Abstract

Quebec's ambition to achieve net-zero emissions by 2050 poses significant challenges in decarbonizing the building and transportation sectors while managing increasing electricity demand. A major obstacle is handling the temporal dynamics of electricity usage, especially unpredictable peak periods influenced by weather, economic activities, and consumer behavior. Efficient demand-side management (DSM) strategies, such as demand response (DR) and Vehicle to Grid (V2G), are essential to shift and flatten peak demand, ensuring a more sustainable and economically viable energy transition.
This thesis introduces the CityEnergy Suite, a comprehensive modeling framework for simulating residential energy-related behavior, electric vehicle (EV) charging, and DR strategies. Leveraging open-access datasets like Census data and energy surveys, the model generates high-resolution load profiles. The CityEnergy Suite employs a modular, agent-based approach to assess the aggregate impact of occupant behavior on the electrical grid and explore future energy scenarios. It comprises three key components: CityAgent, CityLoad, and CityCharge.
CityAgent creates a detailed synthetic population model tailored to the Montreal region, incorporating diverse household compositions and socio-economic characteristics. CityLoad produces stochastic energy load profiles that consider household attributes and appliance usage. CityCharge models urban EV charging demand, enabling analysis of different charging behaviors and penetration scenarios on the grid.
The findings provide crucial insights for developing decarbonization and DSM strategies, highlighting the potential of engaging small customers to support grid stability through load shifting, peak shaving, and emerging V2G technologies. The CityEnergy Suite offers a robust framework for designing inclusive and effective energy policies, contributing significantly to Quebec's decarbonization efforts.