Pub Date : 2010-11-04DOI: 10.1109/SMARTGRID.2010.5622061
Hayden Kwok-Hay So, Sammy H. M. Kwok, E. Lam, K. Lui
The success of future intelligent power deliver and transmission systems across the globe relies critically on the availability of a fast, calable, and most importantly secure communication infrastructure between the energy producers and consumers. One major obstacle to ensure secure communication among various parties in a smart grid network hinges on the technical and implementation difficulties associated with key distribution in such large-scale network with often-time disinterested consumers. This paper proposes the use of an identity-based signcryption (IBS) system to provide a zero-configuration encryption and authentication solution for end-to-end secure communications. The suitability of employing such identity-based cryptosystems in the context of smart grids is studied from the perspective of security requirements, implementation overhead and ease of management. Using the design and implementation experience of our proposed system as an example, we illustrate that IBS is a viable solution to providing a secure and easy-to-deploy solution with close to zero user setup required.
{"title":"Zero-Configuration Identity-Based Signcryption Scheme for Smart Grid","authors":"Hayden Kwok-Hay So, Sammy H. M. Kwok, E. Lam, K. Lui","doi":"10.1109/SMARTGRID.2010.5622061","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5622061","url":null,"abstract":"The success of future intelligent power deliver and transmission systems across the globe relies critically on the availability of a fast, calable, and most importantly secure communication infrastructure between the energy producers and consumers. One major obstacle to ensure secure communication among various parties in a smart grid network hinges on the technical and implementation difficulties associated with key distribution in such large-scale network with often-time disinterested consumers. This paper proposes the use of an identity-based signcryption (IBS) system to provide a zero-configuration encryption and authentication solution for end-to-end secure communications. The suitability of employing such identity-based cryptosystems in the context of smart grids is studied from the perspective of security requirements, implementation overhead and ease of management. Using the design and implementation experience of our proposed system as an example, we illustrate that IBS is a viable solution to providing a secure and easy-to-deploy solution with close to zero user setup required.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116841993","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 : 2010-10-01DOI: 10.1109/SMARTGRID.2010.5622064
Fengjun Li, Bo Luo, Peng Liu
In this paper, we present a distributed incremental data aggregation approach, in which data aggregation is performed at all smart meters involved in routing the data from the source meter to the collector unit. With a carefully constructed aggregation tree, the aggregation route covers the entire local neighborhood or any arbitrary set of designated nodes with minimum overhead. To protect user privacy, homomorphic encryption is used to secure the data en route. Therefore, all the meters participate in the aggregation, without seeing any intermediate or final result. In this way, our approach supports efficient data aggregation in smart grids, while fully protecting user privacy. This approach is especially suitable for smart grids with repetitive routine data aggregation tasks.
{"title":"Secure Information Aggregation for Smart Grids Using Homomorphic Encryption","authors":"Fengjun Li, Bo Luo, Peng Liu","doi":"10.1109/SMARTGRID.2010.5622064","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5622064","url":null,"abstract":"In this paper, we present a distributed incremental data aggregation approach, in which data aggregation is performed at all smart meters involved in routing the data from the source meter to the collector unit. With a carefully constructed aggregation tree, the aggregation route covers the entire local neighborhood or any arbitrary set of designated nodes with minimum overhead. To protect user privacy, homomorphic encryption is used to secure the data en route. Therefore, all the meters participate in the aggregation, without seeing any intermediate or final result. In this way, our approach supports efficient data aggregation in smart grids, while fully protecting user privacy. This approach is especially suitable for smart grids with repetitive routine data aggregation tasks.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121319118","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 : 2010-10-01DOI: 10.1109/SMARTGRID.2010.5621990
Mahdi Kefayati, C. Caramanis
We consider an Energy Services Company (ESCo) acting as a mediator between the wholesale market and the PHEV owners providing them with energy for battery charging using the distribution network as the delivery infrastructure. Furthermore, the ESCo exploits the flexibility in the charging process by offering reserves in the market. To achieve this objective, the ESCo faces the decision problem of purchasing energy, provision of reserves and scheduling PHEVs for minimization of the expected total cost of operation subject to electricity price volatility and uncertainty, demand deadlines and distribution network capacity constraints. We consider this problem as formulated in [1]. In this paper, we first develop an efficiently computable lower bound on the objective function of this problem. Then, inspired by the lower bound, we propose an efficient linear programming based approximate solution for the problem and analyze its performance under different capacity constraints through simulation. In particular, we demonstrate that for practical ranges of uncertainty, the lower bound is tight and the performance of the proposed solution is very close to the optimal DP solution, effectively eliminating the need for complex solutions. Moreover, we show that with current prices of electricity, this energy delivery management model makes a strong business and reliability case by potentially cutting the PHEV energy costs to less than half, providing substantial amounts of efficient and agile reserve to the grid, counterbalancing the intermittency and uncertainty of the renewable generation, and managing PHEV energy demand to observe distribution network limits.
