.png){kind=icon}
返回
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling
.png){kind=icon}
PRE
AI
DOI：10.1109/TIA.2024.3522486.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To coordinate the interests of operator and users in a microgrid under complex and changeable operating conditions, this paper proposes a microgrid scheduling model considering the thermal flexibility of thermostatically controlled loads and demand response by leveraging physical informed-inspired deep reinforcement learning (DRL) based bi-level programming. To overcome the non-convex limitations of Karush-Kuhn-Tucker (KKT)-based methods, a novel optimization solution method based on DRL theory is proposed to handle the bi-level programming through alternate iterations between levels. Specifically, by combining a DRL algorithm named asynchronous advantage actor-critic (A3C) and automated machine learning-prioritized experience replay (AutoML-PER) strategy to improve the generalization performance of A3C to address the above problems, an improved A3C algorithm, called AutoML-PER-A3C, is designed to solve the upper-level problem; while the DOCPLEX optimizer is adopted to address the lower-level problem. In this solution process, AutoML is used to automatically optimize hyperparameters and PER improves learning efficiency and quality by extracting the most valuable samples. The test results demonstrate that the presented approach manages to reconcile the interests between multiple stakeholders in MG by fully exploiting various flexibility resources. Furthermore, in terms of economic viability and computational efficiency, the proposal vastly exceeds other advanced reinforcement learning methods.
Keyword:
Microgrids
Stakeholders
Optimal scheduling
Load modeling
Job shop scheduling
Programming
Renewable energy sources
Deep reinforcement learning
Switches
Iterative methods
Bi-level scheduling
deep reinforcement learning
demand response
microgrid
thermostatically controlled load
期刊
IF:
4.5
论文数:
1.1W
被引数:
3.5W
机构
引用论文
Sustainable and Resilient Distribution Systems With Networked Microgrids.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Optimal scheduling of integrated demand response-enabled integrated energy systems with uncertain renewable generations: A Stackelberg game approach具有不确定可再生能源的集成需求响应支持的集成能源系统的最佳调度: Stackelberg博弈方法OAAI
OAAI分享收藏Deep Reinforcement Learning-Based Approach for Proportional Resonance Power System Stabilizer to Prevent Ultra-Low-Frequency Oscillations基于深度强化学习的比例谐振电力系统稳定器防超低频振荡方法OAAI
OAAI分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects智能电网运营的深度强化学习: 算法、应用和前景PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Decarbonization efforts hindered by China's slow progress on electricity market reformsNATURE SUSTAINABILITY
NATURE SUSTAINABILITYIF27.1
PREAI
分享收藏Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market考虑可再生能源和实时电力市场的多微网随机规划APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏
查看更多内容