Spatial Two-Stage Hierarchical Optimization Analysis for Site Selection of Bitcoin Mining in South Korea

TL;DR

This paper introduces a two-stage hierarchical optimization framework for selecting optimal sites for Bitcoin mining in South Korea, integrating multi-scale spatial data to maximize net profit while considering physical constraints.

Contribution

It develops a novel two-stage optimization approach that combines regional cost-benefit analysis with detailed site screening, overcoming limitations of traditional GIS-based methods.

Findings

Optimal configuration of 3 regions with $K^*=3$ identified, yielding ~$307 million in net profit.

Most profitable region, Yongin, was highly constrained, with 87% of sites filtered out.

Framework effectively integrates multi-scale spatial data for scalable site selection.

Abstract

South Korea faces the dual challenge of managing growing distributed solar energy surpluses and the high energy demand of industries like Bitcoin mining. Leveraging mining operations as a flexible load to monetize this `net-metering surplus' presents a viable synergy, but requires a robust site selection methodology. Traditional GIS-based Multi-Criteria Decision Analysis (MCDA) struggles with subjective weighting and integrating heterogeneous spatial data (areal-level and lattice-level). This thesis develops and implements a Two-Stage Hierarchical Optimization framework to overcome these limitations. Stage 1 (Areal-Level) employs a cost-benefit optimization to determine the optimal number ($K^*$) and combination of regions, maximizing a final adjusted net profit by balancing surplus power revenue against detailed land and non-linear infrastructure costs. Stage 2 (Point-Level) then uses a GIS-based sliding window search within these selected regions, applying topographic (slope $< 6.0^\circ$) and land-use constraints at a 30m resolution to identify physically constructible `unit sites'. The model identified an optimal configuration of $K^*=3$ regions (Yongin, Damyang, Miryang) yielding a maximum potential net profit of approximately \$307 million. Crucially, the Stage 2 screening revealed that Yongin, the most profitable region, was also the most physically constrained, 87\% of sites filtered out. This research contributes a scalable, objective framework for energy infrastructure siting that effectively integrates multi-scale spatial data. It provides a data-driven strategy for policymakers and grid operators (like Korea Electric Power Corporation) to monetize curtailed renewables and enhance grid stability.