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

Distributed residential energy resource scheduling with renewable uncertainties

Distributed residential energy resource scheduling with renewable uncertainties

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Advances in metering and two-way communication technologies foster the studies of Home Energy Management System (HEMS). This study proposes a new HEMS, which optimally schedules the distributed residential energy resources (DRERs) in a smart home environment with varying electricity tariff and high solar penetrations. The uncertainties of solar power output are captured by using Monte Carlo sampling technique to generate multiple solar output scenarios based on the probabilistic solar radiation model. The homeowner's rigid and elastic restrictions on the operations of the automatically controlled household appliances are modelled. Based on this, an optimal DRER scheduling model is proposed to minimise the home operation cost while taking into account the homeowner's requirements. A new heuristic optimisation algorithm recently proposed by the authors, i.e. natural aggregation algorithm, is used to solve the proposed model. Simulations based on real Australian solar data are conducted to validate the proposed method.

References

    1. 1)
      • 1. ‘BCA Building Energy Report’, https://www.bca.gov.sg/GreenMark/others/BCA_BEBR_Abridged_FA.pdf, accessed 10 July 2017.
    2. 2)
      • 2. ‘Building Energy Data Book’, http://buildingsdatabook.eren.doe.gov/ChapterIntro1.aspx, accessed 10 July 2017.
    3. 3)
      • 3. Luo, F., Zhao, J., Dong, Z.Y., et al: ‘Optimal dispatch of air conditioner loads in southern China region by direct load control’, IEEE Trans. Smart Grid, 2016, 7, (1), pp. 439450.
    4. 4)
      • 4. Luo, F., Dong, Z.Y., Meng, K., et al: ‘An operational planning framework for large scale thermostatically controlled load dispatch’, IEEE Trans. Ind. Inf., 2017, 13, (1), pp. 217227.
    5. 5)
      • 5. Luo, F., Zhao, J., Wang, H., et al: ‘Direct load control with distributed imperialist competitive algorithm’, J. Mod. Power Syst. Clean Energy, 2014, 2, pp. 385395.
    6. 6)
      • 6. Wang, H., Meng, K., Luo, F., et al: ‘Demand response through smart home energy management using thermal inertia’. Proc. 2013 Australian Universities Power Engineering Conf. (AUPEC), Hobart, TAS, Australia, 29 September–3 October 2013.
    7. 7)
      • 7. Wang, H., Meng, K., Dong, Z.Y., et al: ‘A MILP approach to accommodate more building integrated photovoltaic system in distribution network’. Proc. IEEE PES General Meeting, Denver, CO, USA, July 2016.
    8. 8)
      • 8. Luo, F., Xu, Z., Meng, K., et al: ‘Optimal operation scheduling for microgrid with high penetrations of solar power and thermostatically controlled loads’, Sci. Technol. Built Environ., 2016, 22, (6), pp. 666673.
    9. 9)
      • 9. Luo, F., Dong, Z.Y., Meng, K., et al: ‘Short-term operational planning framework for virtual power plants with high renewable penetrations’, IET Renew. Power Gener., 2016, 10, (5), pp. 623633.
    10. 10)
      • 10. Zhao, Z., Lee, W., Shin, Y., et al: ‘An optimal power scheduling method for demand response in home energy management system’, IEEE Trans. Smart Grid, 2013, 4, (3), pp. 13911400.
    11. 11)
      • 11. Pedrasa, M.A., Spooner, T., MacGill, I.: ‘Coordinated scheduling of residential distributed energy resources to optimize smart home energy services’, IEEE Trans. Smart Grid, 2010, 1, (2), pp. 134143.
    12. 12)
      • 12. Rastergar, M., Fotuhi-Firuzabad, M., Aminifar, F.: ‘Load commitment in a smart home’, Appl. Energy, 2012, 96, pp. 4554.
    13. 13)
      • 13. Ozturk, Y., Senthikumar, D., Kumar, S., et al: ‘An intelligent home energy management system to improve demand response’, IEEE Trans. Smart Grid, 2013, 4, (2), pp. 694701.
    14. 14)
      • 14. Iwafune, Y., Ikegami, T., da Silva Fonseca, J.G.Jr., et al: ‘Cooperative home energy management using batteries for a photovoltaic system considering the diversity of households’, Energy Convers. Manage., 2015, 96, pp. 322329.
    15. 15)
      • 15. Nguyen, D., Le, L.: ‘Joint optimization of electric vehicle and home energy scheduling considering user comfort preference’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 188199.
    16. 16)
      • 16. Middelberg, A., Zhang, J., Xia, X.: ‘An optimal control model for load shifting with application in the energy management of a colliery’, Appl. Energy, 2009, 86, (7), pp. 12661273.
    17. 17)
      • 17. Vivekananthan, C., Mishra, Y., Ledwich, G., et al: ‘Demand response for residential appliances via customer reward scheme’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 809820.
    18. 18)
      • 18. Luo, F., Ranzi, G., Wang, X., et al: ‘Service recommendation in smart grid: vision, technologies, and applications’. Proc. 9th Int. Conf. Service Science, Chongqing, China, 2016.
    19. 19)
      • 19. Luo, F., Ranzi, G., Kong, W., et al: ‘Non-intrusive energy saving appliance recommender system for smart grid residential users’, IET Gener. Transm. Distrib., 2017, 11, (7), pp. 17861793.
    20. 20)
      • 20. Luo, F., Ranzi, G., Wang, X., et al: ‘Social information filtering based electricity retail plan recommender system for smart grid end users’, IEEE Trans. Smart Grid, 2017, DOI: 10.1109/TSG.2017.2732346.
    21. 21)
      • 21. Luo, F., Zhao, J., Qiu, J., et al: ‘Assessing the transmission expansion cost with distributed generation: an Australian case study’, IEEE Trans. Smart Grid, 2014, 5, (4), pp. 18921904.
    22. 22)
      • 22. Luo, F., Zhao, J., Dong, Z.Y.: ‘A new metaheuristic algorithm for real-parameter optimization: natural aggregation algorithm’. Proc. IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, July 2016.
    23. 23)
      • 23. Luo, F., Dong, Z.Y., Chen, Y., et al: ‘Natural aggregation algorithm: a new efficient metaheuristic tool for power system optimization’. Proc. IEEE Int. Conf. Smart Grid Communications, Sydney, NSW, Australia, November 2016.
    24. 24)
      • 24. ‘Energy Australia’, https://www.energyaustralia.com.au, accessed 10 July 2017.
    25. 25)
      • 25. ‘Smart Grid, Smart City’, http://www.industry.gov.au/ENERGY/PROGRAMMES/SMARTGRIDSMARTCITY/Pages/default.aspx, accessed 20 June 2017.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.1136
Loading

Related content

content/journals/10.1049/iet-gtd.2017.1136
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address