Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm

The rapid developments of advanced metering infrastructure and dynamic electricity pricing provide great opportunities for residential electrical appliances, especially air conditioners (ACs), to participate in demand response (DR) programs to reduce peak power consumptions and electricity bills. One of the biggest challenges faced by residential DR participants is the lack of intelligent DR control methods which enable residential ACs to automatically respond to dynamic electricity prices. Most existing studies on DR control of residential ACs focus on single-speed ACs. However, inverter ACs which have higher part-load efficiencies have been extensively installed in today's residential buildings. This paper presents a novel model-based DR control method for residential inverter ACs to automatically and optimally respond to day-ahead electricity prices. A control-oriented room thermal model and steady-state model of inverter ACs are developed and integrated to predict the coupled thermal response of the room and AC for the purpose of model-based control. Optimal scheduling of indoor air temperature set-points is formulated as a nonlinear programming problem which seeks the preferred trade-offs among electricity costs, thermal comfort and peak power reductions. Genetic algorithm (GA) is used to search the optimal solution of the nonlinear programming problem. Simulation results show that compared with the baseline case, the proposed model-based optimal control method can reduce the whole electricity costs and the peak power demands during DR hours while meeting thermal comfort constraints. Besides, sensitivity analyses on the trade-off weightings in the optimization objective function demonstrate that electricity costs, occupant comfort and peak power reductions are sensitive to the weightings and the use of the weightings is effective in achieving the best trade-off.