Smart Distribution System Design: Automatic Reconfiguration for Improved Reliability

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Smart Distribution System Design: Automatic Reconfiguration for Improved Reliability D. Haughton, Student Member IEEE G. T. Heydt, Life-Fellow IEEE Abstract this paper explores distribution system automation, automatic reconfiguration after a disturbance and the impact on reliability in a smart power distribution system. The use of network incidence or connectivity matrices is shown and an example indicates the potential operational capabilities of a smart distribution system. A discussion of the potential advantages of electronic switching in distribution engineering is also given. The connection with the Smart Grid initiative is made in the paper. or superior levels of safety and reliability as compared to classical designs. References [3-5] are a small sample of the many comprehensive references relating to classical design. The main elements of distribution system assessment are: Reliability Efficiency Voltage regulation Cost Environmental and aesthetic impact Safety. Index Terms Distribution automation, distribution system reconfiguration, distribution system restoration, reliability, distribution engineering, Smart Grid. I. POWER DISTRIBUTION SYSTEMS AND THE SMART GRID S MART GRID technology includes the application of automation and digital controls to power systems in general and to distribution systems in particular. Title XIII of the recently signed Energy Independence and Security Act of 2007 [1] includes the following characteristics of a Smart Grid: Increase in use of digital control and information technology with real time availability Dynamic optimization relating to grid operability Inclusion of demand side response (DSR) Demand side management (DSM) technologies Integration of distributed resources (DR) including renewables and energy storage Deployment of smart metering Distribution automation Smart appliances and customer devices at the point of end use. The details of how the Smart Grid will accomplish these goals and the details of what is (and is not) included in the Smart Grid is a subject of continuing discussion; however, the elements of Fig. 1 illustrate the scope of the Department of Energy (DoE) Grid 2030 vision [2]. It is interesting to contrast Smart Grid technologies with classical distribution system design. One conclusion of such an exercise is that Smart Grid designs should have the same The authors acknowledge the Power Systems Engineering Research Center (PSerc) for supporting this work. G. T. Heydt also acknowledges the support of the Future Renewable Electric Energy Management and Distribution (FREEDM) center, a National Science Foundation Engineering Research Center (award number EEC-08212121). Authors Haughton and Heydt, are with the Department of Electrical, Computer and Energy Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85287 USA (e-mail: {daniel.haughton, heydt}@asu.edu). Fig. 1 Grid 2030 vision The art and science of distribution engineering, distribution system design and system operation present a multitude of complex objectives, as previously identified. For example, reliability, efficiency, and safety must be maximized while simultaneously protecting assets, minimizing costs and customer disturbances. These objectives impact the choice of distribution system assets, substation topologies, locations,

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 primary feeder design, feeder and substation protection schemes and other significant engineering decisions. Transitioning from contemporary systems to smart distribution systems of the future requires a paradigm shift in both design and operations. This paradigm shift is due in part to the increased use of DSM and DSR; even more significant is the burgeoning of distributed resources, many of which are considered renewable energy sources wind and solar. Massive deployment of renewable energy systems is expected to occur in electric distribution systems in the near future [1, 2]. New philosophies of redesigning the existing distribution system topology and rapid restoration following system disturbances are imperative to maximize the use of these resources. This paper focuses on specific aspects of the technical design and operation of a smart distribution system, considering impacts on reliability and incorporating new technology in the design process. No attempt is made to manage any of the aforementioned multiobjectives. Taking the above elements under consideration, this paper describes some design and operational philosophies for the smart distribution system. The paper is organized as follows: Section II briefly describes relevant contemporary indices for measuring system reliability; Section III discusses the upgrading of legacy radial distribution systems; Section IV discusses the promise of electronic switching; Section V illustrates an example; Section VI discusses the use of sensory information in a Smart Grid; Section VII explores practical benefits of automation and Section VIII summarizes the main conclusions. II. SYSTEM RELIABILITY MEASURES Perhaps the best known distribution system reliability measures are the system average interruption duration index (SAIDI) and the system average interruption frequency index (SAIFI), These indices do not capture all information relating to system reliability, and they notably omit capture of the load lost during outages. The indices also suffer from the fact that they are often calculated inconsistently [6]. Since these indices are system averages, they may not give information on specific bus reliability. For specific bus reliability, load point indices are required. Additional similar indices are the customer average interruption duration index (CAIDI) and the average service availability index (ASAI), (1) (2) (3) Note that