{"title":"Efficient Energy Delivery Management for PHEVs","authors":"Mahdi Kefayati, C. Caramanis","doi":"10.1109/SMARTGRID.2010.5621990","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5621990","url":null,"abstract":"We consider an Energy Services Company (ESCo) acting as a mediator between the wholesale market and the PHEV owners providing them with energy for battery charging using the distribution network as the delivery infrastructure. Furthermore, the ESCo exploits the flexibility in the charging process by offering reserves in the market. To achieve this objective, the ESCo faces the decision problem of purchasing energy, provision of reserves and scheduling PHEVs for minimization of the expected total cost of operation subject to electricity price volatility and uncertainty, demand deadlines and distribution network capacity constraints. We consider this problem as formulated in [1]. In this paper, we first develop an efficiently computable lower bound on the objective function of this problem. Then, inspired by the lower bound, we propose an efficient linear programming based approximate solution for the problem and analyze its performance under different capacity constraints through simulation. In particular, we demonstrate that for practical ranges of uncertainty, the lower bound is tight and the performance of the proposed solution is very close to the optimal DP solution, effectively eliminating the need for complex solutions. Moreover, we show that with current prices of electricity, this energy delivery management model makes a strong business and reliability case by potentially cutting the PHEV energy costs to less than half, providing substantial amounts of efficient and agile reserve to the grid, counterbalancing the intermittency and uncertainty of the renewable generation, and managing PHEV energy demand to observe distribution network limits.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123862967","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 : 2010-10-01DOI: 10.1109/SMARTGRID.2010.5621994
Mardavij Roozbehani, M. Dahleh, S. Mitter
The paper proposes a mechanism for real-time pricing of electricity in smart power grids, with price stability as the primary concern. In previous publications the authors argued that relaying the real-time wholesale market prices to the end consumers creates a closed loop feedback system which could be unstable or lack robustness, leading to extreme price volatility. In this paper, a mathematical model is developed for characterization of the dynamic evolution of supply, (elastic) demand, and market clearing (locational marginal) prices under real-time pricing. It is assumed that the real-time prices for retail consumers are derived from the Locational Marginal Prices (LMPs) of the wholesale balancing markets. The main contribution of the paper is in presenting an effective stabilizing pricing algorithm and characterization of its effects on system efficiency. Numerical simulations conform with our analysis and show the stabilizing effect of the mechanism and its robustness to disturbances.
{"title":"Dynamic Pricing and Stabilization of Supply and Demand in Modern Electric Power Grids","authors":"Mardavij Roozbehani, M. Dahleh, S. Mitter","doi":"10.1109/SMARTGRID.2010.5621994","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5621994","url":null,"abstract":"The paper proposes a mechanism for real-time pricing of electricity in smart power grids, with price stability as the primary concern. In previous publications the authors argued that relaying the real-time wholesale market prices to the end consumers creates a closed loop feedback system which could be unstable or lack robustness, leading to extreme price volatility. In this paper, a mathematical model is developed for characterization of the dynamic evolution of supply, (elastic) demand, and market clearing (locational marginal) prices under real-time pricing. It is assumed that the real-time prices for retail consumers are derived from the Locational Marginal Prices (LMPs) of the wholesale balancing markets. The main contribution of the paper is in presenting an effective stabilizing pricing algorithm and characterization of its effects on system efficiency. Numerical simulations conform with our analysis and show the stabilizing effect of the mechanism and its robustness to disturbances.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122201858","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 : 2010-09-29DOI: 10.1109/SMARTGRID.2010.5622025
S. Bose, M. Salsburg, M. F. Mithani
Smart meters, introduced by smart grid technologies, enable power distribution companies to price electricity differently for different times of the day. Such dynamic pricing schemes help the power companies to modulate the aggregate demand of electricity based on the supply. Accordingly, cloud providers, which are often the largest consumers of electricity, should be able to respond to various dynamic pricing signals called demand response (DR) signals by autonomously moving identified applications to other cloud sites. This paper leverages the recent advancements in cloud migration technologies to 'intelligently' move applications across geographically distributed cloud sites at runtime after a DR signal is received by the cloud provider. This helps minimize the cost incurred by a cloud provider and yet provide adequate service level guarantees as stated in the service level agreements (SLA). The current work develops models and solution schemes for identifying suitable applications for migration and placing such applications on remote cloud sites to take advantages of pricing schemes such as the time-of-use pricing and real time pricing.
