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Handling Load Uncertainty during On-Peak Time via Dual ESS and LSTM with Load Data Augmentation

Author

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  • Jin Sol Hwang

    (Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea)

  • Jung-Su Kim

    (Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea)

  • Hwachang Song

    (Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea)

Abstract

This paper proposes a scheduling method of dual ESSs (Energy Storage Systems) for the purpose of reducing the peak load when there are sudden loads or generation changes during the on-peak time. The first ESS is scheduled once a day based on a day-ahead load prediction, and the second ESS is scheduled every 15 min during on-peak time based on a short-term load prediction by LSTM (Long Short-Term Memory). Special attention is paid to training the LSTM for the short-term load prediction by using the augmented past load data which is generated by adding possible uncertainties to the past load and temperature data. Based on the load forecast, optimization problems for the scheduling are formulated. The proposed scheduling method is validated using load and temperature data from a real building. In other words, when the proposed method is applied to the real building energy data in the case study, it not only shaves the peak load during on-peak time interval effectively but also results in lower electricity price although there are sudden load or temperature changes during the time interval.

Suggested Citation

  • Jin Sol Hwang & Jung-Su Kim & Hwachang Song, 2022. "Handling Load Uncertainty during On-Peak Time via Dual ESS and LSTM with Load Data Augmentation," Energies, MDPI, vol. 15(9), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3001-:d:797751
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    References listed on IDEAS

    as
    1. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    2. Jin Sol Hwang & Ismi Rosyiana Fitri & Jung-Su Kim & Hwachang Song, 2020. "Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast," Energies, MDPI, vol. 13(21), pages 1-18, October.
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    Cited by:

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    2. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.

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