A New Approach for Network Reconfiguration Problem in Order to Deviation Bus Voltage Minimization with Regard to Probabilistic Load Model and DGs
Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering wind turbines. The main objective of the DFR is to minimize the deviation of the bus voltage. Since the DFR is a nonlinear optimization problem, we apply the Adaptive Modified Firefly Optimization (AMFO) approach to solve it. As a result of the conflicting behavior of the single- objective function, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The approach is tested on the IEEE 32-bus standard test system.
[1] W. Ouyang, H. Cheng, X. Zhang, L. Yao, "Distribution network planning method considering distributed generation for peak cutting,” Energy Conversion and Management, vol. 51, pp. 2394–2401, 2010.
[2] Wijayatunga, P., Fernando, W. & Shrestha, R. , " Impact of distributed and independent power generation on greenhouse gas emissions,” Energy Conversion and Management, vol. 45, vo. 20, pp. 3193–206, 2004.
[3] G. Vulasala, S. Sirirgiri, and S. Thiruveedula, "Feeder reconfiguration for loss reduction in unbalanced distribution system using genetic algorithm,” Int. J. Elect. Power Energy Syst. Eng., vol. 2, no. 4, pp. 240–248, Feb. 2009.
[4] T. Ackerman, G. Anderson and L. Soder, "Distributed generation: A definition”, Elsevier Science, pp. 195-204, Dec 2000.
[5] A. Merlin, H. Back H, "Search for a minimal loss operating spanning tree configuration in an urban power distribution system”, Proceeding of the 5th Power System Computation Conference, Cambridge, UK, 1–18, 1997.
[6] F. Vanderson Gomes, S. Carneiro, J.L. Pereira, M.P. Vinagre, P. Garcia, L. Ramos Araujo, " A New heuristic reconfiguration algorithm for large distribution systems”, IEEE Transactions On Power Systems , vol. 20, no. 2, pp.1373-8, 2005.
[7] M. Yiming and N.M Karen, "Switch placement to improve system reliability for radial distribution systems with distributed generation,” IEEE Trans. Power Delivery, vol. 18, pp. 1346-1352, Nov. 2003.
[8] B. Amanulla, S. Chakrabarti, and S. N. Singh, "Reconfiguration of power distribution systems considering reliability and power loss,” IEEE Trans. Power Delivery, vol. 27, pp. 918-926, Apr. 2012.
[9] S. Civanlar, J. Grainger, H. Yin, and S. Lee, "Distribution feeder reconfiguration for loss reduction,” IEEE Trans. Power Del., vol. 3, no. 3, pp. 1217–1223, Jul. 1988.
[10] K. Nara, A. Shiose, M. Kitagawoa, and T. Ishihara, "Implementation of genetic algorithm for distribution systems loss minimum reconfiguration,” IEEE Trans. Power Syst., vol. 7, no. 3, pp. 1044–1051, Aug. 1992.
[11] J. Z. Zhu, "Optimal reconfiguration of electrical distribution network using the refined genetic algorithm,” Elect. Power Syst. Res., vol. 62, pp. 37–42, 2002.
[12] R. Srinivasa Rao, S.V.L. Narasimham, M.R. Raju, and A. Srinivasa Rao, "Optimal network reconfiguration of large-scale distribution system using harmony search algorithm,” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1080–1088, Aug. 2011.
[13] T. Niknam , "Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators,” J Zhejiang Univ Sci , vol. 9, no. 12, pp. 1753–64, 2008.
[14] A. Soroudi, M. Ehsan, R. Caire, N. Hadjsaid, "Possibilistic evaluation of distributed generations Impacts on distribution networks,” IEEE Transactions on Power Systems, no. 26, pp. 26:2293-301, 2011.
[15] M. Madrigal, K. Ponnambalam, V.H. Quintana, "Probabilistic optimal power flow,” In: Proc. 1998 IEEE Can. Conf. Electrical and Computer engineering, Waterloo, Canada;. pp. 385-8, 1989.
[16] E. Rosenblueth , "Point estimate for probability moments,” Proceedings of National Academy of Science of United States of America; 72:3812-4, 1975.
[17] H.P. Hong, "An efficient point estimate method for probabilistic analysis,” Reliability Engineering and System Safet, 59:261-7, 1989.
