Towards dynamic pricing algorithm for residential buildings: A Model Predictive Control framework for load aggregation

With the rise of aggregators, establishing an effective pricing framework for electricity allocation among substations serving residential customers is crucial. This paper proposes a method for large-scale demand-side flexibility, smart thermostat data, along with ambient temperature and heating power measurements, are utilized to calibrate models of reduced-order resistance-capacitance (RC) thermal networks for buildings. A Monte Carlo framework evaluates the optimal level of demand-side management (DSM), strategy diversification, and uncertainty in aggregated demand, guiding an economic Model Predictive Control framework for day-ahead coordination market. Simulations conducted on numerous homes in Toronto, Ontario, demonstrate that implementing medium-to-high DSM can enhance grid stability, leading to a 94% increase in load factor. The findings suggest that a balanced portfolio of aggregation provides the most benefit, and a three-tier tariff structure results more effective for load flattening than a two-tier system. This approach offers a viable pathway to improve electricity grid management in residential areas.