Evaluate the impact of network tariffs on the Swiss energy transition. A fair cost distribution or a driver to reduce expensive network upgrades?

Farhat, Yamshid; M. Lipsa, Gabriel; Braun, Torsten (28 November 2022). Evaluate the impact of network tariffs on the Swiss energy transition. A fair cost distribution or a driver to reduce expensive network upgrades? In: PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe 2022) (pp. 1-6). IEEE Xplore: IEEE 10.1109/ISGT-Europe54678.2022.9960540

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We aim to demonstrate the importance of an optimal network tariff model for future electric energy systems. Due to the lack of available household consumption data and the uncertainty of new actors impact (e.g. electric cars), there are no reliable environments to perform such simulations. We propose a new simulation environment in order to evaluate the impact of different network tariff models in a representative network region. The core of this new simulation environments are advanced machine learning models for household consumption, electric cars charger stations, solar panels productions and the local optimization of batteries behavior at the prosumer level. We propose a new method to evaluate the fairness of the network distribution costs. The presented results demonstrate that existing network tariffs will not be able to fairly distribute the costs and that the penetration of energy storage could put the system at risk if the network tariffs are not correctly adapted for this new environment.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF)
08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)

UniBE Contributor:

Farhat Quinones, Yamshid, Braun, Torsten

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

ISBN:

978-1-6654-8032-1

Publisher:

IEEE

Language:

English

Submitter:

Dimitrios Xenakis

Date Deposited:

23 Sep 2022 17:08

Last Modified:

03 Sep 2023 02:34

Publisher DOI:

10.1109/ISGT-Europe54678.2022.9960540

Related URLs:

Uncontrolled Keywords:

energy transition; data science; network tariffs; electric cars; renewable energies; machine learning

BORIS DOI:

10.48350/173201

URI:

https://boris.unibe.ch/id/eprint/173201

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