Abstract
The renewable economic emission transmit is a significant and new assignment in the modern power system. This article develops oppositional grasshopper optimization algorithm (OGOA) which depends on the social dealings of the grasshopper in nature, to solve renewable energy based economic emission dispatch (EED) considering uncertainty in wind power availability and a carbon tax on emission from the thermal unit. To speed up the convergence speed and advance the simulation results, opposition based learning (OBL) is integrated with the fundamental GOA in OGOA algorithm. To show the nonlinearity of wind power availability the Weibull distribution is used. A standard system, containing of two wind farms and six thermal units is used for testing the dispatch model for three different loads. The statistical outcomes of the applied OGOA technique are compared with basic GOA and quantum-inspired particle swarm optimization (QPSO) optimization. It is observed OGOA is more skillful than basic GOA technique for significantly reducing the computation time and developing hopeful outcomes.
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