Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market-Reference-Cited by-同舟云学术

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Hybrid Deep Neural Network-Based Generation Rescheduling for Congestion Mitigation in Spot Power Market

Published:2022 Issue: Volume:10 Page:29267-29276
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Agrawal Anjali¹,Pandey Seema N.²,Srivastava Laxmi¹^ORCID,Walde Pratima³,Singh Saumya³,Khan Baseem⁴^ORCID,Saket R. K.³

Affiliation:

1. Department of Electrical Engineering, Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India

2. Department of Electrical Engineering, Dr. Bhim Rao Ambedkar Polytechnic College, Gwalior, Madhya Pradesh, India

3. Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India

4. Department of Electrical and Computer Engineering, Hawassa University, Hawassa, Ethiopia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09730881.pdf?arnumber=9730881

Reference30 articles.

1. Optimization of distributed generation size based on line sensitivity using transmission congestion cost

2. Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser

3. Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm

4. Managing multi-line power congestion by using Hybrid Nelder–Mead – Fuzzy Adaptive Particle Swarm Optimization (HNM-FAPSO)

5. GSA–FAPSO-Based Generators Active Power Rescheduling for Transmission Congestion Management

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1. A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines;Energy Exploration & Exploitation;2024-06-13

2. A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions;Applied Sciences;2023-11-16

3. Congestion cost estimation using adaptive red fox algorithm in restructured electricity markets;Journal of Intelligent & Fuzzy Systems;2023-11-04

4. A novel digital twin-assisted prediction approach for optimum rescheduling in high-efficient flexible production workshops;Computers & Industrial Engineering;2023-08

5. Optimal Congestion Management with FACTS Devices for Optimal Power Dispatch in the Deregulated Electricity Market;Axioms;2023-06-21

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