In time‐of‐use (TOU) schemes, most similar clusters to load demand can properly map the consumption pattern. However, the performance of such scheme is affected by the load responsiveness to the obtained clusters. This paper presents a time period clustering approach for TOU schemes to find the optimal clusters, using not only the load patterns but also its modified version obtained by responsiveness of elastic loads. A responsive load model is employed to simulate the load pattern modifications considering the hourly prices, load elasticities, and customers' benefit function. An imperialist competitive algorithm (ICA) has been designed to iteratively solve the problem. The proposed approach maximizes the load factors pertaining to the modified seasonal load patterns that are obtained on the basis of load responsiveness to the allocated clusters in each iteration of ICA. A comprehensive seasonal case study has been conducted to evaluate the performance of the proposed model. Results and comparisons show that the proposed clustering technique can significantly enhance the performance of the existing TOU schemes in terms of load factor improvement and peak reduction as it considers both load patterns and the actual load responsiveness to the obtained clusters.

中文翻译：

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基于弹性载荷的响应度的使用时间方案的最佳时间周期聚类

在使用时间（TOU）方案中，大多数满足负载需求的相似群集都可以正确映射消耗模式。但是，这种方案的性能受到对获得的群集的负载响应性的影响。本文提出了一种用于TOU方案的时间周期聚类方法，该方法不仅使用负载模式，而且还使用通过弹性负载响应获得的修改版本来找到最佳聚类。考虑到每小时价格，负载弹性和客户利益函数，采用响应负载模型来模拟负载模式修改。设计了帝国主义竞争算法（ICA）来迭代解决该问题。所提出的方法使与修改后的季节性负荷模式有关的负荷因子最大化，该季节性负荷模式是基于ICA每次迭代中对分配的群集的负荷响应性而获得的。进行了一个全面的季节性案例研究，以评估该模型的性能。结果和比较结果表明，所提出的聚类技术可以同时考虑负载模式和对获得的群集的实际负载响应性，从而在改善负载因子和降低峰值方面显着提高现有TOU方案的性能。