用模拟推断估计电力调度未知成本，提升未来调度预测可靠性。
Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference
- 通过观测发电计划反推未知成本的后验分布
- 后验分布能准确反映历史数据中的成本范围
- 适合电力系统运营者用于改进成本预测与调度
机组组合（UC）是电力系统中一项关键优化任务，旨在有限时间段内制定发电单元的运行计划，以最小化成本并满足需求与技术约束。然而，许多参数如成本未知。本文针对一个示范性UC问题，采用基于模拟的推断方法，从已知的发电计划和需求中估计未知成本，获得给定观测值下的近似后验分布。结果表明，学习到的后验分布有效捕捉了数据的底层分布，给出在历史观测下未知参数的可能取值范围。该后验分布可用于根据历史发电计划估算过去成本，帮助运营者更准确地预测未来成本，制定更稳健的发电调度方案。未来研究可探索缓解后验估计过自信问题、提升方法可扩展性，并应用于包含网络约束和可再生能源的复杂UC问题。
原文摘要 · Abstract (English)
The Unit Commitment (UC) problem is a key optimization task in power systems to forecast the generation schedules of power units over a finite time period by minimizing costs while meeting demand and technical constraints. However, many parameters required by the UC problem are unknown, such as the costs. In this work, we estimate these unknown costs using simulation-based inference on an illustrative UC problem, which provides an approximated posterior distribution of the parameters given observed generation schedules and demands. Our results highlight that the learned posterior distribution effectively captures the underlying distribution of the data, providing a range of possible values for the unknown parameters given a past observation. This posterior allows for the estimation of past costs using observed past generation schedules, enabling operators to better forecast future costs and make more robust generation scheduling forecasts. We present avenues for future research to address overconfidence in posterior estimation, enhance the scalability of the methodology and apply it to more complex UC problems modeling the network constraints and renewable energy sources.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。