Benchmarking of Aggregate Residential Load Models Used for Demand Response
Benchmarking of Aggregate Residential Load Models Used for Demand Response
复制标题
用于需求响应的住宅总负荷模型的基准测试
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Mathieu
中科院分区：
文献类型：
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作者：
Gregory S. Ledva;Sarah F. Peterson;J. Mathieu
住宅负荷可以为电力系统提供频率调节等辅助服务。综合负荷模型旨在以一种精确且易于计算的方式捕捉需求响应负荷的动态，这使得模型可以被纳入需求响应提供者使用的控制器和观察者中。各种各样的聚合模型已经被开发出来；然而，在可比较的情况下，它们的准确性并没有相互比较。本文比较了两种基于马尔可夫模型和一种基于传递函数的集散空调（AC）模型与具有时变室外空气温度和温度相关AC参数的真实仿真模型的精度。我们还扩展了现有的模型，以应对时变的室外空气温度。我们发现1)越详细的马尔可夫模型越准确，2)更新马尔可夫转换作为室外温度趋势的函数减少了两种马尔可夫模型的预测误差，3)传递函数模型表现最差，可能是因为模拟场景与用于开发模型的假设有很大差异。
Residential loads can provide ancillary services such as frequency regulation to an electric power system. Aggregate load models aim to capture the dynamics of the demand-responsive loads in an accurate and computationally-tractable way, which allows the models to be incorporated into controllers and observers used by demand response providers. A variety of aggregate models have been developed; however, their accuracy has not been benchmarked against one another in comparable scenarios. This paper compares the accuracy of two Markov-based and one transfer function-based aggregate air conditioner (AC) models against a realistic simulation model with time-varying outdoor air temperature and temperature-dependent AC parameters. We also extend the existing models to cope with the time-varying outdoor air temperature. We find that 1) the more detailed Markov model is more accurate, 2) updating the Markov transitions as a function of the outdoor temperature trend decreases prediction error in both Markov models, 3) the transfer function model performs worst, likely because the simulation scenario differs significantly from the assumptions used to develop the model.