Dynamic Economic Dispatch For Power System

Dynamic Economic Dispatch For Power System

The research work in this dissertation deals with dynamic economic dispatch problem for large power systems. The work mathematically proves the dynamicity of the economic dispatch. Many physical and operational constraints were considered in the model of the dynamic economic dispatch problem. The pr...

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Main Author: Hussein, Saif Tahseen
Format: Thesis
Language:English
English
Published: 2016
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T Technology (General)
Online Access:http://eprints.utem.edu.my/id/eprint/18388/1/Dynamic%20Economic%20Dispatch%20For%20Power%20System.pdf
http://eprints.utem.edu.my/id/eprint/18388/2/Dynamic%20Economic%20Dispatch%20For%20Power%20System.pdf
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institution Universiti Teknikal Malaysia Melaka
collection UTeM Repository
language English
English
advisor Ab Ghani, Mohd Ruddin
topic T Technology (General)
T Technology (General)
spellingShingle T Technology (General)
T Technology (General)
Hussein, Saif Tahseen
Dynamic Economic Dispatch For Power System
description The research work in this dissertation deals with dynamic economic dispatch problem for large power systems. The work mathematically proves the dynamicity of the economic dispatch. Many physical and operational constraints were considered in the model of the dynamic economic dispatch problem. The problem is to optimize the total generation costs while satisfying the operational constraints. Through an appropriate utilization of the structural features of the model, a solution algorithm based on Particle Swarm Optimization is developed. The performance of the PSO-based developed algorithm was tested on simple case studies with a small number of generation units and limited constraints, and then on more complex case studies with a large number of variables and complicated constraints. The solution algorithm based on a constraint relaxation and period-by-period is developed and tested. The last part of the dissertation is dedicated to the comparison of solution results obtained by using PSO method and the Dantzig-Wolfe decomposition method for different cases of size and complexity. This research finds large variable size DED problems can be easily implemented, PSO method is reliable and is suitable for real-time analysis. Also, time-segmentation of the solution, or as known as a period by period solution, always results in sub-optimality, while, only by solving the optimization problem in totality can lead to an optimal solution. By modifying constraints, the method can provide alternate solutions to the dispatcher. Trade-offs between the level of convergence to the global solution and the required execution time necessitate finding a mean to enhance the social component and determine an appropriate value that leads to limiting the search space of the swarm.
format Thesis
qualification_name Master of Philosophy (M.Phil.)
qualification_level Master's degree
author Hussein, Saif Tahseen
author_facet Hussein, Saif Tahseen
author_sort Hussein, Saif Tahseen
title Dynamic Economic Dispatch For Power System
title_short Dynamic Economic Dispatch For Power System
title_full Dynamic Economic Dispatch For Power System
title_fullStr Dynamic Economic Dispatch For Power System
title_full_unstemmed Dynamic Economic Dispatch For Power System
title_sort dynamic economic dispatch for power system
granting_institution Universiti Teknikal Malaysia Melaka
granting_department Faculty Of Electrical Engineering
publishDate 2016
url http://eprints.utem.edu.my/id/eprint/18388/1/Dynamic%20Economic%20Dispatch%20For%20Power%20System.pdf
