Optimal Energy Management in Autonomous Power Systems with Probabilistic Security Constraints and Adaptive Frequency Control

TL;DR

This paper presents an optimal energy management system for autonomous power systems that co-optimizes frequency control and energy dispatch, incorporating adaptive uncertainty quantification and stability guarantees, validated on an offshore platform.

Contribution

It introduces a convex reformulation of the energy management problem that integrates probabilistic security constraints and adaptive frequency control for isolated power systems.

Findings

Efficient solution achieved through convex reformulation.

The system guarantees frequency stability under uncertainty.

Validated on a real offshore platform case.

Abstract

The decarbonization of many heavy power-consuming industries is dependent on the integration of renewable energy sources and energy storage systems in isolated autonomous power systems. The optimal energy management in such schemes becomes harder due to the increased complexity and stability requirements, the rapidly varying operating conditions and uncertainty of renewable sources, the conflicting objectives across different timescales, the limited amount of reliable power sources and energy storage. The state of charge management when energy storage is used for multiple services, such as optimal scheduling and frequency support, is one of the most notorious problems in this context. To address this issue, an optimal energy management system is proposed in this paper. It co-optimizes the primary frequency control layer and the dispatch schedule of conventional generators and energy storage by taking advantage of an algorithm that provides adaptive active power demand uncertainty quantification, theoretical guarantees for frequency stability, and bounds for the reserves for frequency support assigned to the energy storage system. A convex reformulation is derived enabling the efficient solution of the involved optimization problem, being a test case of an isolated offshore oil and gas platform presented for validation.