Utilizing genetic algorithm to detect collusive opportunities in deregulated energy markets
Year: 2019
Type: dissertation
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.
Source: Sabanci University
Author: Barış Esen
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2932431308