A considerable penetration of rooftop PV generation and increasing demand for heating
loads will enlarge the peak-to-valley difference, imposing a great challenge to the
reliable operation of distribution systems under cold climates. The objective of this
paper is to establish a distributionally robust demand response (DR) model for building
energy systems for suppressing peak-to-valley load ratios by exploiting cooperative
complementarity and flexible transformation characteris-tics of various household
appliances. The thermodynamic effect of buildings is modeled for harvesting intermittent
renewable energy sources (RESs) on the building roof in the form of thermal energy
storages to reduce RES curtailments and eliminate thermal comfort violations in cold
weather. Furthermore, the Wasserstein metric is adopted to develop the ambiguity set
of the uncertainty probability distributions (PDs) of RESs, and thus, only historical
data of RES output is needed rather than prior knowledge about the actual PDs. Finally,
a computationally tractable mixed-integer linear programming reformulation is derived
for the original distributionally robust optimization (DRO) model. The proposed DRO-based
DR strategy was performed on multiple buildings over a 24 h scheduling horizon, and
comparative studies have validated the effectiveness of the proposed strategy for
building energy systems in reducing the peak/valley ratio and decreasing operation
costs.
