Hybrid Fuzzy and Flower Pollination Optimization Algorithm for Optimal Dispatch of Generating Units in the Existence of Electric Vehicles

Abstract

The primary aim of the utility must be delivering of power supply to the utility customers with the minimal cost. Therefore, it is essential to prepare the optimal load dispatch strategy for minimization of generation cost. However, with the increase in environmental consciousness and impact of global warming, the emission dispatch from the generating stations should be viewed seriously along with generation cost reduction. The joined optimization of generation cost and emission cost has been referred as Dynamic Economic and Emission Dispatch (DEED). The combined objective function is subject to power flow, generator limit and ramp rate constraints for providing better operating conditions at the generation and transmission system. Additionally, the rapid increase of Plug-in Electric Vehicles (PEV's) in power networks makes the allocation of generating units more dynamic. The allocation of generating units under the dynamic behavior of PEV's at the energy network requires a dynamic optimization procedure. This paper proposes a hybrid Fuzzy and Flower Pollination Optimization Algorithm (FFPOA) for optimal load and emission dispatching. FFPOA is used to find the optimal solution and fuzzy is used to combine both economic and emission dispatch together. In addition, the solution process addresses the presence of PEV's in the power network along with the normal electric loads. The validation of the proposed algorithm is done with two benchmark test cases.