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Stochastic Energy Management Operation Strategy for High Penetrated Grid Connected Solar with Incorporation of Battery Storage System

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SMART GRIDS AND SUSTAINABLE ENERGY 2025年第1期10卷 1-11页

作者： Semwal, Gourav Sharma, Sachin Rawat, Tanuj Sharma, Gulshan Bansal, Ramesh C. Graph Era Deemed be Univ Dept Elect Engn Dehra Dun India Manipal Acad Higher Educ Elect & Elect Engn Manipal Inst Technol Manipal Karnataka India Newcastle Univ Sch Engn Newcastle Upon Tyne England Univ Johannesburg Dept Elect Engn Technol Johannesburg South Africa Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

The present distribution systems are heading towards smart distribution systems to attain large socio economic benefits. For achieving these benefits, the distribution system will include the practical aspect of the flexible modern technologies like renewable energy based distributed generators, demand response and battery energy storage system to manage load governance. There is source of uncertainty present in the non-dispatch able based distributed generations (DGs) that affects the operation schedule of distribution system. Therefore, this paper proposes a novel operation strategy for battery energy storage in coordination with uncertain large scale Photo-voltaic system based DG for distribution system. The optimal discharging and charging plans of battery energy storage for accommodation of uncertain photovoltaic are condition to the constraint like nodal power balance, feeder current limit and node voltage limit etc. grey wolf optimization algorithm (GWO) is developed for analyzing the impact of multiple battery energy storage strategies, for controlling the demand deviation and node voltage of the distribution system. GWO algorithm is investigated on IEEE-33 bus radial distribution systems. The efficacy of the result shows the successful achieving the promised voltage profile and grid demand deviation profile.

关键词： Distributed generation Battery energy storage systems Photo-voltaic system grey wolf optimization algorithm

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optimization of Flight Endurance for Turboprop Air Taxis Using Metaheuristic algorithms

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第7期38卷 1685-1698页

作者： Talouki, I. Fozouni Toloei, A. Shahid Beheshti Univ Dept New Technol & Aerosp Engn Tehran Iran

This study aims to optimize the flight endurance of a 12-passenger turboprop air taxi using two metaheuristic optimization algorithms: grey wolf optimization (GWO) and Ant Colony optimization (ACO). Initially, the gradient descent method was employed to estimate the aircraft's maximum weight. Subsequently, the aircraft's performance characteristics were utilized as design variables and flight endurance was optimized under specific constraints without altering the physical structure of the aircraft. The optimization process was implemented, and the results were evaluated and compared in terms of performance and efficiency. This research demonstrated that the two mentioned algorithms, utilizing random and collective strategies, were able to enhance the aircraft's efficiency. Additionally, the optimization of flight endurance for three real aircraft Piper, Beechcraft, and Bombardier was examined compared to their original endurance. In this context, the Ant Colony optimization algorithm exhibited better performance than the grey wolf optimization algorithm, which could have a positive impact on flight operations without refueling or the process of finding alternative airports.

关键词： Air Taxi optimization Gradient Descent grey wolf optimization algorithm Ant Colony optimization algorithm

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A cold chain logistics distribution optimization model: Beijing-Tianjin-Hebei region low-carbon site selection

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2024年第11期124卷 3138-3163页

作者： Zhang, Liyi Fu, Mingyue Fei, Teng Lim, Ming K. Tseng, Ming-Lang Tianjin Univ Commerce Tianjin Peoples R China Tianjin Univ Finance & Econ Tianjin Peoples R China Univ Glasgow Adam Smith Business Sch Glasgow Scotland Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ Dept Med Res Taichung Taiwan Univ Kebangsaan Malaysia UKM Grad Sch Business Bangi Malaysia Khon Kaen Univ Dept Ind Engn Khon Kaen Thailand

PurposeThis study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center ***/methodology/approachThis study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center *** optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain ***/valueThis study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

