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Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm

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RENEWABLE ENERGY 2024年 229卷

作者： Araoye, Timothy Oluwaseun Ashigwuike, Evans Chinemezu Mbunwe, Muncho Josephine Bakinson, Oladipupo Idris Ozue, ThankGod Izuchukwu Univ Nigeria Dept Mechatron Engn Nsukka Nigeria Univ Abuja Dept Elect & Elect Engn Abuja Nigeria Univ Nigeria Dept Elect Engn Nsukka Nigeria Montfont Univ Dept Elect Engn Leicester England Univ Nigeria Dept Elect & Comp Engn Nsukka Nigeria

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel grasshopper optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

关键词： Hybrid renewable energy grasshopper optimization algorithm HOMER pro software Modeling And sensitivity simulation analysis

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Optimal Voltage and Frequency Control of an Islanded Microgrid Using grasshopper optimization algorithm

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ENERGIES 2018年第11期11卷 3191-3191页

作者： Jumani, Touqeer Ahmed Mustafa, Mohd Wazir Rasid, Madihah Md Mirjat, Nayyar Hussain Leghari, Zohaib Hussain Saeed, M. Salman Univ Technol Malaysia Sch Elect Engn Skudai 81310 Johor Bahru Malaysia Mehran Univ Engn & Technol Dept Elect Engn SZAB Campus Khairpur Mirs 66020 Pakistan Mehran Univ Engn & Technol Dept Elect Engn Jamshoro 76090 Pakistan Multan Elect Power Co Dept Power Distribut Multan 66000 Pakistan

Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using grasshopper optimization algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm optimization (PSO) and Whales optimization algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).

关键词： grasshopper optimization algorithm microgrid power quality voltage and frequency control

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Hybridization of grasshopper optimization algorithm With Genetic algorithm for Solving System of Non-Linear Equations

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IEEE ACCESS 2020年 8卷 220944-220961页

作者： El-Shorbagy, M. A. El-Refaey, Adel M. Prince Sattam Bin Abdulaziz Univ Coll Sci & Human Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Menoufia Univ Dept Basic Engn Sci Fac Engn Shibin Al Kawm 32511 Egypt Arab Acad Sci Technol & Maritime Transport Coll Engn & Technol Dept Basic & Appl Sci Smart Village Campus Cairo 125077 Egypt

A novel algorithm for optimization in this article, called hybrid grasshopper optimization algorithm (GOA) with genetic algorithm (GA): hybrid-GOA-GA, is proposed for solving the system of non-linear equations (SNLEs). First, the SNLEs are converted into an optimization problem. Then, the optimization problem is solved by hybrid-GOA-GA. In the hybrid-GOA-GA, a population of randomized solutions is initialized. These solutions, by GOA, are looking for an optimal solution for SNLE in the domain of optimization problem. During this process, the evolution of these solutions is carried out by GA. Hybrid-GOA-GA integrates the merits of both GOA and GA;where GOA's exploitability and GOA's exploration potential are combined. Furthermore, hybrid-GOA-GA has good capability for escaping from local optima with faster convergence. The hybrid-GOA-GA has been tested by eight benchmarks problems which include different applications. Additionally, the effect of changing the initial intervals of the variables on the efficiency of the proposed algorithm is discussed. Also, the computational cost of the proposed algorithm is studied and compared with other methods. The results show that the hybrid-GOA-GA algorithm is superior to other algorithms, and return the best solution of SNLEs. Finally, in terms of accuracy, the effect of changing initial intervals and computational cost, the proposed approach is competitive and better in most benchmark problems compared to other methods. So, we can say that hybrid-GOA-GA is effective to solve a SNLEs.

关键词： optimization Genetic algorithms Statistics Sociology Heuristic algorithms Convergence Mathematical model grasshopper optimization algorithm genetic algorithm system of non-linear equations hybrid algorithm optimization

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A Hybrid Modified grasshopper optimization algorithm and Genetic algorithm to Detect and Prevent DDoS Attacks

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第4期34卷 811-824页

作者： Mohammadi, S. Babagoli, M. KN Toosi Univ Technol Dept Ind Engn Tehran Iran

Cyber security has turned into a brutal and vicious environment due to the expansion of cyber-threats and cyberbullying. Distributed Denial of Service (DDoS) is a network menace that compromises victims? resources promptly. Considering the significant role of optimization algorithms in the highly accurate and adaptive detection of network attacks, the present study has proposed Hybrid Modified grasshopper optimization algorithm and Genetic algorithm (HMGOGA) to detect and prevent DDoS attacks. HMGOGA overcomes conventional GOA drawbacks like low convergence speed and getting stuck in local optimum. In this paper, the proposed algorithm is used to detect DDoS attacks through the combined nonlinear regression (NR)-sigmoid model simulation. In order to serve this purpose, initially, the most important features in the network packages are extracted using the Random Forest (RF) method. By removing 55 irrelevant features out of a total of 77, the selected ones play a key role in the proposed model's performance. To affirm the efficiency, the high correlation of the selected features was measured with Decision Tree (DT). Subsequently, the HMGOGA is trained with benchmark cost functions and another proposed cost function that enabling it to detect malicious traffic properly. The usability of the proposed model is evaluated by comparing with two benchmark functions (Sphere and Ackley function). The experimental results have proved that HMGOGA based on NR-sigmoid outperforms other implemented models and conventional GOA methods with 99.90% and 99.34% train and test accuracy, respectively

