为解决西北地区大规模风电接入下系统弃风限电问题,提出一种考虑源–荷多时间尺度协调优化的大规模风电接入多源电力系统调度策略,该策略通过协调调度源侧常规火电机组、光热(concentrated solar power,CSP)电站与风电出力以及荷侧各类...为解决西北地区大规模风电接入下系统弃风限电问题,提出一种考虑源–荷多时间尺度协调优化的大规模风电接入多源电力系统调度策略,该策略通过协调调度源侧常规火电机组、光热(concentrated solar power,CSP)电站与风电出力以及荷侧各类需求响应(demand response,DR)资源调用计划,在促进系统风电消纳的同时提高了系统运行经济性。首先,根据响应速度的不同对DR资源进行分类;然后,综合考虑CSP电站的能量时移特性与快速调节能力以及DR资源的多时间尺度特性,以系统运行成本以及弃风惩罚成本最低为目标,构建了源–荷多时间尺度协调调度模型。IEEE-30节点算例结果表明所提出的调度策略能够协调优化源–荷侧可调节资源,有利于提高系统风电消纳能力并改善系统运行经济性。展开更多
Achieving strong coupling between plasmonic oscillators can significantly modulate their intrinsic optical properties.Here,we report the direct observation of ultrafast plasmonic hot electron transfer from an Au grati...Achieving strong coupling between plasmonic oscillators can significantly modulate their intrinsic optical properties.Here,we report the direct observation of ultrafast plasmonic hot electron transfer from an Au grating array to an MoS_(2) monolayer in the strong coupling regime between localized surface plasmons(LSPs)and surface plasmon polaritons(SPPs).By means of femtosecond pump-probe spectroscopy,the measured hot electron transfer time is approximately 40 fs with a maximum external quantum yield of 1.65%.Our results suggest that strong coupling between LSPs and SPPs has synergetic effects on the generation of plasmonic hot carriers,where SPPs with a unique nonradiative feature can act as an‘energy recycle bin’to reuse the radiative energy of LSPs and contribute to hot carrier generation.Coherent energy exchange between plasmonic modes in the strong coupling regime can further enhance the vertical electric field and promote the transfer of hot electrons between the Au grating and the MoS_(2) monolayer.Our proposed plasmonic strong coupling configuration overcomes the challenge associated with utilizing hot carriers and is instructive in terms of improving the performance of plasmonic opto-electronic devices.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and obj...To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and objective combination of assignment and technique for order preference by similarity to an ideal solution(TOPSIS)—rank sum ratio(RSR)(TOPSIS-RSR)method.Based on the traditional distribution network evaluation system,a comprehensive evaluation system has been constructed.It fully considers the new development requirements of distributed PV access on the environmental friendliness and absorptive capacity of the distribution grid and comprehensively reflects the impact of distributed PV grid connection.The analytic hierarchy process(AHP)was used to determine the subjective weights of the primary indicators,and the Spearman consistency test was combined to determine the weights of the secondary indicators based on three objective assignment methods.The subjective and objective combination weights of each assessment indicator were calculated through the principle of minimum entropy.Calculate the distance between the indicators to be evaluated and the positive and negative ideal solutions,the relative closeness ranking,and qualitative binning by TOPSIS-RSR method to obtain the comprehensive evaluation results of different scenarios.By setting up different PV grid-connected scenarios and utilizing the IEEE33 node simulation algorithm,the correctness and effectiveness of the proposed subject-object combination assignment and integrated assessment method are verified.展开更多
文摘为解决西北地区大规模风电接入下系统弃风限电问题,提出一种考虑源–荷多时间尺度协调优化的大规模风电接入多源电力系统调度策略,该策略通过协调调度源侧常规火电机组、光热(concentrated solar power,CSP)电站与风电出力以及荷侧各类需求响应(demand response,DR)资源调用计划,在促进系统风电消纳的同时提高了系统运行经济性。首先,根据响应速度的不同对DR资源进行分类;然后,综合考虑CSP电站的能量时移特性与快速调节能力以及DR资源的多时间尺度特性,以系统运行成本以及弃风惩罚成本最低为目标,构建了源–荷多时间尺度协调调度模型。IEEE-30节点算例结果表明所提出的调度策略能够协调优化源–荷侧可调节资源,有利于提高系统风电消纳能力并改善系统运行经济性。
基金supported by the National Key Research and Development Program of China(Grant No.2017YFA0205700)National Basic Research Program of China(Grant Nos.2015CB932403,2017YFA0206000)+4 种基金National Science Foundation of China(Grant Nos.11674012,61422501,11374023,61521004 and 21790364)Beijing Natural Science Foundation(Grant No.L140007)Foundation for the Author of National Excellent Doctoral Dissertation of PR China(Grant No.201420)National Program for Support of Top-notch Young Professionals(Grant No.W02070003)Ministry of Education Singapore under Grant No.MOE2015-T2-2-043.
文摘Achieving strong coupling between plasmonic oscillators can significantly modulate their intrinsic optical properties.Here,we report the direct observation of ultrafast plasmonic hot electron transfer from an Au grating array to an MoS_(2) monolayer in the strong coupling regime between localized surface plasmons(LSPs)and surface plasmon polaritons(SPPs).By means of femtosecond pump-probe spectroscopy,the measured hot electron transfer time is approximately 40 fs with a maximum external quantum yield of 1.65%.Our results suggest that strong coupling between LSPs and SPPs has synergetic effects on the generation of plasmonic hot carriers,where SPPs with a unique nonradiative feature can act as an‘energy recycle bin’to reuse the radiative energy of LSPs and contribute to hot carrier generation.Coherent energy exchange between plasmonic modes in the strong coupling regime can further enhance the vertical electric field and promote the transfer of hot electrons between the Au grating and the MoS_(2) monolayer.Our proposed plasmonic strong coupling configuration overcomes the challenge associated with utilizing hot carriers and is instructive in terms of improving the performance of plasmonic opto-electronic devices.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金support of the project“State Grid Corporation Headquarters Science and Technology Program(5108-202299258A-1-0-ZB)”.
文摘To effectively quantify the impact of distributed photovoltaic(PV)access on the distribution network,this paper proposes a comprehensive evaluation method of distributed PV grid connection combining subjective and objective combination of assignment and technique for order preference by similarity to an ideal solution(TOPSIS)—rank sum ratio(RSR)(TOPSIS-RSR)method.Based on the traditional distribution network evaluation system,a comprehensive evaluation system has been constructed.It fully considers the new development requirements of distributed PV access on the environmental friendliness and absorptive capacity of the distribution grid and comprehensively reflects the impact of distributed PV grid connection.The analytic hierarchy process(AHP)was used to determine the subjective weights of the primary indicators,and the Spearman consistency test was combined to determine the weights of the secondary indicators based on three objective assignment methods.The subjective and objective combination weights of each assessment indicator were calculated through the principle of minimum entropy.Calculate the distance between the indicators to be evaluated and the positive and negative ideal solutions,the relative closeness ranking,and qualitative binning by TOPSIS-RSR method to obtain the comprehensive evaluation results of different scenarios.By setting up different PV grid-connected scenarios and utilizing the IEEE33 node simulation algorithm,the correctness and effectiveness of the proposed subject-object combination assignment and integrated assessment method are verified.
