Distribution grid planning considering sector coupling and waste heat recovery

Master thesis (2022)

Authors

A. Pozzetto Electrical Engineering, Mathematics and Computer Science

Contributors

M. Cvetkovic Intelligent Electrical Power Grids - EEMCS (supervisor 1)
P. Palensky Intelligent Electrical Power Grids - EEMCS (supervisor 2)
P. Manganiello Photovoltaic Materials and Devices - EEMCS (supervisor 2)
Arjan van Voorden Stedin (supervisor 1)
Arjen Jongepier Stedin (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Alessandro Pozzetto
Hydrogen Energy system optimization Energy System Integration District Heating Waste Heat
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Published Date

17-10-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This thesis work investigates and provides an analysis of the potential benefits of an electricity - gas - heat integrated energy system, putting extra focus on the waste heat potential from fuel cells and electrolysers. The main focus is given to the low-voltage distribution grid level, and a case study is presented for the Drechtsteden subnetwork operated by Stedin, which, together with the IEPG department at TU Delft, is the promoter of this study. The novelty items of this work consist in the analysis of the waste heat potential of hydrogen conversion assets connected to a district heating network along with the inclusion of electricity and hydrogen markets, all in the contest of an energy system optimization.

The integration between these three energy sectors is assumed to be chiefly driven by the operation of electrolysers and fuel cells. Two other main assumptions set the base for this work: first, the gas sector is assumed to be entirely repurposed to operate with hydrogen and, secondly, the waste heat coming from fuel cells and electrolysers is assumed to be the main thermal energy input of a district heating network. An electricity and a hydrogen market have also been modelled to simulate the interaction of this region with the external grid.
The analysis is carried out by means of a linear optimization algorithm coded using oemof, an open source python package for multi-energy system modelling and optimization. Most of the input data (i.e. energy demand and generation profiles) comes from the Integrale Ifrastructuurverkenning 2030-2050 study from TenneT, Gasunie and the Dutch DSOs, which has been regionalized for the Drechsteden region is order to optimize the investments on the main energy assets (transformers, hydrogen substations, electrolysers, fuel cells, batteries) needed to run the future energy system.

An optimal system configuration is also calculated for a "reinforcement" scenario, in which energy sectors remain independent, and a comparison with the system integration scenario is presented. The overall system costs appear to be only about 5% lower for the system integration scenario. The costs imputable to assets is higher with system integration due to the addition of expensive hydrogen conversion assets, but, in this scenario, the operation on the energy market (driven chiefly by hydrogen exports) is more advantageous.
Finally, a sensitivity analysis on the share of heat demand to be satisfied by district heating and on the price of batteries is carried out, along with an investigation on the effect of adding thermal storage to the system.