Hydropower Drops, Solar Rises: Lessons for Kazakhstan

Uzbekistan’s hydropower output fell 20% in 2025, down to 6.5 billion kWh, after generating 8.1 billion kWh in 2024. That’s not a rounding error—it’s the kind of swing that forces energy planners to make decisions fast.

The immediate driver is water stress. When rivers run low, hydropower stops being “always-on” and starts behaving like a weather-dependent resource—just with different weather. Uzbekistan’s response, according to reporting summarized by OilPrice.com, is to accelerate solar and wind and to rethink hydropower operations to be more water-efficient.

For Kazakhstan, this isn’t a neighboring-country curiosity. It’s a preview. Central Asia’s energy systems are increasingly shaped by climate variability, water allocation, and demand growth. The practical question for our market is: how do we keep reliability high while the resource base becomes less predictable? My take: renewables will be part of the answer, but the part many companies still underestimate is AI in energy—not as hype, but as the operating layer that makes a more complex grid workable.

Why Uzbekistan’s hydropower decline matters for Central Asia

Hydropower is only “cheap and stable” when hydrology is stable. The moment inflows change—because of drought, glacier melt patterns, irrigation demand, or upstream releases—hydropower becomes a constrained asset.

Uzbekistan’s 2025 numbers underline three realities that apply across the region:

1. Water risk is energy risk. A 20% drop in a single year creates gaps that must be filled by other generation sources or by demand cuts.
2. Seasonality gets sharper. Lower summer flows can collide with summer peaks (cooling) and agricultural water needs.
3. Planning assumptions break. Long-term generation forecasts built on historical averages become less reliable.

Here’s the key point: energy diversification isn’t a climate slogan; it’s operational risk management. When one pillar (hydro) wobbles, you need other pillars ready.

Solar and wind aren’t just “new capacity”—they change how the system must be run

Adding solar and wind solves one problem (fuel and water exposure) and introduces another (variability). That trade-off is manageable, but only if system operators modernize how they forecast, dispatch, and maintain assets.

The near-term logic: replace lost kWh fast

When hydropower production drops by 1.6 billion kWh year-over-year (8.1 → 6.5), the system needs an alternative source of energy. Solar and wind are attractive because they:

• Use almost no water in operation (especially PV solar)
• Can be deployed in modular increments
• Reduce dependence on imported fuels (where relevant)

But new renewables don’t behave like a thermal plant or a hydro unit. They require better forecasting, faster balancing resources, and tighter grid visibility.

The medium-term logic: redesign the role of hydropower

The most interesting phrase in the RSS summary is that Uzbekistan is rethinking hydropower strategy to make it more water-efficient. In practice, that usually means shifting hydro toward:

• Flexibility and peak support (generate when it matters most)
• Grid balancing for renewables (fast ramping)
• Coordinated reservoir operations (optimize water across energy + irrigation constraints)

In other words, hydro becomes less of a “baseload kWh factory” and more of a system stabilizer. That shift is exactly where data and AI start paying for themselves.

Where AI fits: making water-constrained energy systems predictable

AI isn’t magic; it’s a way to turn messy, multi-source signals into decisions you can execute. In water-stressed power systems, the highest ROI use cases are the ones that reduce uncertainty and improve timing.

AI use case #1: Hydrology-to-generation forecasting you can dispatch

A useful forecast isn’t a monthly report. It’s an operational tool that answers:

• How much inflow will we have tomorrow, next week, next month?
• What’s the probability distribution (not just one number)?
• What does that mean for feasible generation, reservoir levels, and constraints?

Modern ML models can blend:

• Snowpack and temperature data (for melt timing)
• Satellite-derived basin indicators
• Historical inflow patterns
• Real-time upstream release signals (when available)

A strong result isn’t “more accuracy” in abstract terms. It’s fewer surprises that force emergency dispatch.

Snippet-worthy truth: When water becomes volatile, the winning capability is not more capacity—it’s better prediction and faster coordination.

AI use case #2: Optimal dispatch across hydro + solar + wind + thermal

As renewables grow, dispatch becomes an optimization problem with competing goals:

• Minimize cost
• Maintain reliability
• Respect water and reservoir constraints
• Reduce emissions where policy and economics align

AI-driven optimization (often combined with classic operations research) can recommend:

• When to save water for peak hours
• When to curtail solar vs. preserve reservoir storage
• How to schedule thermal plants to avoid inefficient cycling

For Kazakhstan, this is especially relevant as the system integrates more renewables and modernizes dispatch practices. The complexity rises fast; manual planning doesn’t scale.

