Skip to main content

Intraday Energy Resource Scheduling for Load Aggregators Considering Local Market

  • 419 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1401)

Abstract

Demand response (DR) programs and local markets (LM) are two suitable technologies to mitigate the high penetration of distributed energy resources (DER) that is vastly increasing even during the current pandemic in the world. It is intended to improve operation by incorporating such mechanisms in the energy resource management problem while mitigating the present issues with Smart Grid (SG) technologies and optimization techniques. This paper presents an efficient intraday energy resource management starting from the day-ahead time horizon, which considers load uncertainty and implements both DR programs and LM trading to reduce the operating costs of three load aggregator in an SG. A random perturbation was used to generate the intraday scenarios from the day-ahead time horizon. A recent evolutionary algorithm HyDE-DF, is used to achieve optimization. Results show that the aggregators can manage consumption and generation resources, including DR and power balance compensation, through an implemented LM.

Keywords

  • Aggregator
  • Computational intelligence
  • Demand response
  • Energy resource management
  • Local market
  • Smart grid

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-87869-6_22
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-87869-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    https://www.gams.com/latest/docs/T_SCENRED.html.

References

  1. Dileep, G.: A survey on smart grid technologies and applications. Renew. Energy 146, 2589–2625 (2020). https://doi.org/10.1016/j.renene.2019.08.092

    CrossRef  Google Scholar 

  2. Soares, J., Pinto, T., Lezama, F., Morais, H.: Survey on complex optimization and simulation for the new power systems paradigm. Complexity 2018, 1–32 (2018). https://doi.org/10.1155/2018/2340628

    CrossRef  Google Scholar 

  3. Soares, J., Borges, N., Vale, Z., Oliveira, P.B.: Enhanced multi-objective energy optimization by a signaling method. Energies 9, 807 (2016). https://doi.org/10.3390/en9100807

    CrossRef  Google Scholar 

  4. Lezama, F., Sucar, L.E., de Cote, E.M., Soares, J., Vale, Z.: Differential evolution strategies for large-scale energy resource management in smart grids. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1279–1286. ACM, Berlin (2017). https://doi.org/10.1145/3067695.3082478

  5. Talha, M., Saeed, M.S., Mohiuddin, G., Ahmad, M., Nazar, M.J., Javaid, N.: Energy optimization in home energy management system using artificial fish swarm algorithm and genetic algorithm. In: Barolli, L., Woungang, I., Hussain, O.K. (eds.) INCoS 2017. LNDECT, vol. 8, pp. 203–213. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-65636-6_18

  6. Martnez-Lpez, Y., Rodrguez-Gonzlez, A.Y., Quintana, J.M., Mayedo, M.B., Moya, A., Santiago, O.M.: Applying some EDAs and hybrid variants to the ERM problem under uncertainty. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1–2. ACM, Cancn (2020). https://doi.org/10.1145/3377929.3398393

  7. Soares, J., Lobo, C., Silva, M., Vale, Z., Morais, H.: Day-ahead distributed energy resource scheduling using differential search algorithm. In: 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP), pp. 1–6. IEEE, Porto (2015). https://doi.org/10.1109/ISAP.2015.7325567

  8. Zamani, A.G., Zakariazadeh, A., Jadid, S.: Day-ahead resource scheduling of a renewable energy based virtual power plant. Appl. Energy 169, 324–340 (2016). https://doi.org/10.1016/j.apenergy.2016.02.011

    CrossRef  Google Scholar 

  9. Silva, M., Fernandes, F., Morais, H., Ramos, S., Vale, Z.: Hour-ahead energy resource management in university campus microgrid. In: 2015 IEEE Eindhoven PowerTech. pp. 1–6. IEEE, Eindhoven (2015). https://doi.org/10.1109/PTC.2015.7232449

  10. Liu, W., Qi, D., Wen, F.: Intraday residential demand response scheme based on peer-to-peer energy trading. IEEE Trans. Ind. Inf. 16, 1823–1835 (2020). https://doi.org/10.1109/TII.2019.2929498

    CrossRef  Google Scholar 

  11. Lezama, F., Soares, J., Faia, R., Vale, Z.: Hybrid-adaptive differential evolution with decay function (HyDE-DF) applied to the 100-digit challenge competition on single objective numerical optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 7–8. ACM, Prague (2019)

    Google Scholar 

  12. Lezama, F., Soares, J., Faia, R., Pinto, T., Vale, Z.: A new hybrid-adaptive differential evolution for a smart grid application under uncertainty. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE, Rio de Janeiro (2018). https://doi.org/10.1109/CEC.2018.8477808

Download references

Acknowledgement

This research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017(CENERGETIC), CEECIND/02814/2017, and UIDB/000760/2020.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jos Almeida .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Almeida, J., Soares, J., Canizes, B., Razo-Zapata, I., Vale, Z. (2022). Intraday Energy Resource Scheduling for Load Aggregators Considering Local Market. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_22

Download citation