Abstract
Electricity market participants rely on data-driven methods using public market data to predict locational marginal prices (LMPs) and determine optimal bidding strategies, since they cannot access confidential power system models and operating details. In this paper, system-wide heterogeneous public market data are organized into a 3-dimensional (3D) tensor, which can store their spatio-temporal correlations. A generative adversarial network (GAN)-based approach is proposed to predict real-time locational marginal prices (RTLMPs) by learning the spatio-temporal correlations stored in the historical market data tensor. An autoregressive moving average (ARMA) calibration method is adopted to improve the prediction accuracy. Case studies using public market data from Midcontinent Independent System Operator (MISO) and Southwest Power Pool (SPP) demonstrate that the proposed method is able to learn spatio-temporal correlations among RTLMPs and perform accurate RTLMP prediction.
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