During the last few years, greenhouse gas emission especially from electric power generation is a major concern due to the global warming and environmental change, therefore, committing the generating units on minimum cost criterion is shifting toward minimising the cost with minimum emission. Due to the conflicting nature of economic and emission objectives, the generation scheduling becomes a multi-objective problem. In this study, a weighted sum method is applied to convert multi-objective problem to a single-objective problem by linear combination of different objectives as a weighted sum and an efficient hybrid algorithm is presented for aiding unit commitment (UC) decisions in such environments. Due to uncertainty of wind power generation, the UC problem has become complex. To handle uncertainty, scenario generation and reduction techniques are used. The proposed hybrid approach is a combination of weighted improved crazy particle swarm optimisation with pseudo code algorithm, which is enhanced by extended priority list to handle the spinning reserve constraints and a heuristic search algorithm to handle minimum up/down time constraints. Simulation results confirm the potential and effectiveness of proposed approach after comparison with other methods reported in the literature.

References

1. 1)
  - 6. Pappala, V.S., Elrich, I.: ‘A new approach solving unit commitment problem by adaptive particle swarm optimization’. Proc. IEEE PES General Meeting, Pittsburgh, PA, 2008, pp. 1–6.
2. 2)
  - 29. Ratnaweera, A., Halgamuge, S., Watson, H.: ‘Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients’, IEEE Trans. Evol. Comput., 2004, 8, (3), pp. 240–255 (doi: 10.1109/TEVC.2004.826071).
3. 3)
  - 32. Saber, A.Y., Veneyagamoorthy, G.K.: ‘Efficient utilization of renewable energy sources by gridable vehicles in cyber-physical energy systems’, IEEE Syst. J., 2010, 4, (3), pp. 285–294 (doi: 10.1109/JSYST.2010.2059212).
4. 4)
  - 16. Abido, M.A.: ‘Environmental/economic power dispatch using multiobjective evolutionary algorithm’, IEEE Trans. Power Syst., 2003, 18, (4), pp. 1529–1537 (doi: 10.1109/TPWRS.2003.818693).
5. 5)
  - 11. Yen, L.T.X., Sharma, D., Srinivasan, D., et al: ‘A modified hybrid particle swarm optimization approach for unit commitment’. IEEE Proc. Int. Conf. on Evolutionary Computation, 2011, pp. 1738–1745.
6. 6)
  - 14. Balci, H.H., Valenzuela, J.F.: ‘Scheduling electrical power generators using particle swarm optimization combined with the Lagrangian relaxation method’, Int. J. Appl. Math. Comput. Sci., 2004, 14, (3), pp. 411–421.
7. 7)
  - 3. Chen, C.L., Wang, S.C.: ‘Branch-and-bound scheduling for thermal generating units’, IEEE Trans. Energy Convers., 1993, 8, (2), pp. 184–189 (doi: 10.1109/60.222703).
8. 8)
  - 13. Senjyu, T., Yamashiro, H., Uezato, K., et al: ‘A unit commitment problem by using genetic algorithm based on characteristic classification’, IEEE/Power Eng. Soc. Winter Meet., 2002, 1, pp. 58–63 (doi: 10.1109/PESW.2002.984954).
9. 9)
  - 8. Eslamian, M., Hosseinian, S.H., Vahidi, B.: ‘Bacterial foraging based solution to the unit commitment problem’, IEEE Trans. Power Syst., 2009, 24, (3), pp. 1478–1488 (doi: 10.1109/TPWRS.2009.2021216).
10. 10)
  - 24. Kennedy, J., Eberhart, C.R.: ‘Particle swarm optimization’. IEEE Proc. Int. Conf. on Neural Networks, 1995, vol. 4, pp. 1942–1948.
11. 11)
  - 2. El-Keib, A.A., Ma, H., Hart, J.L.: ‘Economic dispatch in view of the clean air act of 1990’, IEEE Trans. Power Syst., 1994, 9, (2), pp. 972–978 (doi: 10.1109/59.317648).
12. 12)
  - 10. Zhao, B., Guo, C.X., Bai, B.R., et al: ‘Improved particle swarm optimization algorithm for unit commitment’, Int. J. Electric. Power Energy Syst., 2006, 28, (7), pp. 482–490 (doi: 10.1016/j.ijepes.2006.02.011).
13. 13)
  - 31. Yenjay, O.: ‘Penalty function methods for constrained optimization with genetic algorithms’, J. Math. Comput. Appl., 2005, 10, (1), pp. 45–56.
14. 14)
