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The cooling energy cost could be a significant portion of the total energy cost for a large organization or building complex during summer. A hybrid system or thermal energy storage system is usually applied to reduce the energy cost. However, without proper integration and operation, the advantage of using such systems could be limited.
This study presents a general energy cost optimization methodology and mathematical model for a hybrid cooling system under a complex electricity cost structure. The model considers the efficiency of the hybrid cooling system and multiple energy sources. The energy cost evaluation reflects a complex cost structure including electrical energy cost, electrical demand cost, electrical ratchet cost, fuel cost, and electrical energy consumption from other facilities.
The optimization model is constructed as a mixed integer nonlinear program. To reduce high computational intensity, a dual-stage solution method is used by introducing a decision variable of the electrical demand limit as a constraint. This reduced computation provides the possibility of the real time implementation of the model for practical purposes. This study also shows how the optimization model can be used with a simple cooling load forecasting strategy to avoid a high electricity cost.
A case study of the central cooling system in an academic institution shows that the developed methodology and model can be used to reduce around $150,000 in energy cost per year. In particular, the case study shows that the developed optimization model can significantly reduce the high demand punitive costs that are hard to reduce in the current manual operation based on operator experiences. In the case study, this reduction is possible by properly shifting part of the cooling load from electric chillers to steam turbine chillers during peak electrical demand season and thus decreasing the peak electricity consumption under the complex demand charge structure.
Adviser: Jeonghan Ko