Coordinated consensus for smart grid economic environmental power dispatch with dynamic communication network

Coordinated consensus for smart grid economic environmental power dispatch with dynamic communication network

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Combined economic environmental dispatch problem (CEEDP) is one of the greatest challenges of the future smart grids. It aims at reducing the total cost during the power production process considering the growing environmental impact due to the emission of gaseous pollutants of fossil fuels. This study develops a robust distributed algorithm based on consensus protocols in multi-agent systems, to solve the smart grid CEEDP with a practical communication network consisting of a dynamic communication network, randomly communication failure, transmission delay and noise in communication channels. The proposed algorithm is fully distributed and cooperative in such a way that it eliminates the need for a central energy-management unit, or a leader. The performance of the fully decentralised consensus protocol was evaluated on the IEEE 30-bus and the IEEE 118-bus test system. A comparison with previous consensus algorithms proves the supremacy of the proposed approach in terms of its robustness under dynamic communication network with randomly link failure.


    1. 1)
      • 1. Zahurul, S., Mariun, N., Grozescu, I.V., et al: ‘Future strategic plan analysis for integrating distributed renewable generation to smart grid through wireless sensor network: Malaysia prospect’, Renew. Sustain. Energy Rev., 2016, 53, pp. 978992.
    2. 2)
      • 2. Colak, I., Sagiroglu, S., Fulli, G., et al: ‘A survey on the critical issues in smart grid technologies’, Renew. Sustain. Energy Rev., 2016, 54, pp. 396405.
    3. 3)
      • 3. Maria, L.T., Michael, L.A.: ‘A review of the development of smart grid technologies’, Renew. Sustain. Energy Rev., 2016, 59, pp. 710725.
    4. 4)
      • 4. Chowdhury, B.H., Rahman, S.: ‘A review of recent advances in economic dispatch’, IEEE Trans. Power Syst., 1990, 5, (4), pp. 12481259.
    5. 5)
      • 5. Wood, A.J., Wollenberg, B.F., Sheble, G.B.: ‘Power generation, operation and control’ (Wiley-IEEE, Hoboken, NJ, USA, 2013, 3rd edn.).
    6. 6)
      • 6. Bakirtzis, A., Petridis, V., Kazarlis, S.: ‘Genetic algorithm solution to the economic dispatch problem’, IEEE Proc. Gener. Transm. Distrib., 1994, 141, (4), pp. 377382.
    7. 7)
      • 7. Gaing, Z.L.: ‘Particle swarm optimization to solving the economic dispatch considering the generator constraints’, IEEE Trans. Power Syst., 2003, 18, (3), pp. 11871195.
    8. 8)
      • 8. Gong, D.W., Zhang, Y., Qi, C.L.: ‘Environmental/economic power dispatch using a hybrid multi-objective optimization algorithm’, Int. J. Electr. Power Energy Syst., 2010, 32, (6), pp. 607614.
    9. 9)
      • 9. Lu, Y., Zhou, J., Qin, H., et al: ‘Environmental/economic dispatch problem of power system by using an enhanced multi-objective differential evolution algorithm’, Energy Convers. Manag., 2011, 52, (2), pp. 11751183.
    10. 10)
      • 10. Malik, F.H., Lehtonen, M.: ‘A review: agents in smart grids’, Electr. Power Syst. Res., 2016, 131, pp. 7179.
    11. 11)
      • 11. Vincenzo, L., Stefania, T., Alfredo, V.: ‘Using fuzzy transform in multi-agent based monitoring of smart grids’, Inf. Sci., 2017, 388–389, pp. 209224.
    12. 12)
      • 12. Pipattanasomporn, M., Feroze, H., Rahman, S.: ‘Multi-agent systems in a distributed smart grid: design and implementation’. Proc. Int. Conf. Power Systems, Seattle, WA, USA, April 2009, pp. 18.
    13. 13)
      • 13. Zhonghe, H., Yangzhou, C., Jianjun, S., et al: ‘Consensus based approach to the signal control of urban traffic networks’, Proc. Soc. Behav. Sci., 2013, 96, pp. 25112522.
    14. 14)
      • 14. Ren, W., Bead, R.W.: ‘Distributed consensus in multi-vehicle cooperative control: theory and applications’ (Springer, 2008).
    15. 15)
      • 15. Kar, S., Moura, J.M.F.: ‘Distributed consensus algorithms in sensor networks with imperfect communication: link failures and channel noise’, IEEE Trans. Signal Process, 2009, 57, (1), pp. 355369.
    16. 16)
      • 16. Xing, H., Lin, Z., Fu, M.: ‘An ADMM + consensus based distributed algorithm for dynamic economic power dispatch in smart grid’. Proc. Int. Conf. Chinese Control Conf., TBD Hangzhou, China, 2015, pp. 90489053.
    17. 17)
      • 17. Roche, R., Blunier, B., Miraoui, A., et al: ‘Multi-agent systems for grid energy management: A short review’. Proc. Int. Conf. IEEE Industrial Electronics Society, Glendale, AZ, USA, 2010, pp. 33413346.
    18. 18)
      • 18. Zhabelova, G., Vyatkin, V., Zhang, Z., et al: ‘Agent-based distributed consensus algorithm for decentralized economic dispatch in smart grid’. Proc. Int. Conf. IEEE Industrial Electronics Society, Vienna, Austria, 2013, pp. 19681973.
    19. 19)
