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patterns of service zones, line switches, distribution feeders, and main transformers by using customer information in a customer information system (CIS) and ...
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 19, NO. 1, FEBRUARY 2004

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Power Distribution System Switching Operation Scheduling for Load Balancing by Using Colored Petri Nets Yu-Lung Ke, Member, IEEE, Chao-Shun Chen, Member, IEEE, Meei-Song Kang, Jaw-Shyang Wu, Member, IEEE, and Tsung-En Lee

Abstract—This study attempts to determine the daily load patterns of service zones, line switches, distribution feeders, and main transformers by using customer information in a customer information system (CIS) and information about distribution transformers in the outage management information system (OMIS) in Taiwan Power Company (Taipower). When a power distribution system is operating under normal conditions, the reconfiguration of feeders for balancing loads among distribution feeders is obtained by the colored Petri nets (CPN) inference mechanism, which improves the operating performance of distribution systems. A practical Taiwan power distribution system, with daily load patterns derived by a load survey, is used for a computer simulation and, thus, determines the effectiveness of the proposed methodology to improve the balancing of the feeder load for distribution systems by considering the load characteristics of the service customers. Index Terms—Colored Petri nets, feeder reconfiguration, load balancing, load patterns, switching operations.

I. INTRODUCTION

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ITH the vigorous development of the economy in Taiwan, Taipower has experienced the continuous increase of load density over the whole power distribution system. The distribution feeders may become overloaded due to load growth and substation planning and it complicates the distribution system operation in areas with high load density. With the usage of more and more air conditioners in the commercial and residential areas, the air conditioning loading has contributed more than 35% of the summer peak loading of Taipower. It is estimated that the system power consumption temperature rises when the increases by 600 MW per 1 temperature exceeds 28 C. The difference between the system load at the daily peak and the offpeak in the summer season has increased from 4577 MW in 1989 to 8440 MW in 1998. Many main transformers in the substations are heavily loaded

Manuscript received May 26, 2003. This work was supported by the National Science Council of the Republic of China under Contract NSC 89-2213E168-010. Y.-L. Ke is with the Department of Electrical Engineering, Kun Shan University of Technology, Tainan 710, Taiwan, R.O.C. (e-mail: [email protected]). C.-S. Chen is with the Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan, R.O.C. (e-mail: [email protected]). M.-S. Kang is with the Department of Electrical Engineering, Kao Yuan Institute of Technology, Kaohsiung 821, Taiwan, R.O.C. J.-S. Wu and T.-E. Lee are with the Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan, R.O.C. Digital Object Identifier 10.1109/TPWRS.2003.821433

and the loading factors have deteriorated. Fig. 1 presents the total number of main transformers in Taipower and the corresponding loading factor (LF). It is found that the loading factors of 122 main transformers exceeded 95% and 33 main transformers were overloaded. To solve the problem, the mobile transformers have been used for the temporary relief of peak loading. Besides, the load transfer among distribution feeders and main transformers are considered to achieve the load balance by proper reconfiguring the distribution system. Load imbalance and uneven load distribution among feeders are important causes of increased system loss. Planning feeder reconfiguration by switching operations to achieve load balance among distribution feeders will reduce system losses [3]–[12] and increase the operation flexibility by evenly distributing the capacity reserve among the main transformers. In a large power distribution system, feeder reconfiguration can change the system topology by changing the open/close states of the sectionalizing switches and tie switches under both normal and abnormal operating conditions to reduce the power loss and solve the overloading problem [3]–[12]. The feeder reconfiguration is a complex combinatorial and constrained optimization problem because of the enormous number of combinations of candidate switches [13]. It becomes more and more difficult to reconfigure feeders based on the experience of distribution operators. A more systematic operation and computer decision support strategy must be designed to reconfigure feeders quickly and effectively in the power distribution system. An integrated distribution automation system (DAS) has been successfully implemented by Taipower to increase the system reliability and service quality. The optimal switching operation (OSW) program [14]–[19] is to solve the feeder reconfiguration to achieve load balance among distribution feeders and main transformers so that the system loss can be minimized and system overloading due to the use of air conditioners during the summer can be prevented. Furthermore, the quality of power supply has become increasingly important to customers as power distribution systems operate under more constrained operation conditions. Accordingly, power distribution systems must benefit from more efficiently made operation decisions in response to handle the increasingly harsh operating environment, and the reliability of the power supply must increase. To support the decision-making process of optimal interfeeder switching operation, the CPN inference mechanism approach with operation rules is performed to derive the appropriate combination of switching operations for load transfer among distribution feeders under system normal operation

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are defined by the least transformers’ load balance index square formulas in (4) and (5) [14]–[19] respectively, where and represent the percentage current loading of the feeders and transformers, respectively. The load balance index is zero for an ideal load-balanced power distribution system. A smaller load balance index implies better system performance (4)

(5)

Fig. 1.

