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D8.3 & D8.4 Simulation of selected cases for digitalisation in a Dutch hydrogen distribution network

Authors: Octaviano, Ryvo; Palochis, Demetris; Poort, Jonah; Blokland, Huib;

D8.3 & D8.4 Simulation of selected cases for digitalisation in a Dutch hydrogen distribution network

Abstract

Replacing natural gas by hydrogen in the existing DSO infrastructure will give several challenges on the security of supply of energy to the end-users, related to the physical aspects of the assets in the hydrogen network. Within the HyDelta2 program, WP8 Digitalization, the need and benefit of digitalization of the gas grid has been investigated. In the current gas grid the digitalization is limited in all aspects: monitoring, modelling and control. This means that a lot of digitalization aspects could be developed to handle the needs of a future hydrogen grid, which have to deal with the following trends: increasing dynamics in supply and demand, from a stand-alone grid to a multi-connected grid and a need for real-time data on supply and demand. A roadmap has been developed on how to deal with the main challenges in balancing the future hydrogen grid, for all aspects of digitalization. In the frame of the HyDelta2 program we have chosen for the use case ‘Smart sensor placement’ for pressure and flow sensors in the Kapelle area, which covers the main digitalization aspects, with a focus on the short- and mid-term. On this grid several scenarios have been applied, using TNO’s dynamic gas grid simulation tool Aurora. Starting with a base case where natural gas is replaced by hydrogen. Subsequently scenarios have been simulated on adding new supply locations for electrolyzers with a dynamic profile, adding large consumers and a scenario with one ‘broken gas pipe’. The flow and pressure in the whole grid has been simulated for all scenario’s, with realistic supply and demand data. The gas grid simulator has been validated in the current (natural gas) situation by available pressure and flow data. By replacing the natural gas by hydrogen and maintaining the delivery of the same amount of energy to all users, the flows in the grid are about three times higher and the pressures will remain about the same. However, the flows stay below the allowable limits. In the case of adding two realistic electrolyzers with a total capacity of 3 MW, the maximum pressures stay below the allowable limits. A N-1 situation has been simulated by a pipe break in the 4 bar grid, showing the critical pipe segments where the pressure becomes too low. In the next scenario three additional large consumers are added, resulting in a pressure drop which is on some locations just below the allowable limit. Finally the effect of replacing the two current supply’s by one supply on another location in the grid, has been investigated. The simulation tool has been used to find the optimal locations in terms of pressures and flows within the acceptable limits. In all scenario’s an uncertainty in the domestic demand have been introduced and the number of (flow and pressure) sensors and their location have been determined, to minimize the uncertainty in flow and pressure in the whole grid. The overall picture for the scenario’s is that adding two sensors will give the main gain in reducing the uncertainty. Adding two pressure sensors reduces the uncertainty in pressure with about 60% to 70%. Adding flow sensors have a much smaller effect on the reduction due to restriction on placing the sensor. The location for placing the sensor is globally the same for all scenario’s. The different scenario’s show the need for a dynamic modelling tool that is able to calculate flows and pressures in the grid in case of a dynamic supply and demand situation. Only a tool will not be sufficient to get full insight, because of uncertainties in the input data for the grid. Besides uncertainty in the demand profile, there can be incompleteness of the geometrical information (pipe diameters, pipe roughness, etc.) and pressure settings which deviate from the numbers that are used in the model. So, to get a full insight in the grid, always measurement data will be needed. The benefit of a simulation tool to investigate the number and location of sensors has been demonstrated, showing that the number of pressure sensors in the grid should be increased. Furthermore, the insight in the physical behavior of the grid is essential in the future foreseen increase of the number of local decentralized hydrogen suppliers (both from solar/wind and surplus of the electricity grid) and the ability of DSO’s to control and manage the pressure in the distribution network. Overall, we can draw a conclusion on the added value of digitalization of the gas grid. The gas grid is currently facing several broad challenges which can be aided by digital technologies: different heating technologies, declining amount of customers and gas demand, converting of the grid to hydrogen (and biomethane) and decentralized production. Current standard operations such as maintenance planning and security of supply can benefit from digitalization, by allowing the DSO's to make better decisions and proper investments. Digitalisation will create more accurate and real-time insight and a combination of a robust calculation model and online data from a limited number of sensors will generate sufficient insight, moreover digitalisation will facilitate scenario analysis and creates more opportunities for renewable gasses in the gas network.

Dit project is medegefinancierd door TKI Nieuw Gas | Topsector Energie uit de PPS-toeslag onder referentienummer TKI2022-HyDelta.

Keywords

artificial Intelligence, gas grid, hydrogen economy, Transmission System Operator, Digital Twin, Distributed control system, hydrogen in the gas grid, hydrogen, Supervisory Control and Data Acquisition, digital transformation, Distribution System Operators, hydelta, Remote Terminal Unit, Field service management

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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