  Aggregation of heating, ventilation, and air conditioning (HVAC) loads can
provide reserves to absorb volatile renewable energy, especially solar
photo-voltaic (PV) generation. However, the time-varying PV generation is not
perfectly known when the system operator decides the HVAC control schedules. To
consider the unknown uncertain PV generation, in this paper, we formulate a
distributionally robust chance-constrained (DRCC) building load control problem
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 DRCC problem under 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.