Loading factor of main transformers in Taipower system.

conditions or overload contingency conditions by considering the daily load patterns of customers. The daily load profile of each distribution feeder in the Taipower distribution system is determined by considering the typical load patterns of various customers. The power consumption information of customers within each service area is derived by the customer information system (CIS) and outage management information system (OMIS) in Taipower. The CPN inference mechanism, combined with the utility operation rules, can therefore efficiently determine the effective combination of switching operations to increase the system operation performance by balancing the load among distribution feeders and main transformers. II. DISTRIBUTION SYSTEM OPERATION An optimally load-balanced power distribution system is regarded as in an ideal state, such that all feeders and main transformers are operated at the same load level. To determine the load balance among distribution feeders after load transfer, an equivalent distribution feeder current over a day is defined as in (1) [14]–[19], where represents the current loading of the feeder during hour .

To support the CPN inference mechanism for switching operational decision-making, the object-oriented programming language C++ is used to generate an object-oriented database of loads associated with customers, feeders, and main transformers. Given the difference between the typical load patterns of various types of customer, load transfer among distribution feeders by interfeeder switching will be effective to improve load balancing among distribution feeders and main transformers. III. COLORED PETRI NETS AND INFERENCE MECHANISM A. Colored Petri Nets (CPN) [14]–[19], [21]–[27] Colored Petri nets form a bipartite directed weighting multigraph, which is formalized using bag theory. A colored Petri , net structure is a sextuple with where finite nonempty set of place nodes;

:

: (1) The ideal feeder load level , and the ideal transformer are defined by (2) and (3) [14]–[19], respecload level and represent the current loading of tively. The terms and represent feeders and transformers, respectively; and the feeders’ and the transformers’ rated capacities; are the total number of feeders and transformers, respectively

:

:

finite nonempty set of transition nodes. and are disjoint; that is, and ; finite set of types, called color sets, which determine the operations and functions that can be used in the net inscription; input token color transfer function, which communicates between the colors of the place node and each color of the transition node; output token color transfer function that communicates between the colors of the place node and each color of the transition node; initial marking.

B. Enabled Transition Node (2)

The feeders’ load balance index

and the

(3)

A place node is an input location of a transition node and is represented by ; is an output place node of a and is represented by . In a coltransition node ored Petri net, each token color can carry complex information or a database. Transition node is enabled with respect to color for the marking M, if and only if the number of tokens in all is not less than of the input place nodes of transition node the number of colors by the input function of the directed arc that connects place node to transition node . Equation (6)

KE et al.: POWER DISTRIBUTION SYSTEM SWITCHING OPERATION SCHEDULING FOR LOAD BALANCING

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TABLE II PROPOSED SWITCHING OPERATION BY COMPARISON OF CPN AND BIP APPROACHES

Fig. 2.

Switching operation model by colored Petri nets. TABLE I RELATIONSHIP BETWEEN INPUT PLACES AND OUTPUT PLACES FOR EACH TRANSITION

TABLE III SWITCHING OPERATIONS SCHEDULING FOR LOAD BALANCING

[14]–[19], [21]–[27] specifies the enabling rule of transition for colored Petri nets (6) C. Firing an Enabled Transition Node An enabled transition node , with respect to a color , can is obtained after transition node be fired. The new marking is fired with respect to the color . This new marking is obtained from the current marking by the following relationship [14]–[19], [21]–[27]:

D. Inference in CPNs The CPN views a system by describing the state transition of systems using place nodes to represent conditions, and transition nodes to represent events or activities. If all of the place nodes that enter a transition node receive token colors, then the transition node is enabled. If the guard function of the enabled transition node is evaluated to take action, then the transition

node is activated. The activated transition node can be fired and the token colors changed between the entering place nodes and the outgoing place nodes. When the place node stands for completed work, the inference is then determined. In CPN, several

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Fig. 3. Practical 18-feeder distribution system. TABLE IV FEEDER EQUIVALENT CURRENT COMPARISON

operation model using CPN. The numbers of place nodes, transition nodes, and directed arcs in a CPN are calculated from , , , and (7), (8), and (9) [14]–[19], where represent the numbers of service zones, sectionalizing switches, feeders, and faulted zones, respectively. Table I presents the relationships between the input place nodes and output place nodes of each switching operation of switch , according to the switching operation model obtained using Colored Petri nets. The relevant expressions are given by (10)–(13) [14]–[19] (7) (8) (9)

