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Review

Optimal power flow control optimisation problem incorporating conventional and renewable generation sources: a review

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Pages 1119-1150 | Received 05 Apr 2022, Accepted 28 Aug 2022, Published online: 10 Jan 2023
 

Abstract

The study of power system planning, security and control is crucial for determining optimal power flow (OPF), a highly non-linear complicated issue. The functioning of the power system remains complex when Renewable energy sources are connected to the grid along traditional generators. This review paper describes various techniques applicable to the power system networks accessible in the literature. The conventional optimisation algorithms have been reintroduced, while the power systems have been heavily predisposed over the last few years. The most crucial technical, adaptive and economic tool in this context is OPF. In this paper, we compare OPF approaches related to objective functions. Aside from computational power, the case study network and publication dates for these algorithms are described. Finally, we will address some of the basic challenges that arise from the new OPF approach to the modern grid. Mainly, optimisation techniques are compared in terms of reliability, accurateness, faster convergence and applicability for single and multiple objective functions. Initially, the paper discusses types of networks such as AC or DC, constraints and their objectives on OPF, mathematical and different optimisation techniques reviewed. The heuristic and meta-heuristic algorithms, like genetic algorithm and Jaya algorithm, were compared for various operational parameters. In this work, OPF with single- or multi-objective functions such as voltage stability, voltage deviation, emission, cost and losses, consisting of conventional and renewable energy resources were considered. Additionally, Pseudo-code algorithms were presented for some recently evolved algorithms to ease the understanding of the readers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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