Differences and identification on multi-time hydrocarbon generation of carboniferous-permian coaly source rocks in the Huanghua Depression,Bohai Bay Basin

Coal is a solid combustible mineral,and coal-bearing strata have important hydrocarbon generation potential and contribute to more than 12%of the global hydrocarbon resources.However,the deposition and hydrocarbon evolution process of ancient coal-bearing strata is characterized by multiple geological times,leading to obvious distinctions in their hydrocarbon generation potential,geological processes,and production,which affect the evaluation and exploration of hydrocarbon resources derived from coaly source rocks worldwide.This study aimed to identify the differences on oil-generated parent macerals and the production of oil generated from different coaly source rocks and through different oil generation processes.Integrating with the analysis of previous tectonic burial history and hydrocarbon generation history,high-temperature and high-pressure thermal simulation experiments,organic geochemistry,and organic petrology were performed on the Carboniferous-Permian(C-P)coaly source rocks in the Huanghua Depression,Bohai Bay Basin.The oil-generated parent macerals of coal's secondary oil generation process(SOGP)were mainly hydrogen-rich collotelinite,collodetrinite,sporinite,and cutinite,while the oil-generated parent macerals of tertiary oil generation process(TOGP)were the remaining small amount of hydrogen-rich collotelinite,sporinite,and cutinite,as well as dispersed soluble organic matter and unexhausted residual hydrocarbons.Compared with coal,the oil-generated parent macerals of coaly shale SOGP were mostly sporinite and cutinite.And part of hydrogen-poor vitrinite,lacking hydrocarbon-rich macerals,and macerals of the TOGP,in addition to some remaining cutinite and a small amount of crude oil and bitumen from SOGP contributed to the oil yield.The results indicated that the changes in oil yield had a good junction between SOGP and TOGP,both coal and coaly shale had higher SOGP aborted oil yield than TOGP starting yield,and coaly shale TOGP peak oil yield was lower than SOGP peak oil yield.There were significant differences in saturated hydrocarbon and aromatic parameters in coal and coaly shale.Coal SOGP was characterized by a lower Ts/Tm and C31-homohopane22S/(22S+22R)and a higher Pr/n C17compared to coal TOGP,while the aromatic parameter methyl dibenzothiophene ratio(MDR)exhibited coaly shale TOGP was higher than coaly shale SOGP than coaly TOGP than coaly SOGP,and coal trimethylnaphthalene ratio(TNR)was lower than coaly shale TNR.Thus,we established oil generation processes and discriminative plates.In this way,we distinguished the differences between oil generation parent maceral,oil generation time,and oil production of coaly source rocks,and therefore,we provided important support for the evaluation,prediction,and exploration of oil resources from global ancient coaly source rocks.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer model.It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load.First,the principle of wind power obstruction with the involvement of a high-energy load is examined in this work.In this step,highenergy load model with different regulation characteristics is established.Then,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is built.An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the system.Lastly,we take an example of regional power grid in Gansu Province for simulation analysis.Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

Low-carbon generation expansion planning considering uncertainty of renewable energy at multi-time scales

With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a lowcarbon economy.In this paper,a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed.First,renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy.Second,a two-layer generation planning model considering carbon trading and carbon capture technology is established.Specifically,the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale,and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale.Finally,the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid,which demonstrate the effectiveness of the proposed model.

Multi-time scale analysis of precipitation variation in Guyuan, China:1957-2005

Morlet wavelet transformation is used in this paper to analyze the multi time scale characteristics of pre cipitation data series from 1957 to 2005 in Guyuan region.The results showed that(1) the annual precipitation evo lution process had obvious multi time scale variation characteristics of 15 25 years,7 12 years and 3 6 years,and different time scales had different oscillation energy densities;(2) the periods at smaller time scales changed more frequently,which often nested in a biggish quasi periodic oscillations,so the concrete time domain should be ana lyzed if necessary;(3) the precipitation had three main periods(22 year,9 year and 4 year) and the 22 year period was especially outstanding,and the analysis of this main period reveals that the precipitation would be in a relative high water period until about 2012.

