Annual evaluation of a model predictive controller in an integrated thermal-electrical renewable energy system using clustering technique

Whitebox model in a model predictive controller (MPC) for energy systems though does help in developing accurate system model, requires a long time for optimization. In this article, an adaptation of the clustering technique used in hardware-in-the-loop is proposed for evaluation of the MPC on an annual scale with selected six representative days. 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 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, 9% increase in decentral system fraction is reported. Via the use of MPC instead of status quo controllers, the results indicate -18% space heating 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% grid import.