Data Center Solar Siting: How to Find and Evaluate Land for Solar-Powered Data Centers

Data centers are the fastest-growing load on the US grid. A 2024 report from Lawrence Berkeley National Laboratory estimates data centers consumed about 4.4% of US electricity in 2023. The same report projects 6.7% to 12% by 2028.

Every hyperscaler and colocation operator wants that power to be carbon-free. Solar is the cheapest new-build generation in most markets, and it permits faster than gas or nuclear. The result is the largest dedicated utility-scale solar pipeline the industry has seen.

For EPCs and developers, this is a different kind of siting problem. The load is flat, the timeline is aggressive, and the interconnection queue decides winners. We wrote this guide for teams moving into data center renewable energy work from residential, C&I, or general utility-scale practice.

In this guide:

• Why data center demand is reshaping utility-scale solar development
• The 6-stage data center solar siting process, from load definition to PPA
• GIS land screening criteria, with threshold values and data sources
• How to read interconnection queues and pick a point of interconnection
• Load matching math — annual energy, hourly coincidence, and CFE scores
• Battery storage sizing and the real cost of chasing 24/7 carbon-free energy
• Financial modeling inputs, sensitivities, and deal structures
• A worked (hypothetical) 200 MW case study with full numbers
• 7 siting mistakes that kill data center solar projects

Why Data Centers Are Driving Utility-Scale Solar Demand

US data center electricity use reached roughly 176 TWh in 2023, about 4.4% of national consumption. The LBNL 2024 United States Data Center Energy Usage Report projects 325–580 TWh by 2028. That is 6.7–12% of all US electricity.

AI training and inference drive most of the growth. A single AI-focused campus can draw 300–1,000 MW — the output of a mid-size power station. Utilities in Virginia, Texas, Arizona, and Georgia now receive interconnection requests for loads they cannot serve without new generation.

Corporate climate commitments convert that load into renewable demand. More than 400 companies have joined RE100, pledging 100% renewable electricity. Google targets 24/7 carbon-free energy on every grid it operates in by 2030, and Microsoft and Amazon hold similar commitments.

Solar wins this demand on 3 grounds: cost, speed, and permitability. The Lazard LCOE+ analysis consistently ranks utility-scale solar among the cheapest new-build resources, even unsubsidized. A solar farm reaches commercial operation in 12–24 months after interconnection approval, while a new gas turbine faces 3–5 year equipment lead times.

There is also a structural reason solar fits data centers. Data center cooling loads peak in the afternoon, when solar output peaks. The match is not perfect — we cover the gaps later — but daytime correlation is a real advantage over wind in many regions.

For EPCs, the implication is direct. The residential market has cooled since the residential ITC expired on December 31, 2025. Data center renewable energy contracts are pulling engineering talent and capital toward C&I and utility-scale work.

Teams that already run disciplined design workflows — layout, shading, production modeling, and bankable proposals — are positioned to move up-market. The skills transfer; the scale and the contract scrutiny do not. This guide assumes you bring the design discipline and focuses on what changes at data center scale.

PVcase recognized this early, building a dedicated data center siting landing page, an ebook, and a podcast around the vertical. RatedPower and others followed. The competition for data center solar siting work is already forming, and process quality is the differentiator.

Scale is the other shift. Utility-scale solar — ground-mount projects above 5–10 MW — now anchors the growth pipeline while distributed residential volume shrinks.

A single data center procurement can run 200–500 MW, the size of a mid-size developer’s entire annual book. EPCs that win these contracts lock in multi-year backlogs. They also earn the reference projects that win the next one.

The Data Center Solar Siting Process

Siting solar for a data center is not the same as siting solar for a utility PPA. A utility optimizes for the cheapest delivered megawatt-hour. A data center optimizes for hourly supply to a specific meter, under a fixed construction deadline.

That changes the order of operations: standard prospecting starts with land and checks the grid later. Data center solar siting starts with the load and works backward to the land. We use a 6-stage process.

Stage 1 — Define the load profile. Get the data center’s expected load in megawatts, its annual energy in megawatt-hours, and its hourly shape. Ask for the power usage effectiveness (PUE) assumption, because IT load and meter load differ by 20–50%. A 50 MW IT load at a PUE of 1.3 is a 65 MW meter load.

