Absstract-This paper described the coordination of using medium voltage distribution substation SCADA, Smart Meters serving as synchro-sensors and coupled with AMI communication and GIS system for optimizing distribution network operation. Network losses management by improving the system load factor, balancing the system voltage and at the same time combining capacitor bank control and demand response type applications and asset management are topics discussed in this manuscript. This paper also discusses the synergism amongst the different applications and how advantage can be taken to optimize the whole energy delivery system. Index Terms—Synchronization of SCADA with smart meters, synchro-sensors, assets and loss management, optimization of energy delivery, smart distribution grid, harmonic pollution patrol.

I. INTRODUCTION a. The Role of Smart Meters and Remote Sensors.

Smart meters and intelligent remote sensors coupled with Advanced Metering Infrastructure (AMI) allow electric utilities to monitor the electrical power conditions at practically every point of the network. To save monitored data at fixed intervals in contiguous fashion by the smart meters and sensors provide further insight about the behavior of the system with time. If the start and end of each interval are synchronized throughout the whole network using a standard reference clock, then new applications can be generated. The smart meters, remote sensing devices and controllers are linked to the communication infrastructure through an intelligent communication interface device, henceforth called transponder. If in addition the transponders’ locations at the network (bus, feeder/phase) are also known and addressable by logical groups, then a whole set of new advanced control applications can be designed and implemented.

b. SCADA the Master Supervisor The role of SCADA in conjunction with AMI was initially seldom mentioned in the technical literature. However modern day SCADA systems have evolved to such an extent that coupled with the imbedded intelligence in the IEDs, they can generate a wealth of useful information, that when properly utilized and coupled with the AMI network will play a key role allowing utilities to design new applications that enhance the distribution network operation. The authors are with ESTA International, LLC., 11917 Holly Spring Drive, Great Falls, VA 22066. USA. e-mail : Sioe Mak@ESTAinternational.com

SCADA serves as Master Supervisor of the distribution network served by the distribution substation and is directly coupled to the AMI Communication Net-Server computer as shown in FIG. 1

SCADA continuously monitors the network behavior but it also triggers the alarms needed for control. Smart meters serve as remote SCADA sensors whose data help provide explanations about the behavior of the distribution network. The SCADA system also provides feedback information on the effectiveness of a control action.

In addition, the distribution substation SCADA handles only the electric delivery network served by the substation bus. This gives the utility the flexibility to account for the different unique applications needed for the different distribution substations.

FIG. 1 c. The role of AMI

The AMI system plays a major role in monitoring and controlling transactions and data flow between SCADA, remote devices, data base management system and the system command center. The need for stratification of control and monitoring activities by substation bus, feeder or phase imply that remote devices can be accessed, addressed and clustered into groups or subgroups on each phase of a feeder, or on a per feeder basis for a specified distribution substation bus. The AMI communication Net Server computer organizes the communication addressing structure and controls all the transactions and data flow to and from their destinations. The transponders are addressable individually or by group command. This avoids communication latency due to the time needed to set up and form groups that are required for a transaction. The Net Server is responsible for downloading the different addresses

Synchronizing SCADA and Smart Meters Operation For Advanced Smart Distribution Grid Applications

Sioe T. Mak, Life Fellow IEEE Nader Farah ESTA International, LLC ESTA International, LLC

978-1-4577-2159-5/12/$31.00 ©2011 IEEE

to the transponders and storing them at the Net Server data base.

d. The Data Base Management System.

The Data Base Management System computer becomes the source of information for Customer Services, Maintenance and Repair Service and Control groups of the electric utility. The data base should also be structured to provide easy and fast access to the various users of the information without compromising data integrity and security. All stored data are time stamped and tagged with the necessary circuit information.

e. The address design. Each transponder can be accessed individually by the communication Net Server. For specific applications groups of remote units should be addressable by a short single group command. As an example, several hundred load control devices on a particular phase of a feeder have to be activated simultaneously. By using one group address for all the transponders coupled to the load control switches, only one group command is needed to activate those switches. The application engineer decides which transponders are to be grouped under a group address. Group sizes are based on the applications. This also implies that each of the transponders have multi-tier addressing levels.

