Mostafa Esmaeili Shayan

The advancement and proliferation of digitalization and communication infrastructure have facilitated the rise of real-time bidding markets in smart grids. In these dynamic markets, energy distribution companies and power-generating companies interact to establish energy exchange contracts based on offered prices. However, the fluctuation in power flow resulting from contract changes within the real-time bidding market introduces a potential vulnerability that malicious attackers can exploit to launch successful stealthy attacks. To enhance the smart grid resiliency against cyber-attack in the power market bidding environment, a new barrier-function adaptive finite-time trajectory tracking control is proposed in this paper. The developed controller is utilized to actively counteract and mitigate potential cyber-attacks to ensure their rejection and prevention. The stability analysis convincingly demonstrates the rapid convergence of system states within a finite time frame, empowering the system to effectively reject cyber-attacks in real-time. Test results of an IEEE test systems, considering governor dead bound nonlinearity and communication time delay are presented and compared with those obtained from other methods to ensure and demonstrate the performance of proposed method. The Speedgoat real-time target machine, along with Simulink real-time, validates the effectiveness of the proposed method.

Environmentally sustainable metropolitan environments are characterized by their ability to effectively produce and distribute power while reducing their impact on the environment. Smart homes are essential in smart cities since they enhance sustainability and efficiency in urban settings. A key advantage of smart homes is their capacity to diminish energy use and carbon emissions. This is accomplished by optimizing energy consumption in home appliances, which is customized to fulfill the individual requirements and preferences of consumers. However, there is still a need for further academic research to investigate and improve the functioning of intelligent residential homes in microgrids. To efficiently manage microgrids, it is crucial to gather and analyze large amounts of electrical data related to power production from microgrid sources and energy consumption of the loads. This study examines the use of Non-Intrusive Load Monitoring (NILM) methods to monitor electrical parameters of different loads in microgrids. The research focuses on the application of affordable smart meters that are equipped with Internet of Things (IoT) capabilities. An empirical study showcases the possibility of collecting significant data on microgrid operation via the deployment of an operational microgrid that integrates a hybrid wind-solar power source with a variety of home appliances.

Since the initial suggestion that electrically propelled vehicles could be used on the grid-side, numerous significant investigations have been conducted to showcase the capabilities of these technologies, which have proven to be highly advantageous. Nevertheless, there are still many uncertainties surrounding the integration of electric vehicles into the power grid, which is why it has been likened to a black box. These uncertainties include the number of electric vehicles that will be connected to the grid at any given time, the amount of energy that will be stored in their batteries during both the daytime and overnight, and the impact that their charging profiles will have on the overall load placed on the power system. In addition, there are several unanswered questions that need to be addressed. This article presents a novel model that effectively addresses these uncertainties. It is based on a non-stationary Markov chain, and it was introduced in this paper. The findings of the model provide fascinating insights into the number of electric vehicles connected to the grid and the amount of energy saved over the course of a day, as demonstrated by a case study. In addition, this article analyzes and evaluates the ability of the model to accurately represent the load modeling of electric vehicle charging.