Energy Informatics最新文献

{"title":"Full life-cycle cost model for practical application of solar energy system","authors":"Ang Sha,&nbsp;Zhen Xiong,&nbsp;Xiaolin Zang,&nbsp;Wei Zhao,&nbsp;Ruibang Ge,&nbsp;Wanxiang Yao,&nbsp;Marco Aiello","doi":"10.1186/s42162-025-00505-9","DOIUrl":"10.1186/s42162-025-00505-9","url":null,"abstract":"<div><p>In pursuit of carbon neutrality, a swift transformation is underway in the global energy structure, marked by a consistent rise in the installed capacity of solar energy systems. Meanwhile, the substantial reduction of government subsidies in the solar industry intensifies focus on the economic viability of solar energy installations. In this study, we propose a full life-cycle cost model, named the F-LCC model, for calculating the cost of the solar energy system on the long term, e.g., 20–30 years. This model integrates replacement costs, residual value calculation, interest rate, and inflation impacts while supporting market price estimation for individual components, thereby aiding feasibility analysis in the early project phase. We design an investment cost recovery algorithm based on the F-LCC model to calculate the break-even electricity price for solar energy system. Moreover, we analyze component cost distributions, Net Present Value (NPV), and Discounted Payback Period (DPP) for grid-connected and off-grid solar energy systems with capacities of 10 kWp and 100 kWp in the Chinese market. The results show that the proposed model, compared to other models, captures the fact that payback times are longer. In a solar energy system without storage, solar panels have the highest component cost share at 28.8%. With battery storage, batteries dominate the total cost, reaching up to 74.6%. And the the grid-connected systems DPP ranging from a minimum of 5.5 to a maximum of 7.0 years by grid-connected electricity price, while off-grid systems require at least 19.9 years. The 10 kWp off-grid fixed mounting system’s break-even price being 137.1% higher than its grid-connected counterpart. In addition, tracking-mount systems offer greater cost-reduction potential than fixed installations, with the payback period reduced by 20% for 100 kWp grid-connected systems and 15% for off-grid systems. Finally, we develop a plugin based on the F-LCC model. These findings deepen understanding of solar energy economics and inform policy and investment.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00505-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668260","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}

{"title":"Dual-layer scheduling coordination algorithm for power supply guarantee using multi-objective optimization in spot market environment","authors":"Xuanyuan Wang,&nbsp;Xu Gao,&nbsp;Zhen Ji,&nbsp;Wei Sun,&nbsp;Bo Yan,&nbsp;Bohao Sun","doi":"10.1186/s42162-025-00485-w","DOIUrl":"10.1186/s42162-025-00485-w","url":null,"abstract":"<div><p>As the global electricity market continues to evolve, power dispatch in the spot market environment faces unprecedented challenges. Price fluctuations, the intermittency and uncertainty of renewable energy sources, and stringent environmental constraints make traditional dispatch methods inadequate. To address this, this work proposes a two-layer scheduling strategy based on a multi-objective enhanced genetic algorithm. This strategy aims at balancing multiple objectives such as cost efficiency, environmental impact, and system stability to optimize power dispatch in the spot market. The upper-layer scheduling of this strategy focuses on strategic decisions at the macro level, including generation planning and electricity market transactions. Its lower-layer scheduling concentrates on operational execution at the micro level, specifically power transmission and distribution. To validate the model’s effectiveness, this work designs a regional grid model that includes wind, solar, and several conventional generation units. The experimental results show that, compared to the benchmark strategy, the proposed algorithm achieves a cost savings of 8.33% while ensuring a reliable power supply. Additionally, the algorithm reduces carbon dioxide emissions by approximately 15.1% and significantly increases the average utilization rate of renewable energy to 93.4%. The algorithm is iterated 100 times, each simulating a 24-hour scheduling cycle. The experiment demonstrates its excellent performance in high-dimensional decision spaces and multi-objective optimization problems. This work not only provides an innovative multi-objective optimization solution for power dispatch in the spot market environment but also achieves significant improvements in terms of economic efficiency, environmental sustainability, and long-term viability. Through this two-layer scheduling strategy, the dispatch efficiency of the power system is significantly enhanced, and this provides strong support for the development of a green, low-carbon power supply system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00485-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645557","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}

