On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations

TL;DR

This paper addresses degeneracy issues in multi-parametric programming for smart grid optimization, proposing novel methods to identify all critical regions and an improved exploration technique for distributed LP/QP, enhancing computational efficiency.

Contribution

It introduces a new approach to handle degeneracies in mpLP/QP and an enhanced critical region exploration method for distributed optimization in smart grids.

Findings

Effective degeneracy handling method for mpLP/QP.

Accelerated convergence with cutting plane and adaptive stepsize.

Verified computational efficiency on multi-area scheduling benchmarks.

Abstract

Improving renewable energy resource utilization efficiency is crucial to reducing carbon emissions, and multi-parametric programming has provided a systematic perspective in conducting analysis and optimization toward this goal in smart grid operations. This paper focuses on two aspects of interest related to multi-parametric linear/quadratic programming (mpLP/QP). First, we study degeneracy issues of mpLP/QP. A novel approach to deal with degeneracies is proposed to find all critical regions containing the given parameter. Our method leverages properties of the multi-parametric linear complementary problem, vertex searching technique, and complementary basis enumeration. Second, an improved critical region exploration (CRE) method to solve distributed LP/QP is proposed under a general mpLP/QP-based formulation. The improved CRE incorporates the proposed approach to handle degeneracies. A cutting plane update and an adaptive stepsize scheme are also integrated to accelerate convergence under different problem settings. The computational efficiency is verified on multi-area tie-line scheduling problems with various testing benchmarks and initial states.