Pub Date : 2022-12-12DOI: 10.1109/SASG57022.2022.10200928
Ibrahim Alsaleh
The recent government initiatives to decarbonize the power system and minimize the overreliance on fossil fuels will lead to massive and rapid adoption of solar photovoltaics (PVs) in distribution systems. Daytime solar power generation peaks necessitate demand-side flexibility to mitigate voltage dips and spikes that could disrupt the power grid. To this end, a model-free demand response framework is developed based on deep reinforcement learning (DRL) and using OpenAI Gym APIs. The DRL agent assumes the role of a load aggregator that directly controls a percentage of each load in the distribution system. The agent is then trained to optimize the control policy by taking nonuniform actions on each node. The system-wide objective is to minimize the voltage deviations from 3% of the nominal voltage in an effort to properly allocate the energy consumption throughout the day. The DRL-based demand response is trained and tested on the radial IEEE 33-bus distribution feeder, modified to have a high penetration of non-dispatchable PVs plants. Simulation results show that the proposed framework works as intended, shifting flexible demand to times of maximum solar power generation while maintaining an acceptable voltage deviation at each node.
{"title":"Model-free Reinforcement Learning for Demand Response in PV-rich Distribution Systems","authors":"Ibrahim Alsaleh","doi":"10.1109/SASG57022.2022.10200928","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200928","url":null,"abstract":"The recent government initiatives to decarbonize the power system and minimize the overreliance on fossil fuels will lead to massive and rapid adoption of solar photovoltaics (PVs) in distribution systems. Daytime solar power generation peaks necessitate demand-side flexibility to mitigate voltage dips and spikes that could disrupt the power grid. To this end, a model-free demand response framework is developed based on deep reinforcement learning (DRL) and using OpenAI Gym APIs. The DRL agent assumes the role of a load aggregator that directly controls a percentage of each load in the distribution system. The agent is then trained to optimize the control policy by taking nonuniform actions on each node. The system-wide objective is to minimize the voltage deviations from 3% of the nominal voltage in an effort to properly allocate the energy consumption throughout the day. The DRL-based demand response is trained and tested on the radial IEEE 33-bus distribution feeder, modified to have a high penetration of non-dispatchable PVs plants. Simulation results show that the proposed framework works as intended, shifting flexible demand to times of maximum solar power generation while maintaining an acceptable voltage deviation at each node.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115483014","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10199351
A. Klien, Amro Mohamed
Numerous cyber-attacks on critical infrastructure triggered utilities to implement sophisticated cyber security measures to protect the electrical grid. For example, firewalls and “air gaps” are currently used to safeguard substations. However, these can be evaded through remote access tunnels or computers directly attached to the station network. Therefore, measures are needed to detect cyber threats in substation networks to respond quickly and minimize consequences.This document describes how to apply the different NIST Cybersecurity Framework (CSF) functions: Identity, Protect, Detect, Respond, and Recover to substations and which benefits emerge from utilizing what IEC 61850 standard offers. The frequently used attack vector on industrial control systems is the connections to services in corporate IT and control centers or temporary maintenance connections.Another entry point is engineering workstations connected to substation networks. Finally, the storage of settings and test documents could also be an entry point for attackers and malware.
{"title":"Cybersecurity Intrusion Detection for Station and Process Bus Applications in Substations: Challenges and Experiences","authors":"A. Klien, Amro Mohamed","doi":"10.1109/SASG57022.2022.10199351","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10199351","url":null,"abstract":"Numerous cyber-attacks on critical infrastructure triggered utilities to implement sophisticated cyber security measures to protect the electrical grid. For example, firewalls and “air gaps” are currently used to safeguard substations. However, these can be evaded through remote access tunnels or computers directly attached to the station network. Therefore, measures are needed to detect cyber threats in substation networks to respond quickly and minimize consequences.This document describes how to apply the different NIST Cybersecurity Framework (CSF) functions: Identity, Protect, Detect, Respond, and Recover to substations and which benefits emerge from utilizing what IEC 61850 standard offers. The frequently used attack vector on industrial control systems is the connections to services in corporate IT and control centers or temporary maintenance connections.Another entry point is engineering workstations connected to substation networks. Finally, the storage of settings and test documents could also be an entry point for attackers and malware.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116160663","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10200610
Subah M. Alkhaldi
Solar energy is becoming an essential part of the energy mix of several counties. However, there are challenges with implementing such renewable sources including intermittency, difficulty in managing energy flux, integrating & operating the power grid, and the implicit relationship between weather parameters and solar irradiance requiring data analysis techniques to be implemented. These techniques would uncover the hidden patterns and correlations between weather features (e.g., humidity, temperature and solar irradiance) to enhance solar irradiance prediction accuracies for efficient planning of electricity production of solar panels connected to the grid. Saudi Arabian weather features will be employed for prediction assessments of machine learning & artificial neural network algorithms with multiple data sources & public domain entities including Photovoltaic Geographical Information System (PVGIS), King Abdullah City for Atomic and Renewable Energy (KACARE), and the National Renewable Energy Laboratory (NREL).
