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Abstract—The Smart Grid concept is conceived as

a means to optimize the operation and to improve the reliability of the electric delivery system. Many applications operating at the distribution level can be considered as Smart Grid functions because the results of the control applications ripple all the way into the transmission system and the generation plants. Unless these applications are properly integrated, the possibilities that the results create undesirable situations will occur. Accurate assessment of the state of energy system can be obtained from data collected from Smart Meters and other remote monitoring devices. A solid two-way communication system to handle transfer of massive amounts of data to a data repository computer is required. The communication system also serves as a control link between command center and remotely located control devices. This paper describes the requirements that link the architectural building blocks, the Smart Meters, Communication System and Data Repository and Management System, to form a superstructure that supports many functions and applications for Smart Grid applications.

Index Terms-- Smart meters, Smart Grid, Demand

Response, Asset Management, Outage Management, Data Mining, Communication Infrastructure

I. INTRODUCTION HE availability of new technologies in the area of digital electronics, communications and computer technologies

open the door to a multitude of applications that optimize the use and delivery of electric energy to the users. Smart meters are coming down in price and have increased their life and reliability and can generate a wealth of information which can be used to improve the utility operation. Reliable two-way communication and computer technologies, large scale data warehousing and data base management provide the necessary capabilities that many years ago were deemed impossible to implement.. An analysis of the various individual functions and applications that are repeatedly mentioned in the industry to enhance the energy delivery to the customers shows a high degree of interdependency, which through integration will lead to Smart Grid applications. Despite the many different meanings attached to SMART GRID by the electric power industry, this paper is intended to generate a comprehensive integrated aspect of functionalities and applications that form Sioe T. Mak is with ACLARA Technologies of ESCO, Hazelwood, MO 63042 (e-mail : Sioetmak&aol.com)

the basic corner stone to the super structure of the SMART GRID concept.. The requirements, the available tools and operational issues to accomplished the task which will lead to the intended results will be discussed in detail.

An understanding how the energy delivery system is designed and operated and some intrinsic physical behavior of the system during transient and steady state conditions will help what data to collect that will be used to design control algorithms for applications to support Smart Grid. In addition the word transponder is used in this paper to denote intelligent remote devices, such as smart meters, control devices, etc that can be communicated to for data collection or perform control actions on command.

II. THE ELECTRIC ENERGY DELIVERY

INFRASTRUCTURE 1. The Delivery Infrastructure Partitioning Most utility networks can still be viewed as having the following major components.

• Major power conversion plants. Plant capacities range between a few hundred MW to a few thousand MW.

• Transmission network. Transmission voltages range between 34.5 KV to 765 KV.

• The distribution network. Distribution voltages range between 4.0 KV to 34.5 KV and at the service level voltages range between 120 V to 480 V.

2. Circuit Configurations and Unique Properties of the 3-Phase

Network.

Three phase networks operating at 50 Hz or 60 Hz still dominate the existing the delivery infrastructure and the circuit configurations can be 4-wire, 3-wire, 2-wire and single wire with earth return. The transitions to the various voltage levels are accomplished through the use of three phase Y-Y, grounded or ungrounded neutrals, D-Y or single phase transformers. Under steady state operating conditions, the phase to neutral voltages Van, Vbn and Vcn are quite often transformed into line to line voltages at the secondary side due to the 3-phase transformer windings configuration. However, when one views each of the phase to neutral voltages at the medium voltage as base phasors and the line to line voltages as linear combinations of the base phasors, a unique picture emerges showing some basic characteristic features of the network. For any phasor generated at the substation bus there is a remote corresponding phasor at part of the network served by the substation that is slightly phase shifted with respect to the medium voltage substation bus phasor at a magnitude that

Sioe T. Mak, Fellow, IEEE

Knowledge Based Architecture Serving As a Rigid Framework for Smart Grid Applications

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

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depends on the circuit voltage drop and the intervening transformer winding ratio. Fig. 1 shows a simple example of it. Also, since the load current phasor can be referred to the voltage phasor that served as voltage source to the load current, similar behavior can be expected for the current phasor as the voltage phasor. If there are no intervening power