关键词： DDoS Detection Cyber-security grasshopper optimization algorithm Random Forest

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Construction Cost Estimation in Data-Poor Areas Using grasshopper optimization algorithm-Guided Multi-Layer Perceptron and Transfer Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 1332-1339页

作者： Sha, Xuan Dong, Guoqing Li, Xiaolei Sheng, Juan Southeast Univ Sch Civil & Transportat Engn ChengXian Coll Nanjing 210088 Jiangsu Peoples R China Hohai Univ Coll Mech & Engn Sci Nanjing 210024 Jiangsu Peoples R China

Accurate construction cost estimation is crucial for completing projects within the planned timeframe and budget. Using machine learning methods to predict construction costs has become a new trend. However, machine learning methods typically require a large amount of data for model training, which makes it particularly challenging in data-poor areas. This paper proposes a novel method, grasshopper optimization algorithmGuided Multi-Layer Perceptron with Transfer Learning (GOAMLP-TL), specifically designed for construction cost estimation in data-poor areas. GOA-MLP-TL utilizes the global optimal search capability of the GOA to optimize the parameters of the MLP network. Additionally, an adaptation layer is added into the MLP network, using the Maximum Mean Discrepancy (MMD) measure as a regularization to bridge the gap between the source and target domains. The GOA-MLP-TL can effectively leverage the model trained on data-rich area, and transfer the knowledge to adapt the model suitable for data-poor areas. The proposed approach is verified on two datasets from different areas, and the experimental result shows that, compared to the traditional machine learning method MLP and GOA-MLP without transfer learning, the correlation coefficient (R-2) of the proposed GOA-MLP-TL is improved by 12.05% and 6.90%, respectively. This demonstrate the effectiveness of GOA-MLPTL for the construction cost estimation task in the data-poor area.

关键词： Construction - Construction cost estimation multi-layer tron grasshopper optimization algorithm transfer learning chine learning

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Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm

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ENERGY 2023年第1期267卷

作者： Sharifian, Yeganeh Abdi, Hamdi Razi Univ Engn Fac Elect Engn Dept Kermanshah Iran

This research work proposes the hybrid meta-heuristic algorithm to solve the multi-area economic dispatch problem to operate the power systems economically. The proposed hybrid algorithm combines the exchange market and grasshopper optimization algorithms. The grasshopper optimization algorithm sometimes suffers from premature convergence. To overcome this drawback, a hybrid algorithm for the better balance between exploration and exploitation and thus finding optimal solutions with high quality and robustness, is proposed in this paper. The main target of this paper is to investigate the application of the hybrid algorithm for optimizing the multi-area economic dispatch problem, satisfying different constraints of the valve point loading effect, multiple fuels, prohibited operation zones, ramp-rate limit, the tie-line constraints, and transmission loses. In this regard, after validation of the proposed hybrid algorithm by applying it on some benchmark functions of diverse nature, the effectiveness and robustness of the proposed algorithm are evaluated in three case studies of the multi-area economic dispatch problem. The comparative results confirmed the effectiveness of the proposed method in terms of robustness and quality solutions. Also, the applied method reduces the fuel cost in the range of 0.0229%-1.1622%.

关键词： Multi-area economic dispatch Exchange market algorithm grasshopper optimization algorithm Tie-line constraint

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Integrated optimization of Differential Evolution with grasshopper optimization algorithm

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2018年第3期5卷 165-168页

作者： Jitkongchuen, Duangjai Ampant, Udomlux Dhurakij Pundit Univ Coll Innovat Technol & Engn Bangkok Thailand

This paper proposes a scheme to improve the differential evolution (DE) algorithm performance with integrated the grasshopper optimization algorithm (GOA). The grasshopper optimization algorithm mimics the behavior of grasshopper. The characteristic of grasshoppers is slow movement in the larval stage but sudden movement in the adulthood which seem as exploration and exploitation. The grasshopper optimization algorithm concept is added to DE to guide the search process for potential solutions. The efficiency of the DE/GOA is validated by testing on unimodal and multimodal benchmarks optimization problems. The results prove that the DE/GOA algorithm is competitive compared to the other meta-heuristic algorithms. (C) 2018 The Authors. Published by Atlantis Press SARL. This is an open access article under the CC BY-NC license.

