  Aggregation of heating, ventilation, and air conditioning (HVAC) loads can
provide reserves to absorb volatile renewable energy, especially solar
photo-voltaic (PV) generation. In this paper, we decide HVAC control schedules
under uncertain PV generation, using a distributionally robust
chance-constrained (DRCC) building load control model under two typical
ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive
mixed-integer linear programming (MILP) reformulations for DRCC problems under
both sets. Especially, for the Wasserstein ambiguity set, we utilize the
right-hand side (RHS) uncertainty to derive a more compact MILP reformulation
than the commonly known MILP reformulations with big-M constants. All the
results also apply to general individual chance constraints with RHS
uncertainty. Furthermore, we propose an adjustable chance-constrained variant
to achieve a trade-off between the operational risk and costs. We derive MILP
reformulations under the Wasserstein ambiguity set and second-order conic
programming (SOCP) reformulations under the moment-based set. Using real-world
data, we conduct computational studies to demonstrate the efficiency of the
solution approaches and the effectiveness of the solutions.
