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Fast learning optimiser for real-time optimal energy management of a grid-connected microgrid

Fast learning optimiser for real-time optimal energy management of a grid-connected microgrid

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This study proposes a novel fast learning optimiser (FLO) for real-time optimal energy management (OEM) of a grid-connected microgrid. To reduce the optimisation difficulty, the non-convex real-time OEM is decomposed into a two-layer optimisation. The non-convex top-layer optimisation is responsible to determine the direction of tie-line power, and the heat energy outputs of combined heat and power units. Then bottom-layer optimisation is strictly convex with the rest controllable variables, which is solved by the classical interior point method. The model-free Q-learning is employed for knowledge learning and decision making in the top-layer optimisation, thus the feedback reward from the bottom-layer optimisation can effectively realise a coordination between them. The real-coded associative memory is presented for a more efficient optimisation of continuous controllable variables. In order to dramatically reduce the execution time, the knowledge transfer is adopted for approximating the optimal knowledge matrices of a real-time new task by abstracting the optimal knowledge matrices of the predictive source tasks. Simulation results demonstrates that the proposed FLO can rapidly search a high-quality optimum of real-time OEM, in which the computation rate is about 2.75–29.23 times faster than that of eight classical heuristic algorithms.

References

    1. 1)
      • M.S. Mahmoud , M.S.U. Rahman , F.M.A.L. Sunni .
        1. Mahmoud, M.S., Rahman, M.S.U., Sunni, F.M.A.L.: ‘Review of microgrid architectures—a system of systems perspective’, IET Renew. Power Gener., 2015, 9, (8), pp. 10641078.
        . IET Renew. Power Gener. , 8 , 1064 - 1078
    2. 2)
      • A. Anvari-Moghaddam , J.M. Guerrero , J.C. Vasquez .
        2. Anvari-Moghaddam, A., Guerrero, J.M., Vasquez, J.C., et al: ‘Efficient energy management for a grid-tied residential microgrid’, IET Gener. Transm. Distrib., 2017, 11, (11), pp. 27522761.
        . IET Gener. Transm. Distrib. , 11 , 2752 - 2761
    3. 3)
      • S. Mazzola , M. Astolfi , E. Macchi .
        3. Mazzola, S., Astolfi, M., Macchi, E.: ‘A detailed model for the optimal management of a multigood microgrid’, Appl. Energy, 2015, 154, pp. 862873.
        . Appl. Energy , 862 - 873
    4. 4)
      • Y. Riffonneau , S. Bacha , F. Barruel .
        4. Riffonneau, Y., Bacha, S., Barruel, F., et al: ‘Optimal power flow management for grid connected PV systems with batteries’, IEEE Trans. Sustain. Energy, 2011, 2, (3), pp. 309320.
        . IEEE Trans. Sustain. Energy , 3 , 309 - 320
    5. 5)
      • M. Basu , A. Chowdhury .
        5. Basu, M., Chowdhury, A.: ‘Cuckoo search algorithm for economic dispatch’, Energy, 2013, 60, pp. 99108.
        . Energy , 99 - 108
    6. 6)
      • A. Vasebi , M. Fesanghary , S.M.T. Bathaee .
        6. Vasebi, A., Fesanghary, M., Bathaee, S.M.T.: ‘Combined heat and power economic dispatch by harmony search algorithm’, Int. J. Electric. Power Energy Syst., 2007, 29, (10), pp. 713719.
        . Int. J. Electric. Power Energy Syst. , 10 , 713 - 719
    7. 7)
      • C. Chen , S. Duan , T. Cai .
        7. Chen, C., Duan, S., Cai, T., et al: ‘Smart energy management system for optimal microgrid economic operation’, IET Renew. Power Gener., 2011, 5, (3), pp. 258267.
        . IET Renew. Power Gener. , 3 , 258 - 267
    8. 8)
      • C.C. Liao .
        8. Liao, C.C.: ‘Solve environmental economic dispatch of smart microgrid containing distributed generation system – using chaotic quantum genetic algorithm’, Int. J. Electric. Power Energy Syst., 2012, 43, pp. 779787.
        . Int. J. Electric. Power Energy Syst. , 779 - 787
    9. 9)
      • S.J. Pan , Q. Yang .
        9. Pan, S.J., Yang, Q.: ‘A survey on transfer learning’, IEEE Trans. Knowledge Data Eng., 2010, 22, (10), pp. 13451359.
        . IEEE Trans. Knowledge Data Eng. , 10 , 1345 - 1359
    10. 10)
      • M.E. Taylor , P. Stone .