Market-based control strategy for long-span structures considering the multi-time delay issue

To solve the different time delays that exist in the control device installed on spatial structures, in this study, discrete analysis using a 2N precise algorithm was selected to solve the multi-time-delay issue for long-span structures based on the market-based control （MBC） method. The concept of interval mixed energy was introduced from computational structural mechanics and optimal control research areas, and it translates the design of the MBC multi-time-delay controller into a solution for the segment matrix. This approach transforms the serial algorithm in time to parallel computing in space, greatly improving the solving efficiency and numerical stability. The designed controller is able to consider the issue of time delay with a linear controlling force combination and is especially effective for large time-delay conditions. A numerical example of a long-span structure was selected to demonstrate the effectiveness of the presented controller, and the time delay was found to have a significant impact on the results.

Research on multi-time scale doubly-fed wind turbine test system based on FPGA+CPU heterogeneous calculation

As the proportion of renewable energy increases, the interaction between renewable energy devices and the grid continues to enhance. Therefore, the renewable energy dynamic test in a power system has become more and more important. Traditional dynamic simulation systems and digital-analog hybrid simulation systems are difficult to compromise on the economy, flexibility and accuracy. A multi-time scale test system of doubly fed induction generator based on FPGA+ CPU heterogeneous calculation is proposed in this paper. The proposed test system is based on the ADPSS simulation platform. The power circuit part of the test system is setup up using the EMT(electromagnetic transient simulation) simulation, and the control part uses the actual physical devices. In order to realize the close-loop testing for the physical devices, the power circuit must be simulated in real-time. This paper proposes a multi-time scale simulation algorithm, in which the decoupling component divides the power circuit into a large time scale system and a small time scale system in order to reduce computing effort. This paper also proposes the FPGA+CPU heterogeneous computing architecture for implementing this multitime scale simulation. In FPGA, there is a complete small time-scale EMT engine, which support the flexibly circuit modeling with any topology. Finally, the test system is connected to an DFIG controller based on Labview to verify the feasibility of the test system.

Analysis of Changes in Precipitation and Temperature over the Past 60 Years in East China

Based on the mean monthly temperature and precipitation data of East China from 1951 to 2006,we conducted the analysis.The results showed that the mean annual temperature tended to increase in the past 56 years while the variation trend of monthly average temperature was different from the annual one.The obvious increase in temperature happened in early spring and from late autumn to winter.The decrease in temperature happened in summer(August).The precipitation change was not as remarkable as the change in temperature.On the whole,the phase of precipitation change was slightly ahead of temperature change.Continuous wavelet transformation was used to analyze the time-frequency changes of precipitation and temperature in East China and the periodical vibration at different times was obtained.

Study on the Improvement of the Application of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise in Hydrology Based on RBFNN Data Extension Technology

The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult.Currently,some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method,a new time-frequency analysis method based on the empirical mode decomposition(EMD)algorithm,to decompose non-stationary raw data in order to obtain relatively stationary components for further study.However,the endpoint effect in CEEMDAN is often neglected,which can lead to decomposition errors that reduce the accuracy of the research results.In this study,we processed an original runoff sequence using the radial basis function neural network(RBFNN)technique to obtain the extension sequence before utilizing CEEMDAN decomposition.Then,we compared the decomposition results of the original sequence,RBFNN extension sequence,and standard sequence to investigate the influence of the endpoint effect and RBFNN extension on the CEEMDAN method.The results indicated that the RBFNN extension technique effectively reduced the error of medium and low frequency components caused by the endpoint effect.At both ends of the components,the extension sequence more accurately reflected the true fluctuation characteristics and variation trends.These advances are of great significance to the subsequent study of hydrology.Therefore,the CEEMDAN method,combined with an appropriate extension of the original runoff series,can more precisely determine multi-time scale characteristics,and provide a credible basis for the analysis of hydrologic time series and hydrological forecasting.

