SOPF-Based Adaptive Droop Control for Hybrid AC--HVDC Grids Under Offshore Wind Uncertainty

TL;DR

This paper introduces a stochastic optimal power flow-based adaptive droop control method for hybrid AC-HVDC grids with offshore wind, improving voltage regulation under wind uncertainty.

Contribution

It proposes a novel SOPF-based adaptive droop framework that uses Polynomial Chaos Expansion to derive optimal gains directly from wind forecast uncertainties.

Findings

Scenario-adaptive gains outperform fixed gains during extreme wind disturbances.

The method embeds statistical voltage-security guarantees into local converter control.

Validation on a 4-terminal system shows significant error reduction.

Abstract

The integration of massive offshore wind into hybrid AC-HVDC grids demands robust DC voltage regulation, yet conventional fixed-gain droop controllers struggle under severe stochastic volatility. This paper bridges the gap between system-level economic dispatch and converter-level control by proposing a novel Stochastic Optimal Power Flow (SOPF)-based adaptive droop framework. Rather than relying on heuristic or reactive tuning, wind forecast uncertainty is modeled using a zone-wise Beta distribution that accurately captures the heteroscedastic nature of wind errors across low, mid, and high power regimes. By leveraging Polynomial Chaos Expansion (PCE) within a chance-constrained SOPF, the system's stochastic states are formulated analytically. Crucially, the optimal adaptive droop gain is extracted directly from the first-order PCE coefficients via a Jacobian-free sensitivity analysis, embedding statistical voltage-security guarantees directly into the local converter control. Validation on a 4-terminal AC-HVDC system demonstrates that scenario-adaptive gains significantly outperform standard fixed-coefficient approaches, effectively minimizing active-power tracking errors during extreme wind disturbances.