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Intelligent Decision-Making for Smart Home Energy Management

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Abstract

One of the goals of Smart Grids is to encourage distributed generation of energy in houses, hence allowing the user to profit by injecting energy into the power grid. The implementation of a differentiated tariff of energy per time of use, coupled with energy storage in batteries, enables profit maximization by the user, who can choose to sell or store the energy generated whenever it is convenient. This paper proposes a solution to the sequential decision-making problem of energy sale by applying reinforcement learning. Results show a significant increase in the total long-term profit by using the policy obtained with the proposed approach, when compared with a price-unaware selling policy.

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References

  1. Hammoudeh, M.A., Mancilla-David, F., Selman, J.D., Papantoni-Kazakos, P.: Comunication Architectures for Distribution Networks within the Smart Grid Initiative. In: Green Technologies Conference, IEEE, p 65,70 (2013), doi:10.1109/GreenTech.2013.18

  2. Hashmi, M., Hanninen, S., Maki, K.: Survey of smart grid concepts, architectures, and technological demonstrations worldwide. In: IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America), p 1,7 (2011), doi:10.1109/ISGT-LA.2011.6083192

  3. Uluski, R.W.: The role of Advanced Distribution Automation in the Smart Grid. In: Power and Energy Society General Meeting, IEEE, p 1,5 (2010), doi:10.1109/PES.2010.5590075

  4. Palensky, P., Dietrich, D.: Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Trans. Ind. Inf. 7 (3), 381,388 (2011). doi:10.1109/TII.2011.2158841

    Article  Google Scholar 

  5. Chen, C., Kishore, S., Zhifang, W., Alizadeh, M., Scaglione, A.: How will demand response aggregators affect electricity markets? A Cournot game analysis. In: 5th International Symposium on Communications Control and Signal Processing (ISCCSP), p 1,6 (2012), doi:10.1109/ISCCSP.2012.6217839

  6. Parvania, M., Fotuhi-Firuzabad, M., Shahidehpour, M.: Demand response participation in wholesale energy markets. In: Power and Energy Society General Meeting, IEEE, p 1,4 (2012), doi:10.1109/PESGM.2012.6344591

  7. Shishebori, A., Kian, A.R.: Risk analysis for distribution company energy procurement with pool market, DGs and demand response. In: 18th Iranian Conference on Electrical Engineering (ICEE), p 949,954 (2010), doi:10.1109/IRANIANCEE.2010.5506940

  8. Chua-Liang, S., Kirschen, D.: Quantifying the Effect of Demand Response on Electricity Markets. IEEE Trans. Power Syst. 24 (3), 1199,1207 (2009). doi:10.1109/TPWRS.2009.2023259

    Google Scholar 

  9. Keen, P.: Decision support systems : a research perspective. Center for Information Systems Research, Cambridge (1980)

    Google Scholar 

  10. Wright, A., Sittig, D.: A framework and model for evaluating clinical decision support architectures q. J. Biomed. Inform. 41 (2008). doi:10.1016/j.jbi.2008.03.009

  11. Power, D.J.: Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books (2002)

  12. Stephens, W., Middleton, T.: Why has the uptake of Decision Support Systems been so poor?. In: Crop-soil simulation models in developing countries. Wallingford:CABI (2002)

  13. Sol, H. G., Takkenberg, C. A.Th., de Vries Robb, P. F.: Expert systems and artificial intelligence in decision support systems. Second Mini Euroconference, Lunteren (1987). ISBN: 90-277-2437-7

  14. Efraim, T., Jay, E.: Decision Support Systems and Intelligent Systems, p. 574. Prentice Hall (2008). ISBN-13: 978-0137409372

  15. Dusparic, I., Harris, C., Marinescu, A., Cahill, V., Clarke, S.: Multi-agent residential demand response based on load forecasting. In: 1st IEEE Conference on Technologies for Sustainability (SusTech), p 90,96 (2013), doi:10.1109/SusTech.2013.6617303

