access icon openaccess Research on intelligent dispatching strategy of power grid using multi-agent and knowledge discovery algorithm

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 17/07/2018, Accepted 26/07/2018, Published 16/08/2018

Renewable energy has great significance in environmental protection, economical conservation, and energy utilisation. Under the premise of making full use of available energy, the rational allocation and dispatch of distributed energy is the prerequisite for reliable and stable operation of urban power grid. According to the types of energy, this study divides into energy supply agent, energy demand agent, and energy dispatch agent, and establishes the multi-agent system (MAS) model of urban power grid. Use knowledge discovery algorithms to formulate scheduling plans to achieve economic development, environment-friendly, and other multi-objective optimisation. The simulation results show that the use of knowledge discovery algorithm to solve the MAS model for emergency dispatch of electric energy can effectively guarantee the scheduling request and energy supply of the lean areas in various situations.

Inspec keywords: power grids; multi-agent systems; scheduling; optimisation; power engineering computing; renewable energy sources; data mining; power aware computing

Other keywords: knowledge discovery algorithm; multiagent system model; rational allocation; renewable energy; multiagent algorithm; scheduling request; electric energy; multiobjective optimisation; energy supply agent; knowledge discovery algorithms; energy utilisation; energy dispatch agent; energy demand agent; intelligent dispatching strategy; urban power grid; emergency dispatch; economical conservation; MAS model; distributed energy; environmental protection

Subjects: Data handling techniques; Optimisation techniques; Knowledge engineering techniques; Optimisation techniques; Power system management, operation and economics; Power engineering computing; Power electronics, supply and supervisory circuits; Electrical/electronic equipment (energy utilisation); Performance evaluation and testing

References

    1. 1)
      • 15. Chen, C.H., Li, P.K., Chong, Y.T., et al: ‘Knowledge discovery using genetic algorithm for maritime situational awareness’, Int. J. Exp. Syst. Appl., 2014, 41, (6), pp. 2742–2753.
    2. 2)
      • 6. Yan, J.H., Zhang, F.Y., Li, X., et al: ‘Modeling and multi-objective optimization for energy-aware hybrid flow shop scheduling’, 2014.
    3. 3)
      • 14. Cabrera, F., Shin, R., Concha, D., et al: ‘Temporal knowledge discovery in big BAS data for building energy management’, Energy Build., 2015, 109, (4), pp. 75–89.
    4. 4)
      • 18. Yan, J., Yu, H., Xia, X.: ‘Distributed optimization of multi-agent systems with delayed sampled-data’, Neurocomputing, 2018, 296, (28), pp. 100–108.
    5. 5)
      • 1. Viral, R., Khatod, D.K.: ‘Optimal planning of distributed generation systems in distribution system: a review’, Renew. Sustain. Energy Rev., 2012, 16, (7), pp. 5146–5165.
    6. 6)
      • 2. Liu, S., Wu, Z., Dou, X., et al: ‘Optimal configuration of hybrid solar-wind distributed generation capacity in a grid-connected microgrid’, Innovative Smart Grid Technologies, 2013, 44, (44), pp, 1–6.
    7. 7)
      • 4. Elbasuony, G.S., Aleem, S.A., Ibrahim, A.M., et al: ‘A unified index for power quality evaluation in distributed generation systems’, Energy, 2018, 149, (15), pp. 607–622.
    8. 8)
      • 19. Cheng, Y., Chen, K., Sun, H., et al: ‘Data and knowledge mining with big data towards smart production’, J. Ind. Inf. Integr., 2018, 9, pp. 1–13.
    9. 9)
      • 3. Madruga, E.P., Bernardon, D.P., Viriea, R.P., et al: ‘Analysis of transient stability in distribution systems with distributed generation’, Electr. Power Energy Syst., 2018, 99, pp. 555–565.
    10. 10)
      • 16. Li, Z., Liu, J.: ‘A multi-agent genetic algorithm for community detection in complex networks’, Phys. A Stat. Mech. Appl., 2016, 449, pp. 336–347.
    11. 11)
      • 5. Payasi, R.P., Singh, A.K., Singh, D.: ‘Review of distributed generation planning: objectives, constraints, and algorithms’, Int. J. Eng. Sci. Technol., 2011, 3, (3), pp. 133–153.
    12. 12)
      • 7. Wang, F., Zhou, L., Ren, H., et al: ‘Multi-objective optimization model of source-load-storage synergetic dispatch for a building energy management system based on TOU price demand response’, IEEE Trans. Ind. Appl., 2018, 54, (2), pp. 1017–1028.
    13. 13)
      • 11. Cats, O., West, J., Eliasson, J.: ‘A dynamic stochastic model for evaluating congestion and crowding effects in transit systems’, Transp. Res. Part B Methodol., 2016, 89, pp. 43–57.
    14. 14)
      • 9. Li, W., Tan, X., Sun, B., et al: ‘Optimal power dispatch of a centralised electric vehicle battery charging station with renewables’, IET Commun., 2018, 12, (5), pp. 579–585.
    15. 15)
      • 17. Spychalski, P., Arendt, R.: ‘Machine learning in multi-agent systems using associative arrays’, Parallel Comput., 2018, 75, pp. 88–99.
    16. 16)
      • 10. Liang, Z., Liang, X., Yan, O.U., et al: ‘Energy integrated scheduling based on multi-objective-constrained optimization evolutionary algorithm’, Comput. Integr. Manuf. Syst., 2016, 22, (11), pp, 2668–2678.
    17. 17)
      • 8. Wang, J., Yang, W., Du, P., et al: ‘Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system’, Energy, 2018, 148, (1), pp. 59–78.
    18. 18)
      • 12. Logenthiran, T., Srinivasan, D., Khambadkone, A.M.: ‘Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system’, Electr. Power Syst. Res., 2011, 81, (1), pp. 138–148.
    19. 19)
      • 13. Russo, L.S., Bernardino, H.S., Barbosa, J.C.: ‘Knowledge discovery in multi-objective optimization problems in engineering via genetic programming’, Expert Syst. Appl., 2018, 99, (1), pp. 92–102.
    20. 20)
      • 20. Heiss-Czedik, D.: ‘An introduction to genetic algorithms’, Sadhana, 2014, 3, (1), pp. 63–65.
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