http://eprints.utem.edu.my/id/eprint/18388/2/Dynamic%20Economic%20Dispatch%20For%20Power%20System.pdf
_version_ 1747833929211052032
spelling my-utem-ep.183882021-10-08T13:29:48Z Dynamic Economic Dispatch For Power System 2016 Hussein, Saif Tahseen T Technology (General) TK Electrical engineering. Electronics Nuclear engineering The research work in this dissertation deals with dynamic economic dispatch problem for large power systems. The work mathematically proves the dynamicity of the economic dispatch. Many physical and operational constraints were considered in the model of the dynamic economic dispatch problem. The problem is to optimize the total generation costs while satisfying the operational constraints. Through an appropriate utilization of the structural features of the model, a solution algorithm based on Particle Swarm Optimization is developed. The performance of the PSO-based developed algorithm was tested on simple case studies with a small number of generation units and limited constraints, and then on more complex case studies with a large number of variables and complicated constraints. The solution algorithm based on a constraint relaxation and period-by-period is developed and tested. The last part of the dissertation is dedicated to the comparison of solution results obtained by using PSO method and the Dantzig-Wolfe decomposition method for different cases of size and complexity. This research finds large variable size DED problems can be easily implemented, PSO method is reliable and is suitable for real-time analysis. Also, time-segmentation of the solution, or as known as a period by period solution, always results in sub-optimality, while, only by solving the optimization problem in totality can lead to an optimal solution. By modifying constraints, the method can provide alternate solutions to the dispatcher. Trade-offs between the level of convergence to the global solution and the required execution time necessitate finding a mean to enhance the social component and determine an appropriate value that leads to limiting the search space of the swarm. 2016 Thesis http://eprints.utem.edu.my/id/eprint/18388/ http://eprints.utem.edu.my/id/eprint/18388/1/Dynamic%20Economic%20Dispatch%20For%20Power%20System.pdf text en public http://eprints.utem.edu.my/id/eprint/18388/2/Dynamic%20Economic%20Dispatch%20For%20Power%20System.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=100294 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Electrical Engineering Ab Ghani, Mohd Ruddin 1. Ab Ghani, M.R., 1989. Dynamic Secure Economic Dispatch for Large Scale Power Systems. Jurnal Teknologi, UTM, 14(1), pp.22–34. 2. Ab Ghani, M.R., 1989. Optimised Unit Commitment and Dynamic Economic Dispatch for Large Scale Power Systems. PhD Thesis, Department of Computation, UMIST, United Kingdom. 3. Ab Ghani, M.R. and Hindi, K.S., 1989, Multiperiod Secure Economic Dispatch for Large-Scale Power Systems, Generation, Transmission and Distribution, IEE Proceedings C:, Vol. 136, No. 3, pp. 130-136. 4. Ab Ghani, M.R., Hussein, S.T., Mohamad, M.T. and Jano, Z., 2015. An Examination of Economic Dispatch Using Particle Swarm Optimization, Magnt Research Report, 3(8), PP. 193-209. 5. Abido, M.A., 2007. Multiobjective Particle Swarm for Environmental/Economic Dispatch Problem. In Power Engineering Conference, (IPEC 2007). International, pp. 1385-1390. 6. Aliyari, H., Effatnejad, R. and Areyaei, A., 2014. Economic Load Dispatch with the Proposed GA Algorithm for Large Scale System. Journal of Energy and Natural Resources, 3(1), pp.1-5. 7. Alrashidi, M.R. and El-Hawary, M.E., 2008. Impact of Loading Conditions on the Emission- Economic Dispatch. World Academy of Science, Engineering and Technology, 29, pp.148-151. 8. Arora, R.K., 2015. Optimization: Algorithms and Applications, CRC Press. 9. Attai, A.U., 2015. Power System Economic Load Dispatch Using Particle Swarm Optimization. International Journal of Advanced Engineering Research and Technology (IJAERT), 3(6), pp. 202 – 205. 10. Bechert, T.E. and Kwatny, H.G., 1972. On the Optimal Dynamic Dispatch of Real Power. IEEE Transactions on Power Apparatus and Systems, PAS-91(3), pp.889–898. 159 11. Bo, Z., Guo, C. and Yi-jia, C., 2004. Dynamic Economic Dispatch in Electricity Market Using Particle Swarm Optimization Algorithm. In Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on, 6, pp.5050–5054. 12. Bo, Z. and Yi-jia, C., 2005. Multiple Objective Particle Swarm Optimization Technique for Economic Load Dispatch. Journal of Zhejiang University Science A, 6(5), pp.420–427. 13. Cabadag, R.I. and Turkay, B.E., 2013. Heuristic Methods to Solte Optimal Power Flow Problem. IU-Journal of Electrical & Electronics Engineering, 13(2), pp.1653–1659. 14. Cai, J., Ma, X., Li, L. and Haipeng, P., 2007. Chaotic Particle Swarm Optimization for Economic Dispatch Considering the Generator Constraints. Energy Conversion and Management, 48(2), pp.645–653. 15. Chakrabarti, R., Chattopadhyay, P.K., Basu, M. and Panigrahi, C.K., 2006. Particle Swarm Optimization Technique for Dynamic Economic Dispatch. Journal-Insitution of Engineers India Part El Electrical Engineering Division, 87, pp.48–54. 16. Chaturvedi, K.T., Pandit, M. and Srivastava, L., 2008. Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch. Power Systems, IEEE Transactions on, 23(3), pp.1079–1087. 17. Chen, C.H. and Lin, C.C., 2009. Simple Particle Swarm Optimization for Economic Dispatch with Piecewise Quadratic Fuel Cost Function. Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009, pp.412–417. 18. Chen, W.K., Deller, J.R. and Hansen, J., 2005. Processing The Electrical Engineering Handbook–Methods, Models, and Algorithms for Modern Speech, Academic Press. 19. Chen, W.N., Zhang, J., Chung, H.S., Zhong, W.L., Wu, W.G. and Shi, Y.H., 2010. A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems. IEEE Transactions on Evolutionary Computation, IEEE Transactions on, 14(2), pp.278–300. 20. Chowdhury, B.H. and Rahman, S., 1990. A review of Recent Advances in Economic Dispatch. IEEE Transactions on Power Systems, 5(4), pp.1248–1259. 160 21. Chuanwen, J. and Bompard, E., 2005. A Self-Adaptive Chaotic Particle Swarm Algorithm for Short Term Hydroelectric System Scheduling in Deregulated Environment. Energy Conversion and Management, 46(17), pp.2689-2696. 22. Clerc, M., 2004. Discrete Particle Swarm Optimization, Illustrated by the Traveling Salesman Problem, In New Optimization Techniques in Engineering, pp. 219-239. Berlin Heidelberg: Springer. 23. Coelho, L.D.S. and Lee, C.S., 2008. Solving Economic Load Dispatch Problems in Power Systems using Chaotic and Gaussian Particle Swarm Optimization Approaches. International Journal of Electrical Power & Energy Systems, 30(5), pp.297–307. 24. Coelho, L. D S. and Mariani, V.C., 2007. Economic Dispatch Optimization using Hybrid Chaotic Particle Swarm Optimizer. In Systems, Man and Cybernetics, ISIC. IEEE International Conference on, pp. 1963-1968. 25. Dai, H., Zhang, N. and Su, W., 2015. A Literature Review of Stochastic Programming and Unit Commitment. Journal of Power and Energy Engineering, 03(04), pp.206–214. 26. El-Hawary, M.E. and Christensen, G.S., 1979. Optimal Economic Operation of Electric Power Systems, Vol. 142., New York: Academic Press. 