Power generation cost minimization of the grid-connected hybrid renewable energy system through optimal sizing using the modified seagull optimization technique

Abstract

A hybrid renewable power system is studied in this paper. This system is composed of PV panels, wind turbines, inverter, rectifier, electrolyzer, and fuel cell such that it prioritizes storing excess energy by converting it to hydrogen and using it later in the fuel cell. Additional extra generation power is sold to the main electricity network. The system generates power from clean sources and has zero emission. To achieve minimum power generation cost, the optimal size of each component is obtained using the proposed modified seagull optimization technique. The cooperative generation of wind and PV and energy storage mitigate the uncertain behavior of renewable energy sources. The proposed hybrid power system and optimization are implemented in a real-world case study in Qingdao, China. The consumption and meteorological data of the year 2018 are used as input to the system. To prove the superiority of the proposed optimization method in terms of accuracy and computation time, it is compared to two other optimization methods, namely original seagull optimization algorithm (SOA) and modified farmland fertility algorithm (MFFA), which are used in similar applications. The proposed method has achieved 2.02% and 2.78% better results and converged 69.36% and 47.07% faster compared to conventional SOA and MFFA methods, respectively. In addition, the hybrid system is able to operate with high reliability and loss of power supply probability well below the threshold of 8.96e20.