Suresh Muthusamy, R. Suresh Kumar, N. Karthikeyan, P. Rajesh
{"title":"基于高效制氢的混合可再生能源系统的经济评估:OOA-RBFNN 方法","authors":"Suresh Muthusamy, R. Suresh Kumar, N. Karthikeyan, P. Rajesh","doi":"10.1007/s00202-024-02634-y","DOIUrl":null,"url":null,"abstract":"<p>A sustainable society is thought to be greatly aided by hydrogen (H<sub>2</sub>) energy as it is a clean and efficient energy source in light of the impending energy revolution and global climate change. Identifying and implementing green H<sub>2</sub> production methods is made considerably more difficult by the need for a gradual switch to renewable energy. To address these issues, this study proposes a novel energy management approach for hybrid renewable energy resources (RES) systems using multiple H<sub>2</sub> production methods. The proposed approach combines the osprey optimization algorithm (OOA) with a radial basis function neural network (RBFNN), known as the OOA-RBFNN technique. The principal purpose of the proposed strategy is to minimize net system costs. Specifically, OOA is used to lessen the operational cost of a hybrid microgrid consisting of RES. RBFNN is used to predict uncertain renewable energy generation and demand. This work aims to present a strategy for producing hydrogen from solar and wind energy while reducing system costs by using water electrolyzer. The OOA-RBFNN technique is used to define the optimal size and operating energy management of the system. The proposed technique was implemented in the MATLAB platform and compared with various existing techniques like the salp swarm algorithm, convolutional neural network and random forest algorithm. The computation time of the proposed approach is 0.8 s which is lower, and the cost for energy is 23.22$ which is lower than the existing methods.</p>","PeriodicalId":50546,"journal":{"name":"Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Economic assessment of efficient hydrogen production-based hybrid renewable energy system: OOA-RBFNN approach\",\"authors\":\"Suresh Muthusamy, R. Suresh Kumar, N. Karthikeyan, P. Rajesh\",\"doi\":\"10.1007/s00202-024-02634-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A sustainable society is thought to be greatly aided by hydrogen (H<sub>2</sub>) energy as it is a clean and efficient energy source in light of the impending energy revolution and global climate change. Identifying and implementing green H<sub>2</sub> production methods is made considerably more difficult by the need for a gradual switch to renewable energy. To address these issues, this study proposes a novel energy management approach for hybrid renewable energy resources (RES) systems using multiple H<sub>2</sub> production methods. The proposed approach combines the osprey optimization algorithm (OOA) with a radial basis function neural network (RBFNN), known as the OOA-RBFNN technique. The principal purpose of the proposed strategy is to minimize net system costs. Specifically, OOA is used to lessen the operational cost of a hybrid microgrid consisting of RES. RBFNN is used to predict uncertain renewable energy generation and demand. This work aims to present a strategy for producing hydrogen from solar and wind energy while reducing system costs by using water electrolyzer. The OOA-RBFNN technique is used to define the optimal size and operating energy management of the system. The proposed technique was implemented in the MATLAB platform and compared with various existing techniques like the salp swarm algorithm, convolutional neural network and random forest algorithm. The computation time of the proposed approach is 0.8 s which is lower, and the cost for energy is 23.22$ which is lower than the existing methods.</p>\",\"PeriodicalId\":50546,\"journal\":{\"name\":\"Electrical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electrical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00202-024-02634-y\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electrical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00202-024-02634-y","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Economic assessment of efficient hydrogen production-based hybrid renewable energy system: OOA-RBFNN approach
A sustainable society is thought to be greatly aided by hydrogen (H2) energy as it is a clean and efficient energy source in light of the impending energy revolution and global climate change. Identifying and implementing green H2 production methods is made considerably more difficult by the need for a gradual switch to renewable energy. To address these issues, this study proposes a novel energy management approach for hybrid renewable energy resources (RES) systems using multiple H2 production methods. The proposed approach combines the osprey optimization algorithm (OOA) with a radial basis function neural network (RBFNN), known as the OOA-RBFNN technique. The principal purpose of the proposed strategy is to minimize net system costs. Specifically, OOA is used to lessen the operational cost of a hybrid microgrid consisting of RES. RBFNN is used to predict uncertain renewable energy generation and demand. This work aims to present a strategy for producing hydrogen from solar and wind energy while reducing system costs by using water electrolyzer. The OOA-RBFNN technique is used to define the optimal size and operating energy management of the system. The proposed technique was implemented in the MATLAB platform and compared with various existing techniques like the salp swarm algorithm, convolutional neural network and random forest algorithm. The computation time of the proposed approach is 0.8 s which is lower, and the cost for energy is 23.22$ which is lower than the existing methods.
期刊介绍:
The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed.
Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).