An exact linearization of mixed-binary quadratic model predictive control for real-time demand response

Frequency regulation is a fast-timescale power balancing service which is growing in importance given the increasing penetration of renewable in power systems. Demand response can be utilized for frequency regulation through the control of thermostatic controlled loads. For instance, a heating, ventilation, and air conditioning (HVAC) system can be sequentially turned ON and OFF to modulate the load’s consumption. In this work, we consider a large aggregation of loads, and aim to track a setpoint representing a power imbalance, e.g., due to renewable intermittenary, while always keeping the loads temperature within acceptable bounds. We consider the lockout constraint which prevents successively turning ON and OFF an HVAC. We formulate the demand response as a model predictive control problem to track the regulation setpoint while accounting for the load dynamics. The MPC problem takes the form of a mixed-binary, multi-period quadratic program which is ill-suited to fast-timescale decision making. We propose an exact linearization to reformulate the problem into a mixed-binary linear program. This results in a significant reduction in running time as well as a remarkable computational resource deduction without compromising the quality of the solution. Preliminary results generated from the simulation will also be presented.