Pub Date : 2022-03-04DOI: 10.1109/icgea54406.2022.9791886
Hui Li, Xin Zan, Jiahao Tu
The development of renewable energy industry is a process in which many actors participate together, and different actors have different interests, needs and positions. Based on the investigation and analysis of Shaanxi's renewable energy industry, the paper takes the renewable energy industry as the research object, considers the different needs and positions of different stakeholders, uses the Multi-actor Multi-criteria Analysis (MAMCA) method, takes the four subdivision industries of Shaanxi's renewable energy industry as the decision group, and takes the traditional energy industry of coal-fired power generation as the control group to comprehensively evaluate the development potential and prospects of the four subdivision industries. The results show that solar photovoltaic, clean and efficient use of coal production Industry is the priority area of Shaanxi renewable energy industry. On this basis, combined with the development of Shaanxi renewable energy industry, the corresponding policy recommendations are put forward.
{"title":"The Multi-Actor Multi-Criteria Analysis (MAMCA) as a Tool to Evaluate Shaanxi Renewable Energy Projects","authors":"Hui Li, Xin Zan, Jiahao Tu","doi":"10.1109/icgea54406.2022.9791886","DOIUrl":"https://doi.org/10.1109/icgea54406.2022.9791886","url":null,"abstract":"The development of renewable energy industry is a process in which many actors participate together, and different actors have different interests, needs and positions. Based on the investigation and analysis of Shaanxi's renewable energy industry, the paper takes the renewable energy industry as the research object, considers the different needs and positions of different stakeholders, uses the Multi-actor Multi-criteria Analysis (MAMCA) method, takes the four subdivision industries of Shaanxi's renewable energy industry as the decision group, and takes the traditional energy industry of coal-fired power generation as the control group to comprehensively evaluate the development potential and prospects of the four subdivision industries. The results show that solar photovoltaic, clean and efficient use of coal production Industry is the priority area of Shaanxi renewable energy industry. On this basis, combined with the development of Shaanxi renewable energy industry, the corresponding policy recommendations are put forward.","PeriodicalId":151236,"journal":{"name":"2022 6th International Conference on Green Energy and Applications (ICGEA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129101275","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}
Pub Date : 2022-03-04DOI: 10.1109/icgea54406.2022.9791946
Hafsa Elidrissi, Hafsa Achakir, Yahya Zefri, I. Sebari, G. Aniba, H. Hajji
In the maintenance framework of solar photovoltaic (PV) installations, modules’ defect detection, identification and on field localization play a key role in preserving the reliability and efficiency of the electrical power generation. Remotely sensed imagery by means of Unmanned Aerial Vehicles (UAVs) is actively used in this context as it allows faster, cost-effective and contactless characterization of modules’ surface together with large-scale deployment. We develop herein an end-to-end approach to detect, identify and locate on field defects on PV installations based on RGB imagery acquired by UAVs. The approach is fundamentally designed for large-scale applications and comprises: (1) A photogrammetric image acquisition and post-processing phase that produces one orthorectified and georeferenced support covering the entire inspected site; (2) A module extraction phase that yields the individual images of modules; and (3) A deep learning-based defect detection stage using a fine-tuned instance of the YOLOv4 architecture. The approach was developed, validated and tested using a dataset collected from two large-scale PV sites comprising 35 305 modules. The developed defect detector scored a mean Average Precision (mAP) of 83% and 73% respectively on the validation and test sets.
