Smart Grids Vision

INTERNSHIP REPORT ON SMART GRID

Author:Kamaldeep SinghAravind Avvar

Supervisor:Prof. Matti Latva-ahoProf. Premanandana Rajatheva

1

Report on Smart Grids Report on Smart Grid VisionReport on Smart Grid's VisionReport on Smart Grid's Vision

ABSTRACT

In this report we analyze the background work required to cognize onSmart Grid technology and how it can be effectively implemented. The mainobjective of this report is to consider about the channel models, differentways of measurement, standards and tools used to optimize and allocatethe resources Smart Grid. Firstly we report briefly the definition of SmartGrid and how data is communicated in Smart Grids.Secondly we study onthe different ways of measuring and optimizing the allocation of resources.Finally we study about the present standards and how well are they imple-mented in the real system. At last we conclude with the present scenario inSmart Grids and how we can improve on the present implications.

Key words: Power Line Communications, Smart Grid Technology, Stan-dards, Research Areas in Smart Grids.

2

Contents

1 INTRODUCTION 61.1 What is Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 61.2 Data Communication on Smart Grid . . . . . . . . . . . . . . 8

2 CHANNEL MODELS IN SMART GRID 112.1 Channels used in Smart Grids . . . . . . . . . . . . . . . . . . 112.2 FSK System for Smart Utility Network . . . . . . . . . . . . . 12

2.2.1 Communication Network Architecture . . . . . . . . . 132.2.2 Power Line Intelligent Metering Evolution . . . . . . . 14

3 POWER FLOW MANAGEMENT IN SMART GRID 163.1 CDMA Channel Model in Smart Grid . . . . . . . . . . . . . 163.2 Smart wires (SW) . . . . . . . . . . . . . . . . . . . . . . . . 18

4 TOOLS USED IN SMART GRID 204.1 Simulations Tools used in Smart Grid . . . . . . . . . . . . . 20

5 OPTIMIZATION IN SMART GRID 245.1 Optimization Models for Energy Reallocation in a Smart Grid 24

6 RESOURCE ALLOCATION IN SMART GRID 266.1 Cost Aware Grid Implementation . . . . . . . . . . . . . . . . 266.2 Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

7 STANDARDS USED IN SMART GRID 297.1 IEEE P2030 Standard . . . . . . . . . . . . . . . . . . . . . . 29

7.1.1 IEEE P1901- Broadband over the power line Networks 317.1.2 Next Generation Service Overlay Network IEEE P1903 32

8 SMART GRID DEVELOPMENT VISION 34

9 CONCLUSION 36

10 REFERENCES 37

3

FOREWORD

This Internship report is based on the practical training which we performedin the department of CWC at the University of Oulu. The purpose of thisInternship is to do the literature review on Smart Grids and to understandits functionality.

Oulu, 15th June 2011

Kamaldeep SinghAravind Avvar

4

LIST OF ABBREVIATIONS

FSK Frequency Shift KeyingCDMA Code Division Multiple AccessIEEE Institute of Electrical and Electronics EngineersAMI Advance Metering SystemOFDMA Orthogonal Frequency Division Multiple AccessPLC Power Line CommunicationsMIMO Multiple Input Multiple OutputMISO Multiple Input Single Output3GPP 3rd Generation Partnership ProjectOFDM Orthogonal Frequency Division MultiplexingIDMA Interleave Division Multiple AccessWimax Worldwide Interoperability for Microwave AccessSUN Smart Utility NetworksCPE Customer-premises equipmentEPS Electrical Power SystemQOS Quality of ServiceOSI Open System InterconnectionBPL Broadband over Power LinesCTP Capacitated Transshipment ProblemIP Internet ProtocolBER Bit Error RateQPSK Quadrature Phase Shift KeyingBPSK Binary Phase Shift KeyingDPSK Differential Quadrature Phase Shift KeyingSCADA Supervisory Control and Data Acquisition

5

Chapter 1

INTRODUCTION

This chapter gives a brief outlook of the Smart Grid and its functionality.Data communication on the Smart Grid is explained in detail.

1.1 What is Smart Grid

Smart Grid is a vision that how to generate, distribute and consume en-ergy. In addition to this, it is how to overcome the shortcomings of today’selectricity grids. The main goal is to mitigate the impact of disruptions ofthe energy supply as well as to enhance security and reliability of energyinfrastructure. The operation and meaning of the smart grid can be bestunderstood with the figure given below.

Figure 1.1: Smart Grid[26]

6

The topology of Smart Grid is shown in the fig. 1.1. It mainly consistsof domains, interfaces and its distribution. There have been radical changesin a way to generate, distribute and consume energy. The electric grids wereoriginally designed to distribute the electricity from small number of gener-ators to millions of the consumers based on the concept that supply mustfollow the demand [28]. This phenomenon required additional generatorsto be instantly being available to balance the increase in demand and alsothe installed generation capacity is sufficient to satisfy the demand at thepeak time. In addition, consumers are charged for electricity on a per unitbasis however the real cost of generating the electricity varies throughoutthe day [27]. Clearly, because of the inefficiencies in the previous electricitygrid system, a more dynamic and smarter grid system was paramount.

This vision has led to the innovation of smart grids, which are improvedelectricity grids where household and businesses act as both generators aswell as consumer of electricity, and were the information and electricity flowtogether in network.Smart Grid is an electricity delivery system with com-munication facilities and information facilities for the efficient and reliablegrid operation with improved customer satisfaction and cleaner environ-ment. Smart Grids are also called Green Communication because it is na-ture friendly technology. By using the Two-way communication capabilitiesof the smart meter one can enhance the current power system. Smart Gridwill allow the flow of energy from consumers to outside network dependingon demand and supply conditions. Features of smart grid Includes real timemonitoring and exchange of the Information [28]. Consumers can adjustelectric supply according to their needs, cost, power, level of reliability andenvironmental Impact.

Smart Grids will remove any hindrance in economic growth and facili-tates the delivery of the energy from the renewable sources of energy likewind, sun and water. There would be faster detection of outages and black-outs and rapid system restoration will improve the security and reliabilityof the grid [30]. Moreover, Grid will be less vulnerable to potential attacksand threats. The current electric power grid is outdated and cannot supportthe increase in the energy consumption. Hence, it is expected that smartgrids which are more reliable, secure, economic, efficient and environmentalfriendly grids will replace the old power grids.

The backbone of the Smart Grids is the Advance Metering Infrastruc-ture (AMI) consisting of the Smart Meters and the communication networkwhich has capability to monitor and repair the faulty network in the realtime [1].The utilization structure of smart grid needs to be analyzed beforeimplementation of the AMI. The utilization or the component structure ofthe smart gird is shown in the below figure.

The fig. 1.2 describes the utilization structure of grid from the generationto the consumption. The figure mainly explains the component utilization.The vision of the Smart Grid is to research, develop and demonstrate a

7

Figure 1.2: Smart Grid Structure[29]

two-way electricity network that will meet the increasing energy demands.Electricity power Grids must be:

• Flexible: Grid should easily adapt to the changing and challengingenvironment.

• Accessible: Grid must be accessible to all users and should have highefficiency local generation with zero or low carbon emission.

• Reliable: Grid must assure improved quality of supply and securityand must adapt itself with increase in demand without any hazardsand uncertainties.

• Economic: Grid is best valued through innovations and efficient energymanagement.

1.2 Data Communication on Smart Grid

The smart grid system requires high speed sensing of the date from all thesensor nodes within few power cycles [1]. The AMI is employ the meterson the grid and which are used to provide all the vital information to themaster head end within very short duration of time. The two head endand the meter are located on the different parts of the network. Orthogo-nal Frequency Division Multiple Access (OFDMA) based communication isused over low voltage power line in CENELEC band A and B [3]. In Or-thogonal Frequency Division Multiple Access channel model time varyingand frequency selectivity power grid channels and noise is undertaken. AMIprovides an ability to use electricity more efficiently and monitor and repairthe networks in the real time.

8

The multiuser communication over the low voltage undergoes variouschallenges such as large number of sensors, time varying circuits, high back-ground noise, and varying Grid topologies [18]. The channel model viewscurrent grid configuration as a Multiple Input Multiple Output/Multiple In-put Single Output (MIMO/MISO) channel and use the channel informationto develop on OFDMA based transrecievers.

The time variation of the loads represent the complex frequency depen-dent, switching behavior in the CENELEC band of the residential and com-mercial powered equipment’s. The communication is established betweenthe head end and the meter [3]. Not only the channel frequency selectivitycauses the fading but switching on/off of loads also causes fading. This isdue to the time varying behavior of the circuit elements. Time varying loadscauses non-linear behavior [18]. However, non-linearity changes slowly. Wecan use quasi static approximation and Fourier analysis for time varying andstochastic impedance.

Monte Carlo simulations can be used to estimate various parameterslike mean, correlation etc. OFDMA system have been in use for variouswireless system includes Wimax and 3rd Generation Partnership Project(3GPP) for optimizing the simultaneous use of available bandwidth for thedata transmission from the mobile station to the base station [2].

A unique subset of subcarrier is assigned to each user in an OFDMAsystem to simultaneously transmit the data.

Figure 1.3: Smart Grid Architecture[25]

In OFDM based systems the available bandwidth is divided into numberof sub bands and each sub band is assigned to different users [3]. The Lowvoltage power lines are used in the real time communication, and hence can

9

be used in the smart grid monitoring systems [5]. Any system functionalitycan be best understood with the layered architecture. The architecture ofthe present smart grid is shown in the fig 1.3. It analyzes each functionof the grid to match with the definite layer so that the total automationdoesn’t need any human intervention.

The fig.1.3 describes layer architecture from the bulk generation to trans-mission, distribution and finally end customer who is going to get the ser-vice. The physical layer basically constitutes the generation, distribution.The next layer is communication network layer and it takes cares of the net-working functionality in smart grid. The next layer which is most importantlayer i.e. communication network security layer which take security in toconsideration for the network functionality. Each layer is interdependent oneach another for the efficient smart grid implementation. In this chapter,we reviewed the various definitions on Smart Grids and how the data iscommunicated in Smart Grids.

10

Chapter 2

CHANNEL MODELS INSMART GRID

We will analyze the use of Channel Models in Smart Grids. There aremany Channels Models used in Smart Grid. But we will mainly concentrateand narrow down to the main Communication Channel Models employed inpresent Smart Grids.

2.1 Channels used in Smart Grids

Power Line Communications (PLC) plays an important role in Smart Gridsfor its cost efficiency [13]. Number of methods has been used to deal withthe challenges in PLC like selectivity fading and impulsive noise.

One of the methods namely Orthogonal Frequency Division Multiplex-ing Interleave Division Multiple Access (OFDM-IDMA), which can be usedto solve the problems caused by the frequency selective channel and im-pulse noise. There are two major problems in the PLC that are frequencyselectivity which is caused by the Reflections generated by the impedancediscontinuities [11]. The second problem is noise which consists of back-ground noise and impedance noise. The impedance noise is caused due tothe switching behavior of transient elements.

OFDM systems are used to convert the frequency selective channel intothe frequency flat channels so that use of the complicated equalizers isavoided. However, OFDM is not able to handle the busty errors causedby the impulsive noise in PLC. Interleavers can be used to separate the sub-sequent affected symbols. Hence, to get the better performance than OFDMsystems, a new system have been proposed which is known as OFDM-IDMAwhich is enhanced version of OFDM systems.

OFDM-IDMA is interleave division multiple access uses different chiplevel interleaving sequences in contract to the differential spreading sequencesin OFDM systems to distinguish different users [11] .If the interleaving se-

11

quences are treated as spreading codes then IDMA can regarded as a specialcase of the CDMA.

The performance of OFDM-IDMA is better than the OFDMA becauseof the following reasons:-

• Use of the spreader of long length in OFDM-IDMA enables the col-lection of multipath diversity provided by the PLC channels whileOFDMA fails to collect multipath diversity.

• Spreader couples with interleaver in OFDM-IDMA to alleviate theeffect of impulsive noise by averaging the impulse affected subcarrierswith number of unaffected subcarriers.

OFDM is a feasible solution for converting the frequency fading channelsinto the flat fading channels over and IDMA alleviates the effect of impulsivenoise by averaging a number of sub-carriers.

2.2 FSK System for Smart Utility Network

In parallel to the Smart Grids, Smart Utility Networks generally known asSUN are also gaining popularity these days. SUN is the networking systemthat is used in the utility services such as electricity, water, gas, so as tocover the information from millions of supported nodes across the diversegeographical environment. SUN design is called IEEE 802.15.4g [4]. SUNsystem supports a large number of nodes within the network therefore ittakes into account the homogenous co-existence among its devices. Ho-mogenous co-existence is handled by the physical layer and medium accesscontrol sub layer.