27. El-Wahed, W.A., Mousa, A.A. and Elsisy, M.A., 2009. Solving Economic Emissions Load Dispatch Problem by using Hybrid ACO-MSM Approach. The Online Journal on Power and Energy Engineering (OJPEE), 1(1), pp.31–35. 28. Gaing, Z.L., 2004. Constrained Dynamic Economic Dispatch Solution using Particle Swarm Optimization. in Proc. IEEE Power Engineering Society General Meeting, 2004. 2, pp.153–158. 29. Gaing, Z.L., 2003. Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints. IEEE Transactions on Power Systems, 18(3), pp.1187–1195. 30. Hadi, S., 1999. Power System Analysis. WCB/McGRAW-HILL. 161 31. Happ, H.H., 1977. Optimal Power Dispatch - A Comprehensive Survey. Power Apparatus and Systems, IEEE Transactions on, 96(3), pp.841-854. 32. Haker, M. and Waddams, C., 2006. Introducing Competition and Deregulating the British Domestic Energy Markets: A Legal and Economic Discussion. Centre for Competition Policy, (November 2006), p.24. 33. Inhaber, H., 2002. Deregulation and Its Discontents. Ideas in Action (Grace Creek Media and The George W. Bush Institute). 34. Jayabarathi, T., Chalasani, S., Shaik, Z.A. and Kodali, N.D., 2007. Hybrid Differential Evolution and Particle Swarm Optimization Based Solutions to Short Term Hydro Thermal Scheduling School of Electrical Sciences. WSEAS Transactions on Power Systems, 2(11), pp.245–254. 35. Jeyakumar, D.N., Jayabarathi, T. and Raghunathan, T., 2006. Particle swarm Optimization for Various Types of Economic Dispatch Problems. International Journal of Electrical Power & Energy Systems, 28(1), pp.36–42. 36. Kennedy, J. and Eberhart, R., 1995. Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks, Vol.4, pp.1942–1948. 37. Khan, M.J. and Mahala, H., 2014. Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic load. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(12), pp. 13657- 13662. 38. Kherfane, R.L., Younes, M., Kherfane, N. and Khodja, F., 2014. Solving Economic Dispatch Problem Using Hybrid GA-MGA. Energy Procedia, 50, pp.937–944. 39. Krugman, P., 2001. Laissez Not Fair. The New York Times, 11. 40. Kumar, A.I.S., Dhanushkodi, K., Kumar, J.J. and Paul, C.K.C., 2003,. Particle Swarm Optimization Solution to Emission and Economic Dispatch Problem. In TENCON 2003. Conference on Convergent Technologies for the Asia-Pacific Region , Vol. 1, pp. 435-439. 41. Kumar, G. and Singh, R., 2014. Economic Dispatch of Power System Optimization with Power Generation Schedule Using Evolutionary Technique. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(7), pp.10715–10722. 42. Kumar, R., Sharma, D. and Kumar, A., 2009. Economic Power Dispatch with Valve Point Effects Using Bee Optimization Algorithm. Journal of Electrical Engineering and Technology, 4(1), pp.19–27. 43. Lee, T.Y., 2008. Short Term Hydroelectric Power System Scheduling with Wind Turbine Generators Using the Multi-Pass Iteration Particle Swarm Optimization Approach. Energy Conversion and Management, 49(4), pp.751–760. 44. Lee, T.Y. and Chen, C.L., 2007. Unit Commitment with Probabilistic Reserve: An IPSO Approach. Energy Conversion and Management, 48(2), pp.486–493. 45. Lim, S.Y., Montakhab, M. and Nouri, H., 2009. Economic Dispatch of Power System Using Particle Swarm Optimization with Constriction Factor. International Journal of Innovations in Energy Systems and Power, 4(2), pp.29-34. 46. Ling, S.H., Iu, H.H., Chan, K.Y., Lam, H.K., Yeung, B.C. and Leung, F.H., 2008. Hybrid Particle Swarm Optimization with Wavelet Mutation and its Industrial Applications. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 38(3), pp.743–763. 47. Mahor, A. and Rangnekar, S., 2010. Short Term Generation Scheduling of Cascaded Hydro Electric System Using Time Varying Acceleration Coefficients PSO. International Journal of Energy and Environment, 1(5), pp.769–782. 