Power utilities leverage demand response (DR) events to effectively reduce the peak load at critical times with excessive power demand. DR programs are generally categorized as manual or automated from the automation perspective. The opportunities for automated DR in the residential sector have emerged with the integration of smart and connected loads. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers’ direct involvement, given that many consumers might not engage sufficiently to participate in the manual DR. However, it has been shown that unjustified load shifting from many consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target consumers for participation according to efficacy criteria. To address this issue, in this letter, we propose a data-driven method for the selection of consumers according to their potential for demand reduction in a DR program. The proposed method characterizes the frequency, consistency, and the peak time usage of deferrable loads across several subsequent days. By measuring the impact on peak-load shaving, we evaluated our approach on a subset of electricity dataset from the Pecan Street Dataport. The findings demonstrate the efficacy of the proposed method in selecting consumers with deferrable loads based on their potential for demand reduction in the future events.

中文翻译：

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数据驱动的需求响应程序可延迟负载消费者识别

电力公司利用需求响应 (DR) 事件在电力需求过多的关键时刻有效地降低峰值负载。从自动化的角度来看，DR 程序通常分为手动或自动。随着智能和连接负载的集成，住宅领域的自动化 DR 机会已经出现。例如，可以安排具有可延迟负载的智能设备在没有消费者直接参与的情况下转移负载，因为许多消费者可能没有足够的参与度来参与手动 DR。然而，事实表明，高峰时段许多消费者不合理的负载转移可能会导致高峰期的高需求。因此，公用事业公司必须根据功效标准确定和定位消费者参与。为了解决这个问题，在这封信中，我们提出了一种数据驱动的方法，用于根据消费者在 DR 计划中减少需求的潜力来选择他们。所提出的方法表征了可延迟负载在随后几天内的频率、一致性和峰值时间使用情况。通过测量对削峰填谷的影响，我们在 Pecan Street Dataport 的电力数据集子集上评估了我们的方法。研究结果证明了所提出的方法在根据未来事件中需求减少的潜力来选择具有可延迟负载的消费者方面的有效性。以及随后几天可延迟负载的高峰时间使用情况。通过测量对削峰填谷的影响，我们在 Pecan Street Dataport 的电力数据集子集上评估了我们的方法。研究结果证明了所提出的方法在根据未来事件中需求减少的潜力来选择具有可延迟负载的消费者方面的有效性。以及随后几天可延迟负载的高峰时间使用情况。通过测量对削峰填谷的影响，我们在 Pecan Street Dataport 的电力数据集子集上评估了我们的方法。研究结果证明了所提出的方法在根据未来事件中需求减少的潜力来选择具有可延迟负载的消费者方面的有效性。