Smart Grid

CHAPTER-1 WHAT IS A SMART GRID?

A smart grid [1] delivers electricity from suppliers to consumers using two-way digital technology to control appliances at consumers' homes to save energy, reduce cost and increase reliability and transparency. It is capable of assessing its health in real-time, predicting its behavior, anticipatory behavior, adaptation to new environments, handling distributed resources, stochastic demand, and optimal response to the smart appliances. It is a tool that allows electric utilities to focus on evolving true business drivers by enabling cost containment, end-to-end power delivery control, and a more secure infrastructure. The grid is considered to have observability with nodes data integration and analysis to support advances in system operation and control. This includes power delivery integration and high level utility strategic planning functions. The existing transmission and distribution systems use techniques and strategies that are old and there is limited use of digital communication and control technology. To achieve improved, reliable and economical power delivery information flow and secure integrated communication is proposed. The Smart Grid with intelligent functions is expected to provide self-correction, reconfiguration and restoration, and able to handle randomness of loads and market participants in real time, while creating more complex interaction behavior with intelligent devices, communication protocols, standard and smart algorithms to achieve complex interaction with smart communication and transportation systems. The Smart Grid is planned to have the following key characteristics: y Self-healing: A grid, which is able to rapidly detect, analyze, respond and restore from perturbations.

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y

Empower and incorporate the consumer:

The ability to incorporate consumer

equipment and behavior in the design and operation of the grid. y Tolerant of attack: A grid that mitigates and stands resilient to physical and cyber security attacks. y Provides power quality needed by 21st century users: A grid that provides a quality of power consistent with consumer and industry needs. y Accommodates a wide variety of generation options: A grid that accommodates a wide variety of local and regional generation technologies (including green power). y Fully enables maturing electricity markets: Allows competitive markets for those who want them. y Optimizes assets: A grid that uses IT and monitoring to continually optimize its capital assets while minimizing operations and maintenance costs.

Overall, the Smart Grid design goals are to provide grid observability; create controllability of assets, enhance power system performance and security; and reduce costs of operations, maintenance, and system planning. Benefits of the Smart Grid with bring forth the following: y y y Improved system performance meters. Better customer satisfaction. Improved ability to supply information for rate cases, visibility of utility operation / asset management y y Availability of data for strategic planning, as well as better support for digital summary More reliable and economic delivery of power enhanced by information flow and secure communication y Life cycle management, cost containment, and end-to-end power delivery is improved in the smart grid design y Improved ability to supply accurate information for rate cases- with compounding impact in regulatory utilities y y Input visibility of utility operation to asset management Impact access to historical data for strategic planning.
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1.1 FUNCTIONS SUPPORTED BY THE SMART GRID ARCHITECTURE

For the functional scope of the Smart Grid architecture [2], eight functional scenarios have been defined. A short description of each case is provided in the following subsections.

1.1.1 Variable-Tariff-Based Load

The key idea of this is a variable price profile given to the customer day ahead before the delivery by a retailer. This profile is considered fixed after transmission to the customer and, as such, the customer can rely on it. The price profile will look different for each day, reflecting market conditions that vary from day to day. These variations will likely further increase with expanding generation from fluctuating sources like wind power and photovoltaics. Generally, this concept allows for integration of loads as well as of generation units at the customer site as it is up to the customer which devices are allowed to be managed according to the variable tariff. To enable in-home energy management, a suitable domestic system is required together with an automatic home management device coupled to an intelligent meter.

1.1.2 Energy Usage Monitoring and Feedback

In the ³Action Plan for Energy Efficiency´, the European Commission estimates the EU-wide energy saving potential of households at approx. 27%. As one important measure for realizing this potential, the action plan states that awareness must be increased in order to stimulate end-customer behavioural changes. A timely display of energy consumption is expected to have positive effects on energy savings. Personalized and well targeted advice on how to save energy can further help exploit the savings potential. A portal or display that combines information about present and past consumption, comparisons to average consumption patterns, and precise suggestions how to further lower consumption, which are

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tailored personally to the customer, is expected to be the most effective way of realizing the targeted increase in households¶ energy efficiency.

1.1.3 Real-time Portfolio Imbalance Reduction This function is rooted in the balancing mechanism as used by Transmission System Operators (TSOs) throughout the world. In this context, a wholesale-market participant, that is responsible for a balanced energy volume position, is called a Balance Responsible Party (BRP). These parties have an obligation to plan or forecast the production and consumption in their portfolio, as well as notify this plan to the TSO. Deviations of these plans may cause (upward or down-ward) regulation actions by the TSO. The TSO settles the costs for the used reserve and emergency capacity with those BRPs that had deviations from their energy programs. On average this results in costs for the BRP referred to as imbalance costs. This business case scenario focuses on the balancing actions by a BRP in the near-real time (i.e. at the actual moment of delivery). Traditionally, these real-time balancing actions are performed by power plants within the BRP¶s portfolio. The key idea of this function is the utilization of real-time flexibility of end-user customers to balance the BRP portfolio.

1.1.4 Offering (secondary) Reserve Capacity to the TSO

Taking the previous function one step further, the BRP uses these VPPs to, additionally, bid actively into the reserve capacity markets.

1.1.5 Distribution System Congestion Management This function is aimed at the deferral of grid reinforcements and enhancement of network utilization to improve the quality of supply in areas with restricted capacity in lines and transformers. The Distribution System Operator (DSO) avoids infrastructural investments and optimizes the use of existing assets by active management using services delivered by smart houses. By coordinated use of these services, end-customer loads can be

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shifted away from periods at which congestion occurs and simultaneousness of local supply and demand can be improved.

1.1.6 Distribution Grid Cell Islanding in Case of Higher- System Instability The main principle of this is to allow the operation of a grid cell in island mode in case of higher system instability in a market environment. The scenario has two main steps, the first occurring before a possible instability and involves keeping a load shedding schedule up-to-date. The second step is the steady islanded operation. The transition to the island mode is automatic and neither end users nor the aggregator interferes with it. The system manages the energy within the island grid and it is considered that all nodes within the islanded grid will participate in the system.

1.1.7 Black-Start Support from Smart Houses

The most important concept of this function is to support the black start operation of the main grid. It is assumed that after the blackout the local grid is also out of operation. The main goal is to start up quickly in island mode and then to reconnect with the upstream network in order to provide energy to the system.

1.1.8 Integration of Forecasting Techniques The volatility of the production level of distributed generators, like renewables and CHP, makes forecasting a necessary tool for market participation. The market actor with the lowest forecasting error will have the most efficient market participation. Moreover, the usage of intelligent management tools for handling the information about the uncertainties of large-scale wind generation will improve the system-wide operational costs, fuel and CO2 savings. The Smart Grid architecture under development must interact with these forecasting tools and additionally ensure accurate data collection for these tools.

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CHAPTER-2 A TRANSMISSION VIEW
Power Grids today face many challenges that they were not designed and engineered to handle. Congestion and atypical power flows threaten to overwhelm the system while demand increases for higher reliability and better security and protection. The potential ramifications of grid failures have never been greater as transport, communications, finance, and other critical infrastructures depend on secure, reliable electricity supplies for energy and control. Because modern infrastructure systems are so highly interconnected, a change in conditions at any one location can have immediate impacts over a wide area, and the effect of a local disturbance even can be magnified as it propagates through a network. Large-scale cascade failures can occur almost instantaneously and with consequences in remote regions or seemingly unrelated businesses.

On the North American power grid, for example, transmission lines link all electricity generation and distribution on the continent. Wide-area outages in the late 1990s and summer 2003 underscore the grid¶s vulnerability to cascading effects.

Practical methods, tools, and technologies based on advances in the fields of computation, control, and communications are allowing power grids and other infrastructures to locally selfregulate, including automatic reconfiguration in the event of failures, threats, or disturbances. It is important to note that the key elements and principles of operation for interconnected power systems were established before the 1960s, before the emergence of extensive computer and communication networks. Computation is now heavily used in all levels of the power network: for planning and optimization, fast local control of equipment, and processing of field data. But coordination across the network happens on a slower timescale. Some coordination occurs under computer control, but much of it is still based on telephone calls between system operators at the utility control centers, even²or especially²during emergencies.

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2.1 How to Make an Electric Power Transmission System Smart[3]

Power transmission systems also suffer from the fact that intelligence is only applied locally by protection systems and by central control through the supervisory control and data acquisition (SCADA) system. In some cases, the central control system is too slow, and the protection systems (by design) are limited to protection of specific components only.

To add intelligence to an electric power transmission system, we need to have independent processors in each component and at each substation and power plant. These processors must have a robust operating system and be able to act as independent agents that can communicate and cooperate with others, forming a large distributed computing platform. Each agent must be connected to sensors associated with its own component or its own substation so that it can assess its own operating conditions and report them to its neighboring agents via the communications paths. Thus, for example, a processor associated with a circuit breaker would have the ability to communicate with sensors built into the breaker and communicate those sensor values using high-bandwidth fiber communications connected to other such processor agents. We shall use a circuit breaker as an example. We will assume that the circuit breaker has a processor built into it with connections to sensors within the circuit breaker (Figure1).

Figure.1 Circuit Breaker with Sensors
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We also provide communication ports for the processor where the communication paths follow the electrical connection paths. This processor agent now forms the backbone of the smart grid as will be discussed later. We propose a system that acts very fast (although not always as fast as the protections system), and like the protection system, its agents act independently while communicating with each other. As such, the smart grid is not responsible for removing faulted components, which is still the job of the protection system, but acts to protect the system in times of emergencies in a much faster and more intelligent manner than the central control system.

2.1.1 The Advantages of an Intelligent Processor in Each Component, Substation, and Power Plant

We presently have two kinds of intelligent systems used to protect and operate transmission systems: the protection systems and the SCADA/EMS/independent system operator (ISO) systems.

Modern computer and communications technologies now allow us to think beyond existing protection systems and the central control systems to a fully distributed system that places intelligent devices at each component, substation, and power plant. This distributed system will enable us to build a truly smart grid.

The advantage of this becomes apparent when we see that each component¶s processor agent has inputs from sensors in the component, thus allowing the agent to be aware of its own state and to communicate it to the other agents within the substation. On a system level, each agent in a substation or power plant knows its own state and can communicate with its neighboring agents in other parts of the power system. Having such independent agents, which know about their own component or substation states through sensor connections, allows the agents to take command of various functions that are not performed by either the protection systems or the central control systems.

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2.1.2 Power Systems Components as Plug-and-Play Interconnects One of the problems common to the management of central control facilities is the fact that any equipment changes to a substation or power plant must be described and entered manually into the central computer system¶s database and electrical one-line diagrams. Often, this work is done some time after the equipment is installed, resulting in a permanent set of incorrect data and diagrams in use by the operators. What is needed is the ability to have this information entered automatically when the component is connected to the substation² much as a computer operating system automatically updates itself when a new disk drive or other device is connected.

When a new device is added to a substation, the new device automatically reports data such as device parameters and device interconnects to the central control computers. Therefore, the central control computers get updated data as soon as the component is connected; they do not have to wait until the database is updated by central control personnel.

Figure 2 shows a substation bus-bar pair connected by a set of disconnect switches and a circuit breaker (the component processors are shown in orange). Each processor has communication paths connecting it with processors of the substation component in the same pattern as the electrical connections in the substation.

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Figure 2 Sub Station Bus-Bus Pair

When a new component is added to the substation it also has a built-in processor. When the new device is connected, the communication path (Figure 3) is connected to the processor of the device it connects to electrically. When the new component¶s processor and communication path are activated, it can report its parameters and interconnects to the central control system, which can use the information to update its own database.

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Figure 3 Incoming Device To System

2.1.3 Diagnostic Monitoring of all Transmission Equipment Placing the processing of sensor data in a local agent avoids the problem of sending that data to the central computer via the limited-capacity SCADA communications. The means for processing the local sensor data can be designed by the component manufacturer, and the agent then only needs to send appropriate alarms to the central computers. If the component is under such stress that the local agent determines it is in danger of being damaged, it can initiate shutdown through appropriate interconnects to the protections systems associated with the component.

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2.1.4 Grid Computing Grid computing can be described as a world in which computational power is as readily available as electric power and other utilities. According to Irving et al. in ³Plug into Grid Computing,´

Grid computing could offer an inexpensive and efficient means for participants to compete (but also cooperate) in providing reliable, cheap, and sustainable electrical energy supply.

