Skip to main content

Abstract

This paper presents the demonstration of an energy resources management approach using a physical smart city model environment. Several factors from the industry, governments and society are creating the demand for smart cities. In this scope, smart grids focus on the intelligent management of energy resources in a way that the use of energy from renewable sources can be maximized, and that the final consumers can feel the positive effects of less expensive (and pollutant) energy sources, namely in their energy bills. A large amount of work is being developed in the energy resources management domain, but an effective and realistic experimentation are still missing. This work thus presents an innovative means to enable a realistic, physical, experimentation of the impacts of novel energy resource management models, without affecting consumers. This is done by using a physical smart city model, which includes several consumers, generation units, and electric vehicles.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 641794 (Project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the Project UID/EEA/00760/2013. Bruno Canizes is supported by FCT Funds through the SFRH/BD/110678/2015 Ph.D. scholarship.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Institutional subscriptions

References

  1. IEEE Smart Cities group website. http://smartcities.ieee.org/. Accessed Jan 2017

  2. BSi: The Role of Standards in Smart Cities. http://www.bsigroup.com/LocalFiles/en-GB/smart-cities/resources/The-Role-of-Standards-in-Smart-Cities-Issue-2-August-2014.pdf. Accessed Jan 2017

  3. Lund, H.: Renewable Energy Systems, Renewable Energy Systems – A Smart Energy Systems Approach to the Choice and Modeling of 100% Renewable Solutions, 2nd edn. Academic Press, Burlington (2014)

    Google Scholar 

  4. European Commission: The 2020 climate and energy package (2009). http://ec.europa.eu/clima/policies/package/index_en.htm. Accessed Oct 2016

  5. European Commission: 2030 framework for climate and energy policies (2014). http://ec.europa.eu/clima/policies/2030/index_en.htm. Accessed Oct 2016

  6. European Commission: A roadmap for moving to a competitive low carbon economy in 2050 (2011). http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:52011DC0112R(01). Accessed Jan 2017

  7. Sousa, T., et al.: A hybrid simulated annealing approach to handle energy resource management considering an intensive use of electric vehicles. Energy 67, 81–96 (2014)

    Article  Google Scholar 

  8. Soares, J., et al.: Multi-dimensional signaling method for population-based metaheuristics: solving the large-scale scheduling problem in smart grids. Swarm Evol. Comput. 29, 13–32 (2016)

    Article  Google Scholar 

  9. Iqbal, M., et al.: Optimization classification, algorithms and tools for renewable energy: a review. Renew. Sustain. Energy Rev. 39, 640–654 (2014)

    Article  Google Scholar 

  10. Conejo, A.J., Carrión, M., Morales, J.M.: Decision Making Under Uncertainty in Electricity Markets. Springer, New York (2010)

    Book  MATH  Google Scholar 

  11. BISITE: http://www-03.ibm.com/software/products/es/intelligent-operations-center. Accessed Jan 2017

  12. DREAM-GO Project: http://www.dream-go.ipp.pt. Accessed Jan 2017

  13. Soares, J., et al.: Electric vehicle scenario simulator tool for smart grid operators. Energies 5(6), 1881–1899 (2012)

    Article  Google Scholar 

  14. TOMLAB: http://tomopt.com/tomlab. Accessed Jan 2017

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bruno Canizes .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Canizes, B., Pinto, T., Soares, J., Vale, Z., Chamoso, P., Santos, D. (2018). Smart City: A GECAD-BISITE Energy Management Case Study. In: De la Prieta, F., et al. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. PAAMS 2017. Advances in Intelligent Systems and Computing, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-61578-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61578-3_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61577-6

  • Online ISBN: 978-3-319-61578-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics