An improved pelican optimization algorithm for solving stochastic optimal power flow problem of power systems considering uncertainty of renewable energy resources

• Proposing an improved pelican optimization algorithm (POA) based on mutation strategy, fitness distance balanced (FDB) and exploitation-based gorilla troops optimizer (GTO) strategies to enhance the overall exploration and exploitation strength of the traditional POA. • Solving a stochastic optimal power flow (S-OPF) engineering problem of a power system with uncertain renewable energy resources using improved POA. • Validating the performance of the proposed improved POA in comparison with traditional existsing solvers such as sand cat swarm optimization (SCSO), grey wolf optimizer (GWO), zebra optimization algorithm (ZOA), dandelion optimizer (DO) and pelican optimization algorithm (POA) using statistical and non-parametric tests via CEC 2020 test suite. 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.