This paper illustrates the potential of commercial buildings to act as frequency reserves providers through an experimental demonstration conducted in a multi-zone university building. The proposed control methodology is presented in detail, including the control architecture, the controller design, model identification, and hardware description. Finally, the effectiveness of the presented approach is tested by means of simulations and experiments in a controlled environment.
A method for certifying exact input trackability for constrained discrete time linear systems is introduced in this paper. A signal is assumed to be drawn from a reference set and the system must track this signal with a linear combination of its inputs. Using methods inspired from robust model predictive control, the proposed approach certifies the ability of a system to track any reference drawn from a polytopic set on a finite time horizon by solving a linear program. Optimization over a parameterization of the set of reference signals is discussed, and particular instances of parameterization of this set that result in a convex program are identified, allowing one to find the largest set of trackable signals of some class. Infinite horizon feasibility of the methods proposed is obtained through use of invariant sets, and an implicit description of such an invariant set is proposed. These results are tailored for the application of power consumption tracking for loads, where the operator of the load needs to certify in advance his ability to fulfill some requirement set by the network operator. An example of a building heating system illustrates the results.
Abstract-This paper introduces the OpenBuild toolbox for MATLAB. OpenBuild is a toolbox for advanced controller design for buildings heating ventilation and air conditioning systems, with emphasis on Model Predictive Control. It provides researchers in the control community the ability to test algorithms on a wide range of realistic simulation scenarios, by providing most of the data needed to perform simulation and optimization. It combines the convenience of controller design in MATLAB with the simulation capabilities of the building simulation software EnergyPlus. It includes a building modeling tool to construct linear state-space models of building thermodynamics based on building description data, making it useful for design of optimal controllers requiring a good prediction model, as well as providing the input data necessary for simulation such as weather, occupancy and internal gains data. The ability to co-simulate the building between MATLAB and EnergyPlus enables fast prototyping and validation of the models and controllers. This paper presents the working principles and functionality of OpenBuild.
In this study, we investigate the maximum possible profit for a commercial office building participating in New York's Day-Ahead Demand Response (DADR) program. We formulate an optimal control problem, assuming perfect knowledge of future weather, occupancy, and day-ahead electricity price predictions to examine this potential benefit. Then, a practical control strategy based upon the framework of Model Predictive Control (MPC) is proposed, which enables a building to participate in the DADR program. The controller decides once every day, whether or not to participate in the Demand Response (DR) event, and then optimizes the electric consumption to increase savings. A simulation study is carried out using a building model extracted from an EnergyPlus model, real measured weather data, and real day-ahead spot market price data for New York. Savings in the range of 23% to 33% are reported.
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