H (1), NO 2 (2)] promoted by R 2 NH/R 2 NH 2 + in 70 mol % MeCN(aq) have been studied kinetically. The base-promoted eliminations from 1 proceeded by the E2 mechanism when Y ) Cl, CF 3 , and NO 2 . The mechanism changed to the competing E2 and E1cb mechanisms by the poorer leaving groups (Y ) H, OMe) and to the E1cb extreme by the strongly electron-withdrawing -aryl group (2, X ) NO 2 ). The values of ) 0.14 and | lg | ) 0.10-0.21 calculated for elimination from 1 (Y ) NO 2 ) indicate a reactantlike transition state with small extents of proton transfer and C R -OAr bond cleavage. The extent of proton transfer increased with a poorer leaving group, and the degree of leaving group bond cleavage increased with a weaker base. Also, the changes in the k 1 and k -1 /k 2 values with the reactant structure variation are consistent with the E1cb mechanism. From these results, a plausible pathway of the change of the mechanism from E2 to the E1cb extreme is proposed.

A series of 1,3,5‐tricyano‐2,4,6‐tris(styryl)benzene derivatives (1,2) and dendrimers (3) have been synthesized and their first hyperpolarizabilities (β) were determined. Whereas the λmax values of 1–3 are similar, the oscillator strength increases with the molecular size. For all compounds, the Stokes shifts are significantly larger when NAr2 is used as the donor. The β(0) values of 1–3 range from 252 to 507 × 10–30 esu and increases with the number of octupolar units in the molecule. When the octupolar molecules are linked to the 1,3,5‐position of the benzene ring by flexible ether linkages, the β(0) value increases by 1.7 fold. On the other hand, octupolar monodendron based on the nitrogen atom core and 1,3,5‐tricyano‐2,4,6‐tris(p‐diarylaminostyryl)benzene moieties at the periphery exhibits β(0) = 507 × 10–30 esu, which is one of the largest β(0) values reported for the octupolar molecules.

In this paper, a systematic method is proposed to cluster the energy consumption patterns of residential customers by utilizing extreme points and demographic characteristics. The extreme points of the energy consumption pattern enable effective clustering of residential customers. Additionally, demographic characteristics can be used to determine an effective extreme point for the clustering algorithm. The K-means-based features selection method is used to classify energy consumption patterns of residential customers into six types. Furthermore, the type of energy consumption pattern can be identified depending on the characteristics of residential customers. The analytical results of this paper show that the extreme points are effective in clustering the energy consumption patterns of residential customers.

This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into 'M-shaped' and 'square wave-shaped' load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the 'M-shaped' pattern fixes a 2-cycle operation per day due to battery life, while the 'square wave-shaped' pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits. Energies 2020, 13, 1723 2 of 17 algorithms [6]. Lee et al. classified the electric power consumption characteristics of industrial customers using the standard industry classification code in South Korea [7]. Bidoki et al. applied different clustering algorithms based on K-means, weighted fuzzy mean K-means, modified follow leader (MFTL), self-organizing map (SOM), and layer algorithm to classify load curves of different types of customers, and the results from comparing the clustering performances utilized to determine the adaptive clustering algorithms [8]. Zhou et al. proposed the five-stage process model based on K-means, SOM, Fuzzy c-average (FCM), and hierarchical clustering algorithms to analyze the impact of electric power suppliers and their consumers in smart grid circumstances [9]. Abubaker classified load profiles achieved from electricity consumers in the Tulkarm district based on the K-means algorithm [10].These clustering-based approaches for analyzing customer load profiles can be applied to the development of effective strategies for energy management in smart grid circumstances. The representative strategy utilizes the Energy Storage System (ESS) installed for electricity consumers [11]. The ESS is a device that enables the storage of electrical energy during off-peak times and supplies the stored energy at the requested time to reduce electricity costs for the customer. The South Korean government had established a long-term roadmap and supporting policies to increase the penetration level of ESS, and, consequently, South Korea has been positioned as the second largest nameplate capacity of ESS since 2016 [12].Several studies have proposed methodologies to optimize and deve...