Application of a Modified NSGA Method for Multi-Objective Static Distributed Generation Planning

Arabian Journal for Science and Engineering

Volumn 36, Issue 5, 2011, Pages 809-825

  Soroudi, Alireza ^a   Ehsan, Mehdi ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Active loss reduction; Dispersed generation; Emission reduction; Multi objective optimization; Static planning; Technical constraint

Indexed keywords

EID: 80052197923     PISSN: 2193567X     EISSN: 21914281     Source Type: Journal    
DOI: 10.1007/s13369-011-0077-1     Document Type: Article

References (32)

1. Willis L., Scott W. G.: Distributed Power Generation, planning and evaluation. Marcel Deckker, New York (2000).
2. Pepermans G., Driesen J., Haeseldonckx D., Belmans R., D'haeseleer W.: Distributed generation: definition, benefits and issues. Energy Policy 33(6), 787-798 (2005).
3. Ackermann T., Andersson G., Soder L.: Distributed generation: a definition. Electr Power Syst Res 57(3), 195-204 (2001).
4. International Energy Agency. Distributed Generation in Liberalized Electricity Markets. International Energy Agency, Paris (2002).
5. Zangeneh A., Jadid S., Rahimi-Kian A.: Promotion strategy of clean technologies in distributed generation expansion planning. Renew Energy 34(12), 2765-2773 (2009).
6. Soroudi, A., Ehsan, M.: A life cycle model for optimal allocation of DG units in distribution networks to reduce active losses and investment costs simultaneously. In: International Conference on Genetic and Evolutionary Methods, Las Vegas, June (2007).
7. Wang L., Singh C.: Reliability-constrained optimum placement of reclosers and distributed generators in distribution networks using an ant colony system algorithm. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 38(6), 757-764 (2008).
8. Jabr R. A., Pal B. C.: Ordinal optimization approach for locating and sizing of distributed generation. Gener. Transm. Distrib. IET 3(8), 713-723 (2009).
9. Singh R. K., Goswami S. K.: Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue. Int. J. Electr. Power Energy Syst. 32(6), 637-644 (2010).
10. Hedayati H., Nabaviniaki S. A., Akbarimajd A.: A method for placement of DG units in distribution networks. IEEE Trans. Power Deliv. 23(3), 1620-1628 (2008).
11. Zhu D., Broadwater R. P., Tam K.-S., Seguin R., Asgeirsson H.: Impact of DG placement on reliability and efficiency with time-varying loads. IEEE Trans. Power Syst. 21(1), 419-427 (2006).
12. Harrison G. P., Piccolo A., Siano P., Robin Wallace A.: Hybrid GA and OPF evaluation of network capacity for distributed generation connections. Electr. Power Syst. Res. 78(3), 392-398 (2008).
13. Piccolo A., Siano P.: Evaluating the impact of network investment deferral on distributed generation expansion. IEEE Trans. Power Syst. 24(3), 1559-1567 (2009).
14. Teng J.-H., Liu Y.-H., Chen C.-Y., Chen C.-F.: Value-based distributed generator placements for service quality improvements. Int. J. Electr. Power Energy Syst. 29(3), 268-274 (2007).
15. El-Khattam W., Bhattacharya K., Hegazy Y., Salama M. M. A.: Optimal investment planning for distributed generation in a competitive electricity market. IEEE Trans. Power Syst. 19(3), 1674-1684 (2004).
16. Cormio C., Dicorato M., Minoia A., Trovato M.: Embedding distributed generation in a national energy planning procedure. 2, 755-760 (2004).
17. Golshan M. E. H., Arefifar S. A.: Distributed generation, reactive sources and network-configuration planning for power and energy-loss reduction. Gener. Transm. Distrib. IEE Proc. 153(2), 127-136 (2006).
18. Deb K.: Multi Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2003).
19. Kim I. Y., de Weck O.: Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation. Struct. Multidiscipl. Optim. 31, 105-116 (2006).
20. Mendoza F., Bernal-Agustin J. L., Domiguez-Navarro J. A.: NSGA and SPEA applied to multiobjective design of power distribution systems. IEEE Trans. Power Syst 21(4), 1938-1945 (2006).
21. Selvakumar A. I., Thanushkodi K.: A new particle swarm optimization solution to non-convex economic dispatch problem. IEEE Trans. Power Syst. 22(1), 42-51 (2007).
22. Holland, J. H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975).
23. Lee K. Y., Mohamed P. S.: A real-coded genetic algorithm involving a hybrid crossover method for power plant control system design. Evol. Comput. 2, 1069-1074 (1975).
24. Orero S. O. et al.: Economic dispatch of generators with prohibited operating zones: a genetic algorithm approach. Gener. Transm. Distrib. IEE Proc. 143(6), 529-534 (1996).
25. Cheng C.-P. et al.: Unit commitment by Lagrange relaxation and genetic algorithms. IEEE Trans. Power Syst. 15(2), 707-714 (2000).
26. Basu M.: Dynamic economic emission dispatch using evolutionary programming and fuzzy satisfying method. Int. J. Emerg. Electr. Power Syst. 8(4), 1-15 (2006).
27. http://www. ewh. ieee. org/soc/pes/dsacom/testfeeders. html. Accessed Jan 2009.
28. Prospects for distributed electricity generation, CBO paper, congress of the United States congressional budget office, September (2003).
29. EPRI technical report. Distributed Energy Resources Emissions Survey and Technology Characterization. November 2004, http://www. epriweb. com/public/000000000001011256. pdf.
30. Aarts, EHL.: Simulated Annealing: Theory and Applications Book Description, Springer, Berlin (1987).
31. Clerc, M.: Particle Swarm Optimization, Wiley-ISTE, London (2006).
32. Fred, W.: Glover, Manuel Laguna, Tabu Search. Springer, Berlin (1998).