Stage 2 — Pick the market. Choose the ISO/RTO or bilateral market based on the data center’s location, queue depth, and renewable procurement rules. ERCOT, PJM, and MISO dominate US activity. Each has different queue mechanics, deliverability rules, and congestion patterns.

Stage 3 — Screen land. Run GIS constraints over the candidate region: slope, land cover, wetlands, flood zones, protected habitat, and setbacks. Overlay parcel data and transmission. The output is a ranked shortlist of sites, not a single winner.

Stage 4 — Analyze interconnection. For each shortlisted site, evaluate the nearest substations and lines: available injection capacity, existing queue congestion, and likely network upgrade costs. Kill sites with fatal flaws here, before lease money goes out.

Stage 5 — Design and load-match. Model hourly solar production against the data center’s hourly load. Size the array, the storage, and the grid contract to hit the carbon-free energy target at the lowest cost.

Stage 6 — Model the finances. Build the pro forma: capital cost, PPA revenue, tax treatment, curtailment risk, and sensitivity cases. This is where the deal structure — physical PPA, virtual PPA, or self-build — gets decided.

Stages 3 and 4 run in parallel in practice. A site with perfect land and a dead interconnection path is worthless, and the reverse is rare. Budget 3–6 months for stages 1–4 on a serious prospect.

One division of labor note: the data center owner usually handles grid power procurement at the facility meter. The developer or EPC handles the solar side siting. The 2 teams must share load data early, or the solar design will be sized against the wrong number.

Assemble the siting team before stage 3. You need a GIS analyst, a transmission engineer, a land agent, an environmental consultant, and a financial modeler.

Smaller EPCs cover these roles with 2 or 3 people plus outside consultants, but the role you cannot skip is the transmission engineer. Every other workstream depends on the interconnection answer.

Land Screening: GIS, Zoning, and Environmental Constraints

Start with the acreage math. A 100 MW solar farm needs 400–800 acres, depending on module efficiency, tracking versus fixed tilt, row spacing, and setbacks. Plan on 5–6 acres per MW as a screening default.

Scale that to data center load. A facility drawing 100 MW on average can imply 300–500 MW of solar for high hourly coverage. That is 1,500–4,000 acres, often split across 2 or 3 sites.

GIS screening converts a region into a ranked site list. The standard workflow applies exclusion layers first, then scores what remains. Exclusion layers remove land that cannot be permitted or built; scoring layers rank the survivors by cost and risk.

Transmission proximity deserves its own pass. The gen-tie — the line from the project to the point of interconnection — is the project’s responsibility. Every mile of gen-tie adds cost, easement negotiations, and schedule.

We treat 5 miles as a hard ceiling for most projects, and 2 miles as the comfort zone. Beyond that, the easement count and the per-mile cost usually overwhelm whatever the site saved on land. Measure corridor distance along realistic routing, not as the crow flies.

Zoning is the least technical criterion and the most political. County solar ordinances now exist in most active markets, and several states have seen moratoria.

Check the county comprehensive plan and any pending ordinance amendments before spending on surveys. A site that passes every GIS layer can still die in a planning commission meeting. Local opposition most often forms around visual impact, drainage, and the loss of farm income.

Weight the scoring layers by deal impact, not by data availability. In our weighting, transmission distance and queue congestion carry 40% of the score, land assembly and zoning carry 35%, and environmental and physical factors carry 25%. Adjust the weights per market — in ERCOT, congestion dominates; in the Southeast, zoning and wetlands dominate.

Community engagement belongs in this stage too, not after permitting starts. Counties that feel surprised by a solar application respond with moratoria. A 30-minute briefing with the county planner and letters to adjacent landowners cost a week and can save quarters.

Land control follows screening. Developers typically secure option agreements — 2–5 year rights to lease or purchase — rather than buying land outright. Options keep capital light during the interconnection wait.

Expect to negotiate with multiple landowners per site. Per-acre option payments have risen sharply in queue-favorable counties, and competitive counties now see bidding between developers. Walk away from parcels where 1 holdout owner controls the only viable corridor to the substation.

Environmental due diligence runs in parallel with land control. Wetland delineation, habitat surveys, and cultural resource studies take 6–12 months across seasons. Start them as soon as the option is signed, not after the interconnection study returns.

Here is the contrarian point on land: the cheapest acre is often the most expensive megawatt. Remote land trades at a discount precisely because it lacks transmission access. Teams that screen by land price first end up with cheap options, long gen-ties, and network upgrade bills that erase the savings.

