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Designing an Intuitive Algorithm Based Load Frequency Controller for Electrical Power Systems

In the interconnected system, changes in energy consumption and random energy generation by renewable energy sources cause an increase or decrease in frequency and bus voltages. Load frequency control (LFC) that cannot be controlled within certain limits may cause serious problems. Therefore, LFC is required to keep the interconnection frequency and power sharing of the interconnection line at a certain value. Due to the importance of this issue, researchers have been working on numerous studies to improve LFC. In this article, a cascaded FOPID+(III) controller consisting of a fractional-order PID and three integrators is designed for a two-zone power system, including thermal power plants, electric vehicles using the vehicle-to-grid (V2G) technique and renewable energy sources such as wind farms and photovoltaic panels. Particle Swarm Optimization and Gray Wolf Optimization are used to determine the gain parameters in our new design. The effectiveness and efficiency of the FOPID+(III) controller are tested with load variation, parameter variation in the designed model and RES power variation. As a result of the experiments, it was observed that the PSO-based FOPID+(III) controller provided a 54% improvement in settling time and a 55% improvement in maximum frequency overshoot compared to other controllers.

Design of an Improved Hybrid Lithium-Ion-Ultracapacitor Energy Storage System for Transport Vehicles

Lead-acid batteries (LABs) are mostly used in internal combustion engine (ICE) vehicles for starting lightning ignition (SLI). With the increased in load demand for transport vehicles (TVs), LABs are experiencing performance failure. LABs are not able to meet the load demand, which results in deep discharge, and shorten their lifespan. Therefore, this research study seeks to design a hybrid lithium-ion-ultracapacitor energy storage system (ESS) that will have a high storage capacity and longer lifespan at a reduced weight compared to a single LAB ESS. Power-sharing strategy is used as the technical solution to improve the battery performance. Two bidirectional DC–DC buck-boost converters and two level fuzzy logic control (FLC) will make up the full-active topology of the energy management system. The FLC 1 will provide the total reference power to start the ICE and to charge the energy storages based on the power demand, while ensuring that the state of charge is within the minimum and the maximum range of the battery, protects the battery from overcharging and deep discharging. To monitor the bidirectional buck and boost converters during the charging and discharging of the HESS, the FLC 2 is responsible for allocating complete control and managing the reference power of the energy storage (LIB and UP). The suggested method is expected to limit the power drawn from the battery and increase battery lifespan without changing the batteries current chemical composition. The simulation will be carried out utilizing the MATLAB/Simulink tool.

A Multilevel Model of Energy Market Considering Coupon Incentive Based Demand Response with Wind Power Uncertainty

Demand response (DR) can decrease or increase energy consumption and has the ability to use generating assets more efficiently. Because of the increased power consumption, it has become necessary for the power system networks either to update or upgrade the already existing power system or to inculcate some schemes to minimize the load through effective customer engagement. To address this issue incentive-based demand response proved to be an effective tool to change the pattern of consumption. This work proposes a novel multilevel energy market considering coupon incentive based DR (C-IBDR). The proposed model is operated on a modified PJM 5-Bus system where the independent service operator (ISO) using economic dispatch (ED) computes the localized marginal pricing (LMP) and the load serving entity (LSE) issues coupons to consumers and procures the demand reduction from them. Two wind generators are incorporated into the test system and their effect on the localized marginal pricing with and without demand response is analyzed. The optimization problems are modeled using particle swarm optimization (PSO) and linear programming (LP) in MATLAB. The results demonstrate that the proposed C-IBDR reduces peak demand from 900 to 860 MW through the utilization of wind and DR. This alteration modifies the load curve, increases the power factor from 0.524 to 0.555, raises the revenue of LSEs from $106,646 to $106,946, reduces generation costs from $473,291 to $214,651, and procures a total of 262 MW of demand from the consumer side in the form of demand reduction. The system is further tested for scalability. The practicality of the suggested approach in handling more extensive systems is vividly demonstrated through its application to the modified IEEE 118-bus system.

