Skip to main content
SpringerLink
Log in
Book cover

International Conference on Sustainable Energy for Smart Cities

SESC 2019: Sustainable Energy for Smart Cities pp 110–124Cite as

Comparison of Thermal Load Models for MILP-Based Demand Response Planning

  • Conference paper
  • First Online:

Abstract

Demand response has the potential to reduce end-users electricity costs by promoting judicious use of existing power system infrastructure. This is most often assumed to require the adoption of time-varying electricity prices which can make load scheduling and energy resource management difficult to carry out in a time-effective and comfortable way without computational assistance and automated control. Automated home energy management systems can facilitate this process including by providing users with optimised plans. Creating these plans requires optimisation tools operating on mathematical models of the underlying problem. Mixed-integer linear programming (MILP) has been used extensively for this purpose though increasing complexity and time resolution can render this approach impractical. In this paper, we describe and compare MILP formulations of the same demand response problems using alternative thermal load models. The results, obtained using a state-of-the-art solver, can be summarised as follows: (1) the elimination of continuous temperature variables in one thermal load submodel increased the computation time in 99% of cases and by 981% on average; (2) two new discrete control formulations leading to a 40% reduction in the number of binary variables relative to the standard formulation were found to decrease the computation time in approximately 63% of cases and by 38–40% on average. Efforts are ongoing to evaluate these techniques under more diverse scenarios.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. Strbac, B.: Demand side management: benefits and challenges. Energy Policy 36(12), 4419–4426 (2008)

    Article  Google Scholar 

  2. Siano, P.: Demand response and smart grids—a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)

    Article  Google Scholar 

  3. Hubert, T., Grijalva, S.: Modeling for residential electricity optimization in dynamic pricing environments. IEEE Trans. Smart Grid 3(4), 2224–2231 (2012)

    Article  Google Scholar 

  4. Good, N., Ellis, K.A., Mancarella, P.: Review and classification of barriers and enablers of demand response in the smart grid. Renew. Sustain. Energy Rev. 72, 57–72 (2017)

    Article  Google Scholar 

  5. Shareef, H., Ahmed, M.S., Mohamed, A., Al Hassan, E.: Review on home energy management system considering demand responses, smart technologies, and intelligent controllers. IEEE Access 6, 24498–24509 (2018)

    Article  Google Scholar 

  6. Du, P., Lu, N.: Appliance commitment for household load scheduling. IEEE Trans. Smart Grid 2, 411–419 (2011)

    Article  Google Scholar 

  7. Fux, S.F., Benz, M.J., Guzzella, L.: Economic and environmental aspects of the component sizing for a stand-alone building energy system: a case study. Renew. Energy 55, 438–447 (2013)

    Article  Google Scholar 

  8. Patteeuw, D., Helsen, L.: Residential buildings with heat pumps, a verified bottom-up model for demand side management studies. In: International Conference on System Simulation in Buildings, Liège, Belgium, 10–12 October (2014)

    Google Scholar 

  9. Althaher, S., Mancarella, P., Mutale, J.: Automated demand response from home energy management system under dynamic pricing and power and comfort constraints. IEEE Trans. Smart Grid 6(4), 1874–1883 (2015)

    Article  Google Scholar 

  10. Arteconi, A., Patteeuw, D., Bruninx, K., Delarue, E., Dhaeseleer, W., Helsen, L.: Active demand response with electric heating systems: impact of market penetration. Appl. Energy 177, 636–648 (2016)

    Article  Google Scholar 

  11. Renaldi, R., Kiprakis, A., Friedrich, D.: An optimisation framework for thermal energy storage integration in a residential heat pump heating system. Appl. Energy 186, 520–529 (2017)

    Article  Google Scholar 

  12. Iria, J.P., Soares, F.J., Matos, M.A.: Trading small prosumers flexibility in the energy and tertiary reserve markets. IEEE Trans. Smart Grid 10(3), 2371–2382 (2019)

    Article  Google Scholar 

  13. Schütz, T., Streblow, R., Müller, D.: A comparison of thermal energy storage models for building energy system optimization. Energy Build. 93, 23–31 (2015)

    Article  Google Scholar 

  14. Steen, D., Stadler, M., Cardoso, G., Groissböck, M., DeForest, N., Marnay, C.: Modeling of thermal storage systems in MILP distributed energy resource models. Appl. Energy 137, 782–792 (2015)

    Article  Google Scholar 

  15. Wang, H., et al.: Demand response through smart home energy management using thermal inertia. In: 2013 Australasian Universities Power Engineering Conference (AUPEC), pp. 1–6. IEEE (2013)

