Due to the rapid expansion of electric vehicles (EVs), they are expected to be one of the main contributors to transportation. The increasing use of fossil fuels as one of the most available energy sources has led to the emission of greenhouse gases, which will play a vital role in achieving a sustainable transportation system. Developed and developing countries have long-term plans and policies to use EVs instead of internal combustion vehicles and to use renewable energy to generate electricity, which increases the number of charging stations. Recently, to meet the charging demand for EVs, the main focus of researchers has been on smart charging solutions. In addition, maintaining power quality and peak demand for grids has become very difficult due to the widespread deployment of EVs as personal and commercial vehicles. This paper provides information on EV charging control that can be used to improve the design and implementation of charging station infrastructure. An in-depth analysis of EV types, global charging standards, and the architectures of AC-DC and DC-DC converters are covered in this review article. In addition, investigating the role of EV collectors, as well as EV penetration, in electric energy systems to facilitate the integration of electric energy systems with renewable energy sources is one of the main goals of this paper.

This paper presents an optimal framework for demand response aggregators in wholesale electricity markets. Demand response aggregators provide customers with flexible contracts in the proposed model. These contracts allow for hourly load reductions through load curtailment, load shifting, onsite generation utilization, and energy storage systems. The study suggests the price-based self-scheduling model for demand response aggregators, which seeks to maximize the aggregator's payoff for participation in day-ahead energy markets. The proposed model is implemented on a sample demand response aggregator with uncertainty and without uncertainty. Three methods, box, polyhedral, and ellipsoidal, based on robust optimization, are proposed for uncertainty modeling in this work, and their effectiveness is compared with each other. The results show that if the uncertainty is modeled using the polyhedral method, the value of the objective function will deviate by only 22.72 % compared to the case without uncertainty. However, this deviation in box and ellipsoidal methods is 118.48 % and 49.20 %, respectively, which shows the superiority of the polyhedral method compared to the oval and box methods.

Today, the role of smart machines in our lives is valuable. With the advancement of digital technologies, such as the internet of things (IoT), many embedded systems have been developed for various applications. In homes, all daily activities and even security depend on machines. Therefore, implementing IoT-based smart homes has become a prominent research field. Also, since we are in the era of endless growth of the IoT and its applications, the topic of home automation systems is becoming more popular due to its countless advantages. In general, most home automation systems focus on one function: the ability to control home appliances remotely. In the world of information technology, the creation of rules and standards should always be done in the early stages of development because, after the work is done, incompatibilities between devices are created, which is a severe challenge and limits the use of technology. Therefore, the research on IoT technology has led to the formation of various protocols; this article gives an overview of seven essential protocols. Also, in this review article, energy consumption management, and privacy and security issues are discussed, and smart homes are introduced as critical requirements for the evolution of smart cities.

The transportation sector is one of the main contributors to the emission of greenhouse gases globally, and the electrification of this sector can significantly reduce the emission of pollutants. The widespread connection of electric vehicles (EVs) to the power grid may bring challenges, such as increasing the peak load of the network. Therefore, the optimal use of EVs is necessary to improve the network’s economic, security, and stability indicators. This review article examines the deterministic control model and centralized control model, the types of EV models, and their tabular comparison. Additionally, expressing the communication standards to deal with compatibility challenges in charging stations, the effects of EV integration with the power grid, and various methods such as smart charging, dumb charging, and flexible charging are the main goals of this review article. In addition, since batteries play a crucial role in the electric vehicle industry, this research investigates the oldest type of rechargeable battery to the latest battery technology and the energy management system of these batteries. Finally, the authors have conducted studies on government incentives, the adverse effects of these incentives, and the combination of EVs with renewable energy sources.

Residential photovoltaic generation and household demand are temporally mismatched, affecting grid dependence and local storage use. This study formulates a 24 h mixed-integer linear programming model for a grid-connected residential prosumer with rooftop PV, a stationary behind-the-meter battery, and an electric vehicle capable of vehicle-to-home operation. Four configurations are compared under common external inputs: no storage, battery only, V2H only, and a hybrid battery–V2H system. The model resolves the main power routes, enforces charging, discharging, and cyclic state-of-charge constraints, allows grid charging, and excludes storage-to-grid export. PV generation is reduced, and residential demand is increased through a prescribed deviation-scaling parameter evaluated at five levels for clear-day and synthetic partly cloudy profiles. Numerical consistency is checked using an independent no-storage calculation and equation residuals. At $\rho$ = 0.3 under the clear-day profile, the hybrid configuration reduces daily operating cost from USD 26.113 to USD 17.807, increases PV self-consumption from 69.684% to 89.121%, lowers utility purchase from 106.380 to 88.700 kWh/day, and reduces export from 36.170 to 12.980 kWh/day. Under the partly cloudy profile, all storage-based configurations reach 100% PV self-consumption and zero export, while the hybrid case retains the lowest operating cost. The results are conditional on the adopted capacities, continuous EV connection, tariff structure, and exclusion of degradation and investment costs.