Work

Forecast-Guided Distributionally Robust Scheduling of Hybrid Energy Storage for Stability Support in Offshore Wind Farms

Year: 2026

Type: article

Abstract: High-frequency volatility and extreme tail risks in offshore wind power pose severe challenges to grid stability and economic operation. Conventional storage planning often relies on deterministic profiles or static allocation rules, failing to capture the non-stationary temporal dynamics of marine wind resources. To bridge this gap, this paper proposes a closed-loop framework that integrates ultra-short-term probabilistic forecasting with dynamic hybrid energy storage optimization. A novel Dual-Channel Residual Network is developed to provide well-calibrated predictive uncertainty quantification, which explicitly drives a Prediction-Guided Dynamic Hybrid Storage Optimization Framework. By dynamically coordinating lithium-ion batteries and liquid air energy storage based on evidential predictive variance, the proposed approach achieves superior synergy between short-term power response and long-duration energy shifting. Case studies on an offshore wind farm validate that the framework significantly reduces the Levelized Cost of Energy and loss-of-load risks while enhancing frequency regulation capabilities compared to state-of-the-art benchmarks.

Source: Mathematics

DOI: 10.3390/math14091458

Authors: Yijuan Xu, Tiandong Zhang, Zixiang Shen

Institution: Inner Mongolia University of Technology

Language: English

Topic: Power Systems and Renewable Energy

Subfield: Energy Engineering and Power Technology

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137