Abstract
The focus of this work is to present the feasibility of lowering the supply and return temperatures of district heating networks in order to achieve energy savings through the implementation of feed-forward model predictive control. The current level of district heating technology dictates a need for higher supply temperatures, which is not the case when considering the future outlook. In part, this can be attributed to the fact that current networks are being controlled by operator experience and outdoor temperatures. The prospects of reducing network temperatures can be evaluated by developing a dynamic model of the process which can then be used for control purposes. Two scenarios are presented in this work, to not only evaluate a controller’s performance in supplying lower network temperatures, but to also assess the boundaries of the return temperature. In Scenario 1, the historical load is used as a feed-forward signal to the controller, and in Scenario 2, a load prediction model is used as the feed-forward signal. The findings for both scenarios suggest that the new control approach can lead to a load reduction of 12.5% and 13.7% respectively for the heat being supplied to the network. With the inclusion of predictions with increased accuracy on end-user demand and feed-back, the return temperature values can be better sustained, and can lead to a decrease in supply temperatures and an increase in energy savings on the production side.
Highlights
District heating technology is a well established process for distributing localized heat production in order to meet customers’ spacial and hot water requirements
Presented in this work is a method for the modelling and control of a district heating network
The prospects of evaluating the thermal dynamics inherent in district heating networks is achievable through the development of a simplified dynamic physical model
Summary
District heating technology is a well established process for distributing localized heat production in order to meet customers’ spacial and hot water requirements. Lowering distribution temperatures to the levels of 4GDH will rely on the assumption that customer substations are working properly and that buildings meet higher energy standards Both circumstances will be difficult to overcome when cities with existing networks are considered, due to the fact that the current share of buildings requiring higher temperatures is expected to constitute the majority of the heating demand for decades to come [8]. Model predictive control (MPC) can be utilized to meet the customers’ demands and to achieve lower return temperatures in the network This achievement is of great interest to heat providers as it has the potential to reduce the heat load on the plant side, i.e., it would be possible to reduce peak load production during colder months by lowering the network supply temperature
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