Waste-to-Energy for AI Data Centers: Cooling and Grid Resilience
Analysis
This paper addresses the growing challenge of AI data center expansion, specifically the constraints imposed by electricity and cooling capacity. It proposes an innovative solution by integrating Waste-to-Energy (WtE) with AI data centers, treating cooling as a core energy service. The study's significance lies in its focus on thermoeconomic optimization, providing a framework for assessing the feasibility of WtE-AIDC coupling in urban environments, especially under grid stress. The paper's value is in its practical application, offering siting-ready feasibility conditions and a computable prototype for evaluating the Levelized Cost of Computing (LCOC) and ESG valuation.
Key Takeaways
- •Proposes an integrated Waste-to-Energy-AI Data Center configuration to address cooling and grid constraints.
- •Focuses on energy-grade matching to utilize low-grade thermal output for cooling.
- •Provides a framework for assessing the thermoeconomic feasibility of the integrated system.
- •Offers siting-ready feasibility conditions and a computable prototype for LCOC and ESG valuation.
“The central mechanism is energy-grade matching: low-grade WtE thermal output drives absorption cooling to deliver chilled service, thereby displacing baseline cooling electricity.”