PSO based Multi-Criteria Placement and Impact Evaluation of Distributed Generators in Indian Context

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Naveen Jain
S. N. Singh
S. C. Srivastava

Abstract

This paper presents a new approach for optimal placement of Distributed Generators (DGs) utilizing a generic multi-objective performance function considering dynamic relevance (weight) factors and various levels of the DG penetration. The suggested approach can place the fixed size as well as the variable size of multiple DGs in single or multiple stage(s), considering any type of load model. The technical performance of the system, with the dynamic relevance factors is found to be better than with the fixed relevance factors approach. The effect of considering system constraints on the DG size and its location has been studied. A look up table approach is also suggested to place the DG at locations other than the most optimal one. The impact of the DG placement on the system voltage profile and line loss has also been investigated on 33-bus and 41-bus (Indian system) distribution systems. The critical cases with extreme DG output power and distribution load demand are simulated on these systems to study the technical viability of the DG planning under such circumstances.

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How to Cite
Jain, N., Singh, S. N., & Srivastava, S. C. (2013). PSO based Multi-Criteria Placement and Impact Evaluation of Distributed Generators in Indian Context. Power Research - A Journal of CPRI, 251–270. Retrieved from https://cprijournal.in/index.php/pr/article/view/896

References

  1. “Modeling and control of fuel cell: Distributed generation application”, IEEE press series on power engineering, New Jersey: Wiley, 2009.
  2. Chiradeja P and Ramakumar R. “An approach to quantify the technical benefits of distributed generation”, IEEE Trans. Energy Convers., Vol. 19, pp. 764—773, 2004.
  3. Ochoa L F, Padilha-Feltrin A and Harrison G P. “Evaluating distributed generation impacts with a multi-objective index”, IEEE Trans. Power Del., Vol. 21, pp. 1452—1458, 2006.
  4. Singh D, Verma K S and Singh D. “Multiobjective optimization for DG planning with load models”, IEEE Trans. Power Syst., Vol. 24, No. 1, pp. 427–436, February 2009.
  5. Abou El-Ela A A, Allam S M and Shatla M M. “Maximal optimal benefits of distributed generation using genetic algorithms”, Electr. Power Syst. Res., Vol. 80, pp. 869–877, 2010.
  6. Akorede M F, Hizam H, Aris I and AbKadir M Z A. “Effective method for optimal allocation of distributed generation units in meshed electric power systems”, IEE Proc.-Gener. Transm. Distrib., Vol. 5, pp. 276–287, 2011.
  7. El-Zonkoly A M. “Optimal placement of multi-distributed generation units including different load models using particle swarm optimization”, IEE Proc.-Gener. Transm. Distrib., Vol. 5, 760–771, 2011.
  8. Niimura T and Nakashima T. “Multiobjective tradeoff analysis of deregulated electricity transactions”, Electr. Power Syst. Res., Vol. 25, 179–185, 2003.
  9. Wang C and Nehrir M H. “Analytical approaches for optimal placement of distributed generation sources in power systems”, IEEE Trans. Power Syst., Vol. 19, pp. 2068–2076, 2004.
  10. Acharya N, Mahat P and Mithulananthan N. “An analytical approach for DG allocation in primary distribution network”, Int. J.Electr. Power Energy Syst., Vol. 28, pp. 669–678, 2006.
  11. L ee S H and Park J W. “Selection of optimal location and size of multiple distributed generations by using kalman filter algorithm”, IEEE Trans. Power Syst., Vol. 24, pp. 1393–1400, 2009.
  12. Gözel T and Hocaoglu M H. “An analytical method for the sizing and siting of distributed generators in radial systems”, Electr. Power Syst. Res., Vol. 79, pp. 912–918, 2009.
  13. Esmin A A A, Lambert-Torres G and Zambroni de A C. “A hybrid particle swarm optimization applied to loss power minimization”, IEEE Trans. Power Syst., Vol. 20, pp. 859–866, 2005.
  14. Hedayati H, Nabaviniaki S A and Akbarimajd A. “A method for placement of dg units in distribution networks”, IEEE Trans. Power Del., Vol. 23, pp. 1620–1628, 2008.
  15. Celli G, Ghiani E, Mocci S and Pilo F. “A multi-objective evolutionary algorithm for the sizing and siting of distributed generation”, IEEE Trans. Power Syst., Vol. 20, pp. 750–757, 2005.
  16. Haghifam M R, Falaghi H and Malik O P. “Risk-based distributed generation placement”, IET Gener. Transm. Distrib. Journal, Vol. 2, pp. 252–260, 2008.
  17. Nekooei K, Malihe M Farsangi, Pour H N and Lee L Y. “An improved multi-objective harmony search for optimal placement of DGs in distribution systems”, IEEE Trans. Smart Grid, Vol. 4, pp. 557–567, 2013.
  18. Ochoa L F and Harrison G P. “Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation”, IEEE Trans. Power Syst., Vol. 26, pp. 198–205, 2011.