Fig. 1

conditioning devices, one can make this phasor connection all the way from remote customer end to generation. Any current drawn at the 120 V side has its corresponding phasor at the generation side. Hence, if for any reason a massive cumulative imbalance of load at the distribution side occurs, it will reflect itself also as massive imbalance at the generation side. For distribution networks in particular, there is also a unique property that is true. Since there are no distribution circuits that have lengths close to a quarter wavelength of a 50 Hz or 60 Hz wave, no long line or Ferranti effects are expected to occur at the distribution network. This unique behavior of the phasors mentioned above has important implications for the design of control algorithm and applications which will be discussed in the next sections. Another aspect is the time element which ties in intimately with captured data, transient behavior of the network and speed of response of a control system. When a switching action takes place, whether due to man-made control actions or nature induced actions such as lightning flashover across two conductors or a line fault, the electric network reacts according to some specific patterns. A. The switching transient phenomenon [1,2,8]. Energy storage devices such as capacitors (stray capacitances or capacitor banks), inductors, etc. react to a sudden change similar to the reaction due to a perturbation function. In most cases, the reaction is transient oscillatory and decays within a very short time and in general in less time than the duration of a quarter wave-length of the power frequency. The magnitude also depends on at what part of the waveform of the power frequency voltage the switching action is initiated and the attenuation can be attributed to the energy absorbing parts of the network. Fig. 2 and 3 show some transient oscillatory behavior of switching responses extracted from the voltage at the service voltage and the current response extracted from the neutral current transformer at the medium voltage substation bus. B. Energy conversion devices such as electric motors, heaters, electric welders, etc. have fairly long time constants to reach steady state operating conditions. The time constants typically last multiple numbers of cycles of the 50 Hz or 60 Hz.

Examples are locked rotor starting currents of motors as their output torques increase.

Hence, one can raise questions about what constitute good collected data which provide information needed to create control algorithms for the various utility applications.

Fig.2

Fig.3

III WHAT CONSTITUTE GOOD DATA FOR

GENERATING CONTROL ALGORITHMS Transient phenomena occur at random moments in a distribution network and most of the times are confined to local parts of the network. The ability to capture these transients from dispersed transient recorders is of interest to study transient behavior but will probably be useless for design of energy system control algorithms. Collecting data, such as voltage dips due to switching inrush currents and the energy consumption during these periods cannot be correlated to other parts of a distribution circuit in time. To have any impact on the grid, the inrush currents have to be very large and have sufficient duration that starts to affect the operation of the feeder.

Time interval consumptions of loads (KWHr, KVAHr) or averaged instantaneous values (Voltage, Current, etc.) for interval values much longer than the duration of transients or inrush phenomena are much more valuable to determine the state of the energy delivery system if the collected interval data can be synchronized in time. The averaging method such as measuring KWHr instead of the instant value of KW, basically behaves like a filter to reduce the effects of short duration excursions that are highly local and of short duration and have practically no effect on the general operation of the electric energy delivery system.

Most smart meters have the ability to collect interval data and be synchronized for the whole network by providing start and stop times control. Typical interval durations being considered are 15 minutes, half hour and one hour and stored

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in consecutive fashion by the meter. These collected data are retrieved using the two-way communication network to be delivered to a data repository system.

The perceived idea that one needs a real high speed communication network to collect all the information from all the data gathering devices may not be totally correct. Touting high baud rate communication capability as the only option for success does not necessarily imply high data throughput. The ability to use multiple parallel physical communication paths where each path allows multiple simultaneous communication channels to be used without cross-talk will be in many cases adequate.

A good perspective can been obtained by analyzing some utility applications in detail which will help in determining what will be required to implement Smart Grid functions. Two examples will be analyzed in detail, a Demand Response application and an Outage Management application. 1. Demand Response, a new perspective:

Demand Response cannot be equated with load

management anymore. Load Management was intended to reduce the need to install peaking generation plants by controlling peak demand. Utilities went through great pains to establish candidates who wanted to participate voluntarily in the load management program. Concepts of conservation, TOU rates and load control etc were introduced to create a successful program. Some Smart homes ideas were also proposed and introduced. With remote meter reading incorporating synchronized interval reading capabilities in place, customer energy use profiling can be implemented. This simplifies the determination of possible candidates who will participate in the load control and time of use rates program.

In this paper, an expanded concept will be introduced which couples Demand Response applications to other applications in the area of network optimization. For a start, loads can be cyclic for short or long intervals of time. Loads can also be fairly stable for long periods of time. In a distribution network, despite reducing peak coincident demand through load control it does not necessarily imply circuit losses are reduced or a favorable voltage profile along a feeder is attained.

To set up the stage for discussion a 3-phase feeder with some 3-pase branches and numerous single phase laterals is used for example. If for each load one can identify its phase on the feeder/lateral and also the time at which the load data is taken, then a whole set of information can be generated that become the trigger for new control algorithms. It is even better if the data is taken at specified time intervals that are synchronized for the whole feeder. The types of information generated are:

a. Coincident demand on all the phases of the feeder. b. At what time unbalance of load occurs and which

phase is the main cause of unbalance. c. Which loads are the main culprits of unbalance and

what are the duration and magnitude of each.

d. It provides explanation about the observed variable load factor on each phase at different times of the day.

e. Other useful information that the smart meters can deliver

f. Hybrid vehicles will become new type of controllable loads in a not too distant future.

The costs of not knowing are numerous and a few simple examples are used for illustration [3,4]. A. Circuit losses on the line.

• Assume two coincident peak loads with the same time duration ∆T are carried by a phase conductor which has a resistance value R. The load currents are I1 and I2 .

• The net loss in the circuit for the duration of the coincident peaks is {( I1 + I2 )2 *R*∆T} = (I1

2+ 2I1I2 +I2 2 )*R*∆T

• If the peak loads are controlled and no coincidence is allowed as shown in Fig. 4, the line loss becomes (I1

2 + I2

2)*R*2*∆T. For the case that I1 is equal to I2, the difference in line losses between the two loading scenarios is 100%.

• If a load control device can be applied to shift one peak from the other, not only does it reduce the total peak demand but it also improves the load factor, reduce the line losses in the feeder and improves the feeder voltage.

Fig. 4

The cumulative results of reducing the circuit losses due to improving the load factor on each feeder at a distribution network could be massive. One additional item to look into is not only to improve the load factor at each phase but also to balance the load at each feeder. In grounded neutral 4-wire systems, balancing the load will cause a reduction of the neutral current and stray currents in the ground.

Fig. 5 To express this in terms of the actual voltages, if the three

phase line to line voltages form a triangle and the absolute voltages can be expressed as a =⎟Vab⎟, b = ⎟Vbc⎟ and the third one as c = ⎟Vca⎟ then the Voltage Unbalance Factor is :

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In this expression, assuming that ⎟Vab⎟ has the largest magnitude, x and y have the following values :

x = b/a and y = c/a In many European countries the standards limit the

unbalance to 2% at the medium voltage level. In the USA ANSI C84.1 Annex 1 and NEMA MG1, for voltage unbalances in excess of 1%, a de-rating of motors is needed.

Unbalanced loading of a distribution feeder also dramatically increases the copper losses.

B. Integrated Volt-VAR control. The ability to determine the load distribution as a function

of time on each phase of a feeder and to correlate the data with the voltage profile on the feeder as a function of time will also help one to design more accurate control strategies for capacitor bank and voltage regulator control to improve the voltage profile on the feeder and reduce the line losses. Since interval data gathering by smart meters is an on-going process, one can verify the outcome of a control strategy and feed back corrections if necessary to improve the designed control algorithms. C. Predicting cold load pick-up.

Cold load pick-up causing large inrush currents occurs when cyclic loads come on simultaneously after a certain period of power outage. Air conditioners, electric heating and water heaters are turned off when an outage occurs. If the duration of the outage is sufficiently long, home temperatures rise and the water temperature goes down. A sudden power restoration cause all the air conditioners and water heaters to start simultaneously causing large inrush currents. These inrush currents can sometimes cause the circuit breakers at the substation to operate. D. Harmonic pollution patrol.

Some smart meters also have the capability to determine the THD (Total Harmonic Distortion) of the power voltage. Harmonic distortions on the voltage and current can impair the accuracy of revenue metering and other digital electronic control devices in smart homes [6].

If this information is also gathered together with the load data, then this information will allow the utility to determine where the culprit is that causes the harmonic distortions of the power voltage. The location where the maximum THD is observed on the feeder can be assumed to be the source of the harmonics. 2. Electric Utility Network Outage Management

Outages do occur in the electric distribution network. The effects of an outage are felt not only by the users of electric power, but also by communication infrastructures. Outages can occur at an individual customer premise, to a group of customers served by a distribution feeder or to a large area due to a transmission line fault. When an outage occurs, not only are remote communication devices out of power, but some communication nodes become de-energized too if no provision is made for a standby power source.

Single customer outages and total system blackouts will not be discussed here. Most of the outages occur due to line faults at the distribution feeder, which cause protective devices to disconnect certain segments of the feeder network. They can last for several seconds due to a re-closer operation, and some can last for many hours or days if damage occurs on the line or pole. Occurrence of an outage is unpredictable, and we define outage management as the ability to detect an outage and its extent and to alert the system operator of the outage and notify the necessary department about this event. Fault isolation and system recovery are part of outage management.

Many distribution networks are designed and protected by protective devices. By properly coordinating the protective devices (selective coordination), it is possible to isolate the faulted segment of the network to the nearest fault location. Hence, all transponders connected to the part of the circuit that is de-energized will be out of power.

The ability to monitor transponders that are de-energized, which can be related to the part of the network that is de-energized, will be defined as outage mapping [5,6,7]. For a properly coordinated protection system, when the fault occurs beyond a certain protective device, it will isolate the faulty section of the network from the source. This automatic isolation of part of the network is only the first step of the fault isolation process. The fault occurs only at a small segment of the de-energized part of the network. Disconnecting the faulty segment completes the process of fault isolation. Restoration of power means to energize the healthy remaining portion of the network from a different source.

Fig. 6 Several methods are available to detect outages. The availability of a very fast communication capability, the fast continuous polling technique of transponders scattered throughout the whole distribution network is a method that has been suggested to determine the extent of an outage.

The method, which takes advantage of the ability to communicate with the transponders, is the outage mapping function. The model as shown in Fig.4 is used for illustration. A main distribution feeder has 7 laterals protected by fuses F1, F2, ……., F7. Only 2 transponders are shown per lateral, indicated by the letters T with the appropriate subscripts. Assume a fault occurs at a location

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as indicated in the figure and the breaker S1 opens. Transponders T5, T6, ………, T13 and T14 are all de-energized and will not respond to a polling command. The inference can be made that the fault is beyond switch S1.

The continuous polling of all the transponders method is fine if a two-way very high speed communication technology is available and the polling action does not take away the ability to perform other equally important functions.

For slower communication technologies, the best method is the use of selective polling technique [5,7]. If a problem occurs on the feeder and an alarm signal is available from a SCADA RTU or relay to trigger a polling sequence of all 14 transponders, then the responses quickly show that the first 4 transponders T1, T2, T3 and T4 are still energized and the remaining 10 are out of power. If the transponder locations can be related to a circuit map, one can easily infer that circuit breaker S1 is open. It is even better if the de-energized circuit can be related to the topological map of the area.

Using this method, the communication for outage mapping is only active during the polling sequence. The more precise the information content of the alarm is, the lesser the time is needed for polling. The information content of the alarm can be the distribution substation bus number, feeder and phase number, line to neutral or line-to-line fault, etc. which are typically available from a SCADA system.

Outage information also helps to avoid unnecessary routinely scheduled communications to the remote devices that are de-energized, and the same information is also used to identify communication sub-nodes that are de-energized. Fault isolation is accomplished by opening breaker or switch S2 and restoration is accomplished by connecting the part of the network beyond S2 to another source. As simple as it may sound, restoration is a very complex function if one tries to automate and optimize the transfer.

If several options are available regarding where to connect the network beyond S2, what is the basis for considering a particular option if the expected repair time will take a long time. Some of the considerations are listed below:

1. Overloading of the other source can happen if one does not know what the expected total load is during the course of time.

