Price-Aware Deep Learning for Electricity Markets

TL;DR

This paper introduces a novel deep learning approach that incorporates market-clearing optimization to reduce pricing errors and improve fairness in electricity markets, especially in congested power systems.

Contribution

It proposes embedding market-clearing optimization as a differentiable deep learning layer to balance prediction accuracy and pricing fairness.

Findings

Demonstrates how prediction errors affect electricity prices.

Shows the proposed method reduces spatial disparity of price errors.

Validates approach in wind power forecasting and market clearing context.

Abstract

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.