Confidence Estimation Transformer for Long-term Renewable Energy
Forecasting in Reinforcement Learning-based Power Grid Dispatching
- URL: http://arxiv.org/abs/2204.04612v1
- Date: Sun, 10 Apr 2022 06:18:23 GMT
- Title: Confidence Estimation Transformer for Long-term Renewable Energy
Forecasting in Reinforcement Learning-based Power Grid Dispatching
- Authors: Xinhang Li, Zihao Li, Nan Yang, Zheng Yuan, Qinwen Wang, Yiying Yang,
Yupeng Huang, Xuri Song, Lei Li, Lin Zhang
- Abstract summary: This paper proposes a confidence estimation Transformer for long-term renewable energy forecasting in reinforcement learning-based power grid dispatching.
Experiments carried out on the SG-126 power grid simulator show that Conformer-RLpatching achieves great improvement over the second best algorithm DDPG in security score.
- Score: 17.41017767906797
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The expansion of renewable energy could help realizing the goals of peaking
carbon dioxide emissions and carbon neutralization. Some existing grid
dispatching methods integrating short-term renewable energy prediction and
reinforcement learning (RL) have been proved to alleviate the adverse impact of
energy fluctuations risk. However, these methods omit the long-term output
prediction, which leads to stability and security problems on the optimal power
flow. This paper proposes a confidence estimation Transformer for long-term
renewable energy forecasting in reinforcement learning-based power grid
dispatching (Conformer-RLpatching). Conformer-RLpatching predicts long-term
active output of each renewable energy generator with an enhanced Transformer
to boost the performance of hybrid energy grid dispatching. Furthermore, a
confidence estimation method is proposed to reduce the prediction error of
renewable energy. Meanwhile, a dispatching necessity evaluation mechanism is
put forward to decide whether the active output of a generator needs to be
adjusted. Experiments carried out on the SG-126 power grid simulator show that
Conformer-RLpatching achieves great improvement over the second best algorithm
DDPG in security score by 25.8% and achieves a better total reward compared
with the golden medal team in the power grid dispatching competition sponsored
by State Grid Corporation of China under the same simulation environment. Codes
are outsourced in https://github.com/buptlxh/Conformer-RLpatching.
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