Engineering and Technology Journal

Unit Commitment Solution Based on Improved Particle Swarm Optimization Method

Document Type : Research Paper

Authors

Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

Abstract

This paper presents an algorithm to solve the unit commitment problem using the intelligence technique based on improved Particle Swarm Optimization (IPSO) for establishing the optimal scheduling of the generating units in the electric power system with the lowest production cost during a specified time and subjected to all the constraints. The minimum production cost is calculated based on using the Lambda Iteration method. A conventional method was also used for solving the unit commitment problem using the Dynamic Programming method (DP). The two methods were tested on the 14-bus IEEE test system and the results of both methods were compared with each other and with other references. The comparison showed the effectiveness of the proposed method over other methods.

Highlights

  • The optimal scheduling of power generation
  • The Unit Commitment Solution based on Improved Particle Swarm Optimization.
  • lowest production cost during a specified period of time.
  • Economic Dispatch solution with Lambda Iteration Method.
  • Dynamic Programming a conventional method used for solving the unit commitment.

Keywords

References
[1] S. Sivanagaraju, Power system operation and control. Pearson Education India, (2009).
[2] K. W. Edwin, H. D. Kochs, and R. J. Taud, Integer programming approach to the problem of optimal unit commitment with probabilistic reserve determination, IEEE Trans. Power Appar. Syst., PAS-97, 6, (1978), 2154–2166.doi: 10.1109/TPAS.1978.354719.
[3] A. I. Cohen and M. Yoshimura, A branch-and-bound algorithm for unit commitment, IEEE Trans. Power Appar. Syst., 2, (1983), 444–451.
[4] J. A. Muckstadt and R. C. Wilson, An Application of Mixed-Integer Programming Duality to Scheduling Thermal Generating Systems, IEEE Trans. Power Appar. Syst., 87, 12, (1968), 1968–1978. doi: 10.1109/TPAS.1968.292156.
[5] F. Zhuang and F. D. Galiana, Towards a more rigorous and practical unit commitment by Lagrangian relaxation, IEEE Trans. Power Syst., 3, 2, (1988), 2, 763–77.
[6] G. B. Sheble, Solution of the unit commitment problem by the method of unit periods, IEEE Trans. Power Syst., 5, 1, (1990), 257–260.doi: 10.1109/59.49114.
[7] S. Khunkitti, N. R Watson, R. Chatthaworn, S. Premrudeepreechacharn, and A. Siritaratiwat, An improved DA-PSO optimization approach for unit commitment problem, Energies, 12, 12, (2019), 2335.
[8] V. K. Kamboj, A novel hybrid PSO–GWO approach for unit commitment problem, Neural Comput. Appl., 27, 6, (2016), 1643–1655.
[9] A. Bhadoria, S. Marwaha, and V. K. Kamboj, An optimum forceful generation scheduling and unit commitment of thermal power system using sine cosine algorithm, Neural Comput. Appl., (2019), 1–30.
[10] S. Kigsirisin and H. Miyauchi, Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization, IEEE Access, 9, (2021), 12267–12281.
[11] A. Bhadoria and S. Marwaha, Moth flame optimizer-based solution approach for unit commitment and generation scheduling problem of electric power system, J. Comput. Des. Eng., 7, 5, (2020), 668–683.
[12] M. Farsadi, H. Hosseinnejad, and T. S. Dizaji, Solving unit commitment and economic dispatch simultaneously considering generator constraints by using nested PSO,ELECO 2015 - 9th Int. Conf. Electr. Electron. Eng., (2016), 493–499. doi: 10.1109/ELECO.2015.7394478.
[13] A. Singh and A. Khamparia, A hybrid whale optimization-differential evolution and genetic algorithm based approach to solve unit commitment scheduling problem: WODEGA, Sustain. Comput. Informatics Syst., 28, (2020), 100442. doi: 10.1016/j.suscom.2020.100442.
[14] Y. Zhai, X. Liao, N. Mu, and J. Le, A two-layer algorithm based on PSO for solving unit commitment problem, Soft Comput., 24, 12, (2020), 9161–9178.
[15] A. J. Wood, B. F. Wollenberg, and G. B. Sheblé, Power generation, operation, and control. John Wiley & Sons, (2013).
[16] P. K. Singhal and R. N. Sharma, Dynamic programming approach for solving power generating unit commitment problem, in 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011), (2011), 298–303.
[17] S. U. Rani and C. H. P. Raju, A Solution to Unit Commitment Problem via Dynamic Programming and Particle Swarm Optimization, Int. J. Curr. Eng. Technol., 3, 4, (2013).
[18] J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95-international conference on neural networks,4, (1995), 1942–1948.
[19] P. Sriyanyong and Y. H. Song, Unit commitment using particle swarm optimization combined with Lagrange relaxation, in IEEE Power Engineering Society General Meeting, (2005), 2752–2759.
[20] Y. Shi and R. Eberhart, A modified particle swarm optimizer, in 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), (1998), 69–73.
[21] J. A. Boudreaux, Design, Simulation, and Construction of an IEEE 14-Bus Power System, 42, (2018).
Engineering and Technology Journal
Volume 39, Issue 10
Electrical Engineering
October 2021
Page 1601-1609
Files
Share
Export Citation
Statistics