关键词： Meta-heuristic differential evolution algorithm grasshopper optimization algorithm optimization

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AN IMPROVED grasshopper optimization algorithm FOR TASK SCHEDULING PROBLEMS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2019年第5期15卷 1967-1987页

作者： Zhao, Ran Ni, Hong Feng, Hangwei Song, Yaqin Zhu, Xiaoyong Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr 21North 4th Ring Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn 19 AYuquan Rd Beijing 100049 Peoples R China

grasshopper optimization algorithm (GOA) is a novel meta-heuristic algorithm for optimization problems. GOA is easy to implement but it cannot make full utilization of every iteration, and it is easy to fall into the local optimal. To improve the performance of GOA, an Improved grasshopper optimization algorithm (IGOA) was proposed in this paper. Firstly, the nonlinear comfort zone parameter was used to promote the utilization of the iterations of the algorithm. Then the Levy flight mechanism was applied to increasing the randomness and expanding the local search radius. At last the random jumping strategy was introduced to help the algorithm jump out of the local optimal. Several experiments involving 29 well-known benchmark functions were conducted and the performance of IGOA was compared with the basic GOA, Opposition-Based Learning GOA (OBLGOA), Whale optimization algorithm (WOA), Ant Lion Optimizer (ALO), Dragonfly algorithm (DA) and Particle Swarm optimization (PSO). IGOA was also applied to the task scheduling problem in the resource-constrained system. The results of the experiments demonstrated that the proposed IGOA outperformed GOA and other algorithms.

关键词： grasshopper optimization algorithm Nonlinear parameter Levy flight Random jumping strategy Task scheduling problem

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A Hybrid grasshopper optimization algorithm Applied to the Open Vehicle Routing Problem

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algorithmS 2020年第4期13卷 96-96页

作者： Soto-Mendoza, Valeria Garcia-Calvillo, Irma Ruiz-y-Ruiz, Efrain Perez-Terrazas, Jaime Univ Autonoma Coahuila Ctr Invest Matemat Aplicadas Edificio S Camporredondo Saltillo 25280 Coahuila Mexico Inst Tecnol Saltillo Tecnol Nacl Mexico Div Estudios Posgrad & Invest Blvd Venustiano Carranza 2400 Saltillo 25280 Coahuila Mexico

This paper presents a hybrid grasshopper optimization algorithm using a novel decoder and local search to solve instances of the open vehicle routing problem with capacity and distance constraints. The algorithm's decoder first defines the number of vehicles to be used and then it partitions the clients, assigning them to the available routes. The algorithm performs a local search in three neighborhoods after decoding. When a new best solution is found, every route is locally optimized by solving a traveling salesman problem, considering the depot and clients in the route. Three sets containing a total of 30 benchmark problems from the literature were used to test the algorithm. The experiments considered two cases of the problem. In the first, the primary objective is to minimize the total number of vehicles and then the total distance to be traveled. In the second case, the total distance traveled by the vehicles is minimized. The obtained results showed the algorithm's proficient performance. For the first case, the algorithm was able to improve or match the best-known solutions for 21 of the 30 benchmark problems. For the second case, the best-known solutions for 18 of the 30 benchmark problems were found or improved by the algorithm. Finally, a case study from a real-life problem is included.

关键词： optimization combinatorial optimization open vehicle routing problem grasshopper optimization algorithm

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GPS/INS Integrated Navigation Based on grasshopper optimization algorithm

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IFAC-PapersOnLine 2019年第24期52卷 29-34页

作者： Hao Meng Yu Han Yuting Xun Jian Chen Nannan Du Yi Cao Guangqi Wang Xiangwei Yi Yongjun Zheng College of Engineering China Agricultural University Beijing 100083 China College of Water Resources & Civil Engineering China Agricultural University Beijing 100083 China Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment Beijing China Electronics Information School Wuhan University Wuhan 430072 China

In GPS/INS integrated navigation, Kalman filter is usually used for data fusion between GPS and INS. The filtering algorithm of integrated navigation is related to whether the advantages of each sensor in the integrated navigation can be utilized, the navigation accuracy is improved, the reliable working time of the navigation system is improved and the navigation requirement are met. In this paper, we proposed an approach based on the grasshopper optimization algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarm of grasshoppers. The goal of the proposed approach is to optimize the parameters of the Kalman filter. The Kalman filter method is improved by grasshopper optimization algorithm, which improves the accuracy of integrated navigation and reduces the errors caused by system noise and measurement noise. For verification, the proposed approach is compared with particle swarm optimization. The simulation experiment results show that the proposed approach has a better effect.

关键词： grasshopper optimization algorithm GNSS/INS integrated navigation Kalman filter longitudinal latitude errors simulation experiment

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