• Energy Research

This paper examines the specifications of lithium battery cells, which are considered one of the most vital sources for electrical energy storage units. The specifications have been covered to associate battery performance with its usage for electrically powered motor vehicles. With the motivation of rapid deployment of electric vehicles (EVs) around the world, the key contribution of this study is to provide a comparative investigation of well-known commercially available Li-ion battery cells used as a pack for electric race car. Five lithium cells from different manufacturers were analyzed for start voltage, end voltage, current, and the use of active cooling under different test conditions. Thermal imaging was used to provide more comprehensive analysis of tested battery packs. The outcomes of this experimental investigation are described in the sections below in the order in which the analyses were conducted. The key findings of this study are presented in the conclusion section.

In this manuscript proposes a hybrid approach for locating and sizing Electric vehicle charging stations (EVCSs) optimally and managing the vehicle charging process. The proposed hybrid approach is to work in conjunction with Mexican Axolotl optimization (MAO) and Wild Horse Optimizer (WHO) hence it is named as MAOWHO approach. The major purposes of the proposed approach are to site and size of the electric vehicle parking lot (EVPL) and to improve the benefit of EVPL for the participation of the reserve market. In addition, power loss and voltage fluctuations occur due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) demand load, which is reduced by the proposed approach. To optimally determine the size of the parking lot, the MAOWHO approach is adopted. The integration of the EV and PV systems, especially in parking lots, enhances the reliability and flexibility of the electrical system at critical moments. Multiple objective optimization problems are calculated to achieve objective variables to reduce power losses, voltage fluctuations, charging and supply costs, and EV costs. The location and capacity of the RES and EV charging stations in this optimization problem are objective variables. The MAOWHO approach enhances Solar Powered Electric Vehicle Parking Lot (EVSPL) participation in various energy and ancillary service markets that includes the effects of capacity payments. Besides, the implementation of MAOWHO approach is done by the MATLAB/Simulink platform and the performance of the MAOWHO approach is compared to the existing approaches. From the simulation outcome, it concludes that the proposed approach based performance provides a profit of 880 €compared to other approaches like SMO, CGO, SBLA.

Various electric vehicle charging and discharging strategies (EVs) and V2G technologies are discussed in this article as their impacts on energy distribution networks. The V2G application that can be used on vehicles offers many benefits, as demonstrated. Features such as active power regulation, reactive power support, load balancing, and current harmonic filtering are incorporated into this technology. Although V2G technology has many benefits, there are also several challenges. These challenges include reduced battery life, communication overhead between EVs and grids, and changes in distribution network infrastructure. The article briefly discusses the effects of electric vehicle penetration levels, charging profiles, and various other aspects of controlled charging and discharging from a performance perspective. This includes overloading, deteriorating power quality, and power loss. A comprehensive analysis of controlled and uncontrolled charging–discharging methods, delayed charging–discharging methods, indirect controlled discharging methods, bidirectional charging–discharging methods, and intelligent scheduling is presented in this study. Several challenges and issues regarding electric vehicle applications are discussed from an aggregator’s perspective. Analysis shows that Li-ion batteries can be recharged 2000–4000 times, and a mass-produced Li-ion battery costs $200–$500 per kWh. Degradation costs of batteries at 80% discharge depth are estimated to be $130 per MWh at 300 kWh investment cost. 10% of peak capacity could come from PEVs in the 20% range. Around 87.5% of PEVs are properly charged.

Power electronics is a core subject in electrical and electronics engineering at the undergraduate level. The rapid growth in the field of power electronics requires necessary changes in the curricula and practica for power electronics. The proposed next-generation power electronics teaching laboratory changes the learning paradigm for this subject and is for the first time used for teaching purposes in Pakistan. The proposed controller hardware-in-the-loop (CHIL) laboratory enabled students to design, control, and test power converters without the fear of component failure. CHIL setup allowed students to directly validate the physical controller without the need for any real power converter. This allowed students to obtain more repeatable results and perform extreme digital controller testing of power converters that are otherwise not possible on real hardware. Furthermore, students could start learning power electronics concepts with hardware from the beginning on a safe, versatile, fully interactive, and reconfigurable platform. The proposed laboratory meets the accreditation board for engineering and technology (ABET) student outcome criterion K such that students can continue with the same hardware and software toolset for graduate and research purposes. The knowledge and skills acquired during undergraduate years can help students create new solutions for power electronics systems and develop their expertise in the field of power electronics. The results obtained from the survey indicated that the majority of the students were satisfied with the laboratory setup. They also expressed appreciation over the provision of a high-level graphical language “LabVIEW” for the digital controllers compared to conventional low-level text-based languages such as VHDL, Verilog, C, or C++.

