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Chapter 5 sign space, looking for optimal design configurations. The multi-objective optimization approach based on a genetic algorithm (GA) is adopted in this case. Such design procedure takes care of the typical trade-offs, such as, for example, the one ex- isting between the improvement of turbine performance, i.e., by reducing pressure losses in the recuperator (counter-pressure at the turbine discharge), and the reduction of thermodynamic irre- versibility in the heat exchangers. The first goal might be obtained by reducing the heat transfer surface without altering the flow velocity. However, such surface reduction would lead to a deterio- ration of the heat exchange (larger irreversibility), due to the larger temperature difference between the hot and the cold stream. 5.2.2 Assessment of Dynamic Performance The set-up of the second step of the design procedure requires to identify critical scenarios involving system transients, e.g. sudden load changes, load rejections, or unit trips. Requirements on critical variables are formulated, such as rise time, settling time, damping, maximum deviation, maximum or minimum allowed value during the transient, etc. A nonlinear dynamic model of the plant based on first principles is needed, in such a way that it can be parameterized starting from the detailed design data obtained from the first step of the procedure. An effective way to build such model is to use the fully modular approach of the equation-based, object-oriented modeling language Modelica [11]. On the one hand, this allows to carry out the modeling task reliably and in a short time, by leveraging on existing and well- tested libraries of reusable component models. On the other hand, the equation-based approach of the language makes it possible to easily customize the models for the specific requirements of the design problem at hand. In most cases, the system dynamics is the result of the interaction between the inherent plant dynamics and the control system action, with the controller often playing a crucial role. The defi- nition of the design parameters cannot be complete without the values of the controller parameters. It is then necessary to define control system tuning criteria leading to desirable or optimal perfor- mance, which can be applied automatically given the specific values of the design parameters. The assessment of dynamic performance can then be carried out automatically for each design point on the Pareto front, by first running the simulation code generated from the Modelica model with the specific choice of parameters and then checking if all the requirements on critical variables are met. 5.3 Case of Study Off-shore oil and gas platforms are a proper case of study to incorporate the dynamics of the power generation system directly in the design phase, as they are typically equipped with stand-alone (island) power generation systems. Moreover, in off-shore applications, preventing a failure of the power generation system is crucial as it may cause a loss of oil and gas production and a drop of the economic revenue. The case of study is the power generation system installed on the Draugen oil and gas off- shore platform, located 150 km off-shore from Kristiansund, in the Norwegian Sea. The platform, operated by A/S Norske Shell, produces natural gas, exported to Kårstø (Norway) via the Åsgard gas pipeline, and oil, which is first stored in tanks at the bottom of the sea and then exported via a shuttle tanker once every 1-2 weeks. Three Siemens SGT-500 gas turbines are installed on the platform, supplying an electrical base load of 19 MWE. The power demand is increased up to 25 MWE (peak load) during oil export. In order to guarantee a high reliability of the power generation system, two 124PDF Image | New Concepts FOR Organic Rankine Cycle Power Systems
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