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arXiv 2604.08309eess.SYcs.SY
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贝叶斯推断在电力市场中估计发电成本

Bayesian Inference for Estimating Generation Costs in Electricity Markets

University of Liège(列日大学);Engie(恩智)

Matthias Pirlet, Adrien Bolland, Alexandre Huynen, Quentin Louveaux, Gilles Louppe, Damien Ernst

AI总结:

本文提出利用贝叶斯推断估计电力市场中的发电成本,通过隐变量模型建立发电成本与生产计划的关系,并采用平衡神经后验估计法学习后验分布,验证结果显示边际成本能被准确恢复,而启动成本难以从生产计划中识别。

摘要:

从观测的电力市场数据中估计发电成本对于市场模拟、战略投标和系统规划至关重要。为此,我们用隐变量模型建立试发电成本与生产计划之间的关系。将从观测的生产计划中估计发电成本建模为贝叶斯推断。先验分布编码了对参数的初始信念,推断过程则是通过给定观测的后验分布更新信念。我们使用平衡神经后验估计(BNPE)来学习这个后验。在IEEE RTS-96测试系统上的验证显示,边际成本能够被准确恢复,而启动成本从生产计划中单独识别仍然存在较大困难。该方法与一种反向优化算法相比,后者在没有不确定性量化的情况下表现出更大的参数误差。

英文摘要:

Estimating generation costs from observed electricity market data is essential for market simulation, strategic bidding, and system planning. To that end, we model the relationship between generation costs and production schedules with a latent variable model. Estimating generation costs from observed schedules is then formulated as Bayesian inference. A prior distribution encodes an initial belief on parameters, and the inference consists of updating the belief with the posterior distribution given observations. We use balanced neural posterior estimation (BNPE) to learn this posterior. Validation on the IEEE RTS-96 test system shows that marginal costs are recovered with narrow credible intervals, while start-up costs remain largely unidentifiable from schedules alone. The method is benchmarked against an inverse-optimization algorithm that exhibits larger parameter errors without uncertainty quantification.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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