Advantage of low duty cycle is that it provides technical strength, lowpower consumption and good co-existence capabilities but it reduces thedata rate. There is a long silent period in between the two consecutivesignal transmissions. The silent period enables features such as power savingand effective multiple access [4]. Application of Low duty systems is in theimpulse radios and the spread spectrum radio, both related to the ultra-wideband technology. Low duty systems are also used extensively in severalspecifications of the wireless personal area networks.

The Utility meters are connected through a wireless channel to the datacollectors and further data collectors are further connected to the utilityprovider control center servers through the main station.

In SUN systems the data flows from the end nodes to the utility providersfacilitating billing data collection, load assessment and other relaxed mea-surement [4]. On the other hand, the SUN also facilitates control and man-agement of utilities services such as service connection/disconnection, servicemonitoring and load balancing.

12

There are two types of devices in the network, the coordinator capabledevices and the normal devices. In a network cluster, coordinator capabledevice are used to manage the network timing and resources, while the otherdevices become network nodes. In a cluster, devices may be formed in a staror tree cluster formulation. Multiple clusters can be joined through the net-work coordinator capable devices from the respective clusters. This enablesa topology that extends to complex multicluster architecture, supportingmesh and peer to peer networks [4]. The data collection from the customerside and to implement back to their components needs intelligence and thatis done with the smart meter system. The following figure shows the imple-mentation of data collection and distribution in smart meter system.

Figure 2.1: Analysis of Data Collection and Distribution[29]

The fig. 2.1 analyzes the data collection and distribution form the smartmetering system. The data from the CPE smart meter collects the dataand sends it to the EPN agent which transfers it to the EPN edge collectionpoint. The data is transferred to many EPN edge points before it reachesthe smart grid. The grid analyzes the data and production and distributionis done depending on the utilization.

2.2.1 Communication Network Architecture

An IP-centric heterogeneous and integrated communication network maybe used to meet the communication demands of Smart Grid applicationsthat can included in different power grid segments and in multiple networktechnologies [25]. The integrated IP network supports data communicationrequired for controlling and managing applications such as smart metering,automated demand response, rapid inter-substation response, and distri-bution automation, synch phasors, SCADA systems, EVs and micro gridconnectivity. The communication network is also expected to support otherutility enterprise traffic.

To provide more reliable services to the consumers there is a need ofrobust and real time communication between the remote points of the net-

13

work and the control room [17]. One way to achieve this is to use theexisting power line infrastructure as the communications medium, a processgenerally known as PLC. Though PLC is not a new concept, advancementsin modulation performance and the ever decreasing cost of implementingmodems in hardware now means that a network wide multi-point to pointnetwork without the need for expensive line traps is possible.

2.2.2 Power Line Intelligent Metering Evolution

The Power Line Intelligent Metering Evolution(PRIME) architecture for theimplementation of metering system in smart grid is shown in the followingfigure [17]. It basically constitute exchange agent which gets the informationfrom the different consumption points. The exchange will implement thesmart metering system. The exchange agent will route the data accordinglybased on the consumption.

Figure 2.2: Intelligent Metering System[30]

The fig. 2.2 shows the implemented power line metering system whichintelligently can take decision depending on the utilization Power line Intel-ligent Metering Evolution is one of the power line communication technolo-gies, which is used in smart metering applications. PRIME calls for a newpublic, open and non-proprietary telecommunications architecture that willsupport the new AMM functionality and enable the building of the elec-tricity networks of the future, or Smart Grids. The PRIME PHY / MACspecifications are open, publicly available. PRIME employs OFDM modu-lation in the CENELEC A band (9 - 95 kHz), and achieves data rates from21 kbps to 128 kbps at the PHY layer.

14

There are two basic communication scenarios, one is where we cannotafford to have delays such as control signals in the power system opera-tions currently carried out by Supervisory Control and Data Acquisition (SCADA) system, the other is some delay can be allowed. Application ofwireless and wire line access in these areas should be carefully considered.

The other critical aspect is the communication security. With the ad-vent of smart meters, ’always on’ security is essential as opposed to ’on off’security provided for E-commerce applications. Universal, intelligent andmultifunctional devices controlling power distribution and measurement willbecome the enabling technology of the ICT-driven Smart Grid. Agents canbe used for acquiring and monitoring data, support decision making, rep-resent devices and controls etc. They act autonomously and communicatewith each other across open and distributed environments.

In this chapter, we made a study on the present channel models availableand deployed in smart grids for efficient functionality.

15

Chapter 3

POWER FLOWMANAGEMENT INSMART GRID

In recent years, there has been an increased demand for more efficient waysof managing the power distribution in electricity networks; in particular itis desired to reduce the wasteful electricity consumption in order to reducecosts and the adverse effect of electricity generation on the environment.

3.1 CDMA Channel Model in Smart Grid

In order to meet the changing requirements, more sophisticated methods ofmeasuring and controlling the power consumption are desirable. More, so-phisticated networks, sometimes known as Smart Grids, have been proposed,which may include features such as a capability to turn off certain house-hold appliances or factory processes at times of peak demand [25]. TheseSmart Grids may use sophisticated meters, sometimes known as Smart Me-ters, capable of intermittently measuring power consumption in near realtime, and of indicating energy prices to consumers. However, such metersare typically located at the premises of a customer or provider, and measurethe amount of electrical power flow as a total of all devices located in thepremises [25]. This means that power flows relating to individual devices ata given premises, or a group of devices distributed across multiple premises,cannot easily be measured, particularly in view of the relatively high costof smart meters making it prohibitive to install a separate meter at eachpower consuming and/or providing unit to be measured. There is provideda method of controlling electricity power within an electricity distributionnetwork, the electricity distribution network comprising a measured node,the measured node being arranged to access data store storing data indica-tive of one or more predefined power flow patterns, in which a power unit is

16

electrically connected to the electricity distribution network and is arrangedto consume electric power from/or provide electric power to the electricitydistribution network such that a change in consumption and/or provision ofelectricity distribution network such that a change in consumption and/orprovision of electric power by the power unit results in a change in powerflow in the network.

The method comprises of controlling the power flow to and from thepower unit in accordance with a control sequence, such that the consumptionand /or provision of power by the power unit results in a power flow havinga said predefined power flow pattern, and a characteristic of the power flowresulting from the unit is measured by the measurement node.

