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인공지능을 기반으로 한 에너지저장장치 최적운영

Optimal Operation of Energy Storage Devices based on Artificial Intelligence

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초록

Energy storage system (ESS) is a power storage device to increase power utilization efficiency by storing generated electricity and supplying power, when needed. As the number of ESS explosion accidents has been rapidly increasing over the past two to three years, a failure diagnosis study on ESS fire safety should be preceded for its continuous use. When the ESS is operated at high temperature for a long time, aging is caused owing to deterioration, and hence, stable operation is required. Among the recent 28 fires, 15 of them occurred during charging and resting. Because rapid temperature and humidity changes occur during charging and discharging, it is important to predict the trend of change. This study predicts ESS operation patterns through an artificial intelligence-based prediction algorithm, and also diagnoses ESS failures through a supervised learning-based confusion matrix. When a failure pattern is found in the ESS, a method to reduce damage through air conditioner control is studied.

Abstract

Energy storage system (ESS) is a power storage device to increase power utilization efficiency by storing generated electricity and supplying power, when needed. As the number of ESS explosion accidents has been rapidly increasing over the past two to three years, a failure diagnosis study on ESS fire safety should be preceded for its continuous use. When the ESS is operated at high temperature for a long time, aging is caused owing to deterioration, and hence, stable operation is required. Among the recent 28 fires, 15 of them occurred during charging and resting. Because rapid temperature and humidity changes occur during charging and discharging, it is important to predict the trend of change. This study predicts ESS operation patterns through an artificial intelligence-based prediction algorithm, and also diagnoses ESS failures through a supervised learning-based confusion matrix. When a failure pattern is found in the ESS, a method to reduce damage through air conditioner control is studied.

참고문헌 (11)

  1. Ministry of Trade, Industry and Energy (Korea), "Power System Reliability and Electricity Quality Maintenance Standard", Notification No. 2018-104, ~ (2018)
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  3. National Fire Research Institute, "Basic Research for Analysis of Main Ignition Causes of ESS", ~ (2019)
  4. 2020 Grid Energy Storage Technology Cost and Performance Assessment, "Mongird, K.", Energy, ~ (2020)
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  7. Abutayeh, M., "Optimizing Thermal Energy Storage Operation", Solar Energy, 120, pp.318~329 (2015)
  8. Pereira, J., "Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention", 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.1275~1282 (2018)
  9. Kim, T., "Forecasting Stock Prices with a Feature Fusion LSTM-CNN Model Using Different Representations of the Same Data", PloS one, 14, pp.0212320~ (2019)
  10. Livieris, I. E., "A CNN–LSTM Model for Gold Price Time-series Forecasting", Neural Computing and Applications, 32, pp.17351~17360 (2020)
  11. Wang, J, "Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model", Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2, pp.225~230 (2016)

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