Set-point Optimization in Refrigeration Systems -Minimization of Power Consumption Lars Sloth Larsen Central R&D-Refrigeration and Air Conditioning, Danfoss A/S
Outline Motivation -why look at minimization of power consumption? Optimal Set-points -for minimal power consumption Novel approach for on-line steady state optimization - constrained/uncostrained Summary 2001
Outline Motivation -why look at minimization of power consumption? Optimal Set-points -for minimal power consumption Novel approach for on-line steady state optimization - constrained/uncostrained Summary 2001
Motivation Motivation The potential energy savings using optimal set-points in refrigeration systems can in certain cases be 20-30%. (Arne Jakobsen et al, ESO-project final report and L. F. S. Larsen et al) Present optimization methods are too complex and specific for the individual plant. The optimization scheme should be on-line, such that it continously adapts to changes in the operational conditions. It is hard to make e.g. a table look up instead because the optimal set-point of the individual systems are changing. Furthermore it is not possible to conduct preliminary experiments that covers the set of possible external set of disturbances. 2001
Outline Motivation -why look at minimization of power consumption? Optimal Set-points -for minimal power consumption Novel approach for on-line steady state optimization - constrained/uncostrained Summary 2001
Optimal Set-Points Distributed control in refrigeration system Message Steady state optimization = set point optimization in a distributed control setup 2001
Optimal Set-Points Minimization of Power Consumption Objectives -Minimize the power consumption while upholding the required cooling capacity -Stay within operational constraints (and uphold operation feasibility) 2001
Outline Motivation -why look at minimization of power consumption? Optimal Set-points -for minimal power consumption Novel approach for on-line steady state optimization - constrained/uncostrained Summary 2001
Novel approach for on-line steady state optimization Objective To develop a simple optimizing control scheme that is applicable to a variaty of refrigeration system without any a priori knowlegde of specific component parameters. The optimization scheme should adapt to varying operational conditions. Operational constraints should be taken into account Present optimization methods are too complex and specific for the individual plant. The optimization scheme should be on-line, such that it continously adapts to changes in the operational conditions. It is hard to make e.g. a table look up instead because the optimal set-point of the individual systems are changing. Furthermore it is not possible to conduct preliminary experiments that covers the set of possible external set of disturbances. 2001
Unconstrained on-line steady state optimization Novel approach for on-line steady state optimization Unconstrained on-line steady state optimization Steady state objective function assuming that the individual set-points (xss=[PE,PC]) are controlled by distributed feedback controllers: Basic idea: By using a static model the objective function gradient can be computed and used as input to an outer control loop that ensures: W dot is the total power consumption 2001
Control setup Novel approach for on-line steady state optimization 2001
Constrained on-line steady state optimization Novel approach for on-line steady state optimization Constrained on-line steady state optimization Steady state objective function: By using barrier functions ( ) the constrained optimization can be relaxed and solved as an unconstrained t is a relative weight between the barrier and cost function W dot is the total power consumption 2001
Constrained optimization 2001
Set-Point Optimization 2001
Outline Motivation -why look at minimization of power consumption? Optimal Set-points -for minimal power consumption Novel approach for on-line steady state optimization - constrained/uncostrained Summary 2001
Summary Minimization of Energy Consumption A method for on-line steady state optimization has been presented. The method provides a structure for a simple steady state optimization scheme. It is possible to take constraints into account by using barrier functions and it is possible to uphold operational feasibility (graceful degradation) by allowing the deteriorated control performance. The method drives the system towards the global optimum if the cost function declines globally towards the optimum. 2001