        10. Taylor, M.E., Stone, P.: ‘Transfer learning for reinforcement learning domains: a survey’, J. Mach. Learn. Res., 2009, 10, pp. 16331685.
        . J. Mach. Learn. Res. , 1633 - 1685
    11. 11)
      • J. Pan , X. Wang , Y. Cheng .
        11. Pan, J., Wang, X., Cheng, Y., et al: ‘Multi-source transfer ELM-based Q learning’, Neurocomputing, 2014, 137, pp. 5764.
        . Neurocomputing , 57 - 64
    12. 12)
      • J.C.H. Watkins , P. Dayan .
        12. Watkins, J.C.H., Dayan, P.: ‘Q-learning’, Mach. Learn., 1992, 8, (3–4), pp. 279292.
        . Mach. Learn. , 279 - 292
    13. 13)
      • X. Zhang , T. Yu , B. Yang .
        13. Zhang, X., Yu, T., Yang, B., et al: ‘Accelerating bio-inspired optimizer with transfer reinforcement learning for reactive power optimization’, Know.-Based Syst., 2017, 116, pp. 2638.
        . Know.-Based Syst. , 26 - 38
    14. 14)
      • Y. Liu , Z. Qu , H. Xin .
        14. Liu, Y., Qu, Z., Xin, H., et al: ‘Distributed real-time optimal power flow control in smart grid’, IEEE Trans. Power Syst., 2017, 32, (5), pp. 34033414.
        . IEEE Trans. Power Syst. , 5 , 3403 - 3414
    15. 15)
      • M.R. Hesamzadeh , O. Galland , D.R. Biggar .
        15. Hesamzadeh, M.R., Galland, O., Biggar, D.R.: ‘Short-run economic dispatch with mathematical modelling of the adjustment cost’, Int. J. Electric. Power Energy Syst., 2014, 58, pp. 918.
        . Int. J. Electric. Power Energy Syst. , 9 - 18
    16. 16)
      • S Kamalinia , M. Shahidehpour .
        16. Kamalinia, S, Shahidehpour, M.: ‘Generation expansion planning in wind-thermal power systems’, IET Gener. Transm. Distrib., 2010, 4, (8), pp. 940951.
        . IET Gener. Transm. Distrib. , 8 , 940 - 951
    17. 17)
      • S. Brini , H.H. Abdallah , A. Ouali .
        17. Brini, S., Abdallah, H.H., Ouali, A.: ‘Economic dispatch for power system included wind and solar thermal energy’, Leonardo J. Sci., 2009, 14, pp. 204220.
        . Leonardo J. Sci. , 204 - 220
    18. 18)
      • N. Liu , J. Wang , L. Wang .
        18. Liu, N., Wang, J., Wang, L.: ‘Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response’, J. Mod. Power Syst. Clean Energy, 2017, 5, (3), pp. 478488.
        . J. Mod. Power Syst. Clean Energy , 3 , 478 - 488
    19. 19)
      • S. Karki , M. Kulkarni , M.D. Mann .
        19. Karki, S., Kulkarni, M., Mann, M.D., et al: ‘Efficiency improvements through combined heat and power for on-site distributed generation technologies’, Cogener. Distrib. Gener. J., 2007, 22, pp. 1934.
        . Cogener. Distrib. Gener. J. , 19 - 34
    20. 20)
      • G.S. Piperagkas , A.G. Anastasiadis , N.D. Hatziargyriou .
        20. Piperagkas, G.S., Anastasiadis, A.G., Hatziargyriou, N.D.: ‘Stochastic PSO-based heat and power dispatch under environmental constraints incorporating CHP and wind power units’, Electr. Power Syst. Res., 2011, 81, (1), pp. 209218.
        . Electr. Power Syst. Res. , 1 , 209 - 218
    21. 21)
      • Y. Xu , Z. Li .
        21. Xu, Y., Li, Z.: ‘Distributed optimal resource management based on the consensus algorithm in a microgrid’, IEEE Trans. Ind. Electron., 2015, 62, (4), pp. 25842592.
        . IEEE Trans. Ind. Electron. , 4 , 2584 - 2592
    22. 22)
      • S. Yousefi , M.P. Moghaddam , V.J. Majd .
        22. Yousefi, S., Moghaddam, M.P., Majd, V.J.: ‘Optimal real time pricing in an agent-based retail market using a comprehensive demand response model’, Energy, 2011, 36, (9), pp. 57165727.
        . Energy , 9 , 5716 - 5727
    23. 23)
      • P. Subbaraj , R. Rengaraj , S. Salivahanan .
