  This paper presents a deep-learning framework, Multi-load Generative
Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load
profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the
capture of spatial-temporal correlations among a group of loads that are served
by the same distribution transformer. This enables the generation of a large
amount of correlated SLPs required for microgrid and distribution system
studies. The novelty and uniqueness of the MultiLoad-GAN framework are
three-fold. First, to the best of our knowledge, this is the first method for
generating a group of load profiles bearing realistic spatial-temporal
correlations simultaneously. Second, two complementary realisticness metrics
for evaluating generated load profiles are developed: computing statistics
based on domain knowledge and comparing high-level features via a deep-learning
classifier. Third, to tackle data scarcity, a novel iterative data augmentation
mechanism is developed to generate training samples for enhancing the training
of both the classifier and the MultiLoad-GAN model. Simulation results show
that MultiLoad-GAN can generate more realistic load profiles than existing
approaches, especially in group level characteristics. With little finetuning,
MultiLoad-GAN can be readily extended to generate a group of load or PV
profiles for a feeder or a service area.
