AI, Innovation and Collaboration: Shaping Resilient Economies

Detailed Summary

• Strategic framing – AI is not merely software; it is a massive electricity consumer. The U.S. sees AI as a “foundational technology” that will drive economic growth, national security, and global competitiveness.
• America’s AI Action Plan (2022) – built on three pillars:
  1. Strengthening American AI innovation – avoid over‑precautionary regulation, protect freedom of expression.
  2. Building American AI infrastructure – executive order to speed federal permitting for data‑center builds.
  3. Leading in international AI diplomacy & security – AI export program to share trusted, full‑stack U.S. tech.
• Key Insight – AI infrastructure must be coupled with secure, reliable energy systems; otherwise the AI race could reinforce authoritarian models that dominate energy‑intensive compute.
• Call to Action – Treat high‑efficiency data‑centers as strategic national assets and partner with other nations to develop capabilities, not just to consume AI.

2. Moderator Introduction (Jacqueline Cochran)

• Presented the panel composition (utility, transmission, silicon, hyperscaler, and systems‑integration perspectives).
• Set the agenda: first discuss current impacts of AI data‑centers on power systems, then explore coordination, policy, and technology gaps, and finally run a lightning‑round on cross‑sector collaboration.

3. Panelist Presentations

3.1. Distribution‑Level View (Abhishek Ranjan – BRPL)

• Current situation – No hyperscaler data‑centers inside Delhi proper yet, but several in Noida (≈20–25 km from South/East Delhi).
• Planning challenge – A single hyperscaler may demand ≈1 GW; such loads sit at 220 kV‑66 kV levels, not at 11/23 kV distribution.
• Resource‑planning implications:
  • Need pre‑determined siting (national or state‑level transmission studies).
  • Integrate long‑duration generation (future nuclear, large‑scale renewables).
  • Deploy local storage (6‑10 h battery capacity) and captive generation (solar/wind).
• Risk of ad‑hoc connections – would cause “chaos”, sub‑optimal wire additions, higher tariffs for end‑users.
• Recommendation – Develop a national siting plan for AI data‑center zones, akin to the Central Electricity Authority (CEA) transmission plan, to avoid “piecemeal, transactional” connections.

3.2. Transmission & Real‑Time Balancing (Samir Saxena – Grid India)

• Scale projection – By 2030 AI‑data‑center load could equal 8‑10 GW, comparable to a whole state’s demand.
• Load characteristics – Highly variable, spiky, with sharp ramps; inverter‑based loads can quietly island (disconnect) causing sudden disturbances.
• Infrastructure needs:
  • Robust, high‑capacity transmission with adequate reactive‑power support and fault‑ride‑through capability.
  • Resource adequacy – Data‑centers must meet primary, secondary, and reserve obligations, not rely solely on the grid.
• Planning‑process gap – Grid studies (dynamic, fault, stability) must be provided by data‑center developers; historically developers were “plug‑and‑play” with little grid expertise.
• Policy recommendation – Treat AI‑data‑centers as a “mass load” with a special regulatory category (in GNA regulations), mandating co‑optimization of generation, storage, and demand‑response.

3.3. Silicon‑Level Efficiency (Jal Desai – DOE National Lab)

• Three‑level efficiency strategy (Intel perspective, recapped):
  1. Transistor‑level: Ribbon‑FET technology reduces switching voltage → lower dynamic power.
  2. Packaging‑level: Power‑via on backside of die improves power delivery efficiency by ~15 %.
  3. System‑level: “Forwarder” 3‑D stacking of memory and compute reduces I/O distance, cutting interconnect power.
• Heterogeneous compute – Match workload to appropriate silicon (CPU, GPU, custom ASIC) rather than defaulting to “GPU‑guzzlers”.
• Observability & workload profiling – Continuous monitoring of latency & power enables dynamic workload placement for optimal PUE.

3.4. Hyperscaler Site‑Selection (Kartik Krishnan – AWS)

• Decision drivers (for AI‑data‑center siting):
  1. Speed & certainty of power delivery – Must know infrastructure will be in place within the projected 18‑24 month horizon.
  2. Visibility of 100 % renewable supply – AWS requires clear pathways to renewable contracts (physical/virtual PPAs).
  3. Regulatory coherence – Consistency across data‑center policy, electricity regulations, and open‑access rules.
  4. Long‑term power‑price predictability – 15‑20 year price outlook needed for capex justification.
• Current status in India – AI workloads are still early; AWS anticipates the first large‑scale AI data‑centers within 2‑3 years.

