Abstract
It is considered necessary to implement advanced controllers such as model predictive control (MPC) to utilize the technical flexibility of a building polygeneration system to support the rapidly expanding renewable electricity grid. These can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints, amongst other features. One of the main issues identified in the literature regarding deploying these controllers is the lack of experimental demonstrations using standard components and communication protocols. In this original work, the economic-MPC-based optimal scheduling of a real-world heat pump-based building energy plant is demonstrated, and its performance is evaluated against two conventional controllers. The demonstration includes the steps to integrate an optimization-based supervisory controller into a typical building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms to solve a mixed integer quadratic problem. Technological benefits in terms of fewer constraint violations and a hardware-friendly operation with MPC were identified. Additionally, a strong dependency of the economic benefits on the type of load profile, system design and controller parameters was also identified. Future work for the quantification of these benefits, the application of machine learning algorithms, and the study of forecast deviations is also proposed.
Highlights
The details of the experimental set-up and models are explained in a previous work by the authors [25] and, for the sake of brevity, Sections 2.1 and 2.2 highlight only aspects relevant in the scope of this paper
A demonstration of the receding horizon model predictive control (MPC) is given and its technical feasibility is evaluated using the results of a single MPC iteration, multiple MPC iterations, and the comparison to reference controllers, under almost-identical conditions
The complexity of an MPC problem arising from the interdependence of multiple inputs and outputs—e.g., ambient temperature, initial state, physical constraints, and electricity prices—is illustrated using the results of one iteration of the MPC scheme for a typical heat pump system application in the transition period
Summary
If not operated or controlled intelligently, such an expansion may adversely affect grid supply and conventional control strategies implemented as rule-based control; e.g., following thermal load (FTL) or on–off schedules regulated by the grid-operator’s contract are limited in their conceptualization to realize a coordinated operation with the grid and other prosumers [4,5] Optimal control methods such as model predictive control (MPC) that consider the multiple inputs and constraints occurring in the operation of such complex plants have shown promising results over conventional control in the range of 9.5% to 26% [6], 49% to 84% [7], and 8% to
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