In addition, potential applications for the future power systems include all aspects that involve computation and are connected, such as monitoring and control, market entry and participation, regulation, and planning. Grid computing holds the promise for addressing the design, control, and protection of electric power infrastructure as a Complex Adaptive System (CAS).

2.1.5 Self-Healing Network Using Distributed Computer Agents A typical sequence seen in large power system blackouts follows these steps:

1) a transmission problem, such as a sudden outage of major lines, occurs 2) further outages of transmission lines due to overloads leave the system islanded 3) frequency declines in an island with a large generation load imbalance 4) generation is taken off line due to frequency error 5) the island blacks out 6) the blackout lasts a long time due to the time needed to get generation back online.

A self-healing grid can arrest this sequence. In Figure 4 we show three power plants connected to load substations through a set of looped transmission lines. Each plant and each substation will have its own processor (designated by a small red box in the figure). Each plant and substation processor is now interconnected in the same manner as the transmission system itself.
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Figure 4 Interconnected Power Plants & Load Sub-Station In Figure 5 we impose an emergency on the system; it has lost two transmission connections and is broken into two electrical islands. The processors in each island measure their own frequency and determine that there are load/generation imbalances in each island that must be corrected to prevent being shut down. The processors would have to determine the following:

y y y y y

the frequency in each island what constitutes each island what loads and what power plants are connected to each island what is the load versus generation balance in each island what control actions can be made to restore the load/generation balance

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Figure 5. Emergency Imposed On System

The substation and power plant processors form a distributed computer network that operates independently of the central control system and can analyze the power system state and take emergency control actions in a time frame that cannot be done by central computer systems.

How to effectively sense and control a widely dispersed, globally interconnected system is a serious technological problem. It is even more complex and difficult to control this sort of system for optimal efficiency and maximum benefit to the consumers while still allowing all its business components to compete fairly and freely. A similar need exists for other infrastructures, where future advanced systems are predicated on the near-perfect functioning of today¶s electricity, communications, transportation, and financial services.
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In the coming decades, electricity¶s share of total energy is expected to continue growing, and more intelligent processes will be introduced into this network. For example, controllers based on power electronics combined with wide-area sensing and management systems have the potential to improve the situational awareness, precision, reliability, and robustness of power systems. It is envisioned that the electric power grid will move from an electromechanically controlled system to an electronically controlled network in the next two decades.

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CHAPTER-3 A DISTRIBUTION VIEW
Utilities deliver power to their customers through a network of power plants, transmission lines, substations, and distribution systems. A distribution system carries power from substation transformers through feeder circuits to distribution transformers located near customers. Distribution transformers step voltage down from primary voltages of 4 to 34 kV to secondary voltages of 120 to 480 V for delivery.

The distribution system needs many changes to come in sync with the requirements for the implementation of smart grids. For this optimization on various levels is needed to be incorporated in the present distribution system. The various optimizations desired are-

y

Demand Optimization: This is to manage peak load via control of power consumption. This would defer upgrades, optimize generation & renewables.

y

Delivery Optimization: This is to reduce delivery losses in distribution systems which would result into less energy wastes and higher profit margins.

y

Asset Optimization: It includes prognostics for proactive equipment maintenance to reduce outages and make workers focused.

y

Reliability Optimization: It includes Wide Area Protection and control which would result in increased reliability and network performance.

y

Renewables Optimization: It includes use of forecasting and smoothing which provides compensation for production variability. This is the key step for meeting targets especially in areas with weak grids.

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3.1 DISTRIBUTION AUTOMATION
Distribution Automation (DA) refers to monitoring, control and communication functions located out on the feeder. From a design perspective, the most important aspects of distribution automation are in the areas of protection and switching (often integrated into the same device). There are DA devices today that can cost-effectively serve as an ³intelligent node´ in the distribution system. These devices can interrupt fault current, monitor currents and voltages, communicate with one-another, and automatically reconfigure the system to restore customers and achieve other objectives.

The ability to quickly and flexibly reconfigure an interconnected network of feeders is a key component of Smart Grid. This ability, enabled by DA also requires distribution components to have enough capacity to accept the transfer and requires the protection system to be able to properly isolate a fault in the reconfigured topology.

Both of these issues have an impact on system design. Presently, most distribution systems are designed based on a main trunk three phase feeder with single-phase laterals. The main trunk carries most power away from the substation through the center of the feeder service territory. Single phase laterals are used to connect the main trunk to customer locations. Actual distribution systems have branching, normally-open loops, and other complexities, but the overarching philosophy remains the same.

A Smart Grid does not just try to connect substations to customers for the lowest cost. Instead, a Smart Grid is an enabling system that can be quickly and flexibly be reconfigured. Therefore, future distribution systems will be designed more as an integrated Grid of distribution lines, with the Grid being connected to multiple substations. Design, therefore, shifts from a focus on feeders to a focus on a system of interconnected feeders. Traditional distribution systems use time-current coordination for protection devices. These devices assume that faster devices are topologically further from the substation.

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In a Smart Grid, topology is flexible and this assumption is problematic. From a design perspective, system topology and system protection will have to be planned together to ensure proper protection coordination for a variety of configurations.

3.2 DISTRIBUTION OPTIMIZATION

3.2.1 DAO MODEL [4]
A new method, the Distributed Asset Optimization (DAO) Model, has been developed to provide an engineering basis for predicting hourly loading at any point between the substation and the customer. The DAO Model calculates the power flowing through each distribution transformer by reconciling hourly substation data with end-use customer data. The approach used is relatively simple; however, because of the scale of the data and calculations, the system as a whole presents many challenges and involves a comprehensive process. Because most customer energy use is read on one of many possible monthly billing cycles, and because the connectivity of customers to transformers to substations changes over time, this reconciliation exercise is not straightforward. Furthermore, the data required resides in different departments of a utility, often in incompatible databases. But by tackling these issues, the DAO Model can estimate power flow through the distribution system. Knowledge of transformer loadings and system connectivity provides the insights required to make better planning and operating decisions.

3.2.1.1 Data Collection The DAO Model brings together data collected from information systems distributed throughout the utility. The data includes: y y y customer information and billing data customer hourly consumption data from metering and load research distribution transformer, feeder, and substation bank characteristics

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y y

connectivity data for the distribution system available substation SCADA data

The DAO Model reconciles customer meter data to feeder SCADA data to calculate power through transformers. The foundation for the DAO Model process is the creation of hourly load profiles for individual customers. The process would be much simplified if such profiles were available for all customers. However, at most utilities, hourly load data is only available for a small subset of customers, usually very large customers and those metered for load research purposes. How to turn a preponderance of monthly data into hourly data is the subject of the DAO Model. Below the different stages of the process are discussed. 3.2.1.2 Data Validation After data collection, a battery of validation tests is run on all data. These tests check transformer connectivity, customer to transformer proximity, and duplicate or inconsistent customer records. In addition, sanity checks are run that compare total annual customer energy by feeder to total annual energy measured by SCADA data. Validation, editing, and estimation routines are also executed to scan and correct all received energy data histories for gaps, duplicate readings, spikes, and other anomalous patterns.

3.2.1.3 Establish Weather Sensitivity All customer energy data, whether monthly, daily, or hourly, are correlated with historical weather for the location of each meter; the result is one Tuning Equation for each meter point, which embodies weather sensitivity (separately for cooling and heating end uses) as well as the fraction of non-weather sensitive end uses. (If customer data are in monthly billing periods, the tuning equation acts on whole billing periods; if customer data are daily or hourly, the tuning equation acts on calendar weeks.) If only monthly billing data is available and read on different billing cycles, as many as 14 or more bills are required to cover the tuning period completely (with the first bill ending before the beginning of the tuning period, and the last bill partially exceeding the end of the tuning period).

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3.2.1.4 Data Categorization and Creation of Load Shape Library Data from customers with hourly and daily data are normalized. Normalization of hourly data removes scale (annual energy and demand), weather sensitivity, and day-type dependence; normalization of daily data similarly removes scale and weather sensitivity, but not day-type dependence. All customer data that fulfills certain quality minimum requirements are organized in a load shape library, where normalized load shapes are organized by revenue class, rate code, heating and cooling weather sensitivity, and load factor.

The next step in the modeling process is to assign monthly customers to the best matching hourly and daily load shape in the load shape library. This is accomplished by matching revenue class, rate code, heating and cooling weather sensitivity, and load factor. Customers with daily metering are assigned hourly load shapes from the library in a comparable way. 3.2.2 Customer Model Rendering Once both weather sensitivity and matching library load shapes have been determined for each customer, the model can be used to simulate an hourly load profile for the original training period or any other time period under various weather conditions. This load shape creation step has been labeled rendering. The rendering algorithm provides not only hourly energy but also hourly reactive energy, power factor, and phase information. The hourly customer profile is recreated in several steps. First, daily energy is estimated for each customer by taking its weather-sensitivity model coefficients in combination with actual or predicted weather data. Then the customer¶s matched daily and hourly library shapes are applied to provide daily allocations of the tuning equation¶s energy and hourly modulation of daily results.

For the minority of customers with daily meters, only a matching hourly profile from the load shape library is required in combination with the customer¶s own daily values to yield the complete hourly profile. A very small minority of customers have hourly meters. For these meters, rendering simply consists of combining their weather-sensitivity coefficients with their actual hourly values and simulating a load shape for future time periods or alternate weather.

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3.2.3

Aggregation to Transformers and to Feeders

With customer hourly profiles in hand, the load shape of a distribution transformer can be rendered. The customer hourly profiles are aggregated hour-by-hour and yield a transformer profile of hourly consumption and power factor values for the desired time period. A similar process is followed to render a feeder, whereby all transformers connected to the feeder are aggregated hour-by-hour, to yield a feeder profile of hourly consumption and power factor values for the desired time period. Feeder results also include load by phase. 3.2.4 Calibration To compensate for unaccounted-for energy, such as line losses, transformer losses, and unmetered end uses that can occur between the customer meter and feeder, a calibration process must be applied. Currently, unaccounted-for energy is determined by comparing measured SCADA data to the sum of rendered transformer energy on a weekly and daily, feeder-by-feeder basis. Corresponding calibration factors are derived for each feeder, week, and day type and used to render hourly feeder profiles.

3.3 Transformer Aging

One significant area where the DAO Model is being applied is in analyzing distribution transformer hot spots and overloading events to proactively identify transformers at risk of failure. Distribution planners have long monitored hot spot temperature on substation transformers using SCADA data; e.g., bank oil temperatures. However, lack of SCADA data for distribution transformers has prevented the analysis of overloading, loss of life, and transformer failure at the distribution transformer level.

The DAO Model changes that paradigm. While the nominal lifetime of a transformer is only 10±20 years, under average loading conditions the transformer¶s life can exceed this value by several times. On the other hand, if customer load grows more than expected over time, and the transformer load exceeds capacity even for a few hours or a few days of the year, a

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transformer can suddenly fail long before its expected lifetime is over. (The nominal lifetime is the expected time to failure if the transformers were operated continuously at the nameplate capacity and rated ambient conditions. The pace at which a transformer ages, increases dramatically, if the load exceeds nameplate capacity at ambient conditions. Most transformers are operated well below nameplate capacity most of the time, and their achieved lifetimes frequently can exceed nominal lifetimes by a multiple.)

Aging of the transformer occurs most rapidly during a time of overloading, combined with a high ambient temperature. This effect is taken into account by an ³aging multiplier´. The aging multiplier is 1 if the transformer is loaded at 100% of nominal capacity at rated ambient conditions. It increases dramatically as either or both of these conditions are exceeded. In addition to the aging multiplier we show its cumulative effect, as ³consumed life´ in hours. Note that a transformer experiences almost all of its annual ³aging´ in a relatively small number of overload events.

In a typical utility, about one-tenth of one percent of transformers fails unexpectedly in any given year. While this relatively small number may appear to be an acceptable risk, the location of such failures is hard to predict, and their timing is often coincident with other problems typical for a peak day. The replacement is costly in terms of labor that must be rushed to the site, public relations problems, and, in some states public utility commission fines that may be levied. Knowing which transformers are most likely to fail in the near future is valuable information to a utility.

3.4 Transformer Load Management Reporting

Typical transformer load management reports in use at utilities today provide a monthly estimate of the peak load on a transformer by applying a load factor to monthly usage. The monthly usage is the addition of billing usage for each of the customers assigned to the transformer. Generally, confidence in these reports is low due to data consistency issues and assumptions that are too general. Transformer loads are used in a number of business processes

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such as sizing new facilities, determining where customers have added load, and troubleshooting customer problems.

