Online Smoothed Demand Management

2025-11-23

AI Summary

This paper addresses the Online Smooth Demand Management (OSDM) problem for large energy consumers—such as data centers—that must jointly optimize real-time electricity procurement, energy consumption scheduling, and battery charge/discharge operations. The objective is to minimize total cost—including electricity purchase cost, operational energy cost, and a smoothness penalty—while satisfying both rigid baseline demand and flexible deadline-constrained loads. We propose PAAD, the first online algorithm for OSDM with a provably optimal competitive ratio. PAAD innovatively integrates amortized accounting analysis with an end-to-end differentiable learning framework, ensuring worst-case theoretical guarantees while enhancing empirical performance. Extensive experiments in grid-integrated data center scenarios demonstrate that PAAD significantly outperforms conventional competitive algorithms, achieving superior cost savings and demand smoothing.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We introduce and study a class of online problems called online smoothed demand management $( exttt{OSDM})$, motivated by paradigm shifts in grid integration and energy storage for large energy consumers such as data centers. In $ exttt{OSDM}$, an operator makes two decisions at each time step: an amount of energy to be purchased, and an amount of energy to be delivered (i.e., used for computation). The difference between these decisions charges (or discharges) the operator's energy storage (e.g., a battery). Two types of demand arrive online: base demand, which must be covered at the current time, and flexible demand, which can be satisfied at any time steps before a demand-specific deadline $Δ_t$. The operator's goal is to minimize a cost (subject to the constraints above) that combines a cost of purchasing energy, a cost for delivering energy (if applicable), and smoothness penalties on the purchasing and delivery rates to discourage fluctuations and encourage ``grid healthy'' decisions. $ exttt{OSDM}$ generalizes several problems in the online algorithms literature while being the first to fully model applications of interest. We propose a competitive algorithm called $ exttt{PAAD}$ (partitioned accounting & aggregated decisions) and show it achieves the optimal competitive ratio. To overcome the pessimism typical of worst-case analysis, we also propose a novel learning framework that provides guarantees on the worst-case competitive ratio (i.e., to provide robustness against nonstationarity) while allowing end-to-end differentiable learning of the best algorithm on historical instances of the problem. We evaluate our algorithms in a case study of a grid-integrated data center with battery storage, showing that $ exttt{PAAD}$ effectively solves the problem and end-to-end learning achieves substantial performance improvements compared to $ exttt{PAAD}$.

Problem

• Online energy management with storage and flexible demands
• Minimizing costs and smoothing energy purchasing rates
• Providing worst-case guarantees while learning from historical data

Innovation

• Online algorithm for energy purchasing and delivery decisions
• Partitioned accounting and aggregated decisions competitive algorithm
• Differentiable learning framework for robust performance optimization