论文标题
行为和人口数据驱动的分配系统负载建模
Behavioral and Population Data Driven Distribution System Load Modeling
论文作者
论文摘要
公用事业或独立系统运营商领域内不同地理区域的分配系统住宅负载建模和分析对于实现小规模的，汇总的分布式能源以根据联邦能源监管委员会号2222 [1]参与网格服务至关重要。在这项研究中，我们开发了一种对不同地理区域中的住宅负载谱进行建模的方法，重点是人类行为影响。首先，我们构建了利用最新设备模型的基于行为的负载配置文件模型。我们使用Markov Chain Monte Carlo方法模拟了人类活动和占用率，该方法用美国时间使用调查数据集校准。其次，我们将模型与美国人口普查局清除的当前人口调查数据联系起来。最后，我们分别使用加利福尼亚和德克萨斯人的人口普查数据填充了两套500户家庭，以对具有不同群体特征（例如不同收入水平）的不同地理区域的负载进行初步分析。为了区分人口行为差异对汇总负载的影响，我们模拟了假设固定的物理参数和天气数据的两组负载曲线。分析表明，平均每日负荷轮廓因收入和收入依赖性而有很大差异，随地方性而有所不同。
Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.
