Economically Optimal Control of a Cold Room Using an Artificial Neural Network and Dynamic Programming

• Published in: IFAC-PapersOnLine Vol. 52; no. 13; pp. 2002 - 2007
• Main Authors: Ribault, Alnour, Vercraene, Samuel, Henry, Sébastien, Ouzrout, Yacine, Peguet, Lucie
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2019
• Subjects: Energy Storage Operation; Energy systems; Industrial applications of optimal control; Modeling for control optimization; Neural networks; Planning; Energy systems; Planning; Neural networks; Industrial applications of optimal control; Energy Storage Operation; Modeling for control optimization
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2019.11.497

We consider the economically optimal control of a cold store with a single cold room. The thermal inertia of a cold room acts as an energy storage and can therefore be used for economic optimization in the presence of a dynamic electricity price, under a bounding constraint on the internal temperature of the cold room. However, a high number of frost production startups may induce premature wear of the cold store’s compressors. Since the thermal losses are a function of the internal temperature of the cold room, conventional inventory management solving techniques are not suited for this problem. In this paper, we use an artificial neural network as temperature forecast. A dynamic programming algorithm is used to solve the model that includes the non-linear artificial neural network temperature forecast and a fixed cost at each compressor startup. This allows us to solve industrial instances of the problem optimally and within reasonable time. We show the interest of solving the problem optimally as opposed to using a conventional hysteresis-based control method, and discuss the opportunity of using an dynamic hourly price based on the electricity market instead of a traditional contracted price.