基于智能电网构建的欧洲可再生能源电力系统现状和前景展望(英文)

可再生能源在欧盟未来的能源框架中扮演着重要角色。到2020年,在欧盟总的能源消耗量中,预期20%将来自可再生能源,并且能效将提高20%。智能电网将是未来电力系统接纳大规模可再生能源发电并网的基石。文中介绍了欧洲的可再生能源开发现状和规划以及能源政策,并讨论了欧盟在智能电网相关技术方面的研发进展。丹麦是欧盟国家中可再生能源使用和相关关键技术研发的领跑者,文中重点以其为例进行介绍和阐述。

Hydrogen Production Technologies Overview

Hydrogen energy became the most significant energy as the current demand gradually starts to increase. Hydrogen energy is an important key solution to tackle the global temperature rise. The key important factor of hydrogen production is the hydrogen economy. Hydrogen production technologies are commercially available, while some of these technologies are still under development. This paper reviews the hydrogen production technologies from both fossil and non-fossil fuels such as (steam reforming, partial oxidation, auto thermal, pyrolysis, and plasma technology). Additionally, water electrolysis technology was reviewed. Water electrolysis can be combined with the renewable energy to get eco-friendly technology. Currently, the maximum hydrogen fuel productions were registered from the steam reforming, gasification, and partial oxidation technologies using fossil fuels. These technologies have different challenges such as the total energy consumption and carbon emissions to the environment are still too high. A novel non-fossil fuel method [ammonia NH3] for hydrogen production using plasma technology was reviewed. Ammonia decomposition using plasma technology without and with a catalyst to produce pure hydrogen was considered as compared case studies. It was showed that the efficiency of ammonia decomposition using the catalyst was higher than ammonia decomposition without the catalyst. The maximum hydrogen energy efficiency obtained from the developed ammonia decomposition system was 28.3% with a hydrogen purity of 99.99%. The development of ammonia decomposition processes is continues for hydrogen production, and it will likely become commercial and be used as a pure hydrogen energy source.

Achieving superlubricity in DLC films by controlling bulk, surface, and tribochemistry

Superlubricity refers to a sliding regime in which contacting surfaces move over one another without generating much adhesion or friction[1].From a practical application point of view,this will be the most ideal tribological situation for many moving mechanical systems mainly because friction consumes large amounts of energy and causes greenhouse gas emissions[2].Superlubric sliding can also improve performance and durability of these systems.In this paper,we attempt to provide an overview of how controlled or targeted bulk,surface,or tribochemistry can lead to superlubricity in diamond-like carbon(DLC)films.Specifically,we show that how providing hydrogen into bulk and near surface regions as well as to sliding contact interfaces of DLC films can lead to super-low friction and wear.Incorporation of hydrogen into bulk DLC or near surface regions can be done during deposition or through hydrogen plasma treatment after the deposition.Hydrogen can also be fed into the sliding contact interfaces of DLCs during tribological testing to reduce friction.Due to favorable tribochemical interactions,these interfaces become very rich in hydrogen and thus provide super-low friction after a brief run-in period.Regardless of the method used,when sliding surfaces of DLC films are enriched in hydrogen,they then provide some of the lowest friction coefficients(i.e.,down to 0.001).Time-of-flight secondary ion mass spectrometer(TOF-SIMS)is used to gather evidence on the extent and nature of tribochemical interactions with hydrogen.Based on the tribological and surface analytical findings,we provide a mechanistic model for the critical role of hydrogen on superlubricity of DLC films.

System resilience enhancement: Smart grid and beyond

Boosting the resilience of power systems is a core requirement of smart grids. In fact, resilience enhancement is crucial to all critical infrastructure systems.In this study, we review the current research on system resilience enhancement within and beyond smart grids. In addition, we elaborate on resilience definition and resilience quantification and discuss several challenges and opportunities for system resilience enhancement. This study aims to deepen our understanding of the concept of resilience and develop a wide perspective on enhancing the system resilience for critical infrastructures.

Passivity-based Active Stabilization for DC Microgrid Applications

In this paper,an improved active stabilization strategy of the interface converters in microgrid applications is proposed on the basis of the passivity-based stability criterion(PBSC).As a critical part of AC and DC hybrid microgrids,the DC microgrid is taken as an example.In particular,a stabilization method with a proportional-integral(PI)controller and firstorder high-pass filter(HPF)is proposed to meet the passivity requirements of the overall control diagram with respect to the output voltage.Meanwhile,an output current feedback control loop is introduced to ensure the output impedance passivity.Moreover,a small-signal model of the parallel interface converter system is established to comprehensively study the influence of control parameters on the passivity of the converters.Based on the active stabilization method proposed in this study,by manipulating the control diagram of each interface converter,the passivity and stability of the DC microgrids with variable configuration can be guaranteed.Therefore,a generic and simplified design approach is realized.A simulation model with three interface converters is implemented in MATLAB/Simulink,and the effectiveness of the proposed passivity-based active stabilization algorithm is verified by using this simulation model.

