Real-Time Monitoring of Smart Meters Based on Blockchain Technology

In this chapter, the authors primarily discuss how blockchain is being utilized in smarter grids across the globe and how some use cases can be a good fit as a technology. They ensure the reliability and uninterrupted power supply to end users by using smart metering in micro and macro grids, which is possible with novel technology that is transparent and without any cyberattacks/hackers: blockchain technology (BCT). In this chapter, BCT is implemented significantly at micro/macro smart grid network. Such a network would give efficient improvement and be interesting.

Review of the Optimization of PV System to the Grid Network

Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth of generating electricity. In the modern power systems, the effectiveness of grid-tied PV systems has become a spotlight topic for researchers in this field. This paper presents a review on the optimization of PV system to the grid, considering the improvement that will be done to the grid network after connecting the PV system to the grid. A simulation under Matlab/Simulink have been carried out to prove the performance of the proposed power flow management. The objective is to show that the using of grid-connected PV will make a stabilization and increase the reliability to the network. Keywords: Photovoltaic, Grid connected

Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management

The heterogeneous and interoperable nature of the cyber-physical system (CPS) has enabled the smart grid (SG) to operate near the stability limits with an inconsiderable accuracy margin. This has imposed the need for more intelligent, predictive, fast, and accurate algorithms that are able to operate the grid autonomously to avoid cascading failures and/or blackouts. In this paper, a new comprehensive identification system is proposed that employs various machine learning architectures for classifying stability records in smart grid networks. Specifically, seven machine learning architectures are investigated, including optimizable support vector machine (SVM), decision trees classifier (DTC), logistic regression classifier (LRC), naïve Bayes classifier (NBC), linear discriminant classifier (LDC), k-nearest neighbor (kNN), and ensemble boosted classifier (EBC). The developed models are evaluated and contrasted in terms of various performance evaluation metrics such as accuracy, precision, recall, harmonic mean, prediction overhead, and others. Moreover, the system performance was evaluated on a recent and significant dataset for smart grid network stability (SGN_Stab2018), scoring a high identification accuracy (99.90%) with low identification overhead (4.17 μSec) for the optimizable SVM architecture. We also provide an in-depth description of our implementation in conjunction with an extensive experimental evaluation as well as a comparison with state-of-the-art models. The comparison outcomes obtained indicate that the optimized model provides a compact and efficient model that can successfully and accurately predict the voltage stability margin (VSM) considering different operating conditions, employing the fewest possible input features. Eventually, the results revealed the competency and superiority of the proposed optimized model over the other available models. The technique also speeds up the training process by reducing the number of simulations on a detailed power system model around operating points where correct predictions are made.