# Improved Solar Operation Control for a Solar Cooling System of an IT Center
## 1 Introduction
In order to shorten the preheating time, which is necessary to achieve a desired solar collector outlet temperature and thereby enable a longer solar operation of a single-effect H\({}_{2}\)O/LiBr absorption chiller, [PERSON] et al. [8] recommend a variable speed pump for the solar collector loop. A comparison of the simulation results to a constant flow strategy showed that the solar fraction can be increased by about 11% by the temperature control strategy. [PERSON] et al. [8] concluded that the longer solar operating time of the chiller (which results from the higher collector temperature) is more important for achieving a high solar fraction than the reduced collector efficiency due to a higher collector temperature. Comparably, [PERSON] et al. [9] investigated a constant flow rate versus constant outlet temperature control by transient simulations of an SAC system with parabolic trough collectors and a double-effect H\({}_{2}\)O/LiBr absorption chiller. They also indicated that the constant temperature control reduces the preheating time in the morning by approximately one hour and extends the operating time of the chiller driven by solar energy by approximately half an hour in the afternoon. To make low collector temperatures applicable, [PERSON] et al. [10] described a control strategy similar to the one mentioned in [11] but without the constraint of an overflowing evaporator. [PERSON] et al. [10] used a control strategy based on the characteristic equation method that determines the required cooling water temperature for a predetermined driving temperature (e.g., from the solar collector field) in order to match a certain cooling load and to maintain the chilled water set value. In addition, using the cooling water temperature as the manipulated variable offers the possibility to save electric power and also water--if a wet cooling tower is used--provided the available driving temperature from the collector is higher than necessary to cover the load. In this case, a higher cooling water temperature can be used, which reduces the electricity demand of the reject heat device. Unfortunately, the characteristic equation applied by [PERSON] et al. [10] is valid only for the 10-kW absorption chiller under investigation (i.e., type sun_inverse_). Furthermore, the external supply flow rates in hot, cooling, and chilled water circuit must agree with the nominal flow rates, because otherwise the slope and loss parameter in the characteristic equation would change. Moreover, [PERSON] et al. [10] did not report on the reverse applicability of the characteristic equation method, i.e., calculating a necessary driving temperature from a given cooling water temperature in order to keep the preheating time short. In this contribution, a model predictive control algorithm is developed, which allows an increase of the solar operating hours of an SAC system by calculating the minimum possible driving temperature under the constraint of a maximum allowed auxiliary electricity demand to ensure a short preheatingtime. This new strategy utilizes an improved and more precise method of characteristic equations [12]. The computer code of the method is available in [13]. The same characteristic equation method is used to control the absorption chiller itself, irrespective of whether it is operated in a solar or conventional cooling system [14]. Since the chiller control is based on characteristic equations, it is called a CE controller. Nevertheless, the focus of this contribution is not on the absorption chiller control (which is done by the CE controller and is described, e.g., in [14; 15]), but on the control of the switchover between solar and backup heat operation of the chiller in a solar cooling system. Hence, following a short description of the SAC system at the Federal Environment Agency in Dessau, Germany (which was used to test the switchover strategy), the improved characteristic equation method is explained only briefly. Afterwards, the model predictive control algorithm for the switchover strategy is derived.