ENERGY ARCHITECTURE

Why Energy Economics
Define AI Infrastructure

In AI data centers, energy costs represent 40-60% of operational expenses. NeuroVerse's Hybrid-Dynamic power architecture is designed to deliver a structural cost advantage.

The Uttarakhand Tariff Reality

Industrial power tariffs in India vary significantly by state, time-of-day, and consumption pattern. Uttarakhand offers competitive base rates, but peak-hour pricing can erode margins for 24/7 compute operations.

Most data centers accept grid dependency as inevitable. We engineered around it.

Standard Grid Tariff Structure

Off-Peak (22:00 - 06:00) ₹5.50/kWh
Normal (06:00 - 18:00) ₹7.00/kWh
Peak (18:00 - 22:00) ₹9.50/kWh

The NeuroVerse Approach

Rather than accepting grid dependency, NeuroVerse is engineered around a Hybrid-Dynamic architecture that combines multiple power sources, each optimized for specific time windows.

Target Blended Cost

~₹5.0 /kWh average

Approximately 30% below industry average through strategic source switching and thermal storage.

Hybrid-Dynamic Energy Mix

Three integrated power sources, intelligently managed across the 24-hour cycle.

Grid Power

State electricity board connection with dedicated HT line. Primary source during off-peak hours when rates are lowest.

Optimal Usage

22:00 - 06:00

Group Captive Solar

Equity stake in solar generation facility under Group Captive model. Fixed-cost power during daylight hours.

Generation Window

06:00 - 18:00

Thermal Energy Storage

Ice-based thermal storage charges during off-peak, discharges cooling capacity during peak pricing windows.

Discharge Window

18:00 - 22:00

24-Hour Energy Optimization Strategy

Time-of-Day tariff arbitrage combined with Thermal Energy Storage. TES shifts cooling energy across tariff windows; compute workloads remain continuous.

Time Block Compute Power Cooling Strategy Grid Tariff Window Optimization Logic
00:00 - 06:00 Grid (Off-Peak) Chillers + TES Charging Off-Peak Lowest tariff window—charge TES for later use
06:00 - 12:00 Solar + Grid Backup Chillers + TES Charging Normal Solar generation begins—continue TES charging
12:00 - 18:00 Solar + Grid Backup Chillers + TES Charging Normal Peak solar generation—maximize TES charge
18:00 - 22:00 Grid (Peak Tariff) TES Discharge (Primary) Peak Highest tariff—use stored cooling to minimize grid draw
22:00 - 24:00 Grid (Off-Peak) Chillers + TES Charging Off-Peak Return to off-peak tariff—resume TES charging

Target Blended Electricity Cost

Projected cost based on hybrid mix of grid power (Time-of-Day tariffs), group captive solar, and thermal energy storage optimization

~₹5.0–5.1/kWh

vs ~₹7.2/kWh unoptimized grid-only

THERMAL ENERGY STORAGE

Shifting Cooling Load Away from Peak Hours

Thermal Energy Storage (TES) uses off-peak electricity to create ice, then uses that stored cooling capacity during peak pricing windows. This effectively time-shifts our cooling load to when power is cheapest.

Charge Cycle (22:00 - 06:00)

Chillers run at high efficiency during cool nighttime hours, producing ice at off-peak rates.

Discharge Cycle (18:00 - 22:00)

Stored ice provides cooling, dramatically reducing grid draw during peak tariff hours.

TES Impact Analysis

Peak Hour Grid Dependency
-75%
Cooling OPEX Reduction
40%
Chiller Efficiency Gain
+15%

Nighttime operation = lower ambient temperature = higher COP

Target Blended Cost Model

Projected electricity cost based on hybrid mix of grid power (Time-of-Day tariffs), group captive solar, and thermal energy storage optimization.

NeuroVerse Target Model

Grid (Off-Peak) ~33%
Solar (Group Captive) ~42%
TES + Grid (Peak Mitigation) ~17%
Grid (Normal) ~8%
Target Blended Cost ~₹5.0–5.1/kWh

Unoptimized Grid-Only Model

Grid (Industrial HT Tariff) 100%
No solar integration
No TES optimization
No tariff arbitrage
Typical Cost ~₹7.2/kWh

Projected Cost Advantage

~30%

Reduction compared to unoptimized, grid-only industrial data centers under Uttarakhand HT tariffs

Energy Economics Define AI Infrastructure Returns

Our hybrid-dynamic architecture isn't just about cost savings—it's about building a structural moat that protects margins as AI compute scales.