用神经网络提升电网平衡决策精度与速度
Neural Network-Assisted Model Predictive Control for Implicit Balancing
- 用输入凸神经网络建模电力市场，保证优化可解
- 在比利时数据上提升决策质量并加快计算
- 适合电网调度与智能控制方向研究者
在欧洲，平衡责任方可通过主动承担不平衡位置来支持输电系统运营商（TSOs）维持电网稳定，并获取收益，这一做法称为隐式平衡。模型预测控制（MPC）被广泛用于隐式平衡决策。MPC中平衡市场模型的准确性对决策质量至关重要。以往研究要么采用凸市场出清近似，忽略TSOs的主动干预及市场亚小时级动态；要么使用机器学习方法，但无法直接嵌入MPC。为此，本文提出一种基于数据驱动的平衡市场模型，通过输入凸神经网络确保凸性的同时捕捉不确定性，并引入基于注意力的输入门控机制剔除无关数据以保持计算高效。在比利时数据上的评估表明，该模型既提升了MPC决策质量，又降低了计算时间。
原文摘要 · Abstract (English)
In Europe, balance responsible parties can deliberately take out-of-balance positions to support transmission system operators (TSOs) in maintaining grid stability and earn profit, a practice called implicit balancing. Model predictive control (MPC) is widely adopted as an effective approach for implicit balancing. The balancing market model accuracy in MPC is critical to decision quality. Previous studies modeled this market using either (i) a convex market clearing approximation, ignoring proactive manual actions by TSOs and the market sub-quarter-hour dynamics, or (ii) machine learning methods, which cannot be directly integrated into MPC. To address these shortcomings, we propose a data-driven balancing market model integrated into MPC using an input convex neural network to ensure convexity while capturing uncertainties. To keep the core network computationally efficient, we incorporate attention-based input gating mechanisms to remove irrelevant data. Evaluating on Belgian data shows that the proposed model both improves MPC decisions and reduces computational time.
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