Estimating Demand Response Load Impacts: Evaluation of BaselineLoad Models for Non-Residential Buildings in California
Estimating Demand Response Load Impacts: Evaluation of BaselineLoad Models for Non-Residential Buildings in California
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估计需求响应负载影响：加利福尼亚州非住宅建筑基线负载模型的评估
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
S. Kiliccote
中科院分区：
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作者：
K. Coughlin;M. Piette;C. Goldman;S. Kiliccote
联邦和加州州的政策制定者越来越有兴趣开发更标准化和一致的方法来估计和验证需求响应计划和动态定价关税的负荷影响。本研究描述了一个实用的分析性能的不同模型用于计算的基线电力负荷的商业建筑参与需求响应（DR）计划，强调的重要性，天气的影响。在DR事件期间，可以对建筑物操作进行各种调整，目的是降低建筑物峰值电力负荷。为了确定实际的峰值负荷降低，需要估计在没有任何DR措施的情况下事件发生当天的负荷。此基准负荷配置文件（BLP）是准确评估基于事件的DR计划的负荷影响的关键，也可能影响某些类型的DR计划的支付结算。我们在位于加州的33栋建筑物样本上测试了7个基线模型，这些模型可以大致分为两组：（1）平均方法，使用前几天每小时负荷值的线性组合来预测事件的负荷，（2）显式天气模型，使用基于当地每小时温度的公式来预测负荷。该模型在有和更多»无早晨调整的情况下进行了测试，早晨调整使用事件当天的数据来调整估计的BLP。本研究的主要发现是：-加州公用事业公司目前使用的BLP模型的准确性，以估计几个DR程序中的负荷减少（即，如果对天气敏感的商业和机构建筑物应用早晨调整系数，则前10天中最高3天的每小时使用率）可以得到显著改善。- 应用早晨调整因子显著降低了偏差，提高了我们建筑样本中所有BLP模型的准确性。- 对于低负荷变化的建筑物，所有BLP模型的准确性都相当好。- 对于负载高度可变的客户账户，我们发现没有BLP模型产生准确的结果，尽管平均方法在准确性方面表现最好（但不是偏差）。这些类型的客户很难用依赖于历史负荷和天气数据的标准BLP模型来表征。这些结果对DR程序管理员和决策者的影响是：-大多数DR程序将类似的DR BLP方法应用于商业和工业部门客户。我们的研究结果与其他最近的研究（量子2004年和2006年，Buege等人，2006）建议，灾难恢复计划管理员应具有灵活性和多个选项，以便为特定类型的客户建议最合适的BLP方法。«少
Both Federal and California state policymakers areincreasingly interested in developing more standardized and consistentapproaches to estimate and verify the load impacts of demand responseprograms and dynamic pricing tariffs. This study describes a statisticalanalysis of the performance of different models used to calculate thebaseline electric load for commercial buildings participating in ademand-response (DR) program, with emphasis onthe importance of weathereffects. During a DR event, a variety of adjustments may be made tobuilding operation, with the goal of reducing the building peak electricload. In order to determine the actual peak load reduction, an estimateof what the load would have been on the day of the event without any DRactions is needed. This baseline load profile (BLP) is key to accuratelyassessing the load impacts from event-based DR programs and may alsoimpact payment settlements for certain types of DR programs. We testedseven baseline models on a sample of 33 buildings located in California.These models can be loosely categorized into two groups: (1) averagingmethods, which use some linear combination of hourly load values fromprevious days to predict the load on the event, and (2) explicit weathermodels, which use a formula based on local hourly temperature to predictthe load. The models were tested both with andmore » without morningadjustments, which use data from the day of the event to adjust theestimated BLP up or down.Key findings from this study are: - The accuracyof the BLP model currently used by California utilities to estimate loadreductions in several DR programs (i.e., hourly usage in highest 3 out of10 previous days) could be improved substantially if a morning adjustmentfactor were applied for weather-sensitive commercial and institutionalbuildings. - Applying a morning adjustment factor significantly reducesthe bias and improves the accuracy of all BLP models examined in oursample of buildings. - For buildings with low load variability, all BLPmodels perform reasonably well in accuracy. - For customer accounts withhighly variable loads, we found that no BLP model produced satisfactoryresults, although averaging methods perform best in accuracy (but notbias). These types of customers are difficult to characterize withstandard BLP models that rely on historic loads and weather data.Implications of these results for DR program administrators andpolicymakersare: - Most DR programs apply similar DR BLP methods tocommercial and industrial sector customers. The results of our study whencombined with other recent studies (Quantum 2004 and 2006, Buege et al.,2006) suggests that DR program administrators should have flexibility andmultiple options for suggesting the most appropriate BLP method forspecific types of customers.« less