Nuclear and emerging sources

Wind, solar and batteries can supply most of a data centre's energy cheaply, but as Lesson 5 showed, matching a constant 24-hour load with clean energy in every hour requires firm resources that produce whenever needed. Since 2024, data centre operators have become the leading private buyers of firm clean power: restarting retired nuclear plants, contracting the output of existing ones, funding uprates, ordering small modular reactors, and investing in enhanced geothermal, long-duration storage and even fusion. This lesson examines these sources, their costs and timelines, and how data centre owners contract for them.

Why firm clean power

A firm resource can produce at its rated output whenever called upon, apart from planned and forced outages. Nuclear plants run at capacity factors above 90%, geothermal similarly, and hydro with storage can follow demand. The value of firm resources rises steeply as the target for hourly clean matching rises, because meeting the last 10 to 20% of hours with wind, solar and short-duration batteries requires large overbuilding.

import numpy as np

rng = np.random.default_rng(7)
H = 8760
h = np.arange(H) % 24
day = np.arange(H) // 24
season = 1 + 0.3 * np.cos(2 * np.pi * (day - 172) / 365)      # more sun in summer
clouds = np.repeat(rng.uniform(0.3, 1.0, 365), 24)
solar = np.clip(np.sin(np.pi * (h - 6) / 12), 0, None) * season * clouds
solar /= solar.mean()                                          # 1 MW average per unit
w = np.zeros(H)
for t in range(1, H):                                          # slowly varying wind
    w[t] = 0.97 * w[t - 1] + rng.normal(0, 0.25)
wind = np.clip(0.35 + 0.25 * w, 0, 1)
wind /= wind.mean()
load = np.full(H, 100.0)

# cost of each resource per MWh of its output (illustrative)
cost = {"solar": 50, "wind": 60, "firm": 110}

def score(s, wd, f):
    gen = s * solar + wd * wind + f
    matched = np.minimum(load, gen).sum()
    spend = (cost["solar"] * s * solar.sum() + cost["wind"] * wd * wind.sum()
             + cost["firm"] * f * H)
    return matched / load.sum(), spend / load.sum()

best = {}
for s in range(0, 401, 20):
    for wd in range(0, 401, 20):
        for f in range(0, 101, 10):
            c, k = score(s, wd, f)
            for target in (0.8, 0.9, 0.95, 0.99):
                key = (target, f > 0)
                if c >= target and (key not in best or k < best[key][0]):
                    best[key] = (k, s, wd, f)

for target in (0.8, 0.9, 0.95, 0.99):
    for firm in (False, True):
        tag = "with firm" if firm else "no firm  "
        if (target, firm) not in best:
            print(f"CFE {target:.0%} {tag}: not reached with up to 400 MW each")
            continue
        k, s, wd, f = best[(target, firm)]
        print(f"CFE {target:.0%} {tag}: ${k:4.0f}/MWh  solar {s:3d}, wind {wd:3d}, firm {f:3d} MW")

CFE 80% no firm  : $  80/MWh  solar  40, wind 100, firm   0 MW
CFE 80% with firm: $  77/MWh  solar  20, wind  20, firm  50 MW
CFE 90% no firm  : $ 182/MWh  solar 100, wind 220, firm   0 MW
CFE 90% with firm: $  99/MWh  solar   0, wind   0, firm  90 MW
CFE 95% no firm  : not reached with up to 400 MW each
CFE 95% with firm: $ 110/MWh  solar   0, wind   0, firm 100 MW
CFE 99% no firm  : not reached with up to 400 MW each
CFE 99% with firm: $ 110/MWh  solar   0, wind   0, firm 100 MW

In this simplified model (one year of synthetic hourly solar and wind, firm clean power at $110/MWh, and no credit for surplus energy), reaching 80% hourly matching with wind and solar alone costs about $80/MWh of load. Reaching 90% without firm power more than doubles the cost, because large amounts of wind and solar must be built to cover calm, cloudy periods, and 95% cannot be reached at all within the range searched. With a firm resource, even 99% costs about $110/MWh, far less than the wind and solar cost at 90%. Adding storage, selling surplus energy and using real weather data changes the numbers, but the pattern — rapidly rising costs without firm resources as targets approach 100% — appears in most studies.

Existing nuclear plants

Canada's nuclear fleet is in Ontario (Bruce, Darlington and Pickering) and New Brunswick (Point Lepreau), with nuclear supplying about half of Ontario's electricity. These are CANDU reactors, using natural uranium and heavy water. Ontario is midway through refurbishing ten units at Bruce and Darlington, each a 3- to 4-year project that extends a unit's life by about 30 years; the C$12.8 billion Darlington refurbishment has been widely cited as on schedule and on budget, and Pickering units 5 to 8 are to be refurbished. Because Canadian nuclear output is sold through provincial systems, data centres in Ontario benefit from it mainly through the grid's low emissions and through Clean Energy Credits rather than direct contracts with a specific plant.

Ways to obtain power from existing nuclear plants include:

• Life extension and refurbishment: the main source of incremental nuclear energy in Ontario, at a cost per kW far below new build.
• Uprates: modifications that increase a reactor's output, such as Bruce Power's Major Component Replacement and asset optimization programs, adding hundreds of megawatts.
• Restarts: unlike the US, Canada has no recently retired units suitable for restart, apart from Pickering units planned for refurbishment rather than closure.

New large reactors

Ontario is planning new large reactors: Bruce Power has proposed Bruce C (up to 4,800 MW) and OPG is studying a new large plant at Wesleyville, east of Toronto, which could eventually host up to about 10 GW. Alberta is evaluating a proposed CANDU-based plant in the Peace River region. Large reactors take 10 to 15 years from decision to operation, and their cost per kW depends heavily on whether multiple units are built in sequence.

