  This paper presents a novel solution technique for scheduling multi-energy
system (MES) in a commercial urban building to perform price-based demand
response and reduce energy costs. The MES scheduling problem is formulated as a
mixed integer nonlinear program (MINLP), a non-convex NPhard problem with
uncertainties due to renewable generation and demand. A model predictive
control approach is used to handle the uncertainties and price variations. This
in-turn requires solving a time-coupled multi-time step MINLP during each
time-epoch which is computationally intensive. This investigation proposes an
approach called the Scenario-Based Branch-and-Bound (SB3), a light-weight
solver to reduce the computational complexity. It combines the simplicity of
convex programs with the ability of meta-heuristic techniques to handle complex
nonlinear problems. The performance of the SB3 solver is validated in the
Cleantech building, Singapore and the results demonstrate that the proposed
algorithm reduces energy cost by about 17.26% and 22.46% as against solving a
multi-time step heuristic optimization model.
