Utilizing genetic algorithm to detect collusive opportunities in deregulated energy markets

Warning The system is temporarily closed to updates for reporting purpose.

Esen, Barış (2019) Utilizing genetic algorithm to detect collusive opportunities in deregulated energy markets. [Thesis]

[thumbnail of 10236810_BarisEsen.pdf] PDF
10236810_BarisEsen.pdf

Download (1MB)

Abstract

Deregulated electricity markets allow competition over the electricity price among the power companies. However, in an oligopolistic environment, the strategic behavior of the power companies in the electricity market may lead to collusive opportunities. The independent system operator (ISO) is an authorized entity which is responsible for administrating the electricity market. Therefore, ISO shall be able to detect and avoid collusive opportunities among generators. In this study, we propose a metaheuristics approach to assist ISO in the decision-making process to prevent collusions. We develop a method, based on principles of genetic algorithm to detect the collusive opportunities in deregulated electricity markets. We test our algorithm on three problems of varying size. Our results are promising in terms of both speed and accuracy. For the large-scale problem, our algorithm works much faster than the existing alternatives in the literature.
Item Type: Thesis
Uncontrolled Keywords: Deregulated electricity markets. -- Genetic algorithms. -- Parameter estimating. -- Collusion opportunities. -- Independent system operator. -- Serbestleşmiş elektrik piyasası. -- Genetik algoritması. -- Parametre tahmini. -- Gizli anlaşma olasılıkları. -- Bağımsız sistem yöneticisi.
Subjects: T Technology > T Technology (General) > T055.4-60.8 Industrial engineering. Management engineering
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Industrial Engineering
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 28 Mar 2019 14:43
Last Modified: 26 Apr 2022 10:29
URI: https://research.sabanciuniv.edu/id/eprint/36934

Actions (login required)

View Item
View Item