NuhrGrid Proposal Libya v7

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Proposal ContentsProposal Contents

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Executive Summary

Who We Are - Page 2

Smart Grid - Libya 2016www.nuhrgrid.com | [email protected] | 206-618-5326

This proposal is for the installation and implementation of smart grid technologies in Libya, starting specically with Tripoli, and setting a foundation for spreading smart grid technology to other cities. Smart grid technology is the current trend for electrical grid upgrades and progress in the world. It is being implemented in the United States, Europe and most of the 1st world, and is the next step in electrical grid advancement. By creating a decentralized system there is more eciency, more resiliency to damages and shortages, and more cost eciency than legacy electrical grid designs. Providing real-time control and adaptability, the smart grid utilizes the most advanced state-of-the-art computing and Big Data technology to solve current and future electrical grid problems. As an example, shortages from failures of electrical supply lines will be xed in real-time by automated load balancing from other supply lines. This proposal contains a rough cost estimate and timelines for the various modular and parallel components of smart grid implementation.

Eciency - Page 5

Timelines & Goals - Page 3

Reliability - Page 8

The Cost Eectiveness - Page 10

Overview of Technologies - Page 12

Global Grid Info - Page 32

Modules - Page 37

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Who We AreAs a team, we have decades of experience with global companies and major enterprises, with a wide range of skills.

We have worked with some of the biggest players in the market, in various capacities from software engineering to program management, including Amazon, Microsoft, Nokia, Activision, Divx, Apple, Puget Sound Energy, HP, Luth Research, Amec Foster-Wheeler, NVST, CenturyLink, and Bechtel. Whether it is deploying a high-trac WAN connecting Seattle, San Francisco, Los Angeles, New York, and London, designing and testing Hotmail, working with Fortune 500 companies for enrollment in NTO programs, building an infrastructure management system for the Afghan National Police, developing material for the Xbox 360, organizing the Amazon eCommerce platform metadata or managing global enterprises for Bechtel, we are comfortable in most avenues of technical development and implementation, garnered over many years of experience.

Who We AreWho We Are


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Timelines & GoalsTimelines & Goals


Meter ReplacementOnce the project is underway, it is expected to take between one month and twelve months to fully install the smart grid in Tripoli. This estimate is based on the following: Maximum 1 man-hour per smart meter installation, minimum 20 minutes 400-1000 technicians in the eld Approximately 600,000 meters replaced 6,000 data concentrators installed

Data CentersData Center installation will take approximately three to six months based on the following: Availability of secure data-ready facilities Validation of hardware components for each data center Establishment of high-speed connectivity between data centers Establishment of lower-speed connectivity between concentrators and data center

Software ValidationSoftware validation will occur over six to nine months, including the following: Maximum security distributed data collection and control architecture Disaster planning and recovery testing Virtual control room Automated alerting Real-time automated control infrastructure Data analysis and visualization

TrainingLibyan teams will be trained as portions of the smart grid come online for: Smart grid operations Data analysis and reporting

MeterReplacement

DataCenters

SoftwareValidation Training

Goals & Measures of Success

Reliable power restored to Tripoli

Damage to one portion of grid will not aect remainder of grid

Real-time visibility and control into grid health and operations

Controlled distribution of power, increasing electrical eciency 50-300 percent

Electricity is routed where it is needed

Clear understanding of costs related to each portion of the grid

State-of-the-art infrastructure and control systems

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Timelines & GoalsTimelines & Goals


What Does This Cost?For the city of Tripoli, the smart grid will cost, from beginning to end, between $44,000,000 - $62,000,000. For the entire country, between $159,000,000 - $216,000,000. To begin implementation a 50% retainer is required, based on scope of project.

Future Enhancements

Automated billing with web and phone payment options

Electrical independence for each city

Automatically deployed drone cameras for situational awareness

GPS-based shipping, parcel and mail system to identify delivery locations

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Smart Grid EciencyThe purpose of an electrical power system is to deliver energy in a reliable way from the production points to the consumers. The traditional electrical system architecture is characterized by a unidirectional ow of energy from few production sites to many users.

The current energy distribution networks have been designed for unidirectional energy ow from large plants to users and are not suitable for a massive integration of delocalized small/medium power renewable generation plants. In this architecture, the electric system is fully governed by the system operator that, acting on the generation side, balances the production with consumption requests in real time.

EciencyNumerous contributions to overall improvement of the eciency of energy infrastructure are anticipatedfrom the deployment of smart grid technology. Examples include demand-side management, such as turning o air conditioners during short-term spikes in electricity price, reducing the voltagewhen possible on distribution lines through Voltage/VAR Optimization (VVO), eliminating truck-rolls formeter reading, and reducing truck-rolls by improved outage management using data from AdvancedMetering Infrastructure systems. The overall eect is less redundancy in transmissionand distribution lines, greater utilization of generators, leading to lower power prices and more eciency for the whole system.

Load Adjustment & Load BalancingThe total load connected to the power grid can vary signicantly over time.

Although the total load is the sum of many individual choices of the clients, the overall load is not a stable, slow varying, increment of the

load if a popular television program starts and millions of televisions will draw current instantly. Traditionally, to respond to a rapid

increase in power consumption, faster than the start-up time of a large generator, some spare generators are put on a dissipative

standby mode Using mathematical prediction algorithms it is possible to predict how many standby generators need to be

used, to reach a certain failure rate. In the traditional grid, the failure rate can only be reduced at the cost of more

standby generators. In a smart grid, the load reduction by even a small portion of the clients may eliminate

the problem.

EciencyEciency


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Peak Curtailment, Leveling and Time of Use To reduce demand during the high cost peak usage periods, communications and metering technologies inform smart devices in the home and business when energy demand is high and track how much electricity is used and when it is used. It also gives utility companies the ability to reduce consumption by communicating to devices directly in order to prevent system overloads.To motivate them to cut back use and perform what is calledpeak curtailmentorpeak leveling, prices of electricity are increased during high demand periods, and decreased during low demand periods.It is thought that consumers and businesses will tend to consume less during high demand periods if it is possible for consumers and consumer devices to be aware of the high price premium for using electricity at peak periods. This could mean making trade-os such as cycling on/o air conditioners or running dishes at 9pm instead of 5pm. When businesses and consumers see a direct economic benet of using energy at o-peak times, the theory is that they will include energy cost of operation into their consumer device and building construction decisions and hence become more energy ecient.

This will reduce the amount ofspinning reservethat electric utilities have to keep on stand-by, as theload curvewill level itself through a combination of "invisible hand" free-market capitalism and central control of a large number of devices by power management services that pay consumers a portion of the peak power saved by turning their device o.

SustainabilityThe improved exibility of the smart grid permits greater penetration of highly variable renewable energy sources such assolar powerandwind power, even without the addition ofenergy storage. Current network infrastructure is not built to allow for many distributed feed-in points, and typically even if some feed-in is allowed at the local (distribution) level, the transmission-level infrastructure cannot accommodate it. Rapid uctuations in distributed generation, such as cloudy or gusty weather, present signicant challenges to power engineers who need to ensure stable power levels through varying the output of the more controllable generators such as gas turbines and hydroelectric generators. Smart grid technology is a necessary condition for very large amounts of renewable electricity on the grid for this reason.

EciencyEciency


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Demand Response SupportDemand responsesupport allows generators and loads to

interact in an automated fashion in real time, coordinating demand to atten spikes. Eliminating the fraction of demand that

occurs in these spikes eliminates the cost of adding reserve generators, cutswear and tearand extends the life of equipment,

and allows users to cut their energy bills by telling low priority devices to use energy only when it is cheapest.

Currently, power grid systems have varying degrees of communication within control systems for their high value assets, such as generating plants, transmission lines, substations and major energy users. In general information ows one way, from the users and the loads they control back to the utilities. The utilities attempt to meet the demand and succeed or fail to varying degrees (brownout, rolling blackout, uncontrolled blackout). The total amount of power demand by the users can have a very wideprobability distributionwhich requires spare generating plants in standby mode to respond to the rapidly changing power usage. This one-way ow of information is expensive; the last 10% of generating capacity may be required as little as 1% of the time, and brownouts and outages can be costly to consumers.

Platform for Advanced ServicesAs with other industries, use of robust two-way communications, advanced sensors, and distributed computing technology will improve the eciency, reliability and safety of power delivery and use. It also opens up the potential for entirely new services or improvements on existing ones, such as re monitoring and alarms that can shut o power, make phone calls to emergency services, GPS mail systems, GPS enabled and drone enabled security intelligence gathering, etc.

Power ow control devices clamp onto existing transmission lines to control the ow of power within. Transmission lines enabled with such devices support greater use of renewable energy by providing more consistent, real-time control over how that energy is routed within the grid. This technology enables the grid to more eectively store intermittent energy from renewables for later use.

EciencyEciency


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ReliabilityAlthough a number of OECD countries have recently experienced large-scale blackouts, their electricity systems are regarded as generally secure, according to industry-specic indices that measure the number and duration of outages. Smart grid technologies can maintain and improve system security in the face of challenges such as aging infrastructure, rising demand, variable generation and electric vehicle deployment. By using sensor technology across the electricity system, smart grids can monitor and anticipate system faults before they happen and take corrective action. If outages do occur, smart grids can reduce the spread of the outages and respond more quickly through automated equipment.

Electricity System Considerations - Aging InfrastructureThe electrication of developed countries has occurred over the last 100 years; continued investment is needed to maintain reliability and quality of power. As demand grows and changes, aging distribution and transmission infrastructure will need to be replaced and updated, and new technologies will need to be deployed. Unfortunately, in many regions, the necessary technology investment is hindered by existing market and regulatory structures, which often have long approval processes and do not capture the benets of new, innovative technologies.

Smart grid technologies provide an opportunity to maximize the use of existing infrastructure through better monitoring and management, while new infrastructure can be more strategically deployed. Rapidly growing economies like China have dierent smart grid infrastructure needs from those of OECD countries. Chinas response to its high growth in demand will give it newer distribution and transmission infrastructure than the other three regions examined in detail in this roadmap (OECD Europe, OECD North America and OECD Pacic). In the Pacic region, recent investments in transmission have resulted in newer infrastructure than that in Europe and North America. OECD Europe has the highest proportion of aging transmission and distribution lines, but North America has the largest number of lines and the largest number that are ageing especially at the transmission level. This is an important consideration given the changes in generation and consumption in the IEA scenarios up to 2050, and the need to deploy smart grids strategically.

ReliabilityReliability


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In recent years Japan has invested signicantly in its transmission infrastructure, which is operating with very high reliability levels, and is now focusing on its distribution networks. One example is in Yokahama City, where a large-scale energy management project is using both new and existing houses in urban areas to assess the eects of energy consumption on distribution infrastructure. In the United States, as part of a broad range of smart grid investments, signicant eort is being devoted to deploying phasor measurement units on the transmission system, providing increased information for more reliable operation of aging infrastructure. Peak demand for electricity varies throughout the day and across seasons. Electricity system infrastructure is designed to meet the highest level of demand, so during non-peak times the system is typically underutilized. Building the system to satisfy occasional peak demand requires investments in capacity that would not be needed if the demand curve were atter. Smart grids can reduce peak demand by providing information and incentives to consumers to enable them to shift consumption away from periods of peak demand.

Demand response in the electricity system the mechanism by which end-users (at the industrial, service or residential sector level) alter consumption in response to price or other signals can both reduce peak demand, but also provide system exibility, enabling the deployment of variable generation technologies. Reducing peak demand is likely to be the rst priority, because demand at a system level is relatively predictable and ramps up and down slowly compared with variable generation. As demand response technology develops and human interactions are better understood, the availability, volume and response time of the demand-side resource will provide the exibility necessary to respond to both peak demand and variable generation needs.

The management of peak demand can enable better system planning throughout the entire electricity system, increasing options for new loads such as electric vehicles, for storage deployment and for generation technologies. These benets are essential for new systems where demand growth is very high, and for existing and ageing systems that need to maintain existing and integrate new technologies.

ReliabilityReliability


The Cost Eectiveness of a Smartgrid The smartgrid can create 300% more eciency and electrical coverage, for less than half the cost of one power plant. Libya needs 22 more gas power plants to compensate for its current condition.

Overnight Capital CostsOvernight capital costs area useful measure while determining the current cost of building a power plant. While the measure doesnt take into account nancing costs and ination, it gives a clear indication of the cost of building a power plant at the current moment using a companys own funds. The US Energy Information Administration (or EIA) published estimates for capital costs for various fuel types in April 2013. Note that these costs are indicative costs for plants in the US and dont take into account regional variances.

Capital Costs for Dierent FuelsThe EIAs ndings suggest that natural gasred power plants are cheaper to build with overnight capital costs ranging from $676 to $2,095 per kilowatt (or kW), depending on the technology. Typically, natural gas power plants have capacities ranging from 85 megawatts (or MW) to 620 MW. (1 MW = 1,000 kW.) General Electric (GE), which is part of various ETFs including the SPDR S&P 500 ETF (SPY) and the Industrial Select Sector SPDR ETF (XLI), is the market leader in the gas turbines segment.

Capital costs for coal-red power plants range from $2,934 to $6,599 per kW, depending on the technology. Typical coal-red units have a capacity of 520 MW to 1,300 MW. GE and Siemens (SIEGY) are leaders in the steam turbine segment.

Nuclear plants are costlier to build, with a capital cost of $5,530 per kW for a plant with a capacity of 2,234 MW. GE, Westinghouse, and Fluor (FLR) provide engineering services for nuclear power plants.

Power plants range from the $500,000,000 via gas to the $2,000,000,000 via coal or beyond.

300% 1/2More Ecient The Cost

for

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Timelines & GoalsThe Cost Eectiveness

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Smart grid installation

Between $150 million to $1 billion

6 months to a year

1 incidence of rollout

$500 million to $10 billion

10 years of stability

22 power plants needed in Libya

Cost

Time frame

Amount

Power plant construction


Libyan Electrical DemandThe current installed capacity of Libya is around the 5000 MW mark; but according to available data, the demand is expected to triple within the next decade to a whopping 14,000 MWthat would require the building of 22 new 650 MW gas power plants, with a price-tag of $10 billion USD. And that still only equalizes the supply-demand dichotomy.

BlackoutsAccording to the US government, blackouts cost $500 per person per year. For the US, the total is $150 billion dollars per year. For Saudi Arabia, it is $15 billion dollars per year, and for Libya it is $3 billion dollars per year. This means for Libya, until it can build 22 new power plants, it will lose $30 billion dollars.

By comparison, a smart grid can be installed and built for a fraction of the priceas was done in the United States, in the Pacic Northwest (ve states: Idaho, Washington, Oregon, Wyoming and Montana) for $179 million. Our estimate for Libya, is $158 millionand for Tripoli, $44 million.

Which is the better choice? Read on, for more information.

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Timelines & GoalsThe Cost Eectiveness

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14,000 MW

$10 Billion

Estimated Demand

For 22 New Power Plants$200 Million

For Smart Grid Installationor


Overview of the Technologies Required for the Smart GridThe US Department of Energy lists ve fundamental technologies that will drive the Smart Grid:

Integrated communications, connecting components to open architecture for real-time information and control, allowing every part of the grid to both talk and listen.

Sensing and measurement technologies, to support faster and more accurate responses, such as remote monitoring, time-of-use pricing and demand-side management.

Advanced components, to apply the latest research in superconductivity, storage, power electronics and diagnostics.

Advanced control methods, to monitor essential components, enabling rapid diagnosis and precise solutions appropriate to any event.

Improved interfaces and decision support, to amplify human decision-making, transforming grid operators and managers quite literally into visionaries when it come to seeing into their systems.

Smart Grid TechnologiesThe many smart grid technology areas each consisting of sets of individual technologies span the entire grid, from generation through transmission and distribution to various types of electricity consumers. Some of the technologies are actively being deployed and are considered mature in both their development and application, while others require further development and demonstration. However, not all technology areas need to be installed to increase the smartness of the grid.

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Timelines & GoalsOverview of Technologies

Page 12 Smart Grid - Libya 2016www.nuhrgrid.com | [email protected] | 206-618-5326

Wide-Area Monitoring and ControlReal-time monitoring and display of power-system components and performance, across interconnections and over large geographic areas, help system operators to understand and optimize power system components, behavior and performance. Advanced system operation tools avoid blackouts and facilitate the integration of variable renewable energy resources. Monitoring and control technologies along with advanced system analytics including wide-area situational awareness (WASA), wide-area monitoring systems (WAMS), and wide-area adaptive protection, control and automation (WAAPCA) generate data to inform decision making, mitigate wide-area disturbances, and improve transmission capacity and reliability.

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Phasor Measurement UnitsPopularly referred to as the power systems health meter, Phasor Measurement Units (PMU) sample voltage and current many times a second at a given location, providing an MRI of the power system compared to the X-Ray quality available from earlier Supervisory Control and Data Acquisition (SCADA) technology. Oering wide-area situational awareness, phasors work to ease congestion and bottlenecks and mitigate or even prevent blackouts. Typically, measurements are taken once every 2 or 4 seconds oering a steady state view into the power system behavior. Equipped with SmartGrid communications technologies, measurements taken are precisely time-synchronized and taken many times a second (i.e., 30 samples/second) oering dynamic visibility into the power system. Having the ability to monitor grid conditions and receive automated alerts in real time is essential for ensuring reliability. System-wide and synchronized phasor measurement units take sub-second readings that provide an accurate picture of grid conditions. Adoption of the Smart Grid will enhance every facet of the electric delivery system, including generation, transmission, distribution and consumption. It will energize those utility initiatives that encourage consumers to modify patterns of electricity usage, including the timing and level of electricity demand. It will increase the possibilities of distributed generation, bringing generation closer to those it serves (think: solar panels on your roof rather than some distant power station). The shorter the distance from generation to consumption, the more ecient, economical and green it may be. It will empower consumers to become active participants in their energy choices to a degree never before possible. And it will oer a two-way visibility and control of energy usage.

To fulll the dierent requirements of the Smart Grid, the following enabling technologies must be developed and implemented:

1. Information and communications technologies: These include:

a. Two-way communication technologies to provide connectivity between dierent components in the power system and loads.

b. Open architectures for plug-and-play of home appliances; electric vehicles and micro-generation.

c. Communications, and the necessary software and hardware to provide customers with greater information, enable customers to trade in energy markets and enable customers to provide demand-side response.

d. Software to ensure and maintain the security of information and standards to provide scalability and interoperability of information and communication systems.

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2. Sensing, measurement, control and automation technologies: These include:

a. Intelligent Electronic Devices (IED) to provide advanced protective relaying, measurements, fault records and event records for the power system.

b. Phasor Measurement Units (PMU) and Wide Area Monitoring, Protection and Control (WAMPAC) to ensure the security of the power system.

c. Integrated sensors, measurements, control, automation systems, information and communication technologies to provide rapid diagnosis and timely response to any event in dierent parts of the power system. These will support enhanced asset management and ecient operation of power system components, to help relieve congestion in transmission and distribution circuits and to prevent or minimize potential outages and enable working autonomously when conditions require quick resolution.

d. Smart appliances, communication, controls and monitors to maximize safety, comfort, convenience and energy savings of homes.

e. Smart meters, communication, displays and associated software to allow customers to have greater choice and control over electricity and gas use. They will provide consumers with accurate bills, along with faster and easier supplier switching, to give consumers accurate real-time information on their electricity and gas use and other related information and to enable demand management and demand side participation.

3. Power electronics and energy storage: These include:

a. High Voltage DC (HVDC) transmission and back-to-back schemes and Flexible AC Transmission Systems (FACTS) to enable long distance transport and integration of renewable energy sources;

b. Dierent power electronic interfaces and power electronic supporting devices to provide ecient connection of renewable energy sources and energy storage devices.

c. Series capacitors, Unied Power Flow Controllers (UPFC) and other FACTS devices to provide greater control over power ows in the AC grid.

d. HVDC, FACTS and active lters together with integrated communication and control to ensure greater system exibility, supply reliability and power quality.

e. Power electronic interfaces, integrated communication and control to support system operations by controlling renewable energy sources, energy storage and consumer loads.

f. Energy storage to facilitate greater exibility and reliability of the power system.

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References[1] Erinmez, I.A., Bickers, D.O., Wood, G.F. and Hung, W.W. (1999) NGC Experience with frequency control in England and Wales: provision of frequency response by generator. IEEE PES Winter Meeting, 31 January4 February 1999, New York, USA.

[2] Sun, Q., Wu, J., Zhang, Y. et al. (2010) Comparison of the development of Smart Grids in China and the United Kingdom. IEEE PES Conference on Innovative Smart Grid Technologies Europe, 1113 October 2010, Gothenburg, Sweden.

[3] European Commission (2006) European SmartGrids Technology Platform:Vision and Strategy for Europes Electricity, http://ec.europa.eu/research/energy/pdf/smartgrids_en.pdf (accessed on 4 August 2011).

[4] Department of Energy and Climate Change, UK, Smarter Grids: The Opportunity, December 2009, http://www.decc.gov.uk/assets/decc/what%20we%20do/uk%20energy%20supply/futureelectricitynetworks/1_20091203163757_e_@@_smartergridsopportunity.pdf (accessed on 4 August 2011).

[5] Electricity Networks Strategy Group, A Smart Grid Routemap, February 2010, http://www.ensg.gov.uk/assets/ensg_routemap_nal.pdf (accessed on 4 August 2011).

[6] Kaplan, S.M., Sissine, F., Abel, A. et al. (2009) Smart Grid: Government Series, The Capitol Net, Virginia.

[7] U.S. Department of Energy, Smart Grid System Report, July 2009, http://www.oe.energy.gov/sites/prod/les/oeprod/DocumentsandMedia/SGSRMain_090707_lowres.pdf (accessed on 4 August 2011).

[8] A Compendium of Modern Grid Technologies, July 2009, http://www.netl.doe.gov/smartgrid/referenceshelf/whitepapers/Compendium_of_Technologies_APPROVED_2009_08_18.pdf (accessed on 4 August 2011).

[9] European Commission, ICT for a Low Carbon Economy: Smart Electricity Distribution Networks, July 2009, http://ec.europa.eu/information_society/activities/The Smart Grid 15sustainable_growth/docs/sb_publications/pub_smart_edn_web.pdf (accessed on 4 August 2011)

[10] World Economic Forum (2009) Accelerating Smart Grid Investments, http://www.weforum.org/pdf/SlimCity/SmartGrid2009.pdf (accessed on 4 August 2011).

[11] Pudjianto, D., Ramsay, C. and Strbac, G. (2007) Virtual power plant and system integration of distributed energy resources. Renewable Power Generation, IET, 1 (1), 1016.

[12] Gellings, C.W. (2009) The Smart Grid: Enabling Energy Eciency and Demand Response, The Fairmont Press, Lilburn.

[13] Galvin, R., Yeager, K. and Stuller, J. (2009) Perfect Power: How the Microgrid Revolution Will Unleash Cleaner, Greener, More Abundant Energy, McGraw-Hill, New York.

[14] TheIntegratedEnergyandCommunicationSystemsArchitecture,2004, http://www.epri-intelligrid.com/intelligrid/docs/IECSA_VolumeI.pdf (accessed on 4 August 2011).

[15] XCel Energy Smart Grid: A White Paper, March 2007, http://smartgridcity.xcelenergy.com/media/pdf/SmartGridWhitePaper.pdf (accessed on 4 August 2011).

[16] Southern California Edison Smart Grid Strategy and Roadmap, 2007,http://asset.sce.com/Documents/Environment%20-%20Smart%20Grid/100712_SCE_SmartGridStrategyandRoadmap.pdf (accessed on 4 August 2011).

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Communication Technologies for the Smart GridIntroductionThe communication infrastructure of a power system typically consists of SCADA systems with dedicated communication channels to and from the System Control Centre and a Wide Area Network (WAN). Some long-established power utilities may have private telephone networks and other legacy communication systems. The SCADA systems connect all the major power system operational facilities, that is, the central generating stations, the transmission grid substations and the primary distribution substations to the System Control Centre. The WAN is used for corporate business and market operations. These form the core communication networks of the traditional power system. However, in the Smart Grid, it is expected that these two elements of communication infrastructure will merge into a Utility WAN.

An essential development of the Smart Grid is to extend communication throughout the distribution system and to establish two-way communications with customers through Neighborhood Area Networks (NANs) covering the areas served by distribution substations. Customers premises will have Home Area Networks (HANs). The interface of the Home and Neighborhood Area Networks will be through a smart meter or smart interfacing device.

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The various communication sub-networks that will make up the Smart Grid employ dierent technologies and a key challenge is how they can be integrated eectively. In the ISO/OSI reference model the upper layers deal with applications of the data irrespective of its actual delivery mechanism while the lower layers look after delivery of information irrespective of its application.

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Power Line CommunicationIEEE P1901Under the sponsorship of the IEEE Communication Society, the IEEE P1901 working group was formed in 2005 with the remit to develop a standard for high speed (> 100 Mbps at the physical layer) communication devices via electric power lines, the so-called Broadband over Power Line (BPL) devices. This project is devoted to producing a standard for BPL networks. The in-home and access protocol under IEEE P1901 will support MAC layer and Physical layers that use orthogonal frequency multiplexing (OFDM).

The standard which was in draft form at the end of 2010 will use transmission frequencies below 100 MHz and support BPL devices used for the rst-mile/last-mile connections as well as BPL devices used in buildings for LANs and other data distribution. It ensures that the EMC limits set by national regulators are met so that it is compatible with other wireless and telecommunications systems.

HomePlugHomePlug is a non-standardized broadband technology specied by the HomePlug Powerline Alliance, whose members are major companies in communication equipment manufacturing and in the power industry.

HomePlug Powerline Alliance denes the following standards:

1. HomePlug 1.0: connects devices in homes (110 Mbps).

2. HomePlug AV and AV2: transmits HDTV and VoIP in the home 200 Mbps (AV) and600 Mbps (AV2).

3. HomePlug CC: Command and Control to complement other functions.

4. HomePlug BPL: still a working group addressing last-mile broadband (IEEE P1901).

The transmission technology, OFDM used by HomePlug, is specially tailored for use in the power line environments. It uses 84 equally spaced subcarriers in the frequency band between 4.5 and 21 MHz. Impulsive noise events common in the power line environment are overcome by means of forward error correction and data interleaving.

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Overload and Aging InfrastructureIncreasingly In many countries, the power infrastructure is aging and is being heavily used as demand for electricity rises. This over loading will worsen as large numbers of electric vehicles, heat pumps and other new loads use low-carbon energy from the electric power system. Obtaining planning permission for the installation of new power system equipment, particularly overhead lines, is becoming increasingly dicult. Therefore, demand-side programs have been widely introduced to make better use of the existing power supply infrastructure and to control the growth of demand.

The dual aims of reducing CO2 emissions and improving energy security (energy policy goals in many countries) coincide in the increasing use of renewable energy for electricity generation. However, connection of a large amount of intermittent renewable generation alters the pattern of the output of central generation and the power ows in both transmission and distribution circuits. One solution to this increase in variability is to add large-scale energy storage devices to the power system. This is often not practical at present due to technical limitations and cost. Therefore, exibility in the demand side is seen as another way to enable the integration of a large amount of renewable energy.

Load control or load management has been widespread in power system operation for a long time with a variety of

terminology used to describe it. The name Demand-Side Management (DSM) has been used since the 1970s for a

systematic way of managing loads. Later on, Demand Response (DR), Demand-Side Response (DSR), Demand-Side Bidding (DSB)

and Demand Bidding (DB) were used to describe a range of dierent demand side initiatives. To avoid the confusion caused by such

overlapping concepts and terminologies, as recommended by CIGRE, Demand-Side Integration (DSI) is used in this chapter to refer to all

aspects of the relationships between the electric power system, the energy supply and the end-user load.

Eective implementation of DSI needs an advanced ICT (Information and Communication Technology) infrastructure and good knowledge of system loads. However, the electro-mechanical meters that are presently installed in domestic premises have little or no communication ability and do not transmit information of the load in real time. Smart metering refers to systems that measure, collect, analyze, and manage energy use using advanced ICT. The concept includes two-way communication networks between smart meters and various actors in the energy supply system. The smart meter is seen to facilitate DSI through providing real-time or near-real-time information exchange and advanced control capabilities.

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Key Components of Smart MeteringSmart metering consists of four main components: smart meters, a two-way communication network, a Meter Data Management system (MDM) and HAN. In order to integrate smart metering into the operation and management of the power system, interfaces to a number of existing systems are required, for example, the interface to the load forecasting system, the Outage Management System (OMS) and a Customer Information System (CIS).

Smart Meters: An Overview of the Hardware UsedA traditional electro-mechanical meter has a spinning aluminum disc and a mechanical counter display that counts the revolutions of the disc. The disc is situated in between two coils, one fed with the voltage and the other fed with the current of the load. The current coil produces a magnetic eld, I and the voltage coil produces a magnetic eld, V . The forces acting on the disc due to the interaction between the eddy currents induced by I and the magnetic eld V and the eddy currents induced by V and the magnetic eld I produce a torque. The torque is proportional to the product of instantaneous current and voltage, thus to the power. The number of rotations of the disc is recorded on the mechanical counting device that gives the energy consumption.

The replacement of electro-mechanical meters with electronic meters oers several benets. Electronic meters not only can measure instantaneous power and the amount of energy consumed over time but also other parameters such as power factor, reactive power, voltage and frequency with high accuracy. Data can be measured and stored at specic intervals. Moreover, electronic meters are not sensitive to external magnets or orientation of the meter itself, so they are more tamperproof and more reliable.

Early electronic meters had a display to show energy consumption but were read manually for billing purposes. More recently electronic meters with two-way communications have been introduced. Figure 3 provides a general functional block diagram of a smart meter. In Figure 3, the smart meter architecture has been split into ve sections: signal acquisition, signal conditioning, Analogue to Digital Conversion (ADC), computation and communication.

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Signal AcquisitionA core function of the smart meter is to acquire system parameters accurately and continuously for subsequent computation and communication. The fundamental electrical parameters required are the magnitude and frequency of the voltage and the magnitude and phase displacement (relative to the voltage) of current. Other parameters such as the power factor, the active/reactive power and Total Harmonic Distortion (THD) are computed using these fundamental quantities.

Current and voltage sensors measure the current into the premises (load) and the voltage at the point of supply. In low-cost meters the measuring circuits are connected directly to the power lines, typically using a current-sensing shunt resistor on the current input channel and a resistive voltage divider on the voltage input channel.

Analogue to Digital ConversionCurrent and voltage signals obtained from the sensors are rst sampled and then digitized to be processed by the metering software. Since there are two signals (current and voltage) in a single phase meter, if a single ADC is used, a multiplexer is required to send the signals in turn to the ADC. The ADC converts analogue signals coming from the sensors into a digital form. As the number of levels available for analogue to digital conversion is limited, the ADC conversion always appears in discrete form. Figure 4 shows an example of how samples of a signal are digitized by a 3-bit ADC. Even though 3-bit ADCs are not available, here a 3-bit ADC was used to illustrate the operation of an ADC simply. The 3-bit ADC uses 23 (= 8) levels thus any voltage between 0.8 and 0.6 V is represented by 000 (the most negative range is assigned 000). In other word, 0.8, 0.75, 0.7, and 0.65 are all represented by 000.

