Welcome to the InfoSci Platform
Reference Hub1
Bilevel Optimization of Taxing Strategies for Carbon Emissions Using Fuzzy Random Matrix Generators

Bilevel Optimization of Taxing Strategies for Carbon Emissions Using Fuzzy Random Matrix Generators

Timothy Ganesan, Irraivan Elamvazuthi
Copyright: © 2022 |Pages: 25
ISBN13: 9781799871767|ISBN10: 1799871762|ISBN13 Softcover: 9781799871774|EISBN13: 9781799871781
DOI: 10.4018/978-1-7998-7176-7.ch010
Cite Chapter Cite Chapter

MLA

Ganesan, Timothy, and Irraivan Elamvazuthi. "Bilevel Optimization of Taxing Strategies for Carbon Emissions Using Fuzzy Random Matrix Generators." Smart Cities and Machine Learning in Urban Health, edited by J. Joshua Thomas, et al., IGI Global, 2022, pp. 210-234. https://doi.org/10.4018/978-1-7998-7176-7.ch010

APA

Ganesan, T. & Elamvazuthi, I. (2022). Bilevel Optimization of Taxing Strategies for Carbon Emissions Using Fuzzy Random Matrix Generators. In J. Thomas, V. Geropanta, A. Karagianni, V. Panchenko, & P. Vasant (Eds.), Smart Cities and Machine Learning in Urban Health (pp. 210-234). IGI Global. https://doi.org/10.4018/978-1-7998-7176-7.ch010

Chicago

Ganesan, Timothy, and Irraivan Elamvazuthi. "Bilevel Optimization of Taxing Strategies for Carbon Emissions Using Fuzzy Random Matrix Generators." In Smart Cities and Machine Learning in Urban Health, edited by J. Joshua Thomas, et al., 210-234. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-7176-7.ch010

Export Reference

Mendeley
Favorite

Abstract

Bilevel (BL) optimization of taxing strategies in consideration of carbon emissions was carried out in this work. The BL optimization problem was considered with two primary targets: (1) designing an optimal taxing strategy (imposed on power generation companies) and (2) developing optimal economic dispatch (ED) schema (by power generation companies) in response to tax rates. The resulting interaction was represented using Stackelberg game theory – where the novel fuzzy random matrix generators were used in tandem with the cuckoo search (CS) technique. Fuzzy random matrices were developed by modifying certain aspects of the original random matrix theory. The novel methodology was tailored for tackling complex optimization systems with intermediate complexity such as the application problem tackled in this work. Detailed performance and comparative analysis are also presented in this chapter.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.