Optimal Two-Tier Forecasting Power Generation Model in Smart Grids

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Optimal plug-in hybrid electric vehicles recharge in distribution power systems

oil dependence and greenhouse gases emissions, yielding environmental improvements [8,9]. Many researchers have been developing solutions for PHEV recharging. Clement-Nyns et al. proposed in [6] a coordinated charging to minimize power losses and maximize the main grid power factor. To perform optimization, the authors use quadratic and dynamic programming, since an exact forecast of household loads is not possible. Coordinated recharging and linear programming are used to minimize PHEV recharging costs in [10]. It also investigates the consequences of inserting renewable generation into the grid. The interesting results obtained render the losses monitoring as a good strategy for reactive power, voltage level and branch loading control. The authors in [11] use Monte Carlo simulations in a coordinated recharging process to prove that maximizing power factor and also minimizing load variance are equivalent to power losses minimization. In [12] a real-time smart load management control strategy is proposed and developed for the coordination of recharging process based on real-time minimization of total cost of generation plus the associated grid energy losses. In [13], a power losses minimization is performed by applying a time coordinated optimal power ﬂow. They show that, through the control of PHEV storage units and on-load tap changers transformers, electric network operator can inﬂuence savings in energy losses. Besides those approaches applied to losses minimization problem on [6,7,10–13], evolutionary techniques are also used, as particle swarm optimization (PSO), which ﬁnds the optimal solution using a population of particles. However, this technique has some convergence problems, reaching non-optimal solutions. The mutation feature, present in Artiﬁcial Immune Systems (AIS), can avoid this problem [14].

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•Bill checking to verify that you are only charged for the energy you use •Aggregation of energy consumption, including WAGES, and costallocation per area, per usage, per shift or per time within the same facility •Energy cost and usage analysis per zone, per usage or per time period tooptimize energy usageThe PM2000 series meter is a next-generation energy and power meter that offers all the measurement capabilities required to monitor an electricalinstallation in a single 96 x 96 mm unit. The PM2000 series offers simplicity and reliability for basic energy cost and network management applications in both a LED or LCD display option.This multifunction energy and power meter is the easy choice for quality, value, and reliability. Essential features, such as conﬁgurable data logging, industry- leading measurement accuracy, and power quality analysis. And best of all, it’s easy, safe and fast to choose, purchase, and install the right PM2000 model for your application.The PM2000 series meters help to help maximize energy cost savings, optimize operational efﬁciency, and improve business performance.Cost Management Applications•Metering of electrical parameters to better understand the behavior of your electrical distribution system•Power quality analysis of THD and individual harmonics up to 15th and 31st order for Volts and Amps, per-phase•Measurement of True PF and Displacement PFNetwork Management Applications•PM2100 series:LED display type: Intuitive navigation with self- guided, three buttons,bright red color LEDs of 14.2 mm height. Two columns of LEDs indicate the parameter name chosen for display.•PM2200 series:LCD display type: Monochrome graphical LCD of 128 x 128 resolution lets users read all three phase values simultaneously. The bright display enables easy reading even in extreme lighting conditions and viewing angles with intuitive menus, multi-language text, icons and graphics.PM2000 meters are available in LED and LCD display variants.Versatile, multi-function power and energy metersSelection Guide•Performance without compromise- exceptional reliability in its class •Assured quality that you can trust to meet your needs throughout its life-cycle•Outstanding value for an optimized feature set•Simple to choose and easy to install and use for many applicationsSee your Schneider Electric representative for complete ordering information.PM2000 Series Features© 2020 Schneider Electric. All Rights Reserved. All trademarks are owned by Schneider Electric Industries SAS or its afﬁliated companies. Document Number 3000HO2003Schneider Electric6700 Tower Circle Suite 700Franklin, TN 37067/us/powerandenergy+4 Through communication only.+5Any one IO module can be used at a time with PM2130 or PM2230 meter. The control power range for PM2130 & PM2230 meters with or without IO module shall be 72 to 304 V AC L-N or 90 to 304 V DC.+6 Battery backup duration 3 years when meter is in continuous or accumulated Power OFF condition.。

An engineering approach to the optimal design of distributed energy systems in China

