Attaining a higher flexibility degree in CO2 compressor racks

CO2 compressor racks have shown their suitability for commercial and industrial refrigeration systems at any location and climate. Even if some references state that CO2 units can compete in capital cost with any other alternative solution, often investment costs are still the main barrier for the global expansion of CO2. This work explores, numerically and experimentally, the implementation of “pivoting” compressors, i.e. compressors that can operate in the medium temperature (MT) and parallel compressor suction groups, depending on ambient conditions, cooling loads and use of ejector. The objective is to increase the flexibility of CO2 compressor racks, keeping the efficiency and, potentially, reducing the investment. This study shows that this solution with “pivoting” compressors is beneficial in ejector-supported systems, since the investment cost of the ejectors is compensated by a lower number of installed compressors, as compressor capacities can be applied in more flexible ways. Keywords: Refrigeration, Carbon Dioxide, Compressors, Switching, Pivoting.acceptedVersio

Nonconvex Model Predictive Control for Commercial Refrigeration

We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimize the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimization method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid