Model Predictive Control of Discrete-Continuous Energy Systems via Generalized Disjunctive Programming
Year: 2021
Type: article
Abstract: Generalized Disjunctive Programming (GDP) provides an alternative framework to model optimization problems with both discrete and continuous variables. The key idea behind GDP involves the use of logical disjunctions to represent discrete decisions in the continuous space, and logical propositions to denote algebraic constraints in the discrete space. Compared to traditional mixed-integer programming (MIP), the logic structure in GDP yields tighter relaxations that are exploited by global branch and bound algorithms to improve solution quality. We present a general GDP model for optimal control of hybrid systems that exhibit both discrete and continuous dynamics. Specifically, we use GDP to formulate a model predictive control (MPC) model for piecewise-affine systems with implicit switching logic. As an example, the GDP-based MPC approach is used as a supervisory control to improve energy efficiency in residential buildings with binary on/off, relay-based thermostats. A simulation study is used to demonstrate the efficacy of the proposed approach compared to existing MIP-based approaches.
Source: IFAC-PapersOnLine
Authors: Arnab Bhattacharya, Xu Ma, Draguna L. Vrabie
Institution: Pacific Northwest National Laboratory
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3000329189