{"title":"Two-Stage Optimal Dispatching of Wind Power-Photovoltaic-Solar Thermal Combined System Considering Economic Optimality and Fairness","authors":"Weijun Li, Xin Die, Zhicheng Ma, Jinping Zhang, Haiying Dong","doi":"10.32604/ee.2023.024426","DOIUrl":"https://doi.org/10.32604/ee.2023.024426","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69746719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis of Additional Damping Control Strategy and Parameter Optimization for Improving Small Signal Stability of VSC-HVDC System","authors":"Hui Fang, Jingsen Zhou, Hanjie Liu, Yanxu Wang, Hongji Xiang, Yechun Xin","doi":"10.32604/ee.2023.025163","DOIUrl":"https://doi.org/10.32604/ee.2023.025163","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69747217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cun Wei, Yunpeng Zhao, Mingyang Cong, Zhigang Zhou, Jing Yan, Ruixin Wang, Zhuo Li, J. Liu
{"title":"Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions","authors":"Cun Wei, Yunpeng Zhao, Mingyang Cong, Zhigang Zhou, Jing Yan, Ruixin Wang, Zhuo Li, J. Liu","doi":"10.32604/ee.2023.026337","DOIUrl":"https://doi.org/10.32604/ee.2023.026337","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69748595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A distributed generation system (DG) has several benefits over a traditional centralized power system. However, the protection area in the case of the distributed generator requires special attention as it encounters stability loss, failure re-closure, fluctuations in voltage, etc. And thereby, it demands immediate attention in identifying the location & type of a fault without delay especially when occurred in a small, distributed generation system, as it would adversely affect the overall system and its operation. In the past, several methods were proposed for classification and localisation of a fault in a distributed generation system. Many of those methods were accurate in identifying location, but the accuracy in identifying the type of fault was not up to the acceptable mark. The proposed work here uses a shallow artificial neural network (sANN) model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators. Firstly, a distribution network consisting of two similar distributed generators (DG1 and DG2), one grid, and a 100 Km distribution line is modeled. Thereafter, different voltages and currents corresponding to various faults (line to line, line to ground) at different locations are tabulated, resulting in a matrix of 500 × 18 inputs. Secondly, the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train, validate, and test the neural network. The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.
{"title":"Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation","authors":"S. Awasthi, G. Singh, Nafees Ahamad","doi":"10.32604/ee.2023.026863","DOIUrl":"https://doi.org/10.32604/ee.2023.026863","url":null,"abstract":"A distributed generation system (DG) has several benefits over a traditional centralized power system. However, the protection area in the case of the distributed generator requires special attention as it encounters stability loss, failure re-closure, fluctuations in voltage, etc. And thereby, it demands immediate attention in identifying the location & type of a fault without delay especially when occurred in a small, distributed generation system, as it would adversely affect the overall system and its operation. In the past, several methods were proposed for classification and localisation of a fault in a distributed generation system. Many of those methods were accurate in identifying location, but the accuracy in identifying the type of fault was not up to the acceptable mark. The proposed work here uses a shallow artificial neural network (sANN) model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators. Firstly, a distribution network consisting of two similar distributed generators (DG1 and DG2), one grid, and a 100 Km distribution line is modeled. Thereafter, different voltages and currents corresponding to various faults (line to line, line to ground) at different locations are tabulated, resulting in a matrix of 500 × 18 inputs. Secondly, the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train, validate, and test the neural network. The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69748976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Chen, Na Sun, Zhicheng Ma, Wenfei Liu, Haiying Dong
{"title":"A Two-Layer Fuzzy Control Strategy for the Participation of Energy Storage Battery Systems in Grid Frequency Regulation","authors":"Wei Chen, Na Sun, Zhicheng Ma, Wenfei Liu, Haiying Dong","doi":"10.32604/ee.2023.027158","DOIUrl":"https://doi.org/10.32604/ee.2023.027158","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69749300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Uwem O. Ikitde, Abayomi A. Adebiyi, Innocent E. Davidson, Ayodeji S. Akinyemi
