Personalized cooling as an energy efficiency technology for city energy footprint reduction

2018 
Abstract This study analyses the influence of Personalized Conditioning (PC) systems for potential savings of energy, cost, and CO 2 emissions from commercial buildings in different U.S. cities. This analysis characterizes potential benefits from the deployment of PC systems during peak cooling hours for peak load shifting. PC systems deployed in coordination with the central building air conditioning systems could have a large-scale influence on a city's energy footprint. Specifically, portable PC systems that use Phase Change Materials (PCMs) for heat rejection, allow for heat absorption during the working hours and heat rejection during non-working hours typically coinciding with the off-peak (base) utility rates when the commercial building tend to be unoccupied. However, there are limiting factors for the potential energy and cost savings with the use of PC systems. Therefore, this study assesses the use of PC systems in addition to the existing building air conditioning system during cooling seasons. The assessment entails potential energy end-use savings for 7 major cities located in different geographical/climatic regions of the U.S. Furthermore, the study calculates potential cost savings based on the variations in the peak and off-peak (base) electricity rates for different local Time of Use (TOU) programs. This simulated evaluation of local building systems and utility programs allows for regional various on the city's energy footprint reduction to emerge. The analysis shows that midrise apartments are a better building type than office buildings for the deployment of PC systems during a cooling season. The cash savings per person for the deployment of PC systems for midrise apartments are $62/year, $40/year, and $37/year for Honolulu, NY City, and Phoenix, respectively. The simulations also showed that using extended setpoint temperatures could reduce the CO 2 emissions up to 21.4% per year.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    33
    References
    22
    Citations
    NaN
    KQI
    []