Resource management using calculated sensitivities

• US 7,660,649 B1
• Filed: 07/05/2005
• Issued: 02/09/2010
• Est. Priority Date: 07/02/2004
• Status: Active Grant

• Current Assignee: Landis+Gyr Technology, Inc. (Landis+Gyr Group AG)
• Original Assignee: Optimal Innovations Incorporated
• Inventors: Hope, Gordon, Hope, legal representative, Kuloor, Soorya
• Primary Examiner(s): Jarrett; Ryan A
• Application Number:
  US11/175,980
• Time in Patent Office: 1,680 Days
• Field of Search: 700/28, 700/52, 700/99, 700/173, 700/295
• US Class Current: 700/295
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: H02J 2310/60 Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/144 Demand-response operation of the power transmission or distribution network
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving

Abstract

The present invention is directed to a system and method which includes a new optimization algorithm that not only optimizes the given system but provides sensitivity factors that provide the effect of changes in the system and how the changes are going to affect the overall performance of the system.

Claims

1. A system for controlling system resources comprising:

a CPU-based computer system including code stored on a processor-readable storage medium, said CPU-based computer system performing the following functions;

a. providing system parameters;

b. non-linear calculation of at least two sets of indexes based on the system parameters, including optimization of resource allocation using a Lagrange function, L, wherein L is given as;

$L<br><br>\left(X,\mathrm{\lambda <br><br>},\mathrm{\mu <br><br>},\mathrm{\rho <br><br>},\mathrm{\sigma <br><br>}\right)=f<br><br>\left(X\right)+\underset{i}{\sum <br><br>}<br><br><br><br>{\mathrm{\lambda <br><br>}}_i<br><br>u_i<br><br>\left(X\right)<br><br>\underset{j\in <br><br>T}{\sum <br><br>}<br><br><br><br>{\mathrm{\mu <br><br>}}_j<br><br>v_j<br><br>\left(X\right)+\underset{k\in <br><br>S}{\sum <br><br>}<br><br><br><br>{\mathrm{\rho <br><br>}}_k<br><br>\left(x_k-{\stackrel{\_}{x}}_k\right)+\frac{1}{2}<br><br>\underset{k\in <br><br>S}{\sum <br><br>}<br><br>{{\mathrm{\sigma <br><br>}}_k<br><br>\left(x_k-{\stackrel{\_}{x}}_k\right)}^2$

where

X=[x₁, x₂, . . . , x_Nx] is the set of primal system variables such that x_min,k≦

x_k≦

x_max,k, for k=1, . . . , N_x;

ƒ

(X) is the objective function;

u(X) is a set of equality constraints;

v(X) is a set of inequality constraints;

λ

is the vector of multipliers for equality constraints;

μ

is the vector of multipliers for inequality constraints;

ρ

is the vector of multipliers for violated variable limits;

σ

is the vector of penalties for the violated limits;

x is the violated limit of variable x;

T is the set of unsatisfied inequality constraints; and

S is the set of violated limits; and

c. controlling at least one system resource based on the at least two sets of indexes, wherein the controlling at least one system resource comprises load shedding.

2. The system of claim 1 wherein the system parameters comprise at least one of:

objectives, resources, components, and constraints.

3. The system of claim 2 wherein the constraints comprise hard constraints and soft constraints, and wherein the soft constraints can be violated.

4. The system of claim 3 wherein violation of the soft constraints imposes a penalty.

5. The system of claim 1 wherein the at least two sets of indexes comprises a first set of indexes and a second set of indexes, wherein the second set of indexes gives the sensitivities of the first set of indexes to changes in system parameters.

6. The system of claim 1 wherein the non-linear calculation is a modified Lagrangian Relaxation method.

7. The system of claim 6 wherein a modification to the Lagrangian Relaxation method comprises decoupling the optimization with respect to the primal variables.

8. The system of claim 6 wherein a modification to the Lagrangian Relaxation method comprises predicting the multiplier of a constraint.

