A Hybrid IMFO-RNN Technique for Solving Economic Emission Dispatch Problem in Wind-Thermal-Hydro Power Systems
This paper presents a hybrid technique for tackling the economic emission dispatch (EED) problem in the wind-thermal-hydro power systems. The proposed hybrid technique is the combination of the two effective approaches named as Improved Moth-Fly Optimization Algorithm (IMFOA) and the Recurrent Neural Network, so the proposed hybrid approach is named as IMFO-RNN technique. Subject to the wind power uncertainty and pumped storage units, the combination of the thermal generators is optimized by the IMFO algorithm. Likewise, to guarantee the predominant utilization of wind power, the uncertain events of the wind power are determined by the RNN. Consequently, the total economic cost of the system is limited by the proposed approach. The effectiveness of the proposed method is analyzed by the six and ten generating units of the thermal system at the cost of fuel and emission, while in the meantime the two contradictory objectives are optimized. To investigate the effect of the proposed strategy, this is actualized in the MATLAB/Simulink platform and obtained results are equated with the other techniques; this comparison results in delight that the proposed approach is impressive technique to solve the EED problem in the wind-thermal-hydro power systems.
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