{"slug": "the-ai-power-bill-who-gets-grid-capacity-and-who-bears-the-risk", "title": "The AI Power Bill: Who Gets Grid Capacity, and Who Bears the Risk?", "summary": "Voters backed limits on data center development in 40 of 41 local ballot contests across 10 U.S. states on Nov. 3, signaling growing scrutiny of the electricity, land and public resources large computing facilities require. Ohio regulators in July 2025 approved a dedicated AEP Ohio tariff requiring large data centers to commit to a ramp-up period of up to four years plus eight additional years, with collateral equal to 50% of minimum contractual charges for developers failing credit tests; by February 2026 AEP Ohio reported 5,642 MW of binding contracts under the tariff plus 12,219 MW contracted previously. PJM Interconnection's independent market monitor, Monitoring Analytics, estimated in July 2026 that data center demand accounted for about $6.3 billion of $16.4 billion in capacity charges in the latest auction, and $29.4 billion of $63.6 billion across four auctions, as the International Energy Agency projects global data center electricity use rising from roughly 485 TWh in 2025 to 950 TWh by 2030.", "body_md": "In 40 of 41 local ballot contests across 10 U.S. states on Nov. 3, voters favored limiting data center development.\n\nThe measures included bans, moratoriums, rejected zoning proposals, and nonbinding votes. Their legal consequences vary, but they signal growing scrutiny of the electricity, land, and public resources required by large computing facilities.\n\nMeanwhile, developers are competing to secure power capacity years before their facilities will operate.\n\nThe U.S. relies on utilities, regulators, and local governments to negotiate these commitments. China coordinates computing infrastructure and energy resources through national development programs.\n\n[View All](https://www.eetimes.com/category/sponsored-content/)\n\nBoth face the same uncertainty: How much infrastructure should be built before future computing demand is known, and who pays when forecasts prove wrong?\n\n### **Computing moves faster than the grid**\n\nThe International Energy Agency projects global data center electricity consumption rising from approximately 485 terawatt-hours in 2025 to 950 TWh by 2030. Consumption at AI-focused facilities is expected to triple.\n\nAI accelerator generations change within years. Substations, transformers, and transmission connections are designed to operate for decades. Utilities must therefore finance long-lived infrastructure against demand forecasts shaped by a much faster technology cycle.\n\nThe challenge also involves [power characteristics](https://www.powerelectronicsnews.com/why-800-vdc-power-distribution-is-gaining-traction-in-ai-data-centers/). Training, inference, and facility-level power management create different load profiles. On-site systems can buffer fluctuations, but utilities still need to plan for the facilities they connect.\n\nA 500-megawatt request may require substations, transmission upgrades, and additional generation capacity before the developer consumes its first megawatt.\n\nIf the project is delayed or canceled, those costs do not disappear.\n\n### **Ohio’s answer: make developers commit**\n\nIn July 2025, Ohio regulators approved a dedicated tariff for large data centers served by AEP Ohio.\n\nQualifying developers must enter agreements with a ramp-up period of up to four years followed by eight additional years. Minimum monthly billing demand reflects contracted capacity and historical usage, subject to the tariff’s 85% contracted-capacity limit.\n\nDevelopers failing specified credit-rating and liquidity tests must provide qualifying guarantees or collateral equal to 50% of minimum contractual charges over the agreement. Cancelation or substantial delay before connection can also trigger reimbursement obligations.\n\nBy February 2026, AEP Ohio reported 5,642 MW of binding contracts under the new tariff, alongside 12,219 MW contracted previously. These are commitments, not operating demand.\n\nThe tariff reduces the risk that existing electricity customers will finance infrastructure for projects that never materialize.\n\nIt also changes the economics of competition.\n\nA hyperscaler can support long-term electricity commitments through its balance sheet. A smaller developer may need project financing, customer contracts, and external guarantees before securing comparable capacity.\n\nElectricity access could reinforce the advantages that large technology companies already enjoy in semiconductor procurement and capital markets.\n\n### **Who receives the benefits, and who pays?**\n\nPJM Interconnection operates a wholesale electricity market across parts of 13 states and Washington, DC.\n\nIn July 2026, PJM’s independent market monitor, Monitoring Analytics, estimated that data center demand accounted for approximately $6.3 billion of $16.4 billion in capacity charges associated with the latest auction. Across four auctions, it attributed $29.4 billion of $63.6 billion in total capacity charges to that demand.\n\nThese are the market monitor’s estimates of the contribution of existing and forecast data center demand to wholesale capacity charges, not amounts billed directly to data center operators or households. Actual cost recovery depends on market arrangements and retail tariffs.