激光打孔技术在光伏背板玻璃上的应用

双玻光伏组件以其抗PID性强、防隐裂、防水汽透过、抗蜗牛纹、可靠性优异、轻量化等诸多优点,在晶硅太阳能组件市占比逐步提高。双玻光伏组件用背板玻璃一般需要预留出线孔,光伏背板玻璃的出线孔主要有两种打孔方式:金钢钻上下同步钻孔的模式和激光打孔。激光打孔以其易维护、可异形孔加工、效率高、生产成本低等优势得到各大玻璃厂的认可。通过分析在实际生产中激光打孔出现的打孔缺陷问题,提出了改善措施,有助于工厂的降本增效。     

An Injectable Hydrogel for Simultaneous Photothermal Therapy and Photodynamic Therapy with Ultrahigh Efficiency Based on Carbon Dots and Modified Cellulose Nanocrystals

The convenience of injectable hydrogels that can provide high loading of diverse phototherapy agents and further long-time retention at the tumor site has attracted tremendous interest in simultaneous photothermal and photodynamic cancer therapies. However, to incorporate the phototherapy agents into hydrogels, complex modifications are generally unavoidable. Moreover, these phototherapy agents usually suffer from low efficiency and work at different irradiation wavelengths outside the near infrared windows. Hence, a method for the fabrication of an injectable hydrogel for simultaneous photothermal therapy and photodynamic therapy, through the Schiff-base reaction between amido modified carbon dots (NCDs) and aldehyde modified cellulose nanocrystals is proposed. The NCDs act as both phototherapy agents and crosslinkers to form hydrogels. Significantly, the NCDs demonstrate an extremely high photothermal conversion efficiency of 77.6% which is among the highest levels for photothermal agents and a high singlet quantum yield of 0.37 under a single 660 nm light-emitting diode irradiation. The hydrogels are examined through in vitro and in vivo animal experiments which show nontoxic and effectively tumor inhibition. Thus, the strategy of direct reaction of phototherapy agents and the matrix not only provides new strategies for injectable hydrogel fabrication but paves a new road for advanced tumor treatment.     

光伏阵列倾角及方位角的最优化研究

根据光伏阵列的特点建立了光伏阵列的数学模型,分析光伏倾角和方位角对容量和能量价值最大化的影响,并结合2017年的发电数据进行了对比研究,计算出容量和能量价值最大化的倾角和方位角。实验结果表明:该方法能够获得最佳的光伏安装角度,具有较高的应用价值。     

A comprehensive review of renewable energy resources for electricity generation in Australia

Recently, renewable energy resources and their impacts have sparked a heated debate to resolve the Australian energy crisis. There are many projects launched throughout the country to improve network security and reliability. This paper aims to review the current status of different renewable energy resources along with their impacts on society and the environment. Besides, it provides for the first time the statistics of the documents published in the field of renewable energy in Australia. The statistics include information such as the rate of papers published, possible journals for finding relative paper, types of documents published, top authors, and the most prevalent keywords in the field of renewable energy in Australia. It will focus on solar, wind, biomass, geothermal and hydropower technologies and will investigate the social and environmental impacts of these technologies.     

Melanin-Like Nanomedicine in Photothermal Therapy Applications

Photothermal therapy (PTT) mediated by nanomaterial has become an attractive tumor treatment method due to its obvious advantages. Among various nanomaterials, melanin-like nanoparticles with nature biocompatibility and photothermal conversion properties have attracted more and more attention. Melanin is a natural biological macromolecule widely distributed in the body and displays many fascinating physicochemical properties such as excellent biocompatibility and prominent photothermal conversion ability. Due to the similar properties, Melanin-like nanoparticles have been extensively studied and become promising candidates for clinical application. In this review, we give a comprehensive introduction to the recent advancements of melanin-like nanoparticles in the field of photothermal therapy in the past decade. In this review, the synthesis pathway, internal mechanism and basic physical and chemical properties of melanin-like nanomaterials are systematically classified and evaluated. It also summarizes the application of melanin-like nanoparticles in bioimaging and tumor photothermal therapy (PTT)in detail and discussed the challenges they faced in clinical translation rationally. Overall, melanin-like nanoparticles still have significant room for development in the field of biomedicine and are expected to applied in clinical PTT in the future.     

Harvesting Air and Light Energy via “All-in-One” Polymer Cathodes for High-Capacity,Self-Chargeable,and Multimode-Switching Zinc Batteries

Aqueous rechargeable Zinc (Zn)–polymer batteries are promising alternatives to prevalent Li-ion cells in terms of cost, safety, and rate capability but they suffer from limited specific capacity in addition to poor environmental adaptability. Herein, air and light are successfully utilized from external environments in single near-neutral two-electrode Zn batteries to enable remarkably improved electrochemical performance, fast self-charging, and switchable multimode-operation from Zn–polymer to Zn–air cells. This system is enabled by a well-designed polyaniline-nanorod-array based “all-in-one” cathode combining reversible redox capability, oxygen reduction activity, and photothermal-responsiveness. The initiative design allows perfect integration of multiple energy harvesting from ambient “air” and light, energy conversion, and storage in one single cathode. Thus, it can act as an efficient light-assisted electrically-rechargeable Zn–polymer cell featuring the highest specific capacity of 430.0 mAh g⁻¹ among all existing polymer cathodes. Without external power sources, it can be self-charged to deliver a high discharging capacity of 363.1 mAh g⁻¹ by concurrently harvesting chemical energy from air and light energy for only 20 min. It can also switch to a light-responsive Zn–air battery mode to surmount the output capacity limit of Zn–polymer battery mode for continued electricity supply.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

Influence of photovoltaic generation model and time resolution on the reliability evaluation of distribution systems

This paper aims to investigate the influence of photovoltaic (PV) generation on reliability evaluation of distribution systems. Two PV generation models are used to predict the output power injected into the grid, taking into account the main relevant environmental variables, the irradiance and ambient temperature. Issues that directly affect the output power, such as the spatial smoothing effect due to the plant size and the influence of the irradiance and temperature measurement interval are taken into consideration. Using measurement time series of irradiance and local temperature, the models are used to generate power series in 4‐minute and hourly resolutions. The generated power series are used in a reliability assessment model, with the objective of evaluating the impact of solar resource variability on the reliability indices of the system. Case studies on the IEEE RBTS‐Bus 2 and on the real distribution system of Fernando de Noronha in Brazil are presented and discussed, for power plants of different capacities, considering the effect of the PV generation models, the temporal resolution of the time series and the spatial smoothing of the power output fluctuations. The results show that the power time series in hourly resolution significantly underestimates the frequency of interruptions. For the real system, this index is underestimated at the system level (up to 43%) and at the load points (up to 72%). On the other hand, for the interruption duration index, the temporal aggregation results in a small underestimation (just 4%). The results also indicates that the smoothing effect is irrelevant for typical PV system sizes of distribution systems with discretization equal to or above 4 minutes.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.