(10) (11) (12) tokens may be present and several transition nodes may be activated simultaneously. Consequently, passing tokens can be processed along many paths simultaneously at the same time to achieve the parallel-like inference. In practical distribution system modeling, CPN is used to model the practical switching in a distribution system, the on/off states of line switches and energized/de-energized/terminal states of service zones, which are represented by place nodes, specify the variable distribution system states during the inference process. The service section is defined as the terminal zone (T) when it is the final section in a feeder; every service section of a feeder except the terminal zone is regarded as a nonterminal zone (NT). Fig. 2 presents the switching

(13) In Fig. 2, switch is fired to open to release the loading of z1 when all of the states of three input place nodes of the transition node are met: s(C) implies switch is closed; z2(E&NT) implies z2 is an energized, nonterminal zone; z1(E&T) implies that the objective zone z1 is energized and terminal. The fired transition node will open switch to generate three new output place nodes: s(O) is with switch is opened; z1(DE&NT) implies z1 is a de-energized and nonterminal zone, while z2(E&T) implies that z2 is an energized and terminal zone according to then ” rule as expressed in the notation the “if in (10). Similarly, in (11) is to release the loading of z2 by opening switch .

KE et al.: POWER DISTRIBUTION SYSTEM SWITCHING OPERATION SCHEDULING FOR LOAD BALANCING

Fig. 5.

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Load balance index comparison.

minimized; 2) the radial structure of distribution system is maintained; and 3) additional overload contingency associated with load balancing switching operations is not permitted. IV. COMPUTER SIMULATIONS

Fig. 4. Daily load profiles of distribution feeders.

To demonstrate the load balancing of distribution systems by feeder reconfiguration, a sample distribution system is selected for computer simulation. All of the switches are initially opened to maximize the freedom of load balancing switching operation decisions and are closed one by one, to pick up the adequate loading of service zones by considering the load distribution in each zone such that the redistribution of loading in distribution feeders may be divided equally as possible, subject to the following constraints 1) The number of switching operations is

In this paper, a distribution system with 18 distribution feeders, which serve the mixed loads of various types of customers in Taipower is used for case study. The current ratings of each feeder and main transformer are 450 A and 1350 A, respectively. Fig. 3 shows a one-line diagram and Fig. 4 plots the daily load profiles of distribution feeders. The original feeder load balance index of the original system at 3 P.M. is 80.3; the feeder load balance index was markedly reduced to 62.1 and 57.3 by CPN and BIP [20] approaches, respectively. However, the proposed switching operations involve six and eight switched pairs, obtained by CPN and BIP approaches, respectively, as shown in Table II. The proposed CPN approach has lower operation costs because it involves fewer pairs of switching operation than BIP approach, but the load balances among distribution feeders by CPN and BIP approach are almost equal. Without performing the switching operation, the loading distributions among distribution feeders are uneven and the system loss is large. Table III lists the daily scheduled switching load balancing operations derived by the proposed colored Petri nets inference mechanism. Table IV compares the equivalent current in distribution feeders during switching operations (SW). The feeder load balance index at 3 P.M. and 7 P.M. have been reduced from 64.1 to 50.3, 68.2 to 49.2, and that of main transformers has also been improved from 19.7 to 14.2, 22.8 to 12.6, respectively, by execution of the proposed switching operations. The equivalent feeder current of feeder #3, #17, #10, and #6 have been adjusted from 362 to 314 A, 144 to 170 A, 359 to 304 A, and 178 to 235 A, respectively, by the proposed switching operations. The load distribution among distribution feeders is divided even more effectively better by the balance index of feeders and main transformers as listed in Fig. 5 so that the system losses are reduced and the system operation performance is improved. Fig. 6 presents the resultant daily load profiles of

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allel-like inference mechanism with operation rules in models of the colored Petri nets. One of the Taipower distribution systems, which consists of 18 distribution feeders and serves the mixed loads of various customer types, was selected for computer simulation to demonstrate the effectiveness of the proposed methodology. By executing the optimal switching operation derived by the colored Petri nets inference mechanism, the load distribution among the distribution feeders by load transfer switching operations was effectively improved. According to the computer simulation, the proposed methodology provides an effective tool with which distribution system engineers can solve the system reconfiguration for loss reduction and load balancing among distribution feeders and main transformers. REFERENCES