This study describes the optimization of Distribution Network Structure (DNS) by altering sectionalizing and tie-switches with Reactive Power Injections (RPI) through optimal nodes under three load variations considering five different cases in Electric Distribution Networks (EDNs) to reduce Energy Loss (ELoss), thereby achieving Economic Benefit (EB), which is the first step process. This approach yields EBs only to some extent. To achieve a greater reduction in ELoss, enhancing bus voltage profile and to achieve additional EBs, this study examines the incorporation of Renewable Energy Generations (IREGs) into the optimal EDNs with reactive power support. The optimal energy has been purchased from the Independent Power Producers (IPPs). Besides, the Levy Flight Mechanism (LFM) integrated Seagull Optimization Algorithm (SOA) has been applied to solve the Economic Based Objective Function. The effectiveness of the developed methodology has been validated using IEEE 33-bus and a real 74-bus Myanmar EDN. The reductions in ELoss achieved by LFM-SOA have been compared with those of other existing methods in the literature for all the cases. The results reveals that the developed methodology efficiently achieves more EBs ($) for all the cases by optimizing DNSA with and without RPIs and IREGs.

Energy storage batteries have been described as an ideal way to solve renewable energy problems, improve self-consumption rate (ScR), and pave the way for further growth in renewables penetration. In this work, an optimization framework is proposed to enhance a grid-connected microgrid performance in three stages. The first stage epitomizes maximization of the ScR of the highly-penetrated renewables hosted in the microgrid considered via sodium sulfur batteries allocation. The second stage epitomizes the minimization of the active power losses. The third stage epitomizes the calculation of the optimal energy management relying on diminishing the overall microgrid’s cost of operation depending on the optimal findings of the earlier two stages. The coronavirus herd immunity optimization algorithm is applied on MATLAB’s platform to solve the engineering problem formulated. Numerous linear and nonlinear constraints have been taken into account. The results have gotten validate the usefulness of the developed solutions and algorithm application.

Predicting the need for modeling and solutions is one of the largest difficulties in the electricity system. The static-constrained solution, which is not always powerful, is provided by the Gradient Method Power Flow (GMPF). Another benefit of using both dynamic and transient restrictions is that GMPF will increase transient stability against faults. The system is observed under contingency situations using the Dynamic Stability for Constrained Gradient Method Power Flow (DSCGMPF). The population optimization technique is the foundation of a recent algorithm called Training Learning Based Optimization (TLBO). The TLBO-based approach for obtaining DSCGMPF is implemented in this work. The total system losses and the cost of the individual generators have been optimized. Analysis of the stability limits under contingency conditions has been conducted as well. To illustrate the suggested approaches, a Standard 3 machine 5-bus system is simulated using the MATLAB 2022B platform.

Energy consumption has drastically increased to meet the growing demand of domestic and industrial usage needs. This has led to a significant rise in the contribution of renewable energy sources, owing to their eco-friendly nature. Solar photovoltaic (PV)-based power generation plays an important role and is growing rapidly. However, it faces challenges due to its inherently low output voltage and non-linear characteristics, which limit its efficiency and performance. These limitations necessitate the use of DC–DC converters to optimize voltage levels and ensure efficient energy transfer, making them a crucial component in PV systems. Among them, non-isolated converters were preferred due to their compact size and their ability to effectively control the output of solar PV. This article critically reviews various non-isolated DC–DC converters, such as conventional, hybrid, and high-gain converters, and analyzes their performance for optimal selection. A thorough study, including mathematical modeling and performance validation through simulation, is presented in detail. The critical discussion and comparison of the various converters will significantly help design engineers and researchers in selecting the appropriate converter for solar PV applications.