Some Elements of Design and Operation of a Smart Distribution System

Transcription

1 1 Some Elements of Design and Operation of a Smart Distribution System Abstract this paper relates to several specific aspects of design and operation of a smart power distribution system. The emphasis is on automation in design, reliability enhancement, operations, and reconfiguration after a disturbance. The use of network incidence or connectivity matrices is shown and examples indicate the potential operational capabilities of a smart distribution system. An algorithm described as a sequential feeder approach is illustrated. Index Terms Distribution automation, distribution engineering, distribution system reconfiguration, distribution system restoration, reliability, Smart Grid. I. POWER DISTRIBUTION SYSTEMS AND THE SMART GRID T H. Brown, Student Member IEEE D. A. Haughton, Non-Member IEEE G. T. Heydt, Life-Fellow IEEE S. Suryanarayanan, Member IEEE HIS PAPER relates to the application of automation and digital controls to power distribution systems. 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) and demand side management (DSM) technologies; integration of distributed resources (DR) including renewables and energy storage; and deployment of smart metering, distribution automation, smart appliances and customer devices. Grid 2030, the US DoE s vision for the 21 st century electric infrastructure also calls for introduction of smart controls and appliances to the existing grid [2]. Most of the smartness is expected to be incorporated in distribution systems. In order to create the distribution systems of the 21 st century, several innovative additions to existing distribution systems are envisioned including the deployment of smart devices, meters, appliances, controls, communication pathways and sensors. The intent of increasing the smartness of the electric infrastructure is to increase the efficiency and reliability of the grid. In order to 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 ). Authors Haughton and Heydt, are with the Department of Electrical Engineering, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ USA ( {daniel.haughton, heydt}@asu.edu). Authors Brown and Suryanarayanan are with the Colorado School of Mines, Golden, CO USA ( {hbrown, ssuryana}@mines.edu). achieve significant levels of intelligence and to reliably supply the demands of the 21 st century loads, an imperative requirement is a unique framework for the smart distribution system that takes into account the following: levels and locations of increasing smartness in distribution systems; reconfiguration of distribution system architecture from a radial topology to a partially meshed (networked) structure; placement and utilization of sensors in distribution systems that will aid both supervised and fully automated controls; and enabling strategies and configurations for interconnecting renewable energy sources to distribution systems. 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 or superior levels of safety and reliability as compared to classical designs. There are many comprehensive references relating to classical design, with [3-5] being a small sample. The main elements of distribution system assessment are: Reliability Efficiency Voltage regulation Cost Environmental and aesthetic impact Safety. There are a plethora of complex issues in distribution engineering, distribution system design and operation. Fig. 1 highlights major issues associated with the science of distribution engineering and aspects of each issue that arise as systems transition from conventional design and operation to evolutionary design and operation. This paper focuses on specific aspects of the technical design and operation of a distribution system; reliability and efficiency evolutionary options are investigated. No attempt is made to manage the multiobjectives suggested by Fig. 1. 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 some contemporary indices for measuring system reliability; Section III details a new algorithm for redesigning radial distribution systems with renewable energy generation sources to partially networked systems for increased reliability; Sections IV and V illustrate a novel technique for rapid restoration of a distribution system following an outage; Section VIII summarizes the main conclusions.