2. Difficulties adjusting voltage regulation and capacitor bank control to minimize losses while maintaining a good voltage profile.

In the addressing mode, a group of transponders can be addressed by a single group command. Assume a group of N transponders belongs to one group, out of which N1 are located before switch or breaker S1 and the remaining ones (N - N1) are connected to part of the circuit between S1 and S2. If S1 is open due to the fault and S2 is opened to isolate the faulty segment, and one does not have a clue how large N1 is, then the response to a group command might result in multiple retries to try to retrieve data from the (N - N1) transponders. Organizing group addresses of transponders at the network

has to take into account their locations in relationship to the switching or protective devices of the circuit. No group should be separated into two or more subgroups of unknown number of transponders due to circuit disconnects by the protective devices. Several very important common issues can be observed from the two examples :

1. The need to know the origin of the data in relation to the energy delivery electric network.

2. Real time synchronization of data monitoring at the transponders.

3. The circuit information and time tagged to the collected data that the communication system delivers to the data repository system.

4. The need to design the way collected data are partitioned and stored to render them accessible and useful to various designers of control algorithms and users of control applications.

5. The two-way communication system not only serves as a data gathering tool but also as a control link to the various transponders.

IV. NETWORK COORDINATES AND TIME

PARAMETERS OF COLLECTED DATA

Smart Meters and other remote digital electronic devices that can be reached through a two-way communication network can generate massive amounts of useful information. The ability to bin and to store and also to time stamp the acquired data render these devices even more useful for other types of applications beyond simple meter reading. The massive amount of data generated by these devices scattered over widely dispersed geographical areas and the distribution network, unless properly organized in a systematic fashion to make them accessible to the various users within a utility organization, will cause delays and unnecessary waste of time in implementing and performing many of the functions discussed before in the previous sections.

The same group of similar data either by logical partitioning into sub-groups or as a whole can be used for a multitude of different applications. Previous examples have shown the intricate interdependency between applications and the question is how to logically assign certain attributes to each data point to render them useful and amenable for integration into the various applications.

Each transponder connected to the distribution network either for data gathering or control can be identified not only for its communication address or serial number, but also by its electric network coordinates. Each point of the distribution network where the data is taken can have its location at the electric distribution network defined in terms of the following coordinates and time.

2. Medium voltage substation name and substation bus number. Some very large substations have several power transformers with separate busses

3. Feeder number. Most substations have several feeders served by the same bus

4. Phase or phasor. The need to use phasor instead of phase is dictated by the existence of different 3-phase transformer winding configurations.

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5. Protective device/physical part of the circuit These coordinates are tied to the hierarchical structure of

the energy delivery network. In the previous examples mentioned before, time is also an important parameter in developing the control algorithms. Real time synchronization and the ability to recover after a blackout require serious considerations. Time coincident events when correlated to locations in the electric grid, provide information that will help the design of different control algorithms and also help to define the proper schedules for control [9]. Hence the following can be defined to describe the 4 items mentioned above.

Q( s, b, f, p, d, t) Where, Q is the data (KWHr, KVAHr, Voltage, etc.) s is substation name or number b is the substation bus number f is the feeder number p is the phasor name (line-to-neutral/ line-to- line) with respect to the bus phasor d protective device at the supply side of the line segment t the time at which the data gathering by the transponder starts.

Power-line type communication technologies have the ability to generate the network coordinates because the communication paths are practically identical to the power delivery path. Radio frequency technologies use the airspace to reach every point of the distribution network. The network coordinates can be obtained from the distribution network drawings and one also has to take into account the phase rotations in the distribution network due to the different winding configurations of the intermediate step-down transformers.

V. OPERATIONAL ISSUES

Request for data and perform control operations can originate from different sources. Customer services need data for billing and other revenue related services, such as pre-payment, time-of-use billing, customer service disconnect, etc. which may involve sending messages or alarm signals to the energy users. Demand response operations can include load management for reducing demand and request for control can be from generation and in case of emergency when there is danger of losing system stability that loads be dropped quite fast using the “scram” function. Rotating blackouts is another application that may originate from generation or transmission operators. At the distribution level, outage management, asset management, voltage management, etc. are handled by the maintenance and repair department at the various regional district level.