{"title":"Selection method for hybrid energy storage schemes for supply reliability improvement in distribution networks","authors":"Qian Li,&nbsp;Yuanbao Zhou,&nbsp;Yunxiao Zhang,&nbsp;Yuxin Fu","doi":"10.1186/s42162-025-00495-8","DOIUrl":"10.1186/s42162-025-00495-8","url":null,"abstract":"<div><p>Hybrid energy storage (HES) plays a crucial role in enhancing the reliability of distribution networks. However, the distinct charging and discharging characteristics among different energy storage technologies pose challenges to the evaluation of HES technical features. This paper focuses on addressing two main issues in HES pre-selection. Firstly, regarding the influence of the number of energy storage types on the utility value in HES pre-selection evaluation, we employ the integrated evaluation method of analytic hierarchy process (AHP)-criteria importance through intercriteria correlation (CRITIC)-technique for order preference by similarity to ideal solution (TOPSIS). We propose an improved utility value calculation method based on enhanced utility combination rules. These rules include distance, replacement, addition, and multiplication rules. By establishing these rules, we can effectively eliminate the impact of the number of energy storage types on the combination result. This enables us to accurately calculate the technical characteristics of HES schemes with varying numbers of energy storage technologies, providing a more reliable basis for scheme comparison and selection. Secondly, to eliminate the interference of utility value improvement processing on evaluation results, we introduce a secondary screening method based on TOPSIS evaluation results. This method mitigates the influence of subjective coefficients by evaluating HES schemes under different subjective coefficients and selecting optimal and sub-optimal schemes. We also establish an evaluation system with 11 indices and 10 energy storage technologies, which can efficiently evaluate up to 1785 HES schemes, significantly expanding the scope of evaluation. Finally, we develop a reliability simulation method for distribution networks based on sequential Monte Carlo. Using the IEEE-33 node as an example, we configure the optimal scheme. The results show that the evaluation process has high reliability. The proposed method not only improves the accuracy and rationality of HES pre-selection but also has important practical significance. In actual power grid operation, it can help decision-makers and utility companies to select the most suitable HES schemes more scientifically. This can effectively improve the reliability of power supply, reduce construction costs, and promote the efficient operation of distribution networks. It provides a valuable reference for the wide-scale application of HES in the power industry.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00495-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612241","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}

{"title":"Wind power generation prediction using LSTM model optimized by sparrow search algorithm and firefly algorithm","authors":"Wenjing Zhang,&nbsp;Hongjing Yan,&nbsp;Lili Xiang,&nbsp;Linling Shao","doi":"10.1186/s42162-025-00492-x","DOIUrl":"10.1186/s42162-025-00492-x","url":null,"abstract":"<div><p>As an important renewable energy source, wind power generation is highly stochastic and uncertain due to various environmental factors affecting its output. To raise the accuracy of wind power generation prediction, a bidirectional long short-term memory network combination model based on sparrow search algorithm and firefly algorithm optimization is designed. The model first employs a bidirectional long short-term memory network to capture the long-term dependency features of time series, and uses random forests for nonlinear modeling and feature selection. Then, the sparrow search algorithm and firefly algorithm are combined to optimize the hyperparameter configuration, improving the predictive performance and global search ability of the model. The findings denote that the accuracy of the designed model reaches 98.5%, with a mean square error as low as 0.005 and a prediction time as short as 0.18 s. The simulation analysis results show that the predicted values of the developed model almost coincide with the actual values, with small errors. The research outcomes denote that the optimized model greatly raises the accuracy and efficiency of wind power generation prediction, and has good application prospects.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00492-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583611","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}

{"title":"Real-time monitoring and energy consumption management strategy of cold chain logistics based on the internet of things","authors":"Kang Wang,&nbsp;Ning Du","doi":"10.1186/s42162-025-00493-w","DOIUrl":"10.1186/s42162-025-00493-w","url":null,"abstract":"<div><p>With the rapid development of the cold chain logistics industry, its high energy consumption and low operational efficiency have become increasingly prominent, seriously restricting the sustainable development of the industry. This study focuses on this and proposes a real-time monitoring system for cold chain logistics based on the Internet of Things. It combines the long short-term memory network (LSTM) and the particle swarm optimization (PSO) algorithm to build an energy consumption management strategy. Through the distributed system architecture design, a variety of data transmission protocols are used to ensure real-time and stable data collection and transmission, and to achieve accurate monitoring of key environmental factors in the transportation and storage of cold chain logistics. The experiment was carried out in a simulated cold chain logistics scenario. The data set covers multiple types of sensor data and is compared with multiple baseline models. The results show that compared with the traditional cold chain logistics system, this system significantly improves energy efficiency, reduces energy consumption by about 20%, increases temperature and humidity control accuracy to 94% respectively, improves transportation efficiency, and shortens transportation time by 8.33%. At the same time, the combination of LSTM and PSO algorithms optimizes energy consumption prediction and equipment scheduling, and the equipment group collaborative optimization strategy enhances system stability. This study confirms that the real-time monitoring and energy consumption management strategy based on the Internet of Things can effectively improve the economic and environmental benefits of the cold chain logistics system.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00493-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583610","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}

