Enerhash for Real-Time Energy Optimization in Renewable Power Grids

DOI

10.2991/978-94-6239-658-6_15How to use a DOI?

Keywords

AI-controlled Data Centers; Bitcoin Mining; Flexible Demand; Renewable Integration; Sustainable Energy Systems

Abstract

The integration of intermittent renewable energy sources (RES) into power systems continues to face challenges due to fluctuating supply and limited grid flexibility. This paper examines how Enerhash’s modular Databox infrastructure provides a novel solution by operating as an AI-controlled, flexible consumer that supports grid stability while performing productive computing tasks. Unlike conventional data centers that act as constant loads, the Databox combines Bitcoin mining equipment with AI/HPC servers, transforming surplus electricity into economic value while allowing instantaneous demand-side adjustments in response to operator signals. The research applies a qualitative case study approach based on expert interviews, company documentation, and project data, with a particular focus on two deployments: participation in frequency regulation markets in Sweden and the utilization of flared gas in the United States. The Swedish project demonstrated the technological feasibility of including modular computing units in ancillary services markets, with sub-second response times confirming their capacity for primary frequency regulation. The U.S. deployment highlighted the environmental dimension by converting waste gas into electricity, thereby reducing CO₂ and methane emissions while generating digital outputs. Together, these cases show that the Databox model is both scalable and adaptable, offering benefits across regulated and decentralized contexts. At the same time, limitations remain concerning financing, regulatory heterogeneity, and long-term performance data. Future research should extend comparative analysis with other flexible demand-side technologies and explore the broader role of modular computing in sustainable energy transitions.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Imre Mátyás Kovács
AU  - András Szeberényi
PY  - 2026
DA  - 2026/05/01
TI  - Enerhash for Real-Time Energy Optimization in Renewable Power Grids
BT  - Proceedings of the Kautz Conference on Business and Economics 2025 (KCBE 2025)
PB  - Atlantis Press
SP  - 281
EP  - 292
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-658-6_15
DO  - 10.2991/978-94-6239-658-6_15
ID  - Kovács2026
ER  -