首页|Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

扫码查看
原文链接
  • 维普
  • 万方数据
Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active dis-tribution networks(ADNs)to facilitate integration of distributed renewable generation.Due to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their operations.To tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible samples.However,since ADNs are mostly operated in a normal status,only very few historical samples are infeasible.Thus,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network training.To address this issue,we propose an enhanced constraint learning method.First,it leverages constraint learning to train a neural network as surrogate of ADN's model.Then,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of histori-cal dataset.By incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural network.Furthermore,we establish a trust region to constrain and thereafter enhance reliability of the solution.Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

Deep learningdemand responsedistribution networksimbalanced dataoptimal power flow

Yonghua Song、Ge Chen、Hongcai Zhang

展开 >

State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering,University of Macau,Macao 999078,China

School of Electrical and Computer Engineering,Purdue University,West Lafayette,IN 47907 USA

Science and Technology Development Fund,Macau SAR,ChinaScience and Technology Development Fund,Macau SAR,ChinaScience and Technology Development Fund,Macau SAR,China

SKL-IOTSCUM-2021-20230003/2020/AKP0011/2021/AGJ

2024

10.17775/CSEEJPES.2023.05970

中国电机工程学会电力与能源系统学报(英文版)
中国电机工程学会

中国电机工程学会电力与能源系统学报(英文版)

CSTPCDEI
ISSN：2096-0042
年,卷(期)：2024.10(1)
  • 41