Techno-economic study of gas turbine in pipeline applications - Nasir, Abdulkarim (Cranfield University, 2013)
Natural gas being the cleanest fossil fuel today is receiving tremendous rise in demand for both industrial and domestic energy requirements. The availability of natural gas requires it to be transported from the production area through pipeline in most cases to the consumers; this requires compressor station mostly driven by gas turbine. The development of gas pipeline system requires important data such as appropriate pipe sizes, gas rate, required delivery pressure, appropriate compressor and gas turbine sizes. The investment for the pipeline and compressor station is capital intensive and therefore the techno-economic and environmental risk assessment tool to rapidly assess the pipeline becomes imperative. The objective of this project is to look at advanced pipelines and the close coupling of the compression system with advanced prime mover cycles. The investigation offers a comparative assessment of traditional and novel prime mover options including the design and off-design performance of gas turbine engine and the economic analysis of the system. The originality of the work lies in the technical and economic optimization of gas turbines and fluid movers, based on current and novel cycles for a novel pipeline application in a wide range of climatic conditions. The techno-economic and environmental risk assessment (TERA) tool created is made up of a number of modules, starting with the pipeline and compressor station modules which compute the necessary flow parameters and compressor performance, as well as the required compressor power. The next is the gas turbine performance simulation module, TURBOMATCH software was used to simulate the performance of the selected gas turbine engines at design and off-design conditions and it computes the thermodynamic conditions in the core of the engine. Receiving information from the performance simulation module, the emission module, which is based on combustion equations, estimates the amount of emission over the period of operation of the gas turbine. The economic module, which is essential to the tool, receives information from all the other modules to establish the life cycle cost and use the net present value (NPV) methodology to assess the plant. It also calculates all associated costs, as well as the cost of transporting natural gas. The economic module establishes the economic pipe size for any particular throughput. The electric motor drive module is the parallel arm of the methodology, handling all the modules as explained earlier except the gas turbine performance and emission modules. This allows a comparative assessment of gas turbine and electric motor drives to be carried out under any prevailing conditions. This methodology is unique to natural gas pipeline techno-economic assessment and no previous studies have looked at various aspects of the pipeline project before selecting a prime mover or an economic pipe size. This study further uses a genetic algorithm optimization tool to optimize gas turbine selection and compressor station location along the pipeline, based on total cost objective function. The optimization is limited to a particular pipe size and gas throughput. The use of various pipe sizes as well as varying throughput will be a major area for further studies. The results from the individual models are presented in chapters 3, 4 and 5. The result of the integrated modules under case study one and two shows that the transportation of 0.5 million cubic meter per day of natural gas over long distance interstate pipeline for both prime movers is uneconomical. The economic pipe size for 3.0 million cubic meter per day of natural gas is 609.6 mm (24”) pipe size for the two prime movers with transportation cost of $0.063/m3 and $0.056/m3 for gas turbine and electric motors respectively. This is equivalent to $1.46/GJ and $1.30/GJ which agrees with the cost of natural gas transportation in literature. The result of the optimization shows a clear preference for the selection of a 34 MW plant for the pipeline and throughput considered since this presents the minimum cost which is the definition of the genetic algorithm optimizer. It is worth noting that this techno-economic tool, which is made of many modules, can be used to rapidly assess the profitability or otherwise of a natural gas pipeline project.