{"title":"Optimal scheduling of clean energy storage and charging integrated system by fusing DE algorithm and kernel search algorithm","authors":"Xinhua Wang,&nbsp;Yujie Jia,&nbsp;Hao Su,&nbsp;Hua Dang,&nbsp;Songfu Lu","doi":"10.1186/s42162-025-00494-9","DOIUrl":"10.1186/s42162-025-00494-9","url":null,"abstract":"<div><p>In the context of rapid developments in artificial intelligence and the clean energy industry, the optimal scheduling of clean energy storage and charging systems has become increasingly prominent. This study proposes an optimal scheduling method that integrates Differential Evolution (DE) and Kernel Search Optimization (KSO) algorithms. By incorporating DE’s mutation, crossover, and selection operations into the KSO framework, the method effectively avoids local optima while retaining KSO’s advantages in handling complex structures and large-scale data. Experimental results demonstrate that the convergence speed of the fusion algorithm is improved by 34.2%, 30.8%, 28.6%, and 23.4% over four other algorithms for hybrid functions, and by 56.7%, 52.9%, 25.3%, and 21.4% for combined functions. Additionally, the utilization of renewable energy increased from 40% to nearly 70% within 24 h. It can be seen that the convergence speed and renewable energy utilization of the fusion algorithm are significantly improved compared with the four baseline methods, highlighting its effectiveness in large-scale clean energy systems. This research provides an effective scheduling strategy for optimizing clean energy storage and charging systems. This study provides an effective scheduling strategy for optimizing clean energy storage and charging systems, and supports scalable and efficient energy management of urban and rural energy grids. The results show that the optimization of the integrated charging system can not only achieve optimal scheduling in a shorter time, but also reduce operating costs and resource waste, and effectively improve the overall operating efficiency of the energy system. Research to promote the efficient use of renewable energy will help reduce dependence on fossil fuels, thereby reducing greenhouse gas emissions and environmental pollution, which will have a positive impact on achieving the Sustainable Development goals and addressing climate change, and promote a win-win situation for the economy and the environment.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00494-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564450","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}

{"title":"PIDE: Photovoltaic integration dynamics and efficiency for autonomous control on power distribution grids","authors":"Gökhan Demirel,&nbsp;Natascha Fernengel,&nbsp;Simon Grafenhorst,&nbsp;Kevin Förderer,&nbsp;Veit Hagenmeyer","doi":"10.1186/s42162-025-00489-6","DOIUrl":"10.1186/s42162-025-00489-6","url":null,"abstract":"<div><p>With a focus on larger rooftop or utility-scale solar systems, there is a lack of research on the potential impact of mini photovoltaic (MPV) systems, often referred to as balcony power plants. This work analyzes the impact of varying concentrations of MPV systems, on the stability and control of low-voltage (LV) grids. We offer a comprehensive technical assessment of MPV within a distribution grid and quantify their effects on power quality, losses, transformer loading, and the performance of other inverter-based voltage-regulation devices. For this purpose, this paper introduces the open-source Python-based framework PIDE (Photovoltaic Integration Dynamics and Efficiency), a tool for simulating the integration of distributed energy resources (DER)s and evaluating their impact on autonomous reactive power control in the distribution grid. Our case studies include a one-year sensitivity analysis based on Monte Carlo simulations, compare distributed and decentralized DER control strategies, and demonstrate the role of autonomous inverters in providing ancillary services. With the growing use of battery energy storage (BES) systems in LV grids for these services, the need for adaptable DER control strategies becomes increasingly evident. Our results show that high MPV penetration increases mean transformer load by up to 3%, line load by 2.5% and total power losses by around 17%.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00489-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553767","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}

