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Stochastic optimization of home energy management system using clustered quantile scenario reduction

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  • Kim, Minsoo
  • Park, Taeseop
  • Jeong, Jaeik
  • Kim, Hongseok

Abstract

Recent proliferation of renewable energy has increased the installation of residential energy sources (e.g., roof-top photovoltaic (PV) panel and residential wind turbine) in households. To manage electricity usage incurred by renewable energy and residential load, home energy management systems (HEMSs) provide intelligence to home by real-time monitoring and controlling appliances. In this paper, we propose a novel HEMS framework considering multiple uncertainties from renewable generation and load profiles. First, we generate scenarios of each uncertainty through deep learning. Then, we propose an algorithm called clustered quantile scenario reduction (CQSR) to reduce computation time while preserving the stochastic properties of generated scenarios based on the Wasserstein-1 distance. We prove that solution of CQSR is determined by the number of clustered scenarios. Also, we show provable upper bound of performance degradation incurred by the scenario reduction. Simulation results show that the optimality gap and computation time of the proposed framework is substantially reduced compared to other HEMS algorithms, e.g., by up to 81.4% and 93.7%, respectively. Furthermore, although the original scenarios are generated through different scenario generation algorithms, HEMS using CQSR is less vulnerable to performance degradation incurred by scenario reduction.

Suggested Citation

  • Kim, Minsoo & Park, Taeseop & Jeong, Jaeik & Kim, Hongseok, 2023. "Stochastic optimization of home energy management system using clustered quantile scenario reduction," Applied Energy, Elsevier, vol. 349(C).
    • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923009194
    DOI: 10.1016/j.apenergy.2023.121555
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    References listed on IDEAS

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    1. Yohwan Choi & Hongseok Kim, 2016. "Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost," Energies, MDPI, vol. 9(6), pages 1-19, June.
    2. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    3. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
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    Cited by:

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    2. Chen, Shuiwang & Wu, Lingxiao & Ng, Kam K.H. & Liu, Wei & Wang, Kun, 2024. "How airports enhance the environmental sustainability of operations: A critical review from the perspective of Operations Research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).

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