{"title":"Automatic on Field Detection and Localization of Defective Solar Photovoltaic Modules from Orthorectified RGB UAV Imagery","authors":"Hafsa Elidrissi, Hafsa Achakir, Yahya Zefri, I. Sebari, G. Aniba, H. Hajji","doi":"10.1109/icgea54406.2022.9791946","DOIUrl":"https://doi.org/10.1109/icgea54406.2022.9791946","url":null,"abstract":"In the maintenance framework of solar photovoltaic (PV) installations, modules’ defect detection, identification and on field localization play a key role in preserving the reliability and efficiency of the electrical power generation. Remotely sensed imagery by means of Unmanned Aerial Vehicles (UAVs) is actively used in this context as it allows faster, cost-effective and contactless characterization of modules’ surface together with large-scale deployment. We develop herein an end-to-end approach to detect, identify and locate on field defects on PV installations based on RGB imagery acquired by UAVs. The approach is fundamentally designed for large-scale applications and comprises: (1) A photogrammetric image acquisition and post-processing phase that produces one orthorectified and georeferenced support covering the entire inspected site; (2) A module extraction phase that yields the individual images of modules; and (3) A deep learning-based defect detection stage using a fine-tuned instance of the YOLOv4 architecture. The approach was developed, validated and tested using a dataset collected from two large-scale PV sites comprising 35 305 modules. The developed defect detector scored a mean Average Precision (mAP) of 83% and 73% respectively on the validation and test sets.","PeriodicalId":151236,"journal":{"name":"2022 6th International Conference on Green Energy and Applications (ICGEA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126872982","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}
Pub Date : 2022-03-04DOI: 10.1109/icgea54406.2022.9791902
Charivil Sojy Rajan, M. Ebenezer
The traditional grid is undergoing a rapid transition from its conventional unidirectional form to an interactive, smart, bidirectional form. Microgrids are an integral part of smart grids playing a prominent role in supplying power to regions lacking electrical infrastructure. Since there may be diverse Distributed Generation (DG) sources in a microgrid, it is challenging to maintain the bus voltage at the desired value, which may adversely affect the performance of microgrids. This demands the implementation of controllers. The classical PID controller would be an apt choice in such a scenario. To obtain the desired output, it is necessary to perform the tuning of control parameters-proportional, integral and derivative gains, Kp, Ki and Kd, respectively. This paper presents the implementation of Grey Wolf Optimizer (GWO) Algorithm tuned PID Controller to maintain the DC link voltage of a microgrid under study. The latter part of the paper presents a multi-microgrid interconnection scheme. The GWO Algorithm has been implemented for the cost optimization of this multi-microgrid interconnection scheme, consisting of thermal units, solar PV array and wind generation. It has been proved that there is considerable savings in the total cost due to the integration of solar PV array and wind generation. The microgrid modeling and simulations in both cases are performed in the MATLAB/Simulink environment.
{"title":"Grey Wolf Optimizer Algorithm for Performance Improvement and Cost Optimization in Microgrids","authors":"Charivil Sojy Rajan, M. Ebenezer","doi":"10.1109/icgea54406.2022.9791902","DOIUrl":"https://doi.org/10.1109/icgea54406.2022.9791902","url":null,"abstract":"The traditional grid is undergoing a rapid transition from its conventional unidirectional form to an interactive, smart, bidirectional form. Microgrids are an integral part of smart grids playing a prominent role in supplying power to regions lacking electrical infrastructure. Since there may be diverse Distributed Generation (DG) sources in a microgrid, it is challenging to maintain the bus voltage at the desired value, which may adversely affect the performance of microgrids. This demands the implementation of controllers. The classical PID controller would be an apt choice in such a scenario. To obtain the desired output, it is necessary to perform the tuning of control parameters-proportional, integral and derivative gains, Kp, Ki and Kd, respectively. This paper presents the implementation of Grey Wolf Optimizer (GWO) Algorithm tuned PID Controller to maintain the DC link voltage of a microgrid under study. The latter part of the paper presents a multi-microgrid interconnection scheme. The GWO Algorithm has been implemented for the cost optimization of this multi-microgrid interconnection scheme, consisting of thermal units, solar PV array and wind generation. It has been proved that there is considerable savings in the total cost due to the integration of solar PV array and wind generation. The microgrid modeling and simulations in both cases are performed in the MATLAB/Simulink environment.","PeriodicalId":151236,"journal":{"name":"2022 6th International Conference on Green Energy and Applications (ICGEA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127622164","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}
Pub Date : 2022-03-04DOI: 10.1109/icgea54406.2022.9791470
Kaiyan Luo, Rui Wang, Qing Liu
With the goal of peaking carbon emission and carbon neutrality, China is developing a renewable-based power system. Investors pay more attend to hybrid generation project, which is friendly to power system. Based on the method of levelized cost of electricity, this study builds an investment planning model of wind-solar photovoltaic-battery storage hybrid project. Results show that the model effectively optimizes the capacity combination of wind, solar PV and battery storage, and improves the economic competiveness of the project, which can support decision making in renewable energy investment.