2 2 N 9 = -log 10 (1-ASAI). (5) Thus N 9 for ASAI = would be 4 as an example [7]. The reliability associated with an event of probability p is shown in Table I. TABLE I RELIABILITY OF SPECIFIC EVENTS Event p N 9 N 9 representation of ASAI for SAIDI = hours (per year) N 9 representation of ASAI for SAIDI = hour (per year) One day in 200 years minutes per year seconds per year Six sigma reliability US Federal Aviation Administration guidelines for aircraft reliability 6-12 Fig. 1 Various aspects of distribution engineering, distribution system design and operation. 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), (1) 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. Additional similar indices are the customer average interruption duration index (CAIDI) and the average service availability index (ASAI), Note that the ASAI and similar indices may be expressed as a number of nines, N 9, where (2) (3) (4) The unserved energy U captures the energy that is demanded by system loads but cannot be delivered to those loads. The energy U may be calculated as either a kwh figure for a given outage, or as a total over an entire year. References [8-10] further elaborate on measures of distribution system reliability. Reference [6] discusses the advantage of using these indices in benchmarking (i.e., comparing in a repeatable environment). Massive deployment of renewable energy systems is expected to occur in electric distribution systems in the near future [1, 2]. In order to maximize the benefit of such inclusion, new philosophies of redesigning the existing distribution system topology and rapid restoration following system disturbances are imperative. Some of the abovementioned reliability metrics may serve as a tool for realizing configurations that incorporate emerging philosophies of redesign and restoration. III. AN ALGORITHM FOR REDESIGNING DISTRIBUTION SYSTEM TOPOLOGY As the transmission system has become networked and automated for improved reliability, the distribution system has remained largely radial and passive. A typical distribution system topology consists of radial feeders interconnected by tie-switches. One method of connecting two feeders using several sectionalizing switches is the loop-type primary feeder [11]. Envisioned at this juncture is a redesign or retrofit of an existing primary distribution system. As renewable distributed generation (RDG) sources increase in the system, benefits from redesigning radial systems into partially meshed systems are expected to increase [12]. Many researchers have explored the redesign of radial distribution systems: approaches to reconfiguration have objectives such as minimizing losses [13-20]; increasing reliability [21, 22]; and achieving load balance [23, 25]. From a utility perspective, RDGs may be connected at any of the distribution class voltages as long as interconnection requirements such as the recommendations of IEEE 1547 and UL 1741 are met [25, 26]. During an outage, the RDGs may be used to supply local demand. This operation

3 3 occurs requires disconnection at the point of common coupling from the system supply. One approach to realize reliability benefits is the migration from radial to meshed topologies. Another approach is the networking of the secondary distribution [27]. Integrating reliability assessment into planning and design of distribution systems leading to installations of new assets such as substations and feeders and replacing aging infrastructure is practiced by the power industry [28]. An algorithm for redesigning radial distribution systems into partially networked systems is described at this point. This algorithm uses a priori information regarding location and rating of RDG installation. The system load to optimally balance the cost vs. benefits of adding new laterals between distribution feeders is included. The cost being considered is the fixed (i.e., capital) cost of redesign, and the benefit is the value of the avoided unserved energy. Most radial distribution systems have laterals which may be connected during certain system events. A possible multiobjective function for planning new laterals in a radial distribution system is given as maximize [r] and minimize [C] subject to r r 0 (6) C C 0 m = 0 where the cost, C, and benefit, r, are found such that the project cost, C, shown in (8), does not exceed the maximum cost allowed for the project C o and the benefit of the system r has improved above the base case benefit r o. Depending on the benefit used, the relationship between r o and r may need to be altered. The power flow constraints, m, must be satisfied for any topology to be acceptable. The result of this constrained multiobjective optimization is a set of plausible solutions satisfying the power flow requirements, falling within the project budget, and offering an increase in benefit. Then one may select the preferred balance of cost and improved benefit. Although the benefit examined here is the unserved energy, other benefits such as increased reliability or increased power quality may be used. The example shown in Section IV solves the multiobjective optimization problem given in (6) heuristically. IV. AN ILLUSTRATIVE EXAMPLE OF REDESIGN: THE SEQUENTIAL FEEDER APPROACH Three representations are necessary to complete this optimization problem: the existing topology of the distribution system; the locations and ratings of the RDGs; and the existing level of benefit (or reliability). For the purpose of describing the algorithm for adding new laterals in presence of RDGs, a simple example is presented. A test bed named Example System I (ES-I) shown in Fig. 2 is used. Some of the system data for ES-I were synthesized using example data from [4]. ES-I has three feeders, ten buses, and two voltage levels, V 1 and V 2, where V 1 is greater than V 2. There are three loads, at buses 4, 7, and 10, and two RDGs at buses 3 (a PV array) and 5 (a wind turbine). The ES-I system topology is represented by a sparse adjacency matrix or incidence matrix describing the connectivity between buses. The binary bus connection matrix B is this incidence matrix and is defined as, Fig. 2 ES-I with 10 buses, 3 