Research Themes
- Optimize EV charge and discharge scheduling to maximize user benefit and convenience
- Assessing the Impact of High Volume EV Charging on the Distribution System
- Probabilistic prediction of EV charging demand in a region using machine learning
- Reinforcement learning for battery charging and discharging
Related Publication
- D. Kodaira, W. Jung, and S. Han, “Optimal Energy Storage System Operation for Peak Reduction in a Distribution Network Using a Prediction Interval,” IEEE Trans. Smart Grid, vol. 11, no. 3, pp. 2208–2217, 2020.
- D. Kodaira and J. Kondoh, "Probabilistic Forecasting Model for Non-normally Distributed EV Charging Demand," 2020 International Conference on Smart Grids and Energy Systems (SGES), 2020, pp. 623-626.
- M. Seo, D. Kodaira and S. Han, "Reduction of Computational Complexity for Optimal Electric Vehicle Schedulings," 2020 IEEE Power & Energy Society General Meeting (PESGM), 2020, pp. 1-5. Selected as one of the best papers! (top 5% in 2200 papers)
- M. A. Acquah, D. Kodaira, and S. Han, “Real-time demand side management algorithm using stochastic optimization,” Energies, vol. 11, no. 5, p.1166－1179, 2018.
Related Grants
- Research on model development and demonstration of power aggregation using reinforcement learning, JSPS KAKENHI Grant-in-Aid for Young Scientists, 2023-2024, 4,550,000 JPY
研究テーマ
- EVの充放電スケジューリングを最適化し、ユーザーの利益と利便性を最大化
- 配電系統における大量のEV充電の影響の評価
- 機械学習を用いた、地域におけるEV充電需要の確率的予測
- バッテリーの充放電のための強化学習
関連出版物
- D. Kodaira, W. Jung, and S. Han, “Optimal Energy Storage System Operation for Peak Reduction in a Distribution Network Using a Prediction Interval,” IEEE Trans. Smart Grid, vol. 11, no. 3, pp. 2208–2217, 2020.
- D. Kodaira and J. Kondoh, "Probabilistic Forecasting Model for Non-normally Distributed EV Charging Demand," 2020 International Conference on Smart Grids and Energy Systems (SGES), 2020, pp. 623-626.
- M. Seo, D. Kodaira and S. Han, "Reduction of Computational Complexity for Optimal Electric Vehicle Schedulings," 2020 IEEE Power & Energy Society General Meeting (PESGM), 2020, pp. 1-5. Selected as one of the best papers! (top 5% in 2200 papers)
- M. A. Acquah, D. Kodaira, and S. Han, “Real-time demand side management algorithm using stochastic optimization,” Energies, vol. 11, no. 5, p.1166－1179, 2018.
関連助成金
- Research on model development and demonstration of power aggregation using reinforcement learning, JSPS KAKENHI Grant-in-Aid for Young Scientists, 2023-2024, 4,550,000 JPY