Online Optimization in Power Systems with High Penetration of Renewable Generation: Advances and Prospects

TL;DR

This paper reviews recent advances in online optimization techniques for power systems with high renewable penetration, emphasizing their distinctions, applications, and future prospects in managing fluctuating generation and controllable loads.

Contribution

It provides a comprehensive comparison of four key online optimization methods used in power systems, clarifying their differences and potential applications.

Findings

Four main online optimization techniques are identified and compared.

The paper highlights the importance of online methods for integrating renewable energy.

Future research directions in online power system optimization are proposed.

Abstract

Traditionally, offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation. The increasing penetration of fluctuating renewable generation and Internet-of-Things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain, and have redirected attention to online optimization methods. However, online optimization is a broad topic that can be applied in and motivated by different settings, operated on different time scales, and built on different theoretical foundations. This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used. In particular, we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain, i.e., optimization-guided dynamic control, feedback optimization for single-period problems, Lyapunov-based optimization, and online convex optimization techniques for multi-period problems. Lastly, we recommend some potential future directions for online optimization in the power systems domain.