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.

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.

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.

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.

EV charging combined with distributed energy storage (ES) devices could be utilized to send power to the grid during peak hours, mitigating the effects of load shedding. In order to achieve these goals, a hybrid multi-port charging system is developed in this study to properly manage power flows and balance energy. It is made up of a PFC bridge rectifier that is linked to an isolated three-port dc-dc converter that includes a series resonant (SRC) and a DAB converter for fast and slow charging applications, respectively. To maximize efficiency and power density, the ports share a 400 V common DC link. Grid-to-vehicle (G2V) mode is used to charge EV and ES vehicles from the utility grid; PV-to-grid (PV2G) mode is used to deliver PV energy to the grid; renewable-to-vehicle (R2V) mode is used to charge EV and ES batteries; and vehicle-to-grid (V2G) mode is used by EV and ES to deliver stored energy to the grid. The charging system’s performance and operation are validated in real time using MATLAB/Simulink under various operating situations. The system’s total harmonic distortion (THD) for the supply current is 2.07%. The suggested system is also compared against a state-of-the-art multiport charging system to assess its performance.

The traditional nonintrusive load identification (NILD) algorithms result in high computational costs based on classification models. And they cannot accurately identify loads with similar voltage-current trajectories that are frequently encountered in practical applications. Aiming at these deficiencies, an NILD algorithm is proposed based on combined weighting-the technique for order preference by similarity to ideal solution (TOPSIS) of feature fusion. The feature is fused by one image feature and eight numerical features with the combination weighting method. The weights are calculated by combining the principal component analysis, entropy, and the criteria importance through intercriteria correlation weighting methods to improve the utilization of features. The similarity between these nine features of a load and the corresponding features of other loads is calculated by the TOPSIS algorithm. Similarity analysis is used to determine whether or not a load is a "known load," and to obtain an identification result. If it is an "unknown load", the result can be obtained by dynamically updating the database and identifying it again. The results obtained indicate that this proposed algorithm can significantly improve the accuracy of load identification while reducing the computational costs.

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.

Nowadays Grid connected photovoltaic (PV) systems increases its influence in Indian power system. The current rise in load and demand means that linked power networks are essential. The efficiency of a load is significantly impacted by power quality issues, especially when it comes to frequency. The power system’s frequency and tie-line power should be as close as possible to the anticipated specifications. This article proposes artificial raindrop method with proportional- integral (PI) controller, to manage the load frequency control (LFC) of a PV-thermal-thermal interconnected three-area power system. A meta-heuristic method that takes inspiration from natural rainfall is the proposed artificial raindrop algorithm. A comparison is made between the suggested system and standard PI controller. A number of loads are simulated using Matlab to assess the full system. To validate the performance of proposed system, it is compared with the LFC using PI and fuzzy gain scheduling controller. Matlab is used to evaluate the entire system with varying loads. Take into account area 1 in 10% load perturbation: FGS reduces frequency deviation undershoot by 22% compared to the PI controller, while the ARD algorithm reduces it by 32%, where PI results in an undershoot of 0.1781 Hz.

Multi-inverters have higher quality output waveform as they have lower total harmonic distortion (THD) but the individual harmonics do not satisfy the Grid Code EN 50160 and CIGRE Code WG 36-05. Therefore, mitigation of these individual harmonics is necessary to satisfy the mentioned codes. In this paper, a novel solution for mitigating individual harmonics has been proposed. The proposed harmonic mitigation technique uses nearest level control (NLC) for reducing individual harmonics in a grid-cascaded H-bridge multilevel inverter (CBH-MLI). The proposed technique involves solving simultaneous non-linear equations in real-time. For this, first non-linear equations have been derived and then proposed individual harmonics mitigation techniques are digitally applied for 7-level and 9-level CHB-MLI. The main advantage of the proposed novel technique is that it is easy to implement in real time due to less computational burden and lower complexity. Further, an experimental setup has been developed to validate the simulation results. From the results it is obtained that individual harmonics has been reduced for 7-level and 9-level CHB-MLI.

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.