A Day-Ahead Chance Constrained Volt/Var Control Scheme With Renewable Energy Sources by Novel Scenario Generation Method in Active Distribution Networks

With the integration of huge renewable energy sources (RESs) into active distribution networks, how to address the uncertainty outputs of RESs for the day-ahead volt/var control (VVC) is a significant challenge. This paper presents a chance constrained mixed integer second order cone model to handle the nodal power uncertainties and nonlinear branch flow equations. A direct and fast scenario generation method is proposed by employing the group division method and the seven-step probability distribution model of RESs outputs. An efficient and accurate solution method which only uses few larger probability level scenarios instead of all reserved scenarios is also proposed. Numerical simulations on the IEEE 69 standard system show the superiority of the proposed algorithm over traditional Monte Carlo sampling (MCS)-based methods.

Automated summary

This paper introduces a chance constrained mixed integer second order cone model to handle uncertainties in nodal power and nonlinear branch flow equations due to the integration of large amounts of renewable energy sources. A direct and fast scenario generation method is proposed for RES outputs using a seven-step probability distribution model, along with an efficient solution that utilizes only a few larger probability level scenarios. Numerical simulations on the IEEE 69 standard system demonstrate the superiority of the proposed algorithm over traditional Monte Carlo sampling-based methods.