II OPTIMIZATION CONCEPTS

a. Line losses determination and control To generate the concept of distribution network optimization, the illustration in FIG. 2 is used. A single phase feeder with a line impedance (R + jX) from a medium voltage substation bus supplies load currents to the total circuit losses at the feeder is equal to :load1 and load2 for a period of time ΔT. The net load current is equal to (I1 + I2). If the bus SCADA RTU monitors the net delivered energy for a period ΔT and, in synchronism, the smart meters also monitor the consumptions at each load for the same duration of time, then for scenario 1 when both loads coincide in real time, the total circuit losses at the feeder is equal to :

Ploss = [( I1 + I2 )2 R ΔT = [(I1)2R + (I2)2R + (2I1I2)R ]*ΔT ( 1 ) For the case when the absolute values of both currents are equal I1 = I2 = I, then Ploss = 4I2R *ΔT. From a monitoring standpoint this number can be obtained by subtracting the net delivered energy as measured by the SCADA RTU minus the sum of the monitored energy by the smart meters for the same period of time ΔΤ. In other words, Ploss = PSCADA – ( Pload 1 + Pload 2 ) ( 2 ) Hence one can use SCADA time interval energy monitoring capability in conjunction with smart metering time interval energy consumption data to determine the feeder losses. This is only true if the monitoring operations are synchronized.

For scenario 2 type loading, the loading interval is equal to 2ΔΤ.

FIG.2

The total line losses is equal to : Ploss = [ ( I1 )2R]*ΔT +[( I2 )2R ]*ΔT ( 3 ) If the current I1 is equal to I2 in magnitude and phase, the line losses for scenario 2 is equal to Ploss = [2I2R]* ΔT and reduced by 50%. Here is a case where by load shifting to reduce the coincident peak demand the load factor is improved and a dramatic reduction in line losses is accomplished. In addition the peak demand on the transmission and generation is also reduced. Another benefit is also a reduction in voltage drop at the loads. The voltage drop at scenario 1 is higher by virtue of the larger current. b. VAR monitoring and control. If each of the current can be expressed as follows I1 = I1re + jI1im I2 = I2re + jI2im Then the imaginary component due to the loads is equal to j(I1im + I2im) for scenario 1 operation. The imaginary component of the current is caused by the VAR component of the network and of the loads. For the period of time ΔT the substation bus and the capacitor bank have to supply approximately -j(I1im + I2im) to compensate for the VAR requirement. When the system is operated as depicted in scenario 2, the VAR requirement is less severe. The substation bus and capacitor banks have to be able to compensate jI1im or jI2im whichever current is larger even though for a longer period of time 2ΔT. The voltage regulation on the feeder is also improved. In the next sections a more detailed description will be offered about methods of how to implement the optimization principles as described in this section.

III LINE LOSSESS AND VOLTAGE BALANCE ISSUES AND THE ROLE OF SCADA AND CUSTOMER

PROFILE DATA. a. Customers’ and Total System’s Demand Profile

For a specific bus, feeder and phase, SCADA can provide the total substation load, voltage and current data. If the SCADA system can be provided with interval energy reading in synchronism with the interval meter reading at the customer premises and also selectively partition the result by bus, feeder and phase, then the coincident demand due to customers load on each phase of a feeder can be correlated to the total load profile

that the SCADA provides. The information can be used to determine the line loss on a phase of the feeder in question by using equation (2). The total coincident customers load demand profile obtained by summing the demand profiles of each smart meter served by the phase wire of the feeder is then subtracted from the SCADA demand profile of the same phase of the feeder. The net result is the feeder losses profile for the same time interval duration of the SCADA and coincident customers demand profile. This information which can be extracted from the data base is valuable for determining how the line losses behave. When the maximum occurs, by going through the individual customer load profiles one can determine which of the customers cause the line loss to maximize. If in addition the AMI data base also contains information of the phase wire serving the customers, one can also identify all the culprits causing the line losses to maximize.

