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Optimal distributed generators allocation with various load models under load growth using a meta-heuristic technique

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RENEWABLE ENERGY FOCUS 2024年 49卷

作者： Iftikhar, Muhammad Zubair Imran, Kashif Akbar, Muhammad Imran Ghafoor, Saim Natl Univ Sci & Technol NUST US Pakistan Ctr Adv Studies Energy USPCAS E H-12 Islamabad 44000 Pakistan Multan Elect Power Co MEPCO Multan Pakistan Atlantic Technol Univ Donegal Ireland

Distribution network planning and operation are facing several problems, including asset congestion, voltage fluctuations, and system instability. The adequate planning and modeling of distributed generators and capacitor banks must quantify these problems. This article presents the optimal allocation of distributed generators in parallel with capacitor banks in distribution networks with single and multi-objectives using the Gazelle Optimization algorithm (GOA) and Mountain Gazelle Optimization algorithm (MGOA). The single objective framework includes technical objectives like minimization of active power losses. The multi-objective framework includes technical and non-technical objectives like simultaneously minimization of active power losses, voltage stability, and voltage deviation, and minimization of polluting greenhouse gases and total electricity purchase cost. Furthermore, these planning problems are investigated by three case studies on different nonlinear voltagedependent models at two different loading conditions from future planning perspectives. The effectiveness and feasibility of the MGOA are evaluated on the IEEE standard 33 bus system. As a result, the MGOA demonstrates a remarkable reduction in technical and non-technical objectives in all types of distributed generator placement. Moreover, a comparative analysis of other existing research works validated the efficiency and feasibility of established algorithms at each use case with different load models by improving all the objective functions of network planning. In single-objective and multi-objective frameworks, the active power losses reduce to 94.42% and 93.57% in the voltage-independent model, respectively. Meanwhile, the non-technical objectives are also significantly improved for each load model, further validating the efficiency of the proposed algorithms.

关键词： Distributed generators Distribution network Environmental emission meta -heuristic algorithm Multi -objectives framework Voltage -dependent load model

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A novel gray-scale image watermarking framework using harmony search algorithm optimization of multiple scaling factors

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期83卷 21801-21822页

作者： Agarwal, Charu Mishra, Anurag Dubey, Gaurav Ajay Kumar Garg Engn Coll Dept Comp Sci & Engn Ghaziabad 201009 Uttar Pradesh India Univ Delhi Deendayal Upadhyay Coll Dept Elect Delhi 110078 India ABES Engn Coll Dept Comp Sci & Engn Ghaziabad 201009 Uttar Pradesh India

A Digital image watermarking is a cutting-edge problem that deals with copyright protection, content authentication and ownership identification. Precisely due to this reason, it is quite clear to the media industry. Particularly for images and videos, falling under un-compressed and compressed domain, it deals with minimizing the trade-off between two essential performance evaluation metrics - Visual Quality of the signal and the Robustness criteria. Although several metaheuristic technqiues have been applied to this problem, we are yet to apply new nature inspired techniques to develop watermarking applications for images and video. In this paper, we propose a novel watermark embedding scheme for gray-scale images using Harmony Search algorithm (HSA). The HSA optimizes the Objective Function which in turn produces the best Multiple Scaling Factors (MSFs) to be used for embedding the watermark coefficients in the most suitable image coefficients in hybrid transform domain. On signed and attacked images, the PSNR show that their visual quality is very good. This scheme is also found to be very robust against common image processing operations except cropping attack of different variants. It is concluded that the proposed scheme is well optimized in terms of aforesaid performance evaluation metrics and proves improvement over other similar state of the art methods.

关键词： Optimization Visual Quality Gray-Scale Image Watermarking meta -heuristic algorithm Harmony Search algorithm (HSA) Robustness

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The corona virus search optimizer for solving global and engineering optimization problems

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ALEXANDRIA ENGINEERING JOURNAL 2023年 78卷 614-642页

作者： Golalipour, Keyvan Davoudkhani, Iraj Faraji Nasri, Shohreh Naderipour, Amirreza Mirjalili, Seyedali Abdelaziz, Almoataz Y. El-Shahat, Adel Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran Islamic Azad Univ Dept Elect Engn Najafabad Branch Najafabad Isfahan Iran Univ Teknol Malaysia Inst High Voltage & High Current Fac Engn Sch Elect Engn Johor Baharu Malaysia Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Purdue Univ Sch Engn Technol Energy Technol Program W Lafayette IN 47906 USA

