Zimmerman Nathan, Kyprianidis Konstantinos, Lindberg Carl-Fredrik
Department of Automation in Energy and Environment, School of Business, Society and Engineering, Mälardalen University, Box 883, 721 23 Västerås, Sweden.
ABB Force Measurement, Tvärleden 2, 721 36 Västerås, Sweden.
Materials (Basel). 2019 Aug 2;12(15):2465. doi: 10.3390/ma12152465.
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.
这项工作的重点是展示降低区域供热网络供回水温度的可行性，以便通过实施前馈模型预测控制来实现节能。当前区域供热技术水平要求较高的供水温度，但从未来前景来看并非如此。部分原因在于，当前网络由运营商经验和室外温度控制。降低网络温度的前景可通过开发该过程的动态模型来评估，然后将其用于控制目的。这项工作提出了两种方案，不仅要评估控制器在提供较低网络温度时的性能，还要评估回水温度的边界。在方案1中，历史负荷用作控制器的前馈信号，在方案2中，负荷预测模型用作前馈信号。两种方案的结果表明，新的控制方法分别可使供应给网络的热量负荷降低12.5%和13.7%。随着对终端用户需求预测准确性的提高以及反馈的加入，回水温度值能够得到更好的维持，并可导致供水温度降低以及生产端节能增加。
