A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant

Abstract

This article focuses on addressing the chiller sequencing problem of chiller plants by establishing a comprehensive energy management framework. The contribution of this work is threefold. Firstly, a distributive modeling architecture is presented that establishes five concurrent models as input to the framework. A synergy among these models is exploited to formulate a chance-constrained stochastic chiller sequencing problem. Secondly, a quantified life expectancy model of the chiller plant is introduced and a case is made for why it is influential in delivering industrially applicable solutions. The model attempts to strike a balance between economic optimality and improved reliability. Thirdly, a chiller data pre-processing protocol incubating two heuristic algorithms is proposed to address measurement uncertainties of the chiller plant state variables. Furthermore, this work develops a robust ensemble model to accurately forecast the cooling load and embeds a chain of reformulations to improve the global solution's optimality. The developed framework is realized with the plant at the Indian Institute of Technology Gandhinagar. The results confirm that the proposed framework leads to a significant amount of power savings. In comparison to conventional scheduling, the chiller plant power consumption can be reduced by up to 6.2 %, thereby illustrating its efficacy.