Speakers
Description
Microgrids are considered as the crucial element in the integration of various distributed energy resources in buildings. They are capable of operating in both grid-connected and islanded mode and have shown immense potential in absorbing renewable energy. However, the widespread implementation of intermittent renewable energy sources, coupled with variable electricity pricing, has significantly increased the operational uncertainty of microgrids. This paper presents an analysis of the operational strategies of an integrated energy system that includes a micro gas turbine, a ground-source heat pump, PV panels, and batteries, with the aim of meeting the heating and electricity demands of a commercial building. To facilitate this endeavor, a neural network model for micro gas turbines was developed with a focus on fast computation time and high accuracy in capturing off-design performance. Furthermore, mathematical models for ground-source heat pump, PV panel, and battery were developed and validated using the Modelica language. Dymola optimization package was utilized to derive the day-ahead scheduling followed by one-hour intervals for the system, with the purpose of minimizing the electricity and heating costs associated with the system. The results demonstrate that the total costs could be reduced by approximately 10% during the analyzed winter period, indicating a promising avenue for cost savings in the system's operation.
| Conference Topic Areas | Track9: Smart Energy Storage, Integration and Utilization |
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