Integrated HVAC management and optimal scheduling of smart appliances for community peak load reduction

Of the total electricity-generating capacity in the United States, 20% is dedicated to meet peak loads. Strategies to mitigate volatility in energy consumption have the potential to reduce the need for this surplus capacity. Here we investigate the potential for residential consumers to lower community-level peak demand through home energy management systems. We focus on the combination of air-conditioning use with the operation of time-shiftable appliances in the southern U.S. A centralized model predictive control (MPC) scheme minimizes peak air-conditioning (A/C) energy use by altering the thermostat set-points in individual homes. We simultaneously schedule the operation of time-shiftable appliances to further reduce the community peak load. The scheduling problem is formulated as a mixed-integer linear program (MILP) aimed at minimizing peak load under constraints that reflect the start times and allowed delays of individual appliances (e.g. dishwashers, washing machines, dryers) in each house. Using sample data collected from residential homes and consumer survey data located in Austin, TX, USA we show that the proposed integrated control and scheduling approach can minimize the peak load for the neighborhood by leveraging the physical differences and individual preferences between houses. On average, our framework is able to reduce the daily peak load for the group of houses by 25.5% (18.2 kW) when compared with the load for individually controlled thermostat settings and appliance start times.