Waste-to-Energy-Coupled AI Data Centers: Cooling Efficiency and Grid Resilience

TL;DR

This paper introduces an integrated waste-to-energy and AI data center system that improves cooling efficiency and grid resilience by using waste heat for cooling, with a detailed economic and feasibility analysis.

Contribution

It proposes a novel coupled system model that treats cooling as a first-class energy service and provides a decision framework for its implementation.

Findings

Thermoeconomic benefits depend on cooling coverage, parasitic electricity, and delivery distance.

The system can be economically viable within specific operational corridors.

The framework enables siting decisions for WtE-AIDC in urban environments under grid stress.

Abstract

AI data-center expansion is increasingly constrained by the coupled availability of deliverable electricity and heat-rejection (cooling) capacity. We propose and evaluate an integrated Waste-to-Energy-AI Data Center configuration that treats cooling as a first-class energy service rather than an unavoidable electricity burden. The coupled system is modeled as an input-output 'black box' with transparent boundaries and a standalone benchmark in which mechanical chilling is powered by grid electricity. The central mechanism is energy-grade matching: low-grade WtE thermal output drives absorption cooling to deliver chilled service, thereby displacing baseline cooling electricity. We show that thermoeconomic superiority is governed by three first-order determinants, (i) cooling coverage of IT heat load, (ii) parasitic electricity for transport and auxiliaries, and (iii) distance-driven delivery decay, yielding a break-even corridor beyond which net benefits vanish. Comparative statics characterize sensitivity to IT utilization, feedstock quality (waste LHV and throughput), climate parameterization, and corridor distance. We translate these accounting gains into decision language through a computable prototype for Levelized Cost of Computing (LCOC) and an ESG valuation channel grounded in measurable mechanisms, without re-deriving full lifecycle inventories. The framework provides siting-ready feasibility conditions for WtE-AIDC coupling in urban AI corridors under grid stress.