  19. Ehsan S M, Caire R and Hadjsaid N. “Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment”, IEE Proc.-Gener. Transm. Distrib., Vol. 5, pp. 961–972, 2011.
  20. El-Khattam W, Hegazy Y G and Salama M M A. “An integrated distributed generation optimization model for distribution system planning”, IEEE Trans. Power Syst. Vol. 20, pp. 1158–1165, 2005.
  21. O’Malley K and O’Malley M. “Optimal allocation of embedded generation on distribution networks”, IEEE Trans. Power Syst., Vol. 20, pp. 1640–1646, 2005.
  22. Singh D and Misra R K. “Effect of load models in distributed generation planning”, IEEE Trans. Power Syst. 22 (2007) 22042212.
  23. Singh R K and 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., Vol. 32, pp. 637–644, 2010.
  24. Kennedy J, Eberhart R C. “Particle swarm optimization”, in: Proc. IEEE Int. Conf. on Neural Networks, pp. 1942–1948, 1995.
  25. Shi Y and Eberhart R. “A modified particle swarm optimizer”, in: IEEE Int. Conf. on Computational Intelligence, pp. 69–73, 1998.
  26. Pindoriya N M and Singh S N. “MOPSO based day-ahead optimal self-scheduling of generators under electricity price forecast uncertainty”, in: IEEE Power Energy Society General Meeting, pp. 1–8, 2009.
  27. Banks A, Vincent J andAnyakoha C. “A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications”, Natural Computing, Vol. 7, pp. 109–124, 2008.
  28. Eberhart R C and Shi Y. “Comparing inertia weights and constriction factors in particle swarm optimization”, in: Congress on Evolutionary Computation, pp. 84–88, 2000.
  29. Gomez-Gonzalez M, López A and Jurado F. “Optimization of distributed generation systems using a new discrete PSO and OPF”, Electr. Power Syst. Res., Vol. 84, pp. 174–180, 2012.
  30. Moradi M H and Abedini M. “A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems”, Int. J. Elect. Power & Energy Systems, Vol. 34, pp. 66 – 74, 2012.
  31. Maciel R S, Rosa M, Miranda V and P.-Feltrin A, “Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation”, Electr. Power Syst. Res., Vol. 89, pp. 100– 108, 2012.
  32. Quezada V H M, Abbad J R and Roman T G S. “Assessment of energy distribution losses for increasing penetration of distributed generation”, IEEE Trans. Power Syst., Vol. 21, pp. 533–540, 2006.
  33. Prommee W and Ongsakul W. “Optimal multiple distributed generation placement in microgrid system by improved reinitialized social structures particle swarm optimization”, Euro. Trans. Electr. Power, Vol. 21, pp. 489–504, 2011.
  34. Gözel T, Eminoglu U and Hocaoglu M H. “A tool for voltage stability and optimization (VS&OP) in radial distribution systems using matlab graphical user interface (GUI)”, Simulation Modelling Practice and Theory, Vol. 16, pp. 505–518, 2008.
  35. Z hang J, Cheng H and Wang C. “Technical and economic impacts of active management on distribution network”, Int. J. of Electr. Power & Energy Syst., Vol. 31, pp. 130–138, 2009.
  36. Gautam D and Mithulananthan N. “Optimal DG placement in deregulated electricity market”, Electr. Power Syst. Res., Vol. 77, Vol. 1627–1636, 2007.
  37. Atwa Y M and El-Saadany E F. “Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems”, IET Renewable Power Generation, Vol. 5, pp. 79–88, 2011.
  38. Chen C, Duan S, Tao Cai, Bangyin L and Guozhen H. “Optimal allocation and economic analysis of energy storage system in microgrids”, IEEE Trans. Power Electron, Vol. 26, pp. 2762–2773, 2011.
  39. Niknam T, Mojarrad H D and Nayeripour M. “A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch,” Energy, Vol. 35, No. 4, pp. 1764– 1778, June 2010.
  40. IEEE Application Guide for IEEE Std 1547, IEEE standard for interconnecting distributed resources with electric power systems, IEEE Std 1547.2-2008, pp. 1–207, 2009.
  41. Moghaddas-Tafreshi S M and Mashhour E. “Distributed generation modeling for power flow studies and a three-phase unbalanced power flow solution for radial distribution systems considering distributed generation”, Electr. Power Syst. Res. Vol. 79, pp. 680– 686, 2009.
  42. Khushalani S, Solanki J M and Schulz N N. “Development of 3-phase unbalanced power flow using PV and PQ models for distributed generation and study of the impact of DG models”, IEEE Trans. Power Syst., Vol. 22, pp. 1019–1025, 2007.
  43. Eminoglu U and Hakan Hocaoglu M. “A new power flow method for radial distribution systems including voltage dependent load models”, Electr. Power Syst. Res., Vol. 76, pp. 106–114, 2005.
  44. Jain N, Singh S N and Srivastava S C. “A generalized approach for DG planning and viability analysis under market scenario,” IEEE Trans. Ind. Electronics (Accepted and in early access) Digital Object Identifier10.1109/TIE.2012.2219840.
  45. Kumar V, Gupta I, Gupta H O and Agarwal C P., “Voltage and current sensitivities of radial distribution network: a new approach”, IEE Proc.-Gener. Transm. Distrib., Vol. 152, pp. 813–818, 2005.