The main aim of this paper is to illustrate the added value at system level of explicitly accounting for the closed-loop feedback aspect in the open-loop optimal control problem of model predictive control strategies for thermostatically controlled loads participating in demand response programs, when subject to uncertainties. To this end, an integrated system-level optimization problem is set up, merging an economic dispatch problem to represent the supply side and an open-loop stochastic optimal control problem incorporating affine disturbance feedback to represent the demand side. The incorporation of affine disturbance feedback enables the simultaneous optimal scheduling of the demand for electrical energy, reserve capacity and real-time flexibility required to guarantee thermal comfort at the demand side, thereby disclosing very valuable information for an aggregator or system operator, since the load uncertainty can be revealed and controlled ahead of real time. To solve the mathematically complex integrated problem, a distributed solution strategy based on the alternating direction method of multipliers is developed. With the help of an illustrative case study, it is demonstrated that the day-ahead coordination of the demand for reserve capacity in addition to the energy demand is able to reduce the system operating cost while guaranteeing thermal comfort, and hence, enables a more cost-efficient electrification of the residential heating sector. Cost reductions up to 10.7 % are shown to be achievable for a demand side consisting of 900 000 flexible heat pumps combined with low-temperature radiators.

In this paper, we propose a data-driven methodology to identify the optimal placement of sensors in a multi-zone building. The proposed methodology is based on statistical tests that study the (in) dependence of measurements from various available sensors. The tests advice on a set of most dissimilar sensors to be retained, as they would convey the maximum information. The method starts with an initial setup that can provide measurements of every building zone to carry out this study; any of these sensors can be removed eventually to decrease costs in normal operation. The method has the advantages of being purely data driven and computationally efficient, as against several methods proposed in the scientific literature, that operate under the premise that detailed building models are available, to evaluate the number/position of the required sensors. This property makes the method scale to different buildings, in an expert free manner. The methodology can help towards better characterization of a building for optimal control and monitoring applications. It is validated against a widely used method – Kalman filtering with Grey-box models, using two different case studies. In both cases, the proposed approach agrees with the results using grey box models, suggesting that the method is reliable, while being quick and efficient.