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Intraday Energy Resource Scheduling for Load Aggregators Considering Local Market

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


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.


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

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  • DOI: 10.1007/978-3-030-87869-6_22
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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.

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Correspondence to Jos Almeida .

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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.

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