{"title":"Leveraging Smart-Meters for Initiating Application Migration across Clouds for Performance and Power-Expenditure Trade-Offs","authors":"S. Bose, M. Salsburg, M. F. Mithani","doi":"10.1109/SMARTGRID.2010.5622025","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5622025","url":null,"abstract":"Smart meters, introduced by smart grid technologies, enable power distribution companies to price electricity differently for different times of the day. Such dynamic pricing schemes help the power companies to modulate the aggregate demand of electricity based on the supply. Accordingly, cloud providers, which are often the largest consumers of electricity, should be able to respond to various dynamic pricing signals called demand response (DR) signals by autonomously moving identified applications to other cloud sites. This paper leverages the recent advancements in cloud migration technologies to 'intelligently' move applications across geographically distributed cloud sites at runtime after a DR signal is received by the cloud provider. This helps minimize the cost incurred by a cloud provider and yet provide adequate service level guarantees as stated in the service level agreements (SLA). The current work develops models and solution schemes for identifying suitable applications for migration and placing such applications on remote cloud sites to take advantages of pricing schemes such as the time-of-use pricing and real time pricing.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131240760","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 : 2010-06-23DOI: 10.1109/SMARTGRID.2010.5621993
M. Neely, Arash Saber Tehrani, A. Dimakis
We investigate the problem of allocating energy from renewable sources to flexible consumers in electricity markets. We assume there is a renewable energy supplier that provides energy according to a time-varying (and possibly unpredictable) supply process. The plant must serve consumers within a specified delay window, and incurs a cost of drawing energy from other (possibly non-renewable) sources if its own supply is not sufficient to meet the deadlines. We formulate two stochastic optimization problems: The first seeks to minimize the time average cost of using the other sources (and hence strives for the most efficient utilization of the renewable source). The second allows the renewable source to dynamically set a price for its service, and seeks to maximize the resulting time average profit. These problems are solved via the Lyapunov optimization technique. Our resulting algorithms do not require knowledge of the statistics of the time-varying supply and demand processes and are robust to arbitrary sample path variations.
{"title":"Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers","authors":"M. Neely, Arash Saber Tehrani, A. Dimakis","doi":"10.1109/SMARTGRID.2010.5621993","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5621993","url":null,"abstract":"We investigate the problem of allocating energy from renewable sources to flexible consumers in electricity markets. We assume there is a renewable energy supplier that provides energy according to a time-varying (and possibly unpredictable) supply process. The plant must serve consumers within a specified delay window, and incurs a cost of drawing energy from other (possibly non-renewable) sources if its own supply is not sufficient to meet the deadlines. We formulate two stochastic optimization problems: The first seeks to minimize the time average cost of using the other sources (and hence strives for the most efficient utilization of the renewable source). The second allows the renewable source to dynamically set a price for its service, and seeks to maximize the resulting time average profit. These problems are solved via the Lyapunov optimization technique. Our resulting algorithms do not require knowledge of the statistics of the time-varying supply and demand processes and are robust to arbitrary sample path variations.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127239764","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 : 2010-06-02DOI: 10.1109/SMARTGRID.2010.5622037
Feng Pan, R. Bent, A. Berscheid, D. Izraelevitz
Plug-in hybrid electric vehicles (PHEVs) are an environmentally friendly technology that is expected to rapidly penetrate the transportation system. Renewable energy sources such as wind and solar have received considerable attention as clean power options for future generation expansion. However, these sources are intermittent and increase the uncertainty in the ability to generate power. The deployment of PHEVs in a vehicle-to-grid (V2G) system provide a potential mechanism for reducing the variability of renewable energy sources. For example, PHEV supporting infrastructures like battery exchange stations that provide battery service to PHEV customers could be used as storage devices to stabilize the grid when renewable energy production is fluctuating. In this paper, we study how to best site these stations in terms of how they can support both the transportation system and the power grid. To model this problem we develop a two-stage stochastic program to optimally locate the stations prior to the realization of battery demands, loads, and generation capacity of renewable power sources. We develop two test cases to study the benefits and the performance of these systems.
