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Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic algorithms

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IEEE ACCESS 2023年 11卷 68379-68394页

作者： Pompern, Natsawat Premrudeepreechacharn, Suttichai Siritaratiwat, Apirat Khunkitti, Sirote Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50200 Thailand Khon Kaen Univ Fac Engn Dept Elect Engn Khon Kaen 40002 Thailand

In this research, the optimal placement and capacity of battery energy storage systems (BESS) in distribution networks integrated with photovoltaics (PV) and electric vehicles (EVs) have been proposed. The main objective function is to minimize the system costs including installation, replacement, and operation and maintenance costs of the BESS. The replacement cost has been considered over 20 years while the operation and maintenance costs are the costs incurred by transmission line loss, voltage regulation, and peak demand. To solve the problem, three metaheuristic algorithms, namely particle swarm optimization (PSO), african vultures optimization algorithm (AVOA), and salp swarm algorithm (SSA), are employed. The proposed approach is evaluated on the IEEE 33- and 69-bus distribution systems integrated with PV and EVs. The results provided by the considered algorithms are compared in terms of the objective function, system efficiency enhancement, payback period, and statistical analysis. The simulation results show that after the BESS installation, the voltage profile can be improved, transmission loss is reduced, and peak demand is decreased where PSO provides the best objective values and AVOA achieves the fastest payback periods in both systems.

关键词： ~Battery energy storage systems photovoltaics electric vehicles metaheuristic algorithms particle swarm optimization african vultures optimization algorithm salp swarm algorithm

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A robust chaos-inspired artificial intelligence model for dealing with nonlinear dynamics in wind speed forecasting

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2393-e2393页

作者： Baris, Caner Yanarates, Cagfer Altan, Aytac Zonguldak Bulent Ecevit Univ Dept Elect & Elect Engn Zonguldak Turkiye Gumushane Univ Kelkit Aydin Dogan Vocat Sch Dept Elect & Energy Gumushane Turkiye

The global impacts of climate change have become increasingly pronounced in recent years due to the rise in greenhouse gas emissions from fossil fuels. This trend threatens water resources, ecological balance, and could lead to desertification and drought. To address these challenges, reducing fossil fuel consumption and embracing renewable energy sources is crucial. Among these, wind energy stands out as a clean and renewable source garnering more attention each day. However, the variable and unpredictable nature of wind speed presents a challenge to integrating wind energy into the electricity grid. Accurate wind speed forecasting is essential to overcome these obstacles and optimize wind energy usage. This study focuses on developing a robust wind speed forecasting model capable of handling non-linear dynamics to minimize losses and improve wind energy efficiency. Wind speed data from the Band & imath;rma meteorological station in the Marmara region of Turkey, known for its wind energy potential, was decomposed into intrinsic mode functions (IMFs) using robust empirical mode decomposition (REMD). The extracted IMFs were then fed into a long short-term memory (LSTM) architecture whose parameters were estimated using the african vultures optimization (AVO) algorithm based on tent chaotic mapping. This approach aimed to build a highly accurate wind speed forecasting model. The performance of the proposed optimization algorithm in improving the model parameters was compared with that of the chaotic particle swarm optimization (CPSO) algorithm. Finally, the study highlights the potential of utilizing advanced optimization techniques and deep learning models to improve wind speed forecasting, ultimately contributing to more efficient and sustainable forward in wind energy research and its practical applications.

关键词： Wind speed forecasting Robust signal decomposition african vultures optimization algorithm Tent chaotic mapping Long short-term memory

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Employing Adaptive Neural Fuzzy Inference System Model Via Meta-heuristic algorithms for Predicting Maximum Dry Density

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INDIAN GEOTECHNICAL JOURNAL 2024年 1-20页

