.png){kind=icon}
.png){kind=icon}
Return
Energy Optimization for Microgrids Based on Uncertainty-Aware Deep Deterministic Policy Gradient
.png){kind=icon}
OA
AI
DOI：10.3390/pr13041047.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The randomness, volatility, and intermittency of renewable energy sources such as wind and solar energy present significant challenges to energy management in microgrids, resulting in low management efficiency and poor accuracy. This paper proposes an energy optimization method for microgrids based on an uncertainty-aware deep deterministic policy gradient (DDPG) algorithm. First, considering the uncertainty of renewable energy output, an uncertainty awareness model is constructed based on information gap decision theory (IGDT). Second, the DDPG algorithm is employed to optimize the energy scheduling strategy, incorporating a bidirectional feedback collaborative optimization framework. The uncertainty radius is used for forward feedback adjustment of the optimization step size of the DDPG model, while the risk-aversion coefficient of the IGDT model is adjusted via backward feedback based on the DDPG optimization results. This approach enables adaptive regulation in dynamic and complex environments. The research demonstrates that the proposed algorithm significantly enhances the robustness, convergence, and adaptability of the microgrid in uncertain environments, improving peak shaving and valley filling performance as well as the adaptability to fluctuations in renewable energy sources. The proposed method demonstrates significant improvements in robustness, convergence speed, and adaptability when applied to microgrid energy management. Numerical results show a 5.44% and 70.26% improvement in total microgrid revenue compared to baseline algorithms, highlighting the effectiveness of the uncertainty-aware DDPG algorithm in dynamic and uncertain environments.
Keywords:
microgrid
energy optimization
uncertainty awareness
DDPG
bidirectional feedback
Journal
IF:
2.8
Papers:
6.7K
Citations:
3.7W