Stochastic Second-Order Conic Programming for Optimal Sizing of Distributed Generator Units and Electric Vehicle Charging Stations

The increased penetration of electric vehicles (EVs) and distributed generator (DG) units has led to uncertainty in distribution systems. These uncertainties—which have not been adequately considered the literature—can entail risks optimal sizing EV charging stations (EVCSs) DG active network planning. This paper proposes a method for obtaining EVCSs (considering uncertainty), achieve exact power system analysis ensure driver satisfaction. To model uncertainties planning, this study first generates scenarios each asset using probability that considers characteristics. In step, wind-turbine (WT), PV, EVCS are modeled applying Weibull, exponential, kernel density estimation (KDE), generated random sampling. Then, k-means clustering is carried out scenario reduction representative abstract. occurrence assigned depending on number observations within cluster. integrated into all assets through joint probability. applied optimization problem framework. proposed (to minimize line loss voltage deviation) formulated via stochastic second-order conic programming, reflect under an AC flow; convex can be solved polynomial time. tested modified IEEE 15 bus system, simulation performed with various objective functions. results demonstrate effectiveness method.