Estimating energy consumption rates is a necessary step when building infrastructure for charging and schedule optimization of battery-powered vehicles utilized in public urban driving patterns. This study examined several input factors for the prediction of vehicle performance. Input conditions were energy management controls, State of Charge (SOC) power train batteries, and ultra-capacitor vehicle models; output metrics included consumption rates, battery loads, and trip distances. To examine the experimental design, an L9 design was used with four control factors at three different levels each. Artificial neural network (ANN) models were developed employing four learning algorithms: quick propagation (QuP), batch backpropagation (BBaP), Levenberg-Marquardt backpropagation (LMBaP), and incremental backpropagation (IBaP). Post-simulation results were summarized and validated using the root mean square error (RMSE), which indicated that the values collected experimentally were close to those predicted by the models. This paper built an ANN-based prediction model and accurately predicted vehicle performance and potential energy shortfalls in public transportation networks. These insights can be applied to interventions like charging stations or reshaping bus timings to avoid power loss.

The integration of renewable energy resources (RESs) into the existing power grid is an effective approach to reducing harmful emission content. Environmental economic dispatch is one of the complex constrained optimization problems of power systems. These problems have become more complex as a result of integrating RESs, as the availability of solar and wind power is stochastic in nature. To obtain the solution of such types of complex constrained optimization problems, a robust optimization method is required. Literature shows that chaotic maps help to boost the search capability through improvisation in the exploration and exploitation phases of an algorithm; hence, they are able to provide superior solutions during optimization. Therefore, in this study, a new optimization technique was developed based on the Jaya algorithm called the chaotic Jaya algorithm. Here the main aim was to investigate the impact of RES integration into conventional thermal systems on total power generation cost and emissions released to the environment. The proposed approach was tested for two standard cases: (i) scheduling of a committed generating unit for a specific time and (ii) scheduling of a committed generating unit for a time period of 24 hours with 24 intervals of 1 hour each. The simulation results show that a tent map is the best-performing map for a sample problem under consideration, as it provides better results. Hence, it has been considered for detailed analysis.

Renewable energy and electric vehicles are used globally for reducing fuel dependency and carbon footprints. Here, I explore the complex field of "Sustainable Energy Technologies for Emerging Renewable Energy and Electric Vehicles" and examined the most recent advancements, obstacles, and ideas that are promoting the effective use of renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicles (EVs). I focused on the integration of renewable energy (RE), energy storage (ES), and EVs into modern sustainable power grids. With the recent advances in power generation technologies, the fluctuations in generation and electrical demand are common in hybrid power systems. This special issue was aimed at addressing the economic challenge of integrating RESs, ES, and EVs to improve the resilience and flexibility of grid operations. The widespread adoption of intermittent RESs and EVs makes it difficult to maintain a consistent power supply and significantly impacts grid stability. To address this challenge, ESSs are emerging as a potential solution. With the right investments and policies in place, RE, ES, and EVs can play a significant role in creating a more sustainable energy landscape worldwide.

The development of microgrid systems forces to integration of various distributed generators (DG) and battery energy storage (BES) systems. The integration of a BES system in MG provides several benefits such as fast response, short-term power supply, improved power quality, ancillary service, and arbitrage. The system constraints as power balance and the assets constraints as power limit of different DGs, energy, and charge/discharge power limit of BES increase the complexity of the original problem. Therefore, to tackle such a problem an efficient, robust, and strong optimization algorithm is required. In this paper, a recently developed optimization method known as the wild geese algorithm (WGA) has been applied to solve the problem. The WGA is a population-based metaheuristic approach inspired by the different aspects of the living behavior of wild geese. This algorithm has developed with the inspiration of different phases of wild geese's lives, such as their evolution, well-organized and coordinated long-distance group migration, and fatality. The WGA has tested on the MG problem and the obtained simulation results are validated by comparison of results obtained from the other methods. The result shows the WGA is efficiently able to handle the MG operational problem with numerous constraints and shows the potential to produce a high-quality solution in terms of cost reduction. The incorporation of BES reduces operating costs for MG's off-grid and on-grid operational modes by 5.91% and 8.62%, respectively. Further, the analysis for off-grid mode under different seasonality, reduction in the operational cost by 4.47%, 9.28%, 6.37%, and 7.22% was measured in the summer, autumn, winter, and spring seasons, respectively, with the integration of BES. Additionally, the integration of BES in on-grid mode results in a decrease in operating costs by 7.15%, 12.54%, 7.56%, and 11.07% in the summer, autumn, winter, and spring, respectively.

The demand for energy derived from non-conventional sources is increasing. It is essential to optimize the efficiency of renewable energy from sources such as wind and solar. This article introduces high-gain Quasi Z-Source inverters (QZSI) for grid-tied PV/wind energy applications to improve the limitations of conventional six-switched Voltage Source Converters (VSC). This new approach aims to revolutionize grid-tied renewable energy systems by integrating advanced technology and optimization techniques. By utilizing the unique features of QZSI and implementing the ABC algorithm, the proposed model achieves exceptional efficiency, reliability, and adaptability levels. Furthermore, the article utilizes the Artificial Bee Colony (ABC) algorithm to optimize control and track solar and wind systems' Maximum Power Point (MPPT). The model's performance is evaluated by testing system dynamics such as DC-link voltage control, power flow regulation, and grid/PV/wind energy generation on a scaled prototype developed using MATLAB SIMULINK. The simulation results demonstrate the effectiveness of the ABC algorithm and QZSI power converter in various operational modes, both with and without fault conditions.