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.
Many engineering problems that involve hierarchical control applications, such as demand side ancillary service provision to the power grid, can be posed as a robust tracking commitment problem. In this setting, the lower-level controller commits a set of possible reference trajectories over a finite horizon to an external entity in exchange for a reward corresponding to the size of the reference set and the allowed margin of tracking error. If the commitment is accepted, the lower-level system is required to track any reference trajectory that can be sampled from the committed set. This paper presents the framework of robust tracking commitment and a method to solve the optimal commitment problem for constrained linear systems subject to uncertain disturbance and reference signals. The proposed method allows tractable computations via convex optimization for conic representable uncertainty sets and lends itself to distributed solution methods. We demonstrate the proposed method in a simulation based case study with a commercial building that offers frequency regulation service to the power grid.
This paper presents a coordinated primal-dual interior point (PDIP) method for solving structured convex linear and quadratic programs (LP-QP) in a distributed manner. The considered class of problems represents a multi-agent setting, where the aggregated cost is to be minimized while respecting coupling constraints as well as local constraints of the agents. Unlike fully distributed methods, a central agent is utilized, which coordinates the Newton steps taken within the interior-point algorithm. In practical PDIP implementations, predictor-corrector variants are widely used, due to their very fast convergence. We show that in the coordinated case, a naive implementation of a PDIP with predictor-corrector scheme introduces extra communication steps between local and central agents. We propose a decentralized predictor-corrector scheme that uses a non-uniform perturbation on the complementary slackness condition, which is able to reduce the number of communication steps while preserving fast convergence of the original methods. The proposed coordinated PDIP method with decentralized predictor-corrector scheme can be analysed in the general framework of PDIP methods with non-uniform complementarity perturbations, for which convergence and complexity results are provided.
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