In this paper, a new and effective meta-heuristic method named corona-virus search optimizer (CVSO) is proposed inspired based on the movement and search of the corona-virus among different societies, prevalence, and death of individuals. The CVSO has local and global search suitable balance due to the use of evolutionary strategies as well as appropriate social learning. This algorithm has desirable optimization power, easy implementation and requires only a control parameter. The CVSO algorithm is implemented on 30 modern and standard test functions of CEC2014 and also 10 test functions of CEC2019. The performance and capability of CVSO is compared with new and well-known meta-heuristics including particle swarm optimization (PSO), artificial bee colony (ABC) tree growth algorithm (TGA), and Lion optimization algorithm (LOA), bat algorithm (BA) and harris hawks optimization (HHO). The results showed that the CVSO can be more effective than other algorithms to solve the test functions and also various complex real-world engineering optimization problems in terms of optimization accuracy and convergence rate. Also, the superiority of CVSO is demonstrated in the designing of a hybrid energy system by achieving a lower cost of M$ 2.7492 compared to TGA, LOA, ABC, PSO, BA, and HHO algorithms, which achieve costs of M$ 2.7878, M$ 2.7556, M$ 2.8521, M$ 2.9503, M$ 3.0942, and M$ 2.9614, respectively. In addition, due to the optimal balance between local and global search and strength, it is able to avoid getting stuck in locally optimal solutions while obtaining global optimal solutions with a higher convergence rate than other algorithms. The source code of CVSO is publicly available at https://***/Ira jfaraji/CVSO.

关键词： Optimization meta -heuristic algorithm Engineering optimization problem Hybrid energy system sizing Global optimal Corona -virus search optimizer

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Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications

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ALEXANDRIA ENGINEERING JOURNAL 2023年 83卷 257-285页

作者： Golalipour, Keyvan Nowdeh, Saber Arabi Akbari, Ebrahim Hamidi, Seyed Saeed Ghasemi, Danyal Abdelaziz, Almoataz Y. Kotb, Hossam Yousef, Amr Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Golestan Tech & Vocat Training Ctr Gorgan Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Islamic Azad Univ Dept Comp Engn Qaemshahr Branch Qaemshahr Iran Islamic Azad Univ Dept Elect Engn Aliabad Katoul Branch Aliabad Katoul Iran Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Alexandria Univ Fac Engn Dept Elect Power & Machines Alexandria 21544 Egypt Alexandria Univ Fac Engn Engn Math Dept Alexandri 21544 Egypt Univ Business & Technol Elect Engn Dept Jeddah 23435 Saudi Arabia

This paper proposes a novel efficient inspired algorithm based on snow avalanches in nature which is named the Snow Avalanches algorithm (SAA), for solving the benchmark and engineering optimization problems and determining the global solution. The proposed algorithm is modeled using four phases including avalanche due to mountain slope, human factors, weather in the region as well as normal conditions and it has only one control parameter. The advantages of this algorithm are low control parameters, simple structure and also easy implementation. The effectiveness of the SAA algorithm is examined on 23 classic benchmark test functions. Then, the effectiveness of the SAA to achieve accurate results in different aspects is examined and proved on engineering problems including six different cases. The superiority of the SAA to solve the classic benchmark test functions is compared with spotted hyena optimization (SHO), particle swarm optimization (PSO), Aquila optimizer (AO), differential evolution (DE), bat algorithm (BA), dwarf mongoose optimization (DMO), genetic algorithm (GA), artificial bee colony (ABC), and ant colony optimization (ACO). The simulation results provide evidence for the well-organized and efficient performance of the SAA in solving a great diversity of engineering problems. The results demonstrated that the SAA can be more effective than other algorithms to solve the test functions in terms of optimization accuracy and convergence rate. Moreover, the results proved that the SAA obtained more competitive results than the previous methods to solve constrained engineering optimization problems, especially hybrid energy system design as well as economic load dispatch problems.

关键词： Optimization meta -heuristic algorithm Engineering Optimization Problem Benchmark Test Functions Snow Avalanches algorithm

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An enhanced particle swarm optimization with position update for optimal feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Tijjani, Sani Wahab, Mohd Nadhir Ab Noor, Mohd Halim Mohd Univ Sains Malaysia Sch Comp Sci Minden 11800 Malaysia

