Data-driven Network-aware Control of Thermostatically Controlled Loads with Unknown Dynamics

This paper presents a novel data-driven control algorithm that coordinates a large aggregation of heterogeneous thermostatically controlled loads (TCLs) with unknown temperature dynamics and disturbance distributions to provide balancing services to the grid. Each TCL is subject to local constraints on their temperatures due to user comfort and global constraints on the number of TCLs in a mode, called mode-counting constraints, for the safe operation of the distribution network. We first develop a data-driven method to compute sets of modes at each discretized interval of the temperature state space in which the probability of moving outside of the temperature dead-band at the next step is bounded at a certain confidence level. Subsequently, we design a model predictive control (MPC) algorithm that instructs every TCL to switch to the modes within the obtained set while satisfying the mode-counting constraints. A case study compares the proposed control algorithm with a benchmark and verifies its effectiveness in maintaining end-user comfort and the safe operation of the distribution network.