Multi-Agent Large Language Model-Based Economic Dispatch Framework for Optimal Energy Management of Data Center Microgrids

publications

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Motivation

AI workloads are pushing hyperscale data centers toward extreme, volatile power demand that destabilizes grids. To stay reliable and cost-efficient, data centers are becoming localized microgrids with distributed energy resources and storage. But the conventional way to operate them has real barriers:

• High expertise barrier: MILP-based economic dispatch requires specialized optimization and modeling skills that facility operators rarely have.
• Rigid to change: Whenever tariffs (Time-of-Use rates) or regulations (Renewable Energy Certificates) change, the entire model often needs a slow, complete redesign.
• Slow scenario testing: Validating “what-if” operational strategies is time-consuming under rigid formulations.

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System Configuration

The target is a grid-connected hyperscale data center microgrid sized for AI workloads.

Component	Parameter	Value
Data Center Load	Peak load	400 MW (AI/HPC workload)
SMR	Unit capacity / ramp / cost	100 MW · 0.75 MW/15 min · ≈ 7 USD/MWh
Gas Turbine (GT)	Unit capacity / ramp / fuel	170 MW · 15 MW/min · LNG
ESS	Energy / power / efficiency / SOC	160 MWh · 40 MW · 95% · 10–90%

Load modeling. The MIT Supercloud TX-GAIA HPC dataset — chosen for its high volatility relative to commercial data centers — was normalized from its native 300 kW scale up to a 400 MW peak and resampled to a 15-minute resolution (96 steps over 24 hours):

$$P_{load}(t) = \frac{P_{raw}(t)}{P_{raw}^{max}} \times 400\ \text{MW}$$

Generator modeling. The GT uses a quadratic cost function $C_{GT}(P) = aP^2 + bP + c$ (derived via GasTurb at 800 KRW/kg LNG) capturing partial-load efficiency drop, with a 15 MW/min ramp limit. The SMR (SMART-100, 100 MW) operates as baseload at 7 USD/MWh with a strict 0.75 MW/15 min ramp limit for operational safety.

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Method Overview

We propose a four-stage sequential multi-agent LLM pipeline that converts a natural-language prompt into an optimized dispatch schedule — no manual model rebuilding required.

1. Parsing Agent — extracts optimization parameters from the user’s natural-language prompt.
2. Formulation Agent — translates the parameters into a formal mathematical model (objective + constraints).
3. Solver Agent — executes the computational dispatch and returns the optimal schedule.
4. Explanation Agent — synthesizes the numerical results into a comprehensive, human-readable strategic report.

A feedback loop returns infeasibility signals from the solver back to the formulation stage, enabling automatic correction without operator intervention.

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Mathematical Formulation

Objective — minimize total daily operational cost $J$ (base demand charge + generation + grid purchase − sales revenue + ESS degradation):

$$\min J = C_{base} + \sum_{t=1}^{T} \Big[ \sum_{i \in \mathcal{G}} C_{gen,i}(P_i(t)) + \rho_{grid}(t)P_{grid}(t) - R_{sales}(t) + \sum_{k \in \mathcal{E}} \lambda_{deg} P_{dis,k}(t) \Big]$$

Key constraints:

• Power balance — supply equals demand at every step $t$ (grid + generators + PV + ESS discharge = load + ESS charge).
• ESS dynamics — $SOC(t) = SOC(t-1) + \big(P_{chg}(t)\eta - P_{dis}(t)/\eta\big)\Delta t / E_{cap}$, with round-trip efficiency $\eta = 0.95$ and $10\% \le SOC(t) \le 90\%$.
• Generator limits — capacity bounds $P_{min,i} \le P_i(t) \le P_{max,i}$ and ramp limits $|P_i(t) - P_i(t-1)| \le Ramp_i$.

The Solver Agent casts this as a Mixed-Integer Quadratic Program (MIQP) and solves it with Gurobi for the global cost-minimizing optimum.

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Results

The framework was validated on the MIT TX-GAIA data center load profile scaled to 400 MW over a 24-hour horizon, with a portfolio of small modular reactors (SMR), gas turbines (GT), photovoltaics (PV), and a BESS.

Key findings:

• SMR as baseload: The framework seamlessly utilizes the SMR for stable baseload supply (~121 MW average, capacity factor ~100%).
• Peak shaving: ESS and gas turbines are dispatched strategically for peak shaving during expensive TOU periods.
• Cost structure: Total cost was driven primarily by dispatch strategy — baseload generation accounted for only ~2.5% of total cost, while variable operating cost dominated at ~90.9%, confirming that operational decisions matter more than raw asset mix.
• Interactive testbed: The pipeline serves as a rapid, interactive testbed for verifying strategies against uncertainties in cost, load dynamics, and grid stability — and scales to other load systems.

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Future Work

• Capacity optimization: Move from fixed capacities to optimal sizing of GTs and ESS for a given load profile.
• PV curtailment & RE100: Add objective penalty terms for curtailed renewables and minimum-usage constraints to meet sustainability targets.
• Conformal prediction: Integrate distribution-free prediction intervals with guaranteed coverage into the demand-forecasting module, letting the Formulation Agent build uncertainty-aware robust optimization models that hedge against AI-workload volatility — reducing the risk of shortage or over-generation.

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Try the Live Demo

The full agent pipeline is deployed as an interactive web app. Enter a natural-language scenario and watch the agents parse, formulate, solve, and explain the optimal dispatch in real time.