IMPROVEMENT OF DEMAND RESPONSE QUICK ASSESSMENT TOOL (DRQAT) AND TOOL VALIDATION CASE STUDIES
IMPROVEMENT OF DEMAND RESPONSE QUICK ASSESSMENT TOOL (DRQAT) AND TOOL VALIDATION CASE STUDIES
复制标题
需求响应快速评估工具 (DRQAT) 和工具验证案例研究的改进
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
D. Black
中科院分区：
文献类型：
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作者：
Rongxin Yin;D. Black
作者(S)：尹荣新；布莱克，道格|摘要：2006年，劳伦斯伯克利国家实验室需求响应研究中心启动了建筑需求响应快速评估工具的开发，并于2007年发布了第一个版本的需求响应快速评估工具供公众使用。在过去的几年里，DRRC一直在根据用户的反馈改进DRQAT工具，并使用EnergyPlus能源模拟工具升级引擎。目前，DRQAT允许用户一次评估单个灾难恢复策略配置。用户可以一次运行多个策略配置，并在单个输出报告中直接比较它们的性能，这将使用户受益匪浅。本报告中描述的DRQAT的最新更新使用户能够这样做，以比较不同的预冷和重置策略。此外，为了帮助客户更好地了解其设施的需求响应性能，本报告提供了几个案例研究，以比较需求响应预测和实测值。先前的一项研究表明，在用测量数据校准模型后，DRQAT模拟模型的预测值可以显著提高。大多数用户不熟悉模型校准，这一过程可能很耗时。此报告显示了典型用户在没有校准的情况下生成的DRQAT结果的比较。结果表明，DRQAT工具可以在整个需求响应事件小时内生成对高峰节电和负荷形状的可靠预测。
Author(s): Yin, Rongxin; Black, Doug | Abstract: In 2006, the Demand Response Research Center (DRRC) at Lawrence Berkeley National Laboratory (LBNL) initiated the development of a quick assessment tool for demand response in buildings and, in 2007 the DRRC released the first version of the Demand Response Quick Assessment Tool (DRQAT) for public use. Over the past few years, the DRRC has been improving the DRQAT tool based on users’ feedback and upgrading the engine with the EnergyPlus energy simulation tool. Currently, DRQAT enables users to evaluate a single DR strategy configuration at a time. Users could greatly benefit from being able to run multiple strategy configurations at a time and directly compare their performance in a single output report. The latest update of DRQAT, described in this report, enables users to do just that to compare different pre-cooling and reset strategies. Also, to help customers better understand the demand response performance of their facilities; this report presents several case studies to compare demand response predictions with measured values. A previous study indicated that the predictive value of the DRQAT simulation model could be significantly improved after calibrating the model with measured data. Most users are not familiar with model calibration, a process that can be time consuming. This report shows a comparison of DRQAT results generated as a typical user would—without calibration. The results show that the DRQAT tool can generate credible predictions of peak demand savings and load shapes throughout demand response event hours.