\n\nLocal governments see a different balance sheet.\n\nFairfax County, Virginia, estimated approximately $110.6 million in data center-related general fund tax revenue for tax year 2026.\n\nThose revenues benefit the county, while electricity infrastructure costs may extend across a wider network. The Fairfax and PJM figures cover different jurisdictions and do not establish a direct subsidy.\n\nThey illustrate a structural problem: The institutions approving projects and collecting tax revenues are not necessarily those allocating electricity infrastructure costs.\n\n### **Regulation becomes a competitive variable**\n\nIn June 2026, the Federal Energy Regulatory Commission issued orders requiring six regional grid operators to justify or propose reforms to rules for connecting large electricity users and allocating associated costs. The proceedings do not themselves impose new tariffs.\n\nDevelopers must increasingly evaluate collateral requirements, connection charges, and regulatory uncertainty alongside land prices, fiber connectivity, and computing equipment.\n\nThere is no cost-free solution.\n\nWeak commitments expose electricity customers to speculative projects. Strict requirements can favor companies with stronger balance sheets, potentially excluding viable competitors.\n\nAmerica’s decentralized system permits local scrutiny and commercial discipline, but coordination across utilities, markets, and regulators can be expensive and slow.\n\nChina is testing a different model.\n\n### **China’s coordinated approach**\n\nChina’s Eastern Data, Western Computing initiative uses nationally coordinated computing hubs and data center clusters to align infrastructure investment with regional electricity resources, land, and network connectivity.\n\nSuitable workloads are encouraged toward resource-rich western regions, while latency-sensitive services remain closer to major economic centers.\n\nThat distinction matters technically. Training and inference have different requirements for latency, bandwidth, and data movement. Cheap electricity does not make every location suitable for every workload.\n\nChina can coordinate electricity infrastructure, industrial development, and state-owned investment more directly than the U.S. This may reduce barriers created by fragmented planning and approval processes.\n\nBut coordination does not guarantee productive demand.\n\nInfrastructure built around development targets can remain underutilized. When commercial demand disappoints, losses may be absorbed by state-owned enterprises, lenders, or institutions supporting the projects.\n\nAmerica risks delaying viable capacity or concentrating development among financially stronger companies. China risks committing capital to capacity that customers cannot use economically.\n\nThe outcome cannot be judged by the number of announced projects or installed megawatts alone.\n\n### **Four tests for 2030**\n\nThe competition between these infrastructure models should be evaluated against four measures:\n\n1. **Time to operation:** How long does it take to turn an electricity request into a commercially operating computing facility?\n2. **Delivered cost:** What is the total cost of securing reliable power, including network investments, financing, and contractual commitments?\n3. **Utilization:** How much installed computing capacity generates productive workloads rather than remaining idle or constrained?\n4. **Financial exposure:** Who absorbs losses when demand forecasts fail?\n\nDifferences in electricity pricing, financing structures, and public reporting will complicate comparisons, but these questions reveal more than headline investment announcements.\n\nBy 2030, leadership in AI infrastructure may depend as much on electricity access and financial capacity as on processor performance.\n\nFor both the U.S. and China, the decisive measure will be how efficiently investment in electricity infrastructure becomes computing capacity that customers use.\n\n[AI Data Centers Make Power, Cooling Critical to Scaling](https://www.eetimes.com/ai-data-centers-make-power-cooling-critical-to-scaling/)\n\n[Amperesand Aims to Smooth AI Data Center Power Spikes](https://www.eetimes.com/amperesand-aims-to-smooth-ai-data-center-power-spikes/)\n\n[Why Data Centers Must Become System‑Aware](https://www.eetimes.com/why-data-centers-must-become-system%E2%80%91aware/)", "url": "https://wpnews.pro/news/the-ai-power-bill-who-gets-grid-capacity-and-who-bears-the-risk", "canonical_source": "https://www.eetimes.com/the-ai-power-bill-who-gets-grid-capacity-and-who-bears-the-risk/", "published_at": "2026-11-10 15:00:00+00:00", "updated_at": "2026-11-10 15:22:22.843662+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-policy", "ai-chips"], "entities": ["AEP Ohio", "PJM Interconnection", "Monitoring Analytics", "International Energy Agency", "Ohio"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/the-ai-power-bill-who-gets-grid-capacity-and-who-bears-the-risk", "markdown": "https://wpnews.pro/news/the-ai-power-bill-who-gets-grid-capacity-and-who-bears-the-risk.md", "text": "https://wpnews.pro/news/the-ai-power-bill-who-gets-grid-capacity-and-who-bears-the-risk.txt", "jsonld": "https://wpnews.pro/news/the-ai-power-bill-who-gets-grid-capacity-and-who-bears-the-risk.jsonld"}}