作者： Yan, Fengjun Xian Siyuan Univ Fdn Dept Xian 710038 Shaanxi Peoples R China

This article presents a novel approach to estimating the maximum dry density (MDD) of soil stabilization mixtures utilizing the Adaptive Neural Fuzzy Inference System (ANFIS) technique. The approach involves crafting intricate and comprehensive models that establish relationships between the MDD of stabilized soil and a variety of natural soil characteristics, including parameters like liquid limit, cement content, and more. The models are constructed and assessed by utilizing a diverse set of soil types extracted from previously documented soil stabilization tests. To optimize the precision of the models, two meta-heuristic algorithms, african vultures optimization algorithm, Aquila Optimizer, and Flying Foxes optimization, are incorporated into the analysis, leading to the creation of three hybrid models referred to as ANAV, ANAO, and ANFF. The ANFF model demonstrates exceptional predictive accuracy with a high R2 value of 0.994277 and achieves the most favourable RMSE of 18.31230 during the training phase. This underscores its superior performance and ability to generalize compared to the other models created in this study. The primary goal of this study is to establish a dependable model through the utilization of hybridization techniques. The results obtained in this study serve as a testament to the successful achievement of this objective. Overall, this approach presents a promising method for precise MDD prediction in soil stabilization mixtures using ANFIS and the integration of meta-heuristic algorithms, offering potential advantages in a wide range of engineering applications.

关键词： Maximum dry density Adaptive Neural Fuzzy Inference System african vultures optimization algorithm Aquila Optimizer Flying Foxes optimization

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The implementation of a least square support vector regression model for predicting the ultimate bearing capacity of rock-socketed piles

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 4605-4618页

作者： Yang, Xiaochuan Chongqing Coll Finance & Econ Coll Design Chongqing 402160 Peoples R China

The ultimate load-bearing capacity (Qu) of rock-socketed piles, drilled shafts, or piers is contingent on various factors. Rock-socketed piles find widespread application in foundation engineering, particularly when it comes to supporting structures erected on rock formations. Determining the Qu of these piles presents a challenging engineering endeavor, demanding a comprehensive grasp of geotechnical and structural principles, consideration of site-specific circumstances, and meticulous planning and execution during design and construction phases. Accurately predicting the Qu of rock-socketed piles is of paramount importance for various reasons, including ensuring safety, optimizing costs, complying with regulations, and mitigating environmental impacts. Precise forecasts enable the optimization of foundation designs and significantly minimize potential risks inherent in construction projects. The primary aim of this research is to employ two machine learning-based models for the Qu prediction process as innovative research. These models comprise Radial Basis Function and Least Squares Support Vector Regression. Additionally, two distinct meta-heuristic optimization techniques, namely the african vultures optimization algorithm and the Pelican optimization algorithm, were chosen to fine-tune the base models, aiming to optimize their predictive performance. Various performance criteria, such as R2, RMSE, MSE, MAPE, and the SI index, were utilized to assess the predictive performance of the models. These assessments illustrate that, when it comes to practical utility, the LSPO (LSSVR + POA) model emerges as the most superior predictor, attaining an impressive maximum R2train of 0.994 and a minimal RMSEtrain value of 762.93. Properly designed foundations based on accurate Qu predictions can minimize the environmental impact of construction projects. This includes reducing the need for excessive excavation or altering the natural landscape.

关键词： Ultimate bearing capacity Rock-socketed piles Radial basis function Least square support vector regression Pelican optimization algorithm african vultures optimization algorithm

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Estimating high-performance concrete compressive strength with support vector regression in hybrid method

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 477-490页

作者： Wang, Li Baicheng Normal Univ Coll Civil Engn Baicheng 137000 Jilin Peoples R China

Save time and energy by accurately predicting the mechanical parameters of concrete by employing artificial intelligence (AI) methods. Existing nonlinear relationships between concrete members have introduced uncertainties in estimating compressive strength (CS), among the most essential variables in concrete design. The use of conventional methods for individual use of AI models in predicting dependent components has been adopted in many investigations. The objective of this paper was to create estimation models for the CS of high-performance concrete (HPC) using hybrid approaches. Specifically, the study combined support vector regression (SVR) models with optimization algorithms such as Multi-objective Artificial vultures optimization (MAVO), african vulture's optimization algorithm (AVOA), and Tunicate Swarm algorithm (TSA). Linking the three predictive models and tuning their internal settings via optimization algorithms can result in different sorts of models. After evaluating the results of the proposed models utilizing multiple metrics, the performance of the SVMiA is computed to be greater than that of the other hybrid models, with R2 = 0.9672 and RMSE = 2.1884, respectively. In general, employing advanced kinds of individual models, such as hybrids, showed improved performance with a lower cost modeling process.

