Joint Electricity Price and Load Forecasting for Data Center Battery Dispatch Using Polynomial Diffusion Models

Matthew Van Ham; Yiping Liu; Xiaozhe Wang

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arXiv · 2611.08470

Joint Electricity Price and Load Forecasting for Data Center Battery Dispatch Using Polynomial Diffusion Models

Matthew Van Ham·Yiping Liu·Xiaozhe Wang

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Abstract

The rapid growth of large-scale data centers (DCs) is increasing electricity demand, market volatility, and operating costs. Battery energy storage systems (BESSs) can mitigate these challenges by shifting electricity purchases from high-price to low-price periods. This paper proposes a real-time BESS dispatch framework using polynomial diffusion models (PDMs) to forecast both electricity prices and DC loads. Leveraging the closed-form conditional expectations of PDMs, the forecasts are integrated into a dynamic programming (DP) framework to minimize electricity procurement costs. Across the evaluated BESS configurations, the proposed approach achieves 2.63 times the cost savings of a rule-based (RB) strategy.

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Matthew Van Ham, Yiping Liu, Xiaozhe Wang. 2026-11-05. Joint Electricity Price and Load Forecasting for Data Center Battery Dispatch Using Polynomial Diffusion Models. https://arxiv.org/abs/2611.08470

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