DOI QR코드

DOI QR Code

Fully Distributed Economic Dispatching Methods Based on Alternating Direction Multiplier Method

  • Yang, Linfeng (School of Computer Electronics and Information, Guangxi University) ;
  • Zhang, Tingting (School of Computer Electronics and Information, Guangxi University) ;
  • Chen, Guo (School of Electrical Engineering and Telecommunications, University of New South Wales) ;
  • Zhang, Zhenrong (School of Computer Electronics and Information, Guangxi University) ;
  • Luo, Jiangyao (School of Computer Electronics and Information, Guangxi University) ;
  • Pan, Shanshan (College of Electrical Engineering, Guangxi University)
  • 투고 : 2017.11.10
  • 심사 : 2018.02.02
  • 발행 : 2018.09.01

초록

Based on the requirements and characteristics of multi-zone autonomous decision-making in modern power system, fully distributed computing methods are needed to optimize the economic dispatch (ED) problem coordination of multi-regional power system on the basis of constructing decomposition and interaction mechanism. In this paper, four fully distributed methods based on alternating direction method of multipliers (ADMM) are used for solving the ED problem in distributed manner. By duplicating variables, the 2-block classical ADMM can be directly used to solve ED problem fully distributed. The second method is employing ADMM to solve the dual problem of ED in fully distributed manner. N-block methods based on ADMM including Alternating Direction Method with Gaussian back substitution (ADM_G) and Exchange ADMM (E_ADMM) are employed also. These two methods all can solve ED problem in distributed manner. However, the former one cannot be carried out in parallel. In this paper, four fully distributed methods solve the ED problem in distributed collaborative manner. And we also discussed the difference of four algorithms from the aspects of algorithm convergence, calculation speed and parameter change. Some simulation results are reported to test the performance of these distributed algorithms in serial and parallel.