By controlling the power flow at the power unit according to a predefinedpower flow pattern, a measurement node in a network to which the unit isconnected having the access to the pattern can detect and measure thepower flow resulting from the power unit, allowing the power flow to beremotely detected and measured. Further, since the method requires onlythat the power flow to and/or from a power unit to be controlled, it doesnot require complicated and expensive measuring equipment, such as smartmeters [25]. Each of a distributed group said power units is connected tothe electricity distribution network, each of which having an associated saidpower flow control device, and the method comprises using the power flowcontrol devices to control the power flow to and/or from the plurality ofunits in accordance with the control sequence, such that the consumptionand/or provision of power by the plurality of power units is coordinated tocollectively provide a power flow having the predefined power flow patternand a characteristic measurable by the measured node.

By providing a group of, perhaps distribute, power units with the samecontrol sequence, so that they collectively provide a combined power flowaccording to the predefined pattern, the combined power flow resulting fromgroup can be measured [25]. In some embodiments, a plurality of the groupsis connected to the network, and the method comprises controlling the powerflow to and/or from each of the groups according to different control se-quences, such that the power flow patterns resulting from the said groupsmutually orthogonal, or quasi orthogonal, such that a power flow character-istic associated with each of the power flow patterns can be measured at themeasurement node independently of each of the other patterns.

By using orthogonal power flow patterns, power flow from multiple groupsof devices can be measured simultaneously[25]. There is provided a methodof controlling the electricity flow in an electricity distribution network, theelectricity distribution network comprising a plurality of distributed groupsof power units, each of said power units being arranged to consume and/or provide electricity associated with the electricity distribution network,wherein each power unit in a given group is arranged to be controlled bya control sequence assigned to the group, the control sequence controlling

17

power consumption and/or provision by each unit of the group according toa predefined pattern, resulting in a power flow pattern and each of the mea-surement nodes being arranged to measure a characteristic of power flowingin the network according to power consumption of one or more group.

The distribution network is 105 which are distributing the energy to 108networks which has different types of consumption units with the CDMAspreading code separation. The CDMA code is used to separate the groupsand users in the smart grid.

3.2 Smart wires (SW)

SW is a technology which enables to realize low cost transmission line mon-itoring and power flow control in meshed networks. SW allows to utilitiesincreased power transfer in meshed networks by increasing average line uti-lization. Georgia Tech has developed the SW technology which convertedexisting transmission line to a smart asset, able to monitor and regulateits power flow, thereby shifting excess power to underutilized lines in thenetwork [7]. The smart wire circuit schematic constitutes the power linewhere it is received by the step down transformer. The smart wire has acontrol circuit which controls the flow of electricity in the network. Thecircuit schematic of the smart wire is shown in the below figure.

Figure 3.1: Smart Wire Circuit Schematic[7]

The fig. 3.2 describes the circuit schematic of SW when it connected.The simplest version of the technology, SW, monitors line current and takesautonomous action. As current builds up on SW, the modules autonomouslytake action, gradually increasing the impedance of the line by sensing linecurrent and comparing it against a reference current based on the line ca-pacity.

The heart of each module is a ’single-turn transformer’(STT) couplingthe line current with control circuitry, along with a fast acting switch that

18

inserts the leakage impedance of the STT in series with the transmissionline when the switch is closed. When the switch is open, the leakage andmagnetizing impedances of the STT are inserted in series.

The SW modules are self-powered using the line current and do notrequire communications among the devices or to a central control center.The module operates at line potential and does not connect to the ground,eliminating isolation issues.

We have analyzed different ways of measuring power which would beessential for the implementation of smart grids.

19

Chapter 4

TOOLS USED IN SMARTGRID

In this chapter, we will analyze Tools that are utilized in Smart Grids. TheTools used in Smart Grids are done by Simulation as it requires lot of costfor its implementation. We will also study, the methods used for evaluationof performance of particular Modulation Technique.

4.1 Simulations Tools used in Smart Grid

There are many parameters which should be taken in to consideration whilemodeling and simulating power line communication models. To simulatemost likely integrated /hybrid communication architecture consisting bothPLC and wireless connections is needed. This leads to rather challengingmathematical and simulation models. Furthermore, user mobility modelsgiving different scenarios of plug-in electric vehicle charging is also importantgiven the predictions of high level of penetration over the coming years.Weather forecasting models to optimize the network in a predictive mannerfor wind, solar or hydro energy production will be needed for fostering theimplementation of smart grids with renewable energy resources [6]. Theother aspects such as distribution analysis tools, market models, buildingmodels, renewable resource models and also simulation models for moretheoretical research also play a crucial role.

Communication network model are used by information technology com-panies and national defense researchers and application developers for com-munication network design, engineering, and planning. Some of the commu-nication models for designing communications models are Qualnet, Opnet,Washington State University [6]. One of the most important enabling com-ponents of Smart Grid is reliable communications infrastructure that linkstogether many elements of the grid. The design of communication model isquite very prominent in the implementation of the smart grid.

20

The next step is how to distribute and analyze the smart grid resources.This can be done with the help of Distribution Engineering Analysis Tool.Dynamic analysis tools are used primarily by utilities, ISOs and RTOs fortransmission system engineering and planning, including offline studies ofdynamic stability issues and the production of nomograms describing stabil-ity limits. The dynamic analysis tool helps us to determine the distributionpoint of view and analyze it much more effectively. Some of the tools whichare presently used are PSCad (Manitoba HVDC Research), SIM power sys-tems (The Math works). Renewable resource models are used by utilityplanners and operators, researchers, and investors to analyze resource avail-ability and energy output for wind and solar generation thereby the sys-tem become much more efficient. Some of the models used for analysis areLEAP, BCHP Screening Tool, energy PRO, Solar Advisor Model (SAM),TRNSYS16 [19].Market Models study market design and consumer impactissues, Transmission companies, market operators: to analyze system andmarket performance. Some generation companies study market models toanalyze corporate strategies.

Research-Oriented Simulation Environments is used for analysis of dis-tribution and smart grid assets, controls, and operational strategies, to in-vestigate the technical and economic potential of smart grids, developingand analyzing operational strategies, control algorithms, market/incentivestructures, and communication requirements. The research oriented simu-lation environments allow us for determining the requirements of the smartgrids which would be useful for design of smart grids [19]. co-simulationenvironment would allow engineers to assess the reliability of using a givennetwork technology to support communication-based Smart Grid controlschemes on an existing segment of the electrical grid; and conversely, todetermine the range of control schemes that differing communications tech-nologies can support. It helps us to analyze and compare different strategiesof technologies before the implementation of the actual desired grid. In thisreport we described simulation of different modulation techniques so as todetermine the performance.