关键词： Compressive strength High-performance concrete Support vector regression Multi-objective artificial vultures optimization african vultures optimization algorithm Tunicate swarm algorithm

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AVOA-Based Tuning of Low -Cost Fuzzy Controllers for Tower Crane Systems

AVOA-Based Tuning of Low -Cost Fuzzy Controllers for Tower C...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Precup, Radu-Emil Hedrea, Elena-Lorena Roman, Raul-Cristian Petriu, Emil M. Bojan-Dragos, Claudia-Adina Szedlak-Stinean, Alexandra-Iulia Paulescu, Flavius-Catalin Politehn Univ Timisoara AAI Dept Timisoara Romania Univ Ottawa EECS Sch Ottawa ON Canada

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

This paper proposes the african vultures optimization algorithm (AVOA)-based tuning of low-cost fuzzy controllers for the payload position control of tower crane systems. The fuzzy controllers are built around first order discrete-time intelligent Proportional-Integral (iPI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. The parameters of the fuzzy controllers are optimally tuned using the recent metaheuristic AVOA, which solves an optimization problem with the cost function defined as the sum of squared control error multiplied by time, and its variables are the controller tuning parameters. The control system performance improvement is proved in terms of applying only five iterations of AVOA, and the comparison with other metaheuristic algorithms that solve the same optimization problem is carried out on the basis of real-time experimental results.

关键词： african vultures optimization algorithm intelligent Proportional-Integral controllers low-cost fuzzy controllers Takagi-Sugeno-Kang Proportional-Derivative fuzzy terms tower crane systems

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Optimal Placement and Capacity of BESS and PV in EV Integrated Distribution Systems: The Tenth Feeder of Phitsanulok Substation Case Study

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BATTERIES-BASEL 2024年第6期10卷 212页

作者： Khunkitti, Sirote Pompern, Natsawat Premrudeepreechacharn, Suttichai Siritaratiwat, Apirat Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50200 Thailand Khon Kaen Univ Fac Engn Dept Elect Engn Khon Kaen 40002 Thailand

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the african vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations.

关键词： african vultures optimization algorithm battery energy storage systems distribution systems electric vehicles particle swarm optimization

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Techno-economic configuration of a hybrid backup system within a microgrid considering vehicle-to-grid technology: A case study of a remote area

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ENERGY CONVERSION AND MANAGEMENT 2024年 301卷

作者： Elkholy, M. H. Said, Taghreed Elymany, Mahmoud Senjyu, Tomonobu Gamil, Mahmoud M. Song, Dongran Ueda, Soichiro Lotfy, Mohammed Elsayed Univ Ryukyus Dept Elect & Elect Engn Okinawa 9030213 Japan Zagazig Univ Fac Engn Elect Power & Machines PO 44519 Zagazig Egypt Higher Technol Inst Elect Engn Dept 10th Ramadan City Egypt Cent South Univ Sch Automat Changsha 410083 Peoples R China

This paper assesses the feasibility of the hybrid backup system within the microgrid that incorporates vehicle-to -grid (V2G) technology. The proposed strategy is verified through a real case study in a remote area of Egypt. Several operating configurations for the hybrid backup system are studied. In this study, the proposed backup sources are the battery energy storage system (BESS), the hydrogen energy storage system (HESS), and the electric vehicle battery (EVB). To improve the robustness and reliability of the system, an enhanced power management strategy for the hybrid backup system is implemented. The main objectives are to minimize the operational costs, the wasted energy in the dummy load, and the loss of power supply probability (LPSP). The optimization problem is performed using the african vultures optimization algorithm (AVOA). The results clearly indicate that the proposed AVOA outperforms other recent algorithms, specifically the reptile search algorithm (RSA) and the snake optimizer (SO). The AVOA shows a 14.377 % reduction in computational time compared to the RSA, while it is nearly equal to the computational time of SO. Based on the technical and economic analysis, the configuration that combines the three backup sources has superiority over the other configurations. When using AVOA, the cumulative operating cost throughout the day for this configuration is approximately 13066.21 $. The BESS/EVB backup system has an operating cost of 13120.66 $, while the BESS/HESS and BESS backup systems have operating costs of 13084.27 $ and 13,085 $, respectively. Regarding accuracy, the three algorithms achieve close results.

