1. Set-point Optimization in Refrigeration Systems -Minimization of Power Consumption Lars Sloth Larsen Central R&D-Refrigeration and Air Conditioning, Danfoss A/S

2. 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

3. 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

4. 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

5. 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

6. Optimal Set-Points Distributed control in refrigeration system
Message Steady state optimization = set point optimization in a distributed control setup
2001

7. 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

8. 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

9. 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

10. 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

11. Control setup Novel approach for on-line steady state optimization
2001

12. 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

13. Constrained optimization
2001

14. Set-Point Optimization
2001

15. 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

16. 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