关键词： Low-carbon cold chain logistics Distribution center site selection model grey wolf optimization algorithm Artificial fish swarm algorithm

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Dynamics analysis and parameter optimization of a vibration absorber with geometrically nonlinear inerters

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JOURNAL OF VIBRATION AND CONTROL 2024年第21-22期30卷 5031-5046页

作者： Shen, Yongjun Sui, Peng Shijiazhuang Tiedao Univ Dept Mech Engn Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ Dept Mech Engn 17 North Second Ring East Rd Shijiazhuang 050043 Hebei Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn 17 North Second Ring East Rd Shijiazhuang 050043 Hebei Peoples R China

The inerter-based dynamic vibration absorber (DVA) is a promising technique for vibration control. In most studies, the inerter is usually mounted in the same direction as that of the motion, which may not adequately reflect the vibration suppression capability and the two-terminal inertial feature of the inerter, and even weakens the performance of vibration absorbers. Considering the potentiality of improving the control performance by unconventional mounting methods, a rarely studied structure of geometrically nonlinear inerters is introduced into the vibration absorber system. This novel vibration absorber is presented to investigate the following issues, that is, the unknown coupled dynamical behavior between the complex nonlinear force with inertia, damping, and stiffness terms generated by this structure and the vibration absorber system, the possibility of vibration absorption facilitated by this structure, and the full utilization of the two-terminal inertial feature of the inerter. The approximate solutions of the system are obtained using the harmonic balance method. The influence of each variable on the system response is analyzed, and the system parameters are optimized by using the grey wolf algorithm. In addition to the ordinary resonance phenomenon, there are also dynamic features such as soft characteristic jump behavior and response loops at certain parameter range. As a nonlinear system, this model is more stable than the nonlinear energy sink (NES). The optimized amplitude-frequency curves are equal-peak stable, similar to the linear vibration absorbers. The robustness of system parameters is high for small inerter-mass ratios or excitation amplitudes, which is better than DVAs. Compared to the classical linear vibration absorber, NES, and improved NES, the vibration suppression capacity and damping bandwidth of this model are enhanced. In comparison, it is also found that this model has smaller optimum parameters than the classical NES and equiv

关键词： inerter geometrical nonlinearity nonlinear vibration absorber parameter optimization grey wolf optimization algorithm

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A Novel Optimal Planning and Operation of Smart Cities by Simultaneously Considering Electric Vehicles, Photovoltaics, Heat Pumps, and Batteries

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PROCESSES 2024年第9期12卷 1816-1816页

作者： Shokri, Masoud Niknam, Taher Sarvarizade-Kouhpaye, Miad Pourbehzadi, Motahareh Javidi, Giti Sheybani, Ehsan Dehghani, Moslem Shiraz Univ Technol Dept Elect & Elect Engn Shiraz *** Iran Fars Reg Elect Co Shiraz *** Iran Comillas Pontif Univ Inst Res Technol IIT Sch Engn ICAI Madrid 28015 Spain Univ S Florida Muma Coll Business Sch Informat Syst & Management Tampa FL 33620 USA

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed;hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm.

关键词： grey wolf optimization algorithm optimal planning and operation regenerative braking energy smart city

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Robust Control Frameworks for Heave Disturbance Suppression in Offshore Managed Pressure Drilling Environments

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IEEE ACCESS 2025年 13卷 79137-79149页

作者： Rehman, Atif Ghias, Rimsha Ahmad, Iftikhar Sherazi, Hammad Iqbal NUST Sch Interdisciplinary Engn & Sci SINES Islamabad 44000 Pakistan NUST SEECS Islamabad 44000 Pakistan Qassim Univ Coll Engn Dept Elect Engn Buraydah 52571 Saudi Arabia