키워드

참고문헌

  1. B.H. Chowdhury and S. Rahman, "A review of recent advances in economic dispatch," IEEE Trans. Power Systems, vol. 5, no. 1, pp. 1248-1259, Nov. 1990.
  2. G.F. Reid and L. Hasdorff, "Economic Dispatch Using Quadratic Programming," IEEE Trans. PAS, vol. PAS-92, no. 6, pp. 2015-2023, Nov. 1973. https://doi.org/10.1109/TPAS.1973.293582
  3. C.B. Somuah and N. Khunaizi, "Application of linear programming redispatch technique to dynamic generation allocation," IEEE Trans. Power Systems, vol. 5, no. 1, pp. 20-26, Feb. 1990.
  4. J.G. Waight, F. Albuyeh, and A. Bose, "Scheduling of Generation and Reserve Margin Using Dynamic and Linear Programming," IEEE Trans. PAS, vol. PAS-100, no. 5, pp. 2226-2230, May 1981. https://doi.org/10.1109/TPAS.1981.316713
  5. C. Wang and S. M. Shahidehpour, "Optimal generation scheduling with ramping costs," IEEE Trans. Power Systems, vol. 10, no.1, pp. 60-67, Feb. 1995. https://doi.org/10.1109/59.373928
  6. T. Guo, M.I. Henwood, and M.V. Ooijen, "An algorithm for combined heat and power economic dispatch," IEEE Trans. Power Systems, vol. 11, no. 4, pp. 1778-1784, Nov. 1996. https://doi.org/10.1109/59.544642
  7. R.R. Shoults, S.K. Chang, S. Helmick, and W.M. Grady, "A Practical Approach to Unit Commitment, Economic Dispatch, and Savings Allocation for Multiple-Area Pool Operation with Import/Export Constraints," IEEE Trans. PAS, vol. PAS-99, no. 2, pp. 625-635, Mar. 1980. https://doi.org/10.1109/TPAS.1980.319654
  8. L. Imen, B. Mouhamed, and L. Djamel, "Economic dispatch using classical methods and neural networks," in Proc. of International Conference on Electrical and Electronics Engineering, Turkey, Nov. 2013.
  9. A. Mohammadi, M.H. Varahram, and I. Kheirizad, "Online Solving of Economic Dispatch Problem Using Neural Network Approach and Comparing it with Classical Method," in Proc. of International Conference on Emerging Technologies, Pakistan, Nov. 2006.
  10. G.B. Sheble and K. Brittig, "Refined genetic algorithm-economic dispatch example," IEEE Trans. Power Systems, vol. 10, no. 1, pp. 117-124, Feb. 1995. https://doi.org/10.1109/59.373934
  11. Z. L. Gaing, "Particle swarm optimization to solving the economic dispatch considering the generator constraints," IEEE Trans. Power Systems, vol. 18, no. 3, pp. 1187-1195, Jul. 2003.
  12. T. Sen and H.D. Mathur, "A new approach to solve Economic Dispatch problem using a Hybrid ACO-ABC-HS optimization algorithm," International Journal of Electrical Power & Energy Systems, vol. 78, pp. 735-744, Jun. 2016. https://doi.org/10.1016/j.ijepes.2015.11.121
  13. A.Y. Saber, "Economic dispatch using particle swarm optimization with bacterial foraging effect," International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, pp. 38-46, Jan. 2012. https://doi.org/10.1016/j.ijepes.2011.09.003
  14. C.C. Fung, S.Y. Chow, and K.P. Wong, "Solving the economic dispatch problem with an integrated parallel genetic algorithm," in Proc. of International Conference on Power System Technology, Australia, Dec. 2000.
  15. P. Subbaraj, R. Rengaraj, S. Salivahanan, and T.R. Senthilkumar, "Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem," International Journal of Electrical Power & Energy Systems, vol. 32, no. 9, pp. 1014-1023, Nov. 2010. https://doi.org/10.1016/j.ijepes.2010.02.003
  16. F. Guo, C. Wen, and L. Xing, "A distributed algorithm for economic dispatch in a large-scale power system," in Proc. of International Conference on Control, Automation, Robotics and Vision, Nov. 2016.
  17. H. Yang, D. Yi, J. Zhao, and Z. Dong, "Distributed optimal dispatch of virtual power plant via limited communication," IEEE Trans. Power Systems, vol. 28, no. 3, pp. 3511-3512, Mar. 2013. https://doi.org/10.1109/TPWRS.2013.2242702
  18. G. Chen, C. Li, and Z. Dong, "Parallel and Distributed Computation for Dynamical Economic Dispatch," IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 1026-1027, Mar. 2017. https://doi.org/10.1109/TSG.2016.2623980
  19. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein., "Distributed optimization and statistical learning via the alternating direction method of multipliers," Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1-122, 2010. https://doi.org/10.1561/2200000016
  20. M.J. Feizollahi, M. Costley, S. Ahmed, and S. Grijalva, "Large-scale decentralized unit commitment," International Journal of Electrical Power & Energy Systems, vol. 73, pp. 97-106, Dec. 2015. https://doi.org/10.1016/j.ijepes.2015.04.009
  21. S. Magnusson, P.C. Weeraddana, and C. Fischione, "A distributed approach for the optimal power flow problem based on ADMM and sequential convex approximations," IEEE Trans. on Control of Network Systems, vol. 2, no. 3, pp. 238-257, Sept. 2015. https://doi.org/10.1109/TCNS.2015.2399192
  22. M. Fukushima, "Application of the alternating direction method of multipliers to separable convex programming," Computational Optimization and Applications, vol. 1, no. 1, pp. 93-111, Oct. 1992. https://doi.org/10.1007/BF00247655
  23. C.H. Chen, B.S. He, Y.Y. Ye, and X.M. Yuan, "The direct extension of ADMM for multiblock convex minimization problems is not necessarily convergent," Math. Program., vol. 155, no. 1-2, pp. 57-79, Jan. 2016. https://doi.org/10.1007/s10107-014-0826-5
  24. Y.G. Peng, A. Ganesh, J. Wright, W.L. Xu, and Y. Ma, "Robust alignment by sparse and low-rank decomposition for linearly correlated images," IEEE Trans. PAMI, vol. 34, no. 11, pp. 2233-2246, Nov. 2012. https://doi.org/10.1109/TPAMI.2011.282
  25. M. Tao and X.M. Yuan, "Recovering low-rank and sparse components of matrices from incomplete and noisy observations, SIAM J. Optim., vol. 21, no. 1, pp. 57-81, Jan. 2011. https://doi.org/10.1137/100781894
  26. B.S. He, M. Tao, and X.M. Yuan. "Alternating direction method with Gaussian Back substitution for separable convex programming," SIAM J. Optim., vol. 22, no. 2, pp. 313-340, Apr. 2012. https://doi.org/10.1137/110822347
  27. J.H Mathews and K.K. Fink, Numerical Methods Using Matlab(4th Edition), Prentice-Hall Inc., 2004.
  28. J. Ostrowski, M.F. Anjos, and A. Vannelli, "Tight mixed integer linear programming formulations for the unit commitment problem," IEEE Trans. Power Systems, vol. 27, no. 1, pp. 39-46, Feb. 2012. https://doi.org/10.1109/TPWRS.2011.2162008
  29. L.F. Yang, C. Zhang, J.B. Jian, K. Meng, Y. Xu, and Z.Y. Dong, "A novel projected two-binary-variable formulation for unit commitment in power systems. Applied Energy, vol. 187, pp. 732-745, Feb. 2017. https://doi.org/10.1016/j.apenergy.2016.11.096
  30. M. Nick, R. Cherkaoui, and M. Paolone, "Optimal siting and sizing of distributed energy storage systems via alternating direction method of multipliers," International Journal of Electrical Power & Energy Systems, vol. 72, pp. 33-39, Nov. 2015. https://doi.org/10.1016/j.ijepes.2015.02.008