PaperlayerView exact revision on arXiv

Source-linked AI summary

LLMs are Few-Shot Decision-Makers: Generalized Context-Aware Microgrid Frequency Control through Prompt Decision Transformer

Xu Yang, Chenhui Lin, Haotian Liu, Kaihang Deng, Yunhe Li, Wenchuan Wu

arXiv:2608.21858v1eess.SYcs.AI

TL;DR

Microgrid frequency control must generalize across heterogeneous sites and unseen environments despite low inertia, limited local resources, and unavailable system parameters. The paper introduces Prompt-DT, which uses expert-trajectory prompts, context-aware contrastive training, physics-informed prompt selection, and lightweight finetuning. The method approaches expert-level performance across diverse environments, improves substantially over uncontextualized Prompt-DT in a low-inertia case, and adapts to unseen environments with limited data while matching full-parameter finetuning and outperforming LoRA.

  • Problem

    Microgrid frequency-control methods struggle to generalize across diverse configurations and unseen environments when explicit environmental parameters are unavailable and local computing and data resources are limited.

  • Method

    Prompt-DT conditions sequence modeling on few-shot expert trajectories, adds context-aware contrastive environment recognition and physics-informed prompt selection, and finetunes only lightweight trainable components.

  • Results

    The method achieves highly comparable results to experts across most environments, improves low-inertia control over Prompt-DT, and lightweight finetuning matches full-parameter finetuning while outperforming LoRA.

  • Takeaways & Limitations

    Prompt-DT supports single-training and multi-site deployment and transfer to unseen microgrids with minimal samples and computational requirements.

Abstract

from arXiv · show

The rapid evolution of energy structures has positioned microgrids as pivotal components of next-generation power systems, offering enhanced resilience and renewable energy integration. However, the inherent low inertia, complex dynamics, and poor model conditions of microgrids necessitate advanced data-driven frequency control strategies. Although reinforcement learning (RL) has demonstrated certain potential and advantages, existing RL methods often struggle with generalization across diverse microgrid configurations and lack adaptability to unseen environments, particularly when explicit system parameters are unavailable. To address these challenges, in this paper, we introduce a novel prompt decision transformer (Prompt-DT) architecture for microgrid frequency control. Unlike traditional approaches that rely on hard-to-obtain environmental characteristic parameters, the proposed method leverages few-shot expert historical trajectories as prompts to guide autonomous perception and adaptive decision-making. In addition, we propose a context-aware training and execution mechanism utilizing self-supervised contrastive learning to enhance environment recognition and prompt utilization efficiency. In addition, a physics-informed prompt design technique that filters prompts based on cumulative reward and frequency volatility is proposed, ensuring high-quality physical guidance during online execution. Finally, to ensure generalization in unseen environments with limited data, we develop a lightweight finetuning approach that achieves performance comparable to full-parameter finetuning with minimal adjustments.

I. INTRODUCTION

Microgrid frequency control requires adaptive data-driven methods because low inertia, narrow safety margins, heterogeneous configurations, and limited local resources make generalization across sites and unseen environments difficult. The proposed Prompt-DT uses expert trajectories as prompts, context-aware contrastive training, physics-informed prompt selection, and lightweight finetuning to support single-training multi-site deployment and transfer with limited data.

  • Microgrids require adaptive data-driven frequency control because they have low inertia, narrow safety margins, and unfavorable model conditions.
  • Single-training and multi-site deployment demands task versatility across microgrids with distinct configurations, plus extrapolation to unseen environments using few samples.
  • Hard-to-obtain or dynamically changing environmental parameters limit conventional adapter-based approaches in real-world microgrid frequency control.
  • Prompt-DT prepends few-shot expert trajectories to target sequences, guiding control without relying on explicit environmental characteristic parameters or system modeling.
  • Context-aware training combines environment-feature extraction, an environment query, and self-supervised contrastive learning to improve environment recognition and prompt utilization.
  • Physics-informed prompt filtering uses cumulative reward and frequency volatility, while lightweight finetuning freezes the LLM backbone and trains only task-specific heads and the environment query.
  • The lightweight finetuning method achieves performance comparable to full-parameter finetuning with minimal computational resources and outperforms mainstream LoRA finetuning methods.

II. PRELIMINARIES

The paper models each heterogeneous microgrid with linearized frequency-control dynamics involving generators, ESSs, PVs, WTs, loads, and control commands. Nonlinear governor dead bands and turbine generation-rate limits are incorporated by revising the relevant dynamics and clipping power variation rates.

  • A. Microgrid Frequency Control Problem Formulation: Each microgrid’s linearized dynamics represent frequency deviation and power deviations from generators, ESSs, PVs, WTs, and net load disturbance.
  • A. Microgrid Frequency Control Problem Formulation: The model includes heterogeneous damping coefficients, inertia time constants, equipment time constants, droop coefficients, and control commands.
  • A. Microgrid Frequency Control Problem Formulation: Net load disturbance combines load-demand disturbance with baseline PV and WT output fluctuations.
  • A. Microgrid Frequency Control Problem Formulation: Governor dead bands and turbine generation-rate constraints revise the generator and governor dynamics to represent nonlinear operating limits.
  • A. Microgrid Frequency Control Problem Formulation: The clip function constrains turbine power variation rate within [-σ_m,i, σ_m,i], where σ_m,i is the generation-rate limit.

B. Sequence Modeling of Microgrid Frequency Control

The paper formulates microgrid frequency control as conditional sequence modeling over returns-to-go, states, and actions. Unlike RL’s current-state decisions, DT and Prompt-DT learn from expert trajectories to autoregressively predict control actions.

  • MDP formulation: The MDP state contains frequency status and the power-output status of each microgrid component.The action contains generator, ESS, PV, and WT control commands; the reward measures control effectiveness and penalizes frequency deviations and violations.
  • MDP formulation: The paper uses a simplified MDP because its state, action, and reward definitions sufficiently characterize the corresponding optimization problem.Additional indicators such as area control error or control costs could be incorporated for richer dynamics or multi-objective optimization.
  • Sequence modeling: DT and Prompt-DT model complete historical trajectories and long-term returns rather than selecting actions exclusively from the current state.The trajectory combines return-to-go, state, and action variables, with return-to-go computed as the future cumulative reward over the control process.
  • Sequence modeling: DT and Prompt-DT use supervised learning on expert control trajectories instead of RL’s trial-and-error exploration.At each decision step, the preceding sequence is provided to autoregressively predict the next action.

III. METHODS

Prompt-DT extends decision-transformer sequence modeling by prepending few-shot expert trajectories to the target sequence. Encoders and a Transformer-based LLM backbone process the combined tokens, whose target embeddings are converted into actions through an action head.

  • Input construction: Prompt-DT forms its input by prepending a few-shot expert trajectory prompt to the target sequence.The prompt and target together contain return-to-go, state, and action tokens, with total token length 3(k_prompt+k_target).
  • Token encoding: Prompt and target tokens share RTG, state, action, and positional encoders before entering the LLM backbone.These multilayer perceptron encoders project inputs into a common vector space and add positional information.
  • LLM backbone: The LLM backbone stacks Transformer blocks that apply layer normalization, multi-head attention, residual connections, and feed-forward processing.The resulting target embeddings incorporate information from other tokens through attention, including prompt information.
  • LLM backbone: A causal mask restricts attention to preceding tokens, preventing information leakage during autoregressive processing.The mask is part of the attention mechanism used by the Transformer backbone.
  • Action prediction: The final target-sequence embedding is passed to an MLP action head to predict the next control action.Training minimizes mean squared error between the predicted action and the expert historical action.

B. Context-Aware Training Mechanism

The context-aware mechanism explicitly extracts environment features from prompt embeddings and trains them to distinguish operating environments. It combines a learnable environment query, an environment feature head, and self-supervised contrastive learning with the action-prediction pathway.

  • Environment feature extraction: Simple prompt concatenation is insufficient because Prompt-DT must extract, use, and distinguish environmental features during decision-making.The mechanism explicitly accounts for environmental properties rather than relying only on attention to incorporate prompt information.
  • Environment feature extraction: A learnable environment query attends to prompt embeddings as keys and values to aggregate environment-specific features.The extracted features are combined with target-sequence information and passed into the action head.
  • Environment feature extraction: The action head explicitly incorporates autonomously extracted environment features instead of depending exclusively on the LLM backbone or explicit system parameters.This differs from adapter-based approaches that provide environmental characteristic parameters directly.
  • Contrastive learning: Self-supervised contrastive learning pulls same-environment feature representations closer and separates representations from different environments.The method normalizes features, computes cosine similarities, and uses environment labels to identify positive pairs within each batch.
  • Contrastive learning: The total Prompt-DT loss combines MSE action loss with weighted contrastive loss, while dynamically scheduling the contrastive weight during training.The schedule initially stabilizes MSE learning before increasing focus on contrastive learning to avoid early training instability.

C. Physics-Informed Prompt and Lightweight Finetuning

The method selects physically informative expert prompts using reward and frequency variability, then adapts to unseen environments by finetuning only lightweight components. This preserves the pretrained backbone and supports reuse across microgrids.

  • Physics-Informed Prompt Design: Random or poor prompts can mislead inference, while highly steady segments may omit key characteristics such as inertia, damping, and equipment response.Prompt quality therefore affects how much environment information the model can extract during execution.
  • Physics-Informed Prompt Design: High-value expert trajectories are filtered by cumulative reward, then dynamic segments with large local frequency variance are selected as prompts.These segments are intended to provide both high-quality decisions and rich information about system dynamics.
  • Lightweight Finetuning: For unseen environments, lightweight finetuning freezes the LLM backbone and trains only the action head, environment feature head, and environment query.The approach is designed to improve target-environment adaptability without changing the general Prompt-DT capabilities.
  • Lightweight Finetuning: The lightweight approach adjusts approximately 10% of total parameters, requires limited finetuning data, and reduces computational cost relative to full-parameter finetuning and LoRA.The pretrained model supplies general frequency-control knowledge, reducing data needs in data-scarce new microgrids.
  • Lightweight Finetuning: Unmodified LLM backbone parameters allow the adapted model to be reused in future microgrids without complete retraining.This gives the approach a reusability advantage for successive deployment scenarios.
  • Scope: The paper focuses on microgrid frequency control, while extending the method to energy management or power-market participation requires corresponding MDP adjustments.This defines the current application scope and the stated route to other microgrid tasks.

