Print Email Facebook Twitter Evaluating different methods of creating capacity for injecting green gas in the distributional gas grid Title Evaluating different methods of creating capacity for injecting green gas in the distributional gas grid Author Verbaas, Max (TU Delft Technology, Policy and Management) Contributor Heijnen, P.W. (mentor) Correljé, A. (mentor) Degree granting institution Delft University of Technology Programme Engineering and Policy Analysis Date 2021-10-25 Abstract In the Dutch climate agreement, the Klimaatakkoord, the Dutch government has set an ambition to produce 70 PJ worth of green gas in the country by 2030. This ambition has been set to reduce the carbon emissions from the use of natural gas, as green gas is a low carbon alternative. Green gas is produced by anaerobically digesting biomass - i.e.,manure, agriculturalwaste, sewage sludge - and removing any unwanted compounds during the upgrading stage of the production process. The result is green gas, a gas which is of the same characteristics as natural gas and can be used for the same appliances. It can thus, also be injected into the Dutch gas network where it can be transported to end consumers such as households and the industry. As of January 2020, the production capacity of green gas is 0.18 billion cubic meters per year which converts to 6.33 PJ, only 9% of the production capacity goal set by the government. Change is thus necessary in order for the ambition to be reached. One of the major obstacles holding back the growth of green gas production in the country is the injection capacity of the distributional networks of the gas system. Green gas is mostly produced in small-scale decentralized anaerobic digesters which are connected to the distributional grid. These distributional grids have been designed to distribute gas fromthe high pressure transmission network operated by Gasunie to the end users. They have not been designed to receive gas on a decentralized level. The problem with green gas is that production and injection occurs at a constant rate due to the biochemical nature of the production process. The supply of green gas is constant. On the other hand, demand is not constant. Demand fluctuates on a seasonal and daily basis. During summertime for example, demand is very low due to the higher outside temperature. There is thus less need for gas for spatial heating. The constant supply and fluctuating demand therefore result in imbalances which need to be managed in the distributional grid, or else the producers will not be able to inject the gas. The distributional system operators will thus need to invest in strategies that can increase the injection capacity of green gas or the ambition of the Dutch government will be difficult to reach. The goal of this study is to evaluate the cost-effectiveness of different capacity-creating strategies for different distributional gas networks and gain insights that can be used by decision-makers. To determine the cost-effectiveness of the different strategies for different networks, a model has been developed with which different gas networks and their network-specific variables can be simulated over a period of 15 years. This part of the model is used to determine when imbalances between supply and demand occur, and what the extent of the imbalances are while also taking demand and supply trends into account. The capacity-creating strategies can be applied to the network in the model which can then be used to analyze how effective the strategies are in decreasing the occurrence and extent of the imbalances. A decrease in imbalance means more green gas can be injected into the network and thus more green gas producers can connect to that network. It also means more natural gas can then be substituted by the low carbon green gas resulting in lower carbon emissions. This part of the model is finally combined with a social cost benefit modelwith which the costs of the investment and the benefits of decreasing the imbalances can be translated to monetary terms. This will aid decision-makers in the decision-making process regarding investing in the distributional gas grid. The general analysis showed that there is a clear distinction between strategies that can create a lot of injection capacity and strategies that are very limited in their ability to create injection capacity. Static and dynamic pressure adjustments, and connecting CNG refueling stations to add additional demand to the network are strategies that are limited in their effectiveness. Pressure adjustments are affected by the pipeline volume and average pressure in that pipeline. Both are relatively low in distributional grids compared to the transmission grid thus adjusting the pressure in the grid to createmore storage capacity is not very effective in creating capacity. Static pressure adjustments however have negligible costs and are therefore deemed a costeffective measure. Performing dynamic pressure adjustments require the placement of a costly system and has been found to be outperformed by static pressure adjustments for all analyzed networks. CNG refueling stations add a small amount of demand and some storage capacity. However, due to the expected decrease in gas demand fromthe transport sector, the effectiveness of connecting to CNG refueling stations decreases severely to the point that connecting to new stations does not make sense froma cost-wise perspective. Using the storage tanks of the stations to strategically store gas during the more problematic nighttime - imbalances occur more often during nighttime than during the day - however is a cost-effective strategy. DSOs should consider reaching out to CNG refueling station operators for this strategy. The other strategies have a much higher capability of creating injection capacity. Their cost-effectiveness is however very location specific. A gas booster needs to be placed close to the transmission network and an industrial user or other distributional network to connect to can be difficult to find in some cases. Their effectiveness is determined by the feed-in limit for the gas booster, and the additional demand for the industrial user and other distributional network. The costs are governed by the distance to the strategy as new pipelines will have to be constructed to reach them. Also, dependent on the urbanization level of the region the pipelines will be built in, the costs can differ. Constructing in amore urbanized region will be more costly. Finally, a storage facility has been found to create a decent amount of injection capacity too - though this is affected by its scale - however, it is a very costly option resulting in a negative net present value and should thus only be considered as a last resort strategy. Finally, the model is used to analyze the Stedin network in Friesland as a case study. It is expected that this network will experience a lot of imbalances in the future because of the large amount of expected green gas producers in that area. The analysis was used to gain insights with which recommendations are developed for Stedin. First of all, static pressure adjustments and strategically using the storage tanks of CNG refueling stations that are already connected to the network should be used to create additional injection capacity. Both strategies are very cost-effective however they do not create enough capacity for all the expected green gas producers to connect. In order to accommodate the connection of all the expected green gas producers, Stedin will have to make use of a gas booster to inject surpluses of green gas into the transmission network. A possible location for a gas booster in Friesland is on the gas grid in Leeuwarden operated by the DSO Liander. Stedin will thus have to connect its network to that of Liander in Friesland. The most cost-effective option for this is to connect the network from Hallum to Leeuwarden through the network of Stiens. The total net present value of the investments mentioned above is estimated to be 22 million euros, thus making it a very beneficial investment. 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