the ASAI and similar indices may be expressed as a number of nines, N 9, where N 9 = -log 10 (1-ASAI). (5) Thus N 9 for ASAI = 0.9999 would be 4 as an example [7]. Eq. (5) can be approximated by the Taylor series expansion, 0.43429 (6) 1 where a system upgraded from an ASAI of A to an ASAI of A + ΔA results in a concomitant increase in reliability of N 9 to. Similarly, for an upgrade of SAIDI to SAIDI + ΔSAIDI (i.e., ΔSAIDI < 0), 0.43429 (7). Indices like SAIDI, SAIFI and others provide system wide reliability measures. The reliability measures of distribution system components or individual buses (also referred to as load point indices in references [8, 9]) can be determined. These indices include the average failure rate, λ (expected number of failures/year); expected outage time, r (h); annual unavailability, U (h/y); and expectation of unserved energy, E (kwh/outage or kwh/y), that captures the energy demanded by system loads that cannot be delivered to those loads. [8]. These probabilistic data may be adjusted using historical data or factors unique to the area of study, such as weather effects, aging infrastructure or other data pertinent to assessing expected component lifetime. Load point indices can be used to evaluate the relative performance of alternative system designs, restoration plans or system topology changes. Reference [6] discusses the advantage of using these indices for benchmarking in a repeatable and standardized environment. Standardized test beds are useful in calculating reliability indices and in evaluating the reliability of systems that have been seen before and solved before (e.g., a restoration plan has been calculated in the open literature). Reference [9] presents a realistic power system test bed, complete with generation, transmission and distribution along with system reliability analysis and data. As indicated by the authors of reference [9], the distribution system is the most significant part of the integrated power system that negatively impacts system reliability indices. Therefore, distribution systems are the most appropriate location to realize system reliability improvement. References [8-13] further elaborate on measures of distribution system reliability. III. UPGRADING THE DISTRIBUTION SYSTEM FOR IMPROVED PERFORMANCE DURING RESTORATION The Smart Grid initiative is visualized by the authors as a coordinated transition from contemporary legacy distribution systems to an automated, self-healing, and more reliable system. The Smart Grid includes the elements discussed in Section I and refined tools for real-time assessment of system operating conditions as indicated in Fig. 2. Reference [14] further elaborates on the topic of self-healing power systems, (4)

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 3 communications and controls required, and the Smart Grid initiative. The distribution system of the future must be designed so that rapid restoration is possible. Research on system restoration has largely been confined to transmission systems. This is because transmission systems are networked. However, it is envisioned that future distribution systems will increasingly migrate to networked configurations especially in areas of high load density. Rapid distribution restoration can accomplish multiple objectives, including reduction of the classical system average interruption duration and frequency indices, and/or the minimization of unserved energy to loads. A highly reliable, reconfigurable, and fault tolerant system, must contain multiple redundant paths. More important than multiple paths are smart strategies for fault detection, isolation and reconfiguration (FDIR) to manage redundancy. The existence of FDIR in contemporary networked distribution systems is limited to local protection schemes which usually do not communicate with each other. Thus, in order to selectively convert existing distribution systems into smart distribution systems, it is proposed to add FDIR mechanisms and other automatic control devices. The methods used for FDIR may replicate transmission system concepts that use system restoration logic to automatically restore the distribution system. Using circuit matrix concepts, the optimization of the reliability at system buses shall be performed based on the reliability of individual components. each other through any number of intervening buses. Thus a zero entry in B n-1 identifies buses that are not energized [10]. B is an n x n matrix and contains binary entries. It is possible to raise B to higher powers using Boolean operations, e.g., where and are Boolean OR and AND operators respectively. This matrix and its powers (i.e. B n ) can be used to trace connectivity of the networked system. The modeling of circuit breakers and switches is such that breakers or switches are represented as ones when closed and zero when open. As an example, an objective may be the minimization of unserved energy after a component failure and consequent reconfiguration. The B matrix allows all energized loads, line segments and buses to be identified for all allowable network configurations. IV. THE PROMISE OF ELECTRONIC SWITCHING Determination of whether a low voltage or outage event counts toward some reliability metric (e.g., SAIDI, SAIFI) depends on the duration and severity of the event. This subject has been examined in connection with the electric power quality of voltage at a given bus. As an example, consider the Information Technology Industry Council (ITIC) curve as shown in Fig. 3. 