A. Cases and Methods Setup

The experiments use nine distinct microgrid environments with varying configurations, parameters, and equipment capacities. PPO supplies expert trajectories, while the proposed model is trained and evaluated under constrained computing resources.

  • Experimental Cases: Nine microgrid frequency-control environments vary in configurations, parameters, and equipment regulation capacities.Eight environments train and evaluate task versatility, while the ninth is reserved for lightweight finetuning.
  • Experimental Cases: The first eight environments assess whether policies recognize encountered settings and achieve effective control across them.This setup evaluates task versatility rather than only performance in one configuration.
  • Expert Data: PPO is trained separately in each environment, and each converged policy serves as the corresponding expert policy.These expert policies generate the historical trajectories used for subsequent model training.
  • Expert Data: Expert policies provide 240 hours of microgrid operation data as historical trajectories for training the proposed Prompt-DT.The experiments use these trajectories as the data source for expert imitation and prompt-based learning.
  • Implementation: The proposed Prompt-DT has 1,332,356 trainable parameters and is trained on a personal laptop with an NVIDIA GeForce RTX 4070 Laptop GPU with 8 GB of memory.The small-sized backbone enables training on a single GPU or personal computer.

B. Performance on Encountered Environments

The encountered-environment evaluation deploys each policy across the first eight microgrids for 48 hours and compares frequency deviations and hourly frequency violations. Table III marks the strongest non-expert policy in bold.

  • Evaluation Protocol: Policies are deployed in the first eight microgrid environments for a 48-hour testing period to evaluate encountered-environment frequency control.The comparison includes the proposed and baseline methods alongside the expert policy.
  • Evaluation Metrics: Table III-A reports step-wise frequency deviations, while Table III-B reports hourly frequency violations.These two metrics provide separate views of control performance over the testing period.
  • Method Comparison: The best-performing policy excluding the expert policy is marked in bold.This convention identifies the strongest learned or baseline method in each reported comparison.
  • Method Comparison: The table compares On-RL, Off-RL, BC, DT, Prompt-DT, Proposed, and Expert policies across the tested environments.The displayed rows contain environment-specific values for the listed policy categories.

B. FREQUENCY VIOLATIONS ACROSS THE TEST 48 HOURS MICROGRID OPERATION (HZ)

Across diverse microgrid environments, Prompt-DT substantially improves frequency control over DT by using target-specific expert trajectories as prompts. Context-aware training further improves performance in challenging low-inertia conditions, while lightweight finetuning adapts the model to unseen environments with limited parameters and data.

  • Frequency control performance: Prompt-DT substantially outperforms baseline DT and approaches expert decision-making across many environments by prepending target-specific expert trajectories.This avoids reliance on explicitly obtained environmental characteristic parameters and supports single-training, multi-site deployment.
  • Context-aware training: Context-aware training reduces average frequency deviation by 275% and frequency violations by roughly 400% versus Prompt-DT in the second low-inertia microgrid environment.The mechanism uses feature extraction and self-supervised contrastive learning to improve prompt utilization efficiency.
  • Lightweight finetuning: Approximately 10% of total parameters are trainable in the proposed lightweight finetuning approach because the LLM backbone is frozen and selected heads are adjusted.This reduces computational cost relative to full-parameter finetuning and avoids modifying the backbone.
  • Lightweight finetuning: With two hours of expert trajectories, unseen-environment performance improves and continues improving as finetuning samples increase, whereas LoRA performs worst among the compared approaches.The proposed approach can slightly outperform full-parameter finetuning with few samples, while full-parameter finetuning performs best as sample quantity increases.
  • Inference latency: 4.77 ms is the proposed method’s one-step inference latency, and all evaluated methods complete inference within 5 ms.Prompt-DT and the proposed method are slightly slower than DT because prepended prompts increase sequence length.

V. CONCLUSION

The paper presents Prompt-DT with context-aware mechanisms and lightweight finetuning to support frequency control across diverse microgrid scenarios and transfer to unseen environments. It concludes that safety, robustness, and scalability require further investigation.

  • Conclusion: Prompt-DT, context-aware training and execution, and lightweight finetuning address generalization across diverse microgrid scenarios and configurations.The proposed method is intended to enable single-training, multi-site deployment and transfer to unseen environments with minimal samples and computational requirements.
  • Conclusion: Safety, robustness, and scalability remain areas requiring further and more in-depth investigation for this novel dispatch and control paradigm.This is identified as future work rather than as a demonstrated result.
Loading 2608.21858v1…