Title: PSO-based optimal online operation strategy for multiple chillers energy conservation

Authors: Jing An; Luyuan Xu; Zheng Fan; Kefan Wang; Qi Deng; Qi Kang

Addresses: School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 201418, China ' Department of Control Science and Engineering, Tongji University, Shanghai 201804, China ' Shanghai Civil Aviation, East China Air Traffic Control Engineering Technology Co., Ltd, Shanghai, China ' School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 201418, China ' Department of Control Science and Engineering, Tongji University, Shanghai 201804, China ' Department of Control Science and Engineering, Tongji University, Shanghai 201804, China

Abstract: As a key part of energy conservation in HVAC system, reasonable operation strategy of multiple chillers is essential to most industrial buildings. In traditional chiller control strategies, the operation state of chillers mainly depends on the experience of on-site workers. Therefore, it is important to analyse the characteristics and integrate them into a set of effective control strategy of the chiller system. In this paper, we propose an efficient control strategy for energy conservation of multiple chillers. The system energy consumption and the constrains of the chillers are firstly modelled, and a two-layer control strategy for the chillers is proposed, which is respectively used to control the selection of starting scheme of the chillers under the cooling load at the current time and the setting of control parameter values of the chiller under the selected starting scheme. The core of the two-layer strategy is the use of PSO algorithm. Experimental results have suggested that the strategy can effectively optimise the energy consumption of the multiple chillers system and realise the accurate control in different periods.

Keywords: multiple chillers; energy conservation; PSO; two-layer control structure.

DOI: 10.1504/IJBIC.2021.119999

International Journal of Bio-Inspired Computation, 2021 Vol.18 No.4, pp.229 - 238

Received: 20 Dec 2020
Accepted: 04 May 2021
Published online: 04 Jan 2022 *

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