In Heating, Ventilation and Air Conditioning (HVAC) plants of medium-high cooling capacity, multiple chiller systems are often employed. System performance is evaluated in terms of user comfort, energy use, and financial costs. The problem of efficiently managing multiple chiller systems is complex in many respects. In particular, the electrical energy consumption markedly increases if the machines are not properly managed. Therefore significant energy savings can be achieved by optimizing chiller operation. In this paper an unified method for efficient management of multiple chiller systems, by means of a Particle Swarm Optimization (PSO) based algorithm, is presented. In particular, the results show that it is possible to achieve substantial energy savings while granting good load profile tracking with respect to standard approaches. The performance of the algorithm is evaluated by resorting to a simulation environment, where the plant dynamics are accurately described. The results show that PSO satisfactorily deals with such kind of nonlinear constrained optimization problem, while offering advantages, with respect to other optimization techniques, in terms of ease of implementation.

A PSO-based Algorithm for Optimal Multiple Chiller Systems Operation

BEGHI, ALESSANDRO;RAMPAZZO, MIRCO
2012

Abstract

In Heating, Ventilation and Air Conditioning (HVAC) plants of medium-high cooling capacity, multiple chiller systems are often employed. System performance is evaluated in terms of user comfort, energy use, and financial costs. The problem of efficiently managing multiple chiller systems is complex in many respects. In particular, the electrical energy consumption markedly increases if the machines are not properly managed. Therefore significant energy savings can be achieved by optimizing chiller operation. In this paper an unified method for efficient management of multiple chiller systems, by means of a Particle Swarm Optimization (PSO) based algorithm, is presented. In particular, the results show that it is possible to achieve substantial energy savings while granting good load profile tracking with respect to standard approaches. The performance of the algorithm is evaluated by resorting to a simulation environment, where the plant dynamics are accurately described. The results show that PSO satisfactorily deals with such kind of nonlinear constrained optimization problem, while offering advantages, with respect to other optimization techniques, in terms of ease of implementation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2475957
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