Stochastic energy optimization of residential heat pumps in uncertain electricity markets

The penetration of renewable energy sources is increasing in power systems all over the world. Due to the intermittency and volatility of renewable energies, demand-side management is a practical solution to overcome the problem. This paper proposes a stochastic model predictive control for heat pumps to supply space heating and domestic hot water consumption of residential buildings. Continuous-Time Stochastic Model is coded in R language to address the model identification approach. The approach uses the sensor data of households to extract the thermal dynamics of the building. The controller participates in three floors of power markets with high renewable power penetration. The three-stage stochastic programming is suggested to unlock power-to-heat flexibility in the day-ahead, intraday, and balancing markets on long, mid, and short advance notices, respectively. Regarding the close correlation between renewable power availability and electricity price, the price data is modeled as probabilistic scenarios through Auto-Regressive Integrated Moving Average. The ambient temperature, as well as the domestic hot water consumption, are addressed as envelope-bounds with upper and lower thresholds. Finally, the operational strategies of the controller are examined on a 150 m² test house under uncertain electricity prices, weather variables, and occupancy patterns.