feeders, and 2 RDGs. V 1 is kv and V 2 is 2.4 kv. Any zero entry in B is a possible candidate for a new connection. For the purpose of this example, it is assumed that the RDGs are coupled with an energy storage system so that a certain amount of power (P 3 and P 5, for ES-I) may always be supplied to the rest of the system. The data for ES-I are presented in Table II in Appendix I. At this point, consider the upgrading of a distribution circuit for example, through the addition of lines or through the closing of lateral circuits across several feeders. The number of possibilities is large, even in the case of small systems. Assume that to limit the total number of possible topologies, the added line is between existing feeders. For example, in ES-I, buses 5 and 7 cannot be connected via new laterals in the example system. Such a requirement allows the partitioning of the B matrix into an array of allowed new connections. Fig. 3 shows the partitioned B matrix and its subset of allowed connections shown with shaded background. The B matrix is symmetric. However, the partitioning in the lower left triangle of Fig. 3 is not shaded for simplicity. Fig. 3 The binary bus connection matrix, B, for ES-I. The possible connections between feeders are in the shaded area of the matrix. (7)

4 4 The cost of the system addition is calculated as, where T is the allowed topology representation and C is the subset of the cost matrix. The cost is a function of only topology because only fixed costs in the system are considered in this example. A new heuristic algorithm called the sequential feeder approach is used to solve the stated constrained optimization problem. This approach cannot guarantee optimality, but may be useful if the project cost limits the possible additions to one or two new connections. The sequential feeder approach will work for more than two connections as well. The iterative sequential feeder approach is conceived such that a new connection in the system results in the maximum improvement in benefit for that iteration. The flow chart describing this technique is shown in Fig. 4. The algorithm begins with gathering the system data and arranging them into matrices and vectors. The possible new lateral connections are examined. The connections are limited to those between different feeders. For each possible connection, the power flow and benefit calculations are completed, and the corresponding cost and improvement in benefit are calculated. If the topology does not yield any improvement in the benefit, then the solution is discarded. After each possible connection has been examined, the solution that offers the best improvement in the benefit is permanently added to the topology. The process is repeated for the addition of a second new connection. The iterative procedure stops when the cost exceeds the total project cost. The sequential feeder approach is applied to ES-I shown in Fig. 2. The benefit examined is the unserved load energy U when all three feeders were isolated from the grid tie possibly after a system outage (circuit breakers 1, 2, and 3 were open). It is assumed that the required loads on Feeders 1 and 2 are rated less than the RDGs located on those feeders. Each load bus has a non-critical and a critical load component. The critical load is the load that must be served and cannot be interrupted. The critical and non-critical load components for ES-I are given in Appendix I, Table III. Following the sequential feeder approach, possible new connections between feeders in ES-I were added, so that the load served at Bus 10 was maximized. The initial unserved energy was kwh. This was determined by assuming that the system had an ASAI of corresponding to outages totaling hours per year [29]. It was found that the unserved energy could be decreased to 48.7 kwh if a lateral was added between Feeders 2 and 3. Since the benefit was the same for several different new connections, the least cost solution was chosen to be added to the system. The next greatest reduction in unserved energy at Bus 10 was achieved by the addition of a lateral between Feeders 1 and 2, which reduced the unserved energy to zero. The result of the heuristic optimization is given in Fig. 5. The result calls for the addition of a line between Buses 5 and 8, and the addition of a line between Buses 3 and 7. The total cost of this addition is $358,900 (based on synthetic data given in Appendix I, Table IV). (8) For ES-I, it is shown that the addition of laterals in the presence of RDGs could decrease the amount of unserved energy. The sequential feeder approach is similar to certain sequential switching methods used in heuristic approaches to the loss minimization problem. Reference [13] uses an approach that closes all normally open switches, performs an AC power flow study, and subsequently the switch corresponding to the branch with the least power flow is opened. This approach can achieve a near-optimal result. Fig. 4 Flow chart of the sequential feeder approach. The heuristic sequential feeder approach may not yield all optimal solutions. If the power flow and reliability requirements are inside the iterative loop of the algorithm, the opportunity to balance cost and reliability is precluded. The solution is simply the new connection which maximizes the benefit during each iteration. However, the sequential feeder approach does guarantee that the solution improves upon the base case. The fact that the method is sequential means that

