With recent advances in information and communication technology (ICT), the bleeding edge concept of digital twin (DT) has enticed the attention of many researchers to revolutionize the entire modern industries. DT concept refers to a digital representation of a physical entity that is able to reflect its physical behavior by applying platforms and bidirectional interaction of data in real-time. The remarkable deployment of the internet of things in the power grid has led to reliable access to information that improves its performance and equips it with a powerful tool for real-time data management and analysis. This paper aims to trace the continuous investigation and propose practical ideas in originating and developing DT technology, according to various application domains of power systems, and also describes the proposed solutions to deal with the challenges associated with DT. Indeed, with the development of modern cities, different energy layers such as transportation systems, smart grids, and microgrids have emerged facing various issues that challenge the multi-dimensional energy management system. For example, in transportation systems, traffic is a major problem that requires real-time management, planning, and analysis. In power grids, remote data transfer within the grid and also various analyzes needing real data are just some of the current challenges in the field. These problems can be cracked by providing and analyzing a real twin framework in each section. All in all, this paper aims to survey different applications of DT in the development of the various aspects of energy management within a city including transportation systems, power grids, and microgrids. Besides, the security of DT technology based on ML is discussed. It also provides a complete view for the readers to be able to develop and deploy a DT technology for various power system applications.

Using blockchain technology as one of the new methods to enhance the cyber and physical security of power systems has grown in importance over the past few years. Blockchain can also be used to improve social welfare and provide sustainable energy for consumers. In this article, the effect of distributed generation (DG) resources on the transmission power lines and consequently fixing its conjunction and reaching the optimal goals and policies of this issue to exploit these resources is investigated. In order to evaluate the system security level, a false data injection attack (FDIA) is launched on the information exchanged between independent system operation (ISO) and under-operating agents. The results are analyzed based on the cyber-attack, wherein the loss of network stability as well as economic losses to the operator would be the outcomes. It is demonstrated that cyber-attacks can cause the operation of distributed production resources to not be carried out correctly and the network conjunction will fall to a large extent; with the elimination of social welfare, the main goals and policies of an independent system operator as an upstream entity are not fulfilled. Besides, the contracts between independent system operators with distributed production resources are not properly closed. In order to stop malicious attacks, a secured policy architecture based on blockchain is developed to keep the security of the data exchanged between ISO and under-operating agents. The obtained results of the simulation confirm the effectiveness of using blockchain to enhance the social welfare for power system users. Besides, it is demonstrated that ISO can modify its polices and use the potential and benefits of distributed generation units to increase social welfare and reduce line density by concluding contracts in accordance with the production values given.

Accurate prediction of solar energy is an important issue for photovoltaic power plants to enable early participation in energy auction industries and cost-effective resource planning. This article introduces a new deep learning-based multistep ahead approach to improve the forecasting performance of global horizontal irradiance (GHI). A deep convolutional long short-term memory is used to extract optimal features for accurate prediction of the GHI. The performance of such deep neural networks directly depends on their architectures. To deal with this problem, a swarm evolutionary optimization method, called the sine-cosine algorithm, is applied and advanced to automatically optimize the network architecture. A three-phase modification model is proposed to increase the diversity of population and avoid premature convergence in the optimization mechanism. The performance of the proposed method is investigated using three datasets collected from three solar stations in the east of the United States. The experimental results demonstrate the superiority of the proposed method in comparison to other forecasting models.

This paper addresses the optimal operation and scheduling of reconfigurable microgrids incorporating the dynamic line rating limitations during the islanded and grid-connected mode operations. The incorporation of the dynamic line rating of overhead feeders can potentially improve the system security when providing economical and technical benefits for the microgrid. The proposed framework takes into account a realistic formulation to minimize the total microgrid costs in both grid-connected and multi-period islanded modes. Also, a stochastic framework based on unscented transform (UT) is proposed to model the uncertainties associated with renewable energy sources (RESs) output power, market energy price, load demand as well as the weather uncertain parameters such as solar radiation, wind speed, and ambient temperature. Due to the high nonlinearity and complexity of the proposed problem, an efficient optimization algorithm based on collective decision optimization algorithm (CDOA) is proposed. A new two-stage modification method is also developed to improve the algorithm search ability and avoid premature convergence. The proposed problem is examined on the IEEE 32-bus microgrid test system. The simulation results show the effectiveness of the proposed model and verify its economic and reliability merits.

Power grid resilience, reliability, and sustainability can be improved significantly, by decomposing the large grids into networked microgrids (NMGs). However, optimal energy management problem and preserving the security in NMGs are more complicated and challenging. This paper aims to propose an effective secured stochastic energy management framework for NMGs based on the modified blockchain approach utilizing Directed Acyclic Graph (DAG). Using the decentralized and transparent blockchain technology will help to have higher security, lower risks within the network, eliminating the financial fraud, and cutting down the total operational cost. In order to address the issues arising in the traditional blockchain models mainly due to the storage and high complexities of hash address calculations, in this paper, a new modified blockchain technology is developed based on the DAG method. Also, a novel data restoration technique is developed to provide a way to restore the data with appropriate accuracy. Moreover, the unscented transform (UT) approach is employed to model the uncertainties of forecast errors in hourly load demand, solar power, and wind turbines output power. The proposed model is tested on an NMG system with four MGs including two residential MGs (as the non-crucial loads), a commercial MG (as the intermediate level loads), and a hospital MG (as the crucial loads).

Due to the simultaneous development of DC-microgrids (DC-MGs) and the use of intelligent control, monitoring and operation methods, as well as their structure, these networks can be threatened by various cyber-attacks. Overall, a typical smart DC-MG includes battery, supercapacitors and power electronic devices, fuel cell, solar Photovoltaic (PV) systems, and loads such as smart homes, plug-in hybrid electrical vehicle (PHEV), smart sensors and network communication like fiber cable or wireless to send and receive data. Given these issues, cyber-attack detection and securing data exchanged in smart DC-MGs like CPS has been considered by experts as a significant subject in recent years. In this study, in order to detect false data injection attacks (FDIAs) in a MG system, Hilbert-Huang transform methodology along with blockchain-based ledger technology is used for enhancing the security in the smart DC-MGs with analyzing the voltage and current signals in smart sensors and controllers by extracting the signal details. Results of simulation on the different cases are considered with the objective of verifying the efficacy of the proposed model. The results offer that the suggested model can provide a more precise and robust detection mechanism against FDIA and improve the security of data exchanging in a smart DC-MG.