      • 19. Yu, W., Li, C., Yu, X., et al: ‘Distributed consensus strategy for economic power dispatch in a smart grid’. Proc. Int. Conf. Control Conf., Kota Kinabalu, Malaysia, 2015, pp. 16.
    20. 20)
      • 20. Zhang, Z., Chow, M.Y.: ‘Incremental cost consensus algorithm in a smart grid environment’. Proc. Int. Conf. Power and Energy Society General Meeting, Detroit, MI, USA, 2011, pp. 16.
    21. 21)
      • 21. Zhang, Z., Chow, M.Y.: ‘The leader election criterion for decentralized economic dispatch using incremental cost consensus algorithm’. Proc. Int. Conf. IEEE Industrial Electronics Society, Melbourne, Australia, November 2011, pp. 27302735.
    22. 22)
      • 22. Yang, S., Tan, S., Xu, J.X.: ‘Consensus based approach for economic dispatch problem in a smart grid’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 44164426.
    23. 23)
      • 23. Binetti, G., Davoudi, A., Lewis, F.L., et al: ‘Distributed consensus-based economic dispatch with transmission losses’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 17111720.
    24. 24)
      • 24. Elsayed, W.T., El-Saadany, E.F.: ‘A fully decentralized approach for solving the economic dispatch problem’, IEEE Trans. Power Syst., 2014, 30, (4), pp. 21792189.
    25. 25)
      • 25. Zhang, Z., Chow, M.: ‘Convergence analysis of the incremental cost consensus algorithm under different communication network topologies in a smart grid’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 17611768.
    26. 26)
      • 26. Kars, S., Hugh, G.: ‘Distributed robust economic dispatch in power systems: A consensus + innovations approach’. Proc. Int. Conf. Power and Energy Society General Meeting., San Diego, CA, USA, 2012, pp. 18.
    27. 27)
      • 27. Zhang, Y., Rahbari-Asr, N., Chow, M.Y.: ‘A robust distributed system incremental cost estimation algorithm for smart grid economic dispatch with communications information losses’, J. Netw. Comput. Appl., 2016, 59, pp. 315324.
    28. 28)
      • 28. Zhang, Z., Rahbari-Asr, N., Chow, M.: ‘Asynchronous distributed cooperative energy management through gossip-based incremental cost consensus algorithm’. Proc. Int. Conf. North American Power Symp., Manhattan, KS, USA, 2013, pp. 16.
    29. 29)
      • 29. Zhang, X., Xu, H., Yu, T., et al: ‘Robust collaborative consensus algorithm for decentralized economic dispatch with a practical communication network’, Electr. Power Syst. Res., 2016, 140, pp. 597610.
    30. 30)
      • 30. Zhang, X., Yu, T., Yang, B., et al: ‘Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid’, Energy, 2016, 101, pp. 3451.
    31. 31)
      • 31. Boyd, S., Ghosh, A., Prabhakar, B., et al: ‘Randomized gossip algorithms’, IEEE Trans. Inf. Theory, 2006, 52, (6), pp. 25082530.
    32. 32)
      • 32. Olfati-Sabe, R., Fax, J.A., Murray, R.M.: ‘Consensus and cooperation in networked multi-agent systems’, Proc. IEEE, 2007, 95, (1), pp. 215233.
    33. 33)
      • 33. Stefania, T., Matteo, G., Vincenzo, L.: ‘Quasi–consensus in second–order multi–agent systems with sampled data through fuzzy transform’, J. Uncertain Syst., 2016, 10, (4), pp. 243250.
    34. 34)
      • 34. Yujuan, W., Yongduan, S., Miroslav, K., et al: ‘Adaptive finite time coordinated consensus for high-order multi-agent systems: adjustable fraction power feedback approach’, Inf. Sci., 2016, 372, pp. 392406.
    35. 35)
      • 35. Zhou, J., Wang, Q.: ‘Convergence speed in distributed consensus over dynamically switching random networks’, Automatica, 2009, 45, (6), pp. 14551461.
    36. 36)
      • 36. Ming, P., Liu, J., Tan, S., et al: ‘Consensus stabilization in stochastic multi-agent systems with Markovian switching topology, noises and delay’, Neurocomputing, 2015, 200, pp. 110.
    37. 37)
      • 37. Capriglione, D., Ferrigno, L., Paciello, V., et al: ‘Experimental characterization of consensus protocol for decentralized smart grid metering’, Measurement, 2016, 77, pp. 292306.
    38. 38)
      • 38. Liu, S., Xie, L., Zhang, H.: ‘Distributed consensus for multi-agent systems with delays and noises in transmission channels’, Automatica, 2011, 47, (5), pp. 920934.
    39. 39)
      • 39. Rajan, A., Malakar, T.: ‘Electrical power and energy systems optimum economic and emission dispatch using exchange market algorithm’, Int. J. Electr. Power Energy Syst., 2016, 82, pp. 545560.
    40. 40)
      • 40. Blaze, G., Marko, C.: ‘A multi-objective optimization based solution for the combined economic-environmental power dispatch problem’, Eng. Appl. Artif. Intell., 2013, 26, (1), pp. 417429.
    41. 41)
      • 41. Younes, M., Khodja, F., Kherfane, R.L.: ‘Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration’, Energy, 2014, 67, pp. 595606.
    42. 42)
      • 42. Modiri-Delshad, M., AbdRahim, N.: ‘Multi-objective backtracking search algorithm for economic emission dispatch problem’, Appl. Soft Comput.., 2016, 40, pp. 479494.

Related content

This is a required field
Please enter a valid email address