Satellite networks using their own data concentrators and intelligent networks and controllers such as Home Automation Networks, etc. will require information from the generation operator or distribution service center to trigger internal

actions which will benefit the utility as well as the energy user.

Since each task require specific types of information that may different for each application, and the need to issue control commands that are very likely different for each application and not quite predictable from a time stand point, smart integration of access to the system operating computer and data base management system requires good understanding of the control processes for the individual applications. Smart queuing, preventing loss of commands and data, recovery and necessary retry procedures to insure reliable operation are all part of system structural requirements. There is a need to have a command center that monitors and control the links to the communication network to the smart meters and remote transponders and to the data repository and other control center computers of the utility. The issue of cyber security is another item that requires important considerations.

All the issues and items discussed above should provide a clue how the general system architecture should be designed. The major building blocks used to support the system will be discussed in the next sections.

VI. SMART METERS

First, there is the issue of being able to observe, to monitor and to collect locally all necessary information at a cellular level. This task is performed by the smart meters at the customer premises of the utility distribution network. Hence smart meters should also be provided with communication interfaces.

To mention just a few items which a smart meter is capable to deliver, the total KW-Hr consumption, the local voltage, local number of outages count, total harmonic distortion (THD), interval energy consumption, etc. All these data can be time stamped and stored for near future retrieval. For a typical distribution substation serving N thousand customers, to collect hourly interval meter reading from each smart meter, requires the ability of a communication network to transfer 24*N* 103 data points from the geographically scattered sites per 24 hours to a central data base management system. In addition, there might be other types of information that needs to be collected from the remote sites of the network. Smart meters also keeps real time which can be initialized and downloaded by the control center. Several modes of group address assignments for specific functional tasks which can be downloaded from the main communication control computer provide another level of sophistication.

VII. THE ROLE OF COMMUNICATION

One of the very important building blocks of the system architecture is the communication network. The communication network can be congruent with the energy delivery network (eg. power line communication technology) or it overlays the electric distribution network such as the radio technologies. Hybrid power-line and radio technology also exist. Its function is two-fold, which is to bring back data

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from the remote sites of the energy delivery infrastructure in a timely fashion and also to serve as a control link between control centers and remote control devices. Inter-phasing with single transponders, selective group of transponders or all transponders in a global fashion at different periods of time whether scheduled or unscheduled is a basic requirement. The need for downloading assigned addresses to the smart meters nd other remote devices and also to maintain communication traffic control, are of critical importance.

The likelihood that problems can occur at the physical communication infrastructure should receive important considerations in the communication network design. Assuming that the distribution energy delivery network remains static, problems at the communication infrastructure nodes will require a comprehensive communication routing algorithm design. The problem becomes even more complex, if the distribution network is not static. Man-made circuit changes whether due to outages or other reasons do happen and it is necessary to track these changes, especially when it involves remote control that depends heavily on the integrity of the communication network [3,4].

Recovery issues after problems occurring in the communication network and the energy delivery network are not necessarily congruent but are from a communication and remote control standpoint intimately intertwined. The cost of not knowing can be massive. Wasteful communication retries to transponders at a de-energized part of a distribution network, loss of data, uncorrected control strategies, inability to reach transponders, etc. are the most commonly encountered problems.

The Communication Network Server computer, besides serving all possible communications and transactions to the remote devices also becomes a major node to the back-haul communication network which provides the link to the various control centers, data repository system management and other departments within the electric utility organization. The network serving computer handles the necessary queuing, routing, emergency overrides and alerting the existence of persistent problems in the communication infrastructure.

VIII. DATA REPOSITORY AND DATA BASE

MANAGEMENT SYSTEM The huge amount of data collected from the smart meters

and other end devices for immediate, near future or future use by multiple parties within the electric utility organization poses specific requirements on the data base management system. Data mining from the repository system has to provide the flexibility of organizing the data as required by the different applications. We mention here just a few possible uses of the collected data.

1. For scheduled energy billing 2. Time-of-Use rates as a Demand Response

strategy 3. Pre-pay metering 4. Monitoring coincident demand at various levels

of granularity from a network and time standpoint.