250 200 OVERVOLTAGE CONDITIONS 150 PERCENT CHANGE IN BUS VOLTAGE 100 50 0-50 ACCEPTABLE POWER 8.33 ms 0.5 CYCLE RATED VOLTAGE UNDERVOLTAGE CONDITIONS + -- 10% Fig. 2 Visualization of the transition from legacy distribution systems to the Smart Grid, enabling technologies, and tools Various methods are available to accomplish system reconfiguration. In this paper, a binary connection matrix, B, is used to model the distribution system for connectivity. Branches are modeled as ones when energized and zero when deenergized, 1 1 0 For a system with n buses, the matrix B n-1 will contain all ones except in positions ij where i and j are not connected to -100 0.0001 0.001 0.01 0.1 1 10 100 1000 TIME IN SECONDS Fig. 3 ITIC power acceptability curve, total outage tolerated for about 16.7 ms Fig. 3 defines a region in the T- ΔV plane (T is the duration of an event, ΔV is the depth of low (or high) root mean square voltage at a load bus. A low voltage event as low as -100% (i.e., a total outage) could be tolerated for about one cycle (60 Hz system). If electronic switches were used to reconfigure a distribution system, the 16.7 ms objective is readily realized. Note that the system must detect the low voltage condition within this period and this is presently commercially attainable in the one-quarter cycle range. As an example, the dq0 transformation may be used to track the voltage magnitude in real time [15] and the dq0 signal amplitude may be used to initiate circuit reconfiguration. It is concluded that with elec-

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 4 tronic switching of the primary distribution system, it is possible to insure continuous operation of loads that comply with the ITIC curve. Electronic components in the distribution system offer the possibility of energy management in the presence of distributed renewable resources. That is, as renewable resources become available, these generation resources usually require electronic converters to interface to the distribution system. The converters could be controlled to effect energy management. The promise of electronic control in distribution systems does not come without its challenges. Table I shows some of these challenges. These challenges are the subject of present research. For example: Silicon Carbide offers the possibility of high power switching at high speed; cost of components is frequently reduced in mass production of semiconductor devices; voltage withstand capability is possible with series connections and optically controlled semiconductors; and it may be that the value of integration of renewable resources and high speed control comes at a manageable dollar cost. It should be noted that the greatest improvement in system reliability is obtained through networking with appropriate interruption of unserviceable circuits [8, 16]. This generally means the inserting of network interrupters (fuses, circuit breakers, load break switches, solid state interruption devices) and sensors should be used to effectuate the interruption. References [16, 17] discuss networking the secondary distribution system a method that has given improvement in SAIDI and SAIFI by nearly a factor of ten in some cases. Note that there are disadvantages to a solid state controlled energy processor (e.g., a solid state transformer). Some of the disadvantages can be obviated by passing a certain percentage of the power that is ultimately delivered to the load through a conventional magnetic transformer. This configuration is shown in Fig. 4. Note that this hybrid configuration avoids some of the problematic conditions indicated in Table I, namely: Reduces losses due to electronic switching (the magnetic transformer losses are generally below 2% for 50% load) Renders manufacturing problems of the semiconductor controlled elements easier Reduces cooling requirements Improves component life. TABLE I CHALLENGES OF SEMICONDUCTOR CONTROLLED DISTRIBUTION CLASS DEVICES Phenomenon Basic problem Mitigation possibilities Basic impulse level insulation Voltage breakdown of typical semiconductor components may be problematic Use of voltage limiting devices Use lower distribution voltages coordination (below distribution Development of more class voltages) suitable semiconductor materials Switching losses Bulk resistive losses in semiconductors Cost of components Cooling semiconductor components Isolation and safety Component lifetime High power loss, proportional to switching frequency I 2 R loss in semiconductors High cost of high power switches Losses in semiconductor switches No ohmic isolation afforded by semiconductor switches Loss of life due to heat Low loss switching strategies (e.g., zero voltage or zero current switching) Development of more suitable semiconductor materials Use of low current configurations Mass production Development of better manufacturing techniques Oil and air cooled technologies Reduce losses in semiconductor switches Principle of insulation by isolation Judicious use of circuit breakers to isolate circuits Use a magnetic transformer for isolation Better cooling Reduce losses Fig. 4 Two configurations for a controlling energy flow to a load (A) via a solid state transformer and (B) a hybrid approach using a magnetic transformer as well as an SST. V. AN EXAMPLE OF RAPID RESTORATION A rapidly restorative system will follow an algorithm similar to that identified in Fig. 5 where real time sensory monitoring and analysis of the distribution network takes place. It is important in many automated systems that controls exist such that an operator may assume control of the distribution system. For example, maintenance work or construction may require that automated system reconfiguration or restoration be disengaged for the safety of utility or construction workers. References [8, 9] provide details of the test bed used in this paper, better known as the RBTS. The RBTS is a six bus test system with five load buses, eleven generators, nine transmission lines, 240 MW of installed capacity and a peak load of 185 MW. The system also has voltages at the 230 kv, 138 kv, 33 kv, 11 kv and 4 kv. The system is sufficiently small that it can be studied and analyzed in detail but sufficiently large to be considered non-trivial. [9].