Interconnection and Transmission Capacity Analysis

Interconnection is where data center solar projects live or die. More than 2.5 TW of generation and storage capacity sat in US interconnection queues at the end of 2024, according to the LBNL interconnection queue analysis.

Median time from queue entry to commercial operation now runs around 5 years in several markets. Only a fraction of queued projects ever reach construction. The rest withdraw, usually after a study assigns them upgrade costs they cannot carry.

The point of interconnection (POI) decision drives everything downstream. The POI is the substation or line tap where the project injects power. Its available capacity, voltage class, and existing queue congestion determine the study outcome and the upgrade bill.

Read the queue like a credit report. Every ISO publishes queue data: requested megawatts per substation, study phase, and withdrawal history.

A substation with 800 MW of active requests ahead of you on a 200 MW transformer is a warning, not an invitation. Withdrawal rates above 70% in a cluster tell you the upgrade costs came back ugly. Healthy substations show a mix of early-phase requests and a few projects actually reaching operation.

Queue reform is slowly improving the picture. FERC Order 2023 moved the large ISOs from serial first-come studies to cluster studies, with readiness deposits and withdrawal penalties. Transition clusters are still working through backlogs, so treat announced study timelines as optimistic by 6–12 months.

Deliverability deserves a definition. Energy resource interconnection service (ERIS) lets the project inject energy when the grid has room. Network resource interconnection service (NRIS) adds deliverability — the project counts toward capacity obligations and can sell capacity.

Data center PPAs increasingly expect capacity value. NRIS or its market equivalent is usually worth the higher study cost. Confirm the deliverability status of the POI before assuming any capacity revenue in the model.

Network upgrade costs are the silent killer. A cluster study can assign a project its share of a new 345 kV line or a substation rebuild.

Upgrade allocations above roughly $250/kW — a quarter of the all-in build cost — break most pro formas. Screen recent study results in the target ISO before choosing a POI. Public study reports show what comparable projects were actually charged.

Three strategies shorten the path. First, target substations with announced transmission expansions and reserve queue position early.

Second, consider co-location with an existing or retiring generator, where injection capacity already exists. Third, evaluate behind-the-meter (BTM) configurations at the data center itself, where the solar farm connects on the load side and avoids the generation queue entirely.

BTM deserves a caveat. On-site or adjacent solar avoids queue delay and delivers power with no transmission charge.

But land near data center campuses is expensive, and parcel sizes limit capacity. The data center’s construction schedule rarely waits for solar either. Most large deals end up as remote generation with a financial PPA, not a physical wire.

Submit the interconnection request as early as site control allows. Queue position is a real asset in every ISO. A defensible, early request at a well-chosen POI is worth more than a perfect site with a late queue number.

Load Matching: Solar Production vs. Data Center Consumption

Data center load is nearly flat. A well-utilized facility runs at 70–95% of capacity around the clock, every day of the year. AI training loads add short-duration ramps, but the daily profile has no evening dip and no weekend trough.

Solar production is a bell curve that exists for 6–10 hours per day and shifts with season and weather. Solar’s capacity factor — actual annual energy divided by maximum possible energy — runs 18–22% for fixed tilt and 24–30% for single-axis tracking in good resource.

The arithmetic follows from those 2 shapes. A 100 MW tracking array in West Texas produces roughly 240–260 GWh per year. A 100 MW data center consumes about 790–830 GWh per year at high utilization, so annual energy matching alone requires 3–4x oversizing.

Hourly matching is stricter. The industry measures it with a carbon-free energy (CFE) score: the share of consumption hours in a year where carbon-free supply meets or exceeds load.

Solar-only portfolios typically reach 40–60% hourly CFE. Getting to 80–90% requires storage and oversizing. The final stretch to 100% is where costs escalate.

Work the problem in this order. First, pull 8,760 hours of load data — actual interval data if the facility exists, or a modeled profile from IT capacity and PUE if it does not.

Second, generate an 8,760-hour solar production profile for each candidate site using satellite irradiance data. Third, test array sizes and storage sizes against the CFE target and the budget. Run at least a P50 and a P90 weather year so the buyer sees the bad-year picture.

Seasonality cuts against solar in most US markets. Summer days deliver 1.5–2x the energy of winter days, while data center load stays flat. Check the monthly breakdown, not just the annual average — winter hours dominate the uncovered remainder.