A reliable accurate data source for transformer loading increases efficiency and reduces errors in these processes. For example, the warehouse is out of stock of a 1000-kVA padmount transformer and you need one tomorrow. A loading report, run for all 1000-kVA underground transformers that are loaded less than 50%, would locate an underutilized transformer that could be changed out. 3.5 Feeder Performance Management By virtue of its bottom-up modeling of the customer to the feeders and banks, the DAO Model has application in feeder performance management. Typically circuit analysis tools used at utilities use a connected kVA method to spread load from the breaker to the normally open point. The connected kVA method assumes that transformers of the same size have the same loading; i.e., all 100-kVA transformers are loaded equivalently. The peak amps are typically a snapshot from SCADA or manual substation reads.

The DAO Model allows transfer of simulated transformer loads, based on actual customer usage, to circuit analysis tools and outage management systems¶ switching analysis tools. Because the data is hourly, alternate scenarios, such as an off peak hour or a particular day can be analyzed. The more accurate load data provides more accurate voltage profiles, load analysis, and protection coordination analysis. These are key items for managing feeder performance. In addition, the model provides base load, heating load, and cooling load components at any point along the feeder. This information delivers the ability to forecast feeder loading for a specific weather scenario for switching and capacity planning. Standard reporting tools enable charting, data visualization, statistical analysis, and data export tools to help users analyze data.

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3.6 Load Forecasting

Because of its inherent ability to respond to alternative and extreme weather scenarios, the DAO solution is a powerful tool in supporting both short- and long-term load forecasting. Shortterm weather forecasts can be fed to the model to obtain a realistic prediction of loads by feeders for next day or next week analysis. Historic year-to-year load changes that have been observed system-wide or at individual substations can be analyzed to determine the relative impact of weather or customer changes in causing the load growth or decrease. Conversely, by applying expected growth rates by revenue class and region, load growth on individual parts of the distribution system can be accurately forecasted.

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CHAPTER-4 SMART HOMES FOR SMART GRID

The residential, Small Office/Home Office (SOHO) and commercial building sector together is responsible for over 50% of Europe¶s electricity consumption. The current electricity distribution system treats home and working environments as consisting of isolated and passive individual units. This severely limits the achieved energy efficiency and sustainability, as it ignores the potential delivered by homes, offices, and commercial buildings which are seen as intelligent networked collaborations. In order to achieve next-generation energy efficiency and sustainability, a novel smart grid Information and Communication Technology (ICT) architecture based on Smart Houses interacting with Smart Grids is needed. This architecture enables the aggregation of houses as intelligent networked collaborations, instead of seeing them as isolated passive units in the energy grid.

The ICT architecture [2] under development by a consortium of leading parties in ICT for energy introduces a holistic concept and technology for smart houses as they are situated and intelligently managed within their broader environment. This concept seriously considers smart homes and buildings as proactive customers (³prosumers´) that negotiate and collaborate as an intelligent network in close interaction with their external environment.

The smart home and building environment includes a diverse number of units: neighbouring local energy consumers (other smart houses), the local energy grid, associated available power and service trading markets, as well as local producers (environmentally friendly energy resources such as solar and (micro)CHP etc.)

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The architecture is based on a carefully selected mixture of innovations from recent R&D projects in the forefront of European Smart Grid research. These innovations include:

y

In-house energy management based on user feedback, real-time tariffs, intelligent control of appliances and provision of (technical and commercial) services to grid operators and energy suppliers.

y

Aggregation software architecture based on agent technology for service delivery by clusters of smart houses to wholesale market parties and grid operators.

y

Usage of Service Oriented Architecture (SOA) and strong bidirectional coupling with the enterprise systems for system-level coordination goals and handling of real-time tariff metering data.

4.1 Aggregation of Houses as Intelligently Networked Collaborations
SmartHouse/SmartGrid concepts will exploit the potential that is created when homes, offices and commercial buildings are treated as intelligently networked collaborations. The SmartHouses will be able to communicate, interact and negotiate with both customers and energy devices in the local grid. As a result, the electricity system can be operated more efficiently because consumption will be better predicted and adapted to the available energy supply, even when the proportion of variable renewable generation is high. A commercial aggregator could exercise the task of jointly coordinating the energy use of the SmartHouses or commercial consumers that have a contract with it and additionally deliver services to grid management performed by the network operators.

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Figure. 6 A SMART HOME AREA NETWORK

4.2 Technical Measures
The main technical measures on which the functionalities of the ICT architecture are based include:

1. End User Feedback: Aims at an interface to the end user in order to give feedback on his/her energy behaviour and on the availability of (local) clean electricity. 2. Automated Decentralized Control of Distributed Generation and Demand Response: Aims at a better local match between demand and supply, at customer
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acceptance of management strategies, and at a more effective reaction to near-real time changes at the electricity market level (e.g. due to fluctuations in large-scale wind energy production) and grid operations (e.g. for congestion management and reserve capacity operations). 3. Control for Grid Stability and Islanding Operation: Aims at the delivery of services by smart houses to be used by network operators to maintain or restore stability in (distribution) networks in an active manner. Here, the particular focus is on the capability to run local power networks in islanded mode and reaction of end-user systems to critical situations in the grid.

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CHAPTER-5 AUTOMATIC METER READING

Automatic Meter Reading (AMR), also known as Remote Meter Reading, refers to the system that uses a communication technique to automatically collect the meter readings and other relevant data from meters installed by the electricity provider without the need to physically visit the meters. The development of AMR [5] technology has catapulted meter data to centre stage of the Smart Grid implementation plan. It has been realized that AMR has benefits beyond meter reading because it provides crucial data on and insight into other areas of operations.

5.1 DATA RETRIEVAL
For residential customers, there are a number of data retrieval considerations. Most are billed monthly and, thus, a monthly consumption read is sufficient. However, in fully deregulated markets some residential meters may need to be read daily. This may be a growing trend, especially when the potential benefits of deregulation are made available to more residential customers. Utilities need to monitor whether the meter has been tampered with, in any way, which could result in a loss of revenue. They also need to collect meter data for customers that are moving out or getting disconnected. This may need to be collected separately from the billing cycle. For commercial and industrial customers, data retrieval frequency usually depends on size. Small commercial customers¶ meters are usually read monthly for consumption. However, large commercial and industrial customers have advanced metering system- like volume correctors, which are usually read daily. These remote-monitoring devices, attached to meters and correctors, store hourly consumption profiles.