{"title":"Locating PHEV Exchange Stations in V2G","authors":"Feng Pan, R. Bent, A. Berscheid, D. Izraelevitz","doi":"10.1109/SMARTGRID.2010.5622037","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5622037","url":null,"abstract":"Plug-in hybrid electric vehicles (PHEVs) are an environmentally friendly technology that is expected to rapidly penetrate the transportation system. Renewable energy sources such as wind and solar have received considerable attention as clean power options for future generation expansion. However, these sources are intermittent and increase the uncertainty in the ability to generate power. The deployment of PHEVs in a vehicle-to-grid (V2G) system provide a potential mechanism for reducing the variability of renewable energy sources. For example, PHEV supporting infrastructures like battery exchange stations that provide battery service to PHEV customers could be used as storage devices to stabilize the grid when renewable energy production is fluctuating. In this paper, we study how to best site these stations in terms of how they can support both the transportation system and the power grid. To model this problem we develop a two-stage stochastic program to optimally locate the stations prior to the realization of battery demands, loads, and generation capacity of renewable power sources. We develop two test cases to study the benefits and the performance of these systems.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133487579","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 : 2010-06-01DOI: 10.1109/SMARTGRID.2010.5622044
K. Turitsyn, N. Sinitsyn, S. Backhaus, M. Chertkov
The anticipated increase in the number of plug-in electric vehicles (EV) will put additional strain on electrical distribution circuits. Many control schemes have been proposed to control EV charging. Here, we develop control algorithms based on randomized EV charging start times and simple one- way broadcast communication allowing for a time delay between communication events. Using arguments from queuing theory and statistical analysis, we seek to maximize the utilization of excess distribution circuit capacity while keeping the probability of a circuit overload negligible.
{"title":"Robust Broadcast-Communication Control of Electric Vehicle Charging","authors":"K. Turitsyn, N. Sinitsyn, S. Backhaus, M. Chertkov","doi":"10.1109/SMARTGRID.2010.5622044","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5622044","url":null,"abstract":"The anticipated increase in the number of plug-in electric vehicles (EV) will put additional strain on electrical distribution circuits. Many control schemes have been proposed to control EV charging. Here, we develop control algorithms based on randomized EV charging start times and simple one- way broadcast communication allowing for a time delay between communication events. Using arguments from queuing theory and statistical analysis, we seek to maximize the utilization of excess distribution circuit capacity while keeping the probability of a circuit overload negligible.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114413856","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 : 2010-06-01DOI: 10.1109/SMARTGRID.2010.5622021
K. Turitsyn, P. Šulc, S. Backhaus, M. Chertkov
High penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit may degrade power quality due to voltage sags and swells caused by rapidly varying PV generation during cloud transients coupled with the slow response of existing utility compensation and regulation equipment. Fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides a new tool for distribution utilities to minimize the thermal losses in circuit. We suggest a local control scheme that dispatches reactive power from each PV inverter based on local instantaneous measurements of the real and reactive components of the consumed power and the real power generated by the PVs. Using one adjustable parameter per circuit, we balance the requirements on power quality and desire to minimize thermal losses. The performance of the proposed control scheme is evaluated via numerical simulations of realistic rural lines in several generation/consumption scenarios. Simultaneous improvement of both the power quality and the magnitude of losses is observed for all the scenarios, even when the renewable generation in excess of the circuit own load.
{"title":"Local Control of Reactive Power by Distributed Photovoltaic Generators","authors":"K. Turitsyn, P. Šulc, S. Backhaus, M. Chertkov","doi":"10.1109/SMARTGRID.2010.5622021","DOIUrl":"https://doi.org/10.1109/SMARTGRID.2010.5622021","url":null,"abstract":"High penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit may degrade power quality due to voltage sags and swells caused by rapidly varying PV generation during cloud transients coupled with the slow response of existing utility compensation and regulation equipment. Fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides a new tool for distribution utilities to minimize the thermal losses in circuit. We suggest a local control scheme that dispatches reactive power from each PV inverter based on local instantaneous measurements of the real and reactive components of the consumed power and the real power generated by the PVs. Using one adjustable parameter per circuit, we balance the requirements on power quality and desire to minimize thermal losses. The performance of the proposed control scheme is evaluated via numerical simulations of realistic rural lines in several generation/consumption scenarios. Simultaneous improvement of both the power quality and the magnitude of losses is observed for all the scenarios, even when the renewable generation in excess of the circuit own load.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"51 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126316564","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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