    Google Scholar 

  16. Wang, H., Meng, K., Dong, Z.Y., Xu, Z., Luo, F., Wong, K.P.: A MILP approach to accommodate more building integrated photovoltaic system in distribution network. In: 2015 IEEE Power & Energy Society General Meeting, pp. 1–5. IEEE (2015)

    Google Scholar 

  17. Bradac, Z., Kaczmarczyk, V., Fiedler, P.: Optimal scheduling of domestic appliances via MILP. Energies 8, 217–232 (2015)

    Article  Google Scholar 

  18. Antunes, C.H., Rasouli, V., Alves, M.J., Gomes, Á.: A mixed-integer linear programming model for optimal management of residential electrical loads under dynamic tariffs. In: 2018 International Conference on Smart Energy Systems and Technologies (SEST), Sevilla, 2018, pp. 1–6 (2018)

    Google Scholar 

  19. Baeten, B., Rogiers, F., Patteeuw, D., Helsen, L.: Comparison of optimal control formulations for stratified sensible thermal energy storage in space heating applications. In: The 13th International Conference on Energy Storage (2015)

    Google Scholar 

  20. Bapat, T., Sengupta, N., Ghai, S.K., Arya, V., Shrinivasan, Y.B., Seetharam, D.: User-sensitive scheduling of home appliances. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Green Networking, pp. 43–48. ACM (2011)

    Google Scholar 

  21. Antunes, C.H., Rasouli, V., Alves, M.J., Gomes, Á., Costa, J.J., Gaspar, A.: A discussion of mixed integer linear programming models of thermostatic loads in demand response. In: 2nd International Symposium on Energy System Optimization, Karlsruhe, Germany, 10–11 October 2018 (2018)

    Google Scholar 

  22. Bozchalui, M.C., Hashmi, S.A., Hassen, H., Canizares, C.A., Bhattacharya, K.: Optimal operation of residential energy hubs in smart grids. IEEE Trans. Smart Grid 3(4), 1755–1766 (2012)

    Article  Google Scholar 

  23. Alves, M.J., Antunes, C.H., Carrasqueira, P.: A hybrid genetic algorithm for the interaction of electricity retailers with demand response. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 459–474. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31204-0_30

    Chapter  Google Scholar 

  24. Gonçalves, I., Gomes, Á., Antunes, C.H.: Optimizing the management of smart home energy resources under different power cost scenarios. Appl. Energy 242, 351–363 (2019)

    Article  Google Scholar 

  25. Gomes, Á., Antunes, C.H., Martinho, J.: A physically-based model for simulating inverter type air conditioners/heat pumps. Energy 50, 110–119 (2013)

    Article  Google Scholar 

  26. Harrington, L., Aye, L., Fuller, B.: Impact of room temperature on energy consumption of household refrigerators: lessons from analysis of field and laboratory data. Appl. Energy 211, 346–357 (2018)

    Article  Google Scholar 

  27. Mutch, J.: Residential water heating: fuel conservation, economics, and public policy. R-1498-NSF. RAND Corporation (1974)

    Google Scholar 

  28. Vielma, J.P.: Mixed integer linear programming formulation techniques. SIAM Rev. 57(1), 3–57 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was partially supported by projects UID/MULTI/00308/2013 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT - Portuguese Foundation for Science and Technology and Regional Operational Program of the Center Region (CENTRO2020) within projects ESGRIDS (POCI-01-0145-FEDER-016434) and MAnAGER (POCI-01-0145-FEDER-028040).

Author information

Authors and Affiliations

  1. INESC Coimbra, Departamento de Engenharia Electrotécnica e de Computadores, Universidade de Coimbra, Rua Sílvio Lima, 3030-290, Coimbra, Portugal

    Pedro L. Magalhães & Carlos Henggeler Antunes

Authors
  1. Pedro L. Magalhães
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Henggeler Antunes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro L. Magalhães .

Editor information

Editors and Affiliations

  1. Department of Industrial Electronics, University of Minho, Guimaraes, Portugal

    João L. Afonso

  2. Department of Industrial Electronics, University of Minho, Guimaraes, Portugal

    Vítor Monteiro

  3. Department of Industrial Electronics, University of Minho, Guimaraes, Portugal

    José Gabriel Pinto

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Magalhães, P.L., Antunes, C.H. (2020). Comparison of Thermal Load Models for MILP-Based Demand Response Planning. In: Afonso, J., Monteiro, V., Pinto, J. (eds) Sustainable Energy for Smart Cities. SESC 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-030-45694-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-45694-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45693-1

  • Online ISBN: 978-3-030-45694-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation