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Exploring LBWO and BWO algorithms for Demand Side optimization and Cost Efficiency: Innovative Approaches to Smart Home Energy Management

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IEEE ACCESS 2024年 12卷 28831-28852页

作者： Youssef, Heba Kamel, Salah Hassan, Mohamed H. Mohamed, Ehab Mahmoud Belbachir, Nasreddine Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Minist Elect & Renewable Energy Cairo 11517 Egypt Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Addawasir Dept Elect Engn Wadi Ad Dawasir 11991 Saudi Arabia Univ Mostaganem Dept Elect Engn Mostaganem 27000 Algeria

Demand side management (DSM) involves technologies and strategies that allow customers to actively participate in the optimization of their energy usage patterns, ultimately contributing to a more sustainable and efficient energy system. In this paper, leader beluga whale optimization improvement (LBWO) and original beluga whale optimization (BWO) are used to implement a DSM scheme that enables lower peak-to-average ratio (PAR) and decreasing the expenses associated with electricity consumption. In the context of this research, electricity consumers decide to store, buy, or sell the electricity to maximize profits while minimizing its costs and PAR. Electricity consumers make their decisions based on the amount of electricity generated from their mini-grid, electricity prices and demand from the public network. The mini-grid is a combination of a photovoltaic (PV) panel and a wind turbine connected to an energy storage system (ESS). An ESS is used for maintaining power system stability because the power generated from renewable energy source (RES) has intermittent characteristics depending on environmental conditions. The proposed scheme is tested on three different cases from a study, the first case is the traditional house, the second case is the smart house with DSM, and the last case is the smart house with its mini-grid and DSM. Simulation results indicate that in case 2, LBWO and BWO achieved a remarkable reduction in electricity cost by 61% and 51% respectively. In case 3, the reduction was even more significant, with LBWO and BWO lowering the cost by 76% and 64% respectively. Moreover, LBWO generated a revenue of 154 (cents), while BWO generated a revenue of 118 (cents). The results confirm the effectiveness and robustness of the suggested scheme in reducing electricity costs and the PAR (Peak to Average Ratio), while simultaneously increasing profits for electricity consumers.

关键词： optimization Costs Renewable energy sources Smart homes Peak to average power ratio Energy storage Scheduling whale optimization algorithms Demand side management Energy management Storage management Beluga whale optimization demand side management leader beluga whale improvement mini-grid renewable energy source storage system

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PI Parameters Tuning for Frequency Tracking Control of Wireless Power Transfer System Based on Improved whale optimization Algorithm

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IEEE ACCESS 2024年 12卷 13055-13069页

作者： Yang, Xiong Guan, Jiamin State Grid Jiangsu Elect Power Co Ltd Distribut Network Technol Dept Res Inst JSEPRI Nanjing 211103 Peoples R China Nanjing Inst Technol Sch Mech Engn Nanjing 211167 Peoples R China

For the efficiency optimization problem of wireless power transfer system, the most important thing is that the frequency tracking control is needed to make the system work in a resonant state. The parameters of the frequency tracking controller directly determine the control performance. In this paper, we consider using an improved whale optimization algorithm to complete the PI parameter tuning of the frequency tracking controller as a way to save personnel's effort and time on the controller design. This improved whale optimization algorithm mainly has the following improvements: firstly, it uses Kent chaotic mapping to initialize the whale population, which improves the variegation of the initial solution;secondly, it introduces adaptive weight coefficients and nonlinearly improves the convergence factor, which achieves a balance between the algorithm's global and local searching ability;finally, it introduces sine-cosine algorithmic strategy and Cauchy reverse learning strategy, which avoids the problem that the algorithm is easy to mature prematurely. The superiority of the improved algorithm is verified through the testing of eight benchmark functions and the Wilcoxon rank sum test method. Finally, the algorithm is applied to the PI parameter optimization of frequency tracking control, and the phase difference curves under the control of different PI parameters are compared by simulation. The results show that the improved algorithm proposed in this paper has a better performance for the PI parameter tuning of frequency tracking control in wireless power transfer system, which can effectively help reduce the investment of human resources.

关键词： Resonant frequency whale optimization algorithms Tuning Heuristic algorithms Convergence Statistics Social factors Wireless power transfer PI control Energy efficiency frequency tracking control whale optimization algorithm

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Optimal Configuration of Distributed Generation Based on an Improved Beluga whale optimization

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IEEE ACCESS 2024年 12卷 31000-31013页

作者： Li, Jifang Zhou, Xingyao Zhou, Yifan Han, Aishan North China Univ Water Resources & Elect Power Coll Elect Engn Zhengzhou 45000 Peoples R China

Large-scale distributed generation (DG) access to the distribution network brings many uncertainties to the distribution network, considering the impact of different types of distributed power supply on the optimization results as well as the uncertainty and correlation of wind energy, photovoltaic power generation, and load. A metaheuristic algorithm the Improved Beluga whale optimization Algorithm (IBWO) is used to optimize the capacity and location of DG. This algorithm incorporates the elite reverse learning strategy and cyclone foraging strategy while adjusting the balance factor to enhance the diversity of the algorithm population and further balance local search capability with global search capability. This study optimizes the configuration of distributed energy resources considering the uncertainty and correlation of wind power, photovoltaic power, and load. The optimization objective is to reduce active power loss, improve voltage stability, and minimize investment and operating costs. By conducting simulations on IEEE 33-bus and IEEE 118-bus test cases, the active power network losses are enhanced by 55.49% and 45.39% respectively, and the algorithm outperforms other methods regarding other data, demonstrating its superiority and effectiveness.

关键词： Costs optimization Distribution networks Voltage Uncertainty Investment Power system stability Distributed power generation whale optimization algorithms Capacity planning Configuration management Distributed generation distribution network uncertainty correlation improved beluga whale optimization

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Hybrid optimization Based Harmonic Minimization in Three Phase Multilevel Inverter With Reduced Switch Topology

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IEEE ACCESS 2024年 12卷 71010-71023页

作者： Yabalar, Mehmet Halil Ercelebi, Ergun Hasan Kalyoncu Univ Dept Elect & Elect Engn TR-27010 Gaziantep Turkiye Gaziantep Univ Dept Elect & Elect Engn TR-27310 Gaziantep Turkiye

This study presents an innovative hybrid optimization approach that combines teaching-learning based optimization (TLBO) with the whale optimization algorithm (WOA) for selective harmonic elimination (SHE) technique in a modified reduced switch topology three phase multilevel inverter (MLI). The proposed topology requires fewer switches than a conventional cascaded H-bridge MLI and another reduced switch topology in a single phase MLI. Once applied to an 11-level inverter, this hybrid strategy effectively tackles the issues of harmonic reduction and total harmonic distortion (THD) on the line-to-line voltage, significantly improving the quality of the output power through the optimal determination of switching angles. The study leverages the TLBO and WOA to solve the non-linear set of equations associated with the SHE controls technique, aiming to overcome the limitations of classical methods prone to local optimal solutions and dependent on initial controlling parameters. This method has been performed in two steps, during the first step TLBO has been executed and in the next step the solutions derived from TLBO has been used as an initial guess for WOA which ensures the attainment of the precisely converged solution. By using MATLAB (R)/Simulink software environment, the performance of the hybrid TLBO with WOA method has been simulated and benchmarked against traditional standalone metaheuristic techniques. The simulation results reveal that proposed hybrid approach becomes advantageous in terms of SHE and output voltage quality across various modulation indices. The experimental results verified that the proposed algorithm has been validated through the implementation of a three-phase 11-level inverter. This study highlights the significant potential of the hybrid optimization method in progressing harmonic minimization techniques within the multilevel inverters.

关键词： Switches Harmonic analysis Topology optimization Voltage Multilevel inverters Switching loss whale optimization algorithms Teaching-learning based optimization (TLBO) whale optimization algorithm (WOA) selective harmonic elimination (SHE) reduced switch topology multilevel inverter (MLI) total harmonic distortion (THD) modulation index

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Multimodal Renewable Energy Hybrid Supply optimization Model Based on Heterogeneous Cloud Wireless Access

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IEEE ACCESS 2024年 12卷 78286-78303页

作者： Tian, Feng Wang, Hongjiang Jiang, He Zhao, Baida Shenyang Inst Engn Coll Informat Shenyang 110036 Peoples R China Shenyang Inst Engn Sch Renewable Energy Shenyang 110036 Peoples R China Shenyang Inst Engn Innovat & Entrepreneurship Educ Ctr Shenyang 110036 Peoples R China