Managed Pressure Drilling (MPD) systems have transformed drilling operations by enabling precise wellbore pressure control, particularly in challenging conditions. Automatic MPD, leveraging advanced choke valve mechanisms, effectively mitigates pressure fluctuations caused by drill string oscillations induced by Heave Motion. This study introduces advanced nonlinear control strategies, including Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC), to enhance pressure regulation. Controller gains are optimized using an improved grey wolf optimization algorithm, targeting the minimization of the Integral Time Absolute Error. Stability of the controllers is rigorously analyzed through Lyapunov criteria. Comprehensive performance evaluations via MATLAB/Simulink simulations, complemented by real-time validation using a C2000 Delfino MCU F28379D Launchpad, demonstrate the robustness and efficacy of the proposed control methodologies.

关键词： Drilling Mathematical models Fluctuations Sliding mode control Stability criteria Inductors Pressure control optimization Testing Robustness Advanced drilling techniques dynamic pressure regulation grey wolf optimization algorithm nonlinear control strategies

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Hybrid machine learning approach for accurate prediction of the drilling rock index

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Shahani, Niaz Muhammad Zheng, Xigui Wei, Xin Jiang, Hongwei China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China China Univ Min & Technol State Key Lab Geo Mech & Deep Underground Engn Xuzhou 221116 Peoples R China Liupanshui Normal Univ Sch Mines & Civil Engn Liupanshui Peoples R China Guizhou Guineng Investment Co Ltd Guiyang Peoples R China

The drilling rate index (DRI) of rocks is important for optimizing drilling operations, as it informs the choice of appropriate methods and equipment, ultimately improving the efficiency of rock excavation projects. This study presents a hybrid machine learning approach to predict the DRI of rocks accurately. By integrating grey wolf optimization with support vector machine (GWO-SVM), random forest (GWO-RF), and extreme gradient boosting (GWO-XGBoost) models, the aim was to enhance predictive accuracy. Among these, the GWO-XGBoost model exhibited superior predictive performance, achieving a coefficient of determination (R-2) of 0.999, mean absolute error (MAE) of 0.00043, root mean square error (RMSE) of 1.98017, and severity index (SI) of 0.0350 during training. Testing results confirmed its accuracy with R-2 of 0.999, MAE of 0.00038, RMSE of 1.80790, and SI of 0.0312. Furthermore, the GWO-XGBoost model outperformed the other models in terms of precision, recall, f1-score, and multi-class confusion matrix results for each DRI class. The GWO-RF model also demonstrated high accuracy, ranking second, while the GWO-SVM model showed comparatively lower performance. This research aims to advance rock excavation practices by providing a highly accurate and reliable tool for DRI prediction. The results highlight the significant potential of the GWO-XGBoost model in improving DRI predictions, offering valuable intuitions and practical applications in the field.

关键词： Drillability Drilling rate index XGBoost Mining engineering grey wolf optimization algorithm

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Allocation of Distributed Generators Using Parallel grey wolf optimization 9

Allocation of Distributed Generators Using Parallel Grey Wol...

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9th International Conference on Modern Power Systems (MPS)

作者： Younesi, Soheil Ahmadi, Bahman Ceylan, Oguzhan Ozdemir, Aydogan Istanbul Tech Univ Dept Elect Engn Istanbul Turkey Kadir Has Univ Management & Informat Syst Dept Istanbul Turkey

ISBN: (纸本)9781665433815

This paper solves the allocation problem of distributed generators (DGs) in smart grids utilizing a grey wolf optimization (GWO) algorithm. By parallelizing GWO, it presents the impact of using various number of processors on speedup, efficiency. To decrease the computation time required to perform the simulations, different migration rates are applied for different number of processors. Moreover, the accuracy obtained using different number of processors is analyzed. The simulations are performed for a 33-bus distribution test system using MATLAB's parallel computing toolbox. From the simulation results it is observed that parallel GWO can be used as a tool for distribution system optimization.

