.png){kind=icon}
返回
Multi-Objective Interval Optimization Dispatch of Microgrid via Deep Reinforcement Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TSG.2023.3339541.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents an improved deep reinforcement learning (DRL) algorithm for solving the optimal dispatch of microgrids under uncertaintes. First, a multi-objective interval optimization dispatch (MIOD) model for microgrids is constructed, in which the uncertain power output of wind and photovoltaic (PV) is represented by interval variables. The economic cost, network loss, and branch stability index for microgrids are also optimized. The interval optimization is modeled as a Markov decision process (MDP). Then, an improved DRL algorithm called triplet-critics comprehensive experience replay soft actor-critic (TCSAC) is proposed to solve it. Finally, simulation results of the modified IEEE 118-bus microgrid validate the effectiveness of the proposed approach.
Keyword:
Deep reinforcement learning
microgrid
uncertainty
interval optimization
experience replay
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
Data-driven stochastic energy management of multi energy system using deep reinforcement learning.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏
A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties考虑多场景区间不确定性的混合可再生能源CCHP系统的两阶段随机鲁棒优化ENERGY
ENERGYIF9.4
PREAI
分享收藏Improved Soft Actor-Critic: Mixing Prioritized Off-Policy Samples With On-Policy Experiences改进的软行动者-批评家: 将优先的非政策样本与政策经验混合在一起
PREAI
分享收藏分享收藏
查看更多内容