A Heuristic approach for Online Distributed Optimization of Multi-Agent Networks of Smart Sockets and Thermostatically Controlled Loads based on Dynamic Average Consensus

This paper presents a novel heuristic online optimization method and multi-agent control architecture to optimize the Peak-to-Average power Ratio (PAR) of a large population of Thermostatically Controlled Loads (TCLs) over a sliding receding horizon time window. The proposed architecture exploits only local measurements of the TCL power consumption with no knowledge of their internal temperature. No centralized aggregator of information is used and agents preserve their privacy by cooperating only through consensus-based distributed estimation. TCLs interactions occur via Smart Power Sockets (SPSs) which are interconnected through a peer-to-peer (P2P) network over the internet. The control architecture is designed from a multi-agent perspective in which real household appliances can interact with each other via SPSs.Our contribution is twofold: first we introduce a novel hybrid modelling of the TCL-plus-SPS system along with a method for parameter identification and a method estimate the internal state of the TLC through SPS performed power measurements; then we provide a heuristic algorithm for online distributed optimization of the on/off states of the SPSs which exploits a dynamic average consensus algorithm to estimate the planned future average power consumption of the network while preserving the agents' privacy. Numerical simulations and preliminary experimental results performed in a novel low cost testbed are provided.