An engineering approach to the optimal design of distributed energy systems in ChinaZhe Zhou,Pei Liu,Zheng Li*,Weidou NiState Key Laboratory of Power Systems,Department of Thermal Engineering,Tsinghua University,Beijing100084,Chinaa r t i c l e i n f oArticle history:Received11December2011 Accepted31January2012 Available online8February2012Keywords:Energy systems engineering Distributed energy system Distributed energy resource Optimization a b s t r a c tChina faces enormous challenges as it strives to meet its increasing energy demands.Distributed energy systems(DESs)provide a good opportunity to tackle these challenges by integrating various forms of distributed technologies,of which internal combustion engines,solar photovoltaic,and absorption chillers being the typical ones.However,ﬂuctuation in energy demands and supplies,the large number of available distributed technologies and various possible combinations of these technologies make the design of DES a formidable task.The potential beneﬁts of DES might be hampered by their impropriate design and operation.This paper provides a generic energy systems engineering framework toward the optimal design of DES in China,with the purpose of obtaining optimal combination of technologies and capacity of equipment for a given area with given energy demands.A hotel in Beijing is selected as an illustrative example to demonstrate the key steps and features of the proposed approach.Results show that the optimal conﬁguration of such a DES is a rather complex system equipped with various technologies.It is more efﬁcient and economic than conventional centralized energy systems as well as distributed combined cooling,heating and power systems.Ó2012Elsevier Ltd.All rights reserved.1.IntroductionA distributed energy system(DES)refers to an energy system where energy is produced close to end use,typically relying on a number of modular and small-scale technologies.DES has a number of advantages such as high overall efﬁciency,small losses on energy transmission,and small negative environmental impacts [1,2].In recent years,China faces enormous challenges in meeting its immense and rapidly increasing energy demands under more and more stringent environmental constraints,due to the fast growth of its economy[3].Therefore,interest has been intensifying in China in the development of DES,which is an efﬁcient and environmental friendly alternative or complement to the conventional centralized energy system(CES).In October2011,the National Energy Administration(NEA)issued a regulation entitled“the Guidance on the Development of Natural Gas Distributed Energy Systems”.It suggests installing up to one thousand distributed natural gas energy stations,i.e.,combined heating cooling and power(CCHP), during the period of the12thﬁve-year plan,and the total capacity of DES is expected to reach50,000MW by2020.In this context, a large number of demonstration CCHP projects have been started in different areas of China,as listed in Table1[4].These CCHP projects share a similar conﬁguration which generally includes a prime mover(gas turbine or gas engine),a waste heat boiler and an absorption chiller.The regulation and demonstration projects indicate that Chinese government has a strong interest in promoting DESs in China. However,it can also be observed that the government is more interested in classic CCHP systems at the current stage,although possibility of integration of renewable energy with CCHP systems is also mentioned in the Guidance.From a systems engineering viewpoint,focusing on a single or certain types of technologies,for instance,CCHP systems,may not be the best option,as there exist many other alternative energy resources and corresponding conversion technologies for various types of conﬁgurations of DES.These systems,with appropriate design and integration work,may exhibit even better performances than a classic CCHP system.For instance,integration with renew-able energy resources may lead to more environmental beneﬁts such as reduced conventional air pollutants and lower greenhouse gas(GHG)emissions.However,optimal design of DES is not a trivial task as integra-tion of more types of energy resources and conversion technologies*Corresponding author.Tel.:þ861062795739;fax:þ861062795736.E-mail address:lz-dte@(Z.Li).Contents lists available at SciVerse ScienceDirect Applied Thermal Engineeringjournal ho mepage:www.elsevier.co m/locate/apthermeng1359-4311/$e see front matterÓ2012Elsevier Ltd.All rights reserved.doi:10.1016/j.applthermaleng.2012.01.067Applied Thermal Engineering53(2013)387e396may increase the complexity of the system,and the aforemen-tioned beneﬁts may be partly or even totally compensated.In this paper,weﬁrstly present an energy systems engineering framework toward the optimal design of DES,with which energy efﬁciency and complexity of a system can be quantitatively evaluated.For a speciﬁc district,given its electricity,space heating,cooling and hot water loads,energy prices,and technical andﬁnancial infor-mation about optional technologies,the proposed framework helps to minimize the overall annual cost.We then illustrate the appli-cability of the proposed modeling and optimization framework in a case study of a hotel in Beijing.In this case study,the energy, economic and environmental performances of the optimal conﬁg-uration obtained by the proposed method are evaluated and compared with those of a classic CCHP system and a conventional CES.2.Superstructure based model for the optimal design of DESA number of studies have been reported on the optimal design of DES,but most of them focus on distributed CCHP systems[5e18]. Ren et al.and Liu et al.developed models for optimal design of CCHP systems integrated with solar energy and biomass[19e23]. However,these models do not consider certain technologies,for instance,energy storage technologies.Moreover,National Renew-able Energy Laboratory of the United States developed a design tool, namely HOMER for the optimal selection of distributed power generation technologies,which encompassed renewable energy technologies such as solar photovoltaic and wind turbines as well as energy storage technologies such as batteries[24].However, HOMER aims for the design of distributed power generation systems,therefore heating and cooling needs are not considered in the model.In summary,most existing mathematical models for optimal design of DES cannot capture all alternative conﬁgurations due to case-by-case modeling strategy.A generic mathematical model,which can ideally include all alternative distributed gener-ation technologies,is therefore desired to address design issues of DES.In this paper,we propose a superstructure based model for the optimal design of DES,featuring simultaneous determination of the optimal conﬁguration of a DES and its optimal operating conditions via mathematical programming[25].It wasﬁrstly proposed to address process synthesis issues in heat exchanger networks [26e28],and widely used in a broad range ofﬁelds thereafter,such as process synthesis[29]and supply chain network design[30,31]. It has been proved to be an effective method to address issues associated with optimal planning of energy systems,such as bio-energy processing systems[32],polygeneration energy systems [33e35]and hydrogen infrastructure[36].As a DES involves a variety of alternative technologies,superstructure based modeling is well suited for its optimal design.A superstructure representation of the optimal design problem of DES is shown in Fig.1.It consists of an energy generation section, an energy conversion section and an energy storage section.In the energy generation section,a number of technologies are consid-ered,and via these technologies heat and electricity can be produced from various primary energy resources.The secondary energy carriers,i.e.,heat and electricity,are then converted to different forms of tertiary energy carriers,i.e.,heat,cooling and electricity,through different energy conversion technologies.For instance,air-source chiller is a typical energy conversion tech-nology that can produce cooling with electricity.For a more efﬁ-cient use of energy,an energy storage section comes after to shift energy generated during low usage periods for consumption during peak hours.The framework includes six primary energy resources,namely natural gas,diesel,wind,solar energy,biomass,geothermal,four energy demands,namely electricity,space heating,cooling and hot water,and twenty types of equipments,namely internal combus-tion engine,gas turbine,natural gas boiler,fuel cell,diesel engine, wind turbine,solar PV,solar thermal collector,biomass boiler, biogas engine,biogas boiler,electric heater,absorption chiller/heat pump,heat exchanger,water-source chiller/heat pump,air-source chiller/heat pump,ground-source chiller/heat pump,battery,ice storage equipment and thermal storage tank.These equipments in the model constitute the most common and mature distributed energy technologies available in China,while the model can be easily extended to cover more technologies when they become commercially available.3.Mathematical formulationA mathematical model based on mixed-integer linear programming(MILP)optimization is illustrated in this section.We use the symbol e to represent all energyﬂows and its superscripts indicate its position in the superstructure while the subscripts represent the types of energyﬂows.With respect to superscripts, theﬁrst superscript,i.e.,g,c and s,stands for the section that this energyﬂow belongs to,while the second one,i.e.,in and out, represents whether it is an input or an output.The naming rules for the energyﬂows in the optimization model are shown in Fig.2.For example,a variable named with e c;inh;hctmeans the amount of energy carried by a heatﬂow coming into the energy conversion section and converted by a certain heat conversion technology.The nomenclature of all parameters,variables,superscripts and subscripts are presented in Appendix at the end of this paper.3.1.Energy generation sectionEnergy generation section comesﬁrst in Fig.1.In this section, various types of primary energy are converted to electricity and heat by different energy generation technologies.The primary energy consumptions are restrained by local resources availability at any point in time as follows:Table1Demonstration projects of CCHP systems in China and their conﬁgurations. No Location Main equipments1Pudong International Airport,ShanghaiGas turbine,Waste heat boiler 2Huangpu Central Hospital,Shanghai Gas turbine,Waste heat boiler 3Minhang Central Hospital,Shanghai Gas turbine,Waste heat boiler4Shanghai Institute of Technology, Shanghai Micro-turbine,Absorption Chiller, Waste heat boiler5Shuya Liangzi Ministry of Health,ShanghaiDiesel engine,Waste heat boiler6Zizhu Science-based Industrial Park,ShanghaiMicro-turbine,Absorption chiller 7Shanghai Jinqiao Sports Center,ShanghaiGas engine,Waste heat boiler8Shanghai Global Financial Hub,ShanghaiGas engine,Waste heat boiler9Beijing Gas Group MonitoringCenter,BeijingGas engine,Absorption Chiller 10Ciqumen Station Building,Beijing Micro-turbine,Absorption Chiller 11Zhongguancun Software Square,BeijingGas turbine,Absorption Chiller 12International Trade Center,Phase3,BeijingGas turbine,Waste heat boiler13International Business Center,BeijingGas turbine,Waste heat boiler14Olympic Energy Exhibit Center, Beijing Micro-turbine,Absorption chillerZ.Zhou et al./Applied Thermal Engineering53(2013)387e396388e g;in f;m;hr e maxf;m;hr(1)where,e g;inf refers to the primary energy consumption;e maxfis themaximum amount of energy available for energy use;f stands for different types of primary energy resources,including natural gas, diesel,wind energy,solar energy and biomass;m stands for different months in a year and hr stands for different hours in a day. The optimization in this study is conducted on an hourly basis for a typical day of each month,taking into account daily and seasonal ﬂuctuations in energy supplies and demands.We assume that each day in a month follows the same demand pattern of the repre-sentative day of this month.The demand pattern of a representa-tive day is averaged through that month.The time intervals are therefore reduced from8760to288(12month and24h a day).The daily grouping to months can signiﬁcantly reduce the size of the model,thereby improving its computational efﬁciency.Each type of primary energy can be consumed by any tech-nology that uses it as the fuel.The total consumption of each type of primary energy resource at any point in time is given bye g;inf;m;hr¼Xegte g;inegt;f;m;hr(2)where,the subscript egt stands for different types of energy generation technologies.The production of secondary energy carriers,i.e.,electricity and heat,by various energy generation technologies depends on the consumptions of primary energy resources,and the efﬁciency of the on-site generation technologies.It can be characterized as follows:e g;outsec;m;hr¼Xegte g;inegt;m;hr$h egt;sec(3) Fig.1.The superstructure representation of a distributed energysystem.Fig.2.The naming rules for the energyﬂows in the optimization model.Z.Zhou et al./Applied Thermal Engineering53(2013)387e396389where,sec stands for secondary energy carriers,which could be either electricity or heat;sec˛{p,h},p and h refer to electricity and heat;e g;outsecrefers to the production of secondary energy;h egt,sec refers to the efﬁciency of converting a primary energy resource into a secondary energy carrier.For the purpose of model simpliﬁcation,we presume that efﬁ-ciencies of all technologies do not change when these facilities are operated at off-design points.In other words,the energy efﬁcien-cies keep constant in this study.For technologies that produce electricity and heat simultaneously,there is aﬁxed relationship between the amount of recoverable heat and electricity based on the manufacturer’s technical speciﬁcations.Electricity can also be directly purchased from the power grid. Therefore,the total amount of electricity as input to the energy conversion section is the sum of electricity drawn from the grid and the electricity generated in the energy generation section.This can be denoted ase c;in p;m;hr ¼e g;outp;m;hrþe gpp;m;hr(4)The other type of energy carrier,heat,can only be generated by different energy generation technologies,as follows:e c;in h;m;hr ¼e g;outh;m;hr(5)where,e c;insecis the amount of secondary energy for conversion in the energy conversion section;e gp p denotes the amount of electricity drawn from the power grid.3.2.Energy conversion sectionIn the energy conversion section,secondary energy,namely electricity and heat,are converted by energy conversion technolo-gies to tertiary energy carriers,namely electricity,heat and cooling. Accordingly,the consumption of each type of secondary energy is equal to the sum of energy consumed by energy conversion tech-nologies which convert this type of secondary energy,as follows:e c;in sec;m;hr ¼Xecte c;inect;sec;m;hr(6)where,ect stands for different types of energy conversion technologies.Energy conversion technologies such as air-source heat pump/ chiller usually have two working modes:heating mode and cooling mode.The coefﬁcient of performance(COP)of an energy conver-sion technology differs when working in the heating mode and cooling mode.The productions of these tertiary energy carriers can be given bye c;out ter;m;hr ¼Xecte c;inect;sec;m;hr$j heating$h heatingect;sec;terþj cooling$h coolingect;sec;terð7Þwhere,ect stands for different types of energy conversion tech-nologies;ter stands for tertiary energy carriers,i.e.,electricity,heatand cooling;ter˛{p,h,c},c refers to cooling;e c;outter refers to theproduction of tertiary energy in the energy conversion section;h heating ect;sec;ter and h coolingect;sec;terare COPs of energy conversion technologiesoperating in heating mode and cooling mode;j heating and j cooling are binary variables.As an energy conversion technologies cannot produce heat and cooling simultaneously,j heating and j cooling are restrained by j heatþj cooling¼1(8) 3.3.Energy storage sectionFor a more efﬁcient use of energy,the energy conversion section is followed by an energy storage section.The energy inputs of the energy storage section are given bye s;inter;m;hr¼e c;outter;m;hr(9)where,e s;interdenotes the input of an energy storage equipment.Energy storage technologies in this section allow shifting energy generated during low usage periods for use during peak hours.Each end use energy product is equal to the product generated in the energy conversion section plus the net energy ﬂow from the energy storage unit,namely energy stored in the storage unit less released energy,at any point in time,which is denoted ase s;outter;m;hr¼e s;inter;m;hrþeÀter;m;hrÀeþter;m;hr(10)where,e s;outterrefers to the output of an energy storage device;eÀter refers to energy released from the energy storage device;eþter refers to energy stored into the energy storage device.The amount of energy stored at the beginning of each time interval is equal to the energy stored at the beginning of the previous time interval plus the net energyﬂow,given byE ter;m;hrþ1¼E ter;m;hrþeþter;m;hrÀeÀter;m;hr=h es;ter!0(11) Where,E ter represents the inventory of a storage device;h es,ter refers to the efﬁciency of an energy storage technology.Within a certain period of time,T,the total net energyﬂow from the energy storage units sums to zero as given byX ThrE ter;m;hr¼0(12)We set T as a day,which means the energy storage technolo-gies can only deal with short-term,dailyﬂuctuations.This assumption is reasonable,because energy storage technologies are costly and therefore their use tends to be limited to small-scale applications.Finally,each type of end use energy demand product must be satisﬁed by its corresponding tertiary energy carrier coming from the energy storage section,which is given bye s;outh;m;hr!D sh;m;hrþD hw;m;hre s;outp;m;hr!D p;m;hre s;outc;m;hr!D c;m;hr(13)where,D refers to end user’s demands;sh and hw represent space heating and hot water.3.4.Facility capacityThe capacities of all energy generation,storage and conversion technologies must be large enough to meet the maximum amounts of energy generation,conversion and storage over the whole period of time.The capacities of various energy generation and conversion technologies are given byCap tech!max e out tech(14)Z.Zhou et al./Applied Thermal Engineering53(2013)387e396 390where,tech refers to each technology appears in the energy generation section and energy conversion section,i.e., tech˛{egt,ect}.The outputs of different technologies are constrained within their lower and upper bounds of capacity via introduction of binary variables,which can ensure that these technologies are running in reasonable ranges,as follows:k tech;m;hr$Cap Mintech e out tech;m;hr k tech;m;hr$Cap Maxtech(15)where,k is binary variable;Cap Min and Cap Max are minimal and maximal capacities of different technologies.The capacity of energy storage technologies depends on the maximum inventory of a storage device,which can be denoted as: Cap es!max E ter;m;hr(16)3.5.Economic behaviorThe total annual cost is selected as the objective function.It consists of the capital cost,the operation&maintenance(O&M) cost and the fuel cost.The optimization model obtains the most suitable conﬁguration of DES by minimizing the total annual cost, as follows:Min C total¼C capitalþC O&MþC fuel(17) All the associated costs are given byC capital¼Xtech Inv tech$Cap tech$I1Àð1þIÞÀY tech(18)C O&M¼Xtech OM tech$XmXhre gtech;m;hr!þXesOM es$Cap es(19)C fuel¼Xf XmXhre g;inf;m;hr$P f;hrþe gp m;hr$P gp;hr(20)where,Inv tech refers to the unit investment cost;Y tech is the life-time;I denotes the discount rate,speciﬁed at10%in this study; OM tech and OM es represent the unit operation costs;P f,hr and P gp,hr refer to the hourly fuel cost and electricity price.3.6.Model linearizationThe optimization model(Eqs.(1)e(20))presented above is nonlinear due to the multiplication of a binary variable and a continuous variable in Eq.(7).As other parts of the problem are linear,it is desirable to convert the nonlinear terms into linear terms so that a linear model could be obtained.The following linearization techniques are employed:Let d be a binary variable,while x and y be continuous variables, thus the following equation is nonlinear:y¼d$x(21) However,this equation is identical to the following set of equations:xÀð1ÀdÞ$M y xþð1ÀdÞ$M(22) y d$M(23) where,M is a large number.By this means,the optimization model can be converted toa MILP problem.4.Case study4.1.Model inputsA case study has been conducted to illustrate the modeling and optimization framework for the design of DES.The targeted energy consumer is a hypothetic large hotel in Beijing with an area of 30,000m2.The required inputs for the optimization model are the demand information and resource availability of this region,as well as the price information and technological and economic information.4.1.1.Demand informationThe electricity,heating,cooling and hot water loads are obtained through comprehensive investigations about energy demands of hotels in Beijing[37,38].The average electricity,space heating, cooling and hot water loads are listed in Table2and the hourly demand data of this hotel in some representative days are shown in Fig.3.4.1.2.Resource availabilityIn the optimization model,the possible resources include natural gas,wind power,solar energy,biomass and geothermal energy.For a real project,the developer of a DES has to provide information about the availability of these resources as the inputs of the optimization model.For the hypothetic case in this study,we artiﬁcially determined the resource availability of different resources as follows:Natural gas and geothermal energy are unlimited resources in our analysis.Solar energy and wind energy are obtained through meteoro-logical data[39].Assuming that maximum areas for receiving solar energy and installing wind turbines are both6000m2. Assuming that biomass is rare in urban areas.Wind energy is highlyﬂuctuating and difﬁcult to predict.In this study,we adopt a probability distribution approach,complemented by the average wind power density data,to predict the volatility of wind energy.Currently,there are many types of probability distributions available for the simulation of wind energy,such as Weibull distribution,Rayleigh distribution,lognormal distribution and etc.[40].In this study,we adopt a three parameter generalized extreme value(GEV)distribution to predicate the hourly wind energy density data,as follows:fðx j k;m;sÞ¼1s1þkðxÀmÞsÀ1Àð1=kÞeÀ1þkðxÀmÞsÀ1=k(24)Where,k equals0.081;m equals0.654;s equals0.498[41].The average wind energy density in Beijing is about75W/m2[39].Table2The average energy loads of the hypothetic hotel in Beijing.Load,W/m2Electricity Heat Cooling Hot water Winter20.0938.33013.16 Transitional seasons22.7012.158.1 Summer27.73040.0110.35Z.Zhou et al./Applied Thermal Engineering53(2013)387e3963914.1.3.Price informationIn China,current prices of natural gas,diesel and grid power are determined by the National Development and Reform Commission (NDRC)[42],as shown in Fig.4.4.1.4.Technological and economic dataThe technological and economic data used in this study,including energy ef ﬁciency,unit investment cost,operation and maintenance cost,and lifetime of each technology,are listed in Tables 3e 5[4,12,43e 45].4.2.Setting of scenariosIn this study,we provide three different solutions to meet the energy demands in this area.Scenario 1is a conventional central-ized energy system;Scenario 2is a distributed CCHP system;Scenario 3is a DES proposed by the optimization model.The detailed information about these scenarios are described as follows:Scenario 1:Conventional centralized energy system.In this system,the electricity load is satis ﬁed by the power grid;the cooling load is met by electric chillers;the space heating and hot water loads are met by gas boilers.In the conventional system,the electricity is transmitted and distributed via power grid.Scenario 2:Distributed CCHP system.The demonstration projects in Table 1indicate that the existing projects in China are all distributed CCHP systems.This paper chooses one of the typical con ﬁgurations of CCHP systems as an representative example of these projects.In this system,the core facility is an internal combustion engine,which can produce electricity andheat simultaneously.The de ﬁciency of electricity and heat is supplemented by the power grid and gas boilers.The cooling demands are satis ﬁed by absorption chillers,utilizing waste heat recovered in power generation unit.This scenario can represent a classic CCHP system that the current policy is promoting.Scenario 3:DES proposed by the optimization model.In this scenario,the con ﬁguration of the energy supply system and its operating strategy are both determined by the optimization model.5.Results and discussionsIn this study,we conduct the optimization process for the optimal design of DES on the platform developed by GAMS Development Corporations [46].The proposed MIP problem (Scenario 3)involves 13,377continuous variables,6624binary variables and 28,906constraints.It takes 0.39s of CPU time to solve this problem on an Intel(R)Core(TM)2Duo P8600CPU of 2400MHz with a memory size of 2G.The results of the scenario analysis are presented next.5.1.System con ﬁguration of a complex DESThe optimal system con ﬁguration and the capacities of adopted technologies are shown in Fig.5.The optimization model chooses only several technologies,including internal combustion engines,solar heat collectors,wind turbines,various chillers/heat pumps,thermal storage tanks and ice storage equipments,which indicates that the purpose of including various technologies intheFig.3.The hourly energy loads of the hypothetic hotel in Beijing.Z.Zhou et al./Applied Thermal Engineering 53(2013)387e 396392optimization model is to improve the generality of the model rather than proposing a more complex system.As the annual cost is the objective,the model tends to select inexpensive technologies.For instance,among the natural gas-ﬁred energy generation technologies,only internal combustion engine is employed because it is much cheaper than gas turbine and fuel cell.Similarly,the model selects solar collector instead of solar PV to achieve an economic utilization of solar energy.In the energy conversion section,water-source,air-source and absorption chillers/heat pumps are adopted to enhance the production of heat and cooling.Energy storage technologies,such as thermal storage tank and ice storage equipment,are also included in the optimal con ﬁguration to achieve a more proper use of energy.5.2.EconomicsThe annual costs of these three scenarios are shown in Fig.6.DES reveals a much better economics than both the CCHP systemand the centralized energy system.The economic bene ﬁts of DES may stem from:The introduction of renewable energy,which can reduce the fuel costLower investment cost because the model can select inex-pensive technologies to achieve lower capital expenses than that of CCHP systemsSynergy from the integration of various distributed technologies5.3.Total energy consumption and GHG emissionsAs the potential energy system for this case has multiple inputs and outputs,it is hard to de ﬁne an indicator to evaluate its ef ﬁ-ciency.In this study,we use the same energy demands for all these scenarios and convert all kinds of primary energy consumptions to an equivalent amount of natural gas consumption as follows:TE ng equivalent ¼TE ng þTE gp =0:55(25)where,TE refers to annual total energy consumption;ng refers to natural gas;gp refers to grid power.The above equation converts the consumption of grid power to the equivalent natural gas consumption with a ratio of 0.55,as modern gas turbine plants with a triple-pressure heat recovery steam generator can reach ef ﬁciencies above 55%,which can represent the most ef ﬁcient way of power generation from natural gas [47].GHG emissions of these scenarios are also accounted by multi-plying the energy consumption with a reliable CO 2emission factor.The CO 2emission factor of natural gas is speci ﬁed at 0.19kg CO 2/kWh and the emission factor of the northeast power grid in 2009is 0.89kg CO 2/kWh [45].The annual energy consumptions,the equivalent natural gas consumptions and GHG emissions of three scenarios are shown in Fig.7.It can be found that grid power is an important energy source for DESs (Scenarios 2&3).Without the support of the power grid,the capacities of distributed energy technologies must be larger to meet the peak electricity load,which will incur a largerinvestmentFig.4.Current energy prices in Beijing.Table 3The technological and economic data of energy generation technologies.TechnologyInvestment,yuan/kW O&M cost,yuan/kWh Ef ﬁciency LifetimeElectricity Heat Internal combustion engine43570.0720.350.530Gas turbine79000.0680.250.5530Natural gas boiler 8510.00200.920Fuel cell37,2150.20.360.4510Diesel engine 43570.0720.350.520Wind turbine 60000.080.35020Solar PV 20,7000.010.12030Solar heat22500.0100.415Biomass boiler 13870.0800.8515Biogas engine 14,5840.080.280.415Biogas boiler28500.0030.7220Table 4The technological and economic data of energy conversion technologies.TechnologyInvestment,yuan/kW O&M cost,yuan/kWhCOPLifetime Heating mode Cooling mode Electric heater12001020Air-source chiller/heat pump12000.00973320Water-source chiller/heat pump20000.00974520Ground-source chiller/heat pump30000.0097 4.4520Absorption chiller/heat pump12280.0081 1.220Heat exchanger2050.00220.9820Table 5The technological and economic data of energy storage technologies.TechnologyInvestment,yuan/kW O&M cost,yuan/kWh capacity Ef ﬁciencyLifetimeBattery17828.30.7513.5Hot water storage 900.180.920Ice storage 1900.20.6520Z.Zhou et al./Applied Thermal Engineering 53(2013)387e 396393。

VIAVI T-BERD MTS 四通道 OTDR 模块说明书

VIAVI SolutionsData SheetVIAVIT-BERD/MTS Quad OTDR ModuleFor T-BERD®/MTS-2000, -4000 V2, -5800, CellAdvisor 5G and OneAdvisor-800 PlatformsThe VIAVI Quad OTDR module is the ideal test tool for installers/contractors, wireless service providers, or any user dealing with both single-mode and multimode applications every day. It is perfect for use in installing, turning up, and maintaining premises and enterprise, access, metro, and wireless fronthaul/backhaul networks.Key Featuresy Up to 37 dB dynamic range insingle-mode and 26 dB in multimode y Quad-wavelength version with850, 1300, 1310, and 1550 nm and a dual-wavelength version with 850 and 1300 nmy Integrated continuous wave (CW) light source and power meter y TIA/IEC pass/fail thresholds y Propagation delay measurement in multimode (TIA-568-C)y Optimized for 10 MB to 40 GE testing y Certifies Tier 2 premises networks** y IEC 61280-4-1-compliant using an external modal controllery Ready for SLM, FTTA-SLM, and FTTH-SLM intelligent optical application softwareThe VIAVI Quad OTDR module features fast acquisition time, sharp resolution, up to a 26 dB multimode dynamic range, and up to a 37 dB single-mode dynamic range for installing and maintaining fiber links. Its integrated light source and power meter, accessible through both OTDR ports (multimode and single-mode), let users quickly identify fiber without switching ports and conduct a full range of fiber certification tests.The Quad module’s optical performance combined with the T-BERD/MTS, CellAdvisor 5G and OneAdvisor-800 platform’s complete suite of features ensures that testing is done right—the first time.Standard test features include: y Automatic macrobend detectiony Summary results table with pass/fail analysis y Bidirectional OTDR analysisy FastReport on-board report generation*Compatible with models -5811P/L and -5822P.**For Tier 1 certification, see the VIAVI Certifier40GT-BERD/MTS-2000 one-slot handheld modular platform fortesting fiber networksT-BERD/MTS-4000 V2 Two-slot handheld modular platform fortesting fiber networksT-BERD/MTS-5800Handheld test instrument for testing 10 G Ethernet and fiber networksCellAdvisor 5GCell site test solutionOneAdvisor-800All-in-One Cell-site Installation and Maintenance Test SolutionSpecifications1. Using a mode conditioner2. Laser at 25°C3. The one-way difference between the extrapolated backscattering level at the start of thefiber and the RMS noise level after 3-minutes averaging4. Measured at ±1.5 dB down from the peak of an unsaturated reflective event5. Measured at ±0.5 dB from the linear regression using an F/UPC-type reflectanceOrdering Information Array Multimode and Quad OTDR Modules and OptionsMultimode 850, 1300 nm OTDR module E4123MMQuad 850/1300/1310/1550 nm OTDR module E4146QUADContinuous and Modulated Source option E41OTDRLSPower Meter option E41OTDRPMAccessoriesEF modal controller for 50 µm MM fiber−SC/PC EFJEF50CONSCPCEF modal controller for 50 µm MM fiber−FC/PC EFJEF50CONFCPCUniversal Optical ConnectorsStraight connectors (single-mode port)EUNIPCFC,EUNIPCSC,EUNIPCST,EUNIPCDIN,EUNIPCLC8° angled connectors (single-mode port)EUNIAPCFC,EUNIAPCSC,EUNIAPCDIN,EUNIAPCLCStraight connectors (multimode port)EUNIPCFCMM,EUNIPCSCMM,EUNIPCSTMM,EUNIPCDINMM,EUNIPCLCMMFor more information on T-BERD/MTS-2000, -4000 V2,-5800, CellAdvisor 5G and OneAdvisor-800 test platforms,please refer to their respective data sheets and brochures.Contact your VIAVI representative for additionalinformation regarding your specific needs.2 VIAVI T-BERD/MTS Quad OTDR Module© 2021 VIAVI Solutions Inc.Product specifications and descriptions in this document are subject to change without notice.Patented as described at /patentsquad-ds-fop-tm-ae 30168207 906 0721Contact Us+1 844 GO VIAVI (+1 844 468 4284)To reach the VIAVI office nearest you, visit /contactVIAVI Solutions。

Applied Energy

张或，等：重塑工业废弃地集体记忆景观设计研究同时，配合种植先锋植物来改良土壤，由低级到高级，自然的发生演替。

场地主要原生植物有芦苇、狗尾草等耐盐碱植物，通过样方观测的方法确定新增种苗比例，减少人工恢复植被种群的种间竞争。

在覆盖土上层种植天津市长势良好的园林绿化植物，如白腊，臭椿，榆树等乔木，金银木，女贞，紫叶小檗等花灌木。

种植过程中出现长势不好或腐烂的植物可回填土壤中改善生态。

在植被恢复的基础上，还应该促进生态系统中动物和微生物的正常生物循环，保证生物群落的稳定发展。

随着植物的生长，逐渐产生了适合鸟类、昆虫、爬行动物的生活空间，以及微生物的衍生场所，食物网随着植物物种的丰富而逐步完善。

生态系统中运输者，转化者，传播者的营养循环，使当地生态系统良性循环^农村对于农田中蟋蟀、刺猬、田鼠被封存的乡土记忆再次被唤醒，如图8所示。

图8 a . b .c .d 景观恢复过程（图片来源:作者自绘)4 结论自然体制的运行过程本身就是生态景观。

自然体制的运行当然也包括人的参与，这样才能形成一个更加完整的生态机制。

只有充分构建集体记忆、合理考虑人与自然之间的资源配置关系，才能达到真正意义上的人居环境饱和状态；只有人与自然和谐相处，才能真正地找到此片土地正在消失的乡土记忆。

当地居民构筑农家生态孕、廊，不仅是对传统休闲文化的新诠释，同时也是对传统空间可持续发展的表现，从而更好地复兴这片工业废弃地本身的历史人文场所精神。

[2] 杰罗姆•特鲁克，曲公英.对场所的记忆和记忆的场所:集体记忆的哈布瓦赫式社会——K 族，志研究[J ]. M 际社会科，余忐，2012,4(12) ：33 -46.[3] 阿尔多•罗挪国外城市规划与设计理论译丛^城I 丨础筑学[M ].北京:中国建筑工业出版社，2006:34 - 68.[4] 常必.历史环境的再生之道——历史意识与设计探索[M ].北京：中国迚筑丨:业出版社，2008 ,67.[5] 陈小杳，乍洪远.1业废弃物堆积场地小态恢复与景观营造[J ].现代國林，2013,10(11) :19 -26.作者简介：张或（1992 )，山西太原人，硕丨:研究生，研究方向；景观规划设计、城丨1】•附洪管理(494799280@ qq. com) _参考文献：[1]菇纬.灌于文化景观视财下的£业遗产整介[J ].艺术4设讣，2011:124 -125.指导教师：嘗磊（1962 )，大汴人，吨七，教授，研究方向：景观规划设计、以观生态技术。

Fuel_of_the_Future

42CHINA TODAYWHILE stepping up the development of new materials, innovative drugs, and other cutting-edge sectors, China will also focus on hydrogen in its quest forclean, safe, and efficient energy for industry and transportation, according to the 2024 Report on the Work of the Government. Hydrogen is a clean, safe, and efficient energy source that can power industries and transportation, which makes it an attractive fuel for the future. Consequently, devel-oping hydrogen power was one of the focal points of the discussions at the recent “Two Sessions.”Building a Hydrogen Ecosystem“Hydrogen leads the new round of global en-ergy transformation,” NPC deputy and President of Beijing Yihuatong Technology Co. (SinoHytec) Zhang Guoqiang told China Today in an exclusive interview. It is a key direction for strategic emerg-ing industries and crucial for promoting green and low-carbon energy transition, driving industrial upgrading, and shaping new quality productive forces, whose nature is green development, Zhang added.China’s first guideline on accelerating the green development of the manufacturing industry, released on March 1, 2024, focuses on the energy revolution and industrial transformation under the dual carbon goals – achieving carbon peaking by 2030 and carbon neutrality by 2060. The objective is to build a complete industrial chain of technolo-gies and equipment for hydrogen production, stor-age, transportation and utilization, and to improve the economic efficiency.By staff reporter ZHOU LINFuel of the FutureLawmakers call for stepping up hydrogenpower production to facilitate China’s clean and low-carbon energy transformation and create a new engine of growth.Special Report43April 2024hydrogen energy, and about 20 percent to transportation.Currently, there are over 1,000hydrogen refueling stations worldwide.SinoHytec is a national hi-tech enterprise focused on research and industrializationof hydrogen and fuel cells. Zhang said its two majorbusinesses are hydrogen and fuel cells for land transporta-tion. SinoHytec products havepowered over 4,900 vehicles whose cumulative operating mileage is over 200 million kilometers. The company has been holding the largest market share in China for seven consecutive years. Hebei Province has become a significant player in the industry. Last year it issued an action planfor accelerating the development of new energy as well as seven special plans to promote green andlow-carbon transformation of energy, with the goal of forming an integrated system for wind, solar,water, nuclear, and hydrogen energy production and storage. By 2023, a technology research and devel-opment, inspection and testing system covering the entire hydrogen energy industry chain had been formed. Core technologies such as purification ofindustrial by-product hydrogen are being promoted in fields such as transportation, steel and chemical industries, and communication base stations.Zhang said SinoHytec has created a hydrogen ecosystem in Zhangjiakou in Hebei, building the first plant in China that produces hydrogen from renewable sources and Zhangjiakou’s first hydro-gen refueling station. During the Olympic Win-ter Games Beijing 2022, over 700 fuel cell vehiclesequipped with SinoHytec’s products provided transportation for athletes as well as visitors. Zhang said Zhangjiakou already has 35 hydrogen enterprises, which have formed a preliminary hydrogen energy industrial chain.Energizing the Path to Net ZeroThe green hydrogen chemical industry is an-other important area where hydrogen energy can be used.Major countries across the globe have all rolled out national policies for hydrogen energy, with increased investment to boost advanced technol-ogy research and industrialization. According to Hydrogen Insights 2023, released by the Belgium-headquartered Hydrogen Council and McKinsey & Company, the global hydrogen economy is growing rapidly with accelerated development momentum. By 2030, direct investment in hydrogen energy is expected to reach US $320 billion. About half of the projects funded by these investments will be related to large-scale industrial employment of The global hydrogeneconomy is growing rapidly with accelerated development momentum.By 2030, direct invest-ment in hydrogen energy is expected to reachUS $320 billion.ZhangGuoqiang, an NPC deputy and president of Beijing Yi-huatong Tech-nology Co. (SinoHytec).A fuel cell vehicleequipped with Sino-Hytec’s productsprovides transpor-tation for athletesduring the OlympicWinter Games Bei-jing 2022.44CHINA TODAYXu Zixia, an NPC deputy and chief engineer of China National Chemical Engineering No. 13 Con-struction Co., said the chemical industry is both high-energy-consuming and emission-intensive. It is one of the eight key industries covered by the national carbon market. “The chemical industry needs urgent technological and industrial upgrad-ing to accelerate transformation and reach the goal of carbon reduction,” Xu said.In 2022, China’s chemical industry produced over 1.5 billion tonnes of carbon emissions, ac-counting for nearly 15 percent of the country’s total amount, she said. However, the chemical industry is a pillar of the national economy, with a large economic output, a long industrial chain and multiple products. It is key to the security and stability of the industrial and supply chains and people’s well-being. Therefore green and low-carbon development of the industry is of critical importance.“Hydrogen energy is an important part of production in the chemical industry. Using green hydrogen generated via water electrolysis for production can achieve carbon reduction at the source, during the process, and at the end of the production,” Xu pointed out. It is estimated that 10,000 tonnes of green hydrogen can reduce carbon dioxide emissions by more than 100,000 tonnes.“Green hydrogen” means conducting electrolysis by using renewable sources such as wind, hydro, solar, and nuclear power to produce hydrogen, with no carbon emissions produced during the process. Hebei, for example, has a large amount of renewable energy resources and industrial by-product hydrogen resources, which can be tapped to develop hydrogen energy. Zhangjiakou, Chengde, and regions along the Taihang Moun-tains in Hebei have abundant wind and solar pow-er, which in turn means abundant green hydrogen resources.In April 2022, a guideline was issued for high-quality development of the petrochemical and chemical industry, urging enterprises in the two sectors to develop and utilize green hydrogen in accordance with local conditions, and promote the coupling of refining and coal chemical industries with green electricity and green hydrogen.Key enterprises and projects have started using hydrogen energy. In June 2023, the first10,000-tonne hydrogen production project in China – the Narisong Photovoltaic Hydrogen Produc-tion Industry Demonstration Project in Ordos City, Inner Mongolia – began operation. Two months later, China’s largest photovoltaic green hydrogen project – the Xinjiang Kuche Green Hydrogen Demonstration Project – was launched. Xu called them a breakthrough in the industrial applicationof green hydrogen in China.Guo Jianzeng, an NPC deputy and director of the Science and Technology Committee of the 718th Research Institute of China Shipbuilding Industry Corporation.Special Report“The chemical industry needs urgent technolo-gical and indus-trial upgrading to accelerate transformation and reach the goal of carbon reduction.”UPGRADING45April 2024Green Hydrogen as a National StrategyGuo Jianzeng, an NPC deputy and director of the Science and Technology Committee of the 718th Research Institute of China Shipbuilding In-dustry Corporation, said green hydrogen produced from the electrolysis of water is a major solution to tackle global decarbonization. “It is vital to master the key technologies in this field,” Guo said.Water electrolysis uses renewable energy such as offshore wind energy and tidal energy for the mass production of green hydrogen. Advanced technologies are required for high-density stor-age and transportation of high-pressure hydrogen, liquid hydrogen, and metallic hydrogen. Besides enabling large-scale production, storage, and transportation, they also help lower costs. The hydrogen produced can be used in many scenarios – as fuel for ships, in the chemical industry, trans-portation, and construction.Guo pointed out the multiple advantages of having a marine hydrogen industry. It is an effec-tive way of meeting the carbon peaking and car-bon neutrality goals; it boosts the consumption ofoffshore renewable energy; it meets international green shipping requirements; and finally, it is vital to have independent and controllable technology and equipment throughout the hydrogen energy industrial chain.“Oceans are a source of abundant renewable en-ergy, which can be tapped through offshore wind, floating solar photovoltaic, and other emerging ocean energy technologies. Hydrogen obtained from these renewable energy sources is sustain-able and clean, which makes it a key component in the global energy transition,” Guo said. Hydro-gen can also be used for large-scale energy storage and peak shaving, which effectively addresses the challenges of long-term and large-scale storage of electricity, increases the flexibility of the power system, and promotes high-proportion consump-tion of renewable energy.Moreover, hydrogen is an effective intermedi-ate agent for producing green and clean fuels for ships that meet the decarbonization standards. Various forms of hydrogen energy consumption can be created, such as adding carbon, oxygen, or nitrogen into hydrogen to make ammonia and methanol, which can be an alternative fuel for the shipping industry to achieve large-scale decarbonization.“Hydrogen energy covers numerous products in preparation, storage and transportation and appli-cation, and is an important component of China’s strategic emerging industry chain. It is also a key field in China’s high-end equipment manufacturing industry,” Guo said. The electrolysis water hydro-gen production technology of the 718th Research Institute is a premier technology in China. By de-veloping hydrogen energy and promoting the local-ization of its equipment, China can both enhance its international competitiveness and ensure the security and stability of the entire hydrogen energy industry chain.After discussing all the pros and cons, the NPC deputies were unanimous that the development of hydrogen power should be stepped up so that it becomes a new engine of economic growth. It would facilitate China’s clean and low-carbon en-ergy transition and contribute to the development of new quality productive forces. CThe hydrogen stor-age tank installation area of Sinopec Xin-jiang Kuqa Green Hydrogen Pilot Project in Aksu, Xinjiang, China, on October 28, 2023.。