{"title":"Predicting Solar Irradiance in Saudi Arabia via Machine Learning & Artificial Neural Networks for Efficient Grid Integration","authors":"Subah M. Alkhaldi","doi":"10.1109/SASG57022.2022.10200610","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200610","url":null,"abstract":"Solar energy is becoming an essential part of the energy mix of several counties. However, there are challenges with implementing such renewable sources including intermittency, difficulty in managing energy flux, integrating & operating the power grid, and the implicit relationship between weather parameters and solar irradiance requiring data analysis techniques to be implemented. These techniques would uncover the hidden patterns and correlations between weather features (e.g., humidity, temperature and solar irradiance) to enhance solar irradiance prediction accuracies for efficient planning of electricity production of solar panels connected to the grid. Saudi Arabian weather features will be employed for prediction assessments of machine learning & artificial neural network algorithms with multiple data sources & public domain entities including Photovoltaic Geographical Information System (PVGIS), King Abdullah City for Atomic and Renewable Energy (KACARE), and the National Renewable Energy Laboratory (NREL).","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122138155","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10201015
Adnan S. Al-Bukhaytan, A. Al-Awami, Ammar M. Muqbel
This paper proposes a linear planning model for determining the optimal sizing and location of distributed energy resources (DERs) to supply the electrical demand and water desalination facilities (WDFs) while maximizing overall profits for the distribution system operator (DSO). The DERs considered in this research comprise distributed renewable energy resources (RESs), distributed thermal generators (TGs), and battery energy storage system (BESS). Furthermore, the proposed model incorporates coordination between the DSO and the water desalination operator (WDO) in the operation aspect to investigate the effects on the optimal sizing and location of DERs as well as the overall profits to the DSO. The proposed model is validated using a 38-bus radial distribution network. Simulation results indicate that considering coordination increased the overall profits to DSO by 6.8%. Moreover, without coordination, additional BESS is required to be installed in the bus where WDFs are located to smooth the electric load profile. However, the coordination eliminates the need for installing BESS as the coordination can smooth the electric load profile by managing the desalination operational schedules.
{"title":"A Planning Model For Distributed Energy Resources Considering Coordinating with the Water Desalination Operator","authors":"Adnan S. Al-Bukhaytan, A. Al-Awami, Ammar M. Muqbel","doi":"10.1109/SASG57022.2022.10201015","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10201015","url":null,"abstract":"This paper proposes a linear planning model for determining the optimal sizing and location of distributed energy resources (DERs) to supply the electrical demand and water desalination facilities (WDFs) while maximizing overall profits for the distribution system operator (DSO). The DERs considered in this research comprise distributed renewable energy resources (RESs), distributed thermal generators (TGs), and battery energy storage system (BESS). Furthermore, the proposed model incorporates coordination between the DSO and the water desalination operator (WDO) in the operation aspect to investigate the effects on the optimal sizing and location of DERs as well as the overall profits to the DSO. The proposed model is validated using a 38-bus radial distribution network. Simulation results indicate that considering coordination increased the overall profits to DSO by 6.8%. Moreover, without coordination, additional BESS is required to be installed in the bus where WDFs are located to smooth the electric load profile. However, the coordination eliminates the need for installing BESS as the coordination can smooth the electric load profile by managing the desalination operational schedules.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131007986","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10200322
Juliana Chavez, Z. Foroozandeh, S. Ramos, J. Soares, Z. Vale
The growing integration of electric vehicles has attracted a lot of interest. However, they are highly affected by EV charging uncertainties and are, therefore, difficult to forecast accurately. This paper presents an Artificial Neural Network (ANN) method ANN to forecast electric vehicle uncertainties. ANN was trained using historical data from a residential building, such as arrival time, departure time and initial SOC. Then, it was tested during 24 hours through different scenarios. For each one of the cases, the model’s accuracy was assessed by comparing historical data to forecast information. The associated errors were also calculated. The outcomes reveal that the suggested forecasting method is very effective in reducing EV forecasting errors and, as a result, is better at regulating EV uncertainty.