5. Correlating energy use with weather data for load forecasting purposes.

6. Developing trigger functions to active controls. 7. Etc.

In order to be able to activate control, customer alerts or to respond to specific requests, cross-linking the gathered information or its derivatives with the electric network coordinates and communication paths is necessary. Organization of the storage for quick access by different users, data security, backup plans, redundancy, etc., these are all important considerations that have to be taken into account.

There is no doubt that the collected data will also be valuable to the Transmission System Operator and the Generation System Operator.

IX. CONCLUSIONS

The necessary building blocks needed to build the

superstructure for Smart Grid applications are described with sufficient details. Only when properly integrated will the system allow the utility to implement the many applications needed to support the Smart Grid.

Concentrating the efforts on one of the building blocks or on one specific application only, while losing sight on the need to support many of the other intended applications can eventually be very costly to the utility. Retro-fittings, replacements are not only expensive but also very time consuming.

X. REFERENCES

[1]. Sioe T. Mak, “Propagation of Transients in a Distribution Network”, IEEE Transactions on Power Delivery, Vol. 8, No. 1, January 1993 [2]. Sioe T. Mak, “Application of a Differential Technique for Characterization of Waveform Distortions”, Presented at the IEEE Power Engineering Society Winter Meeting in Singapore, Jan. 2000 [3] Sioe T. Mak:, Denny Radford, "A TWACS System Alarm Function for Distribution Automation", IEEE Trans. Paper 93 SM 369-9 PWRD. Presented at the IEEE PES 1993 Summer Meeting at Vancouver, B.C., Canada, July 18-22, 1993. [4] Sioe T. Mak, Denny Radford," Added Utilization Costs Associated with Different Communication Architectures for Distribution Automation and Demand Side Management",IEEE Trans. Paper 94 SM 390-5 PWRD. Presented at the IEEE PES 1994 Summer Meeting, San Francisco, CA, July 24-28, 1994. [5]. Sioe T. Mak, “Synergism Between Intelligent Devices and Communication Systems for Outage Mapping in Distribution Networks.”, Paper presented at CIRED’99, 15eme Congres International des Reseaux Electriques de Distribution, Nice, France, 1-4 June 1999. [6]. Francisco de la Rosa, Sioe T..Mak, “Power Quality Issues Based on Field Data Recordings in MV and LV Facilities.”, Paper presented at the IEEE Power Engineering Society 2005 General Meeting in San Francisco, California, 12 – 16 June 2005.

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[7] Sioe T. Mak, “A Synergistic Approach to Using AMR and Intelligent Electronic Devices to Determine Outages in a Distribution Network”, Power System Conference 2006, March 14-17,Clemson University, Clemson, SC. [8] Francesco de la Rosa, Sioe T. Mak,“A Look into Steady State and Transient Performance of Power Lines Integrating Single Wire Earth Return Circuits” IEEE Conference Paper. Presented at the IEEE Power Engineering Society General Meeting, 24-28 June 2007, Tampa, Florida [9]Sioe T. Mak, “A Synergistic Approach to Implement Demand Response, Asset Management and Service Reliability Using Smart Metering, AMI and MDM Systems.”, Panel Paper presented at the IEEE Power Engineering Society General Meeting, 26-30 July 2009, Calgary, Canada

XI. BIOGRAPHY Sioe T. Mak was born in Indonesia. He received his Diploma in Electrical Engineering from the University of Indonesia, the M. Sc. and Ph.D. in EE from the Illinois Institute of Technology, in Chicago. He was Senior Staff Scientist at the Distribution Control Systems, Inc., a subsidiary of ESCO Technologies Corp. He served at many IEEE Committees, published numerous papers in the area of EHV Pole Fires, High Voltage Insulation Contamination and Power Frequency Communication Technology. He is also involved with Communication Infrastructure Studies, Reliability and Life of Electric Equipments, Remote Metering and Distribution Automation and Power Quality. He holds numerous US and world wide patents in the area of power line communication technology (TWACS), currently used in the USA by many electric utilities for AMI and Distribution Automation. He is also IEEE Life Fellow.