{"title":"Enhanced Electric Power Adaptability Using Hybrid Pumped-Hydro Technology with Wind and Photovoltaic Integration","authors":"Uwem O. Ikitde, Abayomi A. Adebiyi, Innocent E. Davidson, Ayodeji S. Akinyemi","doi":"10.32604/ee.2023.027574","DOIUrl":"https://doi.org/10.32604/ee.2023.027574","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69749324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xincan Song, Lin Wang, Cheng Yu, Jiaxin Chen, Linjie Ma
{"title":"An Experimental Study on the Interaction between Hydrate Formation and Wax Precipitation in Waxy Oil-in-Water Emulsions","authors":"Xincan Song, Lin Wang, Cheng Yu, Jiaxin Chen, Linjie Ma","doi":"10.32604/ee.2023.027637","DOIUrl":"https://doi.org/10.32604/ee.2023.027637","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69749445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Thermodynamic Analysis and Optimization of the C3/MRC Liquefaction Process","authors":"Guisheng Wang","doi":"10.32604/ee.2023.027416","DOIUrl":"https://doi.org/10.32604/ee.2023.027416","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69749484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenhua Xu, Yuming Zhu, Ying Wei, Yanna Su, Yan Xu, Hui Ji, DeHua Liu
{"title":"Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network","authors":"Wenhua Xu, Yuming Zhu, Ying Wei, Yanna Su, Yan Xu, Hui Ji, DeHua Liu","doi":"10.32604/ee.2023.027703","DOIUrl":"https://doi.org/10.32604/ee.2023.027703","url":null,"abstract":"","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69750010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are issues with flexible DC transmission system such as a lack of control freedom over power flow. In order to tackle these issues, a DC power flow controller (DCPFC) is incorporated into a multi-terminal, flexible DC power grid. In recent years, a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability. This work proposes a model predictive control (MPC) strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance. Initially, the mathematical model of a multi-terminal DC power grid with a multi-port interline DC power flow controller is developed, and the relationship between each regulated variable and control variable is determined; The power flow controller is then discretized, and the cost function and weight factor are built with numerous control objectives. Sub module sorting method and nearest level approximation modulation regulate the power flow controller; Lastly, the MATLAB/Simulink simulation platform is used to verify the correctness of the established mathematical model and the control performance of the suggested MPC strategy. Finally, it is demonstrated that the control strategy possesses the benefits of robust dynamic performance, multi-objective control, and a simple structure.
{"title":"Model Predictive Control Strategy of Multi-Port Interline DC Power Flow Controller","authors":"He Wang, Xiangsheng Xu, Guanye Shen, Bian Jing","doi":"10.32604/ee.2023.028965","DOIUrl":"https://doi.org/10.32604/ee.2023.028965","url":null,"abstract":"There are issues with flexible DC transmission system such as a lack of control freedom over power flow. In order to tackle these issues, a DC power flow controller (DCPFC) is incorporated into a multi-terminal, flexible DC power grid. In recent years, a multi-port DC power flow controller based on a modular multi-level converter has become a focal point of research due to its simple structure and robust scalability. This work proposes a model predictive control (MPC) strategy for multi-port interline DC power flow controllers in order to improve their steady-state dynamic performance. Initially, the mathematical model of a multi-terminal DC power grid with a multi-port interline DC power flow controller is developed, and the relationship between each regulated variable and control variable is determined; The power flow controller is then discretized, and the cost function and weight factor are built with numerous control objectives. Sub module sorting method and nearest level approximation modulation regulate the power flow controller; Lastly, the MATLAB/Simulink simulation platform is used to verify the correctness of the established mathematical model and the control performance of the suggested MPC strategy. Finally, it is demonstrated that the control strategy possesses the benefits of robust dynamic performance, multi-objective control, and a simple structure.","PeriodicalId":35610,"journal":{"name":"Energy Engineering: Journal of the Association of Energy Engineers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135755064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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