Dimensioning and techno-economic-assessment of thermal energy storages in the food processing industry using energy load profiles

Abstract

The food industry is a major consumer of electrical energy, which is required for cooling, freezing, drying and heating. Due to the production characteristics, high load peaks often occur in food processing. This leads not only to the need of oversizing the required equipment (e.g. compressors), but also to a shorter lifetime of these, as well as high peak load electricity prices. By integrating a thermal energy storage (TES), supply and demand for thermal energy can be decoupled, thus avoiding peak loads and ensuring a more stable operation of the refrigeration system. At the same time, TES ensures stable and low temperatures and thereby food quality and shelf life. Sensible TES are commonly used in the processing industry in the form of large water tanks, but latent TES using phase change materials (PCM) as storage medium are still under development for different applications. In particular, cold thermal energy storage (CTES) using PCM for storage temperatures below 0 °C are not widely used. In this paper, a python algorithm is presented that uses inputs from a process (hour-based thermal demand and electricity prices) to predict the impact of introducing TES in terms of reducing operating costs. The algorithm uses an optimization-based method to select and dimension the cost-optimal size of pillow-plate PCM thermal storage. In this paper, the Python algorithm is tested using load profiles from the pelagic fish processing industry, with ammonia refrigeration system, which is particularly challenging due to unpredictable and periodic production rhythm.

Dimensioning and techno-economic-assessment of thermal energy storages in the food processing industry using energy load profiles