Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Optimal Relocating of Compensators for Real-Reactive Power Management in Distributed Systems

  • Chintam, Jagadeeswar Reddy (Dept. of EEE, Government College of Engineering, Anna University) ;
  • Geetha, V. (Dept. of EEE, Government College of Engineering) ;
  • Mary, D. (Dept. of EEE, Government College of Technology)
  • Received : 2017.08.08
  • Accepted : 2018.02.17
  • Published : 2018.11.01
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Abstract

Congestion Management (CM) is an attractive research area in the electrical power transmission with the power compensation abilities. Reconfiguration and the Flexible Alternating Current Transmission Systems (FACTS) devices utilization relieve the congestion in transmission lines. The lack of optimal power (real and reactive) usage with the better transfer capability and minimum cost is still challenging issue in the CM. The prediction of suitable place for the energy resources to control the power flow is the major requirement for power handling scenario. This paper proposes the novel optimization principle to select the best location for the energy resources to achieve the real-reactive power compensation. The parameters estimation and the selection of values with the best fitness through the Symmetrical Distance Travelling Optimization (SDTO) algorithm establishes the proper controlling of optimal power flow in the transmission lines. The modified fitness function formulation based on the bus parameters, index estimation correspond to the optimal reactive power usage enhances the power transfer capability with the minimum cost. The comparative analysis between the proposed method with the existing power management techniques regarding the parameters of power loss, cost value, load power and energy loss confirms the effectiveness of proposed work in the distributed renewable energy systems.

