Abstract

In cold-climate regions, residential heating represents a major component of electricity demand, and electric heating systems combined with PV–battery integration offer significant potential for enhancing provision of load energy flexibility in response to grid needs; however, their operation remains strongly influenced by uncertainty in weather forecasts. This thesis develops and evaluates a predictive control framework to enhance heating energy flexibility in electrically heated residential buildings under weather forecast uncertainty. A control-oriented grey-box thermal model based on a three resistance–two capacitance (3R–2C) thermal network represents the thermal dynamics of a typical single-family house in Québec equipped with rooftop photovoltaic (PV) generation and battery storage. The model is integrated within a Model Predictive Control (MPC) framework that incorporates day-ahead weather prediction through scenario-based representations derived from historical variability. The controller coordinates heating operation, PV generation, battery charging and discharging, and grid electricity exchange under time-varying electricity pricing.
The proposed MPC demonstrates robust performance under weather uncertainty by maintaining total heating energy comparable to reactive control while keeping indoor air temperature within the predefined range close to the heating setpoint and reducing peak grid electricity import and operating costs. Under representative winter scenarios, peak-period grid electricity import and daily electricity operating costs are reduced by approximately 69–76% and 30–48%, respectively, compared with reactive control with PV–battery integration. Weather forecast uncertainty in one-day-ahead predictions leads to variations in peak grid import and daily electricity cost of approximately 46% and 38%, respectively. These findings demonstrate the potential of predictive control with PV–battery integration to enhance residential energy flexibility and reduce peak electricity demand in cold-climate regions while accounting for weather uncertainty.