Towards Online Optimization for Power Grids

TL;DR

This paper explores how distributed online optimization techniques can improve real-time decision-making in power grid management, emphasizing scalability and adaptability over traditional offline methods.

Contribution

It introduces a distributed online optimization framework tailored for power grids, highlighting its advantages and differences from offline approaches.

Findings

Distributed algorithms provide scalable solutions for power systems.

Online optimization enhances real-time decision support.

The framework demonstrates suitability for dynamic power grid environments.

Abstract

In this note, we discuss potential advantages in extending distributed optimization frameworks to enhance support for power grid operators managing an influx of online sequential decisions. First, we review the state-of-the-art distributed optimization frameworks for electric power systems, and explain how distributed algorithms deliver scalable solutions. Next, we introduce key concepts and paradigms for online optimization, and present a distributed online optimization framework highlighting important performance characteristics. Finally, we discuss the connection and difference between offline and online distributed optimization, showcasing the suitability of such optimization techniques for power grid applications.