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The purpose of this paper is to examine cyber security issues related to smart grids (SGs), such as prior cyber‐attacks, possible vulnerability issues, and enhanced cyber‐security procedures for SGs on a technical and management basis. When designing public policies for SGs, it is important to consider both the motivations and obstacles to cyber protection and socio‐economic conditions. Furthermore, the importance of suggested policies is viewed from various perspectives. The present paper provides an assessment of a group of policies specified by various stakeholders as having the potential for developing SG cyber security. The methodology for the formation of such a group of policies includes a perfect examination of global experience and meetings with experts from various fields. Subsequently, such policies are evaluated using a Delphi questionnaire aimed at assessing their advantages in pursuing investment goals for cyber security. The first result is that every policy is considered a positive effect of all goals, different in the priority assigned to each of them. The policies that received the most attention were: "regulatory changes policy to foster innovation in cyber security of SGs," "regulation of new business models to improve cyber security and detect malicious attacks" and "establish a cyber‐security governance strategy." [ABSTRACT FROM AUTHOR]

This paper investigates the false data injection attacks (FDIA) in an AC smart island and the detection solution of the attack on distributed energy resources in a smart island. In this study, a new scheme of FDIA detection is proposed based on wavelet singular values as input index of deep learning algorithm. In the proposed method, switching surface based on sliding mode control breaks down for adjusting accurate factors of wavelet transform and then features of wavelet coefficients are extracted by singular value decomposition. Indexes are determined according to the wavelet singular values in switching surface of voltage and current which defines the input indexes of deep machine learning and detecting FDIA. This cyber‐protection plan has been put forward for cyber diagnostic and examined in different types of attacks happening in voltage and current signals derivation of measuring sensors as well as sending and receiving data from communication and control systems. The main priority of the suggested detection plan is the high capability to detect FDIA with a high accuracy. To show the effectiveness of the proposed method, simulation studies are performed on AC smart island in MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Smart cities consist of various energy systems and services that must be optimally scheduled to improve energy efficiency and reduce operation costs. The smart city layout comprises a power distribution system, a thermal energy system, a water system, and the private and public transportation systems. Additionally, several new technologies such as reconfiguration, regenerative braking energy of the metro, etc. are considered. This study is one of the first to consider all these technologies together in a smart city. The proposed power distribution system is a grid‐connected hybrid AC–DC microgrid. The biogeography‐based optimization algorithm was utilized to seek the best solution for scheduling micro‐turbines, fuel cells, heat pumps, desalination units, energy storage systems, AC–DC converters, purchasing power from the upstream, distributed energy resources, and transferring power amongst electric vehicle parking stations and metro for the next day. Also, the reduced unscented transformation layout was used to capture the system's uncertainty. The suggested layout is implemented on an enhanced IEEE 33‐bus test system to show the efficiency of the suggested method. The results show that costs and environmental pollution are reduced. By comparing the proposed smart city with other studies, the efficiency and completeness of the proposed smart city are shown. [ABSTRACT FROM AUTHOR]