A New Efficient Methodology for AC Transmission Network Expansion Planning in The Presence of Uncertainties

TL;DR

This paper introduces a two-stage probabilistic methodology combining a 2m+1-point estimate method and a modified artificial bee colony algorithm to efficiently solve AC transmission network expansion planning problems under uncertainties, reducing computational effort.

Contribution

It presents a novel two-stage approach that significantly decreases computational time while maintaining solution quality for probabilistic AC TNEP with uncertainties.

Findings

Achieves near-optimal solutions with low computational burden.

Effective for medium-sized systems like Garver 6-bus and IEEE 24-bus.

Handles high wind generation scenarios in TNEP.

Abstract

Consideration of generation, load and network uncertainties in modern transmission network expansion planning (TNEP) is gaining interest due to large-scale integration of renewable energy sources with the existing grid. However, it is a formidable task when iterative AC formulation is used. Computational burden for solving the usual ACTNEP with these uncertainties is such that, it is almost impossible to obtain a solution even for a medium-sized system within a viable time frame. In this work, a two-stage solution methodology is proposed to obtain quick, good-quality, sub-optimal solutions with reasonable computational burden. Probabilistic formulation is used to account for the different uncertainties. Probabilistic TNEP is solved by 2m+1-point estimate method along with a modified artificial bee colony (MABC) algorithm, for Garver 6 bus and IEEE 24 bus systems. In both the systems, rated wind generation is considered to be more than one-tenth of the total generation capacity. When compared with the conventional single stage and existing solution methods, the proposed methodology is able to obtain almost identical solutions with extremely low computational burdens. Therefore, the proposed method provides a tool for efficient solution of future probabilistic ACTNEP problems with greater level of complexity.