[18] C. Su, "Probabilistic load-flow computation using point estimate method,” IEEE Transactions on Power Systems, 20:1842-51, 2005.
[19] S. Chun-Lien, L. Chan-Nan L, "two-point estimate method for quantifying transfer capability uncertainty,” IEEE Transactions on Power System, 20:573-9, 2005.
[20] X.S. Yang, "Nature-Inspired Metaheuristic Algorithms,” Frome: Luniver Press. ISBN 1905986106, 2008.
[21] T. Apostolopoulos, A. Vlachos, "Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem,” International Journal of Combinatorics, ID: 523806, 2011.
[22] Y.P. Cai, G.H. Huang, Z.F. Yang, Q. Tan, "Identification of optimal strategies for energy management systems planning under multiple uncertainties,” Appl Energy, 86(4):480–95, 2009.
[23] T. Niknam, "An efficient hybrid evolutionary based on PSO and ACO algorithms for distribution feeder reconfiguration,” European Trans on Elect Power, vol. 20, pp. 575 – 590, 2010.
[24] T. Niknam, S.I. Taheri, J. Aghaei, S. Tabatabaei, M. Nayeripour, "A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources,” Applied Energy , vol. 88 , pp. 4817-4830, 2011.
[25] S.J. Carneiro, J.L. Pereira, M.P. Vinagre, P.A. Garcia, L.R. Araujo, "A New Heuristic Reconfiguration Algorithm for Large Distribution Systems,” IEEE Tran on Power sys, vol. 20 , pp. 1373 – 1378, 2005.
[26] T. Niknam, A. Kavousifard, A. Seifi, "Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power Plants,” Journal of Renewable Energy, vol. 37, pp. 213-225, 2011.
[27] M. Sailaja Kumari, S. Maheswarapu, "Enhanced Genetic Algorithm based computation technique for multi-objective Optimal Power Flow solution,” International Journal of Electrical Power & Energy Systems, vol. 32, pp. 736 – 742, 2010.
[1] W. Ouyang, H. Cheng, X. Zhang, L. Yao, "Distribution network planning method considering distributed generation for peak cutting,” Energy Conversion and Management, vol. 51, pp. 2394–2401, 2010.
[2] Wijayatunga, P., Fernando, W. & Shrestha, R. , " Impact of distributed and independent power generation on greenhouse gas emissions,” Energy Conversion and Management, vol. 45, vo. 20, pp. 3193–206, 2004.
[3] G. Vulasala, S. Sirirgiri, and S. Thiruveedula, "Feeder reconfiguration for loss reduction in unbalanced distribution system using genetic algorithm,” Int. J. Elect. Power Energy Syst. Eng., vol. 2, no. 4, pp. 240–248, Feb. 2009.
[4] T. Ackerman, G. Anderson and L. Soder, "Distributed generation: A definition”, Elsevier Science, pp. 195-204, Dec 2000.
[5] A. Merlin, H. Back H, "Search for a minimal loss operating spanning tree configuration in an urban power distribution system”, Proceeding of the 5th Power System Computation Conference, Cambridge, UK, 1–18, 1997.
[6] F. Vanderson Gomes, S. Carneiro, J.L. Pereira, M.P. Vinagre, P. Garcia, L. Ramos Araujo, " A New heuristic reconfiguration algorithm for large distribution systems”, IEEE Transactions On Power Systems , vol. 20, no. 2, pp.1373-8, 2005.
[7] M. Yiming and N.M Karen, "Switch placement to improve system reliability for radial distribution systems with distributed generation,” IEEE Trans. Power Delivery, vol. 18, pp. 1346-1352, Nov. 2003.
[8] B. Amanulla, S. Chakrabarti, and S. N. Singh, "Reconfiguration of power distribution systems considering reliability and power loss,” IEEE Trans. Power Delivery, vol. 27, pp. 918-926, Apr. 2012.
[9] S. Civanlar, J. Grainger, H. Yin, and S. Lee, "Distribution feeder reconfiguration for loss reduction,” IEEE Trans. Power Del., vol. 3, no. 3, pp. 1217–1223, Jul. 1988.
[10] K. Nara, A. Shiose, M. Kitagawoa, and T. Ishihara, "Implementation of genetic algorithm for distribution systems loss minimum reconfiguration,” IEEE Trans. Power Syst., vol. 7, no. 3, pp. 1044–1051, Aug. 1992.