Review of onsite temperature and solar forecasting models to enable better building design and operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most buildings are in environments that are quite different than those at the airport miles away.Tree cover,adjacent buildings,and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature,humidity,solar irradiance,and wind that are large enough to influence design and operation decisions.In order to overcome this challenge,there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales.This paper reviews and complies knowledge on common weather data resources,data processing methodologies and forecasting techniques of weather information.Commonly used statistical,machine learning and physical-based models are discussed and presented as two major categories:deterministic forecasting and probabilistic forecasting.Finally,evaluation metrics for forecasting errors are listed and discussed.

Influence of tribofilm on superlubricity of highly-hydrogenated amorphous carbon films in inert gaseous environments

In this study,we mainly focus on the structural morphology and inter-atomic bonding state of tribofilms resulting from a highly-hydrogenated amorphous carbon(a-C:H) film in order to ascertain the underlying mechanisms for its superlubric behavior(i.e.,less than 0.01 friction coefficient).Specifically,we achieved superlubricity(i.e.,friction coefficients of down to 0.003) with this film in dry nitrogen and argon atmospheres especially when the tribo-pair is made of an a-C:H coated Si disk sliding against an a-C:H coated steel ball,while the a-C:H coated disk against uncoated ball does not provide superlubricity.We also found that the state of superlubricity is more stable in argon than in nitrogen and the formation of a smooth and uniformly-thick carbonaceous tribofilm appears to be one of the key factors for the realization of such superlubricity.Besides,the interfacial morphology of sliding test pairs and the atomic-scale bond structure of the carbon-based tribofilms also play an important role in the observed superlubric behavior of a-C:H films.Using Raman spectroscopy and high resolution transmission electron microscopy,we have compared the structural differences of the tribofilms produced on bare and a-C:H coated steel balls.For the a-C:H coated ball as mating material which provided superlow friction in argon,structural morphology of the tribofilm was similar or comparable to that of the original a-C:H coating;while for the bare steel ball,the sp^2-bonded C fraction in the tribofilm increased and a fingerprint-like nanocrystalline structure was detected by high resolution transmission electron microscopy(HRTEM).We also calculated the shear stresses for different tribofilms,and established a relationship between the magnitude of the shear stresses and the extent of sp^3-sp^2 phase transformation.

Guest editorial: Special issue on superlubricity

Energy and material losses due to friction and wear in mechanical systems account for huge economic and environmental burdens.Approximately one-third of the world’s primary energy consumption is attributed to friction;in addition,about 80%of the equipment failure is caused by wear in friction processes.Even relatively small improvements in the tribology of mechanical systems would reap enormous societal benefits.Superlubricity is a state in which two contacting surfaces exhibit almost no resistance to sliding,and the friction force between the two sliding surfaces nearly vanishes.Improvement of superlubricity technology and our understanding of its mechanism play an important role for saving energy in industry as well as our daily life.Consequently,superlubricity has attracted a large amount of attention from researchers in many fields,which is leading to a revolution in engineering technology.

ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers

Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach.This has motivated the use of neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system.However,due to the nonlinearities and multi-scale nature of combustion,the predicted solution often diverges from the true solution when these machine learning models are coupled with a computational fluid dynamics solver.This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation solvers.In the present work,a novel neural ordinary differential equations approach to modeling chemical kinetics,termed as ChemNODE,is developed.In this machine learning framework,the chemical source terms predicted by the neural networks are integrated during training,and by computing the required derivatives,the neural network weights are adjusted accordingly to minimize the difference between the predicted and ground-truth solution.A proof-of-concept study is performed with ChemNODE for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions.It is shown that ChemNODE accurately captures the chemical kinetic behavior and reproduces the results obtained using the detailed kinetic mechanism at a fraction of the computational cost.

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management.Without accessibility to other data sources,the onsite observed temperatures or the airport temperatures are used in forecast models.In this paper,we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations(PWS)to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework.The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs.Our approach is distinguished from existing literature in various aspects.First,we leverage the crowdsourcing weather data from PWS in addition to public data sources.In this way,the data is at much finer time resolution(e.g.,at 5-minute frequency)and spatial resolution(e.g.,arbitrary location vs grid).Second,our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance.We demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio,Texas,USA.In general,the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50%compares with persistent model and has 90%chance to outperform airport forecast in short-term forecasting.In a real-time setting,the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon.Moreover,we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance.Finally,we implement our model in other cities such as Syracuse and Chicago to test the model’s performance in different landforms and climate types.