Model Predictive Control of Distributed Air-Conditioning Loads to Compensate Fluctuations in Solar Power

Flexible loads such as residential air-conditioners (ACs) can be directly controlled to provide demand-side regulation and balance services in electricity grids. Large aggregations of ACs offer a resource akin to that of a distributed energy storage system, which may be used to compensate fluctuations in the power output of local renewable energy generation. This paper formulates distributed and centralized model predictive control (MPC) strategies to balance fluctuations in solar power generation by directly controlling the aggregate demand of clusters of distributed residential ACs. The proposed receding-horizon control strategies rely on a new second-order linear time-varying model for the aggregate demand response of a population of heterogeneous ACs to changes in thermostat setpoints under varying ambient temperature. The performance of the proposed MPC strategies is analyzed in a numerical simulation study implementing AC demand tracking of 1-min fluctuations in actual photovoltaic capacity based on persistence and sky imager short-term solar forecasts. The results show that distributed and centralized MPC strategies achieve comparable performance, with better performance of persistence forecasts in a shorter prediction horizon, and better performance with sky imager forecasts on a longer prediction horizon.