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CC BY V4.0, Open Access
Journal article
An improved pelican optimization algorithm for solving stochastic optimal power flow problem of power systems considering uncertainty of renewable energy resources
Results in engineering, Vol.26, 104553
2025
Abstract
This paper proposes solving the non-convex stochastic optimal power flow problem of a power system incorporating uncertain and intermittent renewable energy resources by an improved pelican optimization algorithm (IPOA). The POA, inspired from the foraging the behavior of pelicans, has stagnation problems and may trap into local optima. To avoid these, this paper has developed and implemented three novel improvements of mutation-based strategy, fitness distance balanced and exploitation-based gorilla troops strategies to enhance the exploitation and exploration strength of the traditional POA. The performance and effectiveness of the proposal are validated through statistical and non-parametric tests conducted via CEC 2019 test suite. In addition, IPOA is further used to solve a stochastic optimal power flow problem by integration of solar and wind energy to the modified IEEE 30-bus system to attain the lessen generation cost without and with inclusion of emissions. Statistical and non-parametric tests such as Wilcoxon ranking and Friedman tests validate the effectiveness of the proposed IPOA and the obtained least power generation costs and emissions for the considered numerical case studies.
Details
- Title
- An improved pelican optimization algorithm for solving stochastic optimal power flow problem of power systems considering uncertainty of renewable energy resources
- Authors/Creators
- Raheela Jamal - Qingdao Binhai UniversityNoor Habib Khan - Qingdao Binhai UniversityMohamed Ebeed - Sohag UniversityHamed Zeinoddini-Meymand - Graduate University of Advanced TechnologyFarhad Shahnia - Murdoch University, Centre for Water, Energy and Waste
- Publication Details
- Results in engineering, Vol.26, 104553
- Publisher
- Elsevier B.V.
- Number of pages
- 15
- Identifiers
- 991005753926607891
- Copyright
- © 2025 The Author(s).
- Murdoch Affiliation
- School of Engineering and Energy; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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