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 5 TABLE II BASE CASE RELIABILITY INDICES FOR BUS 3 GIVEN IN [9]* Load Point λ (f/y) r (hr) U (h/y) E (MWh/y) 1 0.3010 11.4352 3.4420 1.6122 3 0.3140 11.1688 3.5070 1.0121 8 0.2210 1.9412 0.4290 0.3634 27 0.3205 10.9626 3.5135 1.9957 41 0.1885 1.8276 0.3445 2.5319 44 0.2015 1.7742 0.3575 1.5689 Total 66.68 SAIDI 3.4726 SAIFI 0.3028 *The data in the table are taken directly from [9] and they do not entail the use of any electronic switching devices. The notation f/y refers to failures per year. The total energy unserved is summed over all 44 load points Fig. 5 Automated monitoring of system connectivity in real-time The largest distribution subnetwork in the RBTS is bus 3; it has peak total load of 85 MW, average load of 52.63 MW, industrial, large user, office buildings, residential and commercial customers. The one line diagram of bus 3 is show in Fig. 6. The one line diagram of the overall system may be found in reference [9]. The RBTS is used for evaluating the performance of incorporating automated, remotely operated electronic switches and the corresponding increase in reliability. A system incorporating electronic switching is consistent with the goals of a Smart Grid. The system of Fig. 6 depicts the distribution network of bus 3 with 44 load buses, 77 line segments, switches and normally open points. Manually operated isolation switches are replaced with remotely controlled switches in selected locations. The algorithm identified by Fig. 5 and the binary bus connectivity matrix may be used for a comparative evaluation of contemporary methods of circuit reconfiguration for restoration versus automated switching. This example assumes FDIR systems operate in an ideal manner; switching times for automated restoration and system reconfiguration scenarios are drastically reduced. Failure probability and frequency of events are held constant for comparison purposes. The results obtained show that average repair times, annual unavailability and expectation of unserved energy can be reduced. Table II presents load point reliability indices of the base case situation presented in reference [9]. Table III presents results of an automated, smart distribution system. As indicated, λ values for each load point are the same in each case, but all other indices are reduced with automation. TABLE III RELIABILITY INDICES FOR BUS 3 WITH ELECTRONIC SWITCHING* Load Point λ (f/y) r (hr) U (h/y) E (MWh/y) 1 0.3010 10.6146 3.1950 1.4965 3 0.3140 10.3822 3.2600 0.9408 8 0.2210 1.1765 0.2600 0.2203 27 0.3205 10.1716 3.2600 1.8517 41 0.1885 1.0345 0.1950 1.4332 44 0.2015 0.9678 0.1950 0.8558 Total 57.14 SAIDI 3.2322 SAIFI 0.3028 *The data in the table reflect system performance with electronic devices inserted in every branch. Note the improvement over data shown in [9] (which are tabulated in Table II). The total energy unserved is summed over all 44 load points It should be noted here that further networking of the primary distribution system, and especially of the secondary distribution system, can produce far greater reductions in r, U, E, and SAIDI, as reliability increases when the number of redundant paths increase [17, 18]. VI. UTILIZATION OF SENSORY INFORMATION In Figs. 1 and 2, there is an implication that a key element of the Smart Grid philosophy is the use of sensory information to effectuate system control. Conventional instrumentation might be used to measure quantities such as voltage magnitude, current, power, reactive power. Additional instrumentation may augment the legacy measurements in areas such as temperature, overhead conductor sag, weather, condition of insulating oil, and customer information. Also, the prospect of using the Global Positioning System allows the synchronization of these measurements via time stamping. Alternative instrumentation concepts are discussed in [24]. As an example of the dimensionality of the sensory requirements, note that in the segment of the RBTS system studied in Section V, approximately 37 active power measurements need to be made and approximately 80 control signals need to be sent. As a reminder, this example has 33 load buses. VII. INCLUSION OF ECONOMIC CONSIDERATIONS IN THE RESTORATION PLAN Assigning value to the cost of unserved energy is a complex, subjective issue. Effects of interruptions may be direct or indirect. Cost of effects such as industrial and manpower efficiency reduction, injury, social disruptions due to unexpected outages (e.g., electrified public transit systems, traffic signals) are not easily quantifiable [13, 20].