SCI写作句型汇总

S C I论文写作中一些常用的句型总结(一)很多文献已经讨论过了一、在Introduction里面经常会使用到的一个句子：很多文献已经讨论过了。

它的可能的说法有很多很多，这里列举几种我很久以前搜集的：A.??Solar energy conversion by photoelectrochemical cells?has been intensively investigated.?(Nature 1991, 353, 737 - 740?)B.?This was demonstrated in a number of studies that?showed that composite plasmonic-metal/semiconductor photocatalysts achieved significantly higher rates in various photocatalytic reactions compared with their pure semiconductor counterparts.C.?Several excellent reviews describing?these applications are available, and we do not discuss these topicsD.?Much work so far has focused on?wide band gap semiconductors for water splitting for the sake of chemical stability.（DOI:10.1038/NMAT3151）E.?Recent developments of?Lewis acids and water-soluble organometalliccatalysts?have attracted much attention.（Chem. Rev. 2002, 102, 3641?3666）F.?An interesting approach?in the use of zeolite as a water-tolerant solid acid?was described by?Ogawa et al（Chem.Rev. 2002, 102, 3641?3666）G.?Considerable research efforts have been devoted to?the direct transition metal-catalyzed conversion of aryl halides toaryl nitriles. (J. Org. Chem. 2000, 65, 7984-7989) H.?There are many excellent reviews in the literature dealing with the basic concepts of?the photocatalytic processand the reader is referred in particular to those by Hoffmann and coworkers,Mills and coworkers, and Kamat.(Metal oxide catalysis,19,P755)I. Nishimiya and Tsutsumi?have reported on（proposed）the influence of the Si/Al ratio of various zeolites on the acid strength, which were estimated by calorimetry using ammonia. (Chem.Rev. 2002, 102, 3641?3666)二、在results and discussion中经常会用到的：如图所示A. GIXRD patterns in?Figure 1A show?the bulk structural information on as-deposited films.?B.?As shown in Figure 7B,?the steady-state current density decreases after cycling between 0.35 and 0.7 V, which is probably due to the dissolution of FeOx.?C.?As can be seen from?parts a and b of Figure 7, the reaction cycles start with the thermodynamically most favorable VOx structures（J. Phys. Chem. C 2014, 118, 24950?24958）这与XX能够相互印证：A.?This is supported by?the appearance in the Ni-doped compounds of an ultraviolet–visible absorption band at 420–520nm (see Fig. 3 inset), corresponding to an energy range of about 2.9 to 2.3 eV.B. ?This?is consistent with the observation from?SEM–EDS. (Z.Zou et al. / Chemical Physics Letters 332 (2000) 271–277)C.?This indicates a good agreement between?the observed and calculated intensities in monoclinic with space groupP2/c when the O atoms are included in the model.D. The results?are in good consistent with?the observed photocatalytic activity...E. Identical conclusions were obtained in studies?where the SPR intensity and wavelength were modulated by manipulating the composition, shape,or size of plasmonic nanostructures.?F.??It was also found that areas of persistent divergent surfaceflow?coincide?with?regions where convection appears to be consistently suppressed even when SSTs are above 27.5°C.(二)1. 值得注意的是...A.?It must also be mentioned that?the recycling of aqueous organic solvent is less desirable than that of pure organic liquid.B.?Another interesting finding is that?zeolites with 10-membered ring pores showed high selectivities (>99%) to cyclohexanol, whereas those with 12-membered ring pores, such as mordenite, produced large amounts of dicyclohexyl ether. (Chem. Rev. 2002, 102,3641?3666)C.?It should be pointed out that?the nanometer-scale distribution of electrocatalyst centers on the electrode surface is also a predominant factor for high ORR electrocatalytic activity.D.?Notably,?the Ru II and Rh I complexes possessing the same BINAP chirality form antipodal amino acids as the predominant products.?(Angew. Chem. Int. Ed., 2002, 41: 2008–2022)E. Given the multitude of various transformations published,?it is noteworthy that?only very few distinct?activation?methods have been identified.?(Chem. Soc. Rev., 2009,?38, 2178-2189)F.?It is important to highlight that?these two directing effects will lead to different enantiomers of the products even if both the “H-bond-catalyst” and the?catalyst?acting by steric shielding have the same absolute stereochemistry. (Chem. Soc. Rev.,?2009,?38, 2178-2189)G.?It is worthwhile mentioning that?these PPNDs can be very stable for several months without the observations of any floating or precipitated dots, which is attributed to the electrostatic repulsions between the positively charge PPNDs resulting in electrosteric stabilization.(Adv. Mater., 2012, 24: 2037–2041)2.?...仍然是个挑战A.?There is thereby an urgent need but it is still a significant challenge to?rationally design and delicately tail or the electroactive MTMOs for advanced LIBs, ECs, MOBs, and FCs.?（Angew. Chem. Int. Ed.2 014, 53, 1488 – 1504）B.?However, systems that are?sufficiently stable and efficient for practical use?have not yet been realized.C.??It?remains?challenging?to?develop highly active HER catalysts based on materials that are more abundant at lower costs. (J. Am. Chem.Soc.,?2011,?133, ?7296–7299)D.?One of the?great?challenges?in the twenty-first century?is?unquestionably energy storage. (Nature Materials?2005, 4, 366 - 377?)众所周知A.?It is well established (accepted) / It is known to all / It is commonlyknown?that?many characteristics of functional materials, such as composition, crystalline phase, structural and morphological features, and the sur-/interface properties between the electrode and electrolyte, would greatly influence the performance of these unique MTMOs in electrochemical energy storage/conversion applications.（Angew. Chem. Int. Ed.2014,53, 1488 – 1504）B.?It is generally accepted (believed) that?for a-Fe2O3-based sensors the change in resistance is mainly caused by the adsorption and desorption of gases on the surface of the sensor structure. (Adv. Mater. 2005, 17, 582)C.?As we all know,?soybean abounds with carbon,?nitrogen?and oxygen elements owing to the existence of sugar,?proteins?and?lipids. (Chem. Commun., 2012,?48, 9367-9369)D.?There is no denying that?their presence may mediate spin moments to align parallel without acting alone to show d0-FM. (Nanoscale, 2013,?5, 3918-3930)(三)1. 正如下文将提到的...A.?As will be described below（也可以是As we shall see below）,?as the Si/Al ratio increases, the surface of the zeolite becomes more hydrophobic and possesses stronger affinity for ethyl acetate and the number of acid sites decreases.（Chem. Rev. 2002, 102, 3641?3666）B. This behavior is to be expected and?will?be?further?discussed?below. (J. Am. Chem. Soc.,?1955,?77, 3701–3707)C.?There are also some small deviations with respect to the flow direction,?whichwe?will?discuss?below.（Science, 2001, 291, 630-633）D.?Below,?we?will?see?what this implies.E.?Complete details of this case?will?be provided at a?later?time.E.?很多论文中，也经常直接用see below来表示，比如：The observation of nanocluster spheres at the ends of the nanowires is suggestive of a VLS growth process (see?below). (Science, 1998, ?279, 208-211)2. 这与XX能够相互印证...A.?This is supported by?the appearance in the Ni-doped compounds of an ultraviolet–visible absorption band at 420–520 nm (see Fig. 3 inset), corresponding to an energy range of about 2.9 to 2.3 eVB.This is consistent with the observation from?SEM–EDS. (Chem. Phys. Lett. 2000, 332, 271–277)C.?Identical conclusions were obtained?in studies where the SPR intensity and wavelength were modulated by manipulating the composition, shape, or size of plasmonic nanostructures.?（Nat. Mater. 2011, DOI: 10.1038/NMAT3151）D. In addition, the shape of the titration curve versus the PPi/1 ratio,?coinciding withthat?obtained by fluorescent titration studies, suggested that both 2:1 and 1:1 host-to-guest complexes are formed. (J. Am. Chem. Soc. 1999, 121, 9463-9464)E.?This unusual luminescence behavior is?in accord with?a recent theoretical prediction; MoS2, an indirect bandgap material in its bulk form, becomes a direct bandgapsemiconductor when thinned to a monolayer.?(Nano Lett.,?2010,?10, 1271–1275)3.?我们的研究可能在哪些方面得到应用A.?Our ?ndings suggest that?the use of solar energy for photocatalytic watersplitting?might provide a viable source for?‘clean’ hydrogen fuel, once the catalyticef?ciency of the semiconductor system has been improved by increasing its surface area and suitable modi?cations of the surface sites.B. Along with this green and cost-effective protocol of synthesis,?we expect that?these novel carbon nanodots?have potential applications in?bioimaging andelectrocatalysis.(Chem. Commun., 2012,?48, 9367-9369)C.?This system could potentially be applied as?the gain medium of solid-state organic-based lasers or as a component of high value photovoltaic (PV) materials, where destructive high energy UV radiation would be converted to useful low energy NIR radiation. (Chem. Soc. Rev., 2013,?42, 29-43)D.?Since the use of?graphene?may enhance the photocatalytic properties of TiO2?under UV and visible-light irradiation,?graphene–TiO2?composites?may potentially be usedto?enhance the bactericidal activity.?(Chem. Soc. Rev., 2012,?41, 782-796)E.??It is the first report that CQDs are both amino-functionalized and highly fluorescent,?which suggests their promising applications in?chemical sensing.(Carbon, 2012,?50,?2810–2815)(四)1. 什么东西还尚未发现/系统研究A. However,systems that are sufficiently stable and efficient for practical use?have not yet been realized.B. Nevertheless,for conventional nanostructured MTMOs as mentioned above,?some problematic disadvantages cannot be overlooked.（Angew. Chem. Int. Ed.2014,53, 1488 – 1504）C.?There are relatively few studies devoted to?determination of cmc values for block copolymer micelles. (Macromolecules 1991, 24, 1033-1040)D. This might be the reason why, despite of the great influence of the preparation on the catalytic activity of gold catalysts,?no systematic study concerning?the synthesis conditions?has been published yet.?(Applied Catalysis A: General2002, 226, ?1–13)E.?These possibilities remain to be?explored.F.??Further effort is required to?understand and better control the parameters dominating the particle surface passivation and resulting properties for carbon dots of brighter photoluminescence. (J. Am. Chem. Soc.,?2006,?128?, 7756–7757)2.?由于/因为...A.?Liquid ammonia?is particularly attractive as?an alternative to water?due to?its stability in the presence of strong reducing agents such as alkali metals that are used to access lower oxidation states.B.?The unique nature of?the cyanide ligand?results from?its ability to act both as a σdonor and a π acceptor combined with its negativecharge and ambidentate nature.C.?Qdots are also excellent probes for two-photon confocalmicroscopy?because?they are characterized by a very large absorption cross section?(Science ?2005,?307, 538-544).D.?As a result of?the reductive strategy we used and of the strong bonding between the surface and the aryl groups, low residual currents (similar to those observed at a bare electrode) were obtained over a large window of potentials, the same as for the unmodified parent GC electrode. (J. Am. Chem. Soc. 1992, 114, 5883-5884)E.?The small Tafel slope of the defect-rich MoS2 ultrathin nanosheets is advantageous for practical?applications,?since?it will lead to a faster increment of HER rate with increasing overpotential.(Adv. Mater., 2013, 25: 5807–5813)F. Fluorescent carbon-based materials have drawn increasing attention in recent years?owing to?exceptional advantages such as high optical absorptivity, chemical stability, biocompatibility, and low toxicity.(Angew. Chem. Int. Ed., 2013, 52: 3953–3957)G.??On the basis of?measurements of the heat of immersion of water on zeolites, Tsutsumi etal. claimed that the surface consists of siloxane bondings and is hydrophobicin the region of low Al content. (Chem. Rev. 2002, 102, 3641?3666)H.?Nanoparticle spatial distributions might have a large significance for catalyst stability,?given that?metal particle growth is a relevant deactivation mechanism for commercial catalysts.?3. ...很重要A.?The inhibition of additional nucleation during growth, in other words, the complete separation?of nucleation and growth,?is?critical(essential, important)?for?the successful synthesis of monodisperse nanocrystals. (Nature Materials?3, 891 - 895 (2004))B.??In the current study,?Cys,?homocysteine?(Hcy) and?glutathione?(GSH) were chosen as model?thiol?compounds since they?play important (significant, vital, critical) roles?in many biological processes and monitoring of these?thiol?compounds?is of great importance for?diagnosis of diseases.(Chem. Commun., 2012,?48, 1147-1149)C.?This is because according to nucleation theory,?what really matters?in addition to the change in temperature ΔT?(or supersaturation) is the cooling rate.(Chem. Soc. Rev., 2014,?43, 2013-2026)(五)1. 相反/不同于A.?On the contrary,?mononuclear complexes, called single-ion magnets (SIM), have shown hysteresis loops of butterfly/phonon bottleneck type, with negligiblecoercivity, and therefore with much shorter relaxation times of magnetization. (Angew. Chem. Int. Ed., 2014, 53: 4413–4417)B.?In contrast,?the Dy compound has significantly larger value of the transversal magnetic moment already in the ground state (ca. 10?1?μB), therefore allowing a fast QTM. （Angew. Chem. Int. Ed., 2014, 53: 4413–4417）C.?In contrast to?the structural similarity of these complexes, their magnetic behavior exhibits strong divergence.?(Angew. Chem. Int. Ed., 2014, 53: 4413–4417)D.?Contrary to?other conducting polymer semiconductors, carbon nitride ischemically and thermally stable and does not rely on complicated device manufacturing. (Nature materials, 2009, 8(1): 76-80.)E.?Unlike?the spherical particles they are derived from that Rayleigh light-scatter in the blue, these nanoprisms exhibit scattering in the red, which could be useful in developing multicolor diagnostic labels on the basis not only of nanoparticle composition and size but also of shape. (Science 2001,? 294, 1901-1903)2. 发现，阐明，报道，证实可供选择的词包括：verify, confirm, elucidate, identify, define, characterize, clarify, establish, ascertain, explain, observe, illuminate, illustrate，demonstrate, show, indicate, exhibit, presented, reveal, display, manifest，suggest, propose, estimate, prove, imply, disclose，report, describe，facilitate the identification of?举例：A. These stacks appear as nanorods in the two-dimensional TEM images, but tilting experiments?confirm that they are nanoprisms.?(Science 2001,? 294, 1901-1903)B. Note that TEM?shows?that about 20% of the nanoprisms are truncated.?(Science 2001,? 294, 1901-1903)C. Therefore, these calculations not only allow us to?identify?the important features in the spectrum of the nanoprisms but also the subtle relation between particle shape and the frequency of the bands that make up their spectra.?(Science 2001,? 294, 1901-1903)D. We?observed?a decrease in intensity of the characteristic surface plasmon band in the ultraviolet-visible (UV-Vis) spectroscopy for the spherical particles at λmax?= 400 nm with a concomitant growth of three new bands of λmax?= 335 (weak), 470 (medium), and 670 nm (strong), respectively. (Science 2001,? 294, 1901-1903)E. In this article, we present data?demonstrating?that opiate and nonopiate analgesia systems can be selectively activated by different environmental manipulationsand?describe?the neural circuitry involved. (Science 1982, 216, 1185-1192)F. This?suggests?that the cobalt in CoP has a partial positive charge (δ+), while the phosphorus has a partial negative charge (δ?),?implying?a transfer of electron density from Co to P.?(Angew. Chem., 2014, 126: 6828–6832)3. 如何指出当前研究的不足A. Although these inorganic substructures can exhibit a high density of functional groups, such as bridging OH groups, and the substructures contribute significantly to the adsorption properties of the material,surprisingly little attention has been devoted to?the post-synthetic functionalization of the inorganic units within MOFs. (Chem. Eur. J., 2013, 19: 5533–5536.)B.?Little is known,?however, about the microstructure of this material. (Nature Materials 2013,12, 554–561)C.?So far, very little information is available, and only in?the absorber film, not in the whole operational devices. （Nano Lett.,?2014,?14?(2), pp 888–893）D.?In fact it should be noted that very little optimisation work has been carried out on?these devices. (Chem. Commun., 2013,?49, 7893-7895)E. By far the most architectures have been prepared using a solution processed perovskite material,?yet a few examples have been reported that?have used an evaporated perovskite layer. (Adv. Mater., 2014, 27: 1837–1841.)F. Water balance issues have been effectively addressed in PEMFC technology through a large body of work encompassing imaging, detailed water content and water balance measurements, materials optimization and modeling,?but very few of these activities have been undertaken for?anion exchange membrane fuel cells,? primarily due to limited materials availability and device lifetime. (J. Polym. Sci. Part B: Polym. Phys., 2013, 51: 1727–1735)G. However,?none of these studies?tested for Th17 memory, a recently identified T cell that specializes in controlling extracellular bacterial infections at mucosal surfaces. (PNAS, 2013,?111, 787–792)H. However,?uncertainty still remains as to?the mechanism by which Li salt addition results in an extension of the cathodic reduction limit. (Energy Environ. Sci., 2014,?7, 232-250)I.?There have been a number of high profile cases where failure to?identify the most stable crystal form of a drug has led to severe formulation problems in manufacture. (Chem. Soc. Rev., 2014,?43, 2080-2088)J. However,?these measurements systematically underestimate?the amount of ordered material. ( Nature Materials 2013, 12, 1038–1044)(六)1.?取决于a.?This is an important distinction, as the overall activity of a catalyst will?depend on?the material properties, synthesis method, and other possible species that can be formed during activation.?(Nat. Mater.?2017,16,225–229)b.?This quantitative partitioning?was determined by?growing crystals of the 1:1 host–guest complex between?ExBox4+?and corannulene. (Nat. Chem.?2014,?6177–178)c.?They suggested that the Au particle size may?be the decisive factor for?achieving highly active Au catalysts.(Acc. Chem. Res.,?2014,?47, 740–749)d.?Low-valent late transition-metal catalysis has?become indispensable to?chemical synthesis, but homogeneous high-valent transition-metal catalysis is underdeveloped, mainly owing to the reactivity of high-valent transition-metal complexes and the challenges associated with synthesizing them.?(Nature2015,?517,449–454)e.?The polar effect?is a remarkable property that enables?considerably endergonic C–H abstractions?that would not be possible otherwise.?(Nature?2015, 525, 87–90)f.?Advances in heterogeneous catalysis?must rely on?the rational design of new catalysts. (Nat. Nanotechnol.?2017, 12, 100–101)g.?Likely, the origin of the chemoselectivity may?be also closely related to?the H?bonding with the N or O?atom of the nitroso moiety, a similar H-bonding effect is known in enamine-based nitroso chemistry. (Angew. Chem. Int. Ed.?2014, 53: 4149–4153)2.?有很大潜力a.?The quest for new methodologies to assemble complex organic molecules?continues to be a great impetus to?research efforts to discover or to optimize new catalytic transformations. (Nat. Chem.?2015,?7, 477–482)b.?Nanosized faujasite (FAU) crystals?have great potential as?catalysts or adsorbents to more efficiently process present and forthcoming synthetic and renewablefeedstocks in oil refining, petrochemistry and fine chemistry. (Nat. Mater.?2015, 14, 447–451)c.?For this purpose, vibrational spectroscopy?has proved promising?and very useful.?(Acc Chem Res. 2015, 48, 407–413.)d.?While a detailed mechanism remains to be elucidated and?there is room for improvement?in the yields and selectivities, it should be remarked that chirality transfer upon trifluoromethylation of enantioenriched allylsilanes was shown. (Top Catal.?2014,?57: 967.?)e.?The future looks bright for?the use of PGMs as catalysts, both on laboratory and industrial scales, because the preparation of most kinds of single-atom metal catalyst is likely to be straightforward, and because characterization of such catalysts has become easier with the advent of techniques that readily discriminate single atoms from small clusters and nanoparticles. (Nature?2015, 525, 325–326)f.?The unique mesostructure of the 3D-dendritic MSNSs with mesopore channels of short length and large diameter?is supposed to be the key role in?immobilization of active and robust heterogeneous catalysts, and?it would have more hopeful prospects in?catalytic applications. (ACS Appl. Mater. Interfaces,?2015,?7, 17450–17459)g.?Visible-light photoredox catalysis?offers exciting opportunities to?achieve challenging carbon–carbon bond formations under mild and ecologically benign conditions. (Acc. Chem. Res.,?2016, 49, 1990–1996)3. 因此同义词：Therefore, thus, consequently, hence, accordingly, so, as a result这一条比较简单，这里主要讲一下这些词的副词词性和灵活运用。