Similarly, voltage between 0.6 and 0.4 V is represented by 001, and so on. There are many established methods for conversion of an analogue input signal to a digital output [3, 4, 5]. The majority of the methods involve an arrangement of comparators and registers with a synchronizing clock impulse. The most common ADCs for metering use the successive approximation and the sigma-delta method.

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ComputationThe computation requirements are split into arithmetic operations on input signals, time-stamping of data, preparation of data for communication or output peripherals, handling of routines associated with irregular input (such as payment, tamper detection), storage of data, system updates and co-ordinating dierent functions. The block diagram shows dierent functional blocks associated with the computation functions of a smart meter.

Due to the relatively large number of arithmetic operations required for the derivation of the parameters, a Digital Signal Processor (DSP) is used.

In addition to routine arithmetic operations, a meter deals with a large number of other procedures (that is, payment, tamper detection, system updates, user interactions) as well as other routine tasks (for example, the communication of billing information). Therefore, a high degree of parallelism (the ability to perform multiple tasks, involving the same data sets, simultaneously) and/or buering (the ability to temporarily pause arithmetical operations so that other needs can be attended to) is required.

For computation, volatile memory (where information is lost on loss of power supply) and non-volatile memory is needed. Volatile memory is used for temporary storage of data to support the processor(s) as operations are undertaken. The amount of volatile memory used depends on the quantity, rate and complexity of computation and the rate of communication to/from ports. A certain amount of non-volatile memory is typically required to store specic information, such as the unit serial number and maintenance access key codes. Additionally, data related to energy consumption should be retained until successful communication to the billing company has been achieved.

In order that the acquired data can be meaningfully interrogated, a time reference must be appended to each sample and/or calculated parameter. For this purpose a real-time clock is used. The accuracy of the real-time clock can vary with temperature. In order to maintain this function during system power losses or maintenance, a dedicated clock battery is typically used.

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Neighborhood Area NetworkThe primary function of the Neighborhood Area Network (NAN) is to transfer consumption readings from smart meters. The NAN should also facilitate diagnostic messages, rmware upgrades and real-time or near real-time messages for the power system support. It is anticipated that the data volume transferred from a household for simple metering is less than 100 kB per day and rmware upgrades may require 400 kB of data to be transferred.

However, these numbers will escalate rapidly if dierent real-time or near real-time smart grid functions are added to the smart metering infrastructure.

The communication technology used for the NAN is based on the volume of data transfer. For example, if ZigBee technology, which has a data transfer rate of 250 kb/s is used, then each household would use the communication link only a fraction of a second per day to transfer energy consumption data to the data concentrator.

Data ConcentratorThe data concentrator acts as a relay between the smart meters and the gateway. It manages the meters by automatically detecting them, creates and optimizes repeating chains (if required to establish reliable communication), coordinates the bi-directional delivery of data and monitors the conditions of the meters.

Meter Data Management SystemThe core of a meter data management system is a database, this typically provides services such as data acquisition, validation, adjustment, storage and calculation (for example, data aggregation). This provides rened information for customer service and system operation purposes such as billing, demand forecasting and demand response.

A major issue in the design and implementation of a meter data management system is how to make it open and exible enough to integrate to existing business/enterprise applications and deliver better services and more value to customers while ensuring data security.

Besides the common database functionalities, a meter data management system for smart metering also provides functions such as remote meter connection/disconnection, power status verication, supply restoration verication and on-demand reading of remote smart meters.

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Protocols for CommunicationsCurrently various kinds of communication and protocol types are used for smart metering. For example, a combination of Power Line Carrier (PLC) and GPRS communication is used in Denmark, Finland and Italy. In these European examples, PLC is used between the meter and data concentrator as the last mile technology and GPRS is used between the concentrator and gateway to the data management system.

Table 5 summarizes the characteristics of the most commonly used protocols for demand-side applications, including local AMR, remote AMR, smart metering and home area automation. In Table 5, Y means applicable, and a blank means not applicable or the information is still not available. With local AMR, the meter readings are collected by sta using hand-held devices and with remote AMR the meter readings are collected from a distance through communication links. For most protocols listed in Table 5, the data frame size is also shown.

The important factors for consideration when assessing communication protocols for smart metering are summarized in.

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Demand-Side IntegrationDemand-Side Integration (DSI) is a set of measures to use loads and local generation to support network operation/management and improve the quality of power supply. DSI can help defer investment in new infrastructure by reducing system peak demand. In practice, the potential of DSI depends on: availability and timing of information provided to consumers, the duration and timing of their demand response, performance of the ICT infrastructure, metering, automation of end-use equipment and pricing/contracts.

There are various terms in use in the demand side, whose meanings are closely related to each other but with slightly dierent focuses. Some widely used denitions are:

Demand-Side Management (DSM): utility activities that inuence customer use of electricity.This encompasses the planning, implementation and monitoring of activities designed to encourage consumers to change their electricity usage patterns.

Demand Response (DR): mechanisms to manage the demand in response to supply conditions.

Demand-Side Participation: a set of strategies used in a competitive electricity market by end-use customers to contribute to economic, system security and environmental benets.

Demand-side resources such as exible loads, distributed generation and storage can provide various services to the power system by modifying the load consumption patterns. Such services can include load shifting, valley lling, peak clipping, dynamic energy management, energy eciency improvement and strategic load growth.

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Load shifting is the movement of load between times of day (from on-peak to o-peak) or seasons. A load such as a wet appliance (washing machine) that consumes 1 kW for 2 hours is shifted to o-peak time.

The main dierence between valley lling and load shifting is that valley lling introduces new loads to o-peak time periods, but load shifting only shifts loads so the total energy consumption is unchanged.Energy eciency programs are intended to reduce the overall use of energy. Approaches include oering incentives to adopt energy-ecient appliances, lighting, and other end-uses; or strategies that encourage more ecient electricity use, for example, the feedback of consumption and cost data to consumers, can lead to a reduction in total energy consumption.

With the deployment of smart metering and the development of home area automaton technologies, domestic appliances can be controlled in a more intelligent way, therefore bringing more exibility to the demand side. The load shape is then exible and can be controlled to meet the system needs. However, for the most eective DSI, the utility needs to know not only which loads are installed in the premises but which are in use. In this case two-way communication between the smart meter and network operators is necessary.

Demand-Side Integration describes a set of strategies which can be used in competitive electricity markets to increase the participation of customers in their energy supply. When customers are exposed to market prices, they may respond as described above. For example, by shifting load from the peak to the o-peak period, and/or by reducing their total or peak demand through load control, energy-eciency measures or by installing distributed generation. Customers are able to sell energy services either in the form of reductions in energy consumption or through local generation.

Traditionally, electric power systems were designed assuming that all loads would be met whenever the energy is requested. Domestic customers (and many other loads) use electricity at dierent times and this allows the design of the power system to benet from diversity. For example, although a house in England may draw up to 10 kW at some times, its distribution supply system will be designed on an After Diversity Maximum Demand (ADMD) of only 1 or 2 kW. The coincidence of domestic demand follows although distribution networks may only serve, say, 100 customers the transformers and cables also have signicant thermal inertia and so a further reduction in the rating used for design may be assumed.

Demand-Side Integration has the potential to negate the benecial eects of diversity. Consider a peak clipping control that sends a signal to switch o one hundred 3 kW water heaters that operate under thermostatic control. Although 100 water heaters have been installed, only 20 will be drawing powerat any one time. Thus the peak will be reduced by 60 kW. When, after, two hours, the water heatersare reconnected, all the water tanks will have cooled and a load of 300 kW will be reconnected. Thus DSI measures must consider both the disconnection of loads but also their reconnection and the payback of the energy that has not been supplied. It is much easier to manage both the disconnection of loads and their reconnection with bi-directional communications whereby the state of the loads can be seen by the control system.

