried to address spatial resolution difference-based challenges in multi-modeling.

The method involved three steps:

  1. Model auditing: a set of questions was prepared as a guide to understand or investigate critical elements related to each model resolution to have a complete model audit.

  2. Coupling auditing: Each model coupling underwent an audit to identify variable-related issues and suggested mitigation strategies.

  3. Coupling implementation: with the knowledge of coupling issues and steps to mitigate them, an actual coupling was implemented.

Results showed that increasing the price of electricity and power supply increases the cash flow from wind farms (ETM-HWP coupling). Changing the electricity price does not have any impact on the constraints of the HWP model, whereas changing the power output has minimal impact. With an increase in the hourly electricity price input to the EVM (ETM-EVM coupling) and an increase in the national population (translated into an increase in municipality population), the mean national power demand and mean national vehicle-to-grid capacity increased. Changing only the electricity price did not impact the aspects mentioned above.

The results provided a clear overview of the effectiveness and limits of the described method to alleviate resolution-based coupling challenges.

Conclusions and future work

This study was an exploratory modeling effort conducted to reflect the feasible consistency limits of various techniques to the stakeholders to which the models belong. Some of the essential conclusions from the couple auditing activity are:

  • the main research question can be answered by using a coupling process based on audits, comprised of questions aimed at detecting issues and checking the effectiveness of the means to solve the problems.

  • System expertise is highly advantageous for identifying and tackling problems encountered during the auditing and coupling process.

  • Coupling models with different resolutions requiring aggregation and disaggregation efforts need an intermediate data model to translate and (possibly) store data to facilitate coupling.

  • The coupling of two models will likely require constructing an A/D effort specifically tailored to the needs of the models in question.

  • It is challenging to determine what constitutes a good consistency or sufficient overlap. There are no benchmarks to compare with.

In the future, we have options to carry on the research by Bram in the following manner:

  • Use the macro case, which performs scaling activity, to analyze the demand distribution of different sectors at a regional level.

  • Identify the differences in the impact of energy infrastructure between the national and regional levels regarding interregional energy flows, investment, and technical characteristics.

    A link to Bram Boereboom’s master thesis work follows:

https://repository.tudelft.nl/islandora/object/uuid%3A6b5867d3-e6bb-46f8-bf2a-aea9399cae17

Bibliography

Boereboom, B. (n.d.). EVM and HWP model. 2022. Retrieved November 16, 2023, from https://github.com/bramboereboom/MSc-thesis

Brandmeyer, J. E., & Karimi, H. A. (2000). Coupling methodologies for environmental models. Environmental Modelling & Software, 15(5), 479–488. https://doi.org/10.1016/S1364-8152(00)00027-X

Degbelo, A., & Kuhn, W. (2018). Spatial and temporal resolution of geographic information: an observation-based theory. Open Geospatial Data, Software and Standards 2018 3:1, 3(1), 1–22. https://doi.org/10.1186/S40965-018-0053-8

Jie Chen, & Xiaoyu Li. (2021). Research on Key Technologies of Multi-resolution Modeling Simulation. 687–693.

Nikolic, I. (2023). Towards integrated decision-making in the energy transition. https://multi-model.nl/

Nikolic, I., Warnier, M., Kwakkel, J. H., Chappin, E. J. L., Lukszo, Z., Brazier, F. M., Verbraeck, A., Cvetkovic, M., & Palensky, P. (2019). Principles, challenges and guidelines for a multi-model ecology. Citation. https://doi.org/10.4233/UUID:1AA3D16C-2ACD-40CE-B6B8-0712FD947840

Quintel, (n.d.). Energy Transition Model. Retrieved December 10, 2019, from https://energytransitionmodel.com/?locale=en

Rabelo, L., Kim, K., Park, T. W., Pastrana, J., Marin, M., Lee, G., Nagadi, K., Ibrahim, B., & Gutierrez, E. (2016). Multi resolution modeling. Proceedings - Winter Simulation Conference, 2016-February, 2523–2534. https://doi.org/10.1109/WSC.2015.7408362

Salome, S. (2021). On the challenge of designing a robust military force: a multi-resolution modelling approach to improve the performance of a naval force support system. https://repository.tudelft.nl/islandora/object/uuid%3Abaa50bd3-e32a-44ee-9fe4-f9593d3e0829

Seck, M. D., & Honig, H. J. (2012). Multi-perspective modelling of complex phenomena. Computational and Mathematical Organization Theory, 18(1), 128–144. https://doi.org/10.1007/S10588-012-9119-9/TABLES/2

Vangheluwe, H., de Lara, J., & Mosterman, P. J. (2002). (PDF) An introduction to multi-paradigm modelling and simulation. https://www.researchgate.net/publication/243776266_An_introduction_to_multi-paradigm_modelling_and_simulation

Xuefei, Y., Qiang, L., Xiaolong, W., Dong, L., & Shoubiao, W. (2017). Non-consistence aggregation-disaggregation technology for battle simulation study of SoS. https://doi.org/10.18178/wcse.2017.06.066