A Demand Response Implementation in Tertiary Buildings Through Model Predictive Control

Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids

“…MPC control of multiple air handling units (AHUs) or rooftop units (RTUs) in multi-zone real buildings are demonstrated in [42][43][44][45][46]. Experiments using MPC to control building systems, behind-the-meter energy storage and DERs have also been demonstrated [47][48][49]. MPC based controllers were deployed in office buildings to determine the setpoints for the supply air temperature, fan speed and the zone air temperature for each AHU [42,43], and West et al [42] additionally controlled the chilled water and hot water valve position.…”

“…Frequency regulation as a grid service (by varying the air flow rate setpoints, which resulted in modifying the fan speed) was implemented in the FLEXLAB R testbed [50] at Lawrence Berkeley National Laboratory [47,48]. A public school building in southern Italy was chosen as a site of demonstration in [49], where the authors performed their optimizations in the cloud and send the control signals to the Internet of Things (IoT) devices and controllers that were retrofitted in the school building to control the battery and various loads through an intermediary gateway. DRL strategies have also been implemented in real buildings, although in small experiments.…”

“…A core barrier in progressing from simulations to real-world deployment of advanced controls is the lack of a robust and reliable software infrastructure to implement those controls in real-world building systems, which often have an eclectic mix of various controllers and systems in place. There has been work on middle-ware software platforms that collect data from various connected sensors and actuators across different systems within a building [42,43,46,48,49,[53][54][55][56]. The ability to retrieve data from various IoT sensors and devices, store these data centrally and use them for simulations of better control algorithms have been demonstrated in [46,49,[53][54][55][56].…”

“…There has been work on middle-ware software platforms that collect data from various connected sensors and actuators across different systems within a building [42,43,46,48,49,[53][54][55][56]. The ability to retrieve data from various IoT sensors and devices, store these data centrally and use them for simulations of better control algorithms have been demonstrated in [46,49,[53][54][55][56]. Interfacing with the existing proprietary control software for gathering data and publishing control signals is another common solution, but this requires site specific implementations as seen in [42,43,48].…”

Optimal Sizing and Operation of Electric and Thermal Storage in a Net Zero Multi Energy System

“…In Ref. [30], it was shown how the actual MPC solution deviates slightly from the ideal open-loop one, even in the presence of significant forecast errors. In this paper, where a sizing problem is studied, having also considered the level of approximation for assuming the annual chronological curves, the ideal open-loop solution is considered detailed enough to represent the daily response of the NZMES energy management system in a long-term evaluation.…”