  16. O’Neill, D., Levorato, M., Goldsmith, A., Mitra, U.: Residential Demand Response Using Reinforcement Learning. In: First IEEE International Conference on Smart Grid Communications (SmartGridComm), p 409,414 (2010), doi:10.1109/SMARTGRID.2010.5622078

  17. Chen, X., Wei, T., Hu, S.: Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home. IEEE Trans. Smart Grid 4 (2), 932,941 (2013). doi:10.1109/TSG.2012.2226065

    Google Scholar 

  18. Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Leon-Garcia, A.: Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid. IEEE Trans. Smart Grid 1 (3), 320,331 (2010). doi:10.1109/TSG.2010.2089069

    Article  Google Scholar 

  19. Atzeni, I., Ordonez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R.: Demand-side management via distributed energy generation and storage optimization. IEEE Trans. Smart Grid 4 (2), 866,876 (2013). doi:10.1109/TSG.2012.2206060

    Article  Google Scholar 

  20. Balijepalli, V.S.K.M., Pradhan, V., Khaparde, S.A., Shereef, R.M.: Review of demand response under smart grid paradigm. In: Innovative Smart Grid Technologies - India (ISGT India), IEEE PES, p 236,243 (2011)

  21. Albadi, M.H., El-Saadany, E.F.: Demand Response in Electricity Markets: An Overview. In: Power Engineering Society General Meeting, IEEE, p 1,5 (2007), doi:10.1109/PES.2007.385728

  22. Carpinelli, G., Celli, G., Mocci, S., Mottola, F., Pilo, F., Proto, D.: Optimal Integration of Distributed Energy Storage Devices in Smart Grids. IEEE Trans. Smart Grid 4 (2), 985,995 (2013). doi:10.1109/TSG.2012.2231100

    Article  Google Scholar 

  23. Zhimin, W., Chenghong, G., Furong, L., Bale, P., Hongbin, S.: Active Demand Response Using Shared Energy Storage for Household Energy Management. IEEE Trans. Smart Grid 4 (4), 1888,1897 (2013). doi:10.1109/TSG.2013.2258046

    Google Scholar 

  24. PJM monthly locational marginal pricing [Online]. Available: http://www.pjm.com/markets-and-operations/energy/real-time/monthlylmp.aspx

  25. Bueno, E.A.B., Utubey, W., Hostt, R.R.: Evaluating the effect of the white tariff on a distribution expansion project in Brazil. In: IEEE PES Conference On Innovative Smart Grid Technologies Latin America (ISGT LA), p 1,8 (2013), doi:10.1109/ISGT-LA.2013.6554479

  26. Russell, S.J., Norvig, P., 2nd edn: Artificial Intelligence: A Modern Approach. Pearson Education, Inc., Upper Saddle River (2003)

    Google Scholar 

  27. Sutton, R.S., Barto, A.G., Hu, S.: Reinforcement Learning: An Introduction. The MIT Press, Cambrige

  28. Watkins, C.J.C.H.: Learning from Delayed Rewards, PhD thesis. Cambridge University, Cambridge (1989)

    Google Scholar 

  29. Kyocera Solar, Data Sheet of KD200-54 P Series PV Modules [On- line]. Available: http://www.kyocerasolar.com/assets/001/5124.pdf

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Authors and Affiliations

  1. Escola Politécnica, Universidade de São Paulo (USP), São Paulo, SP, Brazil

    Heider Berlink, Nelson Kagan & Anna Helena Reali Costa

Authors
  1. Nelson Kagan
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  2. Anna Helena Reali Costa
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Correspondence to Heider Berlink.

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Berlink, H., Kagan, N. & Reali Costa, A.H. Intelligent Decision-Making for Smart Home Energy Management. J Intell Robot Syst 80 (Suppl 1), 331–354 (2015). https://doi.org/10.1007/s10846-014-0169-8

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  • DOI: https://doi.org/10.1007/s10846-014-0169-8

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