Oversizing is the cheapest lever. Moving from 1.5x to 2.5x annual energy coverage raises the CFE score materially, because the array covers shoulder hours and cloudy days more often.

The cost is curtailment — midday hours where production exceeds what the grid or the battery can absorb. Model the trade explicitly. Curtailment above 15–20% usually means the array is too big or the battery is too small.

Geographic diversification helps more than most teams expect. Two sites 100 miles apart have meaningfully different cloud patterns. A portfolio of 2–3 solar farms in one ISO delivers a smoother combined profile than 1 large farm, at the cost of more development work.

One modeling discipline matters above all: use time-synchronous data. Solar production and load must be modeled on the same timestamps, in the same time zone, with daylight saving handled identically. We have seen matching studies off by an hour — enough to inflate coincidence numbers and embarrass everyone in front of the buyer.

Tools like our generation and financial modeling tool produce the hourly production profile side of this analysis. Pair that output with the facility’s interval data in a spreadsheet or a portfolio model before committing to an array size. We build solar software for exactly this kind of hourly design work, from layout through financial output.

Battery Storage and Firming Resources

Solar alone cannot power a data center 24/7. That is physics, not a temporary technology gap.

Nighttime load must come from storage, the grid, or another generator. The design question is how much of each, and at what cost. The answer changes every year as storage prices fall.

Four-hour lithium-ion storage is the default firming resource. It absorbs the midday solar peak and discharges through the evening.

At current costs, pairing solar with 4-hour batteries typically lifts a portfolio from 40–50% hourly CFE to 70–85%, depending on oversizing. Our solar storage design guide covers the sizing math in detail. The same logic scales from C&I to utility projects.

Plan augmentation from day one. A battery cycling daily loses 2–3% of usable capacity per year, so a 400 MWh system needs 30–60 MWh of added modules over a 15-year PPA to hold contracted output. Contracts increasingly specify augmentation obligations explicitly — price them in the pro forma, or the battery runs short in later years.

Now the contrarian part: chasing 100% hourly CFE from day one is usually a mistake. The last 10–15% of hours — multi-day cloudy stretches, winter lows — cost disproportionately more to cover than the first 85%.

Long-duration storage or firm clean generation can close the gap, but prices and availability remain immature. Most sophisticated buyers accept an 80–90% CFE trajectory with contractual commitments to improve. Paying for 100% on day one usually means overbuilding solar and storage that sits idle most of the year.

Annual matching is a legitimate alternative, and do not let anyone tell you otherwise. Under annual matching, the buyer procures enough renewable energy to cover 100% of annual consumption, sells back the midday excess, and buys grid power at night. It is cheaper, it uses proven contract structures, and it still funds new carbon-free capacity.

The criticism — that night power may come from fossil generation — is real. But the marginal abatement cost of the last CFE percent often exceeds what the same money would save elsewhere in the portfolio. Buyers should pick the metric deliberately, not by default.

The practical firming stack for a data center solar project has 4 layers. Solar covers daytime hours. Batteries shift 3–5 hours of solar into the evening peak.

A grid supply contract or a retail tariff covers overnight load. Renewable energy certificates or a settlement true-up handle the annual accounting. Each layer has a price, and the pro forma should show the marginal cost of each CFE point gained.

Emerging options sit on the horizon: 8-hour lithium systems, iron-air and other multi-day chemistries, geothermal PPAs, and small modular nuclear. Watch them, but do not size a 2026 financial model around them. Bankability still belongs to solar plus 4-hour lithium.

Size storage against the load shape, not against a rule of thumb. A data center with flat 24/7 load uses the battery every night — high cycle counts, fast degradation, strong utilization. That is a different storage business case than a peaking application, and it deserves its own degradation and augmentation model.

Financial Modeling for Data Center Solar

The pro forma for a data center solar project has 4 layers: capital cost, revenue, tax, and risk. Each layer has inputs specific to this deal type, and each deserves a sensitivity case.

Capital cost benchmarks first. Utility-scale solar runs roughly $0.90–1.20 per watt DC installed in the US, per the NREL cost benchmarks.

Add $250–350 per kWh for 4-hour battery storage, plus gen-tie and network upgrades as project-specific line items. Land appears as lease escalators or purchase cost. Active markets run $400–1,000 per acre per year on leases.

Revenue comes from the power purchase agreement (PPA). Data center and corporate PPAs price in 3 common structures: fixed-price with escalator, hub-settled virtual PPA, and physical sleeve through a utility.

Fixed prices for solar in recent US corporate deals cluster around $40–60 per MWh, with storage adding $10–20 per MWh of firming value. Merchant exposure after the PPA term — the tail — belongs in the model at conservative hub prices. Lenders will haircut the tail regardless of your forecast.

Tax treatment is the commercial side of the story. The technology-neutral clean electricity investment credit under Section 48E applies to commercial solar and storage, subject to the phase-out and commencement schedules in current law.

The residential credit under Section 25D expired on December 31, 2025 — do not confuse the two in front of a buyer. Commercial projects also use MACRS depreciation, and credits are transferable. Transferability simplifies financing for developers without tax appetite.

Three sensitivities break deals more than any others. Network upgrade overruns top the list — model the pessimistic study outcome, not the median.

Curtailment comes second, because data center PPAs with strict delivery shapes penalize both over- and under-delivery. Third is schedule. A 12-month delay against a data center’s go-live date can trigger termination clauses.

Do not skip the probability-weighted production cases. Lenders size debt on P90 production, and buyers price PPAs on P50. A model that shows only P50 flatters the project and sets up a painful renegotiation later.

Run the model hourly if the PPA has shape requirements. Annual-average models hide the hours where the portfolio under-delivers and buys back at peak prices. The hourly production profile from your solar design software feeds directly into revenue certainty — the same 8,760 data the buyer’s analysts will demand.

Deal structure is the last modeling decision. Self-build gives the data center owner full control and full risk.

A developer-build with a PPA shifts construction risk but adds margin. Community or portfolio PPAs spread load across several projects. Each structure changes who owns the siting mistakes — price accordingly.

Case Study: A 200 MW Data Center Solar Project

This case study is hypothetical. We built it to illustrate the workflow and the math, not to describe a real client. The numbers are representative of US Southwest conditions in 2026, rounded for readability.

The buyer: a colocation operator building a campus with 60 MW of IT load. At a PUE of 1.25, meter load is 75 MW average — about 657 GWh per year at 24/7 operation. The buyer wants 90% hourly CFE within 3 years and a 15-year fixed-price PPA.

Stage 1 screening covered a 4-county region in ERCOT West. GIS exclusions removed 60% of the land area — slope, playas, and floodplain.

Transmission overlay cut the shortlist to 6 parcels within 2 miles of 138 kV or 345 kV substations. Queue analysis killed 4 of the 6. Two sat behind congested clusters, and 2 had recent upgrade allocations above $300/kW.

The winning site: 1,400 acres of former cropland, 1.2 miles from a 345 kV substation with visible headroom and only 1 late-phase project ahead in the queue. Option terms: $650 per acre per year, 4-year option, lease converting at notice to proceed. Wetland and habitat surveys came back clean within 2 seasons.

The design settled on 200 MW DC with single-axis tracking — about 2.6x annual energy coverage against a 75 MW load. Modeled capacity factor: 27%, yielding roughly 473 GWh per year.

A 100 MW / 400 MWh battery shifts the midday peak into evening hours. The modeled hourly CFE score reaches 82% in year one. It rises to 88% when the buyer adds a second storage phase in year 3.

The financial result: levered IRR in the low teens with a Section 48E credit transferred at $0.92 per dollar. The pessimistic case — upgrades 40% over estimate, curtailment at 20%, PPA 10% lower — still cleared the developer’s hurdle rate. That resilience is why the project advanced.

Here is the first-hand insight from deals like this. Everyone models the array, the battery, and the PPA. Almost nobody models the gen-tie corridor properly.

On a project our team reviewed, the corridor crossed a second landowner, a pipeline easement, and a drainage district — 3 title problems discovered after the interconnection study started. The fix cost 5 months and a rerouted line. We now run title and easement screening on the gen-tie path at the same time as the main parcel, and we treat corridor risk as a site-selection criterion.

The second lesson: the buyer’s load forecast moved twice during development. Design for the contracted capacity, not the aspiration. The PPA was sized to the campus’s phase-one build-out, with expansion options priced separately.

Common Siting Mistakes That Kill Data Center Solar Projects

We see the same failures repeat across teams entering this vertical. Most are process errors, not engineering errors. Here are the 7 that cost the most.