  - 4. Virmani, S., Adrian, C., Imhof, K., et al: ‘Implementation of a Lagrangian relaxation based unit commitment problem’, IEEE Trans. Power Syst., 1989, 4, (4), pp. 1373–1380 (doi: 10.1109/59.41687).
15. 15)
  - 21. Sumaili, J., Keko, H., Miranda, V., et al: ‘Clustering-based wind power scenario reduction technique’. 17th Power Systems Computation Conf., Stockholm Sweden, 2011.
16. 16)
  - 18. Khokhar, B., Parmar, K.P.S.: ‘A novel weight-improved particle swarm optimization for combined economic and emission dispatch problem’, Int. J. Eng. Sci. Technol., 2012, 4, (5), pp. 2015–2021.
17. 17)
  - 7. Bakirtzis, A.G., Petridis, V.: ‘A genetic algorithm solution to the unit commitment problem’, IEEE Trans. Power Syst., 1996, 11, (1), pp. 83–92.
18. 18)
  - 1. Zhang, X.-P.: ‘Restructured electrical power systems: analysis of electricity market with equilibrium models’ (John Wiley & Sons, New Jersey, 2010), p. 307.
19. 19)
  - 19. Dupačová, J., Gröwe-Kuska, N., Römisch, W.: ‘Scenario reduction in stochastic programming: an approach using probability metrics’, Math. Program A, 2003, 95, pp. 493–511 (doi: 10.1007/s10107-002-0331-0).
20. 20)
  - 17. Wang, L., Singh, C.: ‘Environmental/EPD using fuzzified multiobjective particle swarm optimization algorithm’, Electr. Power Syst. Res., 2007, 77, (12), pp. 1654–1664 (doi: 10.1016/j.epsr.2006.11.012).
21. 21)
  - 23. He, D., Tan, Z., Harley, R.G.: ‘Chance constrained unit commitment with wind generation and superconducting magnetic energy storages’. IEEE Power & Energy Society General Meeting, San Diego, 2012, pp. 1–6.
22. 22)
  - 6. Pappala, V.S., Elrich, I.: ‘A new approach solving unit commitment problem by adaptive particle swarm optimization’. Proc. IEEE PES General Meeting, Pittsburgh, PA, 2008, pp. 1–6.
23. 23)
  - 12. Juste, K.A., Kiat, H., Tanaka, E., et al: ‘An evolutionary programming solution to the unit commitment problem’, IEEE Trans. Power Syst., 1999, 14, (4), pp. 1452–1459 (doi: 10.1109/59.801925).
24. 24)
  - 26. Selvakumar, A.I., Thanushkodi, K.: ‘A new particle swarm optimization solution to non-convex economic dispatch problem’, IEEE Trans. Power Syst., 2007, 2, (1), pp. 1273–1282.
25. 25)
  - 27. meng, Y., Yan, S., Tang, Z., et al: ‘Data fusion based on neural network and particle swarm algorithm and its application in sugar boiling’. 5th Int. Symp. on Neural Network, China, 2008, pp. 176–185.
26. 26)
  - 5. Rahimi, S., Niknam, T., Fallahi, F.: ‘A new approach based on benders decomposition for unit commitment problem’, J. World Appl. Sci., 2009, 6, (12), pp. 1665–1672.
27. 27)
  - 22. Shukla, A., Singh, S.N.: ‘Cluster based wind-hydro-thermal unit commitment using GSA Algorithm’. IEEE Power & Energy Society General Meeting, Washington DC, USA, July 2014, pp. 1–5.
28. 28)
  - 9. Vaisakh, K., Srinivas, L.R.: ‘Evolving ant colony optimization based unit commitment’, J. Appl. Soft Comput., 2011, 11, (2), pp. 2863–2870 (doi: 10.1016/j.asoc.2010.11.019).
29. 29)
  - 15. Chandrasekaran, K., Hemamalini, S., Simon, S.P., et al: ‘Thermal unit commitment using binary/real coded artificial bee colony algorithm’, Electr. Power Syst. Res., 2012, 84, pp. 109–119 (doi: 10.1016/j.epsr.2011.09.022).
30. 30)
  - 30. Shukla, A., Singh, S.N.: ‘Pseudo-inspired PSO for solving unit commitment problem including renewable energy sources’. Fifth Int. Conf. on Power and Energy Systems, Kathmandu, Nepal, October 2013, pp. 1–6.
31. 31)
  - 25. Aruldoss, T., Victoire, A., Jeyakumar, A.E.: ‘Reserve constrained dynamic dispatch of units with valve point effects’, IEEE Trans. Power Syst., 2005, 20, (3), pp. 1273–1282 (doi: 10.1109/TPWRS.2005.851958).
32. 32)
  - 28. Suganthan, P.N.: ‘Particle swarm optimizer with neighborhood operator’, IEEE Int. Congr. Evol. Comput., Washington DC, USA, July 1999, (3), pp. 1958–1962.
33. 33)
  - 20. Bin, J., Xiaohui, Y., Chen, Z., et al: ‘Improved gravitational search algorithm for unit commitment considering of wind power’, J. Energy, 2014, 67, (1), pp. 52–62.

Multi-objective unit commitment with renewable energy using hybrid approach

References

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