http://iet.metastore.ingenta.com
1887

access icon openaccess Operation strategy of energy hub for commercial buildings based on tiered temperature control

  • XML
    157.3134765625Kb
  • HTML
    179.806640625Kb
  • PDF
    1.6308107376098633MB
Loading full text...

Full text loading...

/deliver/fulltext/joe/2018/17/JOE.2018.8339.html;jsessionid=c3hh21p02gf93.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fjoe.2018.8339&mimeType=html&fmt=ahah

References

    1. 1)
      • 1. Zhou, B., Li, W., Chan, K.W., et al: ‘Smart home energy management systems: concept, configurations, and scheduling strategies’, Renew. Sustain. Energy Rev., 2016, 61, pp. 3040.
    2. 2)
      • 2. Shaikh, P.H., Nor, N.B.M., Nallagownden, P., et al: ‘Intelligent multi-objective control and management for smart energy efficient buildings’, Int. J. Electr. Power Energy Syst., 2016, 74, pp. 403409.
    3. 3)
      • 3. Connelly, K., Wu, Y., Chen, J., et al: ‘Design and development of a reflective membrane for a novel Building Integrated Concentrating Photovoltaic (BICPV) ‘Smart Window’ System’, Appl. Energy, 2016, 182, pp. 331339.
    4. 4)
      • 4. Chai, B., Costa, A., Ahipasaoglu, S.D., et al: ‘Optimal meeting scheduling in smart commercial building for energy cost reduction’, IEEE Trans. Smart Grid, 2016, PP, (99), pp. 11.
    5. 5)
      • 5. Keshtkar, A., Arzanpour, S.: ‘An adaptive fuzzy logic system for residential energy management in smart grid environments’, Appl. Energy, 2017, 186, pp. 6881.
    6. 6)
      • 6. Halvgaard, R., Vandenberghe, L., Poulsen, N.K., et al: ‘Distributed model predictive control for smart energy systems’, IEEE Trans. Smart Grid, 2017, 7, (3), pp. 16751682.
    7. 7)
      • 7. Yang, H., Xiong, T., Qiu, J., et al: ‘Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response’, Appl. Energy, 2016, 167, pp. 353365.
    8. 8)
      • 8. Li, G., Zhang, R., Jiang, T., et al: ‘Optimal dispatch strategy for integrated energy systems with CCHP and wind power’, Appl. Energy, 2016, 192, pp. 408419.
    9. 9)
      • 9. Hu, M., Cho, H.: ‘A probability constrained multi-objective optimization model for CCHP system operation decision support’, Appl. Energy, 2014, 116, (116), pp. 230242.
    10. 10)
      • 10. Ceseña, E.A.M., Good, N., Syrri, A.L.A., et al: ‘Techno-economic and business case assessment of low carbon technologies in distributed multi-energy systems’, Appl. Energy, 2016, 167, pp. 158172.
    11. 11)
      • 11. Wei, T., Zhu, Q., Yu, N.: ‘Proactive demand participation of smart buildings in smart grid’, IEEE Trans. Comput., 2016, 65, (5), pp. 13921406.
    12. 12)
      • 12. Ouammi, A.: ‘Optimal power scheduling for a cooperative network of smart residential buildings’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 13171326.
    13. 13)
      • 13. Zhang, D., Li, S., Sun, M., et al: ‘An optimal and learning-based demand response and home energy management system’, IEEE Trans. Smart Grid, 2016, 7, (4), pp. 17901801.
    14. 14)
      • 14. Brahman, F., Honarmand, M., Jadid, S.: ‘Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system’, Energy Build., 2015, 90, pp. 6575.
    15. 15)
      • 15. Tasdighi, M., Ghasemi, H., Rahimi-Kian, A.: ‘Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 349357.
    16. 16)
      • 16. Nguyen, D.T., Le, L.B.: ‘Joint optimization of electric vehicle and home energy scheduling considering user comfort preference’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 188199.
    17. 17)
      • 17. Menniti, D., Costanzo, F., Scordino, N., et al: ‘Purchase-bidding strategies of an energy coalition with demand-response capabilities’, IEEE Trans. Power Syst., 2009, 24, (3), pp. 12411255.
    18. 18)
      • 18. Good, N., Karangelos, E., Navarro-Espinosa, A., et al: ‘Optimization under uncertainty of thermal storage-based flexible demand response with quantification of residential users’ discomfort', IEEE Trans. Smart Grid, 2015, 6, (5), pp. 23332342.
    19. 19)
      • 19. Kleywegt, A.J., Shapiro, A., Homem-De-Mello, T.: ‘The sample average approximation method for stochastic discrete optimization’, SIAM J. Optim., 2001, 12, (2), pp. 479502.
    20. 20)
      • 20. Morton, D.P., Popova, E.: ‘Monte–Carlo simulations for Stochastic optimization’ (Springer, US, 2001).
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.8339
Loading

Related content

content/journals/10.1049/joe.2018.8339
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address