A Digital Twin-Oriented Decision Support Framework for Lifecycle-Aware Peak-Shaving in Hybrid ETO Off-Shore Vessels

This paper presents a digital twin-oriented decision-support frame work for lifecycle-aware evaluation of energy management strategies in hybrid diesel-battery offshore supply vessels operating under dynamic positioning (DP) conditions. The framework integrates a structured DP load model, including hotel, thruster, and transient burst components, with rule-based peak-shaving control strategies and lifecycle-oriented performance indicators within a unified simulation environment. Three energy management strategies are comparatively gated: a fixed-threshold strategy, a state of charge (SOC)-adaptive strategy, and an SOC load derivative adaptive strategy. The strategies are evaluated using multiple operational and lifecycle-related indicators, including fuel consumption, peak diesel loading, battery energy throughput, and degradation relevance. The objective is to analyze the trade-offs between short-term operational efficiency and long term battery utilization. Simulation results over a 24-h DP operational profile show that adaptive strategies outperform the fixed-threshold baseline in terms of both fuel efficiency and peak mitigation. Among the investigated approaches, the SOC adaptive strategy provides the most balanced overall performance, reducing cumulative battery throughput by approximately 25–30% relative to the fixed-threshold strategy while maintaining competitive peak reduction (15–20%) and fuel savings (8–12%). In contrast, the SOC-load derivative strategy achieves stronger transient peak mitigation at the cost of increased battery utilization. The results demonstrate that computationally lightweight rule-based strategies, when formulated with state awareness and lifecycle considerations, can provide effective decision support for hybrid maritime energy systems. From a digital twin perspective, the proposed framework is intended as a digital twin-oriented simulation and decision-support environment rather than a fully operational real-time digital twin.