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UBC Theses and Dissertations

Evolutionary algorithms for optimal operation of gas networks Lee, Andy Jongsuk


Natural gas is one of the cleanest fossil fuels and most reliable energy source readily available for everyday use. Due to changes in weather conditions and in economic growth and market conditions, demand for natural gas by municipal, as well as commercial and industrial consumers, has greatly increased, and delivering gas to these varied consumers has become more complex. The problem of optimally operating gas transmission networks is generally formulated to minimize the total supply cost of the network while satisfying the demand of consumers at different delivery points, at minimal guaranteed pressures. If necessary, compressors are installed and operated to supply more energy to the network and increase gas pressures, so as to compensate for pressure losses in the pipe network. The nonlinear relationships between system discharges and headlosses due to friction and mechanical devices in pipes form a complex set of nonlinear constraints in optimization models. In order to solve this complex problem, researchers have used approaches that use linear approximation in classical optimization techniques. Recently, heuristic algorithms have been applied, as these techniques can be used to find approximate solutions to complex optimization problems more quickly than classical optimization methods, and can often obtain an acceptable solution when classical methods fail. This thesis compares the performance of four heuristic algorithms for optimizing operations of a gas transmission network, namely, the: Genetic Algorithm (GA), Differential Evolution Algorithm (DE), Artificial Bee Colony Algorithm (ABC), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The algorithms are compared in terms of their computational burden, and the quality of the obtained solutions with respect to practical management concerns. Among the four algorithms, CMA-ES is found to achieve the global optimum, compared with that generated with classical optimization, most consistent results in solutions that meet pressure and flow requirements, and conserve compressor power.

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