{"title":"Investment Planning Model and Economics of Wind-Solar-Storage Hybrid Generation Projects Based on Levelized Cost of Electricity","authors":"Kaiyan Luo, Rui Wang, Qing Liu","doi":"10.1109/icgea54406.2022.9791470","DOIUrl":"https://doi.org/10.1109/icgea54406.2022.9791470","url":null,"abstract":"With the goal of peaking carbon emission and carbon neutrality, China is developing a renewable-based power system. Investors pay more attend to hybrid generation project, which is friendly to power system. Based on the method of levelized cost of electricity, this study builds an investment planning model of wind-solar photovoltaic-battery storage hybrid project. Results show that the model effectively optimizes the capacity combination of wind, solar PV and battery storage, and improves the economic competiveness of the project, which can support decision making in renewable energy investment.","PeriodicalId":151236,"journal":{"name":"2022 6th International Conference on Green Energy and Applications (ICGEA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121541212","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}
Pub Date : 2022-03-04DOI: 10.1109/icgea54406.2022.9792110
Dejenie Birile Gemeda, W. Stork
High penetration of renewable energy resources with highly probabilistic loading in the emerging power transmission network is forcing Transmission System Operators (TSOs) to utilize their resources to the exhaustive extent by making use of intelligent transmission network management methods. The real-time ampacity of overhead conductors is tremendously fluctuating due to its dependence on weather conditions. As a result, the real-time rating of the overhead conductor is better exploited by using dynamic line rating (DLR) than traditional conservative static rating, which depends on the worst-case weather conditions. Since there are high uncertainties associated with point forecast DLR ampacity calculation, probabilistic means of DLR forecasting method provide the possibility for short-term planning and real-time overhead transmission line ampacity monitoring, thus enabling the transmission network to run smoothly without harm to the entire network. In this study, a real-time DLR overhead transmission line is formulated, giving 24-hour ahead ampacity prediction and loading limits by using quantile regression forest (QRF) machine learning model with different quantiles. The proposed method provides better enhancement and safe operation for the lowest quantiles to mitigate decision-makers risk-averse.
{"title":"Probabilistic Ampacity Forecasting of Dynamic Line Rating Considering TSOs Risk-Averse","authors":"Dejenie Birile Gemeda, W. Stork","doi":"10.1109/icgea54406.2022.9792110","DOIUrl":"https://doi.org/10.1109/icgea54406.2022.9792110","url":null,"abstract":"High penetration of renewable energy resources with highly probabilistic loading in the emerging power transmission network is forcing Transmission System Operators (TSOs) to utilize their resources to the exhaustive extent by making use of intelligent transmission network management methods. The real-time ampacity of overhead conductors is tremendously fluctuating due to its dependence on weather conditions. As a result, the real-time rating of the overhead conductor is better exploited by using dynamic line rating (DLR) than traditional conservative static rating, which depends on the worst-case weather conditions. Since there are high uncertainties associated with point forecast DLR ampacity calculation, probabilistic means of DLR forecasting method provide the possibility for short-term planning and real-time overhead transmission line ampacity monitoring, thus enabling the transmission network to run smoothly without harm to the entire network. In this study, a real-time DLR overhead transmission line is formulated, giving 24-hour ahead ampacity prediction and loading limits by using quantile regression forest (QRF) machine learning model with different quantiles. The proposed method provides better enhancement and safe operation for the lowest quantiles to mitigate decision-makers risk-averse.","PeriodicalId":151236,"journal":{"name":"2022 6th International Conference on Green Energy and Applications (ICGEA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132642690","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：