3.5. Systems‑Integration & Execution (Deepesh Kiran Nanda – TCS)

• India’s comparative advantage – Large renewable capacity, flexible gas‑based generation, and government policies (open‑access, renewable targets) give India a head‑start over the US, where generation and transmission are already maxed out.
• Capacity gap – Existing AI‑ready data‑center stock ≈ 1.6 GW (mainly edge/cloud); AI will need 10‑12× more power.
• Policy levers needed:
  • Extend utility contracts beyond the typical 7‑year term to 10‑12 years to allow recovery of capex.
  • Encourage long‑duration storage (e.g., vanadium‑redox flow batteries) and captive generation.
  • Harmonize state‑level renewable PPAs (banking mechanisms, time‑of‑use tariffs).
• Infrastructure considerations – Emphasis on high‑quality utilities, robust cooling (direct‑to‑chip, liquid cooling), and safety standards for massive racks (UPS, diesel back‑up, N+1 redundancy).

4. Joint Discussion – Coordination, Policy, and Technology Gaps

Topic	Key Points & Recommendations
Distribution Planning (Ranjan)	Update Distribution Resource Planning (DRP) rules; create a “mass‑load” classification for hyperscalers; promote vanadium‑redox flow batteries as compact, long‑duration storage.
Transmission Coordination (Saxena)	Require detailed grid studies from developers; enable demand‑side services (load‑shifting, reactive‑power provision) from data‑centers; develop ancillary‑service markets for large loads.
Silicon & System Design (Desai / Intel)	Push performance‑per‑watt improvements through ribbon‑FET, power‑vias, 3‑D stacking; adopt heterogeneous compute to avoid over‑provisioning; improve observability for real‑time power management.
Site‑Selection & Renewable Integration (Krishnan)	Align utility planning horizons (15‑20 yr) with state transmission planning (5 yr) to avoid mismatches; streamline policy coherence across states for PPAs and incentives.
Execution & Long‑Term Contracts (Nanda)	Extend utility‑contract tenures; secure long‑duration storage and captive generation; standardize safety & building codes for ultra‑high‑density racks.

5. Lightning‑Round – One‑Sentence Collaboration Ideas

Panelist	Suggested “quick win” collaboration
Abhishek Ranjan	Adopt heterogeneous silicon designs that are grid‑integrable and can provide reliability services.
Samir Saxena	Encourage data‑centers to offer reliability services (frequency response, voltage support) to the grid.
Jal Desai (Intel)	Foster technology co‑development that yields flexible, grid‑friendly power electronics.
Kartik Krishnan	Increase hyperscaler investment in Indian zones with clear renewable‑procurement pathways.
Deepesh Nanda	Focus regional planning on hotspot zones (e.g., Navi Mumbai, Hyderabad) for early network augmentation.
Audience (battery‑storage question)	Deploy local battery‑energy‑storage (e.g., vanadium‑redox) to absorb ramps and provide ancillary services.

6. Audience Q&A (selected excerpts)

• Battery‑energy‑storage role – Panelists agreed that BES can smooth load ramps, provide voltage support, and enable demand‑shifting, turning storage into a revenue‑generating ancillary service.
• Island‑ing schemes – Jacqueline highlighted recent Mumbai grid failure (2020/21) and stressed that robust island‑ing for critical data‑center zones is essential for national security.

7. Closing Remarks

• Moderator thanked the speakers and announced a follow‑up technical session on data‑center design.
• Audience was invited to stay for the next panel.

Key Takeaways

1. AI data‑centers are emerging as strategic electricity loads – a single hyperscaler can demand ≈1 GW, comparable to a small state’s total demand.
2. Coordinated planning is essential – distribution, transmission, and generation must treat AI data‑centers as a “mass load” with a dedicated regulatory category.
3. Performance‑per‑watt improvements start at the silicon level – ribbon‑FET, power‑via, and 3‑D memory‑compute stacking can cut chip‑level power by ≈15 %, directly easing grid pressure.
4. Renewable‑energy visibility and long‑term price certainty are the top site‑selection criteria for hyperscalers like AWS.
5. India holds a comparative advantage thanks to abundant renewable potential, flexible gas pipelines, and supportive policies, but needs longer utility contracts and standardized safety/quality codes.
6. Battery‑energy‑storage (e.g., vanadium‑redox flow) is a crucial enabler for smoothing AI‑load ramps and providing ancillary grid services.
7. Policy reforms needed – update distribution resource planning, create a mass‑load classification, harmonize state PPAs, and develop ancillary‑service markets for large‑scale loads.
8. Cross‑sector collaboration (utility‑hyperscaler‑chip‑maker) is the fastest path to address reliability, storage, and regulatory challenges.
9. Cooling and safety become architectural concerns – AI data‑centers must be designed from day 1 with integrated power, cooling, and safety systems rather than retrofitted later.
10. Long‑term success hinges on knowledge exchange across utilities, chip manufacturers, and AI application owners to align technology roadmaps with grid evolution.

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