Abstract
This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the central plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm. This work was motivated by the supervisory control need for a campus central plant. Plant operators have to decide a scheduling strategy to mix and match various chillers with a thermal energy storage to satisfy the campus cooling demands, while minimizing the operation cost. This work mathematically characterizes the system dynamics of a campus central plant and establishes a linear model to predict campus cooling load. It proposes a model predictive control (MPC) strategy to optimally schedule the campus central plant based on plant system dynamics and predicted campus cooling load. A heuristic algorithm is proposed to obtain suboptimal solutions for the MPC problem. The effectiveness and efficiency of the proposed approach are well demonstrated for the central plant at the University of California, Irvine.
Highlights
B UILDINGS are one of the primary consumers of energy in the United States
In [23], we present a heuristic algorithm to search for the suboptimal solutions of the mixed-integer nonlinear programming (MINLP) problem by mixed-integer linear programming (MILP)
The original MINLP problem is approximated by the reformulated model predictive control (MPC) problem with the linearized system dynamics and fixed thermal energy storage (TES) operation profile, which leads to a mixed integer linear program (MILP) at each time step
Summary
B UILDINGS are one of the primary consumers of energy in the United States. Buildings are responsible for 41% of primary energy consumption in 2010, compared to 28% by the industrial sector and 31% by the transportation sector [1]. The goal of supervisory control is to optimize TES and chiller bank operations for minimizing energy consumption or electricity cost while satisfying campus cooling demands. A model-based optimization problem is solved over a finite horizon to obtain optimal set-points of operating the TES and chiller bank. In [18], [20], the authors considered the MPC problem of a series of chillers equipped with a water tank–a similar setup as this paper They validated the system model with experimental data and performed closed-loop experimental tests for the proposed control method. The original MINLP problem is approximated by the reformulated MPC problem with the linearized system dynamics and fixed TES operation profile, which leads to a mixed integer linear program (MILP) at each time step.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
