Pub Date : 2024-06-17DOI: 10.1109/TEMPR.2024.3415123
Jamal Faraji;François Vallée;Zacharie De Grève
Local energy communities play a pivotal role in facilitating the transition towards a sustainable and environmentally-friendly energy infrastructure. This paper constructs a novel energy sharing mechanism for a centralized energy community. The proposal includes a product differentiation approach to facilitate green, local, and gray electricity distribution among the community members, ensuring a centralized preference-based sharing and promotion of sustainable practices, and an internal pricing scheme for the community market where members indirectly encounter grid and commodity costs. Initially, a compositional model is employed to measure the socio-economic preferences of members toward various energy products. Then, a product differentiation strategy is proposed based on the part-worth utilities of the preferred energy supply option. Afterward, a robust Stackelberg game is introduced between the community manager (CM) and the members to determine the community's internal electricity prices and energy exchanges. Simulation results, including the robust optimization approach and comparison with non-preference-based energy sharing optimization, demonstrate the efficacy of the proposed framework in terms of both benefits and energy sharing performance.
{"title":"A Preference-Informed Energy Sharing Framework for a Renewable Energy Community","authors":"Jamal Faraji;François Vallée;Zacharie De Grève","doi":"10.1109/TEMPR.2024.3415123","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3415123","url":null,"abstract":"Local energy communities play a pivotal role in facilitating the transition towards a sustainable and environmentally-friendly energy infrastructure. This paper constructs a novel energy sharing mechanism for a centralized energy community. The proposal includes a product differentiation approach to facilitate green, local, and gray electricity distribution among the community members, ensuring a centralized preference-based sharing and promotion of sustainable practices, and an internal pricing scheme for the community market where members indirectly encounter grid and commodity costs. Initially, a compositional model is employed to measure the socio-economic preferences of members toward various energy products. Then, a product differentiation strategy is proposed based on the part-worth utilities of the preferred energy supply option. Afterward, a robust Stackelberg game is introduced between the community manager (CM) and the members to determine the community's internal electricity prices and energy exchanges. Simulation results, including the robust optimization approach and comparison with non-preference-based energy sharing optimization, demonstrate the efficacy of the proposed framework in terms of both benefits and energy sharing performance.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 4","pages":"503-518"},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810698","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}
Although block orders are emerging as an interesting alternative to the multi-part offering / bidding paradigm, existing research on optimal participation in electricity markets employing block orders exhibits two critical limitations. Firstly, it has not focused on flexible demand (FD) participants, and, secondly, it has not explored exclusive groups (EGs), a block order type which is already employed in the European day-ahead market. This paper addresses these limitations by proposing a novel optimal bidding model for a price-taking, stand-alone FD participant, which optimizes the submitted EGs, while factoring realistic market regulations around block orders and the price uncertainty encountered by the participant. The proposed model is deployed to quantitatively support our hypothesis that EGs constitute a particularly valuable type of block orders for a FD participant, by comparing its resulting costs under the submission of simple hourly orders, independent profile block orders, and EGs. This is achieved through both illustrative, small-scale examples and more realistic large-scale and out-of-sample studies, while considering three different types of FD in order to demonstrate the general applicability of the proposed model.
{"title":"Optimal Bidding of Flexible Demand in Electricity Markets With Block Orders","authors":"Makedon Karasavvidis;Dimitrios Papadaskalopoulos;Goran Strbac","doi":"10.1109/TEMPR.2024.3414988","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3414988","url":null,"abstract":"Although block orders are emerging as an interesting alternative to the multi-part offering / bidding paradigm, existing research on optimal participation in electricity markets employing block orders exhibits two critical limitations. Firstly, it has not focused on flexible demand (FD) participants, and, secondly, it has not explored exclusive groups (EGs), a block order type which is already employed in the European day-ahead market. This paper addresses these limitations by proposing a novel optimal bidding model for a price-taking, stand-alone FD participant, which optimizes the submitted EGs, while factoring realistic market regulations around block orders and the price uncertainty encountered by the participant. The proposed model is deployed to quantitatively support our hypothesis that EGs constitute a particularly valuable type of block orders for a FD participant, by comparing its resulting costs under the submission of simple hourly orders, independent profile block orders, and EGs. This is achieved through both illustrative, small-scale examples and more realistic large-scale and out-of-sample studies, while considering three different types of FD in order to demonstrate the general applicability of the proposed model.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 4","pages":"488-502"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810688","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 : 2024-06-13DOI: 10.1109/TEMPR.2024.3404693
{"title":"IEEE Power & Energy Society Information","authors":"","doi":"10.1109/TEMPR.2024.3404693","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3404693","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1109/TEMPR.2024.3404689
{"title":"IEEE Transactions on Energy Markets, Policy, and Regulation Information for Authors","authors":"","doi":"10.1109/TEMPR.2024.3404689","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3404689","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1109/TEMPR.2024.3404695
{"title":"Blank Page","authors":"","doi":"10.1109/TEMPR.2024.3404695","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3404695","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"C4-C4"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment.