5 5 other solutions that may be better in some sense may never be achieved. As the system grows in complexity, the interplay between feeder-to-feeder laterals, the presence of RDGs, and unserved loads would also become more complex. Future tasks in solving the evolving complex problem may resort to stochastic optimization techniques that will take into account the spatial and temporal variability due to intermittent nature of renewable sources - of the problem. The sequential feeder approach is much simpler than the aforementioned methods and for that reason may be useful in systems where the project cost limits the total number of feeders to be added, or the system itself is fairly small. 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. Some of these concepts have been applied in special environments (e.g., space power systems) and specialized hyper reliability applications. It is possible to base designs on a reliability concept [7]. In the foregoing, two distinct areas are examined in distribution engineering: operations and design. Operation includes subjects such as circuit switching strategies, reactive power dispatch, and reconfiguration and restoration after a disturbance (e.g., see Fig. 6 in which an automated (but perhaps operator assisted) reconfiguration and restoration algorithm is envisioned). Design includes system expansion, addition of assets to the system to improve performance, and long term analysis of reliability. Various methods are available for system reconfiguration. The distribution system is modeled for connectivity as a binary connection matrix indicating breaker, bus, load and line status. The problem addressed below lies in the area of both distribution system operation and also distribution system design. As an example, the objective may be the minimization of unserved energy after a fault and consequent reconfiguration. The B matrix contains binary entries and it is possible to raise B to higher powers using Boolean operations, e.g., Fig. 5 Optimal solution given by the sequential feeder approach. The proposed new connections are shown as dashed lines. A redesigned distribution system with new lateral connections needs to be rapidly reconfigured and restored during outages. The following section describes a procedure for doing so. V. DISTRIBUTION SYSTEM PERFORMANCE DURING RESTORATION The distribution system should be designed so that rapid restoration is possible. Rapid restoration could have a number of objectives including reduction of the classical system average interruption duration and frequency indices; and / or the minimization of unserved energy to loads. The intent in this section is to develop the logic for restoring segments of the distribution system after failure or removal of a component in the system. It is known that for a system to be highly reliable and fault tolerant, in addition to multiple redundant paths, it is paramount to have smart strategies such as 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 networked distribution systems into smart distribution systems, it is proposed to add FDIR mechanisms and other automated control concepts. The methods used shall also replicate transmission system concepts that use system restoration logic to automatically where n is the number of buses and also the number of rows and the number of columns of B and are Boolean OR and AND operators respectively [8]. This matrix and its powers (i.e. B n ) can be used to trace connectivity of a networked system. 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 each other through any number of intervening buses. Thus a zero entry in B n-1 can be used to determine which buses are outaged. Using the B matrix, a system status table (SST table) is constructed as shown in Fig. 7. The data from the SST is used in an algorithm shown in Fig. 8. VI. CONFIGURATION DESIGN FOR RAPID RESTORATION EXAMPLE In circuit design applications, it is possible to compare alternative locations of interruption devices, switches, sensors, and circuit routing in order to achieve a given objective. In distribution design, there are many possible objectives including: loss mitigation, high reliability of service, voltage regulation, operation within component ratings, public acceptance, safety, compliance with standards, and keeping the project within appropriate cost bounds (e.g., satisfying cost constraints). In a subsequent example, the discussion of Section V with regard to an algorithm to seamlessly reconfigure the system and restore service is illustrated. In both design and operation applications, possible objectives include: Reducing the system average interruption duration and frequency indices

6 6 Reducing the energy unserved upon reconfiguration after a fault Restoring as much load as possible after a circuit disturbance. Fig. 6 An automated restoration and reconfiguration technique: (Upper) generalized approach (Lower) the relationship of the restore reconfigure algorithm that is continuously running in real-time Fig. 7 The system status table (SST) In the example shown in Fig. 9, the capabilities of the algorithm in both design and operation are illustrated. To illustrate a circuit design application, tie switches were replaced by breakers and four breakers were placed at various locations in the test system of Fig. 9. Bus load data for the example appear in Appendix II, Table V. Assuming that the probability of a fault is determined by the line length (as line length increases, probability increases), pseudorandom fault locations were simulated on the distribution feeders. Breaker locations that allow isolation of lines most likely to fault are selected as the distribution system is stressed by faults. Fig. 6 outlines this procedure. The result obtained is suboptimal because: The bus loads are assumed to be fixed Load flow limitations are not considered The location of n circuit breakers in m lines can occur according to (9) Fig. 8 Flow chart of restoration and reconfiguration algorithm If all cases are not examined, the result will generally be suboptimal. In the example shown, there are 1820 ways to place 4 breakers in 16 lines. In this example, the (sub)optimal location of breakers was found to be lines 2-3, 13-15, and The expectation of the load restored with this configuration is 9 + j4.9 MVA. Note that the example shown is motivated by the desire to migrate radial systems to networked systems and the original radial system shown in Fig. 9 is assumed to be a legacy system. Proceed to an example in operation: in this example, breaker locations are fixed (e.g., at the locations identified above). Assuming that the protection system accurately detects the fault location and locks out only the required circuit breakers, the algorithm then identifies the available subset of breaker combinations given by 2 8-n, where n breakers have been locked out.