Cisco Aironet 1130AG Series产品说明说明书

Data SheetCisco Aironet 1130AG Series IEEE 802.11A/B/GAccess PointLow-profile enterprise-class access point with integrated antennas for easy deployment in offices and similar RF environments.Product OverviewCisco® Aironet® 1130AG Series IEEE 802.11a/b/g access points provide high-capacity, high-security, enterprise-class features in an unobtrusive, office-class design, delivering WLAN access with the lowest total cost of ownership. With high-performing dual IEEE 802.11a and 802.11g radios, the Cisco Aironet 1130AG Series provides a combined capacity of up to 108 Mbps to meet the needs of growing WLANs. Hardware-assisted Advanced Encryption Standard (AES) or temporal key integrity protocol (TKIP) encryption provides uncompromised support for interoperable IEEE 802.11i, Wi-Fi Protected Access 2 (WPA2) or WPA security. The Cisco Aironet 1130AG Series uses radio and network management features for simplified deployment, along with built-in omnidirectional antennas that provide robust and predictable WLAN coverage for offices and similar RF environments. The competitively priced Cisco Aironet 1130AG Series is ready to install and easy to manage, reducing the cost of deployment and ongoing maintenance.The Cisco Aironet 1130AG Series is available in two versions: unified or autonomous. Unified access points operate with the Lightweight Access Point Protocol (LWAPP) and work in conjunction with Cisco wireless LAN controllers and the Cisco Wireless Control System (WCS). When configured with LWAPP, the Cisco Aironet 1130AG Series can automatically detect the best-available Cisco wireless LAN controller and download appropriate policies and configuration information with no manual intervention. Autonomous access points are based on Cisco IOS® Software and may optionally operate with the CiscoWorks Wireless LAN Solution Engine (WLSE). Autonomous access points, along with the CiscoWorks WLSE, deliver a core set of features and may be field-upgraded to take advantage of the full benefits of the Cisco Unified Wireless Network as requirements evolve.The Cisco Aironet 1130AG Series delivers optimal value for offices and similar environments. Built-in antennas provide omnidirectional coverage specifically designed for today’s open workspaces. A multipurpose mounting bracket easily secures Cisco Aironet 1130AG Series access points to ceilings and walls. With an unobtrusive design, Cisco Aironet 1130AG Series access points are aesthetically pleasing and blend into their environments. Formaximum concealment, the access point may be placed above ceilings or suspended ceilings. The UL 2043 rating of the Cisco Aironet 1130AG Series allows the access point to be placed above ceilings in plenum areas regulated by municipal fire codes. Offered at a competitive price, and optimized for easy installation and operation, the Cisco Aironet 1130AG Series helps organizations attain a lower total cost of ownership.ApplicationsIn offices and similarly open environments, Cisco Aironet 1130AG Series access points may be installed on the ceiling to provide users with continuous coverage as they roam throughout a facility. In school buildings and similar facilities, the access points may be installed on the ceiling of each room and hallway to provide users with full coverage and high network availability. In areas where a ceiling installation may not be practical such as retail hotspots or similar small facilities, the access points can be mounted simply and securely on walls for complete coverage with minimal installation cost.Award-Winning SecurityThe Cisco Aironet 1130AG Series has achieved National Institute of Standards and Technology (NIST) FIPS 140-2 level 2 validation and is in process for Information Assurance validation under the National Information Assurance Partnership (NIAP) Common Criteria program. The Cisco Aironet 1130AG Series supports 802.11i, Wi-Fi Protected Access (WPA), WPA2, and numerous Extensible Authentication Protocol (EAP) types. WPA and WPA2 are the Wi-Fi Alliance certifications for interoperable, standards-based WLAN security. These certifications support IEEE 802.1X for user-based authentication, Temporal Key Integrity Protocol (TKIP) for WPA encryption, and Advanced Encryption Standard (AES) for WPA2 encryption. These certifications help to ensure interoperability between Wi-Fi-certified WLAN devices from different manufacturers.The Cisco Aironet 1130AG Series hardware-accelerated AES encryption supports enterprise-class, government-grade secure encryption over the WLAN without compromising performance. IEEE 802.1X authentication helps to ensure that only authorized users are allowed on the network. Backward compatibility and support for WPA client devices running TKIP, the RC4 encryption algorithm, is also supported by the Cisco Aironet 1130AG Series.Cisco Aironet 1130AG Series Access Points operating with LWAPP support Cisco Unified Intrusion Detection System/Intrusion Prevention System (IDS/IPS), a software feature that is part of the Cisco Self-Defending Network and is the industry’s first integrated wired and wireless security solution. Cisco Unified IDS/IPS takes a comprehensive approach to security—at the wireless edge, wired edge, WAN edge, and through the data center. When an associated client sends malicious traffic through the Cisco Unified Wireless Network, a Cisco wired IDS device detects the attack and sends shun requests to Cisco wireless LAN controllers, which will then disassociate the client device.Autonomous or unified Cisco Aironet 1130AG Series Access Points support management frame protection for the authentication of 802.11 management frames by the wireless network infrastructure. This allows the network to detect spoofed frames from access points or malicious users impersonating infrastructure access points. If an access point detects a malicious attack, an incident will be generated by the access point and reports will be gathered on the Cisco wireless LAN controller, Cisco WCS, or CiscoWorks WLSE.Features and BenefitsTable 1 lists features and benefits of Cisco Aironet 1130AG Series access points.Table 1. Features and Benefits of Cisco Aironet 1130AG Series Access PointsFeature BenefitDual 802.11a and 802.11g Radios ●Provides up to 108 Mbps of capacity in a single device for industry-leading capacity and backward compatibility with legacy 802.11b clients.Feature BenefitSupports 15 Nonoverlapping Channels ●Lower potential interference with neighboring access points simplifies deployment ●Fewer transmission errors deliver greater throughputIndustry-Leading Radio Design ●Provides robust signals to long distances●Mitigates the effects of multipath signal propagation for more consistent coverageVariable Transmit Power Settings ●Allows access point coverage to be tuned for differing requirements●Low—dBm setting supports closer spacing of access points in high-density deploymentsIntegrated Antennas ●Complete system is deployable out of the box without external antennas●Specifically designed to provide omnidirectional coverage for offices and similar radio frequency environments Hardware-Assisted AESEncryption●Provides high security without performance degradationCisco Unified IDS/IPS ●This integrated software feature is part of the Cisco Self-Defending Network and is the industry’s firstintegrated wired and wireless security solution. When a trusted client acts maliciously, the wired IDS detectsthe attack and sends shun requests to Cisco WLAN controllers, which will then disassociate the client device. Management Frame Protection ●This feature provides for the authentication of 802.11 management frames by the wireless networkinfrastructure. This allows the network to detect spoofed frames from access points or malicious usersimpersonating infrastructure access points. If an access point detects a malicious attack, an incident will begenerated by the access points and reports will be gathered on the Cisco wireless LAN controller, Cisco WCS,or CiscoWorks WLSE.IEEE 802.11i-Compliant; WPA2-Certified and WPA-Certified●Helps to ensure interoperable security with wireless LAN client devices from other manufacturersLow-Profile Design ●Unobtrusive design blends in to environment●“Quiet” LED does not draw attention to it when operating normally and no action is requiredMultipurpose and Lockable Mounting Bracket ●Installs easily to walls, ceilings, and suspended ceiling railways ●Accommodates standard padlock to prevent theftInline Power Support (IEEE 802.3af and Cisco Inline Power) ●Provides an interoperable alternative to AC power●Simplifies deployment by allowing power to be supplied over the Ethernet cable ●Compatible with 802.3af-compliant power sourcesCisco Green Bulk Packaging To reduce product packaging and preserve the environment, the Cisco Aironet 1130 Series may be ordered in abulk package that includes 10 access points and 10 mounting kits.Summary/ConclusionThe Cisco Aironet 1130AG Series provides the ideal enterprise access point for offices and similar environments. With two high-performance radios, these access points provide simultaneous support for the 802.11a and 802.11g standards, offering 108 Mbps of capacity for your growing WLAN. Incorporating AES encryption in hardware, the Cisco Aironet 1130AG Series complies with the 802.11i security standard and is WPA2-certified, helping to assure that your network employs the strongest security available while maintaining interoperability with products from other manufacturers. Additional design features, including diversity antennas with omnidirectional coverage and an unobtrusive form factor, along with an attractive price, provide low total cost of ownership.For office environments, the Cisco Aironet 1130AG Series is a cost-compelling solution for a high-capacity, high-security, enterprise-class WLAN.Product SpecificationsTable 2 lists the product specifications for Cisco Aironet 1130AG access points.Table 2. Product Specifications for Cisco Aironet 1130AG Access Points Item SpecificationPart Number for Individual Access Points●AIR-AP1131AG-x-K9 (Cisco IOS Software)●AIR-LAP1131AG-x-K9 (Cisco Unified Wireless Network Software)Note: The Cisco Aironet 1130AG Series may be ordered with Cisco IOS Software to operate as an autonomous AP with Cisco Unified Wireless Network Software using LWAPP. When the 1130AG is operating as a lightweight AP a WLAN controller is required.●Regulatory Domains: (x = Regulatory Domain)●A = FCC●C = China●E = ETSI●I = Israel●J = TELEC (Japan)●K = Korea●N = North America (Excluding FCC)●P = Japan2●S = Singapore●T = TaiwanCustomers are responsible for verifying approval for use in their individual countries. To verify approval and to identify the regulatory domain that corresponds to a particular country please visit:/go/aironet/complianceNot all regulatory domains have been approved. As they are approved, the part numbers will be available on the Global Price List.Part Number for Cisco Green Bulk Packaging●AIR-AP1131-x-K9-10 (Cisco IOS Software)●AIR-LAP1131-xK9-10 (Cisco Unified Wireless Network Software)Note: The Cisco Aironet 1130AG Series may be ordered with Cisco IOS Software to operate as an autonomous AP with Cisco Unified Wireless Network Software using LWAPP. When the 1130AG is operating as a lightweight AP a WLAN controller is required.●Regulatory Domains: (x = Regulatory Domain)●A = FCC●E = ETSI●Customers are responsible for verifying approval for use in their individual countries. To verify approval and toidentify the regulatory domain that corresponds to a particular country please visit:/go/aironet/complianceSoftware ●Cisco IOS Software Release 12.3(8)JA or later (autonomous).●Cisco IOS Software Release 12.3(11)JX or later (Lightweight Mode).●Cisco Unified Wireless Network Software Release 4.0 or later. Data Rates Supported ●802.11a: 6, 9, 12, 18, 24, 36, 48, and 54 Mbps●802.11g: 1, 2, 5.5, 6, 9, 11, 12, 18, 24, 36, 48, and 54 Mbps Network Standard IEEE 802.11a, 802.11b, and 802.11gUplink Autosensing 802.3 10/100BASE-T EthernetFrequency Band and Operating Channels Americas (FCC)●2.412 to 2.462 GHz; 11 channels●5.15 to 5.35, 5.725 to 5.825 GHz; 12 channelsChina●2.412 to 2.472 GHz; 13 channels●5.725 to 5.825 GHz; 4 channelsETSI●2.412 to 2.472 GHz; 13 channels●5.15 to 5.725 GHz; 19 channelsIsrael●2.432 to 2.472 GHz; 9 channels●5.15 to 5.35 GHz, 8 channelsJapan (TELEC)●2.412 to 2.472 GHz; 13 channels Orthogonal Frequency Division Multiplexing (OFDM) ●2.412 to 2.484 GHz; 14 channels Complementary Code Keying (CCK)●5.15 to 5.25 GHz; 4 channelsJapan–P (TELEC 2 (Japan2) Cnfg)●2.412 to 2.472 GHz; 13 channels Orthogonal Frequency Division Multiplexing (OFDM) ●2.412 to 2.484 GHz; 14 channels Complementary Code Keying (CCK)●5.15 to 5.35 GHz, 8 channelsJapan-Q●2.412 to 2.472 GHz; 13 channels Orthogonal Frequency Division Multiplexing (OFDM)●2.412 to 2.484 GHz; 14 channels Complementary Code Keying (CCK)●5.15 to 5.35 GHz, 8 channels●5.470 to 5.725 GHz, 11 channelsKorea●2.412 to 2.472 GHz; 13 channels●5.15 to 5.35, 5.46 to 5.72, 5.725 to 5.825, 19 channelsNorth America●2.412 to 2.462 GHz; 11 channels●5.15 to 5.35, 5.725 to 5.825 GHz; 12 channelsSingapore●2.412 to 2.472 GHz, 13 channels●5.15 to 5.35 GHz, 8 channels and 5.725 to 5.825 GHz, 12 channelsTaiwan●2.412 to 2.462 GHz, 11 channels●5.25-5.35 GHz, 5.725 to 5.825, 7 channelsNonoverlapping Channels 802.11a: Up to 19 802.11b/g: 3Receive Sensitivity (Typical) 802.11a:6 Mbps: -87 dBm9 Mbps: -86 dBm12 Mbps: -85 dBm18 Mbps: -84 dBm24 Mbps: -80 dBm36 Mbps: -78 dBm48 Mbps: -73 dBm54 Mbps: -71 dBm 802.11g:1 Mbps: -93 dBm2 Mbps: -91 dBm5.5 Mbps: -88 dBm6 Mbps: -86 dBm 9 Mbps: -85 dBm11 Mbps: -85 dBm12 Mbps: -84 dBm 18 Mbps: -83 dBm 24 Mbps: -79 dBm 36 Mbps: -77 dBm 48 Mbps: -72 dBm 54 Mbps: -70 dBmAvailable Transmit Power Settings (Maximum Power Setting Will Vary by Channel and According to Individual Country Regulations) 802.11a:OFDM:17 dBm (50 mW)15 dBm (30 mW)14 dBm (25 mW)11 dBm (12 mW)8 dBm (6 mW)5 dBm (3 mW)2 mW (2 dBm)-1 dBm (1 mW)802.11b:CCK:20 dBm (100 mW)17 dBm (50 mW)14 dBm (25 mW)11 dBm (12 mW)8 dBm (6 mW)5 dBm (3 mW)2 dBm (2 mW)-1 dBm (1 mW)802.11g:OFDM:17 dBm (50 mW)14 dBm (25 mW)11 dBm (12 mW)8 dBm (6 mW)5 dBm (3 mW)2 dBm (2 mW)-1 dBm (1 mW)Indoor (Distance AcrossOpen Office Environment):Outdoor:Range802.11a:80 ft (24 m) @ 54 Mbps150 ft (45 m) @ 48Mbps200 ft (60 m) @ 36Mbps225 ft (69 m) @ 24Mbps250 ft (76 m) @ 18Mbps275 ft (84 m) @ 12Mbps300 ft (91 m) @ 9 Mbps325 ft (100 m) @ 802.11g:100 ft (30 m) @54Mbps175 ft (53 m) @48Mbps250 ft (76 m) @36Mbps275 ft (84 m) @24Mbps325 ft (100 m) @18Mbps350 ft (107 m) @12Mbps360 ft (110 m) @11Mbps375 ft (114 m) @802.11a:100 ft (30 m) @54Mbps300 ft (91 m) @48Mbps425 ft (130 m) @36Mbps500 ft (152 m) @24Mbps550 ft (168 m) @18Mbps600 ft (183 m) @12Mbps625 ft (190 m) @9Mbps650 ft (198 m) @802.11g:120 ft (37 m) @ 54Mbps350 ft (107 m) @ 48Mbps550 ft (168 m) @ 36Mbps650 ft (198 m) @ 24Mbps750 ft (229 m) @ 18Mbps800 ft (244 m) @ 12Mbps820 ft (250 m) @ 11Mbps875 ft (267 m) @ 9Mbps900 ft (274 m) @ 6Mbps910 ft (277 m) @ 5.5Mbps940 ft (287 m) @ 2Mbps950 ft (290 m) @ 1Mbps6Mbps6Mbps 9Mbps400 ft (122 m) @6Mbps420 ft (128 m) @5.5Mbps440 ft (134 m) @2Mbps450 ft (137 m) @1MbpsRanges and actual throughput vary based upon numerous environmental factors so individual performance may differ. Compliance StandardsSafety●UL 60950-1●CAN/CSA-C22.2 No. 60950-1●UL 2043●IEC 60950-1●EN 60950-1●NIST FIPS 140-2 level 2 validationRadio Approvals●FCC Part 15.247, 15.407●RSS-210 (Canada)●EN 300.328, EN 301.893 (Europe)●ARIB-STD 33 (Japan)●ARIB-STD 66 (Japan)●ARIB-STD T71 (Japan)●AS/NZS 4268.2003 (Australia and New Zealand)EMI and Susceptibility (Class B)●FCC Part 15.107 and 15.109●ICES-003 (Canada)●VCCI (Japan)●EN 301.489-1 and -17 (Europe)Security●802.11i, WPA2, WPA●802.1X●AES, TKIP●FIPS 140-2 Pre-Validation List●Common Criteria (when running Cisco IOS software)Other●IEEE 802.11g and IEEE 802.11a●FCC Bulletin OET-65C●RSS-102Antennas ●2.4 GHz◦Gain 3.0 dBi◦Horizontal Beamwidth 360°●5 GHz◦Gain 4.5 dBi◦Horizontal Beamwidth 360°Security AuthenticationSecurity Standards●WPA●WPA2 (802.11i)●Cisco TKIP●Cisco message integrity check (MIC)●IEEE 802.11 WEP keys of 40 bits and 128 bits802.1X EAP types:●EAP-Flexible Authentication via Secure Tunneling (EAP-FAST)●Protected EAP-Generic Token Card (PEAP-GTC)●PEAP-Microsoft Challenge Authentication Protocol Version 2 (PEAP-MSCHAP)●EAP-Transport Layer Security (EAP-TLS)●EAP-Tunneled TLS (EAP-TTLS)●EAP-Subscriber Identity Module (EAP-SIM)●Cisco LEAPEncryption●AES-CCMP encryption (WPA2)●TKIP (WPA)●Cisco TKIP●WPA TKIP●IEEE 802.11 WEP keys of 40 bits and 128 bitsStatus LEDs External:●Status LED indicates operating state, association status, error/warning condition, boot sequence, and maintenancestatusInternal:●Ethernet LED indicates activity over the Ethernet, status●Radio LED indicates activity over the radios, statusDimensions (H x W x D) 7.5 in. x 7.5 in. x 1.3 in. (19.1 x 19.1 x 3.3 cm)Weight 1.5 lb (0.67 kg)Environmental Operating●Altitude: 0 to 2500m●32 to 104°F (0 to 40°C)●10 to 90% humidity (noncondensing)Non Operating●-40 to 158F (-40 to 70C)●Up to 95% humidity (noncondensing)System Memory ●32 MB RAM●16 MB FLASHInput Power Requirements ●100-240 VAC; 50-60Hz (power supply)●36-57 VDC (device)Power Draw 12.2W maximumWarranty One yearWi-Fi CertificationSystem RequirementsTable 3 lists the system requirements for Cisco Aironet 1130AG access points.Table 3. System Requirements for Cisco Aironet 1130AG Access PointsAccess Utilizing DescriptionBrowser Using the Web browser management GUI, requires a computer running Internet Explorer Version 6.0 or newer, orNetscape Navigator Version 7.0 or newer.Power over Ethernet (PoE)Power sourcing equipment (PSE) compliant with Cisco Inline Power or IEEE 802.3af, and providing at least 12.2W at48 VDC.Service and SupportCisco Systems® offers a wide range of services programs to accelerate customer success. These innovative services programs are delivered through a unique combination of people, processes, tools, and partners, resulting in high levels of customer satisfaction. Cisco services help you protect your network investment, optimize network operations, and prepare your network for new applications to extend network intelligence and the power of your business. For more information about Cisco services, visit Cisco Technical Support Services or Cisco Advanced Services.For More InformationFor more information about the Cisco Aironet 1130AG Series, visit /go/wireless or contact your local account representative.。