{"title":"Demand response and energy dispatch system for intelligent buildings based on improved MOALO algorithm","authors":"Weiwei Han","doi":"10.1186/s42162-025-00490-z","DOIUrl":"10.1186/s42162-025-00490-z","url":null,"abstract":"<div><p>As the rate of energy consumption in intelligent buildings increases, the uneven distribution of energy among different devices in intelligent buildings leads to further acceleration of energy consumption. The study suggested designing an energy dispatch system for intelligent buildings based on the enhanced multi-objective ant-lion optimizer algorithm in an attempt to address the issue that the conventional energy dispatch system for intelligent buildings is unable to carry out energy dispatch in accordance with the electricity price and incentives. The initialization of different energy data parameters was carried out by the multi-objective ant-lion optimizer algorithm, and the variance crossover operation of the data parameters was carried out by the differential evolution algorithm. Based on the improved multi-objective ant-lion optimizer algorithm, a demand response model was constructed, and the energy dispatch system of intelligent buildings was constructed accordingly. The results revealed that the area under the PR curve of the improved multi-objective ant-lion optimizer algorithm was 0.9653, which was significantly higher than the other three algorithms. The root mean square error and the mean absolute error of the algorithm were 0.839 and 0.648, respectively. In the experiments on the practical application of the dispatch system, it was found that the average power of the dispatched energy sources was significantly lower than that of the non-dispatched energy power distribution. The aforementioned findings indicate the suggested approach can more effectively schedule various energy sources in intelligent buildings, offering technical assistance in the area of energy dispatch.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00490-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521574","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}

{"title":"Hybrid energy storage system for intelligent electric vehicles incorporating improved PSO algorithm","authors":"Hui Shu","doi":"10.1186/s42162-025-00488-7","DOIUrl":"10.1186/s42162-025-00488-7","url":null,"abstract":"<div><p>Existing energy storage system is difficult to balance the energy distribution and dynamic response efficiency issues of lithium-ion batteries and supercapacitor, resulting in low energy utilization. Therefore, the study proposes a hybrid energy storage system for intelligent electric vehicles incorporating improved particle swarm optimization. The study analyzes the relationship between vehicle driving speed and power demand through equivalent model, constructs an objective function containing power demand and state of charge, and uses an improved algorithm for optimization and solution. The performance test results indicated that the proposed improved algorithm exhibited the fastest convergence speed by rapidly decreasing the objective function value and approximating the optimal solution within the first 20 iterations in both single-peak and multi-peak functions. The simulation experiments were validated under urban working conditions and highway working conditions, respectively. The results indicated that the energy efficiency in both working conditions was improved to 92.5% and 94.9%, respectively. In addition, good results were achieved in the contribution of supercapacitor, which were 27.2% and 29.6%, respectively. In the test results based on HIL environment, the system proposed by the research institute can also maintain energy efficiency of over 80% under extreme conditions. The findings support the optimal design of intelligent electric vehicle energy storage systems both theoretically and practically, showing that the study’s revised algorithm performs well in both energy allocation efficiency and dynamic response performance.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00488-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496779","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}

{"title":"The design of a real-time monitoring and intelligent optimization data analysis framework for power plant production systems by 5G networks","authors":"Xihong Chuang,&nbsp;Le Li,&nbsp;Lei Zhu,&nbsp;Mingyi Wei,&nbsp;Yongsheng Qiu,&nbsp;Yanqing Xin","doi":"10.1186/s42162-025-00487-8","DOIUrl":"10.1186/s42162-025-00487-8","url":null,"abstract":"<div><p>The current power plant production systems face issues such as insufficient monitoring accuracy, data transmission delays, and low energy utilization efficiency. In response, this study proposes a real-time monitoring and intelligent data analysis system based on Fifth-Generation Mobile Communication Network (5G) technology. Building upon an analysis of the limitations inherent in traditional systems, the experiment capitalizes on the extensive connectivity capabilities of 5G to design an intelligent monitoring architecture tailored for power plant production environments. To enhance system performance, the study introduces an innovative resource scheduling and data analysis model that combines an improved Hybrid Advantage Actor-Critic (A3C) algorithm with a Dueling Deep Q-Network (DQN) algorithm. This model integrates the global optimization capabilities of the A3C algorithm with the efficient learning mechanism of the Dueling DQN algorithm to optimize communication resource scheduling and energy storage management within a 5G Cloud Radio Access Network (C-RAN) environment. Simulation experiments demonstrate that this approach significantly improves system energy efficiency, optimizes resource utilization, and reduces energy waste. The results show that data transmission delays decreased by 25%, energy utilization increased by 18.25%, and renewable energy consumption rose by 12.55%. This study offers a new technical approach for the intelligent upgrade and green, efficient operation of power plant production systems, providing both theoretical and practical support for the optimization of power systems in the 5G era.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00487-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496723","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}