Probabilistic Optimal Power Flow in Large-Scale Electric Transmission Systems through a Matheuristic Solution Approach

This paper proposes a new optimization methodology to solve the AC optimal power flow (OPF) problem considering renewable energy sources (RES). The formulation of the OPF problem comprises the minimization of power generation costs and gas emissions considering a set of operational and physical constraints. This minimization is achieved through controlling power dispatch generators, position changing of the tap transformers, and controllable reactive shunt compensation. RES and demand uncertainties are modeled using the (2m+1) point-estimate method. The mathematical formulation of the OPF problem is a mixed-integer nonlinear programming multiobjective model. A matheuristic algorithm is proposed to solve this problem efficiently, combining a classic nonlinear OPF model and the Variable Neighborhood Descent (VND) metaheuristic algorithm. The potential of the proposed algorithm is shown through numerical experiments carried out using the IEEE 300-bus systems.

Automated summary

This paper proposes a new optimization methodology to solve the AC optimal power flow problem considering renewable energy sources. It minimizes power generation costs and gas emissions by controlling power dispatch generators, adjusting transformer positions, and using controllable reactive shunt compensation. The proposed algorithm combines a mathematical model and a metaheuristic algorithm to efficiently solve the problem, and its potential is demonstrated through numerical experiments.