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Analysis and Optimization of Urban Energy Systems

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Operations Research Proceedings 2019

Part of the book series: Operations Research Proceedings ((ORP))

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Abstract

Cities and municipalities are critical for the success of the energy transition and hence often pursue their own sustainability goals. However, there is a lack of the required know-how to identify suitable combinations of measures to achieve these goals.

The RE3ASON model allows automated analyses, e.g. to determine the energy demands as well as the renewable energy potentials in an arbitrary region. In the subsequent optimization of the respective energy system, various objectives can be pursued—e.g. minimization of discounted system expenditures and emission reduction targets. The implementation of the model employs various methods from the fields of geoinformatics, economics, machine learning and mixed-integer linear optimization.

The model is applied to derive energy concepts within a small municipality. By using stakeholder preferences and multi-criteria decision analysis, it is shown that the transformation of the urban energy system to use local and sustainable energy can be the preferred alternative from the point of view of community representatives.

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References

  1. Mainzer, K.: Analyse und Optimierung urbaner Energiesysteme: Entwicklung und Anwendung eines ĂĽbertragbaren Modellierungswerkzeugs zur nachhaltigen Systemgestaltung. Dissertation, Karlsruhe (2019)

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  2. Microsoft. Bing aerial images: orthographic aerial and satellite imagery. https://www.bing.com/maps/aerial

  3. McKenna, R., Bertsch, V., Mainzer, K., Fichtner, W.: Combining local preferences with multi-criteria decision analysis and linear optimization to develop feasible energy concepts in small communities. Eur. J. Oper. Res. (2018)

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Correspondence to Kai Mainzer .

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Mainzer, K. (2020). Analysis and Optimization of Urban Energy Systems. In: Neufeld, J.S., Buscher, U., Lasch, R., Möst, D., Schönberger, J. (eds) Operations Research Proceedings 2019. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-48439-2_1

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