Estimating Lower Probability Bound of Power System's Capability to Fully Accommodate Variable Wind Generation

TL;DR

This paper proposes a novel method to estimate the lower probability bound of a power system's ability to fully accommodate variable wind generation, addressing uncertainty challenges with a MILP-based optimization approach.

Contribution

It introduces a three-level optimization framework and reformulates the feasibility check into MILP to evaluate power system capacity under wind uncertainty.

Findings

Effective estimation of lower probability bounds demonstrated on IEEE systems

The MILP reformulation enables efficient computation of system capability

Method addresses uncertainty in wind generation for reliable power system operation

Abstract

As the penetration of wind generation increases, the uncertainty it brings has imposed great challenges to power system operation. To cope with the challenges, tremendous research work has been conducted, among which two aspects are of most importance, i.e. making immune operation strategies and accessing the power system's capability to accommodate the variable energy. Driven and inspired by the latter problem, this paper will discuss the power system's capability to accommodate variable wind generation in a probability sense. Wind generation, along with its uncertainty is illustrated by a polyhedron, which contains prediction, risk and uncertainty information. Then, a three-level optimization problem is presented to estimate the lower probability bound of power system's capability to fully accommodate wind generation. After reformulating the inner \emph{max-min} problem, or feasibility check problem, into its equivalent mixed-integer linear program (MILP) form, the bisection algorithm is presented to solve this challenging problem. Modified IEEE systems are adopted to show the effectiveness of the proposed method.