While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing.

翻译：尽管深度学习逐渐渗透到电力系统运行规划中，其固有的预测误差可能显著影响电价。本文探讨了预测误差如何传导至电价，揭示了在阻塞电力系统中显著的定价误差及其空间差异性。为提升公平性，我们提出将电力市场出清优化嵌入为深度学习层。通过该层进行微分运算，可在预测误差与定价误差之间实现平衡，而非单纯最小化预测误差。该层隐含地优化了公平性，并控制了电价误差在系统内的空间分布。我们在风电功率预测与短期电力市场出清的耦合场景中展示了价格感知深度学习的应用效果。