In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

Microgrids have been proposed in an effort to handle the impact of distributed generators (DGs) and make conventional grids suitable for large scale deployments of DGs. However, the introduction of microgrids brings some challenges such as the protection of a microgrid and its entities. Due to the existence of generators at all levels of the distribution system, the fault currents vary substantially. Furthermore, grid connected and islanded modes introduce two different sets of fault currents. Consequently, the traditional fixed current relay protection schemes need to be improved. The authors developed a new protection system which utilizes extensive communication to monitor the microgrid and update relay fault currents according to the variations in the system. This system is designed to respond to dynamic changes in the system such as connection/disconnection of DGs. This paper presents the modeling of a microgrid protection system with logical nodes provided in IEC61850 and IEC61850-7-420 communication standards. It also demonstrates how the proposed communication takes place through logical nodes. Firstly, models for different DGs are provided to detail the implementation of the said logical nodes. Then, a case study is given on a sample microgrid to show how the proposed protection scheme can be run based on these models.

Smart grid is the nexus of advanced Information and Communication Technologies (ICT) and legacy power systems. With increasing awareness on vulnerabilities of smart grids to cyber-attacks, cybersecurity is becoming a prime concern. Earlier, it was assumed that power system communication protocols are very specialized and different, so the security by obscurity approach would be sufficient. However, with the standardization of communication protocols for power utilities and emergence of power market, this approach is no longer valid. IEC 62351 standard has been published to provide security recommendations for different power system communication protocols including IEC 61850. Therefore, in this paper, a detail analysis of security threats, possible attacks and security requirements for IEC 61850 communication is presented. Building on this, the security considerations presented in IEC 62351 for securing different IEC 61850 messages such as GOOSE, SV, Routable-GOOSE (R-GOOSE), Routable-SV (R-SV) and Manufacturing MMS messages have been presented in detail.