# Electrification of Smart Cities
## 2 Short Summary of the Papers  
The variation of energy consumption in transportation and the main influencing factors of decomposition contribute to reducing transportation energy consumption and realizing the sustainable development of the transportation industry. [PERSON], [PERSON], and [PERSON] [1] proposed an improved decomposition model according to the factors governing the direction of change based on existing index decomposition methods. The influencing factors of transportation energy consumption are quantitatively decomposed according to a transportation energy consumption decomposition model. The contributions of transportation turnover, transportation structure, and transportation energy consumption intensity changes to the variation of transportation energy consumption are quantitatively calculated. The results demonstrate that there is great energy conservation potential in the adjustment of transportation structures and that transportation energy intensity is the main factor of energy conservation. [PERSON] et al. [2] presented the optimization and tuning of a simulation framework to improve its simulation accuracy while evaluating the energy utilization of electric buses under various mission scenarios. The simulation framework was developed using a low-fidelity (Lo-Fi) model of a forward-facing electric bus's (e-bus) powertrain to achieve the fast simulation speeds necessary for real-time fleet simulations. The measurement data required verification that the proper tuning of the simulation framework was provided by the bus's original equipment manufacturers (OEMs), and these data were obtained fromvarious demonstrations of 12 m and 18 m buses in the cities of Barcelona, Gothenburg, and Osnabruck. Recently, the increasing winter load peak has applied great pressure on power grids. The demand response on the load side helps alleviate the expansion of the power grid and promote the consumption of renewable energy. However, the response of large-scale electric heat loads to the same electricity price curve leads to new load peaks and regulation failure. [PERSON] and [PERSON] [3] proposed a grouping-coordinated preheating framework based on a demand response model that realizes the integration of information between the central controller and each regulation group. A thermal parameter model of a room and a performance map of an inverter air conditioner/heat pump are integrated into the demand response model. In this framework, a coordination mechanism is adopted to avoid regulation failure, an edge computing structure is applied to consider the users' preferences and plans, and a grouped and parallel computing structure is proposed to improve computing efficiency. Communication networks in power systems constitute a major component of the smart grid paradigm. These networks enable the automation of power grid operation and self-recovery in negative contingencies. However, this dependency on communication networks attracts cyber threats. An adversary can launch an attack on a communication network, which, in turn, can influence a power grid's operation. Such attacks may constitute the injection of false data into system measurements, the flooding of communication channels with unnecessary data, or the interception of messages. The use of machine learning to process data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. [PERSON] et al. [4] presented a co-simulation of cyber-security for a cross-layer strategy. The advantage of such a framework is the augmentation of valuable data, which enhances the detection and identification of anomalies in the operation of a power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three-controller software-defined networking (SDN) framework is proposed that utilizes an Open Network Operating System (ONOS) cluster.