Fig. 6. Resultant load profiles of distribution feeders after scheduled load balancing switching operations.

the distribution feeders by execution of proposed daily load balancing switching operations. V. CONCLUSION In this paper, the typical load patterns of various customer types and the energy consumption of all customers served by each distribution feeder are used to calculate the hourly current flows of line switches and the loads in each service zone. Optimal switching operations for load balancing among distribution feeders were derived by applying the colored Petri nets par-

[1] C. S. Chen, J. C. Hwang, and C. W. Huang, “Application of load survey systems to proper tariff design,” IEEE Trans. Power Syst., vol. 12, pp. 1746–1751, Nov. 1997. [2] , “Determination of customer load characteristics by load survey system at taipower,” IEEE Trans. Power Delivery, vol. 11, pp. 1430–1435, July 1996. [3] M. E. Baran and F. F. Wu, “Network reconfiguration in distribution system for loss reduction and load balancing,” IEEE Trans. Power Delivery, vol. 4, pp. 1401–1407, Apr. 1989. [4] C.-C. Liu, S. J. Lee, and K. Vu, “Loss minimization of distribution feeders: optimality and algorithms,” IEEE Trans. Power Delivery, vol. 4, pp. 1281–1289, Apr. 1989. [5] H.-D. Chiang and R. Jean-Jumeau, “Optimal network reconfigurations in distribution systems: part 1: a new formulation and a solution methodology,” IEEE Trans. Power Delivery, vol. 5, pp. 1902–1907, Nov. 1990. , “Optimal network reconfigurations in distribution systems: part 2: [6] solution algorithms and numerical results,” IEEE Trans. Power Delivery, vol. 5, pp. 1568–1574, July 1990. [7] D. Shirmohammadi, “Service restoration in distribution network reconfiguration,” IEEE Trans. Power Delivery, vol. 7, pp. 952–958, Apr. 1992. [8] K. Aoki, T. Ichimori, and M. Kanezashi, “An efficient algorithm for load balancing of transformers and feeders by switch operation in large scale distribution systems,” IEEE Trans. Power Delivery, vol. 3, pp. 1865–1872, Oct. 1988. [9] J. S. Wu, K. L. Tomsovic, and C. S. Chen, “A heuristic search approach to feeder switching operations for overload, faults, unbalanced flow and maintenance,” IEEE Trans. Power Delivery, vol. 6, pp. 1579–1585, Oct. 1991. [10] T. Taylor and D. Lubkeman, “Implementation of heuristic search strategies for distribution feeder reconfiguration,” IEEE Trans. Power Delivery, vol. 5, pp. 239–245, Jan. 1990. [11] Q. Zhou, D. Shirmohammadi, and W.-H. E.W.-H. Edwin Liu, “Distribution feeder reconfiguration for service restoration and load balancing,” IEEE Trans. Power Syst., vol. 12, pp. 724–729, May 1997. [12] Y. Y. Hsu, Y. J. Hwu, S. S. Liu, Y. W. Chen, H. C. Feng, and Y. M. Lee, “Transformer and feeder load balancing using a heuristic search approach,” IEEE Trans. Power Syst., vol. 8, pp. 184–190, Feb. 1993. [13] Y.-C. Huang, “Switch reconfiguration of electrical distribution systems using improved genetic algorithm,” J. Comput., vol. 13, no. 3, pp. 47–54, Sept. 2001. [14] C.-S. Chen, Y.-L. Ke, and J.-S. Wu, “Switching operation decision making by using Petri-net approach for power distribution systems,” J. Chinese Inst. Eng., vol. 25, no. 1, pp. 17–26, Jan. 2002. [15] , “Colored Petri nets approach for solving distribution system contingency by considering customer load patterns,” Proc. Inst. Elect. Eng., Gen. Transm. Dist., vol. 148, no. 5, pp. 463–470, Sept. 2001. [16] C.-S. Chen, Y.-L. Ke, J.-S. Wu, and M.-S. Kang, “Application of Petri nets to solve distribution system contingency by considering customer load patterns,” IEEE Trans. Power Syst., vol. 17, pp. 417–423, May 2002. [17] J.-S. Wu, C.-C. Liu, K.-L. Liou, and R. F. Chu, “A Petri net algorithm for scheduling of generic restoration actions,” IEEE Trans. Power Syst., vol. 12, pp. 69–75, Feb. 1997. [18] J.-S. Wu, “A Petri-net algorithm for multiple contingencies of distribution system operation,” IEEE Trans. Power Syst., vol. 13, pp. 1164–1171, Aug. 1998.