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 6 Fig. 6 RBTS test bed with one line diagram of bus 3 [9]

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 7 Literature suggests that the average customer may not be willing to pay more for improved reliability. Also, the simple calculation of lost revenue from unsupplied energy does not sufficiently capture the aggregated cost of an interruption. References [19-22] detail a number of attempts to quantify the cost of unserved energy; there is no standardized methodology for assessing interruption cost. An alternate scenario involves assigning a multiplication factor to calculate the value of unserved energy. Industrial, commercial, residential, government and other loads have different multipliers that correlate to the impact of an interruption [20]. For example, a short duration interruption of a manufacturing plant process may halt production from a few minutes to a few hours. This may translate to millions of dollars in lost revenue to the manufacturer [19]. This same philosophy can be applied to large and small industrial, large and small commercial, and residential loads. These multipliers may be applied to a reliability study to determine the cost-to-benefit of alternate cases. The following example incorporates multiplication factors using automated electronic switching described in Section V. Table IV presents a range of multiplication factors for different load types based on data obtained from [19 22] and average historical electricity prices as identified by the U.S. DoE Energy Information Administration (EIA) [23]. Consider as a final example the RBTS system without automation (exactly as in [9]), versus the configuration in Fig. 6 with every switch automated / operated using the algorithm shown in Fig. 5. Incorporating the multipliers shown in Table IV, one obtains improvement in unserved energy which results in a dollar value also indicated in Table IV. In Table IV, the improvement value is shown for selected load points, and also for the entire RBTS. TABLE IV MULTIPLICATION FACTOR METHOD FOR DETERMINING THE COST OF UNSERVED ENERGY DEMONSTRATING POTENTIAL FINANCIAL SAVINGS OF AN AUTOMATED SYSTEM Load Point Load Type Multipliers ($/MWh) Value of automation ($/y) 1 Residential 1-50 11-532 3 Commercial 20-200 119-1190 8 Small industrial 10-100 76-760 27 Office buildings 20 200 240-2400 41 Large industrial 50-300 3000-17,600 44 Large industrial 50-300 1900-11400 Total. 16720-118210 The total energy unserved is summed over all 44 load points In order to fully assess the cost to benefit of automation of the distribution system, one would need a less uncertain set of multipliers than those indicated in Table IV. The results of a cost to benefit analysis would be system dependent; however, the values tabulated suggest that there may be cases for which the proposed automation is favored. VIII. CONCLUSIONS Automated restoration offers the potential of improved reliability over manually restored systems. It is possible to reduce average customer outage times, annual unavailability and expectation of unserved energy by automating distribution systems. There is a considerable potential in improvement of system performance in terms of reliability, speed of restoration, and the integration of distributed resources through the use of electronic switching; but these enhancements come with challenges. The challenges include reduction of losses due to electronic switching; improvement of the reliability of electronic converters (especially microprocessor controlled devices); and innovative solutions to insulation coordination. Cost is also an identified key challenge. The analysis of when to restore, and how to restore service has been proposed through the use of an off-line calculated table and properties of the binary bus connection matrix. A suggestion has been offered for the calculation of the cost to benefit ratio of distribution automation. The ideas indicated are in line with the Smart Grid concept which is basically the use of sensory information to enhance system performance through control and system reconfiguration. IX. REFERENCES [[1] 110 th Congress of United States, Smart Grid, Title XIII, Energy Independence and Security Act of 2007, Washington DC, December 2007. [2] Office of Electric Transmission and Distribution, United States Department of Energy, Grid 2030: a national vision for electricity s second 100 years, Washington DC, April 2003. [3] Westinghouse Electric Co., Electrical Transmission and Distribution Reference Book, East Pittsburgh, PA, 1964. [4] W. Kersting, Distribution System Modeling and Analysis, New York, NY: CRC Press, 2006. [5] I. Novak, Power Distribution Network Design Methodologies, Chicago, IL: IEC Publications, 2008. [6]V. Werner, D. Hall, R. Robinson, C. Warren, Collecting and categorizing information related to electric power distribution interruption events: data consistency and categorization for benchmarking surveys, IEEE Trans. on Power Delivery, v. 21, No. 1, January 2006, pp. 480 483. [7] G. Heydt, Improving distribution reliability (the N9 problem) by the addition of primary feeders, IEEE Transactions on Power Delivery, v. 19, No. 1, January 2004, pp. 434 435. [8] R. N. Allan, R. Billinton, Reliability Evaluation of Power Systems, 2nd ed., Springer, 1996 [9] R. Billinton, S. Jonnavithalu, A test system for teaching overall power system reliability assessment, IEEE Transactions on Power Delivery, v. 11, No. 4, November 1996, pp. 1670-1676 [10] G. Heydt, Computer Analysis Methods for Power Systems, 2nd ed., Scottsdale, AZ: Stars in a Circle Publications, 1996. [11] H. L. Willis, Power Distribution Planning Reference Book, New York, NY: Marcel Dekker, 1997. [12] R. Billinton, R. Ringlee, A. Wood, Power System Reliability Calculations, Cambridge MA: MIT Press, 1973. [13] J. Burke, Power Distribution Engineering Fundamentals and Applications, New York, NY: Marcel Dekker, 1994. [14] S. Amin, B. Wollenberg, Towards a smart grid power delivery for the 21 st century, IEEE Power and Energy Magazine, v. 3, No. 5, pp. 34-41 [15]S. Suryanarayanan, G. T. Heydt, R. Ayyanar, J. D. Blevins, S. W. Anderson, Simulation based Considerations in Placement of Capacitors near a Dynamic Voltage Restorer, Simulation Modeling Practice and Theory, vol. 16, no. 9, October 2008, pp. 1430-1437. [16] R. E. Brown, "Network reconfiguration for improving reliability in distribution systems," in Proc. 2003 IEEE Power Engineering Society General Meeting, Toronto, Canada, July 2003. [17] W. Steeley, C. Perry, M. Vaziri, Interconnection of Distributed Energy Resources in Secondary Distribution Network Systems, EPRI white paper 1012922, Technical Update, Palo Alto, CA, December 2005 [18] R. P. Fanning, Implementation of networked primary and secondary distribution systems for US utilities, Proc. IEEE Power Engineering Society General Meeting, July 2003, vol. 4, pp. 2425 2429. [19] R. Thallam, Impact of grid connected distributed generation on power quality and reliability at a semiconductor fab, presentation at Arizona State University, Tempe, AZ, October 2, 2009

> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 8 [21] P. J. Balducci, J. M. Roop, L. A. Schienbein, J. G. DeSteese, M. R. Weimar, Electrical power interruption cost, estimates for individual industries, sectors, and the U.S. economy, Pacific Northwest National Laboratories, U.S. DoE Office of Power Technologies, February 2002. [20] K. H. LaCommare, J. H. Eto, Cost of power interruptions to electricity consumers in the United States, Ernest Orlando Lawrence Berkeley National Laboratory, February 2006. [22] A. A. Chowdhury, D. Koval, Value-based power system reliability planning, IEEE Transactions on Industry Applications, v. 35, No. 2, March/April 1999, pp. 305 311 [23] Average Retail Price of Electricity to Ultimate Customers: Total by End-Use Sector, Energy Information Administration, [Online]: http://www.eia.doe.gov/fuelelectric.html [24] G. T. Heydt, S. Bhatt, Present and future trends and needs in electric power quality sensors and instrumentation, Journal of Electric Machines and Power Systems, v. 27, No. 7, July, 1999, pp. 691 700. X. BIOGRAPHIES Daniel Haughton (StM 09) was born in Kingston, Jamaica and spent most of his life in Belize City, Belize. His B.S.E.E degree is from the University of South Florida, Tampa FL (2006), and M. S. E. E. from Arizona State University, Tempe AZ (2009).Mr. Haughton has industrial experience with the California ISO, Folsom, CA; Tampa Electric Co., Tampa FL; and Belize Electricity Limited, Belize City. He is presently completing requirements for the PhD. at Arizona State University. Gerald Thomas Heydt (StM 62, M 64, SM 80, F 91, LF 08) is from Las Vegas, NV. He holds the Ph.D. in electrical engineering from Purdue University, West Lafayette, Indiana (1970). His industrial experience is with the Commonwealth Edison Company, Chicago, and E. G. & G., Mercury, NV. He is a member of the National Academy of Engineering. Dr. Heydt is presently the site director of a power engineering center program at Arizona State University in Tempe, AZ where he is a Regents Professor. He is the recipient of the 2010 Harold Kaufmann IEEE Field Award.