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Operational Optimization of a Typical micro Gas Turbine

par De Paepe, Ward ;Abraham, Simon;Tsirikoglou, Panagiotis;Contino, Francesco ;Parente, Alessandro ;Ghorbaniasl, Ghader
Référence Energy Procedia, 142, page (1653-1660)
Publication Publié, 2017

Article révisé par les pairs

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Résumé :

Micro Gas Turbines (mGTs) offer great potential for use in co-, tri or polygeneration applications. In these applications, where the heat from the exhaust gases is used in an efficient way, the mGTs achieve very high efficiencies. To be able to determine the number of units, the nominal parameters of the units and the specific operating strategy of the mGT, it is key to know precisely the performance of these units. Generally in co-, tri-or polygeneration applications with mGTs, this performance is considered to be known and fixed, and is therefore directly used to determine the operational strategy of the network, possibly linked with an economic analysis. In real world operating conditions, the parameters characterizing the operation of the mGT are measured with uncertainties. Depending on the model sensitivity to input parameters, these uncertainties may have a tremendous effect on the performance of the mGT. These uncertainties should be taken into consideration by the designers in an early stage of the design process to achieve a so-called robust design. In this paper, we present the operational optimization of a typical mGT, the Turbec T100 mGT, using a deterministic approach. In this approach, the parameter uncertainties are not taken into account, however, it is an important first step towards a full robust design, since it will set the reference. By varying Turbine Outlet Temperature (TOT) and compressor rotational speed, a Pareto front for maximal electrical power output and electrical efficiency is found. The two objectives are conflicting, making that the maximal electrical efficiency cannot be reached at maximal electrical power output. The results of this optimization will be used in our future study on the design optimization under uncertainties.