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References[1] Kreith, F. and Goswami, D.Y. (2007) Handbook of Energy Eciency and Renewable Energy, CRC Press, New York.

[2] Ziegler, S., Woodward, R.C., Iu, H.H.-C. and Borle, L.J. (2009) Current sensing techniques: A review. IEEE Sensors Journal, 9(4), 354376.

[3] Millman, J. and Grabel, A. (1987) Microelectronics, McGraw-Hill Book Company, Singapore.

[4] Allen, P.E. and Holberg, D.R. (2002) CMOS Analog Circuit Design, Oxford University Press, New York.

[5] Kester, W. (2009) ADC Architectures III: Sigma-Delta ADC Basics, Analogue Devices, MT-022 Tutorial, 2009, http://www.analog.com/static/imported-les/tutorials/MT022.pdf (accessed on 4 August 2011).

[6] Webster, J.G. (1998) The Measurement, Instrumentation and Sensors Handbook, CRC Press, New York.

[7] Kushiro,N.,Suzuki,S.,Nakata,M.etal.(2003)Integratedresidentialgatewaycontroller for home energy management system. IEEE Transactions on Consumer Electronics, 49(3), 629636.

[8] Young, S. and Stanic, R. (2009) SmartMeter to HAN Communications, SmartGrid Australia Intelligent Networking Working Group, July 2009, http://smartgridaustralia.com.au/SGA/Documents/IN_Work_Group_SmartMeter_HAN_Comms.pdf (accessedon 8 August 2011).

[9] Smart Metering Implementation Programme: Statement of Design Requirements, Ofgem E-Serve, July 2010, http://www.decc.gov.uk/assets/decc/consultations/smart-meter-imp-prospectus/225-smart-metering-imp-programme-design.pdf (accessed on 4 August 2011).

[10] Report on Regulatory Requirements, EU project OPENmeter, July 2009, http://www.openmeter.com/les/deliverables/Open_Meter_D1.2_Regulation_v1.1_20090717.pdf (accessed on 4 August 2001).

[11] De Craemer, K. and Deconinck, G. (2010) Analysis of State-of-the-ArtSmart Metering Communication Standards, https://lirias.kuleuven.be/bitstream/123456789/265822/1/SmartMeteringCommStandards.pdf (accessed on 4 August 2011).

[12] IEA Demand Side Management Programme, http://www.ieadsm.org/ (accessed on 4 August 2011).

[13] Gellings, C.W. (2009) The Smart Grid: Enabling Energy Eciency and Demand Response, The Fairmont Press, Lilburn.

[14] Chaudry,M.,Ekanayake,J.andJenkins,N.(2008)OptimumcontrolstrategyofamCHP unit. International Journal of Distributed Energy Resources, 4(4), 265280.

[15] Erinmez, I.A., Bickers, D.O., Wood, G.F. and Hung, W.W. (1999) NGC experience with frequency control in England and Wales: provision of frequency response by generators. IEEE Power Engineering Society Winter Meeting, 1, 590596.

[16] Report of the Investigation into the Automatic Demand Disconnection Following Multriple Generation Losses and the Demand Control Response that Occurred on the 27th May 2008, http://www.nationalgrid.com/NR/rdonlyres/D680C70A-F73D-4484-BA54-95656534B52D/26917/PublicReportIssue1.pdf (accessed on 4 August 2011).

[17] Department of Energy and Climate Change, UK, Smart Metering Implementation Programme: Prospectus, March 2011, http://www.decc.gov.uk/en/content/cms/consultations/smart_mtr_imp/smart_mtr_imp.aspx (accessed on 4 August 2011).

[18] Federal Energy Regulatory Commission, USA, Assessment of Demand Response and Advanced Metering 2007 Sta Report, Sept. 2007, http://www.ferc.gov/legal/sta-reports/09-07-demand-response.pdf (accessed on 4 August 2011).

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Some Notes on DMSElectricity distribution networks connect the high-voltage transmission system to users. Conventional distribution networks have been developed over the past 70 years to accept bulk power from the transmission system and distribute it to customers; generally they have unidirectional power ows. The Smart Grid is a radical reappraisal of the function of distribution networks to include:

Integration of Distributed Energy Resources.

Active control of load demand.

More eective use of distribution network assets.

Distribution systems are extensive and complex and so they are dicult to monitor, control, analyze and manage. Table below shows some of the factors that contribute to the complexity of distribution systems

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Real-time monitoring and remote control are very limited in todays electricity distribution systems and so there is a need for intervention by the system operators particularly during widespread faults and system emergencies. However, it is dicult to deal with such a complex system through manual procedures.

A Distribution Management System (DMS) is a collection of Applications used by the Distribution Network Operators (DNO) to monitor, control and optimize the performance of the distribution system and is an attempt to manage its complexity. The ultimate goal of a DMS is to enable a smart, self-healing distribution system and to provide improvements in supply reliability, quality, eciency and eectiveness of system operation. A DMS should lead to better asset management, the provision of new services and greater customer satisfaction.

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The rst generation of Distribution Management Systems integrated a number of simple Applications into a computer system. An interactive graphical user interface was then added to visualize the network being managed. The subsequent use of large relational databases allowed the management of more complicated distribution networks and a large volume of data. However, as more and more Applications were added, managing the information exchange and maintaining the DMS became a challenge. Standardized models such as the Common Information Model (CIM) were developed to aid information management. For the Smart Grid future, the DMS will use higher-performance ICT hardware, be equipped with greater intelligence, and be deployed in a decentralized manner.

A DMS includes a number of Applications that use modeling and analysis tools together with data sources and interfaces to external systems, as shown above. The modeling and analysis tools are pieces of software which support one or more Applications.

At present, the parameters of distribution system models are obtained from manufacturers data, historical information or site tests. With changes of external conditions, for example, ambient temperature or the aging of equipment, such parameters can change over time and introduce errors into the network modeling and hence result in unreliable system operation.

The ICT infrastructure of a Smart Grid provides the opportunity for more accurate system modeling through the application of statistical system identication techniques. System identication, widely used in control engineering, can be used to build mathematical models of a distribution system based on the large amount of data measured by the ICT system. A set of more accurate, continuously updated models can then be obtained, and used by the DMS Applications.

References[1] Grainger,J.J.andStevenson,W.D.(1994)ElementsofPowerSystemsAnalysis,McGraw-Hill, Maidenhead.

[2] Weedy, B. and Cory, B.J. (2004) Electric Power Systems, John Wiley and Sons, New York.

[3] Jenkins, N., Ekanayake, J.B. and Strbac, G. (2010) Distributed Generation, Institution of Engineering and Technology, Stevenage.

[4] Arrillaga, J. and Watson, N.R. (2001) Computer Modeling of Electrical Power Systems, John Wiley & Sons Ltd, New York.

[5] Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C. (2001) Introduction to Algorithms, MIT Press and McGraw-Hill, New York.

[6] Kersting, W.H. (2001) Distribution System Modelling and Analysis, CRC Press, New York.

[7] Abur, A. and Exposito, A.G. (2004) Power System State Estimation, Marcel Dekker, Inc., New York.

[8] Hatziargyriou, N., Asano, H., Iravani, R. and Marnay, C. (2007) Microgrid: An overview of ongoing research, development and demonstration projects. IEEE Power and Energy Magazine, 5(4), 7894.

