A novel optimization strategy for line loss reduction in distribution
The DG line loss optimization problem in the power electronic distribution network addressed in this paper is a planning problem to minimize the total line loss by reasonably
Two-layer optimization model of distribution network line loss
Scholars have conducted optimization research on distribution networks by constructing single-layer models and adopting artificial intelligence methods.
Research on Line Loss Calculation Method of Distribution Network
This paper proposes a probabilistic line loss calculation method for distribution networks based on the Gaussian Mixture Model (GMM). First, a GMM-based model of node injection current is constructed.
Distribution Network Line Loss Allocation Method Taking into
The method in this paper analyzes the relationship between the operation status change of the distribution network and the line loss after the distributed power supply is connected, and establishes
Distribution network line loss analysis method based on improved
An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out.
Research on loss reduction strategy of distribution network based on
In this paper, we first determine the line loss by the improved power flow algorithm, and then determine the location and capacity of distributed generation by the improved gray wolf
Continuous Line Loss Calculation Method for Distribution Network
For the line loss calculation of medium-voltage distribution networks containing DGs with high-density collection data, a continuous line loss calculation method for the distribution network
Two-layer optimization model of distribution network line loss
This article proposes a scenario generation method using a generative adversarial network (GAN) to handle the uncertainty associated with DGs and constructs a two-layer
Multi-Time Granularity Line Loss Prediction for Distribution Network
With the rapid advancement of smart grid construction and the increasing maturity of big data and artificial intelligence technologies in recent years, the exponential growth of grid operation
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