Quantifying the system-level impacts of gigawatt-scale offshore wind integration using data-driven mathematical modeling

Offshore wind is rapidly emerging as a cornerstone of large-scale renewable energy transitions, yet its integration into power systems traditionally dominated by natural gas and nuclear generation remains a complex modeling and planning challenge. This study develops an end-to-end, mathematical and data-driven framework for analyzing how large offshore wind deployments interact with existing regional grids. We combine climatological wind and solar data with a spatial optimization pipeline that performs wake-aware turbine layout. Applied to New Jersey, whose 11 GW offshore wind target is comparable in scale to its current natural gas and nuclear output, our model quantifies both the spatial configuration of offshore farms and their systemic impacts on statewide supply–demand dynamics. Using representative wind-year climatologies and current award information, we estimate that the planned offshore portfolio could supply roughly half of New Jersey’s annual electricity demand, substantially displacing gas-fired generation and reversing the state’s typical 20% net annual imports. However, the resulting seasonal and synoptic variability introduces significant daily energy oscillations that must be absorbed by reactive resources such as gas and nuclear units unless additional flexibility (e.g., storage or expanded transmission) is added. Each mathematical and computational module in our pipeline relies on generalizable data structures: wind roses, load time series, lease-area polygons, and turbine specifications. This makes the framework directly transferable to other coastal regions. More broadly, this work demonstrates how integrating wake-aware spatial design with system-level energy modeling enables transparent, quantitative evaluation of offshore wind portfolios within evolving energy networks.