Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis. Issue 2 (April 2023)

• Record Type: Journal Article
• Authors: Maheshwari, Ankur; Sood, Yog Raj; Jaiswal, Supriya
• Is Part Of: Wind engineering. Volume 47:Issue 2(2023)
• Page Start: 464
• Page End: 490
• Publication Date: 2023-04
• Subjects: Optimal power flow -- teaching-learning-based optimization -- renewable energy sources -- renewable uncertainty -- cost and emission -- voltage profile enhancement -- transmission losses; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X221124000
• Languages: English
• ISSNs: 0309-524X

Abstract

Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The Teaching Learning Based Optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.