9. The system of claim 1 wherein the optimization is defined as:

$\underset{\left(\mathrm{\lambda <br><br>},\mathrm{\mu <br><br>},\mathrm{\rho <br><br>}\right)}{\mathrm{maximize}}<br><br>\left[\underset{\left(X\right)}{\mathrm{minimize}}<br><br><br><br>L<br><br>\left(X,\mathrm{\lambda <br><br>},\mathrm{\mu <br><br>},\mathrm{\rho <br><br>},\mathrm{\sigma <br><br>}\right)\right].$

10. The system of claim 9 wherein the optimization is operative to:

a. relax all the constraints by setting all multipliers and penalties to 0;

b. minimize the Lagrange function with respect to X;

c. test whether all the constraints are satisfied;

if satisfied and stationary point conditions are met, then stop otherwise, proceed to d;

d. update λ

, μ and

ρ

so that the new value of the Lagrange function is maximized, and return to b.

11. The system of claim 1 wherein the optimization in b is decoupled with respect to the primal variables.

12. The system of claim 1 wherein multipliers are predicted using a sensitivity analysis.

13. A system for controlling a non-linear network, said system comprising:

one or more CPU-based devices including code stored on a processor readable storage medium, performing the following functions;

a. receiving data indicating network resources, network components, objectives for managing the non-linear network, and constraints of the non-linear network;

b. using at least a subset of said received data, computing;

i. a first index indicating a sensitivity of a given objective function with respect to changes in said network resources; and

ii. a second index indicating a sensitivity of said first index with respect to the changes in said network resources;

c. managing the non-linear network based, at least in part, on said first and second indexes, wherein the first index provides congestion levels in the non-linear network.

14. The system of claim 13 wherein said non-linear network comprises a power transmission and distribution network.

15. The system of claim 13 wherein the constraints comprise hard constraints and soft constraints, and wherein the soft constraints can be violated.

16. The system of claim 15 wherein violation of the soft constraints imposes a penalty.

17. The system of claim 13 wherein managing the non-linear network comprises load shedding.

18. The system of claim 13 wherein said data indicating network resources and network components comprises data representing nodes in a power transmission network, and wherein said first index indicates scarcity or surplus of said network resources at each node in the power transmission network, and wherein said second index indicates changes in said first index due to said changes in said network resources.

19. The system of claim 13 where the first index provides resource surplus or deficiency in the non-linear network.

20. The system of claim 19 where the first and second indexes are used to place transmission lines in the non-linear network.

21. The system of claim 19 where the first and second indexes implement incentives for market participants.

22. The system of claim 13 where the first index provides Locational Marginal Prices (LMP).

23. The system of claim 13 where the system is used for financial optimization of the non-linear network, the non-linear network comprising a utility transmission system.

24. The system of claim 13 where the first index is used to rank system resources.

25. The system of claim 24 where the system resources comprise one or more of:

generators, loads, static VAr devices, transformers, and synchronous condensers.

26. The system of claim 13 where the first and second indexes are used to determine optimal system upgrades.

29. The system of claim 13 where the first and second indexes are used for controlling data transfer in a data network.

30. The system of claim 13 where the first and second indexes are used for controlling fluid flows in a fluid flow network.

31. The system of claim 13 where the first and second indexes are used for controlling traffic flow in a transportation network.

32. The system of claim 13 where the first and second indexes are used for controlling passenger flow in a mass transit network.

33. The system of claim 13 where the first and second indexes are used for controlling airplane flows in an air traffic network.

34. The system of claim 13 where the first and second indexes are used to add, remove and control distributed energy resources (DER) in the network.

35. The system of claim 34 where the distributed energy resources comprise one or more of demand response and distributed generation.

36. The system of claim 13 where the first index provides stress levels in the non-linear network.

37. The system of claim 36 where the first index is used as an indicator of voltage stability in the non-linear network.

38. The system of claim 36 where the first and second indexes are used in determining optimal amount of load shedding in the system.

39. The system of claim 36 where first and second indexes are used to add, remove or control resources at a given location in the non-linear network.

40. The system of claim 39 where the first and second indexes are used to add, remove, or control generators in the non-linear network, the non-linear network comprising an electricity transmission network.

41. The system of claim 39 where the first and second indexes are used to control transformers in the non-linear network.

42. The system of claim 39 where the first and second indexes are used to add, remove or control reactive power devices in the non-linear network, the non-linear network comprising an electricity transmission network.

43. The system of claim 42 where the reactive power devices comprise static VAr devices.

27. The system of 13 where the first and second indexes are used for determining a unit commitment solution.

28. The system of claim 27 where objectives of the unit commitment solution comprise one or more of minimum cost and minimum green house gas emission.