Skip to main content
SpringerLink
Log in

Two-Stage Optimization for Building Energy Management

  • Conference paper
  • First Online:
Smart Energy Control Systems for Sustainable Buildings

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 67))

Abstract

In recent years, the energy sector has undergone an important transformation as a result of technological progress and socioeconomic development. The continuous integration of renewable technologies drives the gradual transition from the traditional business model based on a reduced number of large power plants to a more decentralized energy production. The increasing energy demand and intermittent generation of renewable energy sources require modern control strategies to provide an uninterrupted service and guarantee high energy efficiency. Utilities and network operators permanently supervise production facilities and grids to compensate any mismatch between production and consumption. The enormous potential of local energy management contributes to grid stability and can be used to reduce the adverse effects of load variations and production fluctuations. This paper presents a building energy management which determines the optimal scheduling of all components of the local energy system. The two-stage optimization is based on a receding horizon strategy and minimizes two economic functions subject to the physical system constraints. The performance of the proposed building energy management is validated in simulations and the results are compared to the ones obtained with other energy management approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Iwaro, J., Mwasha, A.: A review of building energy regulation and policy for energy conservation in developing countries. Energy Policy 38(12), 7744–7755 (2010)

    Article  Google Scholar 

  2. Pérez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (Mar 2008)

    Google Scholar 

  3. dos Santos, A.H.C., Fagá, M.T.W., dos Santos, E.M.: The risks of an energy efficiency policy for buildings based solely on the consumption evaluation of final energy. Int. J. Electr. Power Energy Syst. 44(1), 70–77 (2013)

    Article  Google Scholar 

  4. Finn, P., O’Connell, M., Fitzpatrick, C.: Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction. Appl. Energy 101, 678–685 (2013)

    Article  Google Scholar 

  5. Moura, P.S., de Almeida, A.T.: The role of demand-side management in the grid integration of wind power. Appl. Energy 87(8), 2581–2588 (2010)

    Article  Google Scholar 

  6. Mohsenian-Rad, A.H., Leon-Garcia, A.: Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans. Smart Grid 1(2), 120–133 (2010)

    Article  Google Scholar 

  7. Palensky, P., Dietrich, D.: Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Trans. Industr. Inf. 7(3), 381–388 (2011)

    Article  Google Scholar 

  8. Tiptipakorn, S., Lee, W.J.: A residential consumer-centered load control strategy in real-time electricity pricing environment. In: Proceedings of the 39th North American Power Symposium (NAPS ’07). pp. 505–510. Las Cruces, NM (30 Sept–2 Oct 2007)

    Google Scholar 

  9. Chen, C., Wang, J., Heo, Y., Kishore, S.: MPC-based appliance scheduling for residential building energy management controller. IEEE Trans. Smart Grid 4(3), 1401–1410 (2013)

    Article  Google Scholar 

  10. Scherer, H.F., Pasamontes, M., Guzmán, J.L., Álvarez, J.D., Camponogara, E., Normey-Rico, J.E.: Efficient building energy management using distributed model predictive control. J. Process Control. 24(6), 740–749 (2014)

    Google Scholar 

  11. Asare-Bediako, B., Kling, W.L., Ribeiro, P.F.: Multi-agent system architecture for smart home energy management and optimization. In: Proceedings of the 4th IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). pp. 1–5. Lyngby, Denmark (6–9 Oct 2013)

    Google Scholar 

  12. Zhao, P., Suryanarayanan, S., Simoes, M.G.: An energy management system for building structures using a multi-agent decision-making control methodology. IEEE Trans. Ind. Appl. 49(1), 322–330 (Jan–Feb 2013)

    Google Scholar 

  13. Malysz, P., Sirouspour, S., Emadi, A.: MILP-based rolling horizon control for microgrids with battery storage. In: Proceedings of the 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013). pp. 2099–2104. Vienna, Austria (10–13 Nov 2013)

    Google Scholar 

  14. Téllez Molina, M.B., Gafurov, T., Prodanovic, M.: Proactive control for energy systems in smart buildings. In: Proceedings of the 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe). pp. 1–8. Manchester, UK (5–7 Dec 2011)

    Google Scholar 

  15. Atzeni, I., Ordőez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R.: Noncooperative and cooperative optimization of distributed energy generation and storage in the demand-side of the smart grid. IEEE Trans. Signal Process. 61(10), 2454–2472 (2013)

    Article  MathSciNet  Google Scholar 

  16. Tarasak, P., Chai, C.C., Kwok, Y.S., Oh, S.W.: Demand bidding program and its application in hotel energy management. IEEE Trans. Smart Grid 5(2), 821–828 (2014)

