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Amalgamation of Machine Learning Techniques with Optical Systems: A Futuristic Approach Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Optimal Rescheduling for Transmission Congestion Management Using Intelligent Hybrid Optimization

Authors : Ward Ul Hijaz Paul, Anwar Shahzad Siddiqui, Sheeraz Kirmani

Published in: Artificial Intelligence of Things

Publisher: Springer Nature Switzerland

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Abstract

Power system congestion seems to be one of the principal issues that organizations like system operators deal with on a routine basis in the current restructured energy market. Congestion on the transmission lines creates significant issues for the efficient operational management of the deregulated power system and raises the cost of transmitting power which is an important factor of consideration. Therefore, it is crucial to examine various methods which can help in reducing this problem of congestion occurring in transmitting power in the power system network. Among the most effective methods to solve this congestion issue is rescheduling the generation side of the power system network. This research proposes a new fuzzy based hybrid optimization technique which is established on hybridization of particle swarm optimization and genetic algorithm optimization technique for minimizing this congestion cost. Utilizing the modified IEEE 57 bus system, the effectiveness of the given methodology is evaluated, and the results are discussed.

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previous chapter Role of Federated Learning for Internet of Vehicles: A Systematic Review
next chapter Predictive Load Management Using IoT and Data Analytics
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Metadata
Title
Optimal Rescheduling for Transmission Congestion Management Using Intelligent Hybrid Optimization
Authors
Ward Ul Hijaz Paul
Anwar Shahzad Siddiqui
Sheeraz Kirmani
Copyright Year
2024
Book
Artificial Intelligence of Things

Print ISBN: 978-3-031-48780-4

Electronic ISBN: 978-3-031-48781-1

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-48781-1_12

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