Tong Ye
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Tong Ye has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Feng Pan
Metrology Center of Guangdong Power Grid Co., Ltd., Qingyuan 511547, China
1 shared publication - Lei Feng
Metrology Center of Guangdong Power Grid Co., Ltd., Qingyuan 511545, China
1 shared publication - Lihua Zhong
Metrology Center of Guangdong Power Grid Co., Ltd., Qingyuan 511545, China
1 shared publication - Shuzhe Qi
Power Measurement Center, Guangdong Power Grid Co., Ltd., Qingyuan 511547, China
1 shared publication - Yuping Huang
Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
1 shared publication - Yuyao Yang
Metrology Center of Guangdong Power Grid Co., Ltd., Qingyuan 511545, China
1 shared publication
Latest Publications
- Open Access
Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks
As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems.
Open AccessDeep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks
As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems.