Hung Ying-Chao, Michailidis George
Department of Statistics, National Chengchi University, Taipei, 11605 Taiwan.
Informatics Institute and Departmenst of Statistics and Computer Science, University of Florida, Gainesville, FL 32611, USA.
IEEE Trans Smart Grid. 2019 Jul;10(4):4116-4127. doi: 10.1109/tsg.2018.2850326. Epub 2018 Jun 25.
Time-of-Use (TOU) pricing is an important strategy for electricity providers to manage supply and hence making the grid more efficient and for consumers to manage their costs. In this paper, we discuss a general stochastic modeling framework for consumer's power demand based on which the TOU contract characteristics can be selected, so as to minimize the mean electricity price paid by the customer. We exploit the characteristics of power demand observed in real grids to propose to model it during homogeneous peak periods as a constant level with fluctuations described by a scaled fractional Brownian motion. We analyze the exceedance process over pre-specified thresholds and use this information for formulating an optimization problem to determine the key features of the TOU contract. Due to the analytical intractability of certain expressions with the exception of short-range dependence fluctuations, the solution of the posited optimization problem requires using techniques such as Monte Carlo simulation and numerical search. The methodology for two pricing schemes is illustrated using real data.
分时定价(TOU)是电力供应商管理供应从而提高电网效率以及消费者管理成本的一项重要策略。在本文中,我们讨论了一种用于消费者电力需求的通用随机建模框架,基于该框架可以选择TOU合同特征,以最小化客户支付的平均电价。我们利用在实际电网中观察到的电力需求特征,提出在同质高峰期将其建模为一个恒定水平,并由缩放分数布朗运动描述波动情况。我们分析超过预先指定阈值的超越过程,并利用此信息制定一个优化问题,以确定TOU合同的关键特征。由于某些表达式在除短程相关波动外具有分析难处理性,所提出的优化问题的解决方案需要使用蒙特卡罗模拟和数值搜索等技术。使用实际数据说明了两种定价方案的方法。