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Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast

Author

Listed:
  • Jin Sol Hwang

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

  • Ismi Rosyiana Fitri

    (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 an optimal Energy Storage System (ESS) scheduling algorithm Building Energy Management System (BEMS). In particular, the focus is placed on how to reduce the peak load using ESS and load forecast. To this end, first, an existing deep learning-based load forecast method is applied to a real building energy prediction and it is shown that the deep learning-based method leads to an accuracy-enhanced load forecast. Second, an optimization problem is formulated in order to devise an ESS scheduling. In the optimization problem, the objective function and constraints are defined such that the peak load is reduced; the cost for electricity is minimized; and the ESS’s lifetime is elongated considering the accuracy-enhanced load forecast, real-time electricity price, and the state-of-charge of the ESS. For the purpose of demonstrating the effectiveness of the proposed ESS scheduling method, it is implemented using a real building load power and temperature data. The simulation results show that the proposed method can reduce the peak load and results in smooth charging and discharging, which is important for the ESS lifetime.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5633-:d:435920
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    References listed on IDEAS

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    1. Nam-Kyu Kim & Myung-Hyun Shim & Dongjun Won, 2018. "Building Energy Management Strategy Using an HVAC System and Energy Storage System," Energies, MDPI, vol. 11(10), pages 1-15, October.
    2. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    3. Rodrigo Martins & Holger C. Hesse & Johanna Jungbauer & Thomas Vorbuchner & Petr Musilek, 2018. "Optimal Component Sizing for Peak Shaving in Battery Energy Storage System for Industrial Applications," Energies, MDPI, vol. 11(8), pages 1-22, August.
    4. Uddin, Moslem & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Abd Halim, Syahirah & Abu Bakar, Ab Halim & Chia Kwang, Tan, 2018. "A review on peak load shaving strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3323-3332.
    5. 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.
    6. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
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    Cited by:

    1. Jieyun Zheng & Linyao Zhang & Jinpeng Chen & Guilian Wu & Shiyuan Ni & Zhijian Hu & Changhong Weng & Zhi Chen, 2021. "Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM," Energies, MDPI, vol. 14(8), pages 1-14, April.
    2. Yunho Kim & Yunha Park & Hyuncheol Seo & Jungha Hwang, 2023. "Load Prediction Algorithm Applied with Indoor Environment Sensing in University Buildings," Energies, MDPI, vol. 16(2), pages 1-14, January.
    3. 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.

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