Optimal gas quality sensor placement for hydrogen blending in natural gas grids

Renewable hydrogen is a key component in achieving a carbon net zero energy system in 2050. One of the possibilities to transport hydrogen in existing natural gas pipelines is by blending it into the natural gas mix. Such gas blends can reduce the carbon footprint of domestic gas use, which accounts for 40% of total global greenhouse gas emissions. Currently, many countries in Europe implement gas billing by measuring the consumed volume of gas. However, the 10-20% hydrogen blending from multiple injection points will reduce the calorific value of the gas, and since the composition is not always the same throughout the grid, not all customers are charged fairly as they need to use more gas to get the same amount of energy. In addition, in many European countries, DSOs (Distribution System Operator) are responsible for monitoring the gas quality in their grids. Thus, gas quality sensors are needed for proper gas billing and monitoring of the grid, but are expensive to install for each individual consumer. Gas quality is not the only important parameter monitored by the DSOs. They also monitor pressure in the station and in some cases flowrate. However, this paper is only focus on optimal placement of gas quality sensor for smart billing. Currently DSO’s only place gas quality sensors at pressure reduction stations, since after the station it is expected to have a fixed quality value. However, it is likely that in the future this strategy will change due to the increase in local injection points located after these stations which contain different gas qualities. There is therefore a clear need for a method that helps DSOs in deciding where to place additional sensors after the stations. The goal of placing sensors in a network is to reduce the uncertainty of the measured quantity (e.g. gas quality) in question. By placing sensors within a gas grid, the total uncertainty in the grid is reduced and a good estimate of the measured quantity throughout the grid can be achieved. On the other hand, when no sensors are available, simulation tools can still be used to get an estimate of the state of the system. However, this estimate comes with its own uncertainties arising from uncertainty in model inputs (for instance, the exact demand in real-time at each consumer is unknown for network operator due to privacy concerns). In this work, an optimal sensor placement approach is proposed that combines model simulations with graph theory to find the minimum set of sensors needed to fully determine the gas quality in an admixed gas grid based on given percentage of uncertainty in the consumer gas usage. With the proposed approach, there is no need to install gas quality sensors for each individual customer, which will help reduce the Capital Expenditure (CAPEX) of gas quality sensor purchasing and installation, and reduce OPEX for maintenance and calibration during a sensor’s lifetime.