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：虽然深度学习逐渐渗透到运行规划中，但其固有的预测误差可能显著影响电力价格。本文探讨了预测误差如何传导至电力价格，揭示了受阻塞电力系统中显著的价格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。通过该层进行差分运算，可在预测误差与价格误差之间实现平衡，而非单纯最小化预测误差。该层隐式优化了公平性，并控制了价格误差在整个系统中的空间分布。我们以风电功率预测与短期电力市场出清的耦合场景为例，展示了价格感知深度学习的应用。