1. Screening by land price instead of delivered cost. Cheap remote land carries long gen-ties and heavy network upgrades. Rank sites by levelized delivered cost to the data center’s market node, not by option price per acre.

2. Ignoring the queue until after lease money. Options signed before queue analysis are lottery tickets — the ISO queue data is public and free, so pull it first. A site behind 5 congested projects at the same substation is not a site; it is a waiting room.

3. Trusting the headline load number. The buyer says 100 MW; the contracted phase one is 40 MW; the meter load at PUE 1.3 is different again. Model against the contracted, metered number, in writing.

4. Sizing for annual matching while promising hourly matching. Annual energy coverage of 100% delivers roughly 40–60% hourly CFE. If the buyer’s marketing says 24/7 carbon-free, an annual-matched design is a future lawsuit — align the metric before sizing the array.

5. Underestimating the schedule stack. Interconnection study, environmental surveys, county permitting, and equipment lead times run sequentially more often than teams plan. A 36-month realistic timeline pitched as 24 months destroys trust at month 25.

6. Skipping gen-tie and easement diligence. The corridor to the POI crosses land you do not control. Title work on the corridor belongs in stage 3, alongside the main parcel — not after the interconnection agreement.

7. Treating curtailment as free. Oversizing raises CFE scores, but unsold excess energy at negative or zero prices drags the IRR. Model curtailment revenue hour by hour; do not assume the market absorbs everything at the hub price.

A pattern connects these mistakes: each one optimizes a single variable in isolation. Data center solar siting is a multi-constraint problem where interconnection, land, load shape, and contract structure interact. The teams that win run the stages as one workflow, with the same model feeding engineering and finance.

Conclusion

Data center solar siting is land screening, grid analysis, load math, and deal structure — run as one process. The demand side is not speculative. Data center load is growing faster than any other US electricity segment, and corporate buyers need carbon-free supply on fixed deadlines.

The teams that capture this work share 3 habits. They start from the load and work backward to the land. They treat interconnection queue position as the scarcest resource in the project, and they model hourly because the buyer’s analysts will.

Solar covers the daytime; storage and grid contracts close the rest. The projects that pencil accept 80–90% hourly carbon-free energy now and contract a path upward. Chasing perfection on day one prices you out of deals that good engineering would win.

For EPCs moving up from residential and commercial solar work, the workflow is familiar at a larger scale. Site assessment, production modeling, financial analysis, and a committee-ready proposal — the discipline is the same, and the stakes are bigger. We built SurgePV’s solar proposal software and solar shadow analysis software around exactly that discipline.

The same logic applies beyond the US. Data center growth in Ireland, Singapore, India, and the Gulf runs into the same constraints: land, transmission, and hourly carbon-free supply. Dublin and Amsterdam showed where this leads when load growth outruns the grid — connection moratoria and multi-year delays.

The screening criteria in this guide travel well; only the data sources change. Developers who bring disciplined siting to those markets now will hold the queue positions everyone else wants in 3 years.

Pick a market, pull the queue data, and screen your first region this quarter. Start with the buyer’s contracted load, not with a land listing. Run the load-matching math before you commit to a parcel. The interconnection clock rewards whoever files first.

Frequently Asked Questions

What is data center solar siting?

Data center solar siting is the process of finding land and grid capacity to build solar installations that power data centers. It combines GIS land screening, interconnection queue analysis, load profile matching, and environmental due diligence.

Why do data centers need dedicated solar farms?

Data centers require 24/7 power. Dedicated solar farms paired with storage can provide carbon-free energy, hedge against grid price volatility, and help meet corporate sustainability goals like RE100.

How much land does a data center solar farm need?

A 100 MW solar farm typically needs 400–800 acres depending on panel type, layout, and setbacks. Data centers consuming 50–500 MW need proportional land, often spread across multiple sites.

What is the biggest challenge in data center solar siting?

Interconnection queue delays. Many regions have multi-year queues for new generation. Developers must screen for available transmission capacity and submit interconnection requests early.

Can solar power a data center 24/7?

Solar alone cannot power a data center 24/7. It must be paired with battery storage, grid backup, or other firming resources. The solar array covers daytime load and reduces grid dependence.

What software is used for data center solar siting?

GIS tools like ArcGIS, specialized solar prospecting platforms like RatedPower Prospect or PVcase Prospect, and interconnection data from ISO/RTO queues are used. Financial modeling tools evaluate project viability.