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Select International Smart Grid Demonstration and Deployment Eorts Globally - Country National Smart Grid Initiatives

ChinaThe Chinese government has developed a large, long-term stimulus plan to invest in water systems, ruralinfrastructures and power grids, including a substantial investment in smart grids. Smart grids are seenas a way to reduce energy consumption, increase the eciency of the electricity network and manageelectricity generation from renewable technologies. Chinas State Grid Corporation outlined plans in 2010for a pilot smart grid program that maps out deployment to 2030. Smart grids investments will reach atleast $96 billion by 2020. In the United States $4.5 billion was allocated to grid modernization under theAmerican Recovery Reinvestment Act of 2009, including: $3.48 billion for the quick integration ofproven technologies into existing electric grids, $435 million for regional smart grid demonstrations,and $185 million for energy storage and demonstrations.

ItalyBuilding on the success of the Telegestore project, in 2011 the Italian regulator (Autorit per lEnergia Elettrica ed il Gas) has awarded eight tari-based funded projects on active medium voltage distribution systems, to demonstrate at-scale advanced network management and automation solutions necessary to integrate distributed generation. The Ministry of Economic Development has also granted over EUR 200 million for demonstration of smart grids features and network modernization in Southern Italian regions.

JapanThe Federation of Electric Power Companies of Japan is developing a smart grid that incorporates solar power generation by 2020 with government investment of over USD 100 million. The Japanese government has announced a national smart metering initiative and large utilities have announced smart grid programs.

South KoreaThe Korean government has launched a USD 65 million pilot program on Jeju Island in partnership with industry. The pilot consists of a fully integrated smart grid system for 6,000 households, wind farms and four distribution lines. Korea has announced plans to implement smart grids nationwide by 2030.

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Global Grid InfoGlobal Grid Info


SpainIn 2008, the government mandated distribution companies to replace existing meters with new smart meters; this must be done at no additional cost to the customer. The utility Endesa aims to deploy automated meter management to more than 13 million customers on the low voltage network from 2010 to 2015, building on past eorts by the Italian utility ENEL. The communication protocol used will be open. The utility Iberdrola will replace 10 million meters.

GermanyThe E-Energy funding program has several projects focusing on ICTs for the energy system.

AustraliaThe Australian government announced the AUD 100 million Smart Grid, Smart City initiative in 2009 to deliver a commercial-scale smart grid demonstration project. Additional eorts in the area of renewable energy deployments are resulting in further study on smart grids.

United KingdomThe energy regulator OFGEM has an initiative called the Registered Power Zone that will encourage distributors to develop and implement innovative solutions to connect distributed generators to the network. OFGEM has set up a Low Carbon Networks fund that will allow up to GPB 500m support to DSO projects that test new technology, operating and commercial arrangements.

FranceThe electricity distribution operator ERDF is deploying 300,000 smart meters in a pilot project based on an advanced communication protocol named Linky. If the pilot is deemed a success, ERDF will replace all of its 35 million meters with Linky smart meters from 2012 to 2016.

BrazilAPTEL, a utility association, has been working with the Brazilian government on narrowband power line carrier trials with a social and educational focus. Several utilities are also managing smart grid pilots, including Ampla, a power distributor in Rio de Janeiro State owned by the Spanish utility Endesa, which has been deploying smart meters and secure networks to reduce losses from illegal connections. AES Eletropaulo, a distributor in So Paulo State, has developed a smart grid business plan using the existing bre-optic backbone. The utility CEMIG has started a smart grid project based on system architecture developed by the IntelliGrid Consortium, an initiative of the California-based Electric Power Research Institute.

Source: Updated from MEF 2009 using feedback from country experts. Projects are listed in order of largest to smallest amount of investment.

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System SecurityAlthough a number of OECD countries have recently experienced large-scale blackouts, their electricity systems are regarded as generally secure, according to industry-specic indices that measure the number and duration of outages. Smart grid technologies can maintain and improve system security in the face of challenges such as aging infrastructure, rising demand, variable generation and electric vehicle deployment. By using sensor technology across the electricity system, smart grids can monitor and anticipate system faults before they happen and take corrective action. If outages do occur, smart grids can reduce the spread of the outages and respond more quickly through automated equipment.

Electricity System ConsiderationsAging InfrastructureThe electrication of developed countries has occurred over the last 100 years; continued investment is needed to maintain reliability and quality of power. As demand grows and changes, aging distribution and transmission infrastructure will need to be replaced and updated, and new technologies will need to be deployed. Unfortunately, in many regions, the necessary technology investment is hindered by existing market and regulatory structures, which often have long approval processes and do not capture the benets of new, innovative technologies. Smart grid technologies provide an opportunity to maximize the use of existing infrastructure through better monitoring and management, while new infrastructure can be more strategically deployed. Rapidly growing economies like China have dierent smart grid infrastructure needs from those of OECD countries. Chinas response to its high growth in demand will give it newer distribution and transmission infrastructure than the other three regions examined in detail in this roadmap (OECD Europe, OECD North America and OECD Pacic). In the Pacic region, recent investments in transmission have resulted in newer infrastructure than that in Europe and North America. OECD Europe has the highest proportion of aging transmission and distribution lines, but North America has the largest number of lines and the largest number that are aging especially at the transmission level. This is an important consideration given the changes in generation and consumption in the IEA scenarios up to 2050, and the need to deploy smart grids strategically.

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In recent years Japan has invested signicantly in its transmission infrastructure, (www.mobilesmartgrid.eu)

which is operating with very high reliability levels, and is now focusing on its distribution networks. One example is in

Yokahama City, where a large-scale energy management project is using both new and existing houses in urban areas to

assess the eects of energy consumption on distribution infrastructure. In the United States, as part of a broad range of

smart grid investments, signicant eort is being devoted to deploying phasor measurement units on the transmission system,

providing increased information for more reliable operation of aging infrastructure. Peak demand for electricity varies throughout the day and

across seasons.

Electricity system infrastructure is designed to meet the highest level of demand, so during non-peak times the system is typically underutilized. Building the system to satisfy occasional peak demand requires investments in capacity that would not be needed if the demand curve were atter. Smart grids can reduce peak demand by providing information and incentives to consumers to enable them to shift consumption away from periods of peak demand.

Demand response in the electricity system the mechanism by which end-users (at the industrial, service or residential sector level) alter consumption in response to price or other signals can both reduce peak demand, but also provide system exibility, enabling the deployment of variable generation technologies. Reducing peak demand is likely to be the rst priority, because demand at a system level is relatively predictable and ramps up and down slowly compared with variable generation. As demand response technology develops and human interactions are better understood, the availability, volume and response time of the demand-side resource will provide the exibility necessary to respond to both peak demand and variable generation needs.

The management of peak demand can enable better system planning throughout the entire electricity system, increasing options for new loads such as electric vehicles, for storage deployment and for generation technologies. These benets are essential for new systems where demand growth is very high, and for existing and aging systems that need to maintain existing and integrate new technologies.

www.meti.go.jp/english/press/data/20100811_01.html

www.naspi.org/

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Smart Grid Demonstration and Deployment EortsThere has been a marked acceleration in the deployment of smart grid pilot and demonstration projects globally, due in part to the recent government stimulus investment initiatives in 2009 and 2010. Investments around the world have enabled hundreds of projects entirely or partly focused on smart gridtechnologies.. Most current smart grid pilot projects focus on network enhancement eorts such as local balancing, demand-side management (through smart meters) and distributed generation.

Demonstration projects have so far been undertaken on a restricted scale and have been hindered by limited customer participation and a lack of a credible aggregator business model.Data (and security) challenges are likely to increase as existing pilots expand to larger-scale projects.Non-network solutions such as ICTs are being used in a growing number of smart grid projects, bringing a greater dependence on IT and data management systems to enable network operation (Boots et al., 2010).

The Telegestore project, launched in 2001 by ENEL Distribuzione S.p.A. (i.e. prior to the current smart grids stimulus funding) addresses many of these issues. The project installed 33 million smart meters (including system hardware and software architecture) and automated 100,000 distribution substations, while also improving management of the operating workforce and optimizing asset management policies and network investments. The project has resulted in fewer service interruptions, and its EUR 2.1 billion investment has led to actual cost savings of more than EUR 500 million per year. Today an active small and medium scale industry is developing technologies for smart grids and ENEL is continually enhancing the system by introducing new features, technologies and exibility. The project clearly demonstrates the value of a large-scale, integrated deployment of smart grid technologies to solve existing problems and plan for future needs.

