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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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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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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 289-334页

作者： Xiangyang Cao Yaojie Zheng Hanbin Xiao Min Xiao School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Sanya Science and Education Innovation Park Wuhan University of TechnologySanya572024China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan430063China

This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.

关键词： Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells

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Optimal operation of new coastal power systems with seawater desalination based on grey wolf optimization

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ENERGY REPORTS 2023年 9卷 391-402页

作者： Gao, Yujie Yang, Hao Zhou, Bowen Chen, Xinyi Hu, Zhijun Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Key Lab Integrated Energy Optimizat & Secure Opera Shenyang 110819 Peoples R China Northeastern Univ Sch Resources & Civil Engn Shenyang 110819 Peoples R China

Seawater desalination is one of the effective means to efficiently consume renewable energy and to improve the flexibility of system control in the new power system. In the optimal operation of new power systems, seawater desalination is usually considered as an entirety, and a motor equivalent model is used to simulate the overall external characteristics of seawater desalination. The characteristics of multiple sets of motors with multiple equipment processes inside the seawater desalination are neglected, which makes operation control characteristics inaccurate. In order to solve the above-mentioned problems, this paper firstly studies a seawater desalination system composed of 12 motors, and introduces the specific parameters of these 12 motors in detail. Secondly, after comprehensively considering the constraints of water flow, water pressure, and other factors, three control strategies are proposed for the 12 motors, respectively. Then, a detailed mathematical model of seawater desalination units and an optimal operation model of a new power system with seawater desalination are established. The optimization model aims at the minimum operating cost of seawater desalination, and considers the constraints of practical problems comprehensively such as motor power requirements and water storage capacity. Finally, the grey wolf optimization algorithm (GWO) is used in this paper to solve the optimization problem. The simulation results show that the model proposed in this paper can precisely control each unit in the seawater desalination system and improve the economic benefits of the system. Compared with the Particle Swarm optimization algorithm (PSO) and the Moth-flame optimization algorithm (MFO), the algorithm used in this paper can find the global optimal solution, and has both faster convergence speed and wider practicability. (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/

关键词： Seawater desalination Detail model Optimal operation grey wolf optimization algorithm

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Improved multi-strategy adaptive grey wolf optimization for practical engineering applications and high-dimensional problem solving

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第10期57卷 277页

作者： Yu, Mingyang Xu, Jing Liang, Weiyun Qiu, Yu Bao, Sixu Tang, Lin Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China New York Inst Technol Dept Biol & Chem Sci New York NY 11568 USA

The grey wolf optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. To address this, we propose an Improved Adaptive grey wolf optimization (IAGWO), which significantly enhances exploration of the search space through refined search mechanisms and adaptive strategy. Primarily, we introduce the incorporation of velocity and the Inverse Multiquadratic Function (IMF) into the search mechanism. This integration not only accelerates convergence speed but also maintains accuracy. Secondly, we implement an adaptive strategy for population updates, enhancing the algorithm's search and optimization capabilities dynamically. The efficacy of our proposed IAGWO is demonstrated through comparative experiments conducted on benchmark test sets, including CEC 2017, CEC 2020, CEC 2022, and CEC 2013 large-scale global optimization suites. At CEC2017, CEC 2020 (10/20 dimensions), CEC 2022 (10/20 dimensions), and CEC 2013, respectively, it outperformed other comparative algorithms by 88.2%, 91.5%, 85.4%, 96.2%, 97.4%, and 97.2%. Results affirm that our algorithm surpasses state-of-the-art approaches in addressing large-scale problems. Moreover, we showcase the broad application potential of the algorithm by successfully solving 19 real-world engineering challenges.

关键词： grey wolf optimization algorithm Search mechanism Inverse multiquadratic function Adaptive strategy Engineering design challenges

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Unsupervised intelligent system based on one class support vector machine and grey wolf optimization for IoT botnet detection

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第7期11卷 2809-2825页

作者： Al Shorman, Amaal Faris, Hossam Aljarah, Ibrahim Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan

Recently, the number of Internet of Things (IoT) botnet attacks has increased tremendously due to the expansion of online IoT devices which can be easily compromised. Botnets are a common threat that takes advantage of the lack of basic security tools in IoT devices and can perform a series of Distributed Denial of Service (DDoS) attacks. Developing new methods to detect compromised IoT devices is urgent in order to mitigate the negative consequences of these IoT botnets since the existing IoT botnet detection methods still present some issues, such as, relying on labelled data, not being validated with newer botnets, and using very complex machine learning algorithms. Anomaly detection methods are promising for detecting IoT botnet attacks since the amount of available normal data is very large. One of the powerful algorithms that can be used for anomaly detection is One Class Support vector machine (OCSVM). The efficiency of the OCSVM algorithm depends on several factors that greatly affect the classification results such as the subset of features that are used for training OCSVM model, the kernel type, and its hyperparameters. In this paper, a new unsupervised evolutionary IoT botnet detection method is proposed. The main contribution of the proposed method is to detect IoT botnet attacks launched form compromised IoT devices by exploiting the efficiency of a recent swarm intelligence algorithm called grey wolf optimization algorithm (GWO) to optimize the hyperparameters of the OCSVM and at the same time to find the features that best describe the IoT botnet problem. To prove the efficiency of the proposed method, its performance is evaluated using typical anomaly detection evaluation measures over a new version of a real benchmark dataset. The experimental results show that the proposed method outperforms all other algorithms in terms of true positive rate, false positive rate, and G-mean for all IoT device types. Also, it achieves the lowest detection time, whi

关键词： Internet of Things Anomaly detection Botnets Feature selection Intrusion detection system grey wolf optimization algorithm Novelty detection One class support vector machine

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Novel grey wolf optimization based on modified differential evolution for numerical function optimization

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APPLIED INTELLIGENCE 2020年第2期50卷 468-486页

作者： Luo, Jun Liu, Zewei Chongqing Univ Chongqing 400030 Peoples R China

grey wolf optimization algorithm (GWO) is a new swarm intelligence optimization algorithm proposed in recent years. Because of few control parameters and easy implementation, GWO is widely used in many fields. Compared with other common swarm optimization algorithms, it is more suitable for the global optimization problems. Nevertheless, the algorithm still has the shortcoming of low accuracy and slow convergence speed. In this paper, a novel hybrid optimization algorithm named MDE-GWO is proposed. Firstly, a JADE with opposition-based learning strategy algorithm (MDE) is embedded in GWO to enhance the ability of avoiding a local optimum. Notably, by introducing the opposition-based learning strategy, the search ability of the improved algorithm is increased greatly. Additionally, in order to balance the global and local search capabilities and speed up the convergence of the GWO algorithm, a leader moving-rate strategy is put forward. 28 typical benchmark functions are utilized to test the performance of the improved algorithm. The experimental results show that MDE-GWO has stronger advantages in search accuracy, stability and convergence speed in most cases.

关键词： grey wolf optimization algorithm Global optimization Opposition-based learning Leader moving-rate strategy Benchmark functions

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Efficient response analysis of the cable of offshore wind turbine at static state: Hybrid of perturbation method and grey wolf optimization

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OCEAN ENGINEERING 2023年第1期269卷

作者： Cao, Liang Dai, Maolin Zhou, Xuhong Huang, Guoqing Chen, Frank Zhu, Ronghua Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China Hunan Univ Key Lab Damage Diag Engn Struct Hunan Prov Changsha 410082 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Penn State Univ Dept Civil Engn Middletown PA 17057 USA Yangjiang Offshore Wind Energy Lab Yangjiang 529500 Guangdong Peoples R China

The mooring cable system is an important component of the offshore floating wind turbine. The mooring system mainly consists of several cables to be used to connect the floating body and seabed. The efficiency of dynamic response analysis of the mooring cable under flow current is generally low and thus complicates the optimal analysis of offshore floating wind turbines. As the determination of the dynamic response of cables highly relies on the static state, the static state response analysis of cables is important. To achieve the static state response analysis of cables, the mooring cable is simplified as a two-point boundary-valued model, which considers the hydrodynamic effect of flow current. Specifically, the perturbation method is first used to linearize the highly nonlinear equations of motion of a cable into the partial differential equations with a constant coefficient for easing the solution of equations. Then, to obtain the undetermined parameters of functions after linearization and reduce the computational cost, the grey wolf optimization algorithm is adopted. Lastly, three examples considering different currents are presented to demonstrate the responses of the mooring cable. Results show that the proposed algorithm can significantly improve the calculation efficiency. To better understand the effects of main parameters on the static response and mooring force of cable, a parametric analysis is carried out under the zero current velocity, considering the cable's stiffness, mass, and diameter as well as the number of nodes. The parametric analysis results indicate that the stiffness influences the response of cables, while the effects from mass and diameter can be ignored.

关键词： Mooring cable grey wolf optimization algorithm Perturbation method Static state Floating wind turbine

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optimization of Dam Reservoir Operation Using grey wolf optimization and Genetic algorithms: A Case Study of Taleghan Dam

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第7期34卷 1644-1652页

作者： Motlagh, A. Davani Sadeghian, M. S. Javid, A. H. Asgari, M. Islamic Azad Univ Tehran Cent Tehran Branch Dept Civil Engn Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Environm Engn Tehran Iran Islamic Azad Univ Cent Tehran Branch Dept Math Tehran Iran

With the growth of population, shortage of water, and severe lack of water resources, optimization of reservoirs operation is a principal step in water resource planning and management. Therefore, in the present study, water was optimally allocated for a period from 2010 to 2020 using two simulation-optimization models based on grey wolf optimization algorithm (GWO) and Genetic algorithm (GA) and WEAP model. System operational indices including volumetric reliability, temporal reliability, vulnerability, and sustainability were used to evaluated the perforemance of optimization algorithms as well as WEAP model. The objective function of resources allocation problem was minimizing sum of the squared relative deficiencies for each month and maximizing reliability over the entire 11-year period. The results showed that optimal allocation solution found by the GWO algorithm with volumetric reliability, vulnerability, and sustainability indices which were 86.93, 0.29, and 21.48%, respectively was better and more suitable than the optimal allocation solution found by GA algorithm (which were 87.12, 0.41, and 21.34%, respectively). Finally, given an increase in the water demands, it is possible to obviate the needs of beneficiaries to an acceptable level and prevent severe draught in dry months through optimizing the use of available resources. According to the calculated indices for the WEAP model, in which volumetric reliability, vulnerability, and sustainability were equal to 87.46, 0.92, and 1.03%, respectively. It can be concluded that the use of optimization algorithm in optimal operation of the dam is more reliable than WEAP model.

关键词： optimization Taleghan Dam grey wolf optimization algorithm Genetic algorithm WEAP Software

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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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