        23. Subbaraj, P., Rengaraj, R., Salivahanan, S.: ‘Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm’, Appl. Energy, 2009, 86, (6), pp. 915921.
        . Appl. Energy , 6 , 915 - 921
    24. 24)
      • M.J. Er , C. Deng .
        24. Er, M.J., Deng, C.: ‘Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning’, IEEE Trans. Syst. Man Cybern. B. Cybern., 2004, 34, (3), pp. 14781489.
        . IEEE Trans. Syst. Man Cybern. B. Cybern. , 3 , 1478 - 1489
    25. 25)
      • R.A.C. Bianchi , L.A. Celiberto , P.E. Santos .
        25. Bianchi, R.A.C., Celiberto, L.A., Santos, P.E., et al: ‘Transferring knowledge as heuristics in reinforcement learning: a case-based approach’, Artif. Intell., 2015, 226, pp. 102121.
        . Artif. Intell. , 102 - 121
    26. 26)
      • K. Krynicki , M.E. Houle , J. Jaen .
        26. Krynicki, K., Houle, M.E., Jaen, J.: ‘An efficient ant colony optimization strategy for the resolution of multi-class queries’, Knowl.-Based Syst., 2016, 105, pp. 96106.
        . Knowl.-Based Syst. , 96 - 106
    27. 27)
      • X. Zhang , T. Bao , T. Yu .
        27. Zhang, X., Bao, T., Yu, T.: ‘Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid’, Energy, 2017, 133, pp. 348365.
        . Energy , 348 - 365
    28. 28)
      • K. Iba .
        28. Iba, K.: ‘Reactive power optimization by genetic algorithm’, IEEE Trans. Power Syst., 1994, 9, (2), pp. 685692.
        . IEEE Trans. Power Syst. , 2 , 685 - 692
    29. 29)
      • M. Clerc , J. Kennedy .
        29. Clerc, M., Kennedy, J.: ‘The particle swarm: explosion, stability, and convergence in a multidimensional complex space’, IEEE Trans. Evol. Comput., 2002, 6, (1), pp. 5873.
        . IEEE Trans. Evol. Comput. , 1 , 58 - 73
    30. 30)
      • D. Karaboga , B. Basturk .
        30. Karaboga, D., Basturk, B.: ‘A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm’, J. Global Optim., 2007, 39, (4), pp. 459471.
        . J. Global Optim. , 4 , 459 - 471
    31. 31)
      • S. He , Q.H. Wu , J.R. Saunders .
        31. He, S., Wu, Q.H., Saunders, J.R.: ‘Group search optimizer: an optimization algorithm inspired by animal searching behavior’, IEEE Trans. Evol. Comput., 2009, 13, (5), pp. 973990.
        . IEEE Trans. Evol. Comput. , 5 , 973 - 990
    32. 32)
      • E.B. Elanchezhian , S. Subramanian , S. Ganesan .
        32. Elanchezhian, E.B., Subramanian, S., Ganesan, S.: ‘Economic power dispatch with cubic cost models using teaching learning algorithm’, IET Gener. Transm. Distrib., 2014, 8, (7), pp. 11871202.
        . IET Gener. Transm. Distrib. , 7 , 1187 - 1202
    33. 33)
      • A. Bhattacharya , P.K. Chattopadhyay .
        33. Bhattacharya, A., Chattopadhyay, P.K.: ‘Application of biogeography-based optimization to solve different optimal power flow problems’, IET Gener. Transm. Distrib., 2011, 5, (1), pp. 7080.
        . IET Gener. Transm. Distrib. , 1 , 70 - 80
    34. 34)
      • S.K. Wang , J.-P. Chiou , C.-W. Liu .
        34. Wang, S.K., Chiou, J.-P., Liu, C.-W.: ‘Non-smooth/non-convex economic dispatch by a novel hybrid differential evolution algorithm’, IET Gener. Transm. Distrib., 2007, 1, (5), pp. 793803.
        . IET Gener. Transm. Distrib. , 5 , 793 - 803
    35. 35)
      • S. Duman , Y. Sonmez , U. Güvenç .
        35. Duman, S., Sonmez, Y., Güvenç, U., et al: ‘Optimal reactive power dispatch using a gravitational search algorithm’, IET Gener. Transm. Distrib., 2012, 6, (6), pp. 563576.
        . IET Gener. Transm. Distrib. , 6 , 563 - 576
    36. 36)
      • N. Duvvuru , K.S. Swarup .
        36. Duvvuru, N., Swarup, K.S.: ‘A hybrid interior point assisted differential evolution algorithm for economic dispatch’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 541549.
        . IEEE Trans. Power Syst. , 2 , 541 - 549
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