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

© The Institution of Engineering and Technology
Received 29/07/2017, Accepted 02/03/2018, Revised 22/01/2018, Published 08/03/2018

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.

Inspec keywords: power system economics; load dispatching; decentralised control; multi-agent systems; protocols; telecommunication channels; environmental factors; telecommunication networks; smart power grids; power engineering computing; distributed algorithms

Other keywords: smart grid; power production process; multiagent system; fossil fuel; IEEE 30 bus test system; cost reduction; gaseous pollutant emission; dynamic communication network; coordinated consensus protocol; randomly communication failure; CEEDP; IEEE 118-bus test system; combined economic environmental dispatch problem; distributed algorithm; central energymanagement unit; transmission delay; communication channel; decentralised consensus protocol

Subjects: Power engineering computing; Protocols; Expert systems and other AI software and techniques; Power system management, operation and economics

References

    1. 1)
      • 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. 4416–4426.
    2. 2)
      • 31. Boyd, S., Ghosh, A., Prabhakar, B., et al: ‘Randomized gossip algorithms’, IEEE Trans. Inf. Theory, 2006, 52, (6), pp. 2508–2530.
    3. 3)
      • 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. 920–934.
    4. 4)
      • 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. 595–606.
    5. 5)
      • 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. 1–6.
    6. 6)
      • 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. 355–369.
    7. 7)
      • 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. 1–6.
    8. 8)
      • 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. 243–250.
    9. 9)
      • 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. 1711–1720.
    10. 10)
      • 32. Olfati-Sabe, R., Fax, J.A., Murray, R.M.: ‘Consensus and cooperation in networked multi-agent systems’, Proc. IEEE, 2007, 95, (1), pp. 215–233.
    11. 11)
      • 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. 1175–1183.
    12. 12)
      • 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. 1–6.
    13. 13)
      • 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. 1761–1768.
    14. 14)
      • 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. 1–10.
    15. 15)
      • 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. 1–8.
    16. 16)
      • 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. 2730–2735.
    17. 17)
      • 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. 9048–9053.
    18. 18)
      • 14. Ren, W., Bead, R.W.: ‘Distributed consensus in multi-vehicle cooperative control: theory and applications’ (Springer, 2008).
    19. 19)
      • 3. Maria, L.T., Michael, L.A.: ‘A review of the development of smart grid technologies’, Renew. Sustain. Energy Rev., 2016, 59, pp. 710–725.
    20. 20)
      • 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. 2511–2522.
    21. 21)
      • 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. 315–324.
    22. 22)
      • 10. Malik, F.H., Lehtonen, M.: ‘A review: agents in smart grids’, Electr. Power Syst. Res., 2016, 131, pp. 71–79.
    23. 23)
      • 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. 34–51.
    24. 24)
      • 35. Zhou, J., Wang, Q.: ‘Convergence speed in distributed consensus over dynamically switching random networks’, Automatica, 2009, 45, (6), pp. 1455–1461.
    25. 25)
      • 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. 3341–3346.
    26. 26)
      • 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. 607–614.
    27. 27)
      • 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. 978–992.
    28. 28)
      • 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. 396–405.
    29. 29)
      • 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. 1968–1973.
    30. 30)
      • 4. Chowdhury, B.H., Rahman, S.: ‘A review of recent advances in economic dispatch’, IEEE Trans. Power Syst., 1990, 5, (4), pp. 1248–1259.
    31. 31)
      • 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. 597–610.
    32. 32)
      • 42. Modiri-Delshad, M., AbdRahim, N.: ‘Multi-objective backtracking search algorithm for economic emission dispatch problem’, Appl. Soft Comput.., 2016, 40, pp. 479–494.
    33. 33)
      • 6. Bakirtzis, A., Petridis, V., Kazarlis, S.: ‘Genetic algorithm solution to the economic dispatch problem’, IEEE Proc. Gener. Transm. Distrib., 1994, 141, (4), pp. 377–382.
    34. 34)
      • 5. Wood, A.J., Wollenberg, B.F., Sheble, G.B.: ‘Power generation, operation and control’ (Wiley-IEEE, Hoboken, NJ, USA, 2013, 3rd edn.).
    35. 35)
      • 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. 545–560.
    36. 36)
      • 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. 2179–2189.
    37. 37)
      • 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. 1–8.
    38. 38)
      • 37. Capriglione, D., Ferrigno, L., Paciello, V., et al: ‘Experimental characterization of consensus protocol for decentralized smart grid metering’, Measurement, 2016, 77, pp. 292–306.
    39. 39)
      • 7. Gaing, Z.L.: ‘Particle swarm optimization to solving the economic dispatch considering the generator constraints’, IEEE Trans. Power Syst., 2003, 18, (3), pp. 1187–1195.
    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. 417–429.
    41. 41)
      • 11. Vincenzo, L., Stefania, T., Alfredo, V.: ‘Using fuzzy transform in multi-agent based monitoring of smart grids’, Inf. Sci., 2017, 388–389, pp. 209–224.
    42. 42)
      • 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. 392–406.
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