Predictive rule-based control for an air-source heat pump system: Environmental and economic impacts in future energy scenarios

Decarbonizing power generation is essential to drastically reduce greenhouse gas emissions. However, intermittent renewable energy sources challenge the electricity system to balance the resulting over- and under-supply. In this context, the flexibility of the energy demand of buildings is a major factor. This study investigates two predictive rule-based control strategies for a German building with an air-to-water heat pump. While one control aims to reduce greenhouse gas emissions from the heat pump's electricity consumption, the other load-shifting strategy aims to reduce electricity costs. To evaluate the saving potentials of the controllers in five different electricity scenarios, a combination of dynamic building and system simulation, linear optimization modeling, and life cycle assessment is used to determine hourly environmental impacts and electricity costs for the years 2019, 2030, 2035, 2040, and 2045. The study shows that the potential for load-shifting depends on the amplitude of the control signal variation, i.e., greenhouse gas emissions or electricity costs. In 2035, the CO2-based control achieves the most considerable GHG reduction at 32 %. The price-based strategy can reduce electricity costs by up to 23 %. Both strategies are complementary, and thus, the greenhouse gas-driven control always leads to a reduction in electricity costs, while the price-based load-shifting also leads to a reduction in CO2-eq emissions. Other environmental impacts of the load-shifting strategies are also examined. The analysis shows that load-shifting has further positive effects on acidification, marine and terrestrial eutrophication, and photochemical ozone creation. However, both predictive rule-based control strategies increase metal and mineral consumption and land use.