Integrated Optimization of Distribution System in The Presence of DGs and EVs: Reconfiguration, Charging Management, and Reactive Power Coordination Using MINLP

This study presents an integrated framework for optimizing distribution systems in the presence of distributed generations (DGs) and electric vehicles (EVs). The proposed model investigates several key scenarios: distribution system reconfiguration (DSR), optimal siting of DGs, simultaneous DSR and DGs siting, and finally, the optimization of active power (P) from the EVs charging process coordinated with the reactive power (Q) support from these vehicles. These elements are formulated within a mixed-integer nonlinear programming (MINLP) framework and are executed in two distinct steps for the scenario involving EVs integration. In this work, trigonometric functions and linearization approximations are eliminated from the power flow equations. They are replaced by a matrix representation based on the real and imaginary components of voltage and current. This approach reduces computational complexity and enables the attainment of a global optimal solution using the branch-and-bound algorithm in the GAMS environment. The model's objectives are threefold: minimizing active power loss (APL), reducing voltage deviation (VD), and improving the voltage profile. Simulation results on the standard 33-bus test system demonstrate that the proposed two-step framework achieves a reduction in APL of up to 96.21%, improves VD by 99.55%, and enhances the voltage profile. Consequently, the system performance indices are significantly enhanced compared to the conventional particle swarm optimization (PSO) method.