{"title":"Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study","authors":"Saroj Khanal;Christoph Graf;Zhirui Liang;Yury Dvorkin;Burçin Ünel","doi":"10.1109/TEMPR.2024.3390760","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3390760","url":null,"abstract":"Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 4","pages":"519-535"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810693","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 : 2024-04-16DOI: 10.1109/TEMPR.2024.3388959
Saeed Mohammadi;Abolfazl Khodadadi;Priyanka Shinde;Evelin Blom;Mohammad Reza Hesamzadeh;Lennart Söder
With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic multi-product optimization model, derived through a series of transformation techniques. Additionally, we present two reformulations that re-frame the problem as a mixed-integer linear programming problem with uncertain parameters. Various aspects of the model are thoroughly examined to observe the optimal multi-product trading behavior of hydro power plant assets, along with numerous case studies. Leveraging historical data from Nordic electricity markets, we construct realistic scenarios for the uncertain parameters. Furthermore, we then proposed an algorithm based on the No-U-Turn sampler to provide probability distribution functions of cleared prices in frequency-regulation and day-ahead markets. These distribution functions offer valuable statistical insights into temporal price risks for informed multi-product optimal-trading decisions.
{"title":"Probabilistic Multi-Product Trading of Hydro Power Plants in Sequential Intraday and Frequency-Regulation Markets","authors":"Saeed Mohammadi;Abolfazl Khodadadi;Priyanka Shinde;Evelin Blom;Mohammad Reza Hesamzadeh;Lennart Söder","doi":"10.1109/TEMPR.2024.3388959","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3388959","url":null,"abstract":"With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic multi-product optimization model, derived through a series of transformation techniques. Additionally, we present two reformulations that re-frame the problem as a mixed-integer linear programming problem with uncertain parameters. Various aspects of the model are thoroughly examined to observe the optimal multi-product trading behavior of hydro power plant assets, along with numerous case studies. Leveraging historical data from Nordic electricity markets, we construct realistic scenarios for the uncertain parameters. Furthermore, we then proposed an algorithm based on the No-U-Turn sampler to provide probability distribution functions of cleared prices in frequency-regulation and day-ahead markets. These distribution functions offer valuable statistical insights into temporal price risks for informed multi-product optimal-trading decisions.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 4","pages":"449-464"},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10502309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.1109/TEMPR.2024.3387270
Jens Hönen;Sjoerd C. Doumen;Phuong Nguyen;Johann L. Hurink;Bert Zwart;Koen Kok
Local electricity markets (LEMs) have progressed significantly in recent years, but a research gap exists in understanding the influence of human preferences on the effectiveness of LEMs when home energy management systems (HEMSs) are involved. Motivated by this, this work aims to model and integrate human preferences into a HEMS, bridging the gap between end-participant and LEM. A sensitivity analysis of the parameter choices of the HEMS and their impact on the performance and outcomes of a LEM is done. Hereby, a behavior model is used to formulate the preferences and motives of households within a LEM in a bottom-up approach. Various distributed energy resources are modeled and controlled via a HEMS, allowing households to input their preferences and motives to output a tailor-made bidcurve for the LEM. A sensitivity analysis reveals that different preference settings result in different consumption profiles, which to a large extent align with the preferences. In addition, the importance of aligning market mechanisms and steering signals with the participants' goals is highlighted.
