Virtual testbed for model predictive control development in district cooling systems

Recently, with increasing cooling demands, district cooling has assumed an important role as it is more efficient than stand-alone cooling systems. District cooling reduces the environmental impact and promotes the use of renewable sources. Earlier studies to optimise the production plants of district cooling systems were focused primarily on plants with compressor chillers and thermal energy storage devices. Although absorption chillers are crucial for integrating renewable sources into these systems, very few studies have considered them from the cooling perspective. In this regard, this paper presents the progress and results of the implementation of a virtual testbed based on a digital twin of a district cooling production plant with both compressor and absorption chillers. The aim of this study, carried out within the framework of INDIGO, a European Union-funded project, was (i) to develop a reliable model that can be used in a model predictive controller and (ii) to simulate the plant using this controller. The production plant components, which included absorption and compressor chillers, as well as cooling towers, were built using the equation-based Modelica programming language, and were cali-brated using information from the manufacturer, together with real operation data. The remainder of the plant was modelled in Python. To integrate the Modelica models into the Python environment, a combination of machine learning techniques and state-space representation models was used. With these techniques, models with a high computational speed were obtained, which were suitable for real-time applications. These models were then used to build a model predictive control for the production plant to minimise the primary energy usage. The improvements in the control and the resultant energy savings achieved were compared with a baseline case working on a standard cascade control. Energy savings up to 50% were obtained in the simulation- based experiments