Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources

Khan, I. U., Javid, N., Gamage, K. A.A. , Taylor, C. J., Baig, S. and Ma, X. (2020) Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources. IEEE Access, 8, pp. 148622-148643. (doi: 10.1109/ACCESS.2020.3015473)

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Today’s electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWObased approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.

Item Type:Articles
Additional Information:The work was in part supported by the UK Engineering & Physical Sciences Research Council (EPSRC) grant EP/R02572X/1.
Glasgow Author(s) Enlighten ID:Gamage, Professor Kelum
Authors: Khan, I. U., Javid, N., Gamage, K. A.A., Taylor, C. J., Baig, S., and Ma, X.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
ISSN (Online):2169-3536
Published Online:14 August 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in IEEE Access 8: 148622-148643
Publisher Policy:Reproduced under a Creative Commons License

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