Optimizing Microgrid Composition for Sustainable Data Centers
🤖 AI Summary
Existing tools for long-term sustainability and power supply reliability assessment in data center microgrid co-planning are inadequate. Method: This paper proposes a multi-timescale optimization framework that integrates computational workloads, renewable energy generation, and energy storage dynamics. It pioneers the deep integration of the Vessim computational simulation platform with the NREL System Advisor Model (SAM) for renewable energy, enabling black-box optimization via a co-simulation system. Contribution/Results: The framework overcomes limitations of current tools by jointly quantifying operational carbon emissions and embodied carbon across the microgrid’s full life cycle. It enables fine-grained optimization of microgrid sizing and component composition, supporting low-carbon, reliability-aware energy configuration decisions during the planning phase. This work fills a critical methodological gap in life-cycle carbon assessment for data center microgrids.
Application Category
📝 Abstract
As computing energy demand continues to grow and electrical grid infrastructure struggles to keep pace, an increasing number of data centers are being planned with colocated microgrids that integrate on-site renewable generation and energy storage. However, while existing research has examined the tradeoffs between operational and embodied carbon emissions in the context of renewable energy certificates, there is a lack of tools to assess how the sizing and composition of microgrid components affects long-term sustainability and power reliability.
In this paper, we present a novel optimization framework that extends the computing and energy system co-simulator Vessim with detailed renewable energy generation models from the National Renewable Energy Laboratory's (NREL) System Advisor Model (SAM). Our framework simulates the interaction between computing workloads, on-site renewable production, and energy storage, capturing both operational and embodied emissions. We use a multi-horizon black-box optimization to explore efficient microgrid compositions and enable operators to make more informed decisions when planning energy systems for data centers.
Problem
Research questions and friction points this paper is trying to address.
Optimizing microgrid composition for sustainable data centers
Assessing microgrid components' impact on sustainability and reliability
Co-simulating computing workloads with renewable energy and storage
Innovation
Methods, ideas, or system contributions that make the work stand out.
Microgrid optimization for sustainable data centers
Co-simulator integrates computing and renewable energy
Multi-horizon black-box optimization for emissions
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