Online Combinatorial Optimization for Interconnected Refrigeration Systems: Linear Approximation and Submodularity

TL;DR

This paper introduces a novel control method for interconnected supermarket refrigeration systems that employs bilevel combinatorial optimization with linear and submodular approximations to reduce energy consumption and compressor switching frequency.

Contribution

It presents a new bilevel optimization framework with scalable approximation algorithms for energy-efficient control of interconnected refrigeration systems.

Findings

Significant energy savings demonstrated in simulations.

Reduced compressor switching frequency achieved.

Algorithms provide near-optimal solutions with performance guarantees.

Abstract

Commercial refrigeration systems consume 7% of the total commercial energy consumption in the United States. Improving their energy efficiency contributes to the sustainability of global energy systems and the supermarket business sector. This paper proposes a new control method that can save the energy consumption of multi-case supermarket refrigerators by explicitly taking into account their interconnected and switched system dynamics. Its novelty is a bilevel combinatorial optimization formulation to generate ON/OFF control actions for expansion valves and compressors. The inner optimization module keeps display case temperatures in a desirable range and the outer optimization module minimizes energy consumption. In addition to its energy-saving capability, the proposed controller significantly reduces the frequency of compressor switchings by employing a conservative compressor control strategy. However, solving this bilevel optimization problem associated with interconnected and switched systems is a computationally challenging task. To solve the problem in near real time, we propose two approximation algorithms that can solve both the inner and outer optimization problems at once. The first algorithm uses a linear approximation, and the second is based on the submodular structure of the optimization problem. Both are (polynomial-time) scalable algorithms and generate near-optimal solutions with performance guarantees. Our work complements existing optimization-based control methods (e.g., MPC) for supermarket refrigerators, as our algorithms can be adopted as a tool for solving combinatorial optimization problems arising in these methods.