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.
|Journal||International Transactions on Electrical Energy Systems|
|Early online date||19 Aug 2021|
|Publication status||E-pub ahead of print - 19 Aug 2021|
Bibliographical noteThis is the peer reviewed version of the following article: Alasali, F, Nusair, K, Obeidat, AM, Foudeh, H, Holderbaum, W. (2021) An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources. Int Trans Electr Energ Syst.; e13060, which has been published in final form at https://doi.org/10.1002/2050-7038.13060. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving.
- emission minimization
- heuristic algorithms
- fuel cost
- manta ray foraging optimization
- optimal power flow (OPF)
- renewable energy
- power loss