Sarantel SL1204 GeoHelix-P2 活动四螺线GPS抗干扰天线说明说明书

GeoHelix S Product Speciﬁ cation V2 Iss 07-06 Sarantel Propriety Information1Speciﬁ cations subject to changeSarantel UK***************** v2 Iss 10-09 © Sarantel Ltd. 2009Product DescriptionBuilt on patented PowerHelix ® ﬁ lteringantenna technology, the SL1204 surface-mount GPS antenna is the smallest active quadriﬁ lar helix antenna available, providing high performance in difﬁ cult GPSapplications. The SL1204 integrates a high-performance, optimised gain, low-noise ampliﬁ er with Sarantel’s GeoHelix ®-P2 antenna for receivers requiring an active input. The SL1204 has been optimally designed to workbest with the latest generation of GPS chipsets. The SL1204 antenna is ideal in applications where:• the device is handheld, body-worn, or otherwise surrounded by high-dielectric materials that would de-tune conventional antennas;• the antenna is tightly integrated with other antennas, e.g., Bluetooth®/GPS receivers or GPS/GSM mobile phones;• there are tight constraints on the size of the device or the amount of space allocated to ground planes;• the 2nd Generation GPS receiver requires optimised LNA gain for optimal GPS performance;• the orientation of the device is random; or • the antenna will be embedded in the device.The SL1204 antenna is balanced, which isolates it from the device and enables the antenna to reject common mode noise resident on the device ground plane. The construction and materials of the antenna constrain its near-ﬁ eld to a very small volume, therefore materials near the antenna have negligible de-tuning effects and the antenna maintains its pattern and efﬁ ciency in the presence of dielectric loading. As a dielectrically -loaded antenna, the SL1204 antenna effectively attenuates signals from common GSM and ISM frequencies, minimizing the need for additional ﬁ ltering.The SL1204 antenna may be deployed in an external, “stub-style” conﬁ guration, but it is also a simple antenna to embed due to its isolation properties.Speciﬁ cationsMinimumTypical Maximum Unit Part Number SL1204(see page 4 for detailed part numbers)Each Type Quadriﬁ lar HelixFrequency 1573.421575.421577.42MHz Polarization Right-hand circular polarizedVoltage 1.8 3.0 3.6V Current 3.1 3.4 3.9mA Gain +17+18dBic Beamwidth 135Degrees Bandwidth (3dB)20MHz Axial Ratio <2.0@ZenithVSWR <2.0:1 2.3:1Impedance 50Noise Figure0.80.9dB Input 3rd Order Intercept Point -7dBm Operating Temperature -40+20+85°C Element Dimensions10 (diameter) x 17 (length)mm Overall Dimensions (w/radome)13.3 (dia) x 12.4 (width) x 34 (length)mm Weight (excl radome or sleeve) 7.0gramsMountingSMTSL1204 (GeoHelix®–M)2nd Generation Active GPS Antenna Product Speciﬁ cationThe SL1204 , with optimised LNA gain and low-noiseﬁgure, provides a typical gain and Noise Figure of 18dBicand 0.8dB, respectively.This antenna is designed to work efﬁ ciently with the latestgeneration of GPS chipsets out in the market today.The strength of the PowerHelix® antenna technology is itsimmunity to de-tuning in the presence of dielectric loading,like human tissues. The SL1204 antenna retains efﬁ ciencyand polarization near the human body. Conventionalantennas lose 5-10dB of gain in similar circumstances.Though it will not electrically couple with a ground plane,the SL1204 antenna can be expected to increase efﬁ ciencyby up to 100% when mounted over a ground plane due tonear-ﬁ eld signal reﬂ ections. Conﬁ guration and orientationof the ground plane with respect to the antenna will varyresults, but efﬁ ciency will not decrease.Radiation Patterns (dBic)Frequency (MHz)S 12 (dB)860GSM 900-40970-371575.42GPS (L1)+181700GSM 1800-41800-71900-92450Bluetooth/Wiﬁ -21Filtering ResponseAntenna Integration OptionsThe SL1204 antennas may be mounted externally or embedded within a device. When mounting externally, the groove in theradome should be used as a mechanical support. Internally embedded antenna requires support ribs in the plastic housing. For further information on integrating the SL1204, please refer to the integration guidelines.Ordering Guide for the SL1204 antennaPart Number Description MOQ Pack SizeM Antenna 400 400SL1204 GeoHelix®M Antenna with Radome 400 400SL1204R GeoHelix®M Antenna with Short Sleeve 400 400SL1204SB GeoHelix®Notesplaced for below MOQ or not in multiples of pack quantities will be subject to a $20 handling 1. Ordersfeefurther guidance on selecting the correct part number please contact Sarantel distributors or 2. ForMechanical Drawings - Overall DimensionsMechanical Drawings - Footprint and Board Proﬁ leRoSH Compliance StatementApplication SupportSarantel are committed to our customers’ success, and sooffer a variety of support options for customers designing RFproducts.Check the Sarantel web site at /products forthe latest production speciﬁ cations, technical notes, andapplication guides for solutions to the most common antennaintegration issues.Contact our applications support group by email at****************************************ﬁ cations,including mechanical drawings, surface mount pad layout,embedding recommendations, and other applicationquestions not answered in the technical literature.For further support options, please contact your local salesrepresentative at /wheretobuy.About SarantelSarantel designs and manufactures dielectricallyloaded antennas based on patented PowerHelix®ﬁltering antenna technology. Sarantel’s antennasare ideal for applications in which the radio deviceis small, handheld, or body-worn, or in deviceswith multiple transceivers and high levels ofcommon mode noise. Sarantel antennas can bemounted externally or easily embedded within adevice.Sarantel antennas are protected by US and othergranted or pending international patents.GeoHelix®, PowerHelix®, and the Sarantel logoare registered trademarks of Sarantel Ltd.Contact SarantelSarantel Ltd. (HQ)Unit 2, Wendel PointRyle Drive, Park Farm SouthWellingborough, NN8 6BAUnited KingdomPh: +44 1933 670560Fax: +44 1933 401155Email:*****************Web: Global Distributors & Representatives/wheretobuySarantel strongly believes in the value of intellectual property and the right of entrepreneurs to protect what they have created. Sarantel dem-onstrates its commitment to this principle by continuously developing its technology and then ﬁ ling patents in a number of regions around the world. Additionally, Sarantel is constantly ﬁ ling new patent applications and has a substantial portfolio of pending applications.A list of Sarantel’s granted patents;Australia; 707488, 716542, 720873, 769570, 2004223229 Austria; 0791978 Brazil; PI9508769-9 Canada; 2198318, 2198375, 2245882, 2250790, 2272389, 2357041, 2373941, 2521493 China; ZL00136656.4, ZL00803562.8, ZL00808144.1, ZL95195772.4, ZL97181567.4, ZL97193099.6,ZL97194742.2, ZL99816387.2 Denmark; 0777922, 1088367 Finland; 0791978, 0876688, 0935826, 0941557, 1088367, 1098392, 1147571, 1153458, 1196963, 1609213 France; 0777922, 0791978, 0876688, 0935826, 0941557, 1081787, 1088367, 1098392, 1147571, 1153458, 1196963, 1609213 Germany; 60003157.8-0, 60029538.9-08, 60034042.2-08, 602004010085.4-08, 69535431.0-08, 69722590.9-0, 69723093.7-0, 69726177.8-0, 69730369.1-08, 69923558.8-08, 69930407.5-08 India; 193515, 193751, 193929, 195085, 206740 Italy; 0777922, 0791978, 0876688, 0935826, 1081787, 1088367, 1153458, 1196963 Japan; 3489684, 3489775, 3923530, 3946955, 4052800, 4057612, 4077197, 4099309, 4147260, 4159749, 4188412 Malaysia; MY-112473-A, MY-119077-A, MY-119465-A, MY-123075-A Mexico; 199890, 205239, 213947, 220048, 213633, 232437, 232438, 231633, 259577 Netherlands; 0791978, 1081787 New Zealand; 291852, 334614 Philippines; 1-1995-51169, 1-1997-55284, 1-1997-55978, 1-1997-58557, 1-1999-03167 Russia; 2173009, 2210146, 2339131 Singapore; 37745, 54891, 56480, 116791, 131698 South Korea; 348441, 366071, 446790, 458310, 523092, 625638, 650620, 650621, 650622, 663873, 667216, 667221, 709688, 767329 Spain; 0777922, 0791978, 1088367, 1196963 Sweden; 0777922, 0791978, 0876688, 0935826, 0941557, 1081787, 1088367, 1098392, 1147571, 1153458, 1196963, 1609213 Switzerland; 0791978, 1081787, 1196963 Taiwan; 094978, 108488, 123671, 144801, 156702, 285980, M 312023 Thailand; 17812, 19360, 19570, 23745 United Kingdom; 1081787, 1147571, 2292257, 2292638, 2309592, 2310543, 2311675, 2321785, 2326532, 2326533, 2338605, 2351850, 2356086, 2367429, 2383901, 2399948, 2419037。