[11] J. Z. Zhu, "Optimal reconfiguration of electrical distribution network using the refined genetic algorithm,” Elect. Power Syst. Res., vol. 62, pp. 37–42, 2002.
[12] R. Srinivasa Rao, S.V.L. Narasimham, M.R. Raju, and A. Srinivasa Rao, "Optimal network reconfiguration of large-scale distribution system using harmony search algorithm,” IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1080–1088, Aug. 2011.
[13] T. Niknam , "Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators,” J Zhejiang Univ Sci , vol. 9, no. 12, pp. 1753–64, 2008.
[14] A. Soroudi, M. Ehsan, R. Caire, N. Hadjsaid, "Possibilistic evaluation of distributed generations Impacts on distribution networks,” IEEE Transactions on Power Systems, no. 26, pp. 26:2293-301, 2011.
[15] M. Madrigal, K. Ponnambalam, V.H. Quintana, "Probabilistic optimal power flow,” In: Proc. 1998 IEEE Can. Conf. Electrical and Computer engineering, Waterloo, Canada;. pp. 385-8, 1989.
[16] E. Rosenblueth , "Point estimate for probability moments,” Proceedings of National Academy of Science of United States of America; 72:3812-4, 1975.
[17] H.P. Hong, "An efficient point estimate method for probabilistic analysis,” Reliability Engineering and System Safet, 59:261-7, 1989.
[18] C. Su, "Probabilistic load-flow computation using point estimate method,” IEEE Transactions on Power Systems, 20:1842-51, 2005.
[19] S. Chun-Lien, L. Chan-Nan L, "two-point estimate method for quantifying transfer capability uncertainty,” IEEE Transactions on Power System, 20:573-9, 2005.
[20] X.S. Yang, "Nature-Inspired Metaheuristic Algorithms,” Frome: Luniver Press. ISBN 1905986106, 2008.
[21] T. Apostolopoulos, A. Vlachos, "Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem,” International Journal of Combinatorics, ID: 523806, 2011.
[22] Y.P. Cai, G.H. Huang, Z.F. Yang, Q. Tan, "Identification of optimal strategies for energy management systems planning under multiple uncertainties,” Appl Energy, 86(4):480–95, 2009.
[23] T. Niknam, "An efficient hybrid evolutionary based on PSO and ACO algorithms for distribution feeder reconfiguration,” European Trans on Elect Power, vol. 20, pp. 575 – 590, 2010.
[24] T. Niknam, S.I. Taheri, J. Aghaei, S. Tabatabaei, M. Nayeripour, "A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources,” Applied Energy , vol. 88 , pp. 4817-4830, 2011.
[25] S.J. Carneiro, J.L. Pereira, M.P. Vinagre, P.A. Garcia, L.R. Araujo, "A New Heuristic Reconfiguration Algorithm for Large Distribution Systems,” IEEE Tran on Power sys, vol. 20 , pp. 1373 – 1378, 2005.
[26] T. Niknam, A. Kavousifard, A. Seifi, "Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power Plants,” Journal of Renewable Energy, vol. 37, pp. 213-225, 2011.
[27] M. Sailaja Kumari, S. Maheswarapu, "Enhanced Genetic Algorithm based computation technique for multi-objective Optimal Power Flow solution,” International Journal of Electrical Power & Energy Systems, vol. 32, pp. 736 – 742, 2010.
@article{"International Journal of Electrical, Electronic and Communication Sciences:67033", author = "Mahmood Reza Shakarami and Reza Sedaghati", title = "A New Approach for Network Reconfiguration Problem in Order to Deviation Bus Voltage Minimization with Regard to Probabilistic Load Model and DGs ", abstract = "Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering wind turbines. The main objective of the DFR is to minimize the deviation of the bus voltage. Since the DFR is a nonlinear optimization problem, we apply the Adaptive Modified Firefly Optimization (AMFO) approach to solve it. As a result of the conflicting behavior of the single- objective function, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The approach is tested on the IEEE 32-bus standard test system.
", keywords = "Adaptive Modified Firefly Optimization (AMFO), Pareto solutions, feeder reconfiguration, wind turbines, bus voltage.", volume = "8", number = "2", pages = "430-6", }
