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access icon free Coalition formation strategies for cooperative operation of multiple microgrids

Energy cooperation among microgrids (MGs) by enabling local energy exchanges among them, is an appealing new solution to cope up with the impending energy crises. Specifically, energy can be wheeled among MGs to supply their deficits using the surplus of others. This study, therefore, presents an interactive model for the coordinated energy management of a distribution network with clustered MGs. Each MG not only schedules its local power generation and load consumption but also trades energy with neighbouring MGs in the distribution network. The power exchanges among the MGs, and that between the MGs and the distribution network are coordinated using a cooperative coalitional game theoretic approach. The framework aims at alleviating the distribution power losses while ensuring that all system variables, such as bus voltages, line flows, and reactive power requirements are within specified limits. Various system intermittencies are captured through scenario generation and reduction processes. Case studies on the IEEE-33 bus and PG & E-69 bus test systems with multiple coalitions forming MGs demonstrate the effectiveness of the proposed method in improving network payoffs, voltage profiles and alleviating the network losses and curtailment of available non-dispatchable resources.

References

    1. 1)
      • 19. Lee, W., Xiang, L., Schober, R., et al: ‘Direct electricity trading in smart grid: a coalitional game analysis’, IEEE J. Sel. Areas Commun., 2014, 32, (7), pp. 13981411.
    2. 2)
      • 15. Zhou, Z, Xiong, F., Huang, F., et al: ‘Game-theoretical energy management for energy internet with big data-based renewable power forecasting’, IEEE Access, 2017, 5, pp. 57315746.
    3. 3)
      • 9. Van Roy, J., Verbruggen, B., Driesen, J.: ‘Ideas for tomorrow: new tools for integrated building and district modeling’, IEEE Power Energy Mag., 2013, 11, (5), pp. 7581.
    4. 4)
      • 26. Asimakopoulou, G.E., Dimeas, A.L., Hatziargyriou, N.D.: ‘Leader follower strategies for energy management of multi-microgrids’, IEEE Trans. Smart Grid, 2013, 4, (4), pp. 19091916.
    5. 5)
      • 41. Tan, S., Xu, J.-X., Panda, S.K.: ‘Optimization of distribution network incorporating distributed generators: an integrated approach’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 24212432.
    6. 6)
      • 39. Pinheiro, J.M.S., Dornellas, C.R.R., Melo, A.C.G.: ‘Probing the new IEEE reliability test system (RTS-96): HL-II assessment’, IEEE Trans. Power Syst., 1998, 13, (1), pp. 171176.
    7. 7)
      • 37. GAMS/SCENRED documentation’. Available at: http://www.gams.com/docs/document.html.
    8. 8)
      • 8. Nunna, H.S.V.S.K., Doolla, S.: ‘Demand response in smart distribution system with multiple microgrids’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 16411649.
    9. 9)
      • 32. Lahon, R., Gupta, C. P.: ‘Risk-based coalition of cooperative microgrids in electricity market environment’, IET Gener. Transm. Distrib., 2018, 12, (13), pp. 32303241.
    10. 10)
      • 24. Ni, J., Ai, Q.: ‘Economic power transaction using coalitional game strategy in micro-grids’, IET Gener. Transm. Distrib., 2016, 10, (1), pp. 1018.
    11. 11)
      • 25. Saad, W., Han, Z., Poor, H.: ‘Coalitional game theory for cooperative micro-grid distribution networks’. Proc. IEEE ICC’, Kyoto, Japan, June 2011, pp. 15.
    12. 12)
      • 29. Tan, X., Lie, T.T.: ‘Application of the Shapley value on transmission cost allocation in the competitive power market environment’, IEE Proc., Gener. Transm. Distrib., 2002, 149, (1), pp. 1520.
    13. 13)
      • 17. Pourahmadi, F., Fotuhi-Firuzabad, M., Dehghanian, P.: ‘Identification of critical generating units for maintenance: a game theory approach’, IET Gener. Transm. Distrib., 2016, 10, (12), pp. 29422952.
    14. 14)
      • 7. Wang, Y., Mao, S., Nelms, R.M.: ‘On hierarchical power scheduling for the macrogrid and cooperative microgrids’, IEEE Trans. Ind. Inf., 2015, 11, (6), pp. 15741584.
    15. 15)
      • 22. Liu, W., Gu, W., Wang, J., et al: ‘Game theoretic non-cooperative distributed coordination control for multi-microgrids’, IEEE Trans. Smart Grid, 2018, 9, (6), pp. 69866997.
    16. 16)
      • 27. Chakraborty, S., Nakamura, S., Okabe, T.: ‘Real-time energy exchange strategy of optimally cooperative microgrids for scale-flexible distribution system’, Expert Syst. Appl., 2015, 42, (10), pp. 46434652.
    17. 17)
      • 28. Kumar Nunna, H.S.V.S., Doolla, S.: ‘Multiagent-based distributed energy-resource management for intelligent microgrids’, IEEE Trans. Ind. Electron., 2013, 60, (4), pp. 16781687.
    18. 18)
      • 30. Stamtsis, G.C., Erlich, I.: ‘Use of cooperative game theory in power system fixed-cost allocation’, IEE Proc., Gener. Transm. Distrib., 2004, 151, (3), pp. 401406.
    19. 19)
      • 45. ANSI Standard C84.1: ‘Electric power systems and equipment voltage ratings (60 Hz)’, 1995.
    20. 20)
      • 23. Hammad, E., Farraj, A., Kundur, D.: ‘Cooperative microgrid networks for remote and rural areas’. IEEE 28th Canadian Conf. on Electrical and Computer Engineering (CCECE), Halifax, NS, Canada, 2015, pp. 14771572.
    21. 21)
      • 43. Jiang, L., Low, S.: ‘Multi-period optimal energy procurement and demand response in smart grid with uncertain supply’. IEEE 50th Int. Conf. on Decision and Control and European Control, Orlando, FL, USA, 2011, pp. 43484353.
    22. 22)
      • 42. Bahrami, S., Therrien, F., Wong, W., et al: ‘Semidefinite relaxation of optimal power flow for AC–DC grids’, IEEE Trans. Power Syst., 2017, 32, (1), pp. 289304.
    23. 23)
      • 44. Jena, S., Chauhan, S.: ‘Solving distribution feeder reconfiguration and concurrent DG installation problems for power loss minimization by multi swarm cooperative PSO algorithm’. Proc. IEEE/PES Trans. and Dist. Conf. & Expo., Dallas, TX, USA, 2016, pp. 19.
    24. 24)
      • 10. Ouammi, A., Dagdougui, H., Sacile, R.: ‘Optimal control of power flows and energy local storages in a network of microgrids modelled as a system of systems’, IEEE Trans. Control Syst. Technol., 2015, 23, (1), pp. 128138.
    25. 25)
      • 13. Nikmehr, N., Najafi-Ravadanegh, S.: ‘Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm’, IET Renew. Power Gener., 2015, 9, (8), pp. 982990.
    26. 26)
      • 5. Guo, L., Liu, W., Jiao, B., et al: ‘Multi-objective stochastic optimal planning method for stand-alone microgrid system’, IET Gener. Transm. Distrib., 2014, 8, (7), pp. 12631273.
    27. 27)
      • 1. 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.
    28. 28)
      • 20. Fele, F., Maestre, J. M., Camacho, E. F.: ‘Coalitional control: cooperative game theory and control’, IEEE Control Syst., 2017, 37, (1), pp. 5369.
    29. 29)
      • 31. Bhakar, R., Sriram, V.S., Padhy, N.P., et al: ‘Probabilistic game approaches for network cost allocation’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 5158.
    30. 30)
      • 18. Sore, F., Rudnick, H., Zolezzi, J.: ‘Definition of an efficient transmission system using cooperative games theory’, IEEE Trans. Power Syst., 2006, 21, (4), pp. 14841492.
    31. 31)
      • 21. Fele, F., Debada, E., Maestre, J.M., et al: ‘Coalitional control for self-organizing agents’, IEEE Trans. Autom. Control, 2018, 63, (9), pp. 28832897.
    32. 32)
      • 2. Amini, M. H., Boroojeni, K. G., Dragicevi, T., et al: ‘A comprehensive cloud-based real-time simulation framework for oblivious power routing in clusters of DC microgrids’. Proc. IEEE 2nd Int. Conf. DC Microgrids, Nuremburg, Germany, 2017, pp. 270273.
    33. 33)
      • 34. Conejo, A.J., Carrion, M., Morales, J.M.: ‘Decision making under uncertainty in electricity markets’ (Springer, New York, NY, USA, 2010).
    34. 34)
      • 33. Kattuman, P., Green, R. J., Bialek, J.: ‘A tracing method for pricing inter-area electricity trades’, 2004, https://doi.org/10.17863/CAM.5179.
    35. 35)
      • 40. Golshanavaz, S., Afsharnia, S., Aminifar, F.: ‘Smart distribution grid: optimal day-ahead scheduling with reconfigurable topology’, IEEE Trans. Smart Grid, 2014, 5, (5), pp. 24022411.
    36. 36)
      • 4. Wasiak, I., Pawelek, R., Mienski, R.: ‘Energy storage application in lowvoltage microgrids for energy management and power quality improvement’, IET Gener. Transm. Distrib., 2014, 8, (3), pp. 463472.
    37. 37)
      • 36. Dupacova, J., Growe-Kuska, N., Romisch, W.: ‘Scenario reduction in stochastic programming: an approach using probability metrics’, Math. Program., 2003, 95, (3), pp. 493511.
    38. 38)
      • 11. Minciardi, R., Sacile, R.: ‘Optimal control in a cooperative network of smart power grids’, IEEE Syst. J., 2012, 6, (1), pp. 126133.
    39. 39)
      • 35. Chen, Y., Wen, J., Cheng, S.: ‘Probabilistic load flow method based on Nataf transformation and Latin hypercube sampling’, IEEE Trans. Sustain. Energy, 2013, 4, (2), pp. 294301.
    40. 40)
      • 38. Arefifar, S.A., Mohamed, Y.A.-R.I., El-Fouly, T.H.M.: ‘Optimum microgrid design for enhancing reliability and supply security’, IEEE Trans. Smart Grid, 2013, 4, (3), pp. 15671575.
    41. 41)
      • 14. Marzband, M., Parhizi, N., Savaghebi, M., et al: ‘Distributed smart decision-making for a multi-microgrid system based on a hierarchical interactive architecture’, IEEE Trans. Energy Convers., 2016, 31, (2), pp. 637648.
    42. 42)
      • 16. Tzavellas, A., Nguyen, P., Ribeiro, P., et al: ‘A game theory approach for coordinating multiple virtual synchronous generators’. IEEE Grenoble PowerTech, Grenoble, France, 2013, pp. 16.
    43. 43)
      • 3. Sanjari, M.J., Gharehpetian, G.B.: ‘Unified framework for frequency and voltage control of autonomous microgrids’, IET Gener. Transm. Distrib., 2013, 7, (9), pp. 965972.
    44. 44)
      • 6. Fathi, M., Bevrani, H.: ‘Statistical cooperative power dispatching in interconnected microgrids’, IEEE Trans. Sustain. Energy, 2013, 4, (3), pp. 586593.
    45. 45)
      • 12. Wang, Z., Chen, B., Wang, J., et al: ‘Coordinated energy management of networked microgrids in distribution systems’, IEEE Trans. Smart Grid, 2015, 6, (1), pp. 4553.
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