Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,...Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.展开更多
随着二氧化碳排放量的迅速攀升,经济、环境和能源的矛盾日益突出,发电行业作为典型的碳排放主体,正面临着低碳转型的迫切要求。文章构建了含电转气(power-to-gas,P2G)的碳捕集电厂,通过分析电厂的经济、环境和能源(economy-environment-...随着二氧化碳排放量的迅速攀升,经济、环境和能源的矛盾日益突出,发电行业作为典型的碳排放主体,正面临着低碳转型的迫切要求。文章构建了含电转气(power-to-gas,P2G)的碳捕集电厂,通过分析电厂的经济、环境和能源(economy-environment-energy,3E)特性,建立电厂的3E综合评价指标体系;为获取3E评价指标的相关数据,构建电厂的两阶段鲁棒优化调度模型,并利用约束生成算法进行求解;设计了组合赋权方法和基于灰色关联度分析的逼近理想解排序方法(grey relational analysis-technique for order preference by similarity to ideal soiution,GRA-POPSIS),形成3E综合评价模型。通过实际数据进行仿真分析,验证了在电-碳市场环境下,含P2G的碳捕集电厂相较于WT-GPPCC系统和WT-GFPP系统具有更好的经济、环境和能源综合效益,碳捕集、利用与封存(carbon capture,utilization and storage,CCUS)技术为系统带来的综合效益足以弥补其较高的运行成本,并且提出的3E综合评价模型具有良好的适用性。展开更多
分布式光伏(distributed photovoltaic,DPV)的大规模无序接入为山区配电网带来了过电压、功率倒送等问题,影响配电网的稳定运行。如何准确计算山区配电网DPV承载能力已成为目前亟待研究和解决的重要问题。针对这一问题,以山区配电网所...分布式光伏(distributed photovoltaic,DPV)的大规模无序接入为山区配电网带来了过电压、功率倒送等问题,影响配电网的稳定运行。如何准确计算山区配电网DPV承载能力已成为目前亟待研究和解决的重要问题。针对这一问题,以山区配电网所能接入的DPV最大容量为目标函数,构建考虑储能系统(energy storage system,ESS)容量健康退化和含有ESS的智能软开关(soft open point integrated with energy storage,E-SOP)的确定性模型。其次,分析DPV出力曲线,并在确定性模型的基础上构建山区柔性配电网DPV承载能力双层鲁棒模型。然后,根据KKT条件将模型转化为混合整数线性规划问题,并采用Gurobi求解器求得模型评估结果。最后,通过改进的IEEE33节点山区柔性配电网进行算例分析,研究ESS容量健康退化和DPV出力不确定性对DPV承载能力的影响。算例分析表明所提模型使DPV承载能力提高了8.43%,验证了该模型的可行性和有效性。展开更多
基金supported by the Project of Stable Support for Youth Team in Basic Research Field,CAS(grant No.YSBR-018)the National Natural Science Foundation of China(grant Nos.42188101,42130204)+4 种基金the B-type Strategic Priority Program of CAS(grant no.XDB41000000)the National Natural Science Foundation of China(NSFC)Distinguished Overseas Young Talents Program,Innovation Program for Quantum Science and Technology(2021ZD0300301)the Open Research Project of Large Research Infrastructures of CAS-“Study on the interaction between low/mid-latitude atmosphere and ionosphere based on the Chinese Meridian Project”.The project was supported also by the National Key Laboratory of Deep Space Exploration(Grant No.NKLDSE2023A002)the Open Fund of Anhui Provincial Key Laboratory of Intelligent Underground Detection(Grant No.APKLIUD23KF01)the China National Space Administration(CNSA)pre-research Project on Civil Aerospace Technologies No.D010305,D010301.
文摘Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.
文摘随着二氧化碳排放量的迅速攀升,经济、环境和能源的矛盾日益突出,发电行业作为典型的碳排放主体,正面临着低碳转型的迫切要求。文章构建了含电转气(power-to-gas,P2G)的碳捕集电厂,通过分析电厂的经济、环境和能源(economy-environment-energy,3E)特性,建立电厂的3E综合评价指标体系;为获取3E评价指标的相关数据,构建电厂的两阶段鲁棒优化调度模型,并利用约束生成算法进行求解;设计了组合赋权方法和基于灰色关联度分析的逼近理想解排序方法(grey relational analysis-technique for order preference by similarity to ideal soiution,GRA-POPSIS),形成3E综合评价模型。通过实际数据进行仿真分析,验证了在电-碳市场环境下,含P2G的碳捕集电厂相较于WT-GPPCC系统和WT-GFPP系统具有更好的经济、环境和能源综合效益,碳捕集、利用与封存(carbon capture,utilization and storage,CCUS)技术为系统带来的综合效益足以弥补其较高的运行成本,并且提出的3E综合评价模型具有良好的适用性。
文摘分布式光伏(distributed photovoltaic,DPV)的大规模无序接入为山区配电网带来了过电压、功率倒送等问题,影响配电网的稳定运行。如何准确计算山区配电网DPV承载能力已成为目前亟待研究和解决的重要问题。针对这一问题,以山区配电网所能接入的DPV最大容量为目标函数,构建考虑储能系统(energy storage system,ESS)容量健康退化和含有ESS的智能软开关(soft open point integrated with energy storage,E-SOP)的确定性模型。其次,分析DPV出力曲线,并在确定性模型的基础上构建山区柔性配电网DPV承载能力双层鲁棒模型。然后,根据KKT条件将模型转化为混合整数线性规划问题,并采用Gurobi求解器求得模型评估结果。最后,通过改进的IEEE33节点山区柔性配电网进行算例分析,研究ESS容量健康退化和DPV出力不确定性对DPV承载能力的影响。算例分析表明所提模型使DPV承载能力提高了8.43%,验证了该模型的可行性和有效性。