All the information generated above will help the utility to design strategies to reduce circuit conductor losses. Some options available are to exercise load control of controllable loads at the customer premises to improve the load factor. Another option is to transfer load to another lightly loaded feeder/phase in a seamless fashion. If a detailed study indicates that some customers can be permanently served by a different feeder without impairing the operation of the feeder, then this will be the most desirable solution. b. Unbalanced loading and voltages. Another issue that requires serious considerations is the load balancing issue. Many feeders form radial networks from the bus with three phases plus grounded neutral. Three phase or single phase laterals feed many single phase transformers which are connected between one phase wire and the neutral. These single phase distribution transformers step down the medium level voltage to 240V/120V service voltages. A center tap from the secondary side of the distribution transformer serves as a neutral wire to the customer premises to provide the necessary 120 V service voltage.

Fig. 3 The transformer is normally grounded at the neutral side of

the high side terminals and is also tied to the center tap of its service voltage side. In addition, the low voltage neutral wire is also grounded at the customer premises. Some of the low

voltage grounds use the metallic water supply lines to the house. Fig. 3 depicts the grounding connections. The triple ground, one at the pole of the lateral, the second one at the distribution transformer and the third one at the customer premises can cause stray currents to flow in the ground. These stray currents sometimes cause step voltage problems with farm animals. These stray currents also quite often flow in the metallic water supply pipes inside the customer homes. These stray currents at the water pipes generate 60 Hz magnetic fields inside the house and generate fears that these magnetic fields might cause cancer at children. EPRI has raised concern about low frequency magnetic fields inside the homes due to stray currents flowing into the copper tubing of the water supply lines.

Theoretically speaking any single phase load can be considered as a cause of unbalance in a three phase balanced system. A large three phase unbalanced load on a feeder generates zero-sequence and negative components of the currents on The zero-sequence components flow into the neutral wire. With a multiplicity of grounds of the neutral wire stray currents in the soil are expected to exist. Balancing the loads on the feeder will reduce the problem of stray currents.

The negative sequence currents on the feeder cause negative sequence voltage drops on the phase wires. These voltage drops cause unbalance voltages to appear on the phase wires. These unbalanced voltages can cause problems with three phase machines. The negative sequence currents generate retardation torques and additional eddy currents in the rotor. This will cause additional temperature rise in the motor and affect the life of the winding insulation.

The unbalance can be defined as the ratio of the negative sequence voltage to the positive sequence voltage. For a three phase voltage triangle the absolute line-to-line voltages can be expressed as a =⎟Vab⎟, b = ⎟Vbc⎟ and c = ⎟Vca⎟. If ⎟Vab⎟ is the largest in magnitude and by defining the following ratios x = b/a and y = c/a , then Voltage Unbalance Factor is equal to :

An unbalance in excess of 2% at the medium voltage level are not permitted in many countries. ANSI C84.1 Annex 1 and NEMA MG1 require a de-rating of motors for voltage unbalances in excess of 1%. Copper losses are also dramatically increased because of unbalanced loading.

IV. PREVENTING COLD LOAD PICK-UP PROBLEMS a. Demand Response issues and associated problems. In the previous discussions it is tacitly assumed that some loads are cyclic and superimposed on a fairly even base load. The cyclic loads are usually air-conditioners, space heater or water heaters, which by using peak demand shifting strategies, operate as depicted by scenario 2 in Fig. 1. If a prolonged outage occurs, at the moment of power restoration, both loads