    Article  Google Scholar 

  17. Costanzo, G.T., Zhu, G., Anjos, M.F., Savard, G.: A system architecture for autonomous demand side load management in smart buildings. IEEE Trans. Smart Grid 3(4), 2157–2165 (2012)

    Article  Google Scholar 

  18. Zhao, Z., Lee, W.C., Shin, Y., Song, K.B.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)

    Article  Google Scholar 

  19. He, M., Murugesan, S., Zhang, J.: A multi-timescale scheduling approach for stochastic reliability in smart grids with wind generation and opportunistic demand. IEEE Trans. Smart Grid 4(1), 521–529 (2013)

    Article  Google Scholar 

  20. Moradzadeh, B., Tomsovic, K.: Two-stage residential energy management considering network operational constraints. IEEE Trans. Smart Grid 4(4), 2339–2346 (2013)

    Article  Google Scholar 

  21. Danandeh, A., Zhao, L., Zeng, B.: Job scheduling with uncertain local generation in smart buildings: Two-stage robust approach. IEEE Trans. Smart Grid 5(5), 2273–2282 (2014)

    Article  Google Scholar 

  22. Li, D., Jayaweera, S.K., Naseri, A.: Auctioning game based demand response scheduling in smart grid. In: Proceedings of the 2011 IEEE Online Conference on Green Communications (GreenCom). pp. 58–63. New York, NY (26–29 Sept 2011)

    Google Scholar 

  23. Qian, L.P., Zhang, Y.J.A., Huang, J., Wu, Y.: Demand response management via real-time electricity price control in smart grids. IEEE J. Sel. Areas Commun. 31(7), 1268–1280 (2013)

    Article  Google Scholar 

  24. Hillier, F., Lieberman, G.: Introduction to operations research. McGraw-Hill, New York, seventh edn. (2001)

    Google Scholar 

  25. Schrijver, A.: Theory of Linear and Integer Programming. Wiley, Chichester (1998)

    MATH  Google Scholar 

  26. Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (2004)

    MATH  Google Scholar 

  27. Maciejowski, J.M.: Predictive Control with Constraints. Prentice Hall, Essex (2002)

    MATH  Google Scholar 

  28. Rossiter, J.A.: Model-Based Predictive Control: A Practical Approach. CRC Press, Boca Raton (2003)

    Google Scholar 

  29. Lofberg, J.: YALMIP: a toolbox for modeling and optimization in MATLAB. In: Proceedings of the 2004 IEEE International Symposium on Computer Aided Control Systems Design. pp. 284–289. Taipei, Taiwan (4 Sept 2004)

    Google Scholar 

  30. Achterberg, T.: SCIP: solving constraint integer programs. Math. Program. Comput. 1(1), 1–41 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  31. Achterberg, T., Berthold, T., Koch, T., Wolter, K.: Constraint integer program-ming: A new approach to integrate cp and mip. In: Perron, L., Trick, M.A. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. Lecture Notes in Computer Science, vol. 5015, pp. 6–20. Springer, Berlin Heidelberg, Berlin (2008)

    Chapter  Google Scholar 

  32. Currie, J., Wilson, D.I.: OPTI: Lowering the barrier between open source opti- mizers and the industrial MATLAB user. In: Proceedings of the 2012 Foundations of Computer-Aided Process Operations (FOCAPO). pp. 1–6. Savannah, GA (8–11 Jan 2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical Systems Unit, IMDEA Energy Institute, 28935, Móstoles, Spain

    Jorn K. Gruber & Milan Prodanovic

Authors
  1. Jorn K. Gruber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milan Prodanovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jorn K. Gruber .

Editor information

Editors and Affiliations

  1. Cardiff School of Art & Design, The Ecological Built Environment group (EBERE), Cardiff Metropolitan University, Cardiff, United Kingdom

    John Littlewood

  2. Bartlett School Env, Energy & Resou, University College London Bartlett School Env, Energy & Resou, London, United Kingdom

    Catalina Spataru

  3. KES International, Shoreham-by-Sea, United Kingdom

    Robert J. Howlett

  4. School of Electrical and Information Engineering, University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Gruber, J.K., Prodanovic, M. (2017). Two-Stage Optimization for Building Energy Management. In: Littlewood, J., Spataru, C., Howlett, R., Jain, L. (eds) Smart Energy Control Systems for Sustainable Buildings. Smart Innovation, Systems and Technologies, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-319-52076-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-52076-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52074-2

  • Online ISBN: 978-3-319-52076-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation