  An operating entity utilizing community-integrated energy systems with a
large number of small-scale distributed energy sources can easily trade with
existing distribution markets. To solve the energy management and pricing
problem of multi-community integrated energy systems (MCIESs) with multi-energy
interaction, this study investigated a hierarchical stochastic optimal
scheduling method for uncertain environments. To handle multiple uncertainties,
a Wasserstein generative adversarial network with a gradient penalty was used
to generate renewable scenarios, and the Kmeans++ clustering algorithm was
employed to generate typical scenarios. A Stackelberg-based hierarchical
stochastic schedule with an integrated demand response was constructed, where
the MCIES operator acted as the leader pursuing the maximum net profit by
setting energy prices, while the building users were followers who adjusted
their energy consumption plans to minimize their total costs. Finally, a
distributed iterative solution method based on a metaheuristic was designed.
The effectiveness of the proposed method was verified using practical examples.