48. Mahor, A., Prasad, V. and Rangnekar, S., 2009. Economic Dispatch Using Particle Swarm Optimization: A Review. Renewable and Sustainable Energy Reviews, 13(8), pp.2134–2141. 49. Malik, T.N., 2009. Economic Dispatch Using Hybrid Approaches. Doctoral Dissertation, University of Engineering & Technology, Taxila (PAKISTAN). 50. Mandal, K.K., Basu, M. and Chakraborty, N., 2008. Particle Swarm Optimization Technique Based Short-Term Hydrothermal Scheduling. Applied Soft Computing, 8(4), pp.1392–1399. 51. Noel, M.M. and Jannett, T.C., 2004. Simulation of A New Hybrid Particle Swarm Optimization Algorithm. In System Theory, 2004. Proceedings of the Thirty-Sixth Southeastern Symposium on, pp. 150-153. 52. Panigrahi, B.K., Ravikumar Pandi, V. and Das, S., 2008. Adaptive Particle Swarm Optimization Approach for Static and Dynamic Economic Load Dispatch. Energy Conversion and Management, 49(6), pp.1407–1415. 53. Park, J.B., Lee, K.S., Shin, J.R. and Lee, K.Y., 2005. A Particle Swarm Optimization for Economic Dispatch with Non-smooth Cost Functions. IEEE Transactions on Power Systems, 20(1), pp.34–42. 54. Park, J.B. and Jeong, Y.W., 2006. An Improved Particle Swarm Optimization for Economic Dispatch with Valve-Point Effect. International Journal of Innovations in Energy Systems and Power, 1(1), pp.1–7. 55. Pinto, H., Magnago, F., Brignone, S., Alsaç, O. and Stott, B., 2006. Security Constrained Unit Commitment: Network Modeling and Solution Issues. IEEE PES Power Systems Conference and Exposition, PSCE 2006 - Proceedings, pp.1759–1766. 56. Praveena, P., Vaisakh, K. and Rao, S., 2010. A Bacterial Foraging PSO-DE Algorithm for Solving Dynamic Economic Dispatch Problem with Security Constraints. In Power Electronics, Drives and Energy Systems (PEDES) & 2010 Power India, 2010 Joint International Conference on, pp.1–7. 57. Rahli, M., Benasla, L., Belmadani, A. and Abdelhakem-Koridak, L., 2015. Real Power System Economic Dispatch Using A Variable Weights Linear Programming Method. Journal of Power Technologies, 95(1), pp.34–39. 58. Rao, L.N., 2014. PSO Technique for Solving the Economic Dispatch Problem Considering the Generator Constraints. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(7), pp.10439–10454. 59. Rao, S.S. and Rao, S.S., 2009. Engineering Optimization: Theory and Practice. John Wiley & Sons. 60. Richter Jr, C.W. and Sheble, G.B., 2000. A Profit-Based Unit Commitment GA for the Competitive Environment. IEEE Transactions on Power Systems, 15(2), pp.715–721. 61. Roy, R. and Ghoshal, S.P., 2008. A Novel Crazy Swarm Optimized Economic Load Dispatch for Various Types of Cost Functions. International Journal of Electrical Power & Energy Systems, 30(4), pp.242–253. 62. Ruiz, P. a, Philbrick, C.R., Zak, E., Cheung, K.W. and Sauer, P.W., 2009. Uncertainty Management in the Unit Commitment Problem. Power Systems, IEEE Transactions on, 24(2), pp.642–651. 63. Samudi, C., Das, G.P., Ojha, P.C., Sreeni, T.S. and Cherian, S., 2008. Hydro Thermal Scheduling Using Particle Swarm Optimization. In Transmission and Distribution Conference and Exposition, IEEE/PES, pp.1–5. 64. Saravanan, B., Das, S., Sikri, S. and Kothari, D.P., 2013. A Solution to the Unit Commitment Problem—A Review. Frontiers in energy, 7(2), pp.223–236. 65. Selvakumar, A.I. and Thanushkodi, K., 2007. A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems. Power Systems, IEEE Transactions on, 22(1), pp.42–51. 66. Selvakumar, A.I. and Thanushkodi, K., 2008. Anti-Predatory Particle Swarm Optimization: Solution to Nonconvex Economic Dispatch Problems. Electric Power Systems Research, 78(1), pp.2–10. 67. Sharma, J. and Mahor, A., 2013. Particle Swarm Optimization Approach For Economic Load Dispatch : A Review. Int J Eng Res Appl (IJERA), 3(1), pp.013–022. 