access icon free Lightning flash algorithm for solving non-convex combined emission economic dispatch with generator constraints

Due to the importance of global warming and environmental impacts that accumulated from emission of gaseous pollutants of fossil-fuelled power plants, the modern combined emission economic dispatch (CEED) is applied. This study proposes a new evolutionary lightning flash algorithm to solve dual-objective CEED problem considering different scenarios with wind power penetration, multiple fuel options and operation constraints on the generators. The lightning flash algorithm is formulated based on the movements of the cloud to ground lightning strikes in a thunderstorm. This method is tested on 11 benchmark functions and then it is applied on six different practical case study systems for solving non-convex CEED. The results of LFA on benchmark functions and the case study systems are compared with other methods and confirm the effectiveness and applicability of the proposed method with higher quality solution, less emission, less costs and better convergence against other methods for solving non-convex practical economic dispatch, CEED and dynamic dispatch problems.

Inspec keywords: wind power plants; power generation economics; evolutionary computation; power generation dispatch; concave programming; global warming; lightning protection; earthing; power generation protection

Other keywords: multiple fuel options; lightning strikes; cloud movements; global warming; environmental impacts; wind power penetration; thunderstorm; evolutionary lightning flash algorithm; dynamic dispatch problems; dual-objective CEED problem; fossil-fuelled power plants; operation constraints; gaseous pollutants emission; generator constraints; grounding; nonconvex combined emission economic dispatch; nonconvex CEED

Subjects: Power system protection; Optimisation techniques; Pollution detection and control; Power system management, operation and economics; Wind power plants