To assess the performances of modulation schemes for PLC is to de-velop a channel model that attempts to accurately describe the power linecommunication channel.

One of the first channel models to gain widespread acceptance was madeby Zimmerman and Dostert. In this model, the multipath effects are re-solved by attributing a weighting factor, attenuation portion and delay toeach path. The model is verified for simple networks but loses accuracy asthe number of paths increases [19]. To resolve this problem, the modulationscheme is directly implemented within the ATP-EMTP software environ-ment using the native FORTRAN based models language. The modulatedsignal can be injected into the network at any point using any couplingscheme. The extracted signal is exported to MATLAB and demodulated.

21

Synchronization algorithms allow the simulation to be ’free running’ in thesense that a frame sent from any node can be demodulated by any othernode without additional user intervention. The main idea of the simulationis to evaluate the performance of modulation schemes employed in power linecommunication channels. The simulation setup is split into three domains:

1) ATP-EMTP domain, where the network model and the inductivecoupler is constructed and simulated.

2) ATP Models domain, where the modulator is simulated in FORTRAN.3) MATLAB domain, where demodulation and post processing takes

place.The overall simulation scheme facilitates the simulation of OFDM mod-

ulation on any ATP- EMTP network model [19]. Within ATP-EMTP, onemay replicate network events such as fault transients or switching surges tostudy the effect on the communication link. Furthermore, the scheme al-lows the noise inherent to the power line to be incorporated in the model. Anumber of modulator can be considered simultaneously, giving the user anindication on how time domain multiplexing schemes operate on the powerline channel. The main disadvantage of the presented simulation scheme isthe uncertainty in the accuracy of the line model at high frequencies.

Figure 4.1: BER vs Cyclic Prefix Length of OFDM system [6]

The outcome of the OFDM simulation is found that for an 11 KV ruraloverhead networks, channel is extremely frequency selective. For frequencydomain differential PSK, the BER varies depending on the phase rotationbetween the adjacent subcarriers. The multipath channel component de-grades BER is also frequency dependent. Positioning on the network wasobserved to affect the BER less than the frequency provided the cyclic prefixexceeded the RMS delay spread of the channel [19]. The BER curves for thethree different modulation techniques DQPSK, D8PSK, DBPSK of OFDMcan be seen in the shown graphs.

22

Figure 4.2: Comparison of Modulation Schemes with OFDM in Smart Grid[6]

The graph 5.2 shows the plot of BER vs. the cyclic prefix of OFDMsystem when implemented in Smart Grids. From the above figure we cansee that higher the cyclic prefix lower is the bit error rate. Even frequencyhas also some effect on the performance on the system as it is evident fromthe graph.

We studied on the tool deployed in smart grids for their implementation.These tools are very useful while analyzing for particular topology, techniqueor environment.

23

Chapter 5

OPTIMIZATION INSMART GRID

In this chapter we study on the performance of smart grid and to optimizethe parameters which would contribute to increase it.

5.1 Optimization Models for Energy Reallocationin a Smart Grid

A Smart Grid is a fully automated electrical distribution and generationsystem that is networked, instrumented and controlled. A Smart Grid is aimportant system, in which the devices are addressable with digital meth-ods such as (IP) addresses (Internet Protocol). Many components are alsoequipped with processors and sensors that are capable of carrying out in-telligent actions. The energy produced in the grid can be conventional ornon-conventional like distributed Energy renewable resources [8].

Self-Healing is very much important and needed in today’s new technolo-gies. Smart Grid should have the ability to take corrective decision to carryon autonomously without human intervention. When there is a fault statein the smart grid, the grid should dynamically adapt to the change andmaintain the same power by dynamic algorithms or either quality issues.Some of the common examples of failure are power outage, poor quality ofpower supply and service disruptions. The topology of the Smart Grid as anetwork of nodes representing demand sites, supply sources and junctions,all connected that represent transmission lines. Failures affect the capabilityof certain supply sources to meet the demands for energy at certain sites [9].

The main criteria of optimization is to ideally design the grid in sucha way that it does not cause any outage at supply site by maximizing thecost effectiveness, overall efficiency and reliability of the system. The mod-els which we design should possess reliability, cost-effectiveness, availabil-ity, and uncertainty and consumer preference. The basic modeling template

24

used while formulating a problem is the Capacitated Transshipment Problem(CTP). The uncertainty of is modeled with the integer linear programmingframework using chance-constrained programming methods. The optimiza-tion models have objective functions that optimize a utility function, andconstraints that ensure feasibility of the resource allocations. The agent-based simulation provides a realistic means of evaluating the performanceof the integer linear programming solutions that would function in a smartgrid when it is on state. The agent-oriented simulation of Smart grid oper-ation is used to test and evaluate optimization parameters. In constructinga Smart Grid self-healing model, there are multiple issues. Some pertain tothe physical infrastructure, such as the generators, buses, relays, and trans-mission lines. Others constitute the cyber information infrastructure theyare related to communication, IP protocols etc.

We concentrate here on the physical issues which are needed to be takenin to consideration while maximizing the output with minimum resources.

Distributed Device Control Functions: All the devices which are con-nected should be able to be accessed remotely and can be monitored re-motely. The best example of such remote monitoring is the ability of circuittripping if the input voltage is beyond the threshold.

Selective Load Control: The ability to switch selectively for customersunder undesirable condition and switch on under desirable conditions is verymuch important. It also helps to increase the efficiency of the system. Thisallows customer also to manage their energy consumption according to theirusage.

Micro-grid Islanding: The customer cluster constitutes small scale powergenerators such as solar arrays, fuel cell and wind farms. The cluster istermed as a micro-grid. This micro-grid disconnects itself when there issome issue with the main grid and it connects back when it is in normalcondition [10]. The above mentioned conditions seem quite small but itaffects a lot in the efficiency, cost effectiveness and reliability of the smartgrid.

In this chapter we analyzed different ways to optimize the parametersfor better performance.

25

Chapter 6

RESOURCE ALLOCATIONIN SMART GRID

Resource allocation is very essential part of the grid which has direct in-fluence on the performance of the grid. The resources should be properlyutilized as has lot of effect on the cost function.

