Development of a Combined Model Predictive and Adaptive Control Strategy for the Operation of a Cold District Heating Network
Abstract
Cold district heating networks, also called anergy networks, reduce thermal losses induced by high operation temperatures using relatively low temperatures for energy distribution and storage. In Order to further optimize the operation of such a network a model predictive control (MPC) including an adaptive control strategy is investigated for controlling both, the thermal energy supply as well as the heat and cold demand. An ice store with a very large thermal capacity represents the main energy store and acts as heat source for the heat pumps within the investigated grid. Since it is discharged during winter for heat supply and charged during summer for cooling purposes it functions as a seasonal energy store. Using model predictive controls over such long time periods is not feasible due to computational restrictions. Hence an adaptive control part is proposed and implemented into an MPC for an optimum control of the ice store throughout the year. First simulation results are shown in terms of results for the optimum seasonal control of the operation of the ice store and system simulation results using both predictive and non-predictive control strategies.