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References

  1. R. A. Verzijlbergh, et al., "Renewable energy sources and responsive demand. Do we need congestion management in the distribution grid?," IEEE Transactions on Power Systems, vol. 29, pp. 2119-2128, 2014. https://doi.org/10.1109/TPWRS.2014.2300941
  2. A. Yousefi, et al., "Congestion management using demand response and FACTS devices," International Journal of Electrical Power & Energy Systems, vol. 37, pp. 78-85, 2012. https://doi.org/10.1016/j.ijepes.2011.12.008
  3. B. Stott and O. Alsac, "Optimal power flow-basic requirements for real-life problems and their solutions," in SEPOPE XII Symposium, Rio de Janeiro, Brazil, 2012.
  4. W. Shi, et al., "Distributed optimal energy management in microgrids," IEEE Transactions on Smart Grid, vol. 6, pp. 1137-1146, 2015. https://doi.org/10.1109/TSG.2014.2373150
  5. M. Sahraei-Ardakani and S. A. Blumsack, "Transfer capability improvement through market-based operation of series FACTS devices," 2015.
  6. S. Chellam and S. Kalyani, "Power flow tracing based transmission congestion pricing in deregulated power markets," International Journal of Electrical Power & Energy Systems, vol. 83, pp. 570-584, 2016. https://doi.org/10.1016/j.ijepes.2016.03.049
  7. C. Luo, et al., "Assessment of market flows for interregional congestion management in electricity markets," IEEE Transactions on Power Systems, vol. 29, pp. 1673-1682, 2014. https://doi.org/10.1109/TPWRS.2014.2297951
  8. M. Esmaili, et al., "Congestion management in hybrid power markets using modified Benders decomposition," Applied Energy, vol. 102, pp. 1004-1012, 2013. https://doi.org/10.1016/j.apenergy.2012.06.019
  9. M. Azam, et al., "Joint admission control, mode selection and power allocation in D2D communication systems," 2015.
  10. Y.-F. Liu, et al., "Joint power and admission control via linear programming deflation," in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 2873-2876.
  11. J. R. Chintam and M. Daniel, "Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm," Energies, vol. 11, p. 183, 2018. https://doi.org/10.3390/en11010183
  12. M. Esmaili, et al., "Locating series FACTS devices for multi-objective congestion management improving voltage and transient stability," European Journal of Operational Research, vol. 236, pp. 763-773, 2014. https://doi.org/10.1016/j.ejor.2014.01.017
  13. R. Li, et al., "Distribution locational marginal pricing for optimal electric vehicle charging management," IEEE Transactions on Power Systems, vol. 29, pp. 203-211, 2014. https://doi.org/10.1109/TPWRS.2013.2278952
  14. K. W. Hedman, et al., "Optimal transmission switching: economic efficiency and market implications," Journal of Regulatory Economics, vol. 40, pp. 111-140, 2011. https://doi.org/10.1007/s11149-011-9158-z
  15. V. Loia, et al., "A self-organizing architecture based on cooperative fuzzy agents for smart grid voltage control," IEEE Transactions on Industrial Informatics, vol. 9, pp. 1415-1422, 2013. https://doi.org/10.1109/TII.2013.2249074
  16. Y. Cao, et al., "Chance-constrained optimization-based unbalanced optimal power flow for radial distribution networks," IEEE Transactions on Power Delivery, vol. 28, pp. 1855-1864, 2013. https://doi.org/10.1109/TPWRD.2013.2259509
  17. S. Huang, et al., "Distribution locational marginal pricing through quadratic programming for congestion management in distribution networks," IEEE Transactions on Power Systems, vol. 30, pp. 2170-2178, 2015. https://doi.org/10.1109/TPWRS.2014.2359977
  18. T. Sansawatt, et al., "Smart decentralized control of DG for voltage and thermal constraint management," IEEE Transactions on Power Systems, vol. 27, pp. 1637-1645, 2012. https://doi.org/10.1109/TPWRS.2012.2186470
  19. S. P. Singh, "Congestion mitigation using UPFC," IET Generation, Transmission & Distribution, 2016.
  20. S. H. Ling, et al., "Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization," Engineering Applications of Artificial Intelligence, vol. 47, pp. 68-80, 2016. https://doi.org/10.1016/j.engappai.2015.03.003
  21. S. Ushasurendra and S. Parathasarthy, "Congestion management in deregulated power sector using fuzzy based optimal location technique for series flexible alternative current transmission system (FACTS) device," Journal of Electrical and Electronics Engineering Research, vol. 4, pp. 12-20, 2012.
  22. A. Mishra and V. N. K. Gundavarapu, "Line utilisation factor-based optimal allocation of IPFC and sizing using firefly algorithm for congestion management," IET Generation, Transmission & Distribution, vol. 10, pp. 115-122, 2016. https://doi.org/10.1049/iet-gtd.2015.0493
  23. A. Mishra and G. N. Kumar, "Congestion management of power system with interline power flow controller using disparity line utilization factor and multi-objective differential evolution," CSEE Journal of Power and Energy Systems, vol. 1, pp. 76-85, 2015. https://doi.org/10.17775/CSEEJPES.2015.00038
  24. X.-S. Yang, "Multiobjective firefly algorithm for continuous optimization," Engineering with Computers, vol. 29, pp. 175-184, 2013. https://doi.org/10.1007/s00366-012-0254-1
  25. S. A. Hosseini, et al., "A new multi-objective solution approach to solve transmission congestion management problem of energy markets," Applied Energy, vol. 165, pp. 462-471, 2016. https://doi.org/10.1016/j.apenergy.2015.12.101
  26. M. Joorabian and E. Afzalan, "Optimal power flow under both normal and contingent operation conditions using the hybrid fuzzy particle swarm optimisation and Nelder-Mead algorithm (HFPSO-NM)," Applied Soft Computing, vol. 14, pp. 623-633, 2014. https://doi.org/10.1016/j.asoc.2013.09.015
  27. J.J.D. Nesamalar, et al., "Managing multi-line power congestion by using Hybrid Nelder-Mead-Fuzzy Adaptive Particle Swarm Optimization (HNMFAPSO)," Applied Soft Computing, vol. 43, pp. 222-234, 2016. https://doi.org/10.1016/j.asoc.2016.02.013
  28. S. Huang, et al., "Uncertainty Management of Dynamic Tariff Method for Congestion Management in Distribution Networks," IEEE Transactions on Power Systems, vol. 31, pp. 4340-4347, 2016. https://doi.org/10.1109/TPWRS.2016.2517645
  29. D. I. Koukoula and N. D. Hatziargyriou, "Gossip Algorithms for Decentralized Congestion Management of Distribution Grids."
  30. S. Gope, et al., "Rescheduling of real power for congestion management with integration of pumped storage hydro unit using firefly algorithm," International Journal of Electrical Power & Energy Systems, vol. 83, pp. 434-442, 2016. https://doi.org/10.1016/j.ijepes.2016.04.048
  31. S. Verma and V. Mukherjee, "Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser," IET Generation, Transmission & Distribution, 2016.
  32. N. Kamaraj, "Transmission congestion management using particle swarm optimization," J. Electrical Systems, vol. 7, pp. 54-70, 2011.