In recent years, feature selection research has quickly advanced to keep up with the age of developing expert systems. This is because the applications of these systems sometimes need massive datasets. Researchers who have an interest in creating novel feature selection methods or enhancing existing technologies have grown their interest in this topic. The current version of binary PSO (BPSO) is not developed as well as continuous PSO and does not follow the main principles of the standard PSO algorithm. Unlike the continuous version of the PSO, in BPSO, particle position is restricted to the Hamming space. Thus, there is no risk of swarm divergence, but the problem of premature convergence of the swarm arises. This paper presents an enhanced particle swarm optimization employed for feature selection to tackle the limitations of the current version of binary BPSO. Its primary quality is the application of a position update mechanism to appropriately select optimal features. The position update mechanism is designed to avoid the premature convergence issue suffered by BPSO, by determining the probability of the particles switching positions using position values rather than velocity. Two variant methods for determining the probability of changing the position of a particle element were introduced. These result in the two variants of enhanced binary particle swarm optimization (EBPSO) for feature selection, called EBPSO1 and EBPSO2. Several experiments were performed to analyze the effectiveness of the proposed method and compare it with state-of-the-art feature selection methods based on eighteen frequently used benchmark datasets. The experimental findings demonstrated that the proposed approach generated the most accurate classification in contrast to the alternative feature selection approach on most of the datasets. As a result, it can be inferred based on the outcomes obtained that this proposed approach performs significantly superior to the other state-of-the-

关键词： Feature selection Particle swarm optimization (PSO) Dimensionality reduction meta -heuristic algorithm

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A p-center mobile hub location in a dynamic environment with capacity constraints and multiple allocation

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AIN SHAMS ENGINEERING JOURNAL 2024年第6期15卷

作者： Eydi, Ali Reza Saghez, Shaho Saeedi Univ Kurdistan Dept Ind Engn Sanandaj Iran

In this paper, the problem of p-center mobile hub location with capacity constraint is studied. According to the rapid developments, paying attention to strategic decisions is considered a significant factor in network design. Thus, the dynamic model is considered to adapt to environmental changes;also, the movability and mobility are simultaneously provided for the hubs in the subsequent periods. Multiple allocations are deemed to connect demand nodes to the hubs to tackle capacity constraints in hubs. A bi-objective model is also employed, which is as follows: the first aim is to reduce travel times, and the second aim is to minimize network costs;As a result, the quality and level of response to demands can be increased. In addition, we attempted to use both classical and intelligent methods for the proposed multi-objective model. The goal programming method and AP dataset are employed for sensitivity analysis and validation, and the Goal attainment method is used to solve the model in small dimensions. Besides, the NSGA-II and MOPSO are developed and used as intelligent methods to approximate the Pareto front of the model in small and large dimensions. Also, a local search algorithm is considered within the intelligent algorithms to calculate the optimal values of the model decision variables. The parameters in intelligent algorithms are tuned using the Taguchi method. Finally, classical and intelligent solutions' results are examined and compared based on five different evaluation criteria. The results of comparisons prove that the Goal attainment method outperforms NSGA-II and MOPSO, and the NSGA-II is better than the MOPSO.

关键词： Transportation Multi -objective optimization Multiple allocation Capacity constraints Mobile hub location meta -heuristic algorithm Dynamic environment

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Optimum management of microgrid generation containing distributed generation sources and energy storage devices by considering uncertainties

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartB期118卷

作者： Valizadeh, Majid Hayati, Alireza Sarvenoee, Amirhosein Khosravi Kouhzadipour, Mohammad Gowtham, N. Aboras, Kareem M. Ilam Univ Dept Elect Engn Ilam *** Iran Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Elect & Elect Engn Manipal India Alexandria Univ Fac Engn Elect Power & Machines Dept Alexandria 21544 Egypt

In recent years, the financial and energy crises have made the economical and safe operation of distribution microgrids one of the main challenges for operators. In this regard, the optimal distribution of the microgrid, considering the favorable indicators of Generation and consumption, significantly improves the quality of the development and economic operation of electricity grids. On the other hand, distribution microgrids have many problems and complications due to the presence of DG sources and the uncertainties caused by them in Generation and consumption, such as the inability to solve optimal load distribution using common and common calculation methods. Therefore, grid technical indicators such as voltage deviation and losses in solving load distribution add to the complexity of related issues. To solve the challenges raised in the distribution microgrid, this paper proposes an optimal load distribution model that models the uncertainties in the distribution microgrids using the Monte Carlo method. Using meta-heuristic algorithms, the optimal Generation function by modeling grid security constraints such as voltage deviation, losses, and a load limit of the system should be in economic operation mode, and then the power distribution equations should be solved. The battery can supply 1 kW of active power to the system at a cost of 1 unit/kW during the high cost period if it absorbs 1 kW of active power at a cost of 0.92 units/kW during the low cost period. Additionally, the active and reactive power losses have been decreased to 0.3kW and 0.2kW, respectively, by utilizing the suggested methodology. The simulation results show that the proposed method performs satisfactorily and that the modeling is simulated in MATLAB software.