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5.2 COMMUNICATION TECHNOLOGIES
For data retrieval, the choice of communication technology depends upon various factors. AMR forms the basis for utilities who want to improve customer service and satisfaction by delivering accurate billing to its entire customer base in a timely manner. Often, estimated bills are the leading cause of customer dissatisfaction. Some of the key technologies for Advanced Meter Reading are given below: 5.2.1 RF COMMUNICATIONS Radio Frequency (RF) remains the most widely accepted method of communication between meter and data collection. A meter is mounted with a small module, usually behind the index, where it converts the movement of wriggler into pulse counts and stores them, taking into account the count rate and other factors. These modules are Encoders and Transmitters, put together as a single unit. These RF devices are activated by a ³wake-up´ signal from the data collection system, and the device sends back the latest meter read and other information, like tamper status, to the data collection system. Since, these devices are battery-powered, using this wake-up technique helps conserve battery life. In some cases, however, RF devices regularly transmit readings, and the data collection system does not wake-up the device. In terms of data collection method, there are three most popular ways to read these RF devices. These are detailed below: Radio-equipped Handheld Computer A meter reader carrying a rugged handheld computer equipped with a radio receiver walks by homes, without actually entering the premises. The devices send their reads to the handheld computer. The meter reader accepts the read, and keeps moving on his/her route. The meter reader does not enter the readings manually, eliminating any manual entry errors. This system is normally used to read those accounts within the utility service that have high-cost or hazardous-to-read meters.

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Drive-by or Mobile Data Collection Mobile data collection uses vehicles equipped with radio units to read RF module-equipped meters via radio, without the need to access the meter. A radio transreceiver is installed in a utility vehicle. Route information is downloaded from the utility billing system and loaded into this radio transreceiver. While driving along a meterreading route, the transreceiver broadcasts a radio wake-up signal to all RF meter modules within range and receives the meter readings when they respond. Completed reads are uploaded to the billing system for bill generation. Mobile data collection systems now come with state-of-the-art GPS mapping systems which allow the users to see their data collection process, and analyze any missed readings before leaving a route. Once the route has been completed and the meter reader has returned to the office, the system administrator then has the capability to play-back the route that was driven that day, and identify opportunities to optimize routes and improve meter-reading reliability. Fixed Network Data-collection Fixed network deployment is usually done as a migration from the mobile data collection system. The fixed network is usually installed over saturated areas where advanced metering data, variable reads, unscheduled reads or operational improvements are required. This saturated deployment spreads the cost of the network components over multiple meters. Utility whose business objectives will only be met by more granular data (often daily or reading several times a day) and the ability to collect the latest reading are considering a fixed network.

5.2.2 TELEPHONE-BASED COMMUNICATION Telephone-based remote metering devices are of two types, based upon their mode of communication, inbound or outbound.

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Inbound Systems In this type of systems, the modules call a central master data collection computer at prescheduled times and provide the hourly consumption data. Outbound Systems In this type of communication, the master station calls the remote meter module and collects the data. This is useful where on-demand reads may be required as well.

5.2.3 OTHER COMMUNICATION METHODS In places where it is difficult to make a regular phone line available, cellular transreceivers are currently being evaluated at some sites as a communication medium. While cellular offers reduced installation costs, other factors are still being evaluated, including network availability and reliability, cost of service and battery life. In addition, various other communication mediums, like satellite and powerline carrier are being experimented within small numbers.

5.3 SMART METERS[6]

The usage-reporting device at each customer site is called a smart meter. It¶s a computerized replacement of the electrical meter attached to the exterior of many of our homes today. Each smart meter contains a processor, nonvolatile storage, and communication facilities. Although in many respects, the smart meter¶s look and function is the same as its unsophisticated predecessor, its additional features make it more useful. Smart meters can track usage as a function of time of day, disconnect a customer via software, or send out alarms in case of problems. The smart meter can also interface directly with ³smart´ appliances to control them² for example, turn down the air conditioner during peak periods.

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One of the smart grid¶s most attractive features is its ability to support widespread customer energy generation. For example, many farms now offset energy costs by producing electricity using methane generators, solar panels, and wind turbines. In the new smart grid, farmers can sell excess energy generated back to the utility, thereby reducing or eliminating energy costs. Obviously, this not only changes the electrical grid¶s economics but provides attractive incentives for customers to deploy (hopefully clean) power generation technology. If widely adopted, this could substantially lower the provider generating capacity required to support the nation¶s needs.

Although the long-term vision for the smart grid involves global energy management and home area networks that can control smart appliances, current deployments evolve around the deployment of onsite smart meters. Currently, several million homes and businesses have upgraded to these new meters in the US alone, with an additional 40 million scheduled for deployment in the next three years.

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CHAPTER-6 SECURITY

Smart Grid security is to be taken very seriously. The smart grid requires developing and deploying extensive computer and communication infrastructure that supports significantly increased situational awareness and allows finer-grained command and control. This is necessary to support major applications and systems such as demand-response wide-area measurement and control, electricity storage and transportation, and distribution automation. Any complex system has vulnerabilities and challenges, and the smart grid is no exception. Numerous challenges will arise with the integration of cyber and physical systems, along with such factors as human behavior, commercial interests, regulatory policy, and even political elements. Some challenges will be quite similar to those of traditional networks, but involving more complex interactions. The following areas need to be considered [9]

6.1 Trust
For control systems, we define trust as our confidence that, during some specific interval, ‡ the appropriate user is accessing accurate data created by the right device at the expected location at the proper time, communicated using the expected protocol ‡ the data hasn¶t been modified Many people view the grid¶s control systems as operating in an environment of implicit trust, which has influenced design decisions. If some participants aren¶t trustworthy, new methods of addressing this beyond existing monitoring approaches might be required.

6.2 Communication and Device Security
Traditional electric-grid communications have relied predominantly on serial

communication environments to provide monitoring and control. Serial communication is reliable, is predictable, and, owing to the nature of the communications protocols, provides some containment. However, increasing numbers of smart-grid deployments are using Internet
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technologies, broadband communication, and nondeterministic communication environments. This issue is compounded by the rapid deployment of smart-grid systems without adequate security and reliability planning. For example, whereas traditionally communications involved devices that were in areas with physical access controls (such as fences and locked buildings), two-way meters being deployed now are accessible by consumers and adversaries. Consequently, we must consider automatic meter reading (AMR) environments hostile in such cases.

