Adaptive Online Optimization for Microgrids with Renewable Energy Sources

TL;DR

This paper introduces an adaptive online optimization algorithm for microgrid management with renewable energy, improving adaptability and performance over existing methods.

Contribution

It presents a novel control-based algorithm using the internal model principle and system identification for microgrid optimization.

Findings

Outperforms state-of-the-art algorithms in long-term microgrid management

Enhances adaptability to internal model changes

Ensures constraint compliance through projection

Abstract

In this paper we propose a novel adaptive online optimization algorithm tailored to the management of microgrids with high renewable energy penetration, which can be formulated as a constrained, online optimization problem. The proposed algorithm is characterized by a control-based design that applies the internal model principle, and a system identification routine tasked with identifying such internal model. In addition, in order to ensure the constraints are verified, we integrate a projection onto the constraint set. We showcase promising numerical results for the microgrid use case, highlighting in particular the enhanced adaptability of the proposed algorithm to changes in the internal model. The performance of the proposed algorithm is shown to outperform state-of-the-art alternative in the long-term, ensuring efficient management of the grid.