access icon free Global energy management system for cooperative networked residential green buildings

This study addresses an optimisation problem faced by a network of green buildings (NGBs) connected to the main electrical grid. The problem is formulated as a cooperative internal power control among interacting residential buildings. The cooperation is reached through a communication infrastructure in the NGB, where the global central controller of the network is the responsible for the coordination of the local residential buildings' energy management systems by properly allowing the optimal management of the internal and external power flows in each building. The main advantage of the cooperation among residential buildings is to better match the load profile of each building internally (at the network level). In order to achieve this goal, components such as energy storage system, distributed generations and loads are included. The uncertainties characteristics of wind speed, solar irradiation, and loads are also considered for the control and operation of the whole system. A small network of five residential buildings has been simulated using the proposed model. Numerical results demonstrate the effectiveness of the proposed network.

Inspec keywords: energy management systems; building management systems; optimisation

Other keywords: solar irradiation; optimisation problem; global energy management system; electrical grid; wind speed; energy management system; energy storage system; cooperative networked residential green buildings

Subjects: Other power utilisation; Optimisation techniques; Power system management, operation and economics

References

    1. 1)
      • 21. Zejli, D., Ouammi, A., Sacile, R., et al: ‘An optimization model for a mechanical vapor compression desalination plant driven by a wind/PV hybrid system’, Appl. Energy, 2011, 88, pp. 40424054.
    2. 2)
      • 8. Ouammi, A., Dagdougui, H., Sacile, R.: ‘Optimal control of power flows and energy local storages in a network of microgrids modeled as a system of systems’, IEEE Trans. Control Syst. Technol., 2015, 23, pp. 128138.
    3. 3)
      • 3. Kanchev, H., Di, L., Colas, F., et al: ‘Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications’, IEEE Trans. Ind. Electron., 2011, 58, pp. 45834592.
    4. 4)
      • 5. Dagdougui, H., Minciardi, R., Ouammi, A., et al: ‘Modeling and optimization of a hybrid system for the energy supply of a ‘Green’ building’, Energy Convers. Manage., 2012, 64, pp. 351363.
    5. 5)
      • 15. Figueiredo, J., Sá da Costa, J.: ‘A SCADA system for energy management in intelligent buildings’, Energy Build., 2012, 49, pp. 8598.
    6. 6)
      • 1. Hatziargyriou, N., Asano, H., Iravani, R., et al: ‘Microgrids’, IEEE Power Energy Mag., 2007, 5, pp. 7894.
    7. 7)
      • 19. Ouammi, A., Dagdougui, H., Dessaint, L., et al: ‘Coordinated model predictive-based power flows control in a cooperative network of smart microgrids’, IEEE Trans. Smart Grid, 2015, 6, (5), pp. 22332244.
    8. 8)
      • 17. Fathi, M., Bevrani, H.: ‘Statistical cooperative power dispatching in interconnected microgrids’, IEEE Trans. Sustain. Energy, 2013, 4, pp. 586593.
    9. 9)
      • 18. Arefifar, S.A., Mohamed, Y.A.I., El-Fouly, T.H.M.: ‘Supply-adequacy-based optimal construction of microgrids in smart distribution systems’, IEEE Trans. Smart Grid, 2012, 3, (3), pp. 14911502.
    10. 10)
      • 13. 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.
    11. 11)
      • 14. Široký, J., Oldewurtel, F., Cigler, J., et al: ‘Experimental analysis of model predictive control for an energy efficient building heating system’, Appl. Energy, 2011, 88, (9), pp. 30793087.
    12. 12)
      • 20. Ouammi, A., Sacile, R., Zejli, D., et al: ‘Sustainability of a wind power plant: application to different Moroccan sites’, Energy, 2010, 35, pp. 42264236.
    13. 13)
      • 11. Che, L., Zhang, X., Shahidehpour, M., et al: ‘Optimal interconnection planning of community microgrids with renewable energy sources’, IEEE Trans. Smart Grid, 2015, DOI: 10.1109/TSG.2015.2456834.
    14. 14)
      • 9. Dagdougui, H., Ouammi, A., Sacile, R.: ‘Optimal control of a network of power microgrids using the Pontryagin's minimum principle’, IEEE Trans. Control Syst. Technol., 2014, 22, (5), pp. 19421948.
    15. 15)
      • 6. Dagdougui, H., Minciardi, R., Ouammi, A., et al: ‘A dynamic decision model for the real-time control of hybrid renewable energy production systems’, IEEE Trans. Syst. J., 2010, 4, pp. 323333.
    16. 16)
      • 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.
    17. 17)
      • 16. Planas, E., Gil-de-Muro, A., Andreu, J., et al: ‘General aspects, hierarchical controls and droop methods in microgrids: a review’, Renew. Sustain. Energy Rev., 2013, 17, pp. 147159.
    18. 18)
      • 7. Mohsenian-Rad, A.H., Leon-Garcia, A.: ‘Optimal residential load control with price prediction in real-time electricity pricing environments’, IEEE Trans. Smart Grid, 2010, 1, pp. 120133.
    19. 19)
      • 2. Chun-Hao, L., Ansari, N.: ‘Decentralized controls and communications for autonomous distribution networks in smart grid’, IEEE Trans. Smart Grid, 2013, 4, pp. 6677.
    20. 20)
      • 10. Arefifar, S.A., Mohamed, Y.A.-R.I., El-Fouly, T.H.M.: ‘Comprehensive operational planning framework for self-healing control actions in smart distribution grids’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 41924200.
    21. 21)
      • 4. Molderink, A., Bakker, V., Bosman, M.G.C., et al: ‘Management and control of domestic smart grid technology’, IEEE Trans. Smart Grid, 2010, 1, pp. 109119.
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