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and the base loads are turned on simultaneously. Scenario 1 operation will result into a cold load pick-up load inrush condition. To minimize the effects of cold load pick-up, a loss of power should automatically cause both loads to be disconnected. At power restoration the cyclic loads are sequentially switched in with the appropriate time delay. This is of course only possible if all the cyclic loads are under control by a demand response program and coupled to an AMI system. The ability to extract the cyclic load part from each customer load profile on a specific phase of a feeder allows the utility to develop cold load pick-up predictor curves. Some customers may not have any controllable load and operate at base load only. After a sufficiently long outage period, the cold load pickup on a feeder phase is approximately equal to the sum total of the base loads plus the coincident demands of all the cyclic loads on that feeder phase. This process should be done on all phases of all the feeders on the bus, The obtained results for different days of the week, season, etc. may be different. However, judicious choices can be made on what the expectations are and when to implement controls to minimize the effects of cold load pick-up inrush currents. Sometimes cold load pickup problems happen during staged rolling blackouts when the utility is stressed to curtail demand because of the generations’ low spinning reserve condition. During restoration of power the cold load pickup currents can cause the feeder circuit breaker to trip open if no controls are used.

V. OUTAGE MANAGEMENT OF DISTRIBUTION NETWORKS AND THE ROLE OF SCADA FOR

OUTAGE DETECTION AND MAPPING. a. Outage Detection and Fault Isolation. Utilities already practice selective coordination of protective devices to minimize the effects of a fault on the network. Illustration Fig.4 depicts a feeder with several laterals. S1 and S2 represent circuit breakers or re-closers. F1, F2, . . , F7 are the protective fuses or cut-outs and T1, . . . T14 represent the customers served by the distribution feeder. A fault at location 1 causes circuit breaker S1 to open. The whole circuit beyond S1 is de-energized. The portion before the breaker S1 remains energized. A fault at location 2 causes only fuse F5 to open. S1 and S2 remain closed. Only customers T9 and T10 are suffering loss of power. The normal procedure after a fault, is to patrol the line after receiving calls from customers to locate the fault and determine which protective device has operated. Restoration efforts follow as soon as possible. If the AMI system can be activated to poll the individual smart meters at the customer premises and tie the polling results to the bus, distribution feeder and phase where the fault is sensed, then it can be inferred that the non-responding units are de-energized. Mapping the locations of the de-energized units on the digitized distribution circuit map gives a quick indication which part of the circuit is de-energized and which protective device has operated and opened

For a fault at location 1 T5, T6 , . . ., T14 will not respond to a polling sequence by the AMI system. T1, T2, T3 and T4

however will respond. By inference based on the circuit map the breaker S1 has opened. Hence the GIS system can be invoked to determine the location of S1 and the area which suffers a power outage.

FIG. 4

The SCADA system plays a major role in determining which smart meters to poll. The SCADA RTU should immediately report to the Service and Maintenance computer that it senses a fault at a particular bus, feeder and phase. This information is needed for the AMI system to selectively poll only those smart meters served by the feeder where the fault is sensed. An intelligent algorithm can be developed to look at the fault current pattern to determine whether it is a breaker, re-closer or a cut-out that has operated. It will dramatically reduce the amount of polling needed by the AMI system.

b. Load Transfer and Service Restoration.

If the fault at location 1 requires a significant time to repair, a possible restoration of power for part of the feeder can be to open breaker S2 and the segment beyond the switch S2 can be connected to another feeder or phase. However this act might cause problems to the other supply line. The loads T9 , T10 , T11 , T12 , T13 and T14 should not cause the coincident demand on the other supply to exceed its allowed limit and cause overload, large voltage drop problems. By looking at the load profile of the other supply line and the coincident demand of all the loads beyond breaker S2 it is possible to predict the effects of the load transfer on the other supply line. Remedial actions can be undertaken by applying some demand response action and voltage control to reduce the peak demand.

VI. DYNAMIC MODELING OF THE DISTRIBUTION NETWORK.

a. Load Flow Issues in System modeling.

The electric distribution network is, under normal operating conditions, fairly rigid in its physical structure. The variable parts are the loads and the capacitor banks that during certain times are switched in or out. In addition, most of the loads

change relatively slowly with respect to time. To study the dynamic changes on the feeders of a network served by a substation bus, such as the voltage distribution along the feeder as a function of loads, currents on the feeder segments, load and voltage unbalance on the feeders, etc. electric network modeling techniques have been used to develop control methods to optimize the network operation. Most of the difficulties encountered are determining the load distribution along the feeders and their dynamic changes with time.