{"title":"Electric Vehicles Uncertainty Forecasting","authors":"Juliana Chavez, Z. Foroozandeh, S. Ramos, J. Soares, Z. Vale","doi":"10.1109/SASG57022.2022.10200322","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200322","url":null,"abstract":"The growing integration of electric vehicles has attracted a lot of interest. However, they are highly affected by EV charging uncertainties and are, therefore, difficult to forecast accurately. This paper presents an Artificial Neural Network (ANN) method ANN to forecast electric vehicle uncertainties. ANN was trained using historical data from a residential building, such as arrival time, departure time and initial SOC. Then, it was tested during 24 hours through different scenarios. For each one of the cases, the model’s accuracy was assessed by comparing historical data to forecast information. The associated errors were also calculated. The outcomes reveal that the suggested forecasting method is very effective in reducing EV forecasting errors and, as a result, is better at regulating EV uncertainty.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133568968","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10199748
A. Alqahtani, E. A. Alammar, W. Koh
With the continuous growth of electrical demand, power grid construction accommodates the required load to be connected and integrated into the existing electrical grid. Many AC power transmission and DC transmission lines are constructed to meet large capacities worldwide. Traditionally High Voltage Alternating Current (HVAC) transmissions have been used for power transmission worldwide in the last century. High Voltage Direct Current (HVDC) technology has specific characteristics which make it unique for transmission system applications, such as long-distance transmission and submarine cable crossings. Studying all types of faults is necessary to protect the design and keep the grid healthy because the faults will significantly impact the transmission system’s operation. This work aims to study the impact of the faults at the proposed transmission line between Central and Western regions in the Kingdom of Saudi Arabia, as well as the feasibility of the project by deep research into the background of the HVDC system and fault analysis at the DC transmission system, which results in knowledge of the impact on the grid before and after the faults occur. The proposed work is modeled and simulated using PSCAD software to simulate the faults on the HVDC transmission line. The results were also analyzed and studied.
{"title":"A Study on Fault Analysis at Proposed HVDC Transmission System between Central and Western Regions in the Kingdom of Saudi Arabia","authors":"A. Alqahtani, E. A. Alammar, W. Koh","doi":"10.1109/SASG57022.2022.10199748","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10199748","url":null,"abstract":"With the continuous growth of electrical demand, power grid construction accommodates the required load to be connected and integrated into the existing electrical grid. Many AC power transmission and DC transmission lines are constructed to meet large capacities worldwide. Traditionally High Voltage Alternating Current (HVAC) transmissions have been used for power transmission worldwide in the last century. High Voltage Direct Current (HVDC) technology has specific characteristics which make it unique for transmission system applications, such as long-distance transmission and submarine cable crossings. Studying all types of faults is necessary to protect the design and keep the grid healthy because the faults will significantly impact the transmission system’s operation. This work aims to study the impact of the faults at the proposed transmission line between Central and Western regions in the Kingdom of Saudi Arabia, as well as the feasibility of the project by deep research into the background of the HVDC system and fault analysis at the DC transmission system, which results in knowledge of the impact on the grid before and after the faults occur. The proposed work is modeled and simulated using PSCAD software to simulate the faults on the HVDC transmission line. The results were also analyzed and studied.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128320743","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10199533
Turki A. Al-Harbi, Maad M. Al-Owaifeer, F. Al-Ismail
This paper presents the importance of developing a Reliability Centered Mmaintenance system (RCM) due to the problems that have arisen during the operation of power transformers. The paper also, aims to provide practical solutions using artificial intelligence, with results and recommendations presented at the end of the paper and a review of important references in this regard.
{"title":"Development of Reliability Centered Maintenance System Using Artificial Intelligence","authors":"Turki A. Al-Harbi, Maad M. Al-Owaifeer, F. Al-Ismail","doi":"10.1109/SASG57022.2022.10199533","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10199533","url":null,"abstract":"This paper presents the importance of developing a Reliability Centered Mmaintenance system (RCM) due to the problems that have arisen during the operation of power transformers. The paper also, aims to provide practical solutions using artificial intelligence, with results and recommendations presented at the end of the paper and a review of important references in this regard.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121458569","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10200591
Ali M. Rafi, Muaiz Ali, M. Hossain, F. Al-Sulaiman, M. Shafiullah
A microgrid is a local electricity network that supplies electrical energy to the local community from various distributed generation (DG) systems. It can be operated independently or in coordination with the national utility grid of a country. Microgrid operators face different operational challenges and uncertainties while meeting the electricity demand of their customers. This research aims to develop a community microgrid (CMG) energy scheduling strategy considering the electricity price, renewable generation, and load demand uncertainty. The considered CMG system is connected to a utility grid and comprises of a solar photovoltaic (PV) plant, a wind generation system, a micro-turbine, an energy storage system (ESS), and a lumped load. A versatile mathematical optimization problem is formulated and solved using an efficient meta-heuristic technique called the whale optimization algorithm (WOA). The proposed strategy combines an intelligent control system with management software to track the CMG customers’ energy requirements and meet the demand with minimal cost by optimizing the available resources. The findings from the research verify the utilization of the ESS in the microgrid to reduce the overall operating costs.
