Sammendrag
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.
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