68. Shi, Y. and Eberhart, R., 1998. A Modified Particle Swarm Optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, pp.69–73. 69. Shi, Y. and Eberhart, R., 2001. Fuzzy Adaptive Particle Swarm Optimization. Proceedings of the Congress on Evolutionary Computation, 1, pp.101–106. 70. Souze, D. and Martha, G.F. eds., 2012. Thermal Power Plant Performance Analysis. Series in Reliability Engineering. Springer-Verlag, London. 71. Sriyanyong, P., 2008. Solving Economic Dispatch Using Particle Swarm Optimization Combined with Gaussian mutation. In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on, Vol. 2, pp. 885-888. 72. Subramanian, R. and Thanushkodi, K., 2013. An Efficient Novel TANAN ’s Algorithm for Solving Economic Load Dispatch Problems. International Journal of Soft Computing and Engineering (IJSCE), 3(3), pp. 215-219. 73. Subramanian, R., Thanushkodi, K. and Neelakantan, P.N., 2013. A Numerical Random Search Algorithm to Solve Economic Load Dispatch Problems with Valve-Point Effect. Australian Journal of Basic and Applied Sciences, 7(14), pp.154–159. 74. Suresh, V. and Sreejith, S., 2015. Economic Dispatch and Cost Analysis on a Power System Network Interconnected With Solar Farm. International Journal of Renewable Energy Research (IJRER), 5(4), pp.1098-1105. 75. Titus, S. and Jeyakumar, A.E., 2007. Hydrothermal Scheduling Using An Improved Particle Swarm Optimization Technique Considering Prohibited Operating Zone. International journal of soft computing, 2(2), pp.313–319. 76. Tiwari, S., Kumar, A., Chaurasia, G.S., Sirohi, G.S. and Email, W.S., 2013. Economic Load Dispatch Using Particle Swarm Optimization. Int J of Application or Innovation in Engineering and Management Sciences, 2(4), pp.476-485. 77. Umayal, S.S.P. and Kamaraj, N., 2005. Stochastic Multi Objective Short Term Hdrothermal Scheduling Using Particle Swarm Optimization. In INDICON, 2005 Annual IEEE, pp. 497-501. 78. Vazquez, M., 2006. Optimizing the Spinning Reserve Requirements. PhD Thesis, UMIST, United Kingdom. (May), p.219. 79. Victoire, T.A.A. and Jeyakumar, A.E., 2005. Reserve Constrained Dynamic Dispatch of Units with Valve-Point Effects. IEEE Transactions on Power Systems, 20(3), pp.1273–1282. 80. Wang L, C.S., 2007. Reserve Constrained Multi-Area Environmental/Economic Dispatch Using Enhanced Particle Swarm Optimization, Swarm Intelligence, Focus on Ant and Particle Swarm Optimization. FelixT.S.Chan and Manoj KumarTiwari (Ed.). ISBN: 978-3-902613-09-7. 81. Wang, L. and Singh, C., 2009. Reserve-Constrained Multi-Area Environmental/Economic Dispatch Based on Particle Swarm Optimization with Local Search. Engineering Applications of Artificial Intelligence, 22(2), pp.298–307. 82. Wood, A.J. and Wollenberg, B.F., 1996. Power Generation Operation and Control (2nd Edition), In Fuel and Energy Abstracts, Vol. 3, No. 37, p. 195. John Wiley & Sons. 83. Wood, A.J., Wollenberg, B.F. and S.G.., 2013. Power Generation, Operation, and Control Series in Wiley-Inter Science. 84. Wright, B., 2013. A Review of Unit Commitment. Columbia Engineering, ELENE4511, 28, pp.1–14. 85. Yang, X.S., Hosseini, S.S.S. and Gandomi, A.H., 2012. Firefly Algorithm for solving Non-Convex Economic Dispatch Problems with Valve Loading Effect. Applied Soft Computing, 12(3), pp.1180–1186. 86. Yu, B., Yuan, X. and Wang, J., 2007. Short-Term Hydro-Thermal Scheduling Using Particle Swarm Optimization Method. Energy Conversion and Management, 48(7), pp.1902–1908. 87. Yuan, X., Wang, L. and Yuan, Y., 2008. Application of Enhanced PSO Approach to Optimal Scheduling of Hydro System. Energy Conversion and Management, 49(11), pp.2966–2972.

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