{"title":"Whale Optimization Algorithm for Community Microgrid Energy Scheduling","authors":"Ali M. Rafi, Muaiz Ali, M. Hossain, F. Al-Sulaiman, M. Shafiullah","doi":"10.1109/SASG57022.2022.10200591","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10200591","url":null,"abstract":"A microgrid is a local electricity network that supplies electrical energy to the local community from various distributed generation (DG) systems. It can be operated independently or in coordination with the national utility grid of a country. Microgrid operators face different operational challenges and uncertainties while meeting the electricity demand of their customers. This research aims to develop a community microgrid (CMG) energy scheduling strategy considering the electricity price, renewable generation, and load demand uncertainty. The considered CMG system is connected to a utility grid and comprises of a solar photovoltaic (PV) plant, a wind generation system, a micro-turbine, an energy storage system (ESS), and a lumped load. A versatile mathematical optimization problem is formulated and solved using an efficient meta-heuristic technique called the whale optimization algorithm (WOA). The proposed strategy combines an intelligent control system with management software to track the CMG customers’ energy requirements and meet the demand with minimal cost by optimizing the available resources. The findings from the research verify the utilization of the ESS in the microgrid to reduce the overall operating costs.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131283659","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 : 2022-12-12DOI: 10.1109/SASG57022.2022.10199824
Ibrahim Altarjami, Lingwei Zhu, E. Farantatos, Muhammad Ijaz, Ahmed H. Al-Mubarak, Amir Saeed, Salem Bashraheel
This paper investigates the impact of multiple wide-area damping controllers (WADCs) on system separation and tie-line power transfer limit using the Real-Time Digital Simulator (RTDS) for the Saudi power grid. The Saudi power grid full model in PSS/e is significantly reduced using the DYNRED tool while preserving the desired dynamic features. Then, the reduced model is converted from PSS/e format to RTDS format for real-time simulation. The WADCs are designed using a measurement-driven approach and integrated into the RTDS model, with the generator units (ex-citer and governor) in three selected power plants as the actuators. Finally, the WADCs are tested in the RTDS model under various contingencies, including an actual incident of a large amount generation trip that occurred in 2017 and resulted in system separation. The test results demonstrated that these WADCs can prevent the entire system from separation after large generation trip disturbances. Meanwhile, these WADCs can further enhance the power transfer limit of the tie-lines between the operation areas in the Saudi power grid.
{"title":"Preventing System Separation and Enhancing Tie-line Transfer Limit Using Multiple Wide- Area Damping Controllers - Saudi Grid Case Study","authors":"Ibrahim Altarjami, Lingwei Zhu, E. Farantatos, Muhammad Ijaz, Ahmed H. Al-Mubarak, Amir Saeed, Salem Bashraheel","doi":"10.1109/SASG57022.2022.10199824","DOIUrl":"https://doi.org/10.1109/SASG57022.2022.10199824","url":null,"abstract":"This paper investigates the impact of multiple wide-area damping controllers (WADCs) on system separation and tie-line power transfer limit using the Real-Time Digital Simulator (RTDS) for the Saudi power grid. The Saudi power grid full model in PSS/e is significantly reduced using the DYNRED tool while preserving the desired dynamic features. Then, the reduced model is converted from PSS/e format to RTDS format for real-time simulation. The WADCs are designed using a measurement-driven approach and integrated into the RTDS model, with the generator units (ex-citer and governor) in three selected power plants as the actuators. Finally, the WADCs are tested in the RTDS model under various contingencies, including an actual incident of a large amount generation trip that occurred in 2017 and resulted in system separation. The test results demonstrated that these WADCs can prevent the entire system from separation after large generation trip disturbances. Meanwhile, these WADCs can further enhance the power transfer limit of the tie-lines between the operation areas in the Saudi power grid.","PeriodicalId":206589,"journal":{"name":"2022 Saudi Arabia Smart Grid (SASG)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131591726","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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