AI Makes 2025’s Clean Energy Wins Scale in 2026

Renewables produced more electricity than coal globally for the first time in 2025. That’s the headline. The operational reality is messier: more variable generation, more devices at the grid edge, faster electrification, and rising demand from data centers and industrial load. If you work in energy and utilities, you already feel the tension—the transition is winning on cost and speed, but it’s stressing planning, operations, and reliability.

Here’s my take: 2025’s “clean energy wins” weren’t just policy victories or market milestones. They were signals that the grid modernization playbook is changing. And the biggest enabler—quietly sitting behind many of these wins—is AI: forecasting, optimization, anomaly detection, digital twins, and automated workflows that let operators move faster without guessing.

This post sits in our “AI for Energy & Utilities: Grid Modernization” series, so I’m going to do more than recap the year. I’ll translate the top 2025 wins into what utilities and energy leaders should actually build in 2026—and where AI is the practical glue that turns momentum into repeatable outcomes.

2025 proved the grid edge is now a grid asset

Distributed energy resources (DERs) moved from “nice to have” to “system resource.” State policy in 2025 accelerated rooftop solar, batteries, and virtual power plants (VPPs)—including permitting reforms that reduce soft costs and speed interconnection. The result isn’t just more DERs. It’s a new operating model: millions of small devices that need to behave like one reliable fleet.

VPPs don’t scale with spreadsheets—AI is the control layer

The hard part of a VPP isn’t signing up customers. It’s delivering firm, measurable capacity during the handful of critical hours that drive reliability and cost.

AI helps VPPs scale because it can:

• Forecast feeder-level net load (not just system load) with weather, occupancy, and device telemetry
• Optimize dispatch across batteries, thermostats, EV charging, and water heaters under constraints (comfort, SOC, export limits)
• Detect non-performance early (device dropouts, misconfigured inverters, comms issues)
• Continuously learn which customers and device types deliver dependable response

Snippet-worthy truth: A VPP is a reliability product. AI is what makes it predictable.

What to do in 2026: a DER operations “minimum viable stack”

If you’re modernizing distribution operations, the most pragmatic stack I’ve seen looks like this:

1. High-resolution visibility: AMI + SCADA + inverter telemetry + weather nowcasts
2. DER forecasting: 5-minute to day-ahead net load forecasts at feeder/zone level
3. Constraint-aware optimization: dispatch that respects transformer loading, voltage, and interconnection limits
4. Measurement & verification (M&V): baseline modeling and performance scoring for every event

Utilities that treat DERs like a program will keep struggling. Utilities that treat DERs like dispatchable infrastructure will win.

Electrification surged—and it’s creating a new peak problem

Heat pumps outsold air conditioners in the US for the first time in 2025. That’s more than a consumer trend. It changes winter peaks, drives new load shapes, and increases the value of flexible demand.

AI turns heat pumps from “load” into “flex”

Heat pumps are inherently controllable because thermal systems have inertia. The comfort requirement is real, but it’s not binary. AI enables what old-school control couldn’t do well at scale: personalized, low-friction flexibility.

Practical applications utilities are deploying (or should):

• Thermal preheating and precooling optimized by tariff, weather, and customer comfort bands
• Clustering and segmentation: grouping homes by building envelope, equipment sizing, and occupancy patterns to improve program performance
• Transformer risk forecasting: identifying neighborhoods where electrification will overload assets before failures happen

Grid modernization implication: forecasting must include adoption curves

Most load forecasts still struggle with rapid end-use adoption. In 2026, load forecasting should explicitly model:

• Heat pump adoption by housing type and vintage
• EV adoption and charging behavior by circuit
• Electrification of water heating and cooking
• New commercial/industrial loads (including data centers)

AI-based demand forecasting isn’t about fancy models. It’s about integrating messy real-world signals—permits, incentives, retail sales, interconnection queues, and AMI patterns—into one planning-grade view.

Clean industry and shipping corridors raised the bar for energy coordination

Corporate demand surged in 2025 for clean steel and lower-carbon concrete, and “green shipping corridors” made progress across the Pacific and beyond. These wins share a common requirement: energy coordination across parties that don’t share systems, data, or incentives.

Why this matters to utilities

Industrial customers aren’t just buying electrons. They’re buying:

• Emissions attributes (hourly matching expectations are rising)
• Power quality and uptime
• Capacity certainty for new processes (hydrogen, electrified heat, high-temp applications)

Utilities that can’t provide transparent, granular operations data will lose opportunities to behind-the-meter solutions—or face drawn-out interconnection and curtailment fights.