关键词： Parallel Computation Smart grids Distributed generators optimization grey wolf optimization algorithm

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Research on wheelchair form design based on Kansei engineering and GWO-BP neural network

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SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Cai, Weilin Wang, Zhengyu Wang, Yi Zhou, Meiyu East China Univ Sci & Technol Sch Art Design & Media Shanghai 200237 Peoples R China

With the increasing emphasis on humanistic care in society, consumers are no longer only concerned about the functional needs of products but also about the spiritual, cultural, and emotional needs that products bring to people. This study proposes a wheelchair form design method based on the Kansei engineering approach, which integrates the evaluation grid method (EGM), grey wolf optimization (GWO) algorithm, and back propagation neural network (BPNN) technology. The aim is to explore the connection between wheelchair form design elements and user emotions and help industrial designers find designs with emotional preferences. In this study method, firstly, the collected wheelchair samples were evaluated using the EGM, extracting upper-level Kansei vocabulary driven by user attractiveness, middle-level original attractiveness items, and lower-level specific design elements and extracting nine sets of Kansei vocabulary mentioned frequently by users. Meanwhile, the morphological analysis method is used to construct a sample library of product morphological elements. Secondly, the semantic difference and factor analysis methods were used to analyze the ratings of 9 pairs of Kansei words, and the weights of Kansei factors were calculated to identify three critical Kansei demand factors. Thirdly, based on the analysis results of the orthogonal experiment, a conceptual plan for the wheelchair was constructed using computer-aided technology Rhinoceros 3D modelling software. Fourthly, the semantic difference method is used to collect users' ratings of critical Kansei words for wheelchair concept schemes, and the evaluation values of critical Kansei words are calculated by weighting. Fifth, a BPNN based on the GWO algorithm will establish a predictive model between wheelchair design elements and vital Kansei images. Finally, the predictive performance of BPNN and GWO-BPNN models will be compared to verify their superiority. The results indicate that the GWO-BPNN model has be

关键词： Wheelchair design Kansei engineering Evaluation grid method grey wolf optimization algorithm Back propagation neural network

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An EEMD-SVD method based on gray wolf optimization algorithm for lidar signal noise reduction

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第17期44卷 5448-5472页

作者： Li, Shun Mao, Jiandong Li, Zhiyuan North Minzu Univ Sch Elect & Informat Engn Yinchuan Peoples R China Key Lab Atmospher Environm Remote Sensing Ningxia Yinchuan Peoples R China

Atmospheric lidar is susceptible to light attenuation, sky background light and detector dark current during detection, which results in a lot of noise in the lidar return signal. In order to improve the SNR and extract useful signals, this paper proposes a new joint denoising method EEMD-GWO-SVD, which includes empirical mode decomposition (EEMD), grey wolf optimization (GWO) and singular value decomposition (SVD). Firstly, the grey wolf optimization algorithm was used to optimize two parameters of EEMD algorithm according to moderate values: the standard deviation Nstd of adding Gaussian white noise to the signal and the number NE of adding Gaussian white noise. Secondly, the mode components obtained by EEMD-GWO decomposition are screened and reconstructed according to the correlation coefficient method. Finally, the SVD algorithm with strong noise reduction ability was used to further remove the noise in the reconstructed signal, and the lidar return signal with high SNR was obtained. In order to verify the effectiveness of the proposed method, the proposed method was compared with empirical mode decomposition (EMD), complete ensemble empirical modal decomposition (CEEMDAN), wavelet packet decomposition and EEMD-SVD-lifting wavelet transform (EEMD-SVD-LWT). The results show that the noise reduction effect of the proposed method was better than that of the other four methods. This method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. In fact, the denoised signal is not distorted, the waveform is smooth, the far-field noise interference can be suppressed and the denoised signal is closer to the real signal with higher accuracy, which indicates the feasibility and practicability of the proposed method.

关键词： Lidar grey wolf optimization algorithm singular value decomposition empirical modal decomposition noise reduction

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