Optimal operation of industrial heat pumps with stratified thermal energy storage for emissions and cost reduction using day-ahead predictions

This paper investigates the reduction of operational costs and CO₂ emissions resulting from an optimal operation of an industrial heat pump paired with a thermal energy storage (TES) in a brewery. The objective of the study is to investigate the potential benefits of load shifting using the TES and the impact of the fluctuations of electricity price, solar radiation and carbon intensity of the grid. The study employs a linear programming model to develop four distinct operation schedules for the heat pump, two aiming to minimize carbon emissions and two for operational costs. It considers both a perfect foresight scenario to illustrate the full potential and a more realistic, limited day-ahead prediction horizon. The results show that the optimal operation schedules can reduce the heat pump operating costs by up to 17.3 % and CO₂ emissions by up to 15.4 %. Even under the constrained day-ahead prediction horizon, the costs could be reduced by up to 15.5 % and the emissions by up to 14.2 %. Furthermore, the study reveals the interplay between cost optimization and emission reduction since the cost-optimal scheduling would also reduce emissions and vice versa. The investigation also includes the implications of these optimal control strategies on system sizing, indicating the potential benefits of adapting to smaller storage volumes and larger heat pumps.