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The smart grid is not a monolithic system, but rather is a collection of several renewable energy resources and enabling technologies in which, intelligent control is an integral part of its mechanism to improve the utilization of assets. The dynamic characteristics of a smart grid upgrade the conventional system requirements using advanced control strategies to provide continuous power to the load from intermittent renewable generation. The communication networks and control systems that enable the accommodation of distributed generation are crucial technologies in monitoring, protecting, and operating the smart grid in a centralized or decentralized manner. This paper improves the earlier published review articles by exploring the evolution of smart grids in light of renewable energy penetration with associated features. Then, the review gives an overview of notable research works in the literature aimed at developing the management and control of smart energy systems. The reader is provided with an in-depth analysis of advanced cloud computing, the internet of things, and blockchain technology with real examples for the related renewable energy projects in smart cities. Furthermore, a special interest has been paid to quantify the performance of communication technologies along with the protocols through the conceptual investigation of real cases using the optimized network engineering tools. The outcomes of the presented review can assist researchers to understand the driving mechanism of smart grid as a route to intelligently utilize renewable energy storage. It is concluded that the amalgamation of blockchain and artificial intelligence for renewable energy management is the key area where the avenue is still open for future research studies. [ABSTRACT FROM AUTHOR]

Vulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN) for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE) for dimensionality reduction of input data. The performance of the RBFNN is compared with the Multi Layer Perceptron Neural Network (MLPNN) so as to evaluate the effectiveness of the RBFNN in assessing the vulnerability of a power system based on the indices, power system loss and possible loss of load. In this study, vulnerability analysis simulations were carried out on the IEEE 300 bus test system using the Power System Analysis Toolbox and the development of neural network models were implemented in MATLAB version 7. Test results prove that the RBFNN give better vulnerability assessment performance than the multilayer perceptron neural network in terms of accuracy and training time. The proposed feature extraction method decreases the training time drastically from hours to less than seconds, this bound to influence the vulnerability classification and increase the speed of convergence. It is also concluded that the reduction in error is achieved by using PSL as an output variable of ANN, in all the cases the error of RBFNN output by PSL is less than 4.87% which is well within tolerable limits. Key words: Vulnerability assessment, vulnerability index, radial basis function, feature extraction, INTRODUCTION Power system operation has become more complex due to the growing demand for electricity and increase in system interconnection. Such a highly interconnected power system when greatly stressed may [...]