Distributed Scenario-Based Optimization for Asset Management in a Hierarchical Decision Making Environment

TL;DR

This paper introduces a hierarchical, stochastic optimization framework for power system asset management, utilizing scenario approximation and distributed algorithms to improve maintenance scheduling efficiency and cost-effectiveness.

Contribution

It presents a novel multi-layered decision-making model with a scalable scenario-based approach and a distributed algorithm for power system asset management.

Findings

The proposed method reduces maintenance costs compared to heuristics.

Efficient Monte-Carlo simulations balance accuracy and tractability.

Demonstrated on PJM 5-bus system with promising results.

Abstract

Asset management attempts to keep the power system in working conditions. It requires much coordination between multiple entities and long term planning often months in advance. In this work we introduce a mid-term asset management formulation as a stochastic optimization problem, that includes three hierarchical layers of decision making, namely the mid-term, short-term and real-time. We devise a tractable scenario approximation technique for efficiently assessing the complex implications a maintenance schedule inflicts on a power system. This is done using efficient Monte-Carlo simulations that trade-off between accuracy and tractability. We then present our implementation of a distributed scenario-based optimization algorithm for solving our formulation, and use an updated PJM 5-bus system to show a solution that is cheaper than other maintenance heuristics that are likely to be considered by TSOs.