7 7 TABLE II EXAMPLE SYSTEM I DATA Fig. 9 One line diagram of the distribution system used in the restoration example For each available breaker combination, the B matrix is used to find the total load connected to the substation buses. The optimum solution selected is the resulting breaker combination that provides for maximum load served after a fault. This is the target operating state shown in Fig. 8. Fault locations were varied to test the performance of the algorithm. With every simulation, a feasible state was found, and at least one solution gives a maximum load served. As a quick example, a permanent fault occurring in line requires opening of breakers in lines 13-15, 17-18, 18-19; by closing breakers T2, T3, T4 and opening all other breakers, j4.9 MVA is restored. Only buses 11, 15 and 18 see an outage. VII. CONCLUSIONS The main conclusions of this work relate to the design and operation of smart distribution systems. The automated capabilities of smart systems are at the heart of the concept, and it is possible to design distribution system configurations so that restoration after an outage is rapid. Also, the dramatic reduction of service outages is possible. This is observed by reduction unserved energy. Classical reliability indices may such as ASAI may also be improved. The approach illustrated uses incidence matrices, a concept from circuit theory. A sequential feeder approach is illustrated as well. The main conclusion is that reliability of an automated distribution system may make the cost / benefit analysis of alternative configurations favor smart designs. VIII. APPENDIX I ES-I TEST BED DATA The following information was synthesized for ES-I discussed in Section III. New system lines were assumed to use the same conductors as the existing lines in the system for a given voltage level. Any new transformer was assumed to have the same properties as the existing transformers. EXISTING CONNECTIONS LENGTH (MI.) R (P.U.) X (P.U.) COST (X 10 3 $) LINE N/A LINE N/A LINE N/A LINE N/A LINE N/A LINE N/A TRANSFORMER 2-3 N/A N/A TRANSFORMER 6-7 N/A N/A TRANSFORMER 8-9 N/A N/A POSSIBLE CONNECTIONS LENGTH (MI.) R (P.U.) X (P.U.) COST (X 10 3 $) LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE LINE TABLE III SYSTEM LOADS AND GENERATION IN ES-I LOAD P (KW) LOAD 4 (NORMAL) LOAD 4 (REQUIRED) 80.2 LOAD 7 (NORMAL) LOAD 7 (REQUIRED) LOAD 10 (NORMAL) LOAD 10 (REQUIRED) 50.8 RDG AT BUS RDG AT BUS TABLE IV COSTS OF SYSTEM ADDITIONS IN ES-I DEVICE COST TRANSFORMER $ KV LINE 100 $/FT KV LINE 50 $/FT FIXED LINE COST $100000

8 8 IX. APPENDIX II SECOND EXAMPLE LOAD DATA Table V shows the example load data used in Fig. 9. TABLE V LOAD DATA EXAMPLE USED IN FIG. 9 Bus P (MW) Q (MVAr) 1 Source N/A Source N/A Source N/A Total demand X. REFERENCES [[1] 110 th Congress of United States, Smart Grid, Title XIII, Energy Independence and Security Act of 2007, Washington DC, December [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 [3] Westinghouse Electric Co., Electrical Transmission and Distribution Reference Book, East Pittsburgh, PA, [4] W. Kersting, Distribution System Modeling and Analysis, New York, NY: CRC Press, [5] I. Novak, Power Distribution Network Design Methodologies, Chicago, IL: IEC Publications, [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 [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 [8] G. Heydt, Computer Analysis Methods for Power Systems, 2nd ed.., Scottsdale, AZ: Stars in a Circle Publications, [9] H. L. Willis, Power Distribution Planning Reference Book, New York, NY: Marcel Dekker, [10] R. Billinton, R. Ringlee, A. Wood, Power System Reliability Calculations, Cambridge MA: MIT Press, [11] T. Gönen, Electric Power Distribution System Engineering, San Francisco, CA: McGraw-Hill, Inc., [12] V. Calderaro, A. Piccolo, and P. Siano, "Maximizing DG penetration in distribution networks by means of GA based reconfiguration," in Proc International Conference on Future Power Systems, Amsterdam, The Netherlands, [13] D. Shirmohammadi and H. W. Hong, "Reconfiguration of electric distribution networks for resistive line losses reduction," IEEE Transactions on Power Delivery, vol. 4, pp , April [14] T. P. Wagner, A. Y. Chikhani, and R. Hackam, "Feeder Reconfiguration for Loss Reduction: An application of distribution automation," IEEE Transactions on Power Delivery, vol. 6, pp , October [15] M. A. Kashem, G. B. Jasmon, and V. Ganapathy, "A new approach of distribution system reconfiguration for loss minimization," Electrical Power and Energy Systems, vol. 22, pp , [16] Y.