6.1 Cost Aware Grid Implementation

Resource Allocation is important issue which maximizes the utility functionand helps us use our resources effectively. It brings high performance andswift flow in the smart grid. The resource allocation problem is modeled asKnapsack problem and design of the resource allocation is mainly to reducethe turnaround time of the grid workflow [14].

The linear programming models which are described in the optimizationof smart grid form the edifice for making intelligent decision making in thegrid. The parameter Grid workflow turnaround is the execution time of theservice offered. The service offered by the grid has also some cost factorwhich needs to be taken in to consideration. More services means shorterturnaround time for allocated grid service. The final outcome expectedfrom the return of investment need proper resource management. There arecurrently three alternative methods [12]: 1. Hierarchical 2. Abstract Owner3. Market Model

We can analyze grid workflow as M/M/C queuing network. The flowof service from the starting till the end of grid is critical path and hasthe average longest execution time. The service average execution time isvery much critical service. This average execution time can be reduced byincreasing the number of abstract owners which is a constraint for cost [15].

26

6.2 Game Theory

Game theory can be used as a potential solution to the above mentionedoptimization and resource allocation problems. It helps us to analyze theequilibriums of the energy infrastructure. Learning and control theory ingame theory allow us to optimize the usage and storage profile of the totalgrid. It also focuses on the system dynamics where all the agents in the sys-tem are given an opportunity to get electricity whenever and wherever theywant. Game theory takes to consideration of the market model while design-ing efficiently utilizing the resources. The operators take peak demands astheir prior importance so that the design, development and implementationbecome efficient with reduced cost [21].

Game theory model decision based on distributed decision making pro-cess. Thereby the roles of the customers are end players in the game. Thesecustomers play the game in such a way to maximize their energy consump-tion with reduced costs. They make the strategies for distribution of energyconsumption depending on their usage. Each customer has their own util-ity function where they try to maximize their utility function and naturallyresulting in better smart grid systems. Each customer while playing theirgame tries to account for preferences ’subscriber preferences’. An optimiza-tion problem can be formulated to maximize the utility of all subscribersby reducing the energy cost. From the operator point of view he can deter-mine the pattern of preferences of the customer depending on his usage anddesign it appropriately. The energy consumption can also change amongdifferent users. Each user has different utility function which is determinedby adopting from the concept of microeconomics.

The game theory also provides flexibility to determine when the deviceshave to interact with the main grid or to make decision when to get con-nected. We can determine when the agent is connected to the main gridand when it gets disconnected we can create a storage profile depending onthe connection thereby minimizing the cost of unnecessary production. Thisstorage profile know the total consumption of the customer and intelligentlymaintain a particular strategy to maximize the parameters which we wouldlike and minimize the cost factor and other factors [18].

Game theory provide strategies to reduce peak demand sites to satiatewith the energy generation and consumption, load management, load shift-ing technologies by storage profiles. In game theory we use distributed loadmanagement profile to control the power demand. This can be done withdynamic pricing algorithms with a focus on real time interaction amongsubscribers. Optimal values of energy consumption optimal price can beadvertised by the operator. We can find distributed energy consumptionsolutions based on congestion games which finally lead us to Nash equilib-rium solution. The optimality criteria designed when implemented in realityneed to be adjusted depending on the implementation. The application of

27

coalition formation in smart grid systems allows us to minimize the cost ofthe whole systems [21].

In this chapter we analyzed the different way to allocate resources as perthe requirement based on different models.

28

Chapter 7

STANDARDS USED INSMART GRID

In this chapter, we will concentrate on the different standards that are de-signed for smart grids. Some of the standards we studied are IEEE P2030,P1901, and P1903. These standards play a very important role in the de-sign of grid. IEEE Standard 2030 Guide for Smart Grid Interoperability ofEnergy Technology and Information Technology operation with the ElectricPower System (EPS) and End-Use Applications and Loads. The first andforemost thing to analyze where we need a standard and why we need it.The reason why we need a standard is to maintain good Quality of Service(QOS) and make each manufacturer understand the minimum requirementsfor the implementation. The reason where we need standard is analyzed inthe following fig..

7.1 IEEE P2030 Standard

Figure 7.1: IEEE P2030 Standard Implementation[27]

29

The fig. 4.1 shows the requirement of standards required for implementa-tion of IEEE P2030 Standard as it specifies interoperability. In recent years,there has been an increased demand for more efficient ways of managing thepower distribution in electricity networks; in particular it is desired to re-duce the wasteful electricity consumption in order to reduce costs and theadverse effect of electricity generation on the environment. In order to meetthe changing requirements, more sophisticated methods of measuring andcontrolling the power consumption are desirable. More, sophisticated net-works, sometimes known as Smart Grids, have been proposed, which mayinclude features such as a capability to turn off certain household appliancesor factory processes at times of peak demand. These Smart Grids may usesophisticated meters, sometimes known as Smart Meters, capable of inter-mittently measuring power consumption in near real time, and of indicatingenergy prices to consumers [16]. The three main components energy, in-formation, communication are very vital in the design of smart grid. Theyform basis for increasing the efficiency of the system.

Figure 7.2: Interoperability of components in Grid[28]

The fig. 4.2 shows the need for interoperability in the components inSmart Grid. Energy Information and Communication are major compo-nents in the implementation of the Smart Grid.Why we need interoperability?

Interoperability is very much important while dealing on broad range ofnetworks. Interoperability the ability of multiple networks, devices and com-ponents to communicate and operate together effectively, securely, withoutuser intervention [29]. The new systems and infrastructure that have beenevolved from the last decade of years are interoperable for better services.

Smart Grid deployment needs lot of planning and analysis to sustain tothe changes after implementation of the system. For this sustainability itneeds to be interoperable and understand the other technologies so as toadapt and become smart. The final aspect of interoperability is backwardcompatibility and smart grid should be able to cope with the previous andpresent standards to become more reliable and efficient. Standards createplatform for the devices and grid for communication irrespective of the lo-

30

cation of the device and the service provider [26]. The introduction of newtechnologies and standards has to be properly secured with proper cybersecurity technologies in order to prevent any breach in the smart grids. Thesecured means of utilization allows providing more efficient smart grids andbetter consumption with smarter networks.

We describe here some of the present standards that are available inthe present day market. So any research or development should take in toconsideration the present standards for interoperability for efficient smartgrid networks.

P2030 Standard Scope and Purpose This standard provides understand-ing and defines smart grid interoperability of the electric power system withend-use applications and loads [26]. Smart grid is a combination of en-ergy technology, Information technology, communication technology whichtogether work for the energy generation, transmission, delivery and com-munication flow among the components. This standard mainly addressesInterconnection and intrafacing frameworks and strategies with design defi-nitions, providing guidance in expanding the current knowledge base. Thisknowledge base is very much required for the architectural design and pro-duction of the efficient electric system. It provides basic knowledge on theinteroperability issues of electric power system with end user taken in toconsideration. It tries to integrate three main domain groups of technol-ogy which are required for implementation of smart grid technology. Theyare information, communication and energy technology. It aims to achieveseamless operation of smart grid technology with the help of the above de-scribed motives. The interoperability of IEEE P2030 can be best understoodby the corresponding fig.4.3

The fig. 4.3 shows the interoperability standard of IEEE which basicallyconstitutes the operation of Smart grid with the interoperability of differentstandards.

7.1.1 IEEE P1901- Broadband over the power line Networks

The P1901 is IEEE working group of the Broadband over power line net-works. The draft published by this group on 1st Feb 2011, mainly addressthe Medium access control and physical layer specifications of the Broad-band over power line networks.

This standard aims to develop communication devices which work atspeeds greater than 100 MBPS over the electric power system. The devicesare termed as Broadband over power line devices BPL. The transmissionfrequencies are below 100 MHZ and they are for both used for first/last milewireless solution for wireless local area network and for distribution points.

This standard defines how these devices are interoperable for all classesof BPLS devices. The standard will take in to consideration of the necessarysecurity questions to ensure the privacy between communicating users and

31

Figure 7.3: IEEE P2030 Interoperability Standard[28]

allow the use of BPL for security sensitive services. This standard is limitedto the physical layer and the medium access sub-layer of the data link layer,as defined by the International Organization for Standardization (ISO) OpenSystems Interconnection (OSI). Purpose of this Standard:

High speed communication links use new modulation techniques and newmedia which are open, and locally shared by several BPL devices. With-out an independent, openly defined standard, BPL devices serving differentapplications will not co-operate with one another and provide unacceptableservice to all parties. The main idea of this standard is fair existence of theBPL devices without getting separated from the main domain. The imple-mentation of this standard will provide with the interoperability with neigh-boring protocols, such as bridging for seamless interconnection via 802.1.The standard also complies with EMC limits set by national regulators, soas to ensure successful co-existence with wireless systems [30].

7.1.2 Next Generation Service Overlay Network IEEE P1903

Next Generation Service Overlay Network IEEE P1903 describes a frame-work of Internet Protocol (IP)-based service overlay networks and specifiescontext-aware, dynamically adaptive services. Some of the services are usinglocally derived information to discover, organize, and maintain traffic flowin the network within a specified local area network. One way is to developnetwork structures, routing and forwarding schemes based on the needs andcapabilities of network structures depending on the customers [30]. The fig.4.4 shows the overlay network of IEEE P1903 standard which uses different

32

Figure 7.4: Overlay Network IEEE P1903[26]

forwarding schemes depending on the load of customers. It describes thecomponents of overlay network IEEE P1903. It shows the options availablefor the networks to switch their load accordingly depending on the customersand network availability.

We have studied different standards which are from IEEE for the betterimplementation of the Smart Grid.

33

Chapter 8

SMART GRIDDEVELOPMENT VISION

In this chapter we study on the future expectations of the smart grid devel-opment in different countries. We study the outcome of grid developmentin the future.

The vision shows the new technologies while retaining the flexibility toadapt to the future developments .Network technologies will increase thepower transfer and will reduce the energy loss and this will improve thequality of services .Advances in the simulation tools will greatly assist toconvert the innovation into the practical application which is beneficial forboth consumers and utilities. Development in the communication, meteringand business system will open up the opportunity at every level on thesystem to increase the market size for technical and commercial field [22].The development vision of the smart grid can be assessed based on theutilization and its functionality. The below figure shows the smart gridvision in future.

The figure 8.1 shows the futuristic vision of Smart Grid which basi-cally constitutes the infrastructure automation utility and data. It needsto support applications from operator to customer which require efficientstructure.

Smart grids are systems which are complicated and composed of intricatedesign that incorporate consumer interactions and decision points. That isthe reason why it makes it difficult for design and development of smartgird. Smart grids are implemented in many countries so development anddemonstration needs to be discussed in global context. But the deploymentis treated to be regional as we need to take in to consideration lot of localfactors which decide efficient deployment. The reason why it needs to bediscussed regional because of the infrastructure, demand growth, generationand market structures [23].

Many countries are motivated by economic, security and environmental

34

Figure 8.1: Smart Grid Vision[26]

factors to choose their own priorities while implementing smart grid tech-nologies. These countries analyze different approaches to assess the impactof potential smart grid deployment [24]. Some of the regional characteristicswhich countries will be taking in to consideration are

• Industry, residential load prevalence or the deployment of electric ve-hicles.

• Status of existing and planned new transmission and distribution net-works.

• Current and planned mix of supply, including fossil, nuclear and re-newable generation.

• Current and future demand and sectoral make-up of demand, such asmanufacturing.

• Ability to interconnect with neighboring regions.

• Regulatory and market structure.

• Climatic conditions and resource availability.

We analyzed on the different ways to implement the smart grid in thenear future with the operator and customer taking in to consideration.

35

Chapter 9

CONCLUSION

In this report we have studied about the Smart Grid technology. It in-cludes the communication on the grid, channel model, resource allocation,optimization, standards and distribution. We have analyzed that currentgrids are outdated, inefficient and overburdened. Smart grids are actuallydesigned to optimize the efficiency and stability. We have studied about thedata communication in Smart Grids and have seen that the communicationin the grid is mainly done using OFDMA technology. We have analyzed howthese channel models are used in practical systems in different applications.We even analyzed how to perform power flow measurement in Smart Gridusing CDMA technology. We then analyzed how we can do simulations toimprove the efficiency of Smart Grid performance. We then analyzed pa-rameters which can optimize and allocate resources in Smart Grids. Finally,we understood the concept behind the present implemented standards inSmart Grids.

