One for all, all for one: A scalable decision-making framework for demand response with a district cooling plant

During demand response events, utility customers typically make decisions to reduce their own load independent of other customers. In the presence of district cooling with a centralized cooling plant, the control decisions of buildings and the district cooling plant become coupled, since the setpoints chosen by the cooling plant affect the consumption of the buildings and vice versa. While past works on demand response address control of building-level heating and cooling systems, control of such systems in the presence of district cooling has not received much attention. We consider the problem of minimizing the discomfort of buildings while meeting the target demand reduction. Specifically, we identify the optimal setpoints for the buildings and district cooling plant, even while obeying the non-linear, coupled thermodynamic constraints between the district cooling plant and buildings. We propose a novel solution strategy using domain knowledge that transforms the complex non-linear optimization problem to a series of quadratic programming which can be then solved conventionally. We validate the performance of the proposed strategy by comparing it with a combinatorial brute-force solution on a small data set. We also evaluate the performance of our strategy on a real-world data set of 416 buildings that are served by a district cooling plant. The results indicate that including the district plant in demand response and solving with the coupled constraints, allows the utility to meet higher target reductions for the same comfort levels. Also, the proposed solution strategy is both fast (at least 4x) and scalable (35x) when compared with conventional optimization solvers.