  We present a stochastic model predictive control (MPC) framework for central
heating, ventilation, and air conditioning (HVAC) plants. The framework uses
real data to forecast and quantify uncertainty of disturbances affecting the
system over multiple timescales (electrical loads, heating/cooling loads, and
energy prices). We conduct detailed closed-loop simulations and systematic
benchmarks for the central HVAC plant of a typical university campus. Results
demonstrate that deterministic MPC fails to properly capture disturbances and
that this translates into economic penalties associated with peak demand
charges and constraint violations in thermal storage capacity (overflow and/or
depletion). Our results also demonstrate that stochastic MPC provides a more
systematic approach to mitigate uncertainties and that this ultimately leads to
cost savings of up to 7.5% and to mitigation of storage constraint violations.
Benchmark results also indicate that these savings are close to ideal savings
(9.6%) obtained under MPC with perfect information.