关键词： african vultures optimization algorithm (AVOA) Backup system configuration Hydrogen energy storage system V2G-equipped microgrid Optimal operation

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A nonlinear african vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Wang, Baiyi Zhang, Zipeng Siarry, Patrick Liu, Xinhua Krolczyk, Grzegorz Hua, Dezheng Brumercik, Frantisek Li, Z. China Univ Min & Technol Sch Mechatron Engn Xuzhou Peoples R China Univ Paris Est Creteil Val de Marne 61 Ave Gen Gaulle F-94010 Creteil France Univ Zilina Fac Mech Engn Dept Design & Machine Elements Univ 1 Zilina 01026 Slovakia Donghai Lab Zhoushan 316021 Zhejiang Peoples R China Opole Univ Technol Dept Mfg Engn & Automat Prod PL-45758 Opole Poland

As a new intelligent optimization algorithm, the african vultures optimization algorithm (AVOA) has been widely used in various fields today. However, when solving complex multimodal problems, the AVOA still has some shortcomings, such as low searching accuracy, deficiency on the search capability and tendency to fall into local optimum. In order to alleviate the main shortcomings of the AVOA, a nonlinear african vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture's initial population;Furthermore, the nonlinear adaptive incremental inertial weight factor is introduced in the location update phase to rationally balance the exploration and exploitation abilities, and avoid individual falling into a local optimum;The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution and strengthen the ability to jump out of the local optimal solution. HWEAVOA and other advanced comparison algorithms are used to solve classical and CEC2022 test functions. Compared with other algorithms, the convergence curves of the HWEAVOA drop faster and the line bodies are smoother. These experimental results show the proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability. Meanwhile, HWEAVOA has reached the general level in the algorithm complexity, and its overall performance is competitive in the swarm intelligence algorithms.

关键词： african vultures optimization algorithm Henon chaotic mapping theory Nonlinear adaptive incremental inertial weight factor Reverse learning competition strategy

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Analysis and optimization of Parabolic Trough Solar Collector to Improve Its Optical Performance

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2023年第3期145卷 031009-031009页

作者： Goel, Anubhav Mahadeva, Rajesh Manik, Gaurav Indian Inst Technol Roorkee Dept Polymer & Proc Engn Roorkee 247667 Uttarakhand India

This article presents a detailed analysis of parameters that affect the optical performance of parabolic trough solar collector (PTSC) and proposes a suitable method to optimize the relevant ones. A mathematical model is drafted and simulated for known geometry and parameters of industrial solar technology (IST) PTSC. The model was evaluated for three different configurations of IST PTSC involving distinct components. A comparison between the experimental results and model estimations indicates a maximum root-mean-square error (RMSE) of 0.7997, confirming the reliability of the proposed model. The influence of variations in absorber diameter (D-ao), length (l(rc)), width (w(rc)), and focal length of PTSC (f(rc)), along with direct normal incidence (I-n), dirt factors (xi(dm),xi(dhc)), and angle of incidence (theta) on the optical performance of PTSC has been investigated. It was established that variation in mentioned parameters exhibits both positive and negative impacts on optical performance. After careful analysis, l(rc), w(rc), f(rc), D-ao, and theta were chosen for optimization as it was perceived that by varying these in a reasonable range, an optimal set of parameters could be obtained that maximize the absorbed solar irradiation for a given PTSC. Genetic algorithm (GA), particle swarm optimization (PSO), and african vultures optimization algorithm (AVOA) are utilized to estimate the optimal values of parameters. Significant improvement in absorbed solar irradiation (similar to 16%) is registered with optimized parameters, suggesting that benefits can be obtained if a study is performed prior to producing PTSC modules for an application.

关键词： parabolic trough solar collector modeling and simulation optical performance genetic algorithm particle swarm optimization african vultures optimization algorithm clean energy efficiency renewable

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