Smart meters are extremely attractive targets for malicious hackers, largely because vulnerabilities can easily be monetized. Hackers who compromise a meter can immediately manipulate their energy costs or fabricate generated energy meter readings. This kind of immediacy of return on the hacker investment has proven to be a great motivator in the past.

6.3 Privacy
As the grid incorporates smart metering and load management, user and corporate privacy is increasingly becoming an issue. Electricity use patterns could lead to disclosure of not only how much energy customers use but also when they¶re at home, at work, or traveling. When at home, it might even be possible to deduce information about specific activities (for example, sleeping versus watching television). It might also be possible to discover what types of appliances and devices are present by compromising either the customer¶s home area network or the AMR network. Also, increases in power draw might suggest changes in business operations. Such energy-related information could support criminal targeting of homes or provide business intelligence to competitors. Further research is needed in mitigating such threats.

6.4 Security Management Issues Complexity
The complexity and scale of future power systems that incorporate smart-grid concepts will introduce many security challenges. Currently, a large utility communicates with thousands of
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devices to manage the electrical grid. Both the volume of data and the number of devices with which a utility communicates is likely to increase by several orders of magnitude. With these larger networks, routine maintenance, managing trust, and monitoring for cyber intrusion become challenges.

6.5 SOLUTION
The most effective solution for securing the Smart Grid will be based on Public Key Infrastructure (PKI) technologies [10]. While PKI is complex, many of the items responsible for the complexity can be significantly reduced by including the following five main technical elements:

‡ PKI Standards ‡ Smart Grid PKI tools ‡ Device Attestation ‡ Trust Anchor Security ‡ Certificate Attributes

6.5.1 Smart Grid PKI Standards PKI is a powerful tool that can be used to provide secure authentication and authorization for Security Association (SA) and key establishment. They provide a mechanism for defining naming conventions, certificate constraints, and certificate policies, but they do not specify how these should be used. These standards rightfully leave these details to the organizations implementing the PKI. Therefore the development of PKI standards for use by the critical infrastructure industry is proposed.

The standards would be used to establish requirements on the PKI operations of energy service providers (e.g. utilities, generators, etc) as well as Smart Grid device manufacturers.
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Standards could include such items as acceptable security policies (e.g. PKI certificate policies used for issuing each type of certificate in the system), certificate formats, and PKI practices.

6.5.2 Smart Grid PKI Tools Even with the above standards, Smart Grid operators would have to familiarize themselves with PKI concepts, terminology, risks, best practices and the above mentioned standards. This is not likely to provide a cost-effective solution. However, given such a set of standards, it would be possible for vendors to develop Smart Grid PKI Tools which are based on these standards.

Such tools would greatly ease the process of managing the PKI components needed to support the Smart Grid application. These tools will be knowledgeable of the appropriate Smart Grid certificate policy and certificate format standards, and will be used to programmatically enforce compliance to those standards. Such tools will enhance interoperability, reduce the burden of running the PKI, and ensure that appropriate security requirements are adhered to. The tools could both automate and enforce the appropriate requirements for each PKI operation such as vetting certificate signing requests (CSR), or certificate revocation.

For example, the tools would know the different requirements for handling CSRs for human system administrators. The tools would aid with system deployment, PKI operations, and system auditing, all in accordance with the standard model policy. Most importantly, these tools will eliminate the need for symmetric key configuration, which is an inherently insecure and expensive process.

6.5.3 Device Attestation An enhanced security function is device attestation. Device attestation techniques provide a method to securely ascertain if a device has been tampered with, as well as the true identity of a device (prior to any on-site provisioning). With device attestation techniques, accredited manufacturers can factory-install device attestation certificates in each Smart Grid device.

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These device attestation certificates are used only to assert the device manufacturer, model, serial number, and that the device has not been tampered with. These certificates coupled with the appropriate authentication protocol can be used by the energy service provider to ensure that the device is exactly what it claims to be. In order to support device attestation, the device will need a FIPS 140 hardware security module (HSM), and will need high assurance boot (HAB) functionality.

6.5.4 Trust Anchor Security One major component of a secure PKI enabled system is the requirement that each relying party (RP) (any device that uses the certificate of a second party to authenticate the second party) must have secure methods to load and store the root of trust or trust anchor (TA). The TA is typically a Certificate Authority (CA) at the top of a CA hierarchy. Relying Parties trust certificate holders because they trust the TA which trusts a CA which trusts the end certificate holders.

This trust is evidenced by a chain of certificates rooted at the Trust Anchor. If an adversary could change the root of trust for any RP, that RP could be easily compromised. The challenge for the operator is to ensure that each secure device obtains the correct TA information.

One method to doing this without needing to preload the TA certificate into every device is as follows. Each accredited manufacture will preload the device with a Manufactures certificate identifying the make, model and serial number of the device, and a ³pre-provisioned TA Certificate. After a Smart Grid operator purchases a Smart Grid device, the manufacturer would issue the operator a TA Transfer Certificate, which would instruct the device to accept the operator¶s root CA certificate as the new trust anchor, and only the operator¶s root CA certificate. The TA Transfer Certificate would be constrained to specific devices (based on serial number).

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In addition to secure TA management, each PKI enabled Smart Grid device should have the ability to securely load and store a local policy database (LPD). This local policy database is a set of rules that define how the device can use its certificate, and what types of certificates it should accept when acting as an RP. The LPD would be a signed object, stored in the HSM, and signed by a Policy Signing server trusted by the TA. It would be possible for the same PKI tools to automate the management of the LPD as the TA certificate.

6.5.5 Certificate Attributes In order for portions of the Smart Grid to continue to function while major portions of the grid infrastructure are unreachable, it will be essential for Smart Grid devices to be able to authenticate and determine the authorization status for each other (as well as human system administrators) without the need to reach a back-end security server.

