WeChat Mini Program
Old Version Features

Energy Planning Based On Economic And Environmental Indices With Optimal Power Flow Approach

JOURNAL OF APPLIED SCIENCE AND ENGINEERING(2024)

Sichuan Agr Univ

Cited 0|Views11
Abstract
Due to the growing trend of load and the high cost of electric energy production, and the limitation in the installation of new power plant units, planning and minimizing the cost of energy generation for active units in the power plants is necessary. The purpose of optimal power flow is to allocate the optimal contribution of the power plants, provide the required power of the network, and minimize the cost of power generation. In this article, the shuffled frog leaping algorithm (SFLA) is employed for the planning of the electrical energy by optimal power flow for minimizing costs and the emission pollution in the power plants. A quadratic objective function is expressed in terms of the generation of units in which constraints are modeled as linear equal and unequal equations. The proposed method is applied to a system of IEEE -30 bus test system. The obtained results show the ability of the proposed algorithm in the optimization of objective functions. Also, the results obtained from this algorithm have been compared with the results from other evolutionary algorithm methods.
More
Translated text
Key words
Optimal power flow,Shuffled frog leaping algorithm (SFLA),Costs and the emission pollution,Quadratic objective function
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest