This paper presents the development of a control approach for a smart polygeneration microgrid using the Model Predictive Control (MPC) paradigm. The importance of distributed generation systems has increased through recent years and questions of grid stability have emerged in the face of high concentrations of non-dispatchable power sources. Numerous proposals for "smart" distribution systems have emerged, including an architecture called the Energy Hub (E-Hub), which is a smart microgrid where both thermal and electrical power are supplied to customers by a mix of generator systems. The problem deals with a constrained Multi-Input Multi-Output (MIMO) optimization problem. This paper describes work underway on a real E-Hub located at the laboratories of the Thermochemical Power Group (TPG) of the University of Genoa (UNIGE), Italy. The TPG E-Hub is being integrated into a larger smart polygeneration grid under construction on the Savona campus of UNIGE as part of the European Union Resilient project. The proposed control approach aims to optimize the loading of various resources of the E-Hub in response to changing electrical and thermal demands from the campus-wide smart grid. This paper presents some of the results from initial testing of this approach to E-Hub control.
Advanced control of a real smart polygeneration microgrid
Banta L.;Rossi I.;Traverso A.;Traverso A. N.
2014-01-01
Abstract
This paper presents the development of a control approach for a smart polygeneration microgrid using the Model Predictive Control (MPC) paradigm. The importance of distributed generation systems has increased through recent years and questions of grid stability have emerged in the face of high concentrations of non-dispatchable power sources. Numerous proposals for "smart" distribution systems have emerged, including an architecture called the Energy Hub (E-Hub), which is a smart microgrid where both thermal and electrical power are supplied to customers by a mix of generator systems. The problem deals with a constrained Multi-Input Multi-Output (MIMO) optimization problem. This paper describes work underway on a real E-Hub located at the laboratories of the Thermochemical Power Group (TPG) of the University of Genoa (UNIGE), Italy. The TPG E-Hub is being integrated into a larger smart polygeneration grid under construction on the Savona campus of UNIGE as part of the European Union Resilient project. The proposed control approach aims to optimize the loading of various resources of the E-Hub in response to changing electrical and thermal demands from the campus-wide smart grid. This paper presents some of the results from initial testing of this approach to E-Hub control.
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