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Identification of Principal Factors in Determining Building Peak Energy Shaving Capacities during Demand Response Events

机译:确定在需求响应事件中确定建筑物峰值节能能力的主要因素

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In the U.S., the building sector consumes 70% of electricity and lays massive pressure on national grids. To avoid electricity blackouts, demand response programs incentivize end-consumers for reducing their electricity demand during peak hours. Therefore, it is essential for grid operators to understand the electricity shaving capacity of buildings. However, previous studies either simplify buildings as black-boxes—resulting in low accuracy in estimations, or represent buildings with detailed information—resulting in over-parameterized models. In this study, the authors provided a computational framework to identify principal factors that dictate peak shaving capacities of buildings. In total, fifteen buildings during twelve DR events were used as testbeds as validation. The results showed that the day-in-the-week and the quantity of relevant equipment are part of the principal factors behind peak capacity determination. With this framework, practitioners can represent buildings beyond black-boxes with less complexity and promising accuracy of peak shaving capacity determination.
机译:在美国,建筑业消耗70%的电力,并给国家电网带来巨大压力。为了避免电力中断,需求响应计划会激励最终用户减少高峰时段的电力需求。因此,对于电网运营商来说,了解建筑物的剃须能力至关重要。但是,先前的研究要么将建筑物简化为黑匣子(导致估算的准确性较低),要么用详细的信息表示建筑物,从而导致参数化过高。在这项研究中,作者提供了一个计算框架,以识别决定建筑物削峰能力的主要因素。总共将十二个灾难恢复事件期间的十五座建筑物用作测试平台进行验证。结果表明,每周的工作量和相关设备的数量是确定高峰容量的主要因素。有了这个框架,从业人员就可以代表黑匣子之外的建筑物,其复杂性较低,并且具有确定峰值削峰能力的准确性。

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