Multi-Objective Control Co-design Using Graph-Based Optimization for Offshore Wind Farm Grid Integration

TL;DR

This paper presents a graph-based control co-design optimization framework for offshore wind farm grid integration, optimizing energy storage sizing and control to improve system reliability and efficiency.

Contribution

It introduces a novel control co-design formulation with a graph-based optimization approach for offshore wind farm grid integration, addressing system complexity and multi-objective optimization.

Findings

Successfully identified Pareto optimal solutions for multi-objective control co-design.

Demonstrated the framework on an IEEE-9 bus system with offshore wind farms.

Enabled decision-makers to select optimal trade-offs among multiple objectives.

Abstract

Offshore wind farms have emerged as a popular renewable energy source that can generate substantial electric power with a low environmental impact. However, integrating these farms into the grid poses significant complexities. To address these issues, optimal-sized energy storage can provide potential solutions and help improve the reliability, efficiency, and flexibility of the grid. Nevertheless, limited studies have attempted to perform energy storage sizing while including design and operations (i.e., control co-design) for offshore wind farms. As a result, the present work develops a control co-design optimization formulation to optimize multiple objectives and identify Pareto optimal solutions. The graph-based optimization framework is proposed to address the complexity of the system, allowing the optimization problem to be decomposed for large power systems. The IEEE-9 bus system is treated as an onshore AC grid with two offshore wind farms connected via a multi-terminal DC grid for our use case. The developed methodology successfully identifies the Pareto front during the control co-design optimization, enabling decision-makers to select the best compromise solution for multiple objectives.