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We propose a method that simultaneously identifies a dynamic model of a building’s temperature in the presence of large, unmeasured disturbances, and a transformed version of the unmeasured disturbance. Our method uses l1-regularization to encourage the identified disturbance to be approximately sparse, which is motivated by the piecewise constant nature of occupancy that determines the disturbance. We test our method using both open-loop and closed-loop simulation data. Results show that the identified model can accurately identify the transfer functions in both scenarios, even in the presence of large disturbances, and even when the disturbance does not satisfy the piecewise-constant property. 

We propose a method that simultaneously identifies a dynamic model of a building’s temperature and a transformed version of the unmeasured disturbance affecting the building. Our method uses l1-regularization to encourage the identified disturbance to be approximately sparse, which is motivated by the piecewise-constant nature of occupancy that determines the disturbance. We test our method on both simulation data (both open-loop and closed-loop), and data from a real building. Results from simulation data show that the proposed method can accurately identify the transfer functions in open and closed-loop scenarios, even in the presence of large disturbances, and even when the disturbance does not satisfy the piecewise-constant property. Results from real building data show that algorithm produces sensible results. 

We propose a decentralized algorithm to help reduce demand-supply imbalance in a power grid by varying the demand from loads, just like charging and discharging a battery. The algorithm ensures strict bounds on the consumers' quality of service (QoS) by constraining the bandwidth of demand variation. A model-predictive-control formulation is adopted to compute local decisions at the loads. The algorithm is decentralized in the sense that loads do not communicate with one another. Instead, loads coordinate using local measurements of the grid frequency, which provide information about global demand-supply imbalance. It is envisioned that consumers will be recruited through long-term contracts, aided by the QoS guarantees provided by the proposed scheme. Simulation results show that loads are able to reduce frequency deviations while maintaining QoS constraints and that the performance of the algorithm scales well with the number of loads. Closed-loop stability is established under some assumptions. 

In prior work, the Distributed Gradient Projection (DGP) algorithm was proposed to allow loads or load aggregators to provide contingency service to the grid using local frequency measurements. The DGP algorithm was shown to perform well in linear simulations. The goal of this work is to evaluate the performance of the DGP algorithm in more realistic scenarios and its robustness to issues of practical implementation, such as time delay, model mismatch, measurement noise, and stochastic disturbance. Simulation results from the IEEE 39-bus system indicate that the DGP algorithm performs well in mitigating the effects of contingencies and that it is robust to issues of practical implementation.