Small modular reactors

Small modular reactors (SMRs) are reactors of up to about 300 MW per unit, designed to be built from factory-made modules with simpler, passive safety systems. Their promise is lower construction risk, shorter schedules, and costs that fall as more identical units are built. Advanced designs use different coolants (sodium, molten salt, helium gas) and fuels, offering higher temperatures and new capabilities. Microreactors of a few MW to tens of MW target remote sites and specialized uses.

Canada has positioned itself as an early SMR adopter. The Canadian Nuclear Safety Commission (CNSC) issued a construction licence for the first BWRX-300 at Darlington in April 2025, and the Ontario government approved construction of the first unit of four, with an estimated total cost of about C$20.9 billion for all four units including shared infrastructure. SaskPower is evaluating BWRX-300 units, New Brunswick is supporting advanced designs at Point Lepreau, and Alberta is studying SMRs for oil sands steam and power. The federal Clean Technology and Clean Electricity ITCs include SMRs, and the Canada Infrastructure Bank has invested in the Darlington project.

SMRs face challenges: first units are expensive (NuScale's first project in Utah was cancelled in 2023 when estimated prices rose to $89/MWh even with subsidies), fuel for many advanced designs (high-assay low-enriched uranium, HALEU) is not yet produced at commercial scale outside Russia, supply chains and skilled workforces must be built, and licensing new technologies takes time. Data centre owners should treat SMRs as a 2030s resource and plan bridging supply accordingly.

Contracting for nuclear power

Data centres access nuclear power through several structures:

• Grid-delivered PPAs: the plant sells into the grid, and the buyer receives energy attribute certificates and a financial hedge (as in a VPPA) or physical delivery through its utility.
• Co-location: the data centre connects directly to the plant, reducing transmission use. This raises the cost-allocation questions discussed in Lesson 7, and regulators have scrutinized it closely.
• Utility tariffs: some utilities offer clean transition or firm clean tariffs letting a data centre pay a premium for new nuclear or geothermal supply procured by the utility.
• Equity and development funding: investing in reactor developers or early project costs in exchange for rights to future output.

Contract terms for new nuclear typically include long durations (20 years or more), price escalation, risk sharing for construction delays and cost overruns, and provisions for licence changes. Because first units carry high risk, buyers increasingly commit to fleets of several units to earn lower prices on later ones.

Geothermal and hydro

Conventional geothermal power uses naturally permeable hot rock and is limited to specific regions (in the US mainly California and Nevada). Enhanced geothermal systems (EGS) apply horizontal drilling and hydraulic stimulation, techniques from the oil and gas industry, to create permeable reservoirs in hot dry rock, greatly expanding the potential resource. Closed-loop systems circulate fluid through sealed wellbores without fractures.

Canadian companies are prominent in next-generation geothermal: Eavor (Calgary) has developed closed-loop technology, with its first commercial project in Germany, and DEEP Earth Energy is developing hot sedimentary aquifer geothermal in Saskatchewan. Alberta's oil and gas expertise, drilling workforce and hot sedimentary basins make it a focus for geothermal power and heat. In the US, Fervo Energy is building Cape Station in Utah, expected to deliver first power in 2026 and about 100 MW by early 2027, and in 2026 signed a nearly 400 MW agreement with Google.

Hydro is Canada's dominant source, about 60% of national generation. Québec, BC and Manitoba sell large amounts to the US, and new interties such as the Champlain Hudson Power Express to New York City carry Québec hydro south. New large hydro is limited and expensive (Site C in BC cost about C$16 billion for 1,100 MW), so the main opportunities for data centres are existing surplus and upgrades of existing stations.

Longer-term options

• Long-duration storage: technologies such as iron-air batteries (about 100 hours), compressed air, pumped hydro and thermal storage can shift wind and solar energy across days, partly substituting for firm generation.
• Gas with carbon capture and storage (CCS): capturing 90% or more of CO₂ from gas plants and storing it underground. Several projects are planned near data centre markets, especially where geology suits storage (Alberta, Texas, Louisiana, North Dakota), but costs and capture rates at scale are still being proven.
• Hydrogen: hydrogen produced with clean electricity can fuel turbines or fuel cells, but conversion losses (about 60 to 70% of the original energy) make it expensive for power generation except for long-duration backup.
• Fusion: private fusion companies have signed agreements with data centre operators, including Helion's agreement to supply Microsoft from about 2028 and Commonwealth Fusion Systems' agreement with Google for its ARC plant in Virginia in the early 2030s. No fusion device has yet produced net electricity, so these are high-risk, long-term options rather than planning assumptions.

Putting it together

A data centre owner planning a decade ahead might combine:

• Near term (now to 2028): grid supply with renewables PPAs, existing nuclear or hydro contracts where available, on-site gas or fuel cells for bridging, and batteries for flexibility.
• Medium term (2028 to 2032): restarts, uprates, geothermal and gas with CCS, plus 24/7 matching programs with utilities.
• Long term (2030s): SMRs and new large reactors, long-duration storage, and possibly fusion, with on-site gas transitioning to backup or being replaced.

Each choice must weigh cost, timing, risk, emissions, community acceptance and regulatory approval. The owners that secured power early with diverse, flexible portfolios have been able to grow; those that waited for the grid alone often could not.

Lesson 11 compares these long-term options with other paths on cost, risk and timing, and Lesson 12 combines them into a staged power strategy.