The facility was established to support the development of optimal dispatch methods for micro gas turbine-based combined heat and power generators in smart grid settings. The facility includes state-of-the-art Capstone C65 ICHP turbine and several engine control units that allow hardware-in-the-loop simulation of micro-turbine network. The turbine is installed and is fully operational with the available liquid fuel supply.
The research aims to deliver real-time solution for unit commitment problem of micro gas turbine network in CHP operation under smart grid environment by solving single unit commitment in smart-grid based on steady state models and forecasted demands, developing detailed CHP unit model towards realistic cycle analysis, and creating model-predictive control approach for optimal operation of micro gas turbine network in dynamic environment.
The simulation model of the recuperated micro gas turbine cycle in CHP mode is based on NASA DYNGEN algorithm, which generates steady-state operation maps. The economic dispatch (ED) problem is then defined as short-term optimal determination of generation unit outputs aimed at satisfying the system load while minimizing the overall costs. The operation of a micro-gas turbine as a CHP unit is considered, and thus the ED problem is to determine optimal schedules for both the power output and heat generation of the MGT that minimizes the overall cost to the user. This includes costs incurred from the operation (mostly attributed to fuel), in addition to the cost of purchasing power and heat from the utility in the event that the turbine is not economically feasible to operate, or when it cannot completely satisfy the demands. Although the thermodynamic behavior of various MGT components is well defined, the solution methodology for finding their steady-state relations can be non-trivial. For that reason, a numerical simulation model flowchart of the recuperated MGT established. Problem solution allows to identify several distinct turbine operation modes that would be most cost-effective in various operational conditions (demand driven, utility cost driven, and maintenance driven). Moreover, dispatch optimization allows to recover the costs associated with turbine purchase and installation within three years.
In following, this approach can be tested experimentally using the Capstone C65 unit and is currently being extended to a network of micro turbines. In this case, the distribution of power and thermal loads between the different units is also a non-trivial problem and necessitates solution over entire network.