While deep learning gradually penetrates operational planning of power systems, its inherent prediction errors may significantly affect electricity prices. This paper 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.

Argonne researchers are using artificial intelligence to speed up the day-ahead electricity market clearing and real-time operations. The electricity market clearing and grid operations rely on the security constrained unit commitment, or SCUC, which helps grid operators set a schedule for daily and hourly power generation. As the SCUC problem is solved multiple times a day, data accumulates that can be used to discover patterns applicable to solving the next round of problems. To that end, Argonne researchers have developed AI that now can solve a SCUC about 12 times faster than conventional methods. Researchers continue to refine the method, an early version of which was used successfully in tests at Midcontinent Independent System Operator (MISO), overseeing electricity market and delivery across 15 states and one Canadian province.