The popularity of electric vehicles (EVs) is increasing day by day due to their environmentally friendly operation and high milage as compared to conventional fossil fuel vehicles. Almost all leading manufacturers are working on the development of EVs. The main problem associated with EVs is that charging many of these vehicles from the grid supply system imposes an extra burden on them, especially during peak hours, which results in high per-unit costs. As a solution, EV charging stations integrated with hybrid renewable energy resources (HREs) are being preferred, which utilize multi-energy systems to produce electricity. These charging stations can either be grid-tied or isolated. Isolated EV charging stations are operated without any interconnection to the main grid. These stations are also termed standalone or remote EV charging stations, and due to the absence of a grid supply, storage becomes compulsory for these systems. To attain maximum benefits from a storage system, it must be configured properly with the EV charging station. In this paper, different types of the latest energy storage systems (ESS) are discussed with a comprehensive review of configurations of these systems for multi-energy standalone EV charging stations. ESS in these charging stations is applied mainly in three different configurations, named single storage systems, multi-storage systems, and swappable storage systems. These configurations are discussed in detail with their pros and cons. Some important expectations from future energy storage systems are also highlighted.

Rapid growth in electrical transportation and a shift towards clean energy from distributed renewable energy sources is the sustainable option to crack down on the demand and usage of electricity. In this paper, grid-assisted electric vehicle rapid charging facilities empowered by photovoltaic modules and battery stacks are utilized. Further, an effective interactive mechanism between electric vehicles (EV) and renewable power sources to improve system reliability- fifteen efficient modes of charging are employed for the grid-connected charging station. Moreover, for long run of the charging facility, sizing components of the EV charging facilities are optimized by meta heuristic barebones Harris-hawk Optimization with a net profit of US 0.0084/kWh in criteria of no profit/loss. This is also confirmed by the swarm-style memetic grasshopper optimization algorithm and multifarious particle swarm optimization.

Now-a-days, electricity has become the most significant part of human life. For this purpose, renewable energy sources as the emergent sources are introduced in the system. In Pakistan, in spite of having abundant renewable sources and favorable climate conditions, there are some remote areas where the extension of national grid is still absent. In this research paper, one of the remote area of Pakistan, Chak 6-DRB, a village of Cholistan, is considered for the hybrid renewable sources. This research has been implemented on HOMER (Hybrid Optimization Model for Electric and Renewable) software, in which two renewable sources; solar and anaerobic biogas have been implemented in terms of COE (Cost of Energy), NPC (Net Present Cost), costs of different equipment used and then find an optimal size and costs of the system. This HDRP (Hybrid Distributed Renewable Power) system contains anaerobic biogas, and solar system which yield most accurate and inexpensive results and are quite feasible for this remote area. This standalone generation and distribution model is also analyzed through HOMER including sensitivity parameters. In future, this role model of proposed hybrid renewable system would be very effective and helpful for other remote areas of the country for implementation

An optimal topographical arrangement of Wind Turbines (WTs) is essential for increasing the total power production of a Wind Farm (WF). This work introduces PSO-GA, a newly formulated algorithm based on the hybrid of Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) method, to provide the best possible and reliable WF Layout (WFL) for enhanced output power. Because GA improves on PSO-found solutions while PSO investigates several regions, PSO-GA can effectively handle issues with multiple local optima. In the first phase of the framework, PSO improves the original variables; in the second phase, variables are changed for improved fitness. The goal function takes into account both the power production of the WF and the total cost of WTs while analyzing wake upshot using the Jenson-Wake model. To evaluate the robustness of this strategy, three case studies are analyzed. The algorithm identifies the best possible position of turbines and strictly complies with industry-standard separation distances to prevent extreme wake interference. The comparative study with the past layout improvement process models demonstrates that the proposed hybrid algorithm has enhanced performance with the power improvement of 0.03-0.04% with the p value< 0.01 and 24-27.3% reduction in the wake loss. The above findings indicate that the proposed PSO-GA can be better than the other innovative methods, especially in the aspects of quality and consistency of the solution.

An optimal topographical arrangement of wind turbines (WTs) is essential for increasing the total power production of a wind farm (WF). This work introduces PSO-GA, a newly formulated algorithm based on the hybrid of Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) method, to provide the best possible and reliable WF layout (WFL) for enhanced output power. Because GA improves on PSO-found solutions while PSO investigates several regions; therefore, hybrid PSO-GA can effectively handle issues involving multiple local optima. In the first phase of the framework, PSO improves the original variables; in the second phase, the variables are changed for improved fitness. The goal function takes into account both the power production of the WF and the cost per power while analyzing the wake loss using the Jenson wake model. To evaluate the robustness of this strategy, three case studies are analyzed. The algorithm identifies the best possible position of turbines and strictly complies with industry-standard separation distances to prevent extreme wake interference. This comparative study on the past layout improvement process models demonstrates that the proposed hybrid algorithm enhanced performance with a significant power improvement of 0.03–0.04% and a 24–27.3% reduction in wake loss. The above findings indicate that the proposed PSO-GA can be better than the other innovative methods, especially in the aspects of quality and consistency of the solution.