A Method to Overcome Key Issues in Joint Chance-Constrained AC Optimal Power Flow

This paper proposes a new method to enhance AC optimal power flow (OPF) solutions for effectively mitigating daily impacts of the uncertainties of the renewable energy sources (RES). To this end a joint chance-constrained AC OPF problem is first formulated. Then, a solution new method is proposed that aims to overcome three issues in chance-constrained OPF area: i) computation tractability while maintaining the accuracy by working with the nonlinear model as well as removing strong assumptions such as (ii) the reliance on conservative affine policies, which is valid only for small changes in operating conditions, and (iii) the RES variations are not needed to be modeled to a specific (i.e. Gaussian) probability distribution function. The proposed method relies on a finite sampling scenarios and evaluates the integrality of the total number of constraint violations based solely on continuous variables. This approach facilitates the representation of the joint chance constraint, preserving the practicality of the proposed method for real-life power system operations. The method is successfully tested on two transmission test systems, comprising 60 and 2,737 buses, respectively.