In this manuscript, an automated framework dedicated to design space exploration and design optimization studies is presented. The framework integrates a set of numerical simulation, computer-aided design, numerical optimization, and data analytics tools using scripting capabilities. The tools used are open-source and freeware, and can be deployed on any platform. The main feature of the proposed methodology is the use of a cloud-based parametrical computer-aided design application, which allows the user to change any parametric variable defined in the solid model. We demonstrate the capabilities and flexibility of the framework using computational fluid dynamics applications; however, the same workflow can be used with any numerical simulation tool (e.g., a structural solver or a spread-sheet) that is able to interact via a command-line interface or using scripting languages. We conduct design space exploration and design optimization studies using quantitative and qualitative metrics, and, to reduce the high computing times and computational resources intrinsic to these kinds of studies, concurrent simulations and surrogate-based optimization are used.

In the current energy scenario, gas turbine combined cycles (GTCCs) are considered key drivers for the transition towards fossil-free energy production. However, to meet this goal, they must be able to cope with rapid changes in power request and extend their operating range beyond the limits imposed by the environmental conditions in which they operate. The European H2020 project PUMP-HEAT (Pump-Heat Project, 2021, D4. 6 – “Validation Results in Energy -Hub of MPC With Cold Thermal Storage,”) aims at achieving this goal thanks to the integration of the GTCC with a heat pump (HP) and a thermal energy storage (TES). To study this setup, a dedicated cyber-physical facility was built at the University of Genova laboratories, Italy. The plant includes physical hardware, such as a 100kWel microgas turbine, (mGT), a 10 kWel HP and a 180 kWh change phase material-based TES. These real devices are up-scaled thanks to performance maps and real-time dynamic models to emulate a full-scale heavy-duty 400 MW GTCC with a cyber-physical approach. The control system determines the optimal configuration of the whole plant and the operative point of the real devices to minimize the mismatch with a real electric power demand curve. Different operative configurations are tested: one for reducing the power production of the plant below the minimum environmental load (MEL) and two for augmenting the plant maximum power under certain ambient conditions. From the analysis of these tests, it is possible to verify the effectiveness of the proposed concept and characterize the transient behavior of the real components.