新型电力系统灵活性资源聚合两阶段调度优化模型

构建以新能源为主体的新型电力系统是实现碳达峰、碳中和目标的关键驱动力。传统的以可控煤电装机为主导的电源结构,转变为以强不确定性、弱可控的新能源为主体的新型电力系统,将面临着灵活性资源短缺等挑战。以提升新型电力系统灵活性为导向,提出了灵活性资源聚合两阶段调度优化模型。第一阶段模型考虑分时价格型需求响应,以净负荷波动最小为目标平滑负荷曲线;第二阶段模型考虑分段激励型需求响应市场交易机制,融合电化学储能、抽水蓄能、改造火电等灵活性资源,以系统运行成本最小为目标设计最优运行方案。最后,算例结果和场景对比表明,需求响应能够充分挖掘负荷跟随系统调节的互动能力;改造后火电机组能够降低煤耗水平,提高调节能力,加强与系统灵活性需求时空匹配;各类储能积极响应电力系统调峰,促进了新能源消纳。     

基于机器学习的配电网异常缺失数据动态清洗方法

针对传统配电网数据清洗过程中异常数据判断阈值需要人为设定、缺失数据填补效率不佳的局限性,提出基于机器学习的配电网异常缺失数据一体化动态清洗方法。首先,基于局部异常因子检测算法和高斯混合模型,提出一种异常数据动态检测改进算法,实现配电网异常数据阈值的准确自动选择。其次,基于随机森林算法与最小二乘回归法,提出一种配电网缺失数据动态填补算法。根据缺失数据时间长度自适应优化填补算法,在保证数据填补精度的同时降低计算时间。在此基础上,通过异常数据检测和缺失数据填补共同构建一体化动态清洗架构。采用湖南某地区配电网数据进行实例验证,结果表明所提方法可实现异常辨识阈值准确自动选择,有效检测配电网异常数据,并且实现缺失数据填补精度与速度的平衡,具有较好的工程应用价值。     

Bifunctional Edge-Rich Nitrogen Doped Porous Carbon for Activating Oxygen and Sulfur

Oxygen reduction reaction (ORR) and sulfur reduction reaction (SRR) play key roles in advanced batteries. However, they both suffer from sluggish reaction kinetics. Here, an interesting nitrogen doped porous carbon material that can simultaneously activate oxygen and sulfur is reported. The carbon precursor is a nitrogen containing covalent organic framework (COF), constituting periodically stacked 2D sheets. The COF structure is well preserved upon pyrolysis, resulting in the formation of edge-rich porous carbon with structure resembling stacked holey graphene. The nitrogen containing groups in the COF are decomposed into graphitic and pyridinic nitrogen during pyrolysis. These edge sites and uniform nitrogen doping endow the carbon product with high intrinsic catalytic activities toward ORR and SRR. The COF derived carbon delivers outstanding performances when assembling as cathodes in the Li-S and Li-O₂ batteries. Simultaneous activation of oxygen and sulfur also enables a new battery chemistry. A proof-of-concept Li-S/O₂ hybrid battery is assembled, delivering a large specific capacity of 2,013 mAh g⁻¹. This study may inspire novel battery designs based on oxygen and sulfur chemistry.     

Simultaneous CO2 and H2O Activation via Integrated Cu Single Atom and N Vacancy Dual-Site for Enhanced CO Photo-Production

Photocatalytic conversion of CO₂ into fuels using pure water as the proton source is of immense potential in simultaneously addressing the climate-change crisis and realizing a carbon-neutral economy. Single-atom photocatalysts with tunable local atomic configurations and unique electronic properties have exhibited outstanding catalytic performance in the past decade. However, given their single-site features they are usually only amenable to activations involving single molecules. For CO₂ photoreduction entailing complex activation and dissociation process, designing multiple active sites on a photocatalyst for both CO₂ reduction and H₂O dissociation simultaneously is still a daunting challenge. Herein, it is precisely construct Cu single-atom centers and two-coordinated N vacancies as dual active sites on CN (Cu₁/N_2CV-CN). Experimental and theoretical results show that Cu single-atom centers promote CO₂ chemisorption and activation via accumulating photogenerated electrons, and the N_2CV sites enhance the dissociation of H₂O, thereby facilitating the conversion from COO* to COOH*. Benefiting from the dual-functional sites, the Cu₁/N_2CV-CN exhibits a high selectivity (98.50%) and decent CO production rate of 11.12 µmol g⁻¹ h⁻¹. An ingenious atomic-level design provides a platform for precisely integrating the modified catalyst with the deterministic identification of the electronic property during CO₂ photoreduction process.     

通信波段稀土离子掺杂固态量子存储进展

量子互联网是实现多方量子通信、分布式量子计算等量子信息技术的重要基础,量子存储器作为实现互联网的重要部件,对量子信息技术的发展、应用具有举足轻重的作用。如今遍布全球的光纤网络已经是信息传输的有力载体,通信波段的量子存储器因容易嵌入到当前的光纤网络中而备受重视。聚焦于稀土离子掺杂固态体系的通信波段光量子存储,首先介绍稀土离子掺杂固态量子存储的基本原理,包括稀土掺杂材料特性以及存储协议等,然后介绍目前的研究现状,最后简要分析其未来的发展趋势,并对量子互联网的构建做出展望。     

基于ESO-UKF的动力电池内部温度在线估计

准确的内部温度估计对动力电池的安全使用至关重要,为了在线获取准确的电池内部温度,本文提出一种基于温度估计模型的ESO-UKF电池内部温度估计方法。其中温度估计模型由Bernardi生热模型与热路传热模型组成,生热模型中端电压由神经网络获取,传热模型参数由递推最小二乘法辨识得到;该算法利用温度估计模型的离散状态空间描述,提出ESO-UKF进行电池内部温度的在线估计,将影响估计精度的传感器偏差视为扩展状态与原状态一起估计,实现了对不确定状态的估计;测试验证表明该算法的估计误差小于1℃,能够实现多工况下内部温度的在线估计,估计精度高、适应性强。     

Multicolor Photo‐Crosslinkable AIEgens toward Compact Nanodots for Subcellular Imaging and STED Nanoscopy

Aggregation induced emission (AIE) has attracted considerable interest for the development of fluorescence probes. However, controlling the bioconjugation and cellular labeling of AIE dots is a challenging problem. Here, this study reports a general approach for preparing small and bioconjugated AIE dots for specific labeling of cellular targets. The strategy is based on the synthesis of oxetane‐substituted AIEgens to generate compact and ultrastable AIE dots via photo‐crosslinking. A small amount of polymer enriched with oxetane groups is cocondensed with most of the AIEgens to functionalize the nanodot surface for subsequent streptavidin bioconjugation. Due to their small sizes, good stability, and surface functionalization, the cell‐surface markers and subcellular structures are specifically labeled by the AIE dot bioconjugates. Remarkably, stimulated emission depletion imaging with AIE dots is achieved for the first time, and the spatial resolution is significantly enhanced to ≈95 nm. This study provides a general approach for small functional molecules for preparing small sized and ultrastable nanodots.