With the increasing emphasis on environmental issues, the utilization of renewable energy has been recognized as a feasible solution to address the energy crisis and reduce environmental pollution. In view of this, this article proposes a multi-modal renewable energy hybrid power supply optimization model based on heterogeneous cloud wireless access. The model innovatively combines heterogeneous cloud wireless access technology and various intelligent optimization algorithms, including k-clustering algorithm, particle swarm optimization algorithm, and whale optimization algorithm, forming a hybrid optimization algorithm. In order to comprehensively evaluate the actual performance of the model, this study recruited 20 experts to provide detailed ratings on four core dimensions: cost-benefit ratio, reliability, robustness, and user satisfaction. The results showed that the model scored 95.1, 96.4, 95.6, and 96.2 in the four dimensions of cost-benefit ratio, reliability indicators, robustness, and user satisfaction, respectively. This series of significant data not only confirms the theoretical superiority of the model, but also demonstrates its strong potential and practical value in practical applications. In summary, this study provides a promising and innovative solution for the field of renewable energy supply.

关键词： optimization Clustering algorithms Heuristic algorithms Renewable energy sources Wireless communication Cloud computing whale optimization algorithms K-clustering algorithm heterogeneous cloud radio access particle swarm optimization algorithm WOA algorithm energy supply optimization

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A Triaxial Magnetometer Calibration Method With Improved Beluga whale optimization Algorithm

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IEEE SENSORS JOURNAL 2025年第6期25卷 9522-9530页

作者： Li, Zhuoxuan Li, Yuguo Luo, Ming Dong, Chenxu Ding, Xuezhen Ocean Univ China Coll Marine Geosci Qingdao 266100 Peoples R China

The triaxial magnetometer is a vector magnetic field sensor widely used in various fields. Due to limitations in manufacturing and installation processes, a nonorthogonal error, a scale factor error, and a zero-bias error can occur. These sensor errors significantly affect the accuracy of magnetic field measurements, so calibration is essential to ensure reliable data. This article proposes a novel error calibration method for triaxial magnetometers based on an improved Beluga whale optimization (BWO) algorithm. In addition, three synthetic simulations, with and without noise, and with different initial search spaces and population sizes are designed to validate the proposed method. Ideally, the minimum absolute error between the calibrated and theoretical results is 10(-13) nT. Furthermore, when the calibration method is applied to the field experiment data, the total geomagnetic field fluctuation is reduced from 600 to 9 nT, which is about 66 times smaller. Both simulation and experimental results further confirm the effectiveness and practicality of the proposed algorithm.

关键词： Magnetometers Calibration Vectors Magnetic sensors Magnetic field measurement optimization Sensitivity Magnetic susceptibility whale optimization algorithms Noise Beluga whale optimization (BWO) calibration model parameter estimation triaxial magnetometers

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Ripple-Induced whale optimization Algorithm for Independent Tasks Scheduling on Fog Computing

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IEEE ACCESS 2024年 12卷 65736-65753页

作者： Khan, Zulfiqar Ali Aziz, Izzatdin Abdul Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Malaysia

Due to the revolution of Internet of Things (IoT), the amount of data generation has been redoubling, leading to higher latency and network traffic. This has resulted in delays in services and increased energy consumption of cloud servers. Fog computing tackles the issues associated with long geographical distance between end-users and cloud servers by extending service provision closer to the network edge, reducing latency and makespan, and optimizing energy consumption during workload execution. Instead of offloading all tasks to the cloud, delay-sensitive tasks are executed at fog nodes, while others are offloaded to the cloud. However, the resources at the fog layer are limited, posing a challenge for task scheduling in fog computing, particularly as a multi-objective optimization problem. Meta-heuristic algorithms have been potent to find an optimal solution for such problems within a reasonable amount of time. The whale optimization Algorithm (WOA) is a relatively new meta-heuristic algorithm that has received significant attention from researchers due to its impressive optimization characteristics. However, being an exploitation-oriented technique, it falls into local optima due to a lack of generating new solutions over time. Limited exploration capabilities also compromise the diversity of the solution space and prolong convergence time. Therefore, in this study, an enhanced Ripple-induced whale optimization Algorithm (RWOA) is proposed, utilizing ripple effects to schedule independent tasks in fog computing. It aims to minimize makespan and energy consumption while maximizing throughput in a fog-cloud infrastructure by improving poor solutions through substantial changes. Extensive simulations are performed to assess the effectiveness of the proposed algorithm. The proposed RWOA outperformed TCaS, HFSGA, MGWO, and WOAmM on two workload datasets: Random and NASA Ames iPSC. The statistical significance of the results is validated by the Friedman test and Wil

