Multiobjective Dynamic Optimal Power Flow Considering Fuzzy-Based Smart Utilization of Mobile Electric Vehicles

In this study, an efficient and smart fuzzy logic controller (FLC) is designed for charging/discharging (C/D) nodes of plug-in electric vehicles (PEVs). The designed controller controls the amount of power to be compensated by these nodes in order to meet the required peak shaving and voltage flattening. The main focus of this work is to practically coordinate the mobile PEVs in a multiobjective security constrained dynamic optimal power flow (OPF) problem that aims at simultaneously minimizing the operation cost and emission over 24-h time horizons. In the formulation of the problem, nonsmooth, nonconvex, and nonlinear natures of valve-point effects, multifuel options, prohibited zones, and ac power flow equations are also considered. This complex problem needs a robust, fast, and powerful optimization algorithm, which is able to extract the Pareto-optimal surface (POS). Hence, a new improved black hole (IBH) algorithm is proposed with a new formulation for updating particles to allow a greater exploration and appropriate exploitation of the search space. The proposed framework is applied on IEEE 118-bus test system incorporating several aggregated PEVs to show its efficiency and ability.