Control Co-Design Under Uncertainty for Offshore Wind Farms: Optimizing Grid Integration, Energy Storage, and Market Participation

2025-04-11

AI Summary

This study addresses the coupled optimization challenge of grid integration, energy storage (ES) configuration, and electricity market participation for offshore wind farms under dual uncertainties in wind resource availability and electricity prices. Method: We propose the first control-design co-optimization framework, integrating stochastic and robust optimization techniques within a multi-stage scenario tree model. The resulting mixed-integer nonlinear programming (MINLP) formulation jointly optimizes wind turbine dispatch, ES sizing and operation, grid interface design, and market bidding strategies. Contribution/Results: A novel methodology is introduced for joint optimization of onshore ES capacity and siting to maximize long-term revenue. Case studies across five representative U.S. West Coast offshore sites demonstrate that the proposed approach improves integrated market revenue by 3.2% compared to conventional sequential design, while significantly enhancing flexibility in responding to power fluctuations and improving scheduling robustness.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. This paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2% and provide flexibility in managing wind resource uncertainties.

Problem

• Optimizing grid integration and market participation for offshore wind farms
• Addressing uncertainties in wind resources and energy prices
• Enhancing revenue through optimal energy storage sizing

Innovation

• Control co-design optimizes grid integration and market participation
• Optimal energy storage sizing enhances revenue for wind farms
• Framework addresses wind and price uncertainties effectively