Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Reinforcement Learning and Stochastic Optimization with Deep Learning based Forecasting on Power Grid Scheduling

Cheng Yang
*,† ,
Jihai Zhang
*,† ORCID logo ,
Wei Jiang
† ,
Li Wang
† ,
Hanwei Zhang
† ,
Zhongkai Yi
*,† ,
Fangquan Lin
*,†
†
These authors contributed equally to this work.
Version 1 : Received: 27 September 2023 / Approved: 28 September 2023 / Online: 28 September 2023 (10:14:29 CEST)

A peer-reviewed article of this Preprint also exists.

Yang, C.; Zhang, J.; Jiang, W.; Wang, L.; Zhang, H.; Yi, Z.; Lin, F. Reinforcement Learning and Stochastic Optimization with Deep Learning-Based Forecasting on Power Grid Scheduling. Processes 2023, 11, 3188. Yang, C.; Zhang, J.; Jiang, W.; Wang, L.; Zhang, H.; Yi, Z.; Lin, F. Reinforcement Learning and Stochastic Optimization with Deep Learning-Based Forecasting on Power Grid Scheduling. Processes 2023, 11, 3188.

Abstract

Continuous greenhouse gas emissions are causing global warming and impacting the habitats of many animals. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and maintaining the power grid in an economic, low-carbon, and stable is challenging. To address the issue, we propose a grid dispatching technique that combines prediction technology, reinforcement learning, and optimization technology. Prediction technology can forecast future power demand and solar power generation, while reinforcement learning and optimization technology can make charging and discharging decisions for energy storage devices based on current and future grid conditions. In the power system, the aggregation of distributed energy resources increases uncertainty, particularly due to the fluctuating generation of renewable energy. This requires the use of advanced predictive control techniques to ensure long-term economic and decarbonization goals. In this paper, we present a real-time dispatching framework that integrates deep learning-based prediction, reinforcement learning-based decision-making, and stochastic optimization techniques. The framework can rapidly adapt to target uncertainty caused by various factors in real-time data distribution and control processes. The proposed framework achieved global Champion in the NeurIPS Challenge 2022 competition and demonstrated its effectiveness in practical scenarios of intelligent building energy management.

Keywords

forecasting; reinforcement learning; power grid; planning and scheduling; uncertainty in AI; agent-based systems; deep learning; stochastic optimization

Subject

Engineering, Electrical and Electronic Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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