# Electrification of Smart Cities
## 2 Short Summary of the Papers
To effectively select the useful feature subset, two regularization terms, namely, I2,1-norm and Frobenius norm penalties, are adopted to conduct hierarchical feature selection for multiview data. Demand response programs (DRs) can be implemented with fewer investment costs than those incurred in power plants or facilities and enable us to control power demand. Therefore, they are widely regarded as an efficient option for power supply-demand-balancing operations. On the other hand, DRs bring new difficulties regarding the evaluation of consumers' cooperation and the setting of electricity prices or rebate levels while reflecting their results. [PERSON] et al. [10] presented a theoretical approach that calculates electricity prices and rebate levels in DRs based on the framework of social welfare maximization. In the authors' proposal, the DR-originated changes in the utility functions of power suppliers and consumers are used to set a guide for DR requests. Moreover, optimal electricity prices and rebate levels are defined from the standpoint of minimal burden in DRs. Through numerical simulations and a discussion of their results, the validity of the authors' proposal is verified. The construction and operation of wind turbines have become important aspects of the development of smart cities. However, a fault in the main drive chain often causes wind turbine outages, thereby seriously impacting the normal operation of wind turbines in smart cities. To overcome the shortcomings of the commonly used main drive chain fault diagnosis method, which only uses a single data source, [PERSON] et al. [11] proposed a fault feature extraction and fault diagnosis approach based on data source fusion. By fusing two data sources, that is, the supervisory control and data acquisition (SCADA) real-time monitoring system data and the main drive chain vibration monitoring data, the fault features of the main drive chain are jointly extracted, and an intelligent fault diagnosis model for the main drive chain in the wind turbine based on data fusion is established. [PERSON] et al. [12] presented a new coordinated operation (CO) framework for electricity and natural gas networks that considers network congestion and demand response. A credit rank (CR) indicator for coupling units was introduced, and gas consumption constraint information of natural-gas-fired units (NGFUs) was provided. A natural gas network operator (GNO) delivers this information to an electricity network operator (ENO). A major advantage of this operation framework is that frequent information interaction between GNO and ENO is unnecessary. The entire framework contains two participants and three optimization problems, namely, GNO optimization sub-problem-A, GNO optimization sub-problem-B, and ENO optimization sub-problem. Blockchain technologies have received considerable attention from academia and industry due to their distinctive characteristics, such as data integrity, security, decentralization, and reliability. However, their adoption rate is still low, which is one of the primary reasons behind conducting studies related to users' satisfaction and adoption. Determining the factors impacting the use and adoption of blockchain technologies can efficiently address their adoption challenges. [PERSON], [PERSON], and [PERSON] [13] performed a systematic review of blockchain technologies to offer a thorough understanding of what impacts their adoption and discuss the main challenges and opportunities across various sectors. Of the 902 studies collected, 30 empirical studies met the eligibility criteria and were thoroughly analyzed. The results confirmed that the technology acceptance model (TAM) and technology-organization-environment (TOE) model were the most common models for studying blockchain adoption. The upward trend of adopting Distributed Energy Resources (DER) reshapes the energy landscape and supports the transition toward a sustainable, carbon-free electricity system.