  In this paper, we propose a model-free unsupervised learning approach to
forecast real-time locational marginal prices (RTLMPs) in wholesale electricity
markets. By organizing system-wide hourly RTLMP data into a 3-dimensional (3D)
tensor consisting of a series of time-indexed matrices, we formulate the RTLMP
forecasting problem as a problem of generating the next matrix with forecasted
RTLMPs given the historical RTLMP tensor, and propose a generative adversarial
network (GAN) model to forecast RTLMPs. The proposed formulation preserves the
spatio-temporal correlations among system-wide RTLMPs in the format of
historical RTLMP tensor. The proposed GAN model learns the spatio-temporal
correlations using the historical RTLMP tensors and generate RTLMPs that are
statistically similar and temporally coherent to the historical RTLMP tensor.
The proposed approach forecasts system-wide RTLMPs using only publicly
available historical price data, without involving confidential information of
system model, such as system parameters, topology, or operating conditions. The
effectiveness of the proposed approach is verified through case studies using
historical RTLMP data in Southwest Power Pool (SPP).
