COMPUTATIONALLY EFFICIENT METHOD FOR MULTI-OBJECTIVE RISK-LIMITING OPTIMAL POWER FLOW PROBLEMS
Shin-Yeu Lin, Yu-Syuan Cheng
Strony: 20-27
Opublikowano: 6 Sep 2018
Wyświetlenia: 2,321
Pobrania: 292
Streszczenie: To balance the non-renewable power generation cost and the risk of violating security constraints in the presence of uncertain renewable power generation, a multi-objective risk-limiting optimal power flow (MORLOPF) problem is presented in this paper. To tackle this problem, a curve fitting based computationally efficient method is proposed. Simulation and comparison results show that the proposed method is computationally much faster than the previously developed golden section method based two-level algorithm, and the obtained results are almost the same.
Słowa kluczowe: renewable energy, security constraints, optimal non-renewable power generation, multi-objective risk-limiting optimal power flow, golden section metho
Cytowanie artykułu: Shin-Yeu Lin, Yu-Syuan Cheng. COMPUTATIONALLY EFFICIENT METHOD FOR MULTI-OBJECTIVE RISK-LIMITING OPTIMAL POWER FLOW PROBLEMS. Journal of International Scientific Publications: Materials, Methods & Technologies 12, 20-27 (2018). https://www.scientific-publications.net/en/article/1001679/
Powrót do spisu treści tomu
© 2026 The Author(s). This is an open access article distributed under the terms of the
Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This permission does not cover any third party copyrighted material which may appear in the work requested.
Disclaimer: The Publisher and/or the editor(s) are not responsible for the statements, opinions, and data contained in any published works. These are solely the views of the individual author(s) and contributor(s). The Publisher and/or the editor(s) disclaim any liability for injury to individuals or property arising from the ideas, methods, instructions, or products mentioned in the content.