Although signicant eort and nancial resources are already being invested in smart grids, the scale of demonstration and deployment co-ordination needs to be increased. Several organizations have created, are creating or are calling for the creation of an inventory or database of detailed case studies to gather the lessons learned from such projects, particularly in the areas of policy, standards and regulation, nance and business models, technology development, consumer engagement and workforce training. These include the International Smart Grid Action Network, Asia-Pacic Economic co-operation, European Union Set Plan, as well as a number of national initiatives.

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Low Estimate

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ModulesModules

Page 37

Region

Tripoli

West

Alwosta

Bengazi

South

Green Mountain

$9,238,322.77

$6,251,265.08

$5,512,199.26

$4,526,778.16

$2,679,113.60

$1,847,664.55

$25,634,237.20

$19,027,167.17

$17,392,428.20

$15,212,776.23

$11,125,928.79

$9,286,847.44

$9,517,736.75

$6,440,335.20

$5,678,916.26

$4,663,691.01

$2,760,143.66

$1,903,547.35

$44,390,296.72

$31,718,767.45

$28,583,543.71

$24,403,245.39

$16,565,186.05

$13,038,059.34

$158,699,098.66

Meters Data Modules Phasers Grand Total

High Estimate

Region

Tripoli

West

Alwosta

Bengazi

South

Green Mountain

$26,818,385.14

$18,147,107.28

$16,001,636.47

$13,141,008.72

$7,777,331.69

$5,363,677.03

$25,634,237.20

$19,027,167.17

$17,392,428.20

$15,212,776.23

$11,125,928.79

$9,286,847.44

$9,517,736.75

$6,440,335.20

$5,678,916.26

$4,663,691.01

$2,760,143.66

$1,903,547.35

$61,970,359.09

$43,614,609.65

$39,072,980.92

$33,017,475.95

$21,663,404.14

$16,554,071.82

$215,892,901.57

Meters Data Modules Phasers Grand Total


Regions

Analytics Infrastructure Data Samples

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ModulesModules

Page 38

Region

PercentPopulation 0.3 0.203 0.179 0.147

Tripoli West Alwosta Bengazi

0.087 0.06

Subscribers 392966 265907 234470 192553 113960 78593

Concentrators 5171 3499 3085 2534 1499 1034

Daily Samples 37724746

Total CPUCores Needed 3314

Total CPUCores Needed 138

Total Annual TB 58

Total TB Needed 174

Archive InfrastructureTotal Annual TB 19

5 Year Total 97

25527078 22509098 18485125 10940176 7544949

Daily Data Bytes 16297090099.2 11027697633.792 9723930425.856 7985574148.608 4726156128.768 3259418019.84

Annual RawData TB 6 4 4 3 2 1

Redundant DataStorage 18 12 11 9 5 4

Data Modules / (2 per 200K subscribers) 5 4 3 3 2 2

Annual DataModule TB 2 2 2 1 1 1

Total ModuleCPU Cores 377 255 225 185 109 75

Cores Per Module 77 70 67 63 51 42

Total Subscribers: 1309887

South Green Mountain

Data SamplesPer Hour 4

Data SamplesPer Day 96

Data SampleBytes 144

ConsumptiveData Per Day 13824

Control / PricingData Per Day 13824

Total RawData per Day 27648


Meters

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ModulesModules

Page 39

Region

Buer(illness, injury) 0.15 0.15 0.15 0.15


0.15 0.15

Subscribers 392966 265907 234470 192553 113960 78593

Days 300 300 300 300 300 300

Working Hours 10 10 10 10 10 10

Installs per Hour 1 1 1 1 1 1

Meter Cost $50.00 $50.00 $50.00 $50.00 $50.00 $50.00

Hourly Labor $11.00 $11.00 $11.00 $11.00 $11.00 $11.00

People Required 151 102 90 74 44 30

Total Devices $19,648,305.00 $13,295,353.05 $11,723,488.65 $9,627,669.45 $5,698,008.45 $3,929,661.00

Total - Labor $4,971,021.17 $3,363,724.32 $2,966,042.63 $2,435,800.37 $1,441,596.14 $994,204.23

Grand Total - Devices: $63,922,485.60

Grand Total - Labor: $16,172,388.86


ConcentratorsRegion

Buer(illness, injury) 0.15 0.15 0.15 0.15


0.15 0.15

Subscribers 5171 3499 3085 2534 1499 1034

Days 300 300 300 300 300 300

Working Hours 10 10 10 10 10 10

Installs per Hour 0.5 0.5 0.5 0.5 0.5 0.5

Meter Cost $400.00 $400.00 $400.00 $400.00 $400.00 $400.00

Hourly Labor $11.00 $11.00 $11.00 $11.00 $11.00 $11.00

People Required 4 3 2 2 1 1

Total Devices $2,068,242.63 $1,399,510.85 $1,234,051.44 $1,013,438.89 $599,790.36 $413,648.53

Total - Labor $130,816.35 $88,519.06 $78,053.75 $64,100.01 $37,936.74 $26,163.27

Grand Total - Devices: $6,728,682.69

Grand Total - Labor: $425,589.18



Modules

NUHRGRID

ModulesModules

Page 40

Region

VirtualizationPlatform $250,000.00 $250,000.00 $250,000.00 $250,000.00


$250,000.00 $250,000.00

AnalyticsPlatform $1,000,000.00 $1,000,000.00 $1,000,000.00 $1,000,000.00 $1,000,000.00 $1,000,000.00

Real-TimeReaction $250,000.00 $250,000.00 $250,000.00 $250,000.00 $250,000.00 $250,000.00

Module Count 5 4 3 3 2 2

Data Lake $500,000.00 $500,000.00 $500,000.00 $500,000.00 $500,000.00 $500,000.00

Backup $100,000.00 $100,000.00 $100,000.00 $100,000.00 $100,000.00 $100,000.00

Visualization $3,000,000.00 $3,000,000.00 $3,000,000.00 $3,000,000.00 $3,000,000.00 $3,000,000.00

Per-Module Total $5,200,000.00 $5,200,000.00 $5,200,000.00 $5,200,000.00 $5,200,000.00 $5,200,000.00

Region Total $25,634,237.20 $19,027,167.17 $17,392,428.20 $15,212,776.23 $11,125,928.79 $9,286,847.44

Archive $100,000.00 $100,000.00 $100,000.00 $100,000.00 $100,000.00 $100,000.00

Region Grand Total: $97,679,385.02


PhasersRegion

Per-SeatSoftware $80,711.64 $80,711.64 $80,711.64 $80,711.64


$80,711.64 $80,711.64

Subscribers 392966 265907 234470 192553 113960 78593

PMU Cost $24,066.40 $24,066.40 $24,066.40 $24,066.40 $24,066.40 $24,066.40

PDC Cost $226,177.00 $226,177.00 $226,177.00 $226,177.00 $226,177.00 $226,177.00

Seats 15 10 9 7 4 3

PMUs 201 136 120 98 58 40

PDCs 15 10 9 7 4 3

Total PMUs $4,837,346.40 $3,273,271.06 $2,886,283.35 $2,370,299.74 $1,402,830.46 $967,469.28

Total PDCs $3,460,508.10 $2,341,610.48 $2,064,769.83 $1,695,648.97 $1,003,547.35 $692,101.62

Total Software $1,219,882.25 $825,453.65 $727,863.07 $597,742.30 $353,765.85 $243,976.45

Region Total $9,517,736.75

Total PMUs 670

Total PDC 51

Total Seats 50

$6,440,335.20 $5,678,916.26 $4,663,691.01 $2,760,143.66 $1,903,547.35



NuhrGrid Proposal Libya v7

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