The ability to profile the loads as a function of time in terms of synchronized consecutive time intervals provides an improvement over the previous techniques used by the electric industry. The SCADA system can be used to provide feedback on the effectiveness of the control actions used.

b. Dynamic equivalent circuit modeling of the distribution network.

In the following Fig.5, a single phase lateral is shown with a distribution transformer serving two loads. Smart meters at both loads can generate information such as incremental magnitudes of Δ KWHr, ΔKVAHr and the average voltage VAVG during a specified period of time ΔT. The average load currents can be calculated from the information provided by the smart meters. Knowing the magnitudes of the average voltages and the currents only are not very helpful unless the phase differences between the customers’ load voltages and load currents are known in order to determine the voltage at the lateral. To overcome these problems, the loads can converted into equivalents impedances.

Assume the following information Δ KWHr, ΔKVAHr and also the average voltage VAVG are known. Then the average power factor and the current I can be calculated as in the following manner. cos(φ) = Δ KWHr / ΔKVAHr ( 4 ) IAVG = [ΔKVAHr /ΔT] / VAVG ( 5 )

Fig. 5

The load can be represented by the following parallel impedances. Rshunt = VAVG / [Icosφ] ( 6 ) Xshunt = VAVG / [Isinφ] ( 7 )

This equivalent load impedance at the secondary side of the transformer can be combined with the low voltage line impedance R+jX using series and parallel combination methods. Using the appropriate voltage reference the equivalent impedance for both loads plus their low voltage service wires can be added to the distribution transformer

impedance. The lateral is now reduced into one shunt load impedance.

c. VOLT-VAR control For each time interval, the lateral shunt impedance can be calculated as a function of time valid for the defined interval ΔT. The shorter the time interval is chosen, the more accurate the results will be to describe the circuit dynamic changes with time. Standard EMTP programs are available that describe the feeder analytically and calculate the voltages and currents at all parts of the circuits for a given input function at the substation bus. For every new interval the currents and voltages on the line can be calculated. Hence the dynamic changes on the line are based on the data provided by the smart meters served by the feeder and not based on assumed load distribution models. For each state of the network as calculated by using the EMTP method one can predict the expected outcomes of certain applications such as changing the capacitor banks switching combinations, transferring laterals to a different phase, voltage regulator setting changes, etc. in order to obtain the most optimal operational gain at the feeders and the whole network served by the substation bus. The desirable gains are for instance improved load balance, good voltage profile along the feeder. The EMTP results can be tested through actual implementation of selected control strategies on the actual network as predicted by the staged controls on the EMTP model. The substation SCADA verifies whether the outcome of the application produces the desirable results as predicted by the EMTP calculations. The equivalent load impedance as calculated by using equations (4), (5), (6) and (7) are based upon the average value of the load within a specified time interval. A cyclic load which lasts less than the duration of the observation interval requires additional considerations. The utility may also be interested what impact the Demand Response applications on the cyclic loads has on the network load factor If this cyclic load can be extracted from the total load profile, one can calculate its equivalent load impedance and connect this in parallel of the equivalent load impedance when the cyclic load is absent. In the EMTP study, the option will be available to have the cyclic load in or not as part of the customer load for the study. This area of study is still wide open for research and development into the area of Integrated VOLT-VAR control, Loss Management and Assets Management applications.

VII. MONITORING POWER QUALITY

a. Harmonic Pollution causes and sources SCADA systems have the ability to detect large intermittent load currents that cause recurrent voltage dips on the feeder. Another level of sophistication as advertised by several vendors is the ability to determine random current spikes that could be caused by high impedance faults at the medium voltage circuit. One of the categories pertaining to voltages under the “power quality” issues is voltage stability. Large steady state loads subjected to frequent switching in and out

