Annual Evaluation of a Model Predictive Controller in an Integrated Thermal-Electrical Renewable Energy System Using Clustering Technique

Abstract

Whitebox model in a model predictive controller (MPC) for energy systems, though does help in developing an accurate system model, requires a long time for optimization. In this article, an adaptation of the clustering technique used in hardware-in-the-loop testbench is proposed for evaluation of the MPC on an annual scale with selected six representative days in a year for that particular system and location. Initially, the various input parameters for clustering (algorithm, distance metric, and datapoint input dimensions) are studied for the selected thermal-electrical integrated renewable energy system (with solar thermal collectors, auxiliary gas boiler, stratified thermal storage, micro fuel cell combined heat and power (FC-CHP), photovoltaic system, a lithium-ion battery) for a Sonnenhaus standard single-family residential building. Finally, the proposed methodology is used to compare the annual derived energy values and key performance indicators (KPIs) for an MPC implementation with a status quo controller as a reference. Also, extreme exemplary weather days are investigated as the selected representative days were only average days in each season. Despite the conflict of using the FC-CHP on cold sunny days, instead of utilizing the battery and increased gas boiler energy input, a 9% increase in decentral system fraction is reported. Via the use of MPC instead of status quo controllers, the results indicate −18% space heating (SH) demand; +30% solar thermal energy production; −29% gas boiler energy supply; −52% power-to-heat thermal energy supply; −52% electrical fuel cell production; +240 kWh battery utilization; and −52% reduced grid import at the expense of 1.2% of the electrical load demand as grid import.