电力定价优化模型：基于峰谷TOU价格的住宅定价说明书

Optimization Model for Residential Tiered Electricity Pricing Based on Peak-Valley TOU PriceL. Yang, K.T. Chen, Z.F. Tan School of Economics and Management North China Electric Power University Changping District, Beijing, ChinaB.B. ZhouLiaoning Province Power Company Ningbo Road, Shenyang, Liaoning, ChinaAbstract—In recent years, with the shortage of energy supply and tremendous of environmental pressure in China, the price of primary energy such as coal continues to rise, as well as the electricity price. However, the adjustable amplitude and frequency for residential electricity are lower than other industries. In order to construct resource conserving and environment friendly society, to gradually reduce the price of cross-subsidization and guide residents to save electricity, it is necessary to implement the residential tiered electricity pricing. This paper optimizes the model of residential tiered electricity pricing through the introduction of peak-valley TOU price, which lays the foundation for promotion of power system reform.Keywords-Residential electricity price; tiered electricity pricing; peak-valley TOU price; modelI.I NTRODUCTIONElectric power industry is an important basic industry of the national economy, and is also an important part of social public utilities. After years of efforts, China’s power system reform has achieved initial results. The price reform is one of the core content of electricity market reform. Throughout the reform system, the tariff is an important economic lever. It not only can optimize the resources allocation, but also can adjust the relations among various interests [1, 2].Residential electricity consumption is an extremely important part for electricity sales market. The establishment of residential electricity price involves thousands of families, and is related to vital interests and living standards of the masses, as well as social efficiency, effectiveness and fairness [3]. Therefore, it is significant to establish a scientific and reasonable price system for the residents. This paper introduces peak-valley TOU price to design and optimize the model of residential tiered electricity pricing, and then obtains the optimization model for operation cost of the system.II.R ESEARCH S TATUS FOR R ESIDENTIAL T IEREDE LECTRICITY P RICINGIn China, the tiered pricing should be described to be the increasing price by ladder. It is refers to replace the current residential price of a single form with the approach of segmented pricing according to residential consumption, in this pricing method, the price of electricity increases gradually in a stepwise form along with the growth of electricity consumption [4,5]. The tiered electricity price divide power level of residential consumption in accordance with three stages of “meet the basic demand”, ”meet the normal and reasonable demand” and ”meet the high quality electricity demand”. Then, the corresponding price is set up. The tariff implements principle of stepping increment [6].Although the existing tiered electricity pricing system has been promoted and has achieved the electricity saving benefit, this system has not considered the distribution of load and lacked reaction for electricity value in peak or valley period. Actually, electric power often needs more standby unit for power generation to meet the residents' consumption in the peak period, so the generation cost is high. Additionally, the load demand is low in valley period, so many units have not been called and utilization efficiency of units is not high. If we can guide residents to use electricity in the valley time, the reducing peak and filling valley will be achieved, and the utilization efficiency of units will be improved [7, 8]. Therefore, this paper introduces the peak-valley TOU price in residential tiered electricity price, and then optimizes the sub-file number, sub-file power and block-tariff.III.D ESIGN M ODEL FOR R ESIDENTIAL T IEREDE LECTRICITY P RICINGTo guide residents using electricity rationally, tiered electricity pricing has been introduced. The upper tiered electricity pricing optimization model is constructed to differ from the traditional concept, targeting as the minimal electricity cost for residents. Optimization cycle is 24h in this paper in order to combine the tiered electricity pricing with the peak-valley price. Specific goal function is as follows.()()1,1,2,2,21,2,21,2,11minT It t i t i t i t i t i t i tt iC P Q P Q Q P Q Q−++===×+×−+×−∑∑(1)In this formula,1,tP refers to electricity price for thefirst-file resident at t time.1,tQ is electricity consumption for the first-file resident at t time.,i tP refers to electricity price for i-file resident at t time.,i tQ is electricity consumption for i-file resident at t time.Introducing tiered electricity price should consider the psychological strategy for residents. For the goal of reducing electricity costs and consumption, electricity costs of some users would rise after implementing tiered electricity price. In order to facilitate the expression, the comparison function()12,F x xis defined.()121211,,0,x xF x xx x>=≤(2)(1) Electricity cost constraintInternational Conference on Computer Information Systems and Industrial Applications (CISIA 2015)Electricity costs of some users should remain unchanged after implementing tiered electricity price. If total resident electricity is m, then:()11,Ii ii F C C Iλ=≥∑ (3)In this formula, i C and iC are electricity costs before and after introducing tiered electricity price for the resident i. 1λ is proportional coefficient which refers to the proportion of residents whose electricity cost is not higher than the cost before tiered electricity price.i i i C P Q =× (4)1Jiij ijj C P Q ==×∑ (5) In formulas above, i P and iQ refer to electricity cost and consumption before introducing tiered electricity price for the resident i. ij P and ij Q refer to electricity cost and consumption after introducing tiered electricity price for the resident i in j-file. At the same time, to ensure the implementation of tiered electricity price, electricity consumption for a certain percentage of residents should cross at least two intervals after implementing tiered electricity price, then: ()112,Ij i F Q Q I λ=≥∑ (6) In this formula, 2λis the proportion of residents whoseelectricity consumptions have crossed at least two intervals. To reduce residential reaction degree after introducingtiered electricity price, the basic electricity demand of residents should be limited, it is as follows.1i Q Q≥ (7)Q is the basic electricity demands of residents.After determining the number of sub-grade, the block-tariff *ij P and block-electricity *ij Q can be obtained by solving the model above.IV.D ESIGN M ODEL FOR P EAK -V ALLEY TOU P RICEFurther, TOU price is introduced to reflect the time valueof electricity. The main psychological factor of residents to respond TOU price is to minimize electricity costs. Therefore, residential peak-valley TOU price model is constructed as the goal of minimal electricity costs. The target function is as follows:11min T Iit itt i C P Q ===∑∑ (8)In this formula, it P and it Q are tariff and electricity consumption for resident i at t moment after introducing TOU price.In order to ensure the load shifting effect of peak-valleyTOU price, a certain proportion of residential electricity peak-valley difference should be decreased, the specific constraint is that:()()max min max min 13,mi i i i i F Q Q Q Q Iλ= −−≥∑ (9)In this formula, max iQand max iQare maximum load forresidents in peak period before and after TOU price. min iQ and min i Q are minimum load for residents in valley period beforeand after TOU price. 3λ is residential proportion whoproduce the load shifting effect./1p p i i P P < (10)/1v v i i PP ≥ (11) In the formulas, p i P and p iP are tariff for resident i in peak period before and after using TOU price. v i P and v iP are tariff for resident i in valley period before and after using TOU price.Considering different price characteristics of timesharingand multi-level in peak-valley step price, a concept of elastic coefficient with peak-valley and multi-level has put forward. For each resident, the tiered-TOU price in each levelcorresponds to an elasticity coefficient, so electricity consumption of the resident in each level is determined by peak-valley elasticity of multi-level, which can be described as follows.()()()()////p p p p i i ip i i v v v v i i iv i i Q Q e Q Q Q Q e Q Q ∆=∆∆=∆(12)In this formula, ijp Q and ijv Q are electricity for resident i at j level in peak and valley periods when implementing a single price (ijp Q =ijvQ ). After introducing peak-valley step price, change amounts of electricity for resident i at j level in peakand valley periods are that: ijp ijp ijp Q Q Q ∆=−，ijv ijv ijv Q Q Q ∆=− . e ij is the demand elasticity for resident i at j level.Solving the model above, we can get optimal tariffs and electricity consumptions in peak and valley periods. Combined with the tiered price in section 2, the residential peak-valley step price can be formed.V. O PTIMIZATION M ODEL FOR P EAK -V ALLEY S TEP P RICE The implementation of residential peak-valley step price will change electrical behavior and transfer using times of residents, which will directly affect the operation cost of the system. In order to ensure the stable operation of the system, the basic benefits of power system should be guaranteed. Therefore, an optimization model for peak-valley step price is constructed as the goal of maximizing system operation efficiency. The goal function is as follows.()111111max TTm m m m t t it st it it st it t t i i i i R R C c Q C c Q π====== =−=−−−∑∑∑∑∑∑ (13) In this formula, t R and tR are system profits before and after implementing peak-valley step price. st c refers to power purchase price for power company.In order to optimize the system operation, system power efficiency should be promoted when reducing residential electricity cost. Specific constraints are as follows.Firstly, changes of electricity sales profits for power companies are in a certain range. That is:(14)Secondly, electricity sales profits should be higher than system operating costs C op after implementing peak-valley step price:(15)Lastly, change magnitude of the total average price should be in a certain range:1121Ii i Iii C P P Q ξξ==−≤≤−∑∑ (16)In this formula, ξ1and ξ2 are constants.Although the objective function is to reduce power consumption, electricity should satisfy the following formula after the introduction of peak-valley step price in order to improve utilization rates of existing power generating equipment.1Igen i geni kQ Q Q =<<∑ (17)In this formula, Q gen refers to generation capacity. k （0<k <1）is a scaling factor to indicate a minimum utilization of power generation equipment.The characteristic of peak-valley step price determines the following relationship among tariffs:max1max10,1,2,,10,1,2,,1p p i i i v v i i i P P P i N P P P i N ++ <<≤=− <<≤=− (18)In this formula, the upper limit of tariff P is decided byelectricity consumption level, policy, economy and other factors, which results in some differences.VI.C ONCLUSIONSIn order to reflect the time value of electricity, this paper introduces peak-valley TOU price under the traditional residential tiered electricity price model, so peak-valley TOU price model is constructed. While the promotion of residential peak-valley TOU price directly influences the system operation cost, in order to ensure stable operation of thesystem, this paper constructs the optimization model forpeak-valley step price with the goal of maximizing system operation benefit.Optimization for residential tiered electricity price plays an important role in promoting the system to consume the random power resources. The final optimized model can promote random power resources into the grid, so the power output of thermal power unit will be reduced, as well as the cost of power generation system. Finally, the significant benefits would be created in economy and environment.R EFERENCES[1] Wu Jianhong. Pricing Model of Tiered Electricity Pricing for Residentsand Policy Simulation Based on Social Equilibrium [D]. North China Electric Power University, 2013, 4-7.[2] National Development and Reform Commission improves residentialtiered electricity price. National Energy Administration, /2013-12/26/c_132998400.htm[3] Wang Ruichun. Analysis of different pricing scheme under the guidanceof residential tiered electricity price [J]. Water Resources and Power, 2013, 31(1), 215-218.[4] Chen Minjun, Ci Xiangyang, Ping Zongfei. Residents Ladder ElectricityPrice Effect Evaluation Based on Analytic Hierarchy Process [J]. Science Technology and Industry, 2014, 14(9), 88-91.[5] Li Gen. Implementation Status and Improvement Strategy of China'sResidents Ladder Price[J]. Information and Communication, 2013, (2), 271-272.[6] ZENG Ming, LI Na, LIU Chao. Energy-Saving Assessment of TieredPricing Program for Residential Electricity Based on Utility Function [J]. East China Electric Power, 2011, 39 (8), 1215-1219.[7] HUANG Hai-tao. Design Model for Residential Block ElectricityPricing Structure and Level [J]. East China Electric Power, 2012, 40 (5), 0721-0727.[8] ZENG Ming. Assessment of Tiered Pricing Program for ResidentialElectricity Based on the Multiple Indexes [J]. East China Electric Power, 2012, 40 (3), 0359-0362.5R RRλ−≤ 1mi opi C C =≥∑。

