Tulasichandra Sekhar Gorripotu

Electric vehicles can significantly improve their energy efficiency through regenerative braking, which recovers kinetic energy during braking for storage in batteries. Supercapacitors are particularly suitable for this purpose due to their high power density and long cycle life. However, to prevent thermal stress and degradation during rapid energy exchange in regenerative braking, the charging current must be carefully managed. The proposed method controls the highest charging current using the supercapacitor temperature as an input parameter. The Levy Enhanced Red Panda Optimization (LE‐RPO) algorithm is employed to optimally tune the FOPID controller parameters and adjust the supercapacitor reference current. In order to improve the utilization of energy, this commit aims to analyze the supercapacitor's maximal charge capacity. The results were compared between a method with fixed peak current and ESR and one that was modified by temperature, peak current, and ESR. Modeling by varying the initial ambient temperatures was also examined. Lastly, the effect of the methods on the life span of the supercapacitor module is studied. According, the result shows that the proposed LE‐RPO method outperforms the conventional models like GWO, SOA and RPO with an improved efficiency of 79.233%. It highlights the superior stability and control system performance of the proposed LE‐RPO method. Likewise, the proposed LE‐RPO algorithm achieves a settling time of 7.714 s, which demonstrates faster stabilization compared to other methods such as Fuzzy, PSO, RTO, LSTM‐PSO, GA‐PSO, and IGWO‐CSO. Moreover, the stability performance of the proposed LE‐RPO‐based FOPID controller was evaluated under four conditions, such as no fault and SLG (single line to ground) faults on Lines 1–1, 1–3, and 1–5. The results show the settling time remains almost unchanged (7.7403–7.7543 s), indicating the system quickly reaches steady state with minimal disturbance.

Deregulated power systems face the difficulty of load frequency control due to different transaction situations and system uncertainties. This paper assesses a Fuzzy PIDN controller that is being tuned via a Moth Flame-Pattern Search (MFO-PS) algorithm to a two-area thermal-gas system under Poolco, bilateral and contract violation conditions. It is hypothesized that the proposed controller has better dynamic performance when compared to GA-PID, DE-PID, TLBO-PID, TLBO-TID, and TLBO-PS:TID. It is done with a MATLAB/Simulink model and the performance is evaluated through settling time, peak deviations, and an integral square error (ISE). Results confirm consistent improvement. In the Poolco case, the proposed controller achieves settling times of 4.07 s (ΔF1), 1.83 s (ΔF2), 1.32 s (ΔPTie) with ISE = 0.0135, outperforming TLBO-PS:TID (ISE = 0.0270). In the bilateral scenario, ISE reduces to 0.0075 compared to 0.0872, and under contract violation to 0.00251 compared to 0.1445. Sensitivity analysis shows stable performance with ISE varying from 0.0125 to 0.0135 under ±25% parameter changes. These findings indicate that the suggested approach can significantly improve damping and minimize frequency deviation compared to the existing best controller, which makes it highly applicable to operation of a deregulated power system.

This paper presents a new hybrid optimization framework that combines the Two-Point Estimation Method (2PEM) and the Enhanced Peafowl Optimization Technique (EPOT) to improve the dynamic voltage stability of Electric Vehicle Charging Stations (EVCSs). To efficiently manage uncertainties in EV demand, renewable energy unpredictability and load changes, the model employs a Voltage Stability Index (VSI)-based technique to coordinate reactive power injection from EVCSs. MATLAB simulations are used to evaluate the framework on a realistic 51-bus distribution network, showing notable advancements over conventional techniques. Additionally, the proposed method outperforms Monte Carlo Simulation (MCS) in terms of accuracy while achieving a 38% reduction in power losses, a 23% improvement in VSI and a 65% reduction in processing time. These findings demonstrate that the EPOT–2PEM hybrid approach can enhance grid resilience and voltage stability in intelligent distribution networks with significant EV integration.

The integration of 5G and cloud computing with electric vehicles has transformative potential to improve the energy efficiency and performance of electric vehicles. This chapter emphasizes integrating high-speed connectivity from 5G and vast computational power from cloud computing to take advantage of optimizing EV systems - focusing on real-time data analysis, predictive maintenance, and dynamic energy management. 5G enables seamless communication between EVs, infrastructure, and cloud platforms, ensuring efficient route planning, battery management, and energy conservation. By leveraging cloud computing, EVs can access advanced analytics and machine learning algorithms for optimizing energy usage, extending battery life, and enhancing overall performance. These technologies converge to present the world with smarter, more sustainable options regarding electric mobility, contributing to energy conservation and reduced carbon emissions.

Short-term load forecasting (STLF) is an integral component of energy management systems. In this paper, fuzzy logic-based algorithm is used for short-term load forecasting. The load changes over time and the goal is to satisfy the shift in demand and to maintain a fault as low as possible between the reference and real powers. The error in the load demand in mega-watt (MW) is compared with proposed technique as well as conventional methods. Three cases were investigated in which the load changes were 1) more random in nature, but the variance to the reference was more; 2) the random load changes were simpler, but a little different from the reference; and lastly, 3) the load changing was random, and the reference deviation was maximum. The results are analyzed for different load changes, and the corresponding results are verified using MATLAB. The deviation of the error value in load response is less experienced with a fuzzy logic controller than with a traditional system, and in fewer iterations, the objective function is also achieved.