AI use case #3: Predictive maintenance for hydro and grid assets

Water variability often increases wear:

• More cycling and ramping stresses turbines
• Sedimentation and debris risks rise with certain flow regimes
• Grid assets face different load profiles under renewable variability

Predictive maintenance models can identify failure risk earlier by learning from:

• Vibration and temperature sensors
• SCADA event logs
• Maintenance history
• Operating regime changes (ramping frequency, starts/stops)

The benefit isn’t just fewer failures; it’s fewer forced outages during tight supply periods.

AI use case #4: Demand forecasting and demand response (the “invisible power plant”)

The cheapest kWh is the one you don’t need to produce. In winter and summer peaks, AI-based demand forecasting helps utilities and large industrials:

• Predict peak hours with higher precision
• Shift flexible loads (pumps, compressors, certain industrial processes)
• Design demand response programs that actually get participation

Kazakhstan’s oil-gas and mining-heavy load profile makes this a serious lever, not a nice-to-have.

What Kazakhstan can learn from Uzbekistan’s pivot

The lesson isn’t “build solar because Uzbekistan did.” The lesson is that resource constraints force strategy changes, and the companies that prepare early spend less later.

1) Treat water as a first-class variable in energy planning

Most energy plans still treat hydrology as an input and electricity as the output. In a climate-stressed region, it’s a joint system:

• Water allocations affect generation
• Generation decisions affect water releases
• Both affect agriculture and regional stability

For Kazakhstan, especially in basins with competing water demands, a realistic plan includes water scenarios—dry, average, wet—and operating rules for each.

2) Invest in “operating intelligence,” not only steel-and-concrete

It’s tempting to respond to volatility with only capacity additions. Capacity matters, but operational intelligence often delivers faster payback:

• Better forecasts reduce reserve requirements
• Better dispatch cuts fuel burn and curtailment
• Better maintenance reduces outage risk

This is where жасанды интеллект (AI) fits naturally into the broader series theme: Kazakhstan’s energy and oil-gas sector is already using data to improve production and safety; the same mindset works for power system efficiency and reliability.

3) Make renewables bankable by fixing integration, not marketing

Renewables integration fails when:

• Grid visibility is poor
• Forecasting is weak
• Flexibility resources aren’t planned
• Curtailment becomes the default solution

AI helps most when paired with concrete grid upgrades: telemetry, automated controls, clear dispatch rules, and market signals that reward flexibility.

A practical 90-day AI roadmap for energy companies in Kazakhstan

If you’re leading strategy, operations, or digital transformation, here’s what works in the real world—small steps that compound.

Step 1: Build a single “truth layer” for operational data

Start by connecting the data you already have:

• SCADA / dispatch data
• Weather feeds
• Maintenance logs
• Metering and load data

The goal is not a perfect data lake. It’s a usable dataset for 1–2 priority models.

Step 2: Pick one high-impact model and define success metrics

Good first models in this context:

• Day-ahead solar/wind forecast improvement
• Hydro inflow forecasting with confidence intervals
• Predictive maintenance for one critical asset class

Define success in operational terms, for example:

• Reduce balancing costs by X%
• Reduce forced outages by Y%
• Cut curtailment hours by Z

Step 3: Put the model into a decision workflow

A model that lives in a slide deck won’t change outcomes. Embed it where decisions happen:

• Dispatch planning
• Maintenance scheduling
• Trading/hedging (where applicable)

Step 4: Expand to optimization (the real payoff)

Forecasting is step one. Optimization is step two:

• Unit commitment recommendations
• Reservoir operation schedules
• Flexibility planning (storage, demand response, reserves)

That’s where AI starts to feel less like “digital” and more like “operations.”

Quick answers people usually ask (and should)

Will solar and wind fully replace hydropower in Central Asia?

No. Hydropower remains valuable, especially for flexibility. The shift is about rebalancing the mix and using hydro smarter when water is tight.

Is AI only for big utilities with big budgets?

No. I’ve seen smaller operators get real value by starting with one model—forecasting or predictive maintenance—then expanding. The constraint is usually governance and data access, not money.

What’s the biggest mistake companies make with AI in energy?

They buy tools before fixing the workflow. If dispatchers, engineers, and planners can’t act on the output, accuracy doesn’t matter.

What happens next in the region

Uzbekistan’s hydropower drop is a clean signal: climate and water variability are already rewriting the energy playbook in Central Asia. Solar and wind will keep growing because they reduce water exposure, but they also demand better forecasting, dispatch, and maintenance practices.

For Kazakhstan, the most competitive posture is to treat AI as the connective tissue between generation types—thermal, hydro, wind, solar—and to make efficiency improvements a core part of energy security. That fits the heart of this series: Қазақстандағы энергия және мұнай-газ саласын жасанды интеллект қалай түрлендіріп жатыр isn’t just about automation; it’s about running critical infrastructure with fewer surprises.

If water-driven volatility becomes the new normal, the uncomfortable question is simple: will your operating model handle uncertainty—or will it amplify it?