Hybrid Optimization of Large-Scale Offshore Wind Systems with Integrated Storage Using Quasi-Monte Carlo Methods

Sammanfattning

The integration of offshore wind power into European energy systems is challenged by the inherent variability of wind resources. This study investigates the technical and economic feasibility of delivering baseload electricity through a geographically distributed network of offshore wind farms combined with large-scale energy storage. A MATLAB-based simulation model was developed to capture the dynamics of wind generation, energy storage, and transmission across three major European regions, using ERA5 wind data and ENTSO-E load profiles. The system architecture includes 18 hypothetical offshore wind parks, each with local energy storage, and a central pumped hydro storage (PHS) facility for seasonal balancing. Transmission losses and cable constraints were incorporated to reflect realistic operational conditions. The optimization problem, sizing storage and transmission capacities to minimize cost while ensuring reliable baseload supply, was formulated as a high-dimensional, non-linear, derivative-free problem. To address this complexity, a hybrid optimization framework was implemented. Global exploration employed quasi-Monte Carlo (QMC) sampling using Beta-warped Sobol sequences, ensuring uniform coverage of the 75-dimensional design space while biasing toward central parameter ranges. This approach mitigated clustering effects typical of random sampling and improved convergence consistency. The top-performing configurations from QMC were refined using MATLAB’s patternsearch algorithm, enabling local optimization within ±2% of the best global candidates. Results demonstrate that a distributed offshore wind system, supported by both local and central storage, can achieve near-continuous baseload delivery with overall efficiencies approaching 90%. The optimized configuration supplied approximately 22% of the EU’s baseload demand at a cost comparable to new nuclear projects in Europe. Sensitivity analysis revealed diminishing returns beyond certain storage and cable capacity thresholds, emphasizing the importance of balanced system design. While the model simplifies grid constraints and cost dynamics, the findings highlight the potential of hybrid optimization methods for renewable energy system planning. Future work will extend the framework to include levelized cost of energy (LCOE) calculations, dynamic pricing, and stochastic failure modes, as well as explore genetic algorithms for further optimization. This study underscores the viability of large-scale offshore wind with integrated storage as a cornerstone of Europe’s carbon-free energy future and demonstrates the effectiveness of combining QMC sampling with local refinement for complex energy system design.