Optimizing Energy Utilization in the Weaving Industry: Advanced Electrokinetic Solutions with Modified Piezo Matrix and Super Lift Luo Converter

This project aims to revolutionize energy utilization in the textile industry by optimizing Modified Piezo Matrix-based electro-kinetic energy generation from weaving power looms. It focuses on harnessing kinetic energy from two sources—the open-end head frame and the Weaving X-Y Shuttle box—both generating sequential kinetic energy during weaving. The experimental setup employs a specialized configuration: a 4(4x2) matrix piezo for the open-end head frame and a 2(2x10) matrix piezo for the Weaving X-Y Shuttle box. This arrangement efficiently captures and converts kinetic energy, resulting in remarkable energy outputs of 9.51 Hp and 2.60 Hp for the Open end Head frame and Weaving X-Y shuttle box, respectively. To further enhance energy utilization and integration, a second-level DC-DC power conversion approach is employed, utilizing the Super Lift Luo converter(90%η). This strategy ensures efficient energy transfer and seamless integration with the Industrial DC microgrid. The project’s objectives encompass minimizing reliance on fossil fuels, promoting sustainability, and highlighting the potential of electrokinetic solutions for industrial energy optimization. By tapping into previously overlooked kinetic energy sources and maximizing their conversion, this project presents a pioneering effort toward sustainable practices in the textile sector, contributing to environmentally-conscious production methods.

A New Comparative Approach Based on Features of Subcomponents and Machine Learning Algorithms to Detect and Classify Power Quality Disturbances

Current measurement systems based on the IEEE-1159 standard have some limitations and robustness problems under noisy and fast-changing conditions. Besides, applying different methods for each Power Quality Disturbance (PQD) to every window is required but time-consuming and not feasible. Therefore, different kinds of two-stage methods, Detection and Classification (D&C), have been improved in many studies. Then, the required measurement can be performed to define disturbance. For this purpose, a new approach based on features of subcomponents with Machine Learning Algorithms (MLAs) to detect and classify PQDs is proposed. 21-class dataset including single and multiple PQDs under different noisy conditions was prepared randomly. Of this dataset, determined features were extracted and some of these were selected. Then, selected features were trained and tested with some MLAs in a workstation. Results obtained from comparative MLAs and the other classification methods show that the best MLA with related features is Random Forest with 96.97% while LightGBM, k-Nearest Neighbors, and XGBoost 96.85%, 96.73%, and 92.82% accuracy, respectively. Because the selected features, optimized parameters, and the related MLA were obtained by investigating for features provided from the PQDs in the whole parameter space, this approach brings the advantages of high accuracy, low D&C complexity, and computing load.

Metaheuristic Algorithms for Evaluating the Effect of Electric Vehicle Charging Infrastructures in Distribution Networks

To address the effect of electric vehicle (EV) load on distribution system efficient optimization techniques are required. In this paper, an attempt is made to work with various new optimization techniques for minimizing the detrimental effect of EV charging station (EVCS) load on the electrical network. The operating conditions of the distribution network, that is, voltage stability, reliability and power loss (VRP) index are optimized in this work in a framework of multiple objectives with various technical constraints. Although many optimization approaches have been developed in recent years, eight well-known techniques are employed in the present work such as modified teaching-learner-based optimization (MTLBO), JAYA, modified JAYA (MJAYA), ant–lion optimization (ALO), whale optimization technique (WOT), grasshopper optimization technique (GOT), modified whale optimization algorithm (MWOA), and hybrid whale particle swarm optimization (HWPSOA). All the eight techniques’ performance is compared under two different cases of operations by testing the objective function on the modified IEEE 33 bus distribution system using MATLAB software. The numerical solutions obtained by the HWPSOA indicate the suitability and effectiveness for optimizing the specified complex multi-objective function compared to other techniques.