Where AI fits: from “reports” to operational carbon intelligence

In practice, AI supports modern energy coordination by:

• Optimizing hourly resource matching (load shifting + storage dispatch + renewable forecasting)
• Predicting congestion and curtailment risk for large new loads
• Automating interconnection studies with scenario libraries and learned heuristics
• Managing flexibility contracts (think: industrial demand response that behaves like a capacity product)

A stance I’ll defend: utilities should treat industrial decarbonization as a grid services design problem, not a PR initiative.

Transparency got real: satellites, carbon data protocols, and AI monitoring

Two 2025 developments are easy to overlook but hard to overstate:

• New satellite-based emissions visibility is making methane and CO₂ “seeable” at asset level.
• Carbon markets are pushing toward connected, standardized data to reduce opacity.

This changes expectations for utilities and energy companies. Regulators, investors, and customers increasingly assume you can answer basic questions fast:

• Where are our biggest emissions sources?
• Which assets are super-emitters?
• Which interventions actually worked?

AI makes monitoring continuous, not annual

Traditional environmental reporting is periodic. Modern operations need continuous monitoring. AI enables:

• Anomaly detection across methane sensors, SCADA signals, and maintenance logs
• Root-cause triage to separate sensor noise from real leaks/failures
• Prioritized work orders based on impact (tons reduced per dollar, or per crew-hour)
• Verification loops that confirm reductions and prevent repeat issues

Practical one-liner: If you can’t measure it weekly, you can’t manage it operationally.

Emerging markets showed what “access + affordability” really means

Solar growth in Africa accelerated sharply in 2025: solar imports rose 60% year-over-year, and pay-as-you-go kit sales increased 54% to 2.35 million units in the first half of the year. India hit a record month for electric two-wheelers, with nearly 144,000 high-speed e-2W sales in October.

These are reminders that the transition doesn’t wait for perfect grid conditions. People adopt what’s affordable to buy and cheaper to run.

The utility lesson: customer economics beats messaging

Programs that win in 2026 will be built around:

• Transparent bill impacts
• Easy enrollment
• Fast permitting and interconnection
• Reliability during extreme events

AI helps on the operational side (forecasting, dispatch, fraud detection, credit risk for pay-as-you-go, outage prediction), but the strategy is simpler: make the clean choice the easy choice.

A 2026 playbook: the five AI capabilities utilities should prioritize

If you’re deciding where AI belongs in grid modernization, I’d prioritize five capabilities that map directly to 2025’s wins.

1) Feeder-level forecasting for renewables and net load

Aim for forecasts that operators can trust at 5-minute, hour-ahead, and day-ahead horizons. The win condition is fewer surprises: ramps, backfeed, and evening peaks.

2) Constraint-aware optimization (DERMS + VPP + outage modes)

Dispatch should respect voltage, thermal limits, and device constraints. Include “storm mode” operating profiles that preserve customer backup and critical loads.

3) Predictive maintenance that connects ops data to work execution

Predictive maintenance only pays off when it changes field actions. Tie AI outputs to:

• crew routing
• parts availability
• inspection intervals
• post-maintenance verification

4) Planning-grade scenario modeling for electrification

Use AI to speed scenario generation, but keep governance tight. Models should be auditable and understandable—especially for regulators.

5) Emissions and reliability transparency dashboards

Not vanity dashboards—operational ones. Executives need leading indicators (risk) as much as lagging indicators (performance).

Quick diagnostic: If your AI outputs don’t change a dispatch decision, a maintenance schedule, or a planning approval, it’s not grid modernization yet.

What to ask your team before you fund the next AI initiative

These are the questions I’d put on the table in Q1 2026 budgeting:

1. What decision does this model improve—and how often is that decision made?
2. What data latency can we tolerate? (real-time, 15-minute, daily)
3. Who owns the outcome? (control room, distribution ops, planning, customer programs)
4. How will we measure success? (SAIDI/SAIFI impacts, peak reduction, deferred capex, interconnection cycle time)
5. What’s the failure mode? (bad dispatch, missed peak, customer comfort complaints, compliance risk)

AI in energy and utilities is mature enough that “pilot forever” isn’t acceptable. Either it ships into operations, or it shouldn’t be funded.

Where 2025 leaves us—and what 2026 demands

2025 delivered proof that clean energy keeps winning on economics and adoption: renewables passing coal in global electricity, DER and VPP policy acceleration, heat pump momentum, cleaner industrial procurement, better emissions transparency, and rapid solar growth in Africa.

For utilities, the next step is obvious and uncomfortable: the grid has to become more observable, more controllable, and faster to operate—even as complexity rises. That’s why AI-driven grid optimization, predictive maintenance, and demand forecasting aren’t “innovation projects.” They’re how you keep reliability high while integrating renewables and electrification at speed.

If you’re planning your grid modernization roadmap for 2026, where do you see the biggest bottleneck right now: forecasting, interconnection, operations visibility, or field execution?