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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
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
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
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
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
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
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
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
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
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
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)
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
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
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., GarcÃa 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
DOI: https://doi.org/10.1007/978-3-030-87869-6_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87868-9
Online ISBN: 978-3-030-87869-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)