An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources. (19th August 2021)

• Record Type: Journal Article
• Authors: Alasali, Feras; Nusair, Khaled; Obeidat, Amr M.; Foudeh, Husam; Holderbaum, William
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-19
• Subjects: emission minimization -- fuel cost -- heuristic algorithms -- manta ray foraging optimization -- optimal power flow (OPF) -- power loss -- renewable energy; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.13060
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: An optimal power management solution is a potential tool to produce cost‐effective and environmentally friendly power supply using renewable energy sources (RESs) for the electrical power network. Therefore, the article introduces a novel optimization algorithm inspired by the vitality, namely, Manta Ray Foraging Optimization (MRFO), to figure out both multi‐ and single‐objective problems of optimal power flow (OPF) incorporating stochastic RES. The OPF problems are designed by considering four different objective functions: transmission power loss, emission index, fuel operational costs, and voltage deviation. The stochastic and volatile nature of RES increases the complexity of the OPF issue. In this study, a new MRFO algorithm and some modern metaheuristic algorithms were used to settle the issue of OPF, enhance the energy efficiency, and environmental and cost performance of the power network. The test cases, with and without RES, different RES locations on the network, increase in the load, and outages of some transmission lines, are considered by addressing the challenge of the proposed OPF. These cases are tested with bus systems as 30 and 118, and the outcome from the suggested MRFO is compared with six metaheuristic optimization algorithms. Moreover, OPF challenges are successfully settled by the MRFO algorithm and outperform the proposed metaheuristic optimization methods. Abstract : The stochastic and volatile nature of RES increases the complexity of the OPF issue or achieve higher energy efficiency, environment and cost performance of the power network. A novel optimization algorithm inspired by the vitality Manta Ray Foraging Optimization (MRFO) and some modern metaheurism algorithms, to figure out and solve Optimal Power Flow (OPF) problems incorporating stochastic RES.