-T. Hsiao, "Multiobjective evolution programming method for feeder reconfiguration," IEEE Transactions on Power Systems, vol. 19, pp , February [17] A. Ahuja, S. Das, and A. Pahwa, "An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration," IEEE Transactions on Power Systems, vol. 22, pp , August [18] G. K. V. Raju and P. R. Bijwe, "An efficient algorithm for minimum loss reconfiguration of distribution system based on sensitivity and heuristics," IEEE Transactions on Power Systems, vol. 23, pp , August [19] W.-M. Lin and H.-C. Chin, "A new approach for distribution feeder reconfiguration for loss reduction and service restoration," IEEE Transactions on Power Delivery, vol. 13, pp , July [20] T. E. McDermott, I. Drezga, and R. P. Broadwater, "A Heuristic Nonlinear Constructive method for distribution system reconfiguration," IEEE Transactions on Power Systems, vol. 14, pp , May [21] R. E. Brown, "Network reconfiguration for improving reliability in distribution systems," in Proc IEEE PES General Meeting, Toronto, Canada, July [22] Y.-T. Hsiao and C.-Y. Chien, "Multiobjective optimal feeder reconfiguration," IEEE Proc. on Generation, Transmission and Distribution, vol. 148, pp , July [23] B. Venkatesh, R. Ranjan, and H. B. Gooi, "Optimal reconfiguration of radial distribution systems to maximize loadability," IEEE Transactions on Power Systems, vol. 19, pp , February [24] M. W. Siti, D. V. Nicolae, A. A. Jimoh, and A. Ukil, "Reconfiguration and load balancing in the LV and MV distribution networks for optimal performance," IEEE Transactions on Power Delivery, vol. 22, pp , October [25] IEEE, Standard for interconnecting distributed resources with electric power systems, IEEE Standard 1547, July [26] Underwriters Laboratories, Inverters, converters, controllers and interconnection system equipment for use with distributed energy resources, Underwriters Laboratories Standard 1741, May [27] W. Steeley, "Interconnection of distributed energy resources in secondary distribution network systems," Electric Power Research Institute (EPRI), Palo Alto, CA, Tech. Rep , December [28]. R. E. Brown, A. P. Hanson, H. L. Willis, F. A. Luedtke, and M. F. Born, "Assessing the reliability of distribution systems," IEEE Computer Applications in Power, vol. 14, pp , January [29] R. C. Dugan, M. F. McGranaghan, S. Santoso, and H. W. Beaty, Electrical Power Systems Quality, 2nd ed., New York, NY: McGraw-Hill, 2003, p. 91. XI. BIOGRAPHIES Hilary Brown (StM 08) received a B.Sc. (2008) in Engineering Physics from the Colorado School of Mines, where she is now pursuing a M.Sc. in Electrical Engineering. She is a member of Tau Beta Pi. Daniel Haughton is from 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. Mr. Haughton is a member of Eta Kappa Nu. 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 (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 also a site director of a new NSF engineering research center on Future Renewable Electric Energy Delivery and Management (FREEDM) Systems. Dr. Heydt is the recipient of the 2010 Richard H. Kaufmann award. Siddharth Suryanarayanan (StM 00, M 04) received the Ph.D. degree in electrical engineering from Arizona State University, Tempe. He is currently an Assistant Professor in the Division of Engineering at Colorado School of Mines, Golden, CO. Previously he held research appointments in the faculties of Florida State University, Tallahassee, FL and Arizona State University.