during short intervals, cause voltage dips or swells that cause undesirable effects to the customers. Recurrent transients, burst transients caused by power electronic switching devices, arc furnaces, arc welders, etc. occur quite often in the electric network and cause voltage and current distortions. Another source of power quality problems are high impedance faults, surface discharges on polluted insulators, bad contacts, etc. The magnitudes of the current changes are not large enough in magnitude to cause circuit breaker operation The SCADA system can report these findings to trigger a polling sequence by the AMI system to strategically located Smart Meters on the feeder/phase where the phenomena are detected. Smart Meters can monitor voltages and some special ones are designed to determine the THD (Total Harmonic Distortion) of the monitored voltages. The returned data from the polling results will quickly indicate where the approximate location is of the cause of problems by determining which polled units indicate the highest voltage dip or maximum THD magnitude. Scheduled polling of these special Smart Meters can also be implemented to determine power quality problems at the remote sites that SCADA does not detect at the substation level. The ability to identify the cause of power quality problems and to locate the culprits of harmonic pollution at the network is becoming a reality. The utility can subsequently take remedial actions to correct the problem. b. Voltage instability problems.

Voltage swells and sags data at many sites, not merely evaluated statistically on an individual site basis, but also correlated by time for all the sites being monitored, will become even more valuable to the electric utility for developing strategies to minimize their detrimental effects. Within a load interval duration, the possibility exists that the system voltage fluctuates between wide limits for a short time durations. For that interval the voltage should be defined in terms of its average values, maximum values, and minimum values. The smart meter should be programmed to generate these values of the voltage for that interval. This information is needed to determine the necessary remedial steps to reduce the voltage instability problem c. The adverse effects of harmonics on revenue metering. Many sensitive digital-electronic devices are also affected by distorted voltages. Digital clocks, relying on the 60 Hz voltage zero-crossings for their proper operation, are affected by recurring transient spikes on the voltage waveform. Other malfunctions are caused by memory contamination of these devices due to burst transient phenomena. The degree of severity of the voltage distortions can be such that many customers’ devices are affected simultaneously due to one main culprit. Monitoring the problems at the point of common coupling quite often produces results, which point to the source of the problem. Revenue meters are also prone to be affected by locally generated distorted voltages and currents. It is hard to quantify the cumulative loss of revenues due to harmonic pollution. The effects of distorted voltage and currents on electric revenue meters and KVAHr metering have been extensively studied by various researchers. [5,6,7,8,9]. There is also the question of what metric can be used to

quantify distortion and what data should a power quality monitoring device generate, time stamp and made available for data gathering using the communication network. However standard distortions for voltage and current which can be replicated by standard calibration laboratories still need to be established. Only then can “goodness of a design” be objectively ranked.

The existing communication system already used for remote meter reading can be used to quickly determine the sites affected by harmonic pollution.

VIII. DISTRIBUTED GENERATION

During the last few years the industry has witnessed a surge in the development of local distributed generation. Smaller wind turbines, and solar panels are becoming more available serving individual homes and in many instances electrically coupled to the utility network at the customer premises. Larger size distributed generation are integrated into the utilities’ network at the medium voltage distribution feeders. Small local distributed generation may generate new rate structures, especially when excess local generation can feed back energy into the utility network. If the smart meter can read reverse energy flow, the load profile might show negative dips on the profile curve. It is not clear yet what impact these local generation might have on the network load factor and voltage profile. For the larger capacity distributed generation, there are different issues to be concerned about. The classical selective coordination of protective devices as shown in Fig. 4 may not be applicable. The fault current comes from the distribution substation as well as from the network side. Assuming the intelligence at the relays manage to isolate the fault, there can be islanding problems to be dealt with. Local generation will suddenly experience sudden overloading conditions or power swing with existing induction motors acting as induction generators.

Hybrid electric vehicles or pure electric vehicles are slowly coming down in price and start to gain popularity. It is still very difficult to assess what the impact is on the electric utility grid. The battery chargers generate additional demands and can also cause harmonic distortions on the voltages and currents. There is also a possibility that these battery charging systems require special metering and possibly load control switches for demand response type actions.

