Probabilistic baseline estimation via Gaussian process
Probabilistic baseline estimation via Gaussian process
复制标题
DOI：
10.1109/pesgm.2015.7285756
复制
发表时间：
2015-07
期刊：
影响因子：
--
通讯作者：
Yang Weng;R. Rajagopal
中科院分区：
文献类型：
--
作者：
Yang Weng;R. Rajagopal
需求响应的目的是利用灵活的负荷，以经济有效的方式运行电力系统。需求响应的一个基本问题是如何进行基线估计，以应对电力系统中日益增加的不确定性。不幸的是，传统的基线估计由于其确定性建模而缺乏表征不确定性的能力。这种缺陷通常会导致系统操作错误和付款计算错误，从而阻碍参与客户。在本文中，我们提出了一种基于高斯过程的方法来缓解这个问题。它的特点是能够使用所有的历史数据作为先验知识，并根据过去类似的日常模式调整估计。为了表征不确定性，该方法提供了一种概率估计，不仅可以用于增加系统运营商的估计置信度，而且可以更公平地对待客户。最后，从太平洋天然气和电力公司的数据仿真结果表明，这种新方法可以产生一个高度准确的估计，这大大减少了固有的不确定性，在配电网。这一工作为基于概率估计的电力系统运行打开了大门。
Demand response aims at utilizing flexible loads to operate power systems in an economically efficient way. A fundamental question in demand response is how to conduct a baseline estimation to deal with increasing uncertainties in power systems. Unfortunately, traditional baseline estimation lacks the ability to characterize uncertainties due to their deterministic modeling. This deficiency often results in erroneous system operations and miscalculated payments that discourage participating customers. In this paper, we propose a Gaussian process-based approach to mitigate the problem. It features the ability to use all historical data as a prior knowledge, and adjust the estimation according to similar daily patterns in the past. To characterize the uncertainties, this method provides a probabilistic estimate that can be used to not only increase estimation confidence for system operators but also to fairer treatment to customers. Finally, simulation results from Pacific Gas and Electric Company data show that this new method can produce a highly accurate estimate, which dramatically reduces the uncertainties inherent in the distribution power grid. Such a work opens the door for power system operation based on probabilistic estimate.