{"title":"Modeling and Analyzing the Effect of Human Preferences on a Local Electricity Market","authors":"Jens Hönen;Sjoerd C. Doumen;Phuong Nguyen;Johann L. Hurink;Bert Zwart;Koen Kok","doi":"10.1109/TEMPR.2024.3387270","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3387270","url":null,"abstract":"Local electricity markets (LEMs) have progressed significantly in recent years, but a research gap exists in understanding the influence of human preferences on the effectiveness of LEMs when home energy management systems (HEMSs) are involved. Motivated by this, this work aims to model and integrate human preferences into a HEMS, bridging the gap between end-participant and LEM. A sensitivity analysis of the parameter choices of the HEMS and their impact on the performance and outcomes of a LEM is done. Hereby, a behavior model is used to formulate the preferences and motives of households within a LEM in a bottom-up approach. Various distributed energy resources are modeled and controlled via a HEMS, allowing households to input their preferences and motives to output a tailor-made bidcurve for the LEM. A sensitivity analysis reveals that different preference settings result in different consumption profiles, which to a large extent align with the preferences. In addition, the importance of aligning market mechanisms and steering signals with the participants' goals is highlighted.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"265-275"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10496199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.1109/TEMPR.2024.3386722
Chang Min Jeong;Hee Seung Moon;Seung Wan Kim
This paper addresses the need for distribution system operators to effectively manage uncertainties related to distributed energy resources. We propose a probabilistic approach using Polynomial Chaos Expansion, allowing the operator to balance economic efficiency with system reliability by setting a pre-determined acceptable violation probability. This approach provides efficient computation and high accuracy in the representation of uncertainties. Moreover, we introduce a framework for integrating the capability of distributed energy resources aggregators to manage uncertainties through the uncertainty band commodity. This allows these aggregators to bid for both energy and a specified uncertainty range, thereby contributing to their active role in the management of system uncertainties. Our methodology fairly distributes responsibility for constraint violations among various stakeholders by employing two allocation strategies: one based on Shapley Value and another based on sensitivity factors. The proposed system significantly improves the overall decision-making process by considering both economic and reliability factors within the prequalification process of distribution system operator.
{"title":"Probabilistic Prequalification Scheme of a Distribution System Operator for Supporting Market Participation of Multiple Distributed Energy Resource Aggregators","authors":"Chang Min Jeong;Hee Seung Moon;Seung Wan Kim","doi":"10.1109/TEMPR.2024.3386722","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3386722","url":null,"abstract":"This paper addresses the need for distribution system operators to effectively manage uncertainties related to distributed energy resources. We propose a probabilistic approach using Polynomial Chaos Expansion, allowing the operator to balance economic efficiency with system reliability by setting a pre-determined acceptable violation probability. This approach provides efficient computation and high accuracy in the representation of uncertainties. Moreover, we introduce a framework for integrating the capability of distributed energy resources aggregators to manage uncertainties through the uncertainty band commodity. This allows these aggregators to bid for both energy and a specified uncertainty range, thereby contributing to their active role in the management of system uncertainties. Our methodology fairly distributes responsibility for constraint violations among various stakeholders by employing two allocation strategies: one based on Shapley Value and another based on sensitivity factors. The proposed system significantly improves the overall decision-making process by considering both economic and reliability factors within the prequalification process of distribution system operator.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 4","pages":"465-475"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810687","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 : 2024-04-08DOI: 10.1109/TEMPR.2024.3386127
Hanning Mi;Qingxin Li;Ming Shi;Sijie Chen;Yutong Li;Yiyan Li;Zheng Yan
Concept drift means the statistical properties of the variable that a predictor is predicting change over time in unforeseen ways. Existing research solves concept drift in the locational marginal price (LMP) prediction process by updating predictors in online approaches. However, new data is indiscriminately utilized to update predictors in these methods. The new property changes can not be accurately captured when concept drift occurs. This paper proposes a stacking framework for online LMP prediction considering the concept drift phenomenon. Long short-term memory networks and graph attention networks are selected as the base predictors to capture the spatio-temporal dependencies in LMPs. When concept drift occurs, data with drift selected by the adaptive windowing algorithm is used to update the stacked predictor. Numerical results based on real data from Australian Energy Market Operator and Midcontinent Independent System Operator validate the effectiveness of the proposed framework. The comparative experiments prove that attempts to change or simplify the proposed framework can undermine prediction accuracy.
{"title":"A Stacking Framework for Online Locational Marginal Price Prediction Considering Concept Drift","authors":"Hanning Mi;Qingxin Li;Ming Shi;Sijie Chen;Yutong Li;Yiyan Li;Zheng Yan","doi":"10.1109/TEMPR.2024.3386127","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3386127","url":null,"abstract":"Concept drift means the statistical properties of the variable that a predictor is predicting change over time in unforeseen ways. Existing research solves concept drift in the locational marginal price (LMP) prediction process by updating predictors in online approaches. However, new data is indiscriminately utilized to update predictors in these methods. The new property changes can not be accurately captured when concept drift occurs. This paper proposes a stacking framework for online LMP prediction considering the concept drift phenomenon. Long short-term memory networks and graph attention networks are selected as the base predictors to capture the spatio-temporal dependencies in LMPs. When concept drift occurs, data with drift selected by the adaptive windowing algorithm is used to update the stacked predictor. Numerical results based on real data from Australian Energy Market Operator and Midcontinent Independent System Operator validate the effectiveness of the proposed framework. The comparative experiments prove that attempts to change or simplify the proposed framework can undermine prediction accuracy.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"254-264"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319611","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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