References

    1. 1)
      • 38. Mendes, O.M.Jr, Domingues, M.O.: ‘Lightning path simulation based on the stepped leader: electrical conductivity effects’, J. Atmos. Sol.-Terr. Phys., 2005, 67, pp. 12871297.
    2. 2)
      • 35. Modiri-Delshad, M., Abd Rahim, N.: ‘Multi-objective backtracking search algorithm for economic emission dispatch problem’, Appl. Soft Comput., 2016, 40, pp. 479494.
    3. 3)
      • 52. Yuan, X., Su, A., Yuan, Y.,, et al: ‘An improved PSO for dynamic load dispatch of generators with valve-point effects’, Energy, 2009, 34, (1), pp. 6774.
    4. 4)
      • 1. Nayeripour, M., Kheshti, M. (Eds.): ‘Sustainable growth and applications in renewable energy sources’ (INTECH Publication, Croatia, 2011).
    5. 5)
      • 16. Zou, D., Li, S., Li, Z., et al: ‘A new global particle swarm optimization for the economic emission dispatch with or without transmission losses’, Energy Convers. Manage., 2017, 139, pp. 4570.
    6. 6)
      • 47. Mandal, K.K., Mandal, S., Bhattacharya, B., et al: ‘Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique’, Appl. Soft Comput., 2015, 28, pp. 188195.
    7. 7)
      • 29. Gherbi, Y.A., Bouzeboudja, H., Gherbi, F.Z.: ‘The combined economic environmental dispatch using new hybrid metaheuristic’, Energy, 2016, 115, pp. 468477.
    8. 8)
      • 50. Victoire, T., Jeyakumar, V.: ‘A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect’, Electr. Power Energy Syst., 2005, 27, (8), pp. 594601.
    9. 9)
      • 49. Attaviriyanupap, P., Kita, H., Tanaka, E., et al: ‘A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function’, IEEE Trans. Power Syst., 2002, 17, (2), pp. 411416.
    10. 10)
      • 14. Basu, M.: ‘Economic environmental dispatch of fixed head hydrothermal power systems using nondominated sorting genetic algorithm-II’, Appl. Soft Comput., 2011, 11, pp. 30463055.
    11. 11)
      • 28. Zhang, R., Zhou, J., Mo, L., et al: ‘Economic environmental dispatch using an enhanced multi-objective cultural algorithm’, Electr. Power Syst. Res., 2013, 99, pp. 1829.
    12. 12)
      • 13. Lu, S.Y., Lou, S.H., Wu, Y.W., et al: ‘Power system economic dispatch under low-carbon economy with carbon capture plants considered’, IET Gener. Transm. Distrib., 2013, 7, (9), pp. 9911001.
    13. 13)
      • 18. Jebaraj, L., Venkatesan, C., Soubache, I., et al: ‘Application of differential evolution algorithm in static and dynamic economic or emission dispatch problem: a review’, Renew. Sustain. Energy Rev., 2017, 77, pp. 12061220.
    14. 14)
      • 41. Meng, A., Li, J., Yin, H.: ‘An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects’, Energy, 2016, 113, pp. 11471161.
    15. 15)
      • 51. Victoire, T, Jeyakumar, A.: ‘Deterministically guided PSO for dynamic dispatch considering valve-point effect’, Electr. Power Syst. Res., 2005, 37, (3), pp. 313322.
    16. 16)
      • 39. Uman, M.A.: ‘The lightning discharge’ (Elsevier, 1987), ch 5, vol. 39, pp. 8298, doi:10.1016/S0074–6142(08)60271-5.
    17. 17)
      • 24. Shilaja, C., Ravi, K.: ‘Optimization of emission/economic dispatch using Euclidean affine flower pollination algorithm (eFPA) and binary FPA (BFPA) in solar photo voltaic generation’, Renew. Energy, 2017, 107, pp. 550566.
    18. 18)
      • 9. Dillon, J.S., Dillon, J.S., Kothari, D.P.: ‘Economic-emission load dispatch using binary successive approximation-based evolutionary search’, IET Gener. Transm. Distrib., 2009, 3, (1), pp. 116.
    19. 19)
      • 31. Thang, T.N., Dieu, N.V.: ‘The application of one rank cuckoo search algorithm for solving economic load dispatch problems’, Appl. Soft Comput., 2015, 37, pp. 763773.
    20. 20)
      • 34. Azizipanah-Abarghooee, R., Dehghanian, P., Terzija, V.: ‘Practical multi-area bi-objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm’, IET Gener. Transm. Distrib., 2016, 10, (14), pp. 35803596.
    21. 21)
      • 27. Manteaw, E.D., Odero, N.A.: ‘Combined economic and emission dispatch solution using ABC_PSO hybrid algorithm with valve point loading effect’, Int. J. Sci. Res. Publ., 2012, 2, (12), pp. 19.
    22. 22)
      • 19. Qu, B.Y., Liang, J.J., Zhu, Y.S., et al: ‘Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm’, Inf. Sci., 2016, 351, pp. 4866.
    23. 23)
      • 53. Pandi, V.R., Panigrahi, B.K..: ‘Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm’, Expert Syst. Appl., 2011, 38, pp. 85098514.
    24. 24)
      • 43. Wang, Y., Li, B., Weise, T.: ‘Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems’, Inf. Sci., 2010, 180, pp. 24052420.
    25. 25)
      • 5. Kheshti, M., Kang, X., Song, G.,, et al: ‘Modeling and fault analysis of doubly fed induction generators for gansu wind farm application’, Can. J. Electr. Comput. Eng., 2015, 38, (1), pp. 5264.
    26. 26)
      • 20. Panigrahi, B.K., Pandi, V.R., Sharma, R., et al: ‘Multiobjective bacteria for-aging algorithm for electrical load dispatch problem’, Energy Convers. Manage., 2011, 52, (2), pp. 13341342.
    27. 27)
      • 23. Abdelaziz, A.Y., Ali, E.S., Abd Elazim, S.M.: ‘Flower pollination algorithm to solve combined economic and emission dispatch problems’, J. Eng. Sci. Technol., 2016, 19, (2), pp. 980990.