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[19] Y.-L. Ke, “A study on switching operation decision making by using petri nets for power distribution systems,” Ph.D. dissertation, National Sun Yat-Sen Univ., Kaohsiung, Taiwan R.O.C., June 2001. [20] C. S. Chen, M. S. Kang, J. C. Hwang, and C. W. Huang, “Temperature adaptive switching operation for distribution systems,” IEEE Trans. Power Delivery, vol. 16, pp. 694–699, Oct. 2001. [21] J. L. Peterson, Petri Net Theory and the Modeling of Systems. Englewood Cliffs, NJ: Prentice-Hall, 1981. [22] K. Jensen and G. Rozenberg, High-Level Petri Nets Theory and Application. New York: Springer-Verlag, 1991. [23] R. David and H.Hassane Alla, Petri Nets and Crafcet. Englewood Cliffs, NJ: Prentice-Hall, 1992. [24] K. Lautenbach, Lectures on Invariant Analysis Methods Definitions, Theoretical Results, Examples. Taipei, Taiwan R.O.C.: National Taiwan Univ., 1999. [25] W. Reisig and G. Rozenberg, Lectures on Petri Nets I: Basic Models. Berlin, Germany: Springer-Verlag, 1998. [26] K. Jensen, Colored Petri Nets Basic Concepts, Analysis Methods and Practical Use. Berlin, Germany: Springer-Verlag, 1997. [27] E. Best, Raymond Devillers and Maciej Koutny, Petri Net Algebra. Berlin, Germany: Springer-Verlag, 2001. [28] Operation Manual of Outage Management Information System, Taiwan Power Company, Business Department, Taipei City, Taiwan, R.O.C.

Yu-Lung Ke (M’98) was born in Kaohsiung, Taiwan, R.O.C., on July 13, 1963. He received the B.S. degree in control engineering from National Chiao-Tung University, Hsin-Chu, Taiwan, R.O.C., in 1988, the M.S. degree in electrical engineering from National Taiwan University, Taipei, Taiwan, R.O.C., in 1991, and the Ph.D. degree in electrical engineering from National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C., in 2001. Currently, he is an Associate Professor with the Department of Electrical Engineering at Kun Shan University of Technology, Tainan, Taiwan, R.O.C., where he has been since 1991. His research interests include distribution automation, energy management, power quality, and artificial intelligence applications in power system operation. Since 2002, he has been as a reviewer for IEEE TRANSACTIONS ON POWER DELIVERY and International Journal of Electrical Power & Energy Systems. Dr. Ke is a Member of IEEE Power Engineering Society (PES), Industry Applications Society (IAS), and Systems, Man, and Cybernetics (SMC) Society.

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Chao-Shun Chen (M’84) received the B.S. degree in electrical engineering from National Taiwan University, Taipei, Taiwan, R.O.C., in 1976 and the M.S. and Ph.D. degrees in electrical engineering from the University of Texas, Arlington, in 1981 and 1984, respectively. Currently, he is a Professor in the Electrical Engineering Department at National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C. From 1984 to 1994, he was a Professor with National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C. He was also in charge of electrical and mechanical system planning with the Kaohsiung Rapid Mass Transport Project, Mass Rapid Transmit Bureau, Kaohsiung City, Taiwan, R.O.C.

Meei-Song Kang received the M.S. and Ph.D. degrees in electrical engineering from the National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C., in 1993 and 2001, respectively. Currently, he is an Associate Professor with the Department of Electrical Engineering at Kao Yuan Institute of Technology, Kaohsiung, Taiwan, R.O.C., where he has been since 1993. His research interest is in the area of load survey and demand subscription service.

Jaw-Shyang Wu (M’95) received the Ph.D. degree in electrical engineering from the National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C., in 1991. Currently, he is a Professor in the Electrical Engineering Department at National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, R.O.C. His research areas are power system operation, expert system, and application of artificial intelligence.

Tsung-En Lee was born in Tainan Hsien, Taiwan, R.O.C., on March 5, 1958. He received the B.Sc. degree in electrical engineering from National Taiwan Institute of Technology, Taipei, Taiwan, R.O.C., in 1985, the M.Sc. degree in electrical engineering from National Taiwan University, Taipei, Taiwan, R.O.C., in 1989, and the Ph.D. degree from National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C., in 1994. Currently, he is an Associate Professor with the Department of Electrical Engineering at National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, R.O.C. His research interests include power system operation, and application of operation research and artificial intelligence.