关键词： Optimal load distribution Uncertainties Modeling Microgrid Monte Carlo meta -heuristic algorithm

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Loading pattern optimization of a PWR using newly developed Multi-Verse optimization algorithm

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NUCLEAR ENGINEERING AND DESIGN 2024年 426卷

作者： Safari, M. Aghaie, M. Salimi, K. Shahid Beheshti Univ Engn Dept GCPOB *** Tehran Iran

In the fuel management of nuclear reactors, which has a direct impact on power generation costs and environmental factors, it is essential to obtain an optimal arrangement of assemblies and core parameters. This complex task relies on both neutronics and thermal hydraulics. This research solves this by employing PARCS and COBRA-EN codes, alongside Multi-Verse Optimization algorithm (MVO) as a novel algorithm. Findings demonstrate the MVO algorithm has reliability and favorable convergence rate. Moreover, integrating the nodal expansion technique with MVO notably decreases the computational time for optimization in the loading pattern optimizations (LPOs). The optimized loading pattern aligns well with the findings in the literature. Also, using multi objective fitness function leads to improving core flatness, cycle length and safety parameters.

关键词： Loading pattern optimization WWER1000 reactor Neutronics Thermal -hydraulic meta -heuristic algorithm COBRA -EN PARCS MVO MFO GSA HS

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In-depth analysis of photovoltaic module parameter estimation

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ENERGY 2024年 291卷

作者： Wang, Shinong Wang, Chuanzhi Ge, Yuan Liu, Shilin Xu, Jie Amer, Ragab Ahmed Minist Educ Key Lab Adv Percept & Intelligent Control Highend Wuhu 241000 Peoples R China Anhui Polytech Univ Coll Elect Engn Wuhu 241000 Peoples R China Menoufia Univ Fac Engn Dept Elect Engn Shibin Al Kawm Egypt

Accurate knowledge of photovoltaic (PV) module model parameters plays an important role in PV power generation system. Therefore, in this study, the single-diode model of PV modules, physical meaning and solution methods of various parameters were introduced firstly. Then, the influence of various parameters on I-V characteristics was analyzed, and the parameter sensitivities of various parameters to the open circuit voltage and short circuit current were quantitatively discussed. Finally, different types of PV modules in a public dataset were used as objects, and different algorithms were employed to estimate the value of each parameter. Through simulation, some important points were provided: (i) the influence of photocurrent and parallel resistance on I-V characteristics is relatively independent, while the influence of reverse saturation current, series resistance, and ideality factor on I-V characteristics is highly coupled;(ii) the system degrees of freedom is totally different in case of considering the reverse saturation current as a dependent and independent variable. The improvement of the system degrees of freedom remarkably reduces the root mean square error of I-V characteristics;(iii) the estimated parameter resulted from meta-heuristic algorithms do not have the characteristic of uniqueness and may not represent the true values of the parameters.

关键词： Photovoltaic module Parameter estimation Parameter sensitivity meta -heuristic algorithm Independent variable Dependent variable

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Hierarchical optimization of policy and design for standalone hybrid power systems considering lifecycle carbon reduction subsidy

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ENERGY 2023年第PartA期262卷

作者： He, Yi Guo, Su Dong, Peixin Huang, Jing Zhou, Jianxu Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211000 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Kowloon Hong Kong Peoples R China

To achieve the target of carbon neutrality, the present work proposes a lifecycle carbon reduction subsidy policy to promote the exploitation of renewable energy technologies in remote areas. The hierarchical optimization of policy and design for a standalone hybrid renewable energy system is further conducted based on the proposed subsidy policy. The upper-level policy optimization model aims to maximize the renewable energy fraction and minimize the subsidy expenses borne by the government simultaneously, and the lower-level design optimization model aims to minimize the net present cost borne by the system operator. Four meta-heuristic algorithms coupled with a standard iterative method are employed to solve the hierarchical optimization problem, and their performances are comprehensively assessed in terms of convergence, robustness, and computational efficiency. Besides, an improved K-means clustering algorithm along with a tailored optimal K-value selection strategy is employed for scenario reduction. The results of the case study show that: (i) teaching-learning-based optimization performs the best convergence and robustness as it achieves the balance of exploration and exploitation. (ii) the nexus between policy and system design presents a piecewise pattern, implying that the system configuration will change significantly only when the subsidy policy exceeds a certain threshold. (iii) the proposed subsidy policy is expected to increase the renewable energy fraction to 99.23% and reduce the lifecycle carbon emissions by 80.7%. Overall, the proposed subsidy policy and its impact mechanism on system design can provide theoretical support for the government to formulate tailored policies for different preferences, and the proposed optimization framework and methodology can be directly extended to practical applications.

关键词： Hybrid renewable energy system Carbon neutrality Subsidy policy Hierarchical optimization Optimal sizing meta -heuristic algorithm

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