First look at Nova Scotia Offshore Lease Area Energy Production and Wake Losses
Canada has officially stepped into the offshore wind arena, and Nova Scotia is leading the charge. A few weeks ago, the province released five proposed lease areas for offshore wind development . These proposed zones mark a significant milestone in Canada’s journey toward expanding renewable energy capacity and decarbonizing its electricity grid. For developers, investors, and policymakers, this announcement provides a clearer picture of where the country’s first large-scale offshore wind farms may take root.
At Veer Renewables, we’ve been closely tracking the development of Canada’s offshore ambitions. To get an early sense of the energy potential and wake losses, we ran 2-km resolution WakeMap simulations over a typical meteorological year (TMY) across the newly proposed lease zones. Our modelling provides a first look at likely energy production, prevailing wake impacts, and key atmospheric conditions that will shape the future of these sites. In this post, we’ll share some early insights from our simulations, which we hope will help inform potential bidders and future lease-holders.
Proposed offshore Nova Scotia lease areas.
The figure above shows the locations of the proposed offshore wind lease areas, with the table below summarizing each area's size and modeled capacity for this study. These proposed zones are extensive, ranging from 1,691 to 5,850 km². Assuming a turbine spacing of 1 nautical mile (approximately 1.85 km)—consistent with standard assumptions for U.S. Atlantic lease areas—these areas could theoretically accommodate nearly 5,700 turbines.
To assess a worst-case scenario for wake impacts, we've modeled a full build-out of each area using 15-MW turbines spaced 1 nautical mile apart, resulting in estimated capacities ranging from 7.4 GW to 27.4 GW across the lease areas. Note that these capacities far exceed Nova Scotia's target of 5 GW by 2030, reflecting an intentionally conservative scenario designed to highlight the potential scale of wake effects.
| Lease Area | Capacity (GW) | Area (km²) |
|---|---|---|
| French Bank | 14.9 | 3,421 |
| Middle Bank | 9.9 | 2,289 |
| Sable Island Bank | 25.6 | 5,850 |
| Sydney Bank | 7.4 | 1,691 |
| Western/Emerald Bank | 27.4 | 6,334 |
Fortunately, the exceptional wind resource across these lease areas will help mitigate the energy losses from wake effects. The figure below shows the distribution of 150-meter wind speeds at the centroid of each lease area, based on a free-stream WakeMap simulation (i.e., no turbines modelled), allowing for an undisturbed view of the atmospheric conditions over a typical meteorological year (TMY). Mean wind speeds across the sites range from 10.4 to 11.0 m/s, underscoring the region’s world-class wind potential. With winds frequently exceeding turbine rated speeds, the relative impact of wakes on annual energy production is expected to be less severe than in lower-wind environments.
Distributions of 150-m wind speeds at the centroid of each proposed lease area.
Next, we examine the combined impact of wake and blockage losses across the proposed lease areas. It’s important to note that within the WRF Wind Farm Parameterisation (WFP) framework, wake and blockage effects are not easily separable. The presence of turbines modifies the flow in complex ways, inducing both downstream wake deficits and upstream slowdowns—together referred to as wind farm–atmosphere interaction effects. We will illustrate the role of blockage more specifically at the end of this post.
The table below compares the gross capacity factors—calculated from the free-stream simulation with no turbines modelled—to the waked capacity factors from the full build-out scenario, and presents the resulting estimated losses due to wake and blockage effects. From a resource perspective, the gross capacity factors are exceptional, ranging from 60.9% at Sydney Bank to 64.4% at the farther offshore Sable Island Bank.
However, due to the density and clustering of turbines across the four primary lease areas, energy losses are significant—ranging from 25.3% to 30.0%. Sydney Bank, which is more geographically isolated and features the fewest modelled turbines, experiences a notably lower loss of 19.9%. As a result, despite having the lowest mean wind speed of the group, Sydney Bank ends up with the highest waked capacity factor.
| Lease Area | Gross Capacity Factor (%) | Waked Capacity Factor (%) | Energy Losses (%) |
|---|---|---|---|
| French Bank | 62.9 | 46.5 | 26.1 |
| Middle Bank | 64.2 | 48.0 | 25.3 |
| Sable Island Bank | 64.4 | 45.1 | 30.0 |
| Sydney Bank | 60.9 | 48.8 | 19.9 |
| Western/Emerald Bank | 64.0 | 46.7 | 27.0 |
The sample animation below, captured during a significant waking event, illustrates the extent of inter-lease area wake losses and highlights the advantage of Sydney Bank’s geographic isolation from the other proposed lease zones.
Animation of wind speed deficits over a sample 2-day period
While these energy losses may appear substantial at first glance, they must be considered in the context of the large-scale development modelled—ranging from 7.4 to 25.6 GW per lease area. Under such aggressive build-out assumptions, the observed losses are actually quite modest, particularly when compared to a similar WRF wind farm parameterization (WFP)-based study in the U.S. Atlantic, where wake losses of 34–38% were estimated for much smaller deployments. The lower losses found in Nova Scotia are likely due to the region’s superior wind resource and the higher frequency of operation above turbine rated wind speeds, where wake effects are diminished or negligible. A more detailed comparison with U.S. Atlantic model results will be the focus of an upcoming blog post.
Finally, we illustrate the important role of blockage—or the induction effect—on total energy losses. In the figure below, we plot wind speed deficit contours for the northeast sector (45.0–76.5 degrees), with enhanced resolution at lower wind speed reductions. As winds approach the large southern lease clusters, we observe a slowdown of more than 5% before the flow even reaches the first row of turbines. This effect is not uniform across all wind sectors and is highly dependent on atmospheric stability, with stronger blockage observed under stable stratification. These represent meaningful pre-wake energy losses and likely contribute significantly to the total losses reported in this study. Future work will explore techniques for isolating blockage effects from downstream wake losses within the WRF-WFP modeling framework.
Contours of mean wind speed deficits for the 45.0-67.5 wind sector.
Nova Scotia’s proposed offshore wind lease areas represent a bold and promising step forward for Canada’s clean energy future. Our early WakeMap modelling highlights both the vast wind energy potential of these sites and the importance of accounting for wake and blockage effects at scale. Despite aggressive full build-out assumptions, modelled wake losses remain relatively modest—thanks to the strength of the wind resource and favourable atmospheric conditions.
As lease planning progresses and more data becomes available, detailed assessments like this will be key to optimizing site layout, turbine spacing, and inter-area coordination to maximize production and minimize energy losses. Stay tuned for future posts where we’ll dive deeper into comparisons with U.S. offshore modelling and explore strategies for disentangling wake and blockage effects in next-generation wind farm simulations.