Price-Aware Deep Learning for Electricity Markets
This addresses fairness issues in electricity markets for operators and consumers, but it is incremental as it builds on existing deep learning and optimization methods.
The paper tackles the problem of deep learning prediction errors affecting electricity prices, revealing significant pricing errors and spatial disparities in congested power systems, and proposes embedding market-clearing optimization as a deep learning layer to balance prediction and pricing errors, implicitly optimizing fairness and controlling price error distribution.
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.