AMI, SCADA, Smart Meters and remote intelligent devices must be able to expand their capabilities for monitoring and control in this new environment.

IX CONCLUSIONS

The integration and synchronizing the operation of SCADA and Smart Meters linked by AMI system open new horizons for new advanced applications for control and optimize the distribution network operation. SCADA serves as watchdog over the distribution network and continuously monitor the integrity of the distribution network together with the remote synchro-sensors to generate detailed information about the state of the network. The synchronization of SCADA

monitoring operation with the remote Smart Meters data gathering provides an accurate picture of how the actions of the end devices at the remote sites affect the operation of the energy delivery system. The wealth of information generated becomes a source and the basis for new and advanced control functions designs that improves the customer satisfaction and the reliability energy delivery system. How distributed generation and electric vehicles affect the distribution network operation are still fuzzy at this moment.

REFERENCES

1. Sioe T. Mak , “Synergism Between Intelligent Devices and Communication Systems for Outage Mapping in Distribution Networks”, CIRED 15th International Conference on Electric Distribution, France, 1–4 June, 1999

2. 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.

3. 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, Calgary, Canada

4. .Sioe T. Mak, “Knowledge Based Architecture Serving as a Rigid Framework for Smart Grid Applications”, Conference Paper presented at the ISGT/IEEE Smart Grid Conference in Washington DC., January 19-21, 2010

5. Sioe T. Mak , Nader Farah, “Strategic Use of Smart Meters Data and AMI Capability to Develop Advanced Smart Distribution Grid Applications”, CIRED 21stInternational Conference on Electricity Distribution, Frankfurt, Germany, 6-9 June 2011.

6. IEEE Working Group on Non-sinusoidal Situations, “Practical Definitions for Powers in Systems with Non-sinusoidal Waveforms and Unbalanced loads : A Discussion”, IEEE Transactions On Power Delivery, Vol. 11, No. 1, Jan. 1996, pp. 79-101.

7. R. Arseneau, New Definitions for Electrical Quantities and Their Effects on Revenue Meters and Billing Charges.

8. Alessandro Ferrero, Antonio Menchetti, Renato Sasdelli, “An Approach to Energy Metering in the Presence of Distortion, Asymmetry and Unbalance”

9. .A. McEachern, W. M. Grady, M. A. Moncrief, G. T. Heydt, M. McGranaghan :”Revenue and harmonics: an evaluation of some proposed rate structures,” IEEE Transactions Power Delivery., vol.10, n0. 1, pp 474-481

10. 10.Alexander Eigeles Emanuel, “Powers in Non-sinusoidal Situations, a Review of Definitions and Physical Meaning”, IEEE Transactions on Power Delivery, Vol. 5, No. 3, July 1990, pp. 1377-1389.

Biographies:

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 Equipment, 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. Nader Farah obtained his Bachelor and M. Sc. In EE from Purdue University. He also has a certificate of management training from Twente School of Management in the Netherlands. He has published technical papers and has experience with consulting, software/solution development and implementation of real-time automation (SCADA, DMS, EMS, OMS, SAS, AMI). He is also member of the Project Management Institute (PMI), IEEE, CIGRE, International Utilities Revenue Protection Association (IURPA) and the United States Energy Association (USEA) and also participates in the National Institute of Standards and Technology (NIST) Smart Grid Interoperability Panel (SGIP). Mr. Farah is founder of ESTA International, providing technology advisory services to the electric utility industry worldwide. Prior to establishing ESTA International he worked for real-time automation suppliers (GE, ABB and OSI) and KEMA. He is also leading ESTA’s efforts for extending the NIST Synchro-Phasor Test Facilities and Communications and the harmonization of DNP 3.0 and IEC 61850. While at KEMA as Senior VP of International Operations, he was responsible for operations in several countries and was the Officer-in-Charge and Program Manager for international projects in over 30 countries. The projects scopes covered the full spectrum of SCADA/DMS systems, development of energy policy and wholesale electric markets, T&D reliability and planning studies, real-time automation projects, sustainable energy, smart grid, AMI and energy efficiency.