Distributed Stochastic ACOPF Based on Consensus ADMM and Scenario Reduction

TL;DR

This paper introduces a distributed stochastic AC optimal power flow method using consensus ADMM and scenario reduction, effectively handling load uncertainty with improved cost and reliability metrics.

Contribution

It develops a novel consensus ADMM-based framework with scenario reduction techniques for efficient stochastic ACOPF solving under load uncertainty.

Findings

Achieves about 2% cost reduction.

More than 15 times lower reliability index.

Effective scenario reduction improves computational efficiency.

Abstract

This paper presents a Consensus ADMM-based modeling and solving approach for the stochastic ACOPF. The proposed optimization model considers the load forecasting uncertainty and its induced load-shedding cost via Monte Carlo sampling. The sampled scenarios are reduced using a clustering method combined with simultaneous backward reduction techniques to reduce the computational complexity. The proposed approach is tested on two IEEE systems, achieving about 2% cost reduction and more than 15 times lower reliability index in stochastic load settings compared to the baseline approach.