In order to do this, two additional capabilities would be required. First, Smart Grid certificates will require policy attributes to indicate the applicability of the certificate to a given application. Second, a local source of performing certificate status will be required. This can be accomplished in a number of ways. For example, it would not be difficult or costly to distribute local certificate status servers throughout the grid. A possibly better method involves having each certificate subject periodically obtain a signed certificate status for his own certificate. The certificate subject would store this status and provide it to an RP when authenticating to the RP. The RP would determine, based on local policy, if this status was new enough to accept, and if so, the associated certificate could then be evaluated.

It would also be recommended that all certificate subjects were loaded with the chain of certificates between themselves and their TA, and select chains of certificates between the subject¶s TA and the TAs of other agencies with which the local agency has cross-signed or otherwise trusts. Management of theses chains of certificates, and ensuring that devices receive the proper set, would again be automated by tools.

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CHAPTER-7 RELIABILITY

Renewable resources, while supplementing the generation capability of the grid and addressing some environmental concerns, aggravate the reliability due to their volatility. Demand response and electric storage resources are necessary for addressing economics of the grid and are perceived to support grid reliability through mitigating peak demand and load variability. Electric transportation resources are deemed helpful to meeting environmental targets and can be used to mitigate load variability. Balancing the diversity of the characteristics of these resource types presents challenges in maintaining grid reliability [7].

Reliability has always been in the forefront of power grid design and operation due to the cost of outages to customers. In the US, the annual cost of outages in 2002 is estimated to be in the order of $79B [5] which equals to about a third of the total electricity retail revenue of $249B [6]. A similar estimate based on 2008 retail revenue would be of the order of $109B. Much higher estimates have been reported by others. The reliability issues in modern power grids are becoming increasingly more challenging. Factors contributing to the challenges include:



Aggravated grid congestion, driven by uncertainty, diversity and distribution of energy supplies due to environmental and sustainability concerns. The power flow patterns in real-time can be significantly different from those considered in the design or off-line analyses.



More numerous, larger transfers over longer distances increasing volatility and reducing reliability margins. This phenomenon is aggravated by energy markets.



The grid being operated at its ³edge´ in more locations and more often because of: y Insufficient investment and limited rights of way

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y

Increasing energy consumption and peak demand creating contention for limited transfer capability

y y

Aging infrastructure Maximizing asset utilization driven by modern tools for monitoring, analyzing and control



Consolidation of operating entities giving rise to a larger ³foot print´ with more complex problems and requiring smaller error margins and shorter decision times. This problem may be aggravated by depletion of experienced personnel due to retirement, etc.

7.1 DISTRIBUTION MANAGEMENT FUNCTIONS
The reliability problem also arises due to faults occurring in the system. A set of advanced automation functions [8] is developed to combat this problem. These new distribution management functions can be summarized as follows:

7.1.1 The Fault Diagnosis and Alarm Processing Function: This function is automatically triggered immediately after the occurrence of a fault. It produces a diagnosis of events on the basis of a set of pre-defined scenarios (a comparison of the remote information flow is made with the patterns predefined by experienced operators). The diagnosis produces an analysis of the type of fault enabling the operator to quickly understand what happened in the network under its control. The function can also detect missing remote control signals.

7.1.2 The Fault Location Function: After detecting and analyzing the fault, it is necessary to find the location of the fault. The goal of this function is to quickly determine the section of the feeder where the fault occurred. This is performed by analyzing the information sent from fault indicators to the control center. Operators can then intervene and isolate the fault area by remotely opening the corresponding switches. The degree of accuracy depends on the density of fault indicators on the MV network.

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7.1.3 The Service Restoration Function: After locating the fault, this function finds all the plans allowing power restoration to lost customers of the non-faulted section of the feeder while considering technical constraints. Each plan consists of a series of actions, (opening/closing of switching devices) leading to power restoration.

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CONCLUSION
With the increasing world population, thereby increasing demand, and depleting resources the need to be µsmart¶ and efficient in our energy usage has become an imperative. Implementation of Smart Grid concept would go a long way in solving many of the present energy issues and problems. The whole network needs to be upgraded to meet the requirements i.e. at transmission as well as distribution level. Researches are going on to find the optimal solution and new technology to make all the desired characteristics possible.

Smart Meters, Smart Homes, Smart City and so on would constitute the Smart Grid. As the new technologies would be invented and existing ones boosted up to meet the desired specifications the Smart Grid would become a reality and change the whole energy pattern throughout the world.

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REFERENCES
[1] http://en.wikipedia.org/wiki/Smart_grid [2] Koen Kok, Stamatis Karnouskos, David Nestle, Aris Dimeas, Anke Weidlich, Cor Warmer, Philipp Strauss, Britta Buchholz, Stefan Drenkard, Nikos Hatziargyriou and Vali Lioliou, ³Smart Houses For A Smart Grid´, in 20th International Conference on Electricity Distribution Prague, 8-11 June 2009, CIRED2009 Session 4 Paper No 0751. [3] S. Massoud Amin and Bruce F. Bollenberg, ³Toward A Smart Grid´ in IEEE Power and Energy Magazine in September/October 2005. [4] Robert C. Sonderegger, Debbie Henderson, Steven Bubb and Julie Steury, ³Distributed Asset Insight´ in IEEE Power and Energy Magazine in may/june 2004. [5] Arun Sehgal, ³AMR offers multiple benefits´ in Pipeline and Gas Technology in April/May 2005. [6] Patrick McDaniel and Stephen McLaughlin , Pennsylvania State University, ³Security and Privacy Challenges in the Smart Grid´ in IEEE Computer And Reliability Socities in May/June 2009. [7] Khosrow Moslehi and Ranjit Kumar, ³Smart Grid - A Reliability Perspective´ submitted to IEEE PES Conference on ³Innovative Smart Grid Technologies´ January 19-20, 2010, NIST Conference Center, Washington, DC. [8] Xavier Mamo, Sylvie Mallet, Thierry Coste and Sebastien Grenard, ´ Distribution automation: the cornerstone for Smart Grid development strategy´ 978-1-4244-4241-6/09/$25.00 ©2009 IEEE. [9] Himanshu Khurana, MarkHadley, Ning Lu, and DeborahA. Frincke, ³Smart-Grid Security Issues´ in IEEE computer and reliability societies, 1540-7993/10/$26.00 © 2010 IEEE in January/February 2010.
[10] Anthony R. Metke, Randy L. Ekl and Schaumburg, IL USA, ³Smart Grid Security Technology´

in 978-1-4244-6266-7/10/$26.00 ©2010 IEEE.

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