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Demonstrates the effectiveness of genetic algorithms for optimizing power flow in offshore wind energy systems, highlighting economic and operational benefits.
The increasing penetration of intermittent renewable sources, such as wind and solar energy, introduces significant operational uncertainties and challenges for power systems. To ensure both economic efficiency and system reliability, advanced optimization methods are required to manage the balance between generation and demand. This paper presents a Genetic Algorithm (GA) based Optimal Power Flow (OPF) approach applied to a grid-connected offshore wind power plant within a Libyan 12-bus network. The proposed GA model minimizes total generation cost while maintaining voltage and power flow limits. In addition, the generated, consumed, and lost power within the network were calculated and analyzed using ETAP software. Implementation in MATLAB/Simulink demonstrates the capability of GA to handle nonlinear and stochastic characteristics associated with renewable energy integration, confirming its effectiveness for cost-efficient and stable operation of modern hybrid power systems.
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Elshaibani et al. (2026) studied this question.
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