From buildings to cities: How household demographics shape demand response and energy consumption

In the face of rapidly evolving energy landscapes, driven by increasing integration of renewable energy and the transition towards a more sustainable grid, the role of demand response (DR) programs in maintaining grid stability and efficiency has become increasingly critical. This study presents an in-depth methodology for assessing the nexus between household demographics and engagement in demand response (DR) programs, investigating their impact on energy consumption and financial outcomes at both building and urban scales. Six household archetypes are defined: single working individual, single retired individual, working couple, retired couple, nuclear family, and lone parent household. Additionally, three energy-use archetypes— austere, average, and wasteful—are considered. Employing a stochastic load profile generator, the study examined variables like annual occupancy hours, thermal energy needs, and appliance consumption. In a Canadian residential context, a shift from a single working adult to a nuclear family occupant archetype led to an increase in annual occupancy by 24%, causing a 21% fluctuation in Energy Use Intensity (EUI). Additionally, diverse consumption behaviors led to a 41% EUI variance. The study evaluated load curtailment capabilities across various household archetypes; notably, a ‘wasteful’ household could reduce their load by 32% through a 2 °C-thermostat adjustment during DR events, and this rose to 54% with pre-heating. At the urban scale, demographic shifts substantially influence load profiles and cause variable peak load times. Areas with more retirees have higher mid-day energy usage. The research quantified DR benefits in load reduction and fiscal gains. By raising DR participation from 10% to 50%, load curtailment grew by 69% in areas with younger families. Pre-heating further enhanced load curtailment by 12% and reduced energy rebound by 4%. Economically, ‘wasteful’ households could accumulate 11% more credits compared to average homes. This research introduced a flexible framework for incorporating household demographics and energy use behavior into DR studies, which can be adapted and re-applied to different case studies based on the availability of relevant data.