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IET Renewable Power Generation




Optimal power flow problem is one of the most important non-linear problems for power system planning and the operation of existing modern power networks. Recently, the incremental usage of renewable energy sources in power systems has revealed the significance of power system planning. Thus, the aim is to model the AC optimal power flow problem using thermal–wind–solar–tidal energy systems. In this study, uncertainties of wind, solar, and tidal energy systems were simulated using Weibull, Lognormal, and Gumbel probability distribution functions. Furthermore, the study presents solutions to the AC optimal power flow problem by including test cases of stochastic wind, solar, and tidal energy systems involving minimisation of cost function, active power loss, voltage deviation, enhancement of voltage stability, and contingency conditions. The solutions were tested via IEEE 30-bus and IEEE 118-bus test systems incorporating renewable energy sources, using different locations according to the selected thermal generating units. The symbiotic organisms search algorithm, which is one of the recently introduced optimisation algorithms, was used to solve the proposed power system planning problem, and simulation results of this algorithm were compared to the results of other algorithms such as the imperialist competitive, harmony search, backtracking search optimisation, and gravitational search algorithms.


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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.