Enhancing Industrial Heat Pump and Thermal Storage's Sizing with Fixed and Volatile Electricity Procurement Using MILP

This paper investigates the optimal sizing and operational strategies for industrial heat pumps (HPs) with and without thermal energy storage (TES) under fixed and day-ahead electricity pricing schemes. Four scenarios are modeled using Mixed Integer Linear Programming (MILP) implemented in Pyomo, incorporating actual demand data from a paper mill and ENTSO-E electricity prices. Results demonstrate that integrating TES and day-ahead pricing reduces operational costs and peak power demands up to 35%. The model accounts for realistic operational constraints such as ramp rates, start restrictions, and minimum load conditions. Findings indicate that leveraging energy flexibility through optimal sizing and innovative operational strategies provides significant economic benefits in industrial heating applications.