Optimal Power Flow with Renewable Generation: A Modified NSGA-II-based Probabilistic Solution Approach

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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of control, automation & electrical systems automation & electrical systems, 2020-08, Vol.31 (4), p.979-989
Main Authors: Araujo, Elaynne Xavier Souza, Cerbantes, Marcel Chuma, Mantovani, José Roberto Sanches
Format: Article
Language:English
Subjects:
Continuity (mathematics)
Control
Control and Systems Theory
Electrical Engineering
Engineering
Genetic algorithms
Integer programming
Mechatronics
Multiple objective analysis
Optimization
Power
Power flow
Power sources
Reactive power
Renewable resources
Robotics
Robotics and Automation
Sorting algorithms
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 2 m 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.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-020-00596-7