Paper Info
Title
Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming
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.
Year
DOI
Venue
2015
10.1109/TASE.2014.2352280
IEEE T. Automation Science and Engineering
Keywords
Field
DocType
Cooling,Electricity,Heuristic algorithms,Mathematical model,Buildings,Nonlinear dynamical systems,Optimization
Dynamic programming,Mathematical optimization,Job shop scheduling,Computer science,Heuristic (computer science),Control theory,Nonlinear programming,Model predictive control,Control engineering,Chiller,Integer programming,Water chiller
Journal
Volume
Issue
ISSN
12
2
1545-5955
Citations 
PageRank 
References 
11
0.90
18
Authors
8
Name
Order
Citations
PageRank
Kun Deng1162.40
Y. Sun2598.54
Sisi Li3608.22
Yan Lu4111.24
Jack Brouwer5110.90
Prashant G. Mehta641452.29
MengChu Zhou78989534.94
Amit Chakraborty827823.81