    28. 28)
      • 30. Vo, D.N., Schegner, P., Ongsakul, W.: ‘Cuckoo search algorithm for non-convex economic dispatch’, IET Gener. Transm. Distrib., 2013, 7, pp. 645654.
    29. 29)
      • 3. Kheshti, M., Nayeripour, M., Majidpour, M.D.: ‘Fuzzy dispatching of solar energy in distribution system’, Appl. Sol. Energy, 2011, 47, (2), pp. 105111.
    30. 30)
      • 6. Wei, W., Liu, F., Wang, J., et al: ‘Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants’, Appl. Energy, 2016, 183, pp. 674684.
    31. 31)
      • 11. Yang, Z., Li, K., Niu, Q.: ‘A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads’, J. Mod. Power Syst. Clean Energy, 2014, 2, (4), pp. 298307.
    32. 32)
      • 26. Güvenç, U., Sonmez, Y., Duman, S., et al: ‘Combined economic and emission dispatch solution using gravitational search algorithm’, Sci. Iranica D, Comput. Sci. Eng. Electr. Eng., 2012, 19, (6), pp. 17541762.
    33. 33)
      • 17. Bhattacharya, A., Chattopadhyay, P.K.: ‘Solving economic emission load dispatch problems using hybrid differential evolutions’, Appl. Soft Comput., 2011, 11, (2), pp. 25262537.
    34. 34)
      • 36. Ghasemi, A., Gheydi, M., Golkar, M.J., et al: ‘Modeling of wind/environment/economic dispatch in power system and solving via an online learning meta-heuristic method’, Appl. Soft Comput., 2016, 43, pp. 454468.
    35. 35)
      • 12. Li, J., Wen, J., Han, X.: ‘Low-carbon unit commitment with intensive wind power generation and carbon capture power plant’, J. Mod. Power Syst. Clean Energy, 2015, 3, (1), pp. 6371.
    36. 36)
      • 40. Shareef, H., Ibrahim, A.A., Mutlag, A.H.: ‘Lightning search algorithm’, Appl. Soft Comput., 2015, 36, pp. 315333.
    37. 37)
      • 33. Zhou, J., Wang, C., Li, Y., et al: ‘A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security’, Appl. Math. Model., 2017, 45, pp. 684704.
    38. 38)
      • 44. Bhattacharjee, K., Bhattacharya, A., Dey, S.H.N.: ‘Oppositional real coded chemical reaction optimization for different economic dispatch problems’, Int. J. Electr. Power Energy Syst., 2014, 55, pp. 378391.
    39. 39)
      • 10. Niu, Q., Zhang, H.Y., Wang, X.H., et al: ‘A hybrid harmony search with arithmetic crossover operation for economic dispatch’, Int. J. Electr. Power Energy Syst., 2014, 62, pp. 237257.
    40. 40)
      • 25. Basu, M.: ‘Economic environmental dispatch using multi-objective differential evolution’, Appl. Soft Comput., 2011, 11, pp. 28452853.
    41. 41)
      • 46. Jadhav, H.T, Bhatia, M., Roy, R.: ‘An application of craziness based shuffled frog leaping algorithm for wind-thermal generation dispatch considering emission and economy’. 10th Int. Conf. on Environment and Electrical Engineering, Rome, Italy, 8–11 May 2011, pp. 14.
    42. 42)
      • 8. Kheshti, M., Tekpeti, B.S., Kang, X.: ‘A study on hybrid solar thermal and geothermal power plant as a key development for shaanxi province in China’. IEEE PES Asia-Pacific Power and Energy Engineering Conf. 2016, Xi'an, China, 25–28 October 2016, pp. 369373.
    43. 43)
      • 48. Gaing, Z.L..: ‘Particle swarm optimization to solving the economic dispatch considering the generator constraints’, IEEE Trans. Power Syst., 2003, 18, pp. 11871195.
    44. 44)
      • 37. Sarajčev, I., Sarajčev, P., Vujević, S.: ‘Mathematical model of lightning stroke development’. 16th Int. Conf. on Telecommunications and Computer Networks, Split, 2008, pp. 3741.
    45. 45)
      • 21. Chatterjee, A., Ghoshal, S.P., Mukherjee, V.: ‘Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm’, Int. J. Electr. Power Energy Syst., 2012, 39, pp. 920.
    46. 46)
      • 45. Bhattacharjee, K., Bhattacharya, A., Dey, S.H.N.: ‘Chemical reaction optimisation for different economic dispatch problems’, IET Gener. Transm. Distrib., 2014, 8, pp. 530541.
    47. 47)
      • 15. Wang, L., Singh, C.: ‘Reserve constrained multi-area environmental/economic dispatch based on particle swarm optimization with local search’, Eng. Appl. Artif. Intell., 2009, 22, pp. 298307.
    48. 48)
      • 42. Chiang, C.L.: ‘Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels’, IEEE Trans. Power Syst., 2005, 20, pp. 16901699.
    49. 49)
      • 22. Abdelaziz, A.Y., Ali, E.S., Abd Elazim, S.M.: ‘Combined economic and emission dispatch solution using flower pollination algorithm’, Int. J. Electr. Power Energy Syst., 2016, 80, pp. 264274.
    50. 50)
      • 4. Pandit, M., Srivastava, L., Sharma, M.: ‘Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection’, Appl. Soft Comput., 2015, 28, pp. 498510.
    51. 51)
      • 2. Nayeripour, M., Kheshti, M. (Eds.): ‘Renewable energy-trends and applications’ (INTECH Publication, Croatia, 2011).
    52. 52)
      • 32. Nguyen, T.T., Vo, D.N.: ‘An efficient cuckoo bird inspired meta-heuristic algorithm for short-term combined economic emission hydrothermal scheduling’, Ain Shams Eng. J., 2016, doi: 10.1016/j.asej.2016.04.003.
    53. 53)
      • 7. Kheshti, M., Kang, X.: ‘Geothermal energy, a key point in energy and development in China’. The 4th IET Renewable Power Generation Conf., Beijing, China, October 2015, pp. 1718.
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