Optimal Power Flow with Renewable Generation: A Modified NSGA-II-based Probabilistic Solution Approach - Araujo, Elaynne Xavier Souza (2020)
The rapid expansion of renewable generation has drastically increased the planning complexity of modern power systems as additional uncertainties, environmental concerns, and technical–economic issues should be accounted for. Within this context, the best operation performance of contemporary power system operators (SOs) depends not just on tractable realistic optimal power flow (OPF) formulations, but also on powerful optimization approaches. In this work, a tractable life-like multi-objective probabilistic OPF-based model for the SO’s medium-term operation considering high penetration of renewable resources is proposed. This model includes an explicit formulation of the operation of dispatchable and non-dispatchable generation, shunt reactive power sources, and under-load tap-changing (ULTC) transformers. The resulting model is a large-scale probabilistic multi-objective non-convex nonlinear mixed-integer programming (NLMIP) problem with continuous, discrete, and binary variables. To ensure tractability, uncertainties are modeled through a fast and efficient 2m probabilistic approach. To handle the nonlinearities and non-continuous variables that characterize the problem, a modified non-dominated sorting genetic algorithm (NSGA)-II solution approach is proposed and effectively tested.