36

Chapter 10

REFERENCES

[1] Slootweg, ”Smart Grids - the future or fantasy” Smart Metering -MakingIt Happen, 2009 IET, Publication Year: 2009 , Page(s): 1 - 19.[2] Galli, Scaglione, Zhifang Wang, ”For the Grid and Through the Grid:TheRole of Power Line Communications in the Smart Grid,” Proceedings of theIEEE, vol.99, no.6, pp.998-1027, 2011.[3] Srinivasa Prasanna, G.N Lakshmi,A.Sumanth, S.Simha, V.Bapat, ”Powerline communications and its application”, 2009.ISPLC 2009, PublicationYear: 2009 , Page(s): 273 - 279, IEEE international Symposium.[4] Sum, Chin-Sean, Rahman, Mohammad Azizur, Lan, Zhou,Kojima, Fu-mihide, Funada, Ryuhei, Harada, Hiroshi, ”Performance analysis of low dutyFSK system for Smart Utility Network”, Wireless Communications and Net-working Conference (WCNC), 2011 IEEE, Publication Year: 2011 , Page(s):1568 - 1573.[5] Niyato, Dusit, Wang, Ping, Han, Zhu, Hossain, Ekram, ”Impact of PacketLoss on power demand estimation and power supply cost in smart grid”.Wireless Communications and Networking Conference 2011, IEEE,PublicationYear: 2011 , Page(s): 2024 - 2029.[6] Robson, Haddad, Griffiths, ”Simulation of Power Line Communicationusing ATP-EMTP and MATLAB”, Innovative Smart Grid TechnologiesConference Europe (ISGT Europe), 2010 IEEE, Publication Year: 2010 ,Page(s): 1 - 8.[7] Kreikebaum, Das, Yang, Lambert, Divan, ”Smart Wires - A Distributed,Low-Cost Solution for Controlling Power Flows and Monitoring Transmis-sion Lines”, Innovative Smart Grid Technologies Conference Europe (ISGTEurope), 2010 IEEE PES,Publication Year: 2010 , Page(s): 1 - 8.[8] Madruga E.P, Canha L.N, ”Allocation and integrated configuration of ca-pacitor banks and voltage regulators considering multi-objective variables inSmart grid distribution system”. Industry Applications (INDUSCON), 20109th IEEE/IAS International Conference, Publication Year: 2010 , Page(s):1 - 6.

37

[9] Pengcheng Xiong, Yushun Fan, ”Cost-Aware Grid Workflow ResourceAllocation” Semantics, Knowledge and Grid, Third IEEE International Con-ference, Publication Year: 2007 , Page(s): 422 - 425.[10] Tomasin, Erseghe, ”Constrained optimization of local sources genera-tion in smart grids by SDP approximation”, Power Line Communicationsand Its Applications (ISPLC), 2011 IEEE International Symposium.[11] Xiang Chen, Fengzhong Qu, Liuqing Yang, ”OFDM-IDMA for PowerLine Communications, ”Power Line Communications and Its Applications(ISPLC), 2011 IEEE International Symposium, Publication Year: 2011 ,Page(s): 187 - 192.[12] Taesik Nam, Hyunseung Choo, Hoon Kim, ”LRU-based ZigBee ChannelAllocation for Interference Avoidance in Smart Grid Network”, ConsumerElectronics (ICCE), 2011 IEEE International Conference, Publication Year:2011 , Page(s): 667 - 668.[13] Tsiropoulos, Sarafi, Cottis, ”Broadband over Power Lines” Power Tech”,PowerTech, 2009 IEEE Bucharest, Publication Year: 2009 , Page(s): 1 - 6.[14] Swearingen M, ”Real Time Evaluation and Operation of the Smart GridUsing Game”, Rural Electric Power Conference (REPC),IEEE 2011, Publi-cation Year: 2011 , Page(s): B3-1 - B3-6.[15] Godfrey, Mullen, Dugan, Rodine, Griffith, Golmie, N.;”Modeling SmartGrid Applications with Co-Simulation”, Smart Grid Communications (SmartGrid Comm), 2010 First IEEE International Conference, Publication Year:2010 , Page(s): 291 - 296.[16] Hua Lin, Sambamoorthy S, Shukla S; Thorp J, Mili L, ”Power Systemand Communication Network Co-Simulation for Smart Grid Applications”,Innovative Smart Grid Technologies (ISGT), 2011 IEEE PES, PublicationYear: 2011 , Page(s): 1 - 6.[17] Pipattanasomporn, M.Feroze, H.Rahman, ”Multi-Agent Systems in aDistributed Smart Grid: Design and Implementation”, Power Systems Con-ference and Exposition, 2009. PSCE ’09. IEEE/PES, Publication Year:2009 , Page(s): 1 - 8.[18] Han Kim, Varadarajan B, Dabak A, ”Performance Analysis and En-hancements of Narrowband OFDM Powerline Communication Systems”,Smart Grid Communications (Smart Grid Comm), 2010 IEEE internationalConference, Publication Year: 2010 , Page(s): 362 - 367.[19] Galli, Scaglione, Zhifang Wang, ”Power Line Communications and theSmart Grid”, Smart Grid Communications (Smart Grid Comm), 2010 FirstIEEE International Conference, Publication Year: 2010 , Page(s): 303 -308.[20] Molderink, A, Bakker, V, Bosman M.G.C, Hurink J.L, Smit G.J.M,”Management and Control of Domestic Smart Grid Technology” ,SmartGrid, IEEE Transactions, Volume: 1 , Issue: 2, Publication Year: 2010 ,Page(s): 109 - 119 .[21] Heile, ”Smart Grids for Green Communications”, Wireless Communi-

38

cations, IEEE Transaction, Volume: 17 , Issue: 3 , Publication Year: 2010, Page(s): 4 - 6.[22] Bakker V, Molderink A, Bosman M.G.C, Hurink J.L, Smit, G.J.M, ”OnSimulating the Effect on the Energy Efficiency of Smart Grid Technologies”,Winter Simulation Conference (WSC), Proceedings of the 2010, PublicationYear: 2010 , Page(s): 393 - 404.[23] Sauter T, Lobashov M, ”End-to-End Communication Architecture forSmart Grids”, Industrial Electronics, IEEE Transactions, Volume: 58 , Is-sue: 4, Publication Year: 2011 , Page(s): 1218 - 1228.[24] Heikki Huomo, ”Power Flow Management and Measurement”, UKPatent Application GB 2469361A.[25] http://www.oe.energy.gov/DocumentsandMedia.[26] http://www.sgiclearinghouse.org/ConceptualModel.[27] http://cse.unl.edu/ byrav/INFOCOM2011/workshops/papers.[28] http://ec.europa.euresearch/energy/pdf.[29] http://www.carbonlighthouse.com.[30] http://www.smartgrids.eu/documents.

39