.png){kind=icon}
返回
Efficient Constraint Learning for Data-Driven Active Distribution Network Operation
.png){kind=icon}
OA
AI
DOI：10.1109/TPWRS.2023.3251724.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Scheduling flexible sources to promote the integration of renewable generation is one fundamental problem for operating active distribution networks (ADNs). However, existing works are usually based on power flow models, which require network parameters (e.g., topology and line impedance) that may be unavailable in practice. To address this issue, we propose an efficient constraint learning method to operate ADNs. This method first trains multilayer perceptrons (MLPs) based on historical data to learn the mappings from decisions to constraint violations and power loss. Then, power flow constraints can be replicated by these MLPs without network parameters. We further prove that MLPs learn constraints by formulating a union of disjoint polytopes to approximate the corresponding feasible region. Thus, the proposed method can be interpreted as a piecewise linearization method, which also explains its desirable ability to replicate complex constraints. Finally, a novel pruning method is developed to remove the useless binary variable solutions in advance, which can enhance the solution's reliability and reduce the computational complexity. Numerical experiments based on the IEEE 33- and 123-bus test systems validate that the proposed method can achieve desirable optimality, feasibility, and computational efficiency simultaneously.
Keyword:
Active distribution networks
deep learning
renewable generation
optimal power flow
flexible sources
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
引用论文
MPC-Based Coordinated Voltage Regulation for Distribution Networks With Distributed Generation and Energy Storage System基于MPC的含分布式发电和储能系统的配电网协调调压
OAAI
OAAI分享收藏A systematic review of the costs and impacts of integrating variable renewables into power grids将可变可再生能源纳入电网的成本和影响的系统回顾.png)
NATURE ENERGY
NATURE ENERGYIF60.1
PREAI
分享收藏
查看更多内容