  In this work, a composite economic model predictive control (CEMPC) is
proposed for the optimal operation of a stand-alone integrated energy system
(IES). Time-scale multiplicity exists in IESs dynamics is taken into account
and addressed using multi-time-scale decomposition. The entire IES is
decomposed into three reduced-order subsystems with slow, medium, and fast
dynamics. Subsequently, the CEMPC, which includes slow economic model
predictive control (EMPC), medium EMPC and fast EMPC, is developed. The EMPCs
communicate with each other to ensure consistency in decision-making. In the
slow EMPC, the global control objectives are optimized, and the manipulated
inputs explicitly affecting the slow dynamics are applied. The medium EMPC
optimizes the control objectives correlated with the medium dynamics and
applies the corresponding optimal medium inputs to the IES, while the fast EMPC
optimizes the fast dynamics relevant objectives and makes a decision on the
manipulated inputs directly associated with the fast dynamics. Meanwhile,
thermal comfort is integrated into the CEMPC in the form of zone tracking of
the building temperature for achieving more control degrees of freedom to
prioritize satisfying the electric demand and reducing operating costs of the
IES. Moreover, a long-term EMPC based on a simplified slow subsystem model is
developed and incorporated into the CEMPC to ensure that the operating state
accommodates long-term forecasts for external conditions. Finally, the
effectiveness and superiority of the proposed method are demonstrated via
simulations and a comparison with a hierarchical real-time optimization
mechanism.
