  A community integrated energy system (CIES) is an important carrier of the
energy internet and smart city in geographical and functional terms. Its
emergence provides a new solution to the problems of energy utilization and
environmental pollution. To coordinate the integrated demand response and
uncertainty of renewable energy generation (RGs), a data-driven two-stage
distributionally robust optimization (DRO) model is constructed. A
comprehensive norm consisting of the 1-norm and infinity-norm is used as the
uncertainty probability distribution information set, thereby avoiding complex
probability density information. To address multiple uncertainties of RGs, a
generative adversarial network based on the Wasserstein distance with gradient
penalty is proposed to generate RG scenarios, which has wide applicability. To
further tap the potential of the demand response, we take into account the
ambiguity of human thermal comfort and the thermal inertia of buildings. Thus,
an integrated demand response mechanism is developed that effectively promotes
the consumption of renewable energy. The proposed method is simulated in an
actual CIES in North China. In comparison with traditional stochastic
programming and robust optimization, it is verified that the proposed DRO model
properly balances the relationship between economical operation and robustness
while exhibiting stronger adaptability. Furthermore, our approach outperforms
other commonly used DRO methods with better operational economy, lower
renewable power curtailment rate, and higher computational efficiency.
