On Wednesday March 7 PhD student Maomao Hu from Hong Kong Polytechnic University gave a presentation with the title “Model Predictive Control of Inverter Air Conditioners Responding to Real-time Electricity Prices in Smart Grids“.
The rapid development of smart grids has proposed a new requirement for residential appliances, i.e. being demand-response-enabled (DR-enabled). The key feature of DR-enabled appliances is able to respond to real-time electricity pricing (RTP) from an electric utility or third-party load aggregator, which helps DR program participants to effectively shift peak power demands from high-RTP to low-RTP periods. Advanced supervisory DR control methods are key to the development of DR-enabled appliances. Residential inverter air conditioners (ACs), as the major contributors to home electricity bills, have been extensively installed in today’s residential buildings due to its high energy efficiency at part-load conditions. In this study, we make the first attempt to apply model predictive control (MPC) method to inverter AC to make it RTP-responsive. In contrast to the classic local control methods, the MPC approach can simultaneously consider all influential variables including weather condition, occupancy and RTP into optimization problems. A TRNSYS-MATLAB co-simulation testbed is developed to test the performances of the integrated building energy system under various control methods. Test results show that compared with conventional PID control, the cost-oriented MPC for inverter AC can help to reduce total energy consumptions, shift peak power demands and reduce electricity costs while satisfying indoor thermal comfort.