Natural gas turbine combined cycles (GTCCs) are playing a fundamental role in the current energy transition phase towards sustainable power generation. The competitiveness of a GTCC in future electrical networks will thus be firmly related to its capability of successfully compensating the discontinuous power demands. This can be made possible by enhancing power generation flexibility and extending the operative range of the plant.
To achieve this goal, a test rig to investigate gas turbine inlet conditioning techniques was developed at the TPG laboratory of the University of Genoa, Italy. The plant is composed of three key hardware components: a micro gas turbine, a butane-based heat pump, and a phase-change material cold thermal energy storage system. The physical test-rig is virtually scaled up through a cyber-physical approach, to emulate a full scale integrated system.
The day-ahead schedule of the plant is determined by a high-level controller referring to the Italian energy market, considering fluctuations in power demands.
By using HP and TES, it is possible to control the mGT inlet air temperature and thus enhance the operational range of the plant optimizing the management of energy flows.
This article (Part 1) introduces the new experimental facility, the real-time bottoming cycle dynamic model, and the four-level control system that regulates the operation of the whole cyber-physical plant. The experimental campaign and the analysis of the system performance are presented in the Part 2.
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.
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