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Optimal Two-Tier Forecasting Power Generation Model in Smart GridsKianoosh G.Boroojeni 1,Shekoufeh Mokhtari 1,M.H.Amini 2,and S.S.Iyengar 11School of Computing and Information Sciences,Florida International University,Miami,Florida,USA 2Department of Electrical and Computer Engineering,Carnegie Mellon University,Pittsburgh,PA,USAAbstractThere has been an increasing trend in the electric power system from a centralized generation-driven grid to a more reliable,environmental friendly,and customer-driven grid.One of the most important issues which the designers of smart grids need to deal with is to forecast the ﬂuctuations of power demand and generation in order to make the power system facilities more ﬂexible to the variable nature of renew-able power resources and demand-side.This paper proposes a novel two-tier scheme for forecasting the power demand and generation in a general residential electrical gird which uses the distributed renew-able resources as the primary energy resource.The proposed forecasting scheme has two tiers:long-term demand/generation forecaster which is based on Maximum-Likelihood Estimator (MLE)and real-time demand/generation forecaster which is based on Auto-Regressive Integrated Moving-Average (ARIMA)model.The paper also shows that how bulk generation improves the adequacy of proposed residential system by canceling-out the forecasters estimation errors which are in the form of Gaussian White noises.Keywords :Adequacy Analysis,ARIMA Model,Forecasting Model,Maximum Likelihood Estima-tion,Smart Grids1INTRODUCTION In recent years,increasing awareness about environmental issues and sustainable energy supply introduced modern power system,called smart grid (SG),to upgrade conventional power system by utilizing novel technologies.There are many inﬂuential elements in the SG which helps power grid to achieve a more reliable,sustainable,eﬃcient and secure level,such as distributed renewable resources (DRRs),advanced metering infrastructure (AMI),energy storage devices,electric vehicles,demand response programs,energy eﬃciency programs,and home area networks (HANs)[1,2].Furthermore,recent advances in deploying communication networks in SG provide two-way communication between utility and electricity consumers and improve market eﬃciency [3].In a related context,conventional generation resources mostly use fossil fuel as their energy source which is a major environmental concern.To overcome this problem,SG will experience a high penetration of DRRs which has two main advantages:1)cost-eﬀective because the main energy source is free (wind energy,sunlight,etc),2)produce no hazardous pollution.Additionally,DRR utilization helps power system to become dispersed.Therefore,not only SG is more distributed than conventional power system but power generation units are trying to implement green-based energy resources [4].In order to reduce the variability of wind power generation,this reference proposes coordination of wind power plant and ﬂexible load[5].One of the most challenging issues in future power system design and implementation is the ﬂexibility ofpower system devices to adapt the stochastic nature of demand and generation [6,7].In other words,high penetration of DRRs,such as wind power and photovoltaics,is not suﬃcient to achieve an acceptable level of reliability in terms of adequate supply of electricity demand;for instance the output power of wind generators requires excessive cost to manage intermittency [8].Ref [9]proposes a Consensus+Innovations distributed control approach for the distributed energy resources.Consequently,there is a foremost obligation to develop an accurate forecasting method to predict the power generation of intermittent DRRs.Based on US Energy Information Administration (EIA)assessment,energy demand will increase by 56%from 2010to 2040.This astonishing consumption growth is driven by economic development [10].Additionally,on the demand side,customers demand depends on many factors and there are many studies performed in order to achieve an accurate forecast methodology.Load forecast uncertainty plays a pivotal role on power system studies such as loss estimation,reliability evaluation,and generation expansion planning.Demand forecasting methods including,but not limited to,fuzzy logic approach,artiﬁcial neural network,linear regression,transfer functions,Bayesian statistics,judgmental forecasting,and grey dynamic models[11].a r X i v :1502.00530v 1 [c s .S Y ] 2 F eb 2015Considering all of the above-mentioned issues,including uncertainty of demand and generation,forecast-ing errors and DRR intermittent generation proﬁle,proposing an accurate demand/generation forecasting scheme is deﬁnitely required to achieve a more reliable and secure power grid.In other words,the purpose of this paper is to propose a framework in which the SG customers are satisﬁed in terms of supplying their demand reliably,independent from the wide variation of DRRs’generation amount.1.1Related WorksUtilizing green power generation units,DRRs,requires electricity demand/generation forecasting which are addressed in recent studies.In[6],deferrable demand is used to compensate the uncontrollable and hard-to-predictﬂuctuations of DRRs.As a result,green-based power generation units can utilize theﬂexibility of the customers to meet demand appropriately.They introduced an eﬃcient solution using stochastic dynamic programming implement their method.Moreover,consumer participation in generation side is modeled in term of demand response.Implementation of demand response programs brings many advantages for the SG:1)customer participation in generating power,2)transmission lines congestion management,and3) reliability improvement.In[12],aﬂexible demand response model is proposed.This model is useful for evaluation customer’s reactions to electricity price and incentives.They deﬁned a strategy success index to evaluate the feasibility of each scenario[12].Additionally,Hernandez et al.performed a comprehensive survey on power grid demand forecasting methods considering SG elements.This study classiﬁes load forecasting based on forecasting horizons:very short term load forecasting(from seconds to minutes),short term load forecasting(from hours to weeks), medium and long term load forecasting(from months to years).The authors also classiﬁed forecasting methods according to the objective of forecast:one value forecasting(next minute’s load,next year’s load), and multiples values forecasting(such as peak load,average load,load proﬁle)[11].Smart load management studies also require load/generation forecasting to eﬃciently balance load and generation in a near-real time manner.In[13],a multi-agent based load management framework is intro-duced.This approach considered renewable resources and responsive demand to achieve an acceptable level of load-generation balance.For a broader treatment on this,there are some other researches on this topic that considered electric energy dispatch in presence of DRRs[14,15].For a broader treatment on this,please see[16,17,18,19,20,21,22,23,24,25,26,27].1.2Our ContributionIn this paper,we propose a novel hybrid(two-tier)scheme for forecasting the power demand and generation in a residential electrical gird.The grid has expanded over a city consisting of a number of communities, Distributed Renewable Resources(DRR),and some bulk generations back-up plan.Our forecasting scheme has two tiers:long-term demand/generation forecaster which is based on Maximum-Likelihood Estimator (MLE)and real-time forecaster which is based on Auto-Regressive Integrated Moving-Average(ARIMA) model(see[28,29,30,31]for related work).In the long-term forecaster,we use the classiﬁcation of historical demand/generation data to build our estimator;while in the real-time one,we predict the time series of power demand/generation using a discrete feedback control system which gets feedback from short-term previous values.We show that how the bulk generators can improve the adequacy of our residential system by canceling-out the forecasters estimation errors which are in the form of Gaussian White noises.The rest of this paper is organized as follows.Section2represents a general framework of the problem.In Section3we discuss our proposed two-tier forecasting scheme in both long-term and real-time time horizons.A far-reaching adequacy analysis framework is presented in Section4.Finally,summary and outlook are given in Section5.2PROBLEM SPECIFICATIONConsider a network of communities in a city.In each community,there are a number of customers and a distributed renewable resource1which supplies the energy needed by the customers in the community.1An industrial facility for the generation of electric power.The power generator of a distributed renewable resource use renewable energy sources.(a)the whole network.(b)More details inside a community.Arrows represent energyﬂow.Figure1:Schematic representation of the electric power distribution network.Moreover,there are a few power plants which are outside the communities and scattered over the city to help the distributed renewable resources generate electricity on demand.Existence of these extra plants improves the performance of our electrical distribution system.We refer to these extra power plants as bulk generators.The electric energy generated by these generators can be transferred to each community in the city through the network of communities schematically represented in Figure1a.Thisﬁgure shows a network representation of our described model.As you see,each community has some connected neighbors so that it can trade electricity with them if it is necessary.We assume that any two neighbors are connected via a two-way transmission power line.Consider some community in our example.Each customer located in the community has some amount of electricity demand which varies from time to time.Let D(q,t)denote the total electricity demand of all of the customers in the q th community at given moment t.Note that D(q,t)is the instantaneous power that have to be supplied at time t.To illustrate,see Figure2which speciﬁes the daily expected value of D(q,t)in New-England in2012.Additionally,we use G(q,t)to represent the total amount of electric power generatedby the DRR located in the community at given time t.Figure2:Daily demand proﬁle of New-England in2012.Figure1b shows more details of a community.As you see,there is a controller unit called Local Load Management Unit(LLMU)which controls the energyﬂow inside the community.Furthermore,the controller may participate in the energy distribution of other communities by forwarding theﬂow received from its neighbors.Assuming that the instantaneous generated power(G(q,t))of the DRR exceeds D(q,t)in a period of time,the energyﬂow of size G(q,t)from the DRR is divided into two parts:a powerﬂow of size D(q,t)moves towards the customers and the other G(q,t)−D(q,t)units is stored by the energy storage unit embedded in each community.On the other hand,if the value of G(q,t)becomes lower than D(q,t)in some time interval,there will be two main energyﬂows in the community to satisfy the customers demand:one is originated from the DRR;the other of magnitude D(q,t)−G(q,t)is from the storage unit.Moreover,in the case that D(q,t)>G(q,t)and there is no enough amount of energy in the storage unit to compensate the shortage of DRR generation,there has to be anotherﬂow from the neighbors towards the community of customers.3OUR PROPOSED HYBRID FORECASTING SCHEMEIn this section,we focus on how to forecast the power demand and generation in short/long-term.At theﬁrst subsection,we propose a Maximum-Likelihood Estimator for long-term foresting which is crucial in the process of low-cost energyﬂow management and also is a basis for real-time forecasting of power demand/generation.In the following subsection,two estimation models will be proposed for real-time forecasting of demand and generation.In theﬁrst one which is a two-tier hybrid model(based on MLE and AR),we assume that the forecast random processes are stationary in few hours;however,the second model(which is an ARIMA)is more appropriate for the case that the forecast random processes doesn’t show stationary behaviors even in few hours.3.1Long-Term ForecastingWe assume that there are a set of customers C distributed over an area.By partitioning the area into n disjoint parts A1,A2,...,A n,we obtain a corresponding partition of set C:C1,C2,...,C n(communities of customers).The demand values of every subset C q has been measured every u units in time period[0,T u] for some integer T and real value u.Assume that a year is divided into m parts(school time,Christmas holidays,Summer break,etc)based on the similarity of electricity usage pattern.We partition time interval[0,T u]into m subsets:I1,I2,...,I m such that set I i contains the i th part of every year belonging to[0,T u].Additionally,every set I i is divided to two parts:weekends I i1and business days I i2.Moreover,assuming that a day is divided into d parts (again based on the similarity of electricity usage pattern during the day),we partition every interval I ij into I ij1,I ij2,...,I ijd.In addition,assume that we have the historical weather data in every area A q over period[0,T u].Considering that W denotes the set of diﬀerent weather conditions,we partition time interval I ijk in thefollowing form for every area A q:I ijk=w∈W I(w,q)ijk(1)for every i=1,2,...m,j=1,2,k=1,2,...,d,q=1,2,...,n.Note that in Equation1,I(w,q)ijkspeciﬁes thesubset of I ijk such that the weather condition in area A q and time t∈I(w,q)ijkis w.Now,assuming that interval[0,T u]containsδdays,let D v speciﬁes the v th day of time interval[0,T u] for every v=1,2,...,δ.Additionally,consider D(q,τ)as the power demanded by the set of customers C qmeasured at moment uτ(for everyτ=0,1,...,T).For every interval I(w,q,v)ijk =I(w,q)ijk∩D v,if I(w,q,v)ijk=∅,we specifyﬁve parameters:X(q)1which is the number of years passed since t=0(till interval I(w,q,v)ijk),X(q)2which is the number of weeks passed since the begining of the i th paritition of a year,X(q)3which is thenumber of days passed since the begining of the j th partition of a week,X(q)4is the temperature in area A qand time interval I(w,q,v)ijk,andy(q)=uτ∈I(w,q,v)ijkD(q,τ)uτ∈I(w,q,v)ijk1,(2)where y(q)speciﬁes the average power demanded by the set of customers C q in time interval I(w,q,v)ijk.For every subset I(w,q)ijk⊂[0,T u],we construct a maximum-likelihood estimator for the dependent variable y(q)based on the following linear model:y(q)=[1X(q)1X(q)2X(q)3X(q)4][ β0 β1... β4]T+N(0, σ2).(3)Considering that condition I(w,q,v)ijk=∅is only true for v=v1,v2,...,v p,we obtain that Y(q)=X(q)β+ε∀q=1,2,...,n such that Y(q)=[y(q)i]p×1,β=[βi−1]5×1,ε=[εi]p×1,and X(q)=[1X(q)1X(q)2X(q)3X(q)4].Symbol y(q)ιspeciﬁes the average power demanded by the set of customers C q intime interval I(w,q,vι)ijk ;moreover,Xι1,...,Xι4denote the parameters on which y(q)ιis dependent(for everyι=1,2,...,p).Using the maximum-likelihood method for the linear model mentioned in Equation3,we obtain that:βML=(X(q)T X(q))−1X(q)T Y(q)T,(4)εML=Y(q)T−X(q)ˆβML∼N(0, σ2ML I),(5)andσ2ML=Y(q)−X(q) βMLT×Y(q)−X(q) βML/p.(6)Note that the ML estimator speciﬁed in Equation3can forecast the average power demand in an interval of few hours.However,by using the estimator repetitively and for diﬀerent intervals I(w,q)ijk,we can forecast the average power demand for longer time;however,the variance of error will increase respectively2.Additionally,the similar estimation model can be made for power generation.The only diﬀerence is that we don’t need to partition a week into two parts.Moreover,we have to partition a year into small parts based on the similarity of power generation pattern.2addition of b i.i.d.jointly normally distributed random variables of varianceσ2is also a normal variable of variance bσ2.3.2Real-Time ForecastingIn the previous subsection,we partitioned the interval[0,T u]intoΘ(md|W|)subsets in the form of I(w,q)ijk (for every set of customers C q).Additionally,for every subset I(w,q)ijk,a maximum likelihood estimator wasconstructed to estimate the average power demanded by customers C q in time interval I(w,q)ijk ∩D v.Our ultimate goal in this section is to construct an estimator for the value of power demanded by set of customers C q in moment t=τu(for some integer valueτ)based on ARIMA(a,0,0)model with drift−µ(q):1−al=1φl L l(D(q,τ)−µ(q))=ετ,(7)whereµ(q)is the average of demand value D(q,t)in time interval t∈I(w,q,v)ijkwhich is estimated by Equation3,ετis a white noise of varianceσ2,τu∈I(w,q,v)ijkfor some i,j,k,w,v,and L is the lag operator:L(D(q,τ))= D(q,τ−1).By replacingµ(q)with y(q)+ε whereε ∼N(0, σ2ML),we obtain that:D(q,τ)=(al=1φl−1) y(q)+al=1D(q,τ−l)estimated value+ετ+(al=1φl−1)ε.estimation error(8)Note that Equation7works only if random process D(q,t)shows stationary behavior;otherwise,we need to use the model with moving average.In fact,assuming that process D(q,t)is not stationary,ARIMA(a,1,0) is much better for short-term forecasting:1−al=1φl L l(1−L)D(q,τ)=ετ.(9)Consequently,we obtain that:D(q,τ)=(φ1+1)D(q,τ−1)+al=2(φl−φl−1)D(q,τ−l)−φa D(q,τ−a−1)+ετ.(10)As you see,ARIMA(a,1,0)model forecasts the demand value using its(a+1)previous values with a white noise error.In addition,the power generation of the g th generator can also be forecast using ARIMA(a ,1,0).As-suming that G(g,t)speciﬁes the instantaneous power generated by the g th generator at moment t,we have:1−al=1φ l L l(1−L)G(g,τ)=ε τ;(11)or equivalently,G(g,τ)=(φ 1+1)G(g,τ−1)+al=2(φ l−φ l−1)G(g,τ−l)−φ a G(g,τ−a −1)+ε τ.(12)In the following section,we analyze the adequacy of the electricity system based on ARIMA(a,1,0) forecasting model.4ADEQUACY ANALYSISBy assumption,we consider the maximum security for our electrical facilities (like wires).Henceforth,the system reliability in our discussion refers to the system adequacy.In order to analyze the system adequacy,we need to use the forecasting models of instantaneous demand and generation presented in the previous section:D (q,τ)= D(q,τ)+D τand G (q,τ)= G (q,τ)+G τsuch that D (q,τ)and G (q,τ)are obtained by the ARIMA estimators speciﬁed in Equations 10and 12;additionally,D t and G t are two independent Gaussian white noises of the following covariance functions (regarding the Central-Limit theorem,the estimationerrors of the instantaneous demand and generation are Gaussian processes):cov(D s ,D t )=σ2d ·δ(s −t )andcov(G s ,G t )=σ2g ·δ(s −t ).Now,assume that community C q uses the q th renewable power plant (DRR)to satisfy its demand.Assuming that at given time t ,community C q has stored S (q,t )units of energy,we obtain that S (q,t )= t 0(G (q,t )−D (q,t ))d t +s q for every t ≥0such that s q is the initial stored energy in the community.By replacing the generation and demand functions with their equivalent random processes,we obtain that S (q,t )= S (q,t )−W t where S (q,t )= t 0( G (q,t )− D (q,t ))d t +s q and W t = t 0(D t −G t )d t .Since G t andD t are two independent Gaussian white noises,W t is a Wiener process of variance (σ2g +σ2d )and covariancefunction cov (W s ,W t )=min {s,t }·(σ2g +σ2d ).Moreover,it is easy to show that the expected value of thestored energy at given time t is S(q,t ).According to the above analysis,the amount of stored energy S (q,t )is equal to the summation ofdeterministic amount S(q,t )and the scaled Wiener process (−W t ).In the rest of our analysis,we assume that the expected value of the stored energy never becomes less than the initial amount of energy (s q );i.e. S(q,t )≥s q for every t ≥0.This condition can be held by providing suﬃcient DRRs for every community (which is designed based on long-term forecasting of power demand and generation).Here,we deﬁne the system adequacy ratio (ρq (0,t ))for the q th community as the probability that theactual stored energy S (q,t )doesn’t meet the low-threshold (s q −λ)for some λ∈[0,s q ]and every t ∈[0,t ].So,ρq (0,t )=Pr ∀t ≤t :S (q,t )>s q −λ ≥Pr M t <λ where M t is the running maximum processcorresponding to the scaled Wiener process W t .So,regarding the characteristics of the running maximum process,we conclude that ρq (0,t )≥erf λ√ such that σ2=σ2g +σ2d .In other words,we assume that if S (q,t )≤s q −λ(the stored energy becomes lower than some threshold in the q th community),the consumers demand will not be satisﬁed anymore.Figure 3shows how the lower-bound of the reliability ratio changes as parameters λand σ2get diﬀerent values.Algorithm 1:LocalLoadManagementUnitInput :Community Index q &time t 1if S(q,t +1)≤s q −λthen 2Ask the bulk generations for s q − S(q,t +1)units of energy;3end4if D (q,t )>G (q,t )then5Create an energy ﬂow of size D (q,t )−G (q,t )originated from the storage unit toward thecustomers.;6end7else8Divide the energy ﬂow originated from the DRR into two branches:one toward the customers andthe other toward the storage unit.9endAs you see in Figure 3,if the DRR of each community is the only source of power for the community customers,the adequacy ratio will substantially decrease over time.In fact,even if the DRRs are suﬃcient to satisfy the customers’demand in long-term( S(q,t )≥s q ),the system adequacy can not be guaranteed because of the white noise errors existed in the short-term forecasting scheme of demand and generation.Subsequently,we have to get help from the bulk generators located outside the community to cancel out the temporary noises and improve the adequacy ratio by generating extra energy on demand .Figure3:Lower bound of the adequacy ratio of a community for three diﬀerent values ofλandσ2. 4.1Canceling out the Noise by Energy RequestsAs mentioned before,if the value of S(q,t)falls below some threshold(s q−λ),the system adequacy will be endanger.In this subsection,we use the bulk generation as a back-up plan to prevent such event.To do this,we design a controller to watch the amount of stored energy in diﬀerent communities during the time. In the case that S(q,t)≤s q−λfor the q th community,the controller asks the bulk generation toﬁll the gap and cancel out the noise−W t by providing the community with W t units of energy.Here,we focus on what has to be done by the LLMU speciﬁed in Figure1b.The recently explained scenario which speciﬁed the functionality of LLMU cannot be implemented in the real-world as the values of G(q,t)and D(q,t)are obtained from random processes.This urges us to forecast theses values in short term(for example,every15minutes).Algorithm1shows the practical way of implementing LLMU using forecasting models for the q t h community.As mentioned before,by forecasting(estimating)the power demand and generation using ARIMA model,we will obtain white noise errors added to the real values of G(q,t)and D(q,t)as the estimated values.These noises on the estimated power values will add some errors in the form of Brownian Motion processes to the estimated values of stored energy.5SUMMARY AND OUTLOOKThis paper proposed a novel hybrid scheme for forecasting the power demand and generation in a residential power distribution network.Our forecasting scheme had two tiers:long-term demand/generation forecaster which is based on Maximum-Likelihood Estimator(MLE)and real-time demand/generation forecaster which is based on Auto-Regressive Integrated Moving-Average(ARIMA)model.The paper also showed how bulk generation improves the adequacy of our residential system by canceling-out the forecasters estimation errors which are in the form of Gaussian White noises.References[1]C.W.Gellings,The smart grid:enabling energy eﬃciency and demand response.The Fairmont Press,Inc.,2009.[2]P.Zhang,F.Li and N.Bhatt.Next-Generation Monitoring,Analysis,and Control for the Future SmartControl Center,IEEE Transactions on Smart Grid,vol.1,no.2,186-192,(September2010).[3]Khosrow Moslehi and Ranjit Kumar.A Reliability Perspective of the Smart Grid,IEEE Transactionson Smart Grid,vol.1,no.1,(June2010).[4]S.M.Amin,and B.F.Wollenberg.Toward a smart grid:power delivery for the21st century.IEEEPower and Energy Magazine,vol.3,no.5,34-41,(Sep.2005).[5]J Mohammadi,A Rahimi-Kian,M-S Ghazizadeh.Aggregated wind power andﬂexible load oﬀeringstrategy,Renewable Power Generation,IET,vol.5,no.6,(December2011).[6]A.Papavasiliou and S.S.Oren,Supplying Renewable Energy to Deferrable Loads:Algorithms andEconomic Analysis,IEEE PES General Meeting,Minneapolis,Minnesota,(July2010).[7]A.Papavasiliou,S.S.Oren,M.Junca,A.G.Dimakis,and T.Dickhoﬀ,Coupling Wind Generators withDeferrable Loads,CITRIS White paper Technical Report,(2008).[8]J.F.Decarolis,and D.W.Keith.The costs of wind’s variability:is there a threshold?,ElectricityJournal,vol.18,no.1,69-77,(2005).[9]Soummya Kar,Gabriela Hug,Javad Mohammadi,J Moura.Distributed State Estimation and EnergyManagement in Smart Grids:A Consensus+Innovations Approach.IEEE Transactions on Selected Topics in Signal Processing,vol.8,no.6,1022-1038,(Dec.2014).[10]EIA,July2013.International Energy Outlook.U.S.Energy Information Administration,DOE/EIA-0484(2013)Edition.[11]L.Hernandez,A Survey on Electric Power Demand Forecasting:Future Trends in Smart Grids,Mi-crogrids and Smart Buildings.IEEE Communications Surveys&Tutorials,vol.16,no.3,1460-1495 (2014).[12]M P.Moghaddam,A.Abdollahi,and M.Rashidinejad.Flexible demand response programs modelingin competitive electricity markets.Applied Energy,vol.88,no.9,3257-3269,(2011).[13]M.H.Amini,B.Nabi,and M.-R.Haghifam.Load management using multi-agent systems in smartdistribution network,in IEEE Power and Energy Society General Meeting(PES),Vancouver,BC,(July 2013).[14]Le Xie,,and M.D.Ilic.Model predictive dispatch in electric energy systems with intermittent resources.IEEE International Conference on Systems,Man and Cybernetics(SMC),(Oct.2008).[15]Masters,Gilbert M.Renewable and eﬃcient electric power systems.John Wiley&Sons,(2013).[16]Dirty Coal Is Hazardous to Your Health:Moving Beyond Coal-Based Energy Oct.2007,/health/eﬀects/coal.[17]Yi Liu,Naveed Ul Hassan,Shisheng Huang,and Chau Yuen,Electricity Cost Minimization for a Resi-dential Smart Grid with Distributed Generation and Bidirectional Power Transactions.IEEE innovative Smart Grid Technologies(ISGT),(Feb.2013).[18]M.J.Neely,A.Tehrani,and A.Dimakis,Eﬃcient Algorithms for Renewable Energy Allocation toDelay Tolerant Consumers,in Proc.IEEE First Intl Conf.Smart Grid Comm.(SmartGridComm), (Oct.2010).[19]Aoife M.Foley,Paul G.Leahy,Antonino Marvuglia,Eamon J.McKeogh.Current methods and advancesin forecasting of wind power generation,Journal of the World Renewable Energy Network.Volume37, Issue1,1-8,(January2012).[20]Khosrow Moslehi and Ranjit Kumar.A Reliability Perspective of the Smart Grid,IEEE Transactionson Smart Grid,vol.1,no.1,(June2010).[21]R.Banos,F.Manzano-Agugliaro,F.G.Montoya,C.Gil,A.Alcayde,and J.Gomez.Optimization meth-ods applied to renewable and sustainable energy:A review,Renewable and Sustainable Energy Reviews, Volume15,Issue4,X1753-1766,(June2010).。