%0 Journal Article
%K Modelica
%K Condenser water set point
%K Model predictive control (MPC)
%K Optimization frequency
%K Optimization starting point
%A Sen Huang
%A Wangda Zuo
%A Michael D Sohn
%B Building and Environment
%D 2017
%G eng
%P 33 - 46
%R 10.1016/j.buildenv.2016.10.011
%T Improved cooling tower control of legacy chiller plants by optimizing the condenser water set point
%V 111
%8 01/2017
%! Building and Environment
%X <div class="rendering rendering_researchoutput  rendering_researchoutput_abstractportal rendering_contributiontojournal rendering_abstractportal rendering_contributiontojournal_abstractportal"><div class="textblock"><p>Achieving the optimal control of cooling towers is critical to the energy-efficient operation of current or legacy chiller plants. Although many promising control methods have been proposed, limitations in their applications exist for legacy chiller plants. For example, some methods require the change of the plant's overall control structure, which can be difficult to legacy chiller plants; some methods are too complicated and computationally intensive to implement in old building control systems. To address the above issues, we develop an operational support system. This system employs a model predictive control scheme to optimize the condenser water set point and can be applied in chiller plants without changes in the control structure. To further facilitate the implementation, we propose to increase the optimization accuracy by selecting a better starting point. The results from a case study with a real legacy chiller plant in Washington D.C. show that the proposed operational support system can achieve up to around 9.67% annual energy consumption savings for chillers and cooling towers. The results also show the proposed starting point selection method can achieve a better accuracy and a faster computational speed than commonly used methods. In addition, we find that we can select a lower optimization frequency for the studied case since the impact of the optimization frequency on the energy savings is not significant while a lower optimization frequency does reduce the computational demand to a great extent.</p></div></div><div class="rendering rendering_researchoutput  rendering_researchoutput_detailsportal rendering_contributiontojournal rendering_detailsportal rendering_contributiontojournal_detailsportal">&nbsp;</div>