Design of 256 bit single-poly MTP memory based on BCD process

We propose a single-poly MTP （multi-time programmable） cell consisting of one capacitor and two transistors based on MagnaChip＇s BCD process. The area of a unit cell is 37.743 75μm^2. The proposed single-poly MTP cell is erased and programmed by the FN tunnelling scheme. We design a 256 bit MTP memory for PMICs （power management ICs） using the proposed single-poly MTP cells. For small-area designs, we propose a selection circuit between V10V and VSV, and a WL （word-line） driver by simplifying its logic circuit. We reduce the total layout area by using pumped internal node voltages from a seven-stage cross-coupled charge pump for V10V （=10 V） and V5V （=5 V） without any additional charge pumps. The layout size of the designed 256 bit MTP memory is 618.250 μm × 437.425μm.

A Multi-time Scale Tie-line Energy and Reserve Allocation Model Considering Wind Power Uncertainties for Multi-area Systems

Continued expansion of the power grid and the increasing proportion of wind power centralized integration leads to requirements in sharing both energy and reserves among multiple areas under a hierarchical control structure,which successively requires a correction between schedule plans within multi-time scale.In order to address this problem,this paper develops an information integration method integrating complicated relationships among fuel cost,total thermal power output,reserve capacity,owned reserves and expectations of load shedding and wind curtailment,into three types of time-related relationship curves・Furthermore,a multi-time scale tieline energy and reserves allocation model is proposed,which contains two levels in the control structure,two time scales in dispatch sequence and multiple areas integrated within wind farms as scheduling objects・The efficiency of the proposed method is tested in a 9-bus test system and IEEE 118-bus system.The results show that a cross-regional control center is able to approach the optimal scheduling results of the whole system with the integrated uploaded relationship curves.The proposed model not only relieves energy and reserve shortages in partial areas but also allocates them to more urgent need areas in a high effectivity manner in both day-ahead and intraday time scales.

Multi-time scale dynamics in power electronics-dominated power systems

Electric power infrastructure has recently undergone a comprehensive transformation from electromagnetics to semiconductors. Such a development is attributed to the rapid growth of power electronic converter applications in the load side to realize energy conservation and on the supply side for renewable generations and power transmissions using high voltage direct current transmission. This transformation has altered the fundamental mechanism of power system dynamics, which demands the establishment of a new theory for power system control and protection. This paper presents thoughts on a theoretical framework for the coming semiconducting power systems.

Wavelet analysis of rainfall variation in the Hebei Plain

Based on the monthly and annual rainfall data of 1955―2000,the multi-time scales characteristics of seasonal and annual rainfall in the past 45 years in the Hebei Plain have been analyzed using Mexican Hat wavelet analysis in this article.The periodic oscillation of rainfall variation and the points of abrupt change at different time scales along the time series are dis-covered.According to the main periods,the trend of rainfall variation in the future has also been estimated.The results indicate that there are obvious periodic oscillations of 8―12 years and 4―6 years for the seasonal and annual rainfalls variation.The variation trend of the summer rainfall is in agreement with that of the annual rainfall and both of them have the main periods of 1 year and 12 years.It is estimated,based on the main period of 1 year,that the amount of rainfall will be relatively small around 2003 and abundant around 2004―2007 in the Hebei Plain.

Schedulable capacity forecasting for electric vehicles based on big data analysis

Fast and accurate forecasting of schedulable capacity of electric vehicles(EVs)plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems.Traditional methods are insufficient to deal with large-scale actual schedulable capacity data.This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales.The time scale of these data analysis comprises the real time of one minute,ultra-short-term of one hour and one-day-ahead scale of 24 hours.The predicted results for different time scales can be used for various ancillary services.The proposed algorithm is validated using operation data of 521 EVs in the field.The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm,the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.

A Two-Level Hierarchical Markov Decision Model with Considering Interaction between Levels

Decision in reality often have the characteristic of hierarchy because of the hierarchy of an organization＇s structure. In this paper, we propose a two-level hierarchic Markov decision model that considers the interactions of agents in different levels and different time scales of levels. A backward induction algo- rithm is given for the model to solve the optimal policy of finite stage hierarchic decision problem. The proposed model and its algorithm are illustrated with an example about two-level hierar- chical decision problem of infrastructure maintenance. The opti- mal policy of the example is solved and the impacts of interactions between levels on decision making are analyzed.