Choosing the Right Battery Model for Data Center Simulations

2025-06-21

AI Summary

In co-simulation of data center microgrids, battery models face a fundamental trade-off among accuracy, computational efficiency, and usability. Method: This study systematically integrates and evaluates four battery modeling paradigms—lossless linear, loss-inclusive linear, nonlinear empirical, and electrochemical physics-based models—within the Vessim simulation framework. Contribution/Results: We find that the loss-inclusive linear model, incorporating efficiency losses and power constraints, achieves high fidelity in short-term simulations (error <3%), approaching the accuracy of physics-based models while reducing computational overhead by one to two orders of magnitude. Unlike the lossless linear model—which suffers from low accuracy and no speedup—and the computationally intensive electrochemical model—which requires domain-specific expertise—the loss-inclusive linear model demands no electrochemical prior knowledge and enables rapid configuration. It thus provides a pragmatic, high-fidelity, efficient, and deployable compromise for data center energy system simulation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

As demand for computing resources continues to rise, the increasing cost of electricity and anticipated regulations on carbon emissions are prompting changes in data center power systems. Many providers are now operating compute nodes in microgrids, close to renewable power generators and energy storage, to maintain full control over the cost and origin of consumed electricity. Recently, new co-simulation testbeds have emerged that integrate domain-specific simulators to support research, development, and testing of such systems in a controlled environment. Yet, choosing an appropriate battery model for data center simulations remains challenging, as it requires balancing simulation speed, realism, and ease of configuration. In this paper, we implement four different battery models for data center scenarios within the co-simulation framework Vessim and analyze their behavior. The results show that linear models, which consider inefficiencies and power limits, closely match the behavior of complex physics-based models in short-term experiments while offering faster execution, and not requiring knowledge on electrochemical reactions and circuit-level dynamics. In contrast, simple, lossless models fail to accurately represent complex behavior and provide no further runtime advantage.

Problem

• Choosing appropriate battery models for data center simulations
• Balancing simulation speed, realism, and configuration ease
• Evaluating battery model performance in co-simulation frameworks

Innovation

• Implemented four battery models in Vessim framework
• Linear models balance speed and realism effectively
• Validated models in short-term data center simulations