关键词： Task analysis Cloud computing Costs Edge computing Scheduling Energy consumption Delays whale optimization algorithms Task scheduling meta-heuristic whale optimization Algorithm (WOA) fog computing

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Wide-Angle Scanning Thinned Phased Array Synthesis Based on Improved Multiobjective Beluga whale optimization Algorithm

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第11期23卷 3511-3515页

作者： Zheng, Yuqing You, Chang Jiang Zhang, Naibo Zhu, Xueyong Ding, Yufeng He, Hao Univ Elect Sci & Technol China Chengdu 611731 Peoples R China UESTC Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China China Elect Technol Grp Corp Beijing R&D Ctr Res Inst 54 Beijing 100070 Peoples R China

This letter proposes an algorithm that combines the improved multiobjective beluga whale optimization algorithm with the iterative Fourier transform (IFT) technique to optimize the sidelobe level (SLL) and directivity of the phased array. The algorithm is derived from the traditional beluga whale optimization algorithm, based on the framework of multiobjective optimization. By improving the traditional algorithm and combining with IFT technology and opposition-based learning strategy, the population diversity is enhanced, and the optimization ability of the algorithm is improved. By optimizing the array layout, the proposed method can effectively synthesize thinned planar phased arrays with low SLL and nondegraded directivity. Some specific examples are provided to confirm the effectiveness of the proposed method.

关键词： Phased arrays optimization Statistics Sociology Sorting whales whale optimization algorithms Beluga whale optimization (BWO) directivity phased array sidelobe level (SLL) thinned array synthesis

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Grey Wolf Optimizer and whale optimization Algorithm for Stochastic Inventory Management of Reusable Products in a Two-Level Supply Chain

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IEEE ACCESS 2023年 11卷 40278-40297页

作者： Sadeghi, Amir Hossein Bani, Erfan Amani Fallahi, Ali Handfield, Robert North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27606 USA Sharif Univ Technol Dept Ind Engn Tehran *** Iran North Carolina State Univ Dept Business Management Raleigh NC 27607 USA

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to establish a more realistic problem. The presented problem is formulated using a nonlinear programming mathematical model. The chance-constrained approach is suggested to deal with the constraints' uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.

关键词： Supply chains Inventory management Uncertainty Stochastic processes Metaheuristics whale optimization algorithms Programming Reuse and recovery chance-constrained programming grey wolf optimizer whale optimization algorithm Taguchi method

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Cloudlet Based Computing optimization Using Variable-Length whale optimization and Differential Evolution

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IEEE ACCESS 2023年 11卷 45098-45112页

作者： Muwafaq, Layth Noordin, Nor K. K. Othman, Mohamed Ismail, Alyani Hashim, Fazirulhisyam Univ Putra Malaysia Fac Engn Dept Comp & Commun Syst Engn Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia Fac Engn Res Ctr Excellence Wireless & Photon Network WiPNE Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia UPM Dept Commun Technol & Networks Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia UPM Inst Math Res INSPEM Lab Computat Sci & Math Phys Serdang 43400 Selangor D E Malaysia

Cloudlet-based optimization involves deploying a set of cloudlets in an environment and assigning user tasks to optimize various metrics, including energy consumption, quality of service (QoS), and cost. Typically, approaches deal with them separately, which might cause sub-optimality. Furthermore, assuming the fixed location of the cloudlets will limit the dynamic adaptability of the problem. Enabling more optimality and adaptability to the dynamic nature of cloudlet-based computing, we propose a novel Variable-Length multi-objective whale optimization Integrated with Differential Evolution designated as VL-WIDE. Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. Furthermore, it enables a non-dominated evaluation of solutions based on four objectives using crowding distance for selection. It provides an application-oriented solutions repair operator for repairing non-valid solutions and assuring that all solutions are generated in the feasible region. The proposed algorithm enables moving the cloudlets among pre-defined locations to increase the quality of service according to the change in the user density caused by user mobility. Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. VL-WIDE has provided the best in a number of non-dominated solutions and delta metrics and was competitive in other metrics.

关键词： optimization Edge computing Cloud computing whale optimization algorithms Costs Measurement Heuristic algorithms Mobile edge computing environment cloudlet deployment task offloading multi-objective optimization variable-length optimization

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