用大模型生成电网安全切负荷的可靠操作方案，提升准确性与电压稳定性。
Fine-Tuning LLMs to Generate Economical and Reliable Actions for the Power Grid
- 通过监督微调将电网优化模型转化为可控动作语法，确保生成操作可验证。
- 结合电压惩罚评估改进策略，使生成方案在真实电力系统中失败率降至个位数。
- 适合电网调度、电力算法研发人员快速获取高可靠性切换方案。
公共安全断电（PSPS）引发的快速拓扑变化会使标准运行点不可行，需迅速制定修正输电切换方案以减少负荷切除并维持电压稳定。本文提出一种可验证的多阶段微调流程，将指令微调的大语言模型（LLM）用于从紧凑的PSPS场景摘要中生成仅开路的修正切换计划，并满足明确的开关预算。首先，监督微调将直流最优潮流混合整数规划（DC-OPF MILP）代理转化为带约束的动作语法，实现可靠解析与可行性检验；其次，直接偏好优化利用交流潮流评估的偏好对，基于电压惩罚指标优化策略，引入超越直流模仿的电压感知能力；最后，采用最佳N选一方法在推理阶段选择目标指标最优的可行候选。在IEEE 118节点的PSPS场景下，微调显著提升直流目标值，将交流潮流失败率从50%降至个位数，并在共成功集上改善电压惩罚结果。代码与数据生成脚本已开源，支持复现。
原文摘要 · Abstract (English)
Public Safety Power Shutoffs (PSPS) force rapid topology changes that can render standard operating points infeasible, requiring operators to quickly identify corrective transmission switching actions that reduce load shedding while maintaining acceptable voltage behavior. We present a verifiable, multi-stage adaptation pipeline that fine-tunes an instruction-tuned large language model (LLM) to generate \emph{open-only} corrective switching plans from compact PSPS scenario summaries under an explicit switching budget. First, supervised fine-tuning distills a DC-OPF MILP oracle into a constrained action grammar that enables reliable parsing and feasibility checks. Second, direct preference optimization refines the policy using AC-evaluated preference pairs ranked by a voltage-penalty metric, injecting voltage-awareness beyond DC imitation. Finally, best-of-$N$ selection provides an inference-time addition by choosing the best feasible candidate under the target metric. On IEEE 118-bus PSPS scenarios, fine-tuning substantially improves DC objective values versus zero-shot generation, reduces AC power-flow failure from 50\% to single digits, and improves voltage-penalty outcomes on the common-success set. Code and data-generation scripts are released to support reproducibility.
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