Decision support system and method

• US 7,657,480 B2
• Filed: 07/27/2001
• Issued: 02/02/2010
• Est. Priority Date: 07/27/2001
• Status: Expired due to Fees

• Current Assignee: Air Liquide Large Industries U S LP (L'Air Liquide SA), L'Air Liquide Societe Anonyme Pour L'Etude et L'Exploitation Des Procedes Georges Claude (L'Air Liquide SA)
• Original Assignee: Air Liquide Large Industries U S LP (L'Air Liquide SA)
• Inventors: Harper, Charles N.
• Primary Examiner(s): Oyebisi; Ojo O
• Application Number:
  US09/916,548
• Publication Number:
  US 20030023466A1
• Time in Patent Office: 3,112 Days
• Field of Search: 705/7, 705/8, 705/10, 705/37, 705/35, 703/18, 700/286
• US Class Current: 705/37
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A decision support system and method is provided for responding to a deregulated electrical energy environment for use in making decisions related to supplying and purchasing electrical power. In a preferred embodiment, the decision support system comprises a plurality of interconnected models that produce information related to various business operations. A contract valuation model produces a value of energy contracts based upon timely data such as contract terms, real-time commodity pricing, and forecasted commodity pricing. A potential action valuation model generates value for potential, predefined operational actions at a given production facility in response to a particular opportunity. A forecasting and planning model provides forecasting services related to energy based upon historical and current real-time data for use in other models. A supply chain optimizer provides overall supply information related to supplying a plurality of electrical generating facilities to thereby indicate economies for overall system operation. A risk management model allows the entry of risk tolerance parameters. A financial position management model is utilized as a tool to support risk reduction.

Claims

1. A computer-implemented method for identifying an excess energy capacity in a production supply chain operated by a supply chain operator, comprising:

identifying, by a supply chain optimizer, a potential production configuration for the production supply chain, wherein;

(i) the supply chain operator also operates at least one power generation facility to sustain industrial production by the production supply chain,

(ii) the supply chain operator is capable of both consuming and selling electricity produced by the power generation facility while operating the production supply chain,

(iii) the potential production configuration is related to a target electricity production by the power generation facility, and

(iv) the potential production configuration reduces a production output and energy consumption for at least some portion of the production supply chain or increases electricity production by the power generation facility during a given time period;

determining, using a potential action valuation model, whether to reduce the production output of the production supply chain or increase electricity production by the power generation facility according to the potential production configuration to create the excess energy capacity during the time period; and

if production output is determined to be reduced or electricity production by the power generation facility is determined to be increased, selling the excess energy capacity created by implementing the potential production configuration during the time period for the production supply chain and the power generation facility.

2. The method of claim 1, wherein the potential action valuation model determines whether to reduce the production output of the production supply chain using a risk management model.

3. The method of claim 2, wherein the risk management model may be configured according to a set of risk tolerance criteria and risk performance criteria.

4. The method of claim 1, wherein the forecasted price for electricity during the time period is determined using a forecasting and planning model utilizing historical and real-time data.

5. The method of claim 1, wherein, if production output is determined to be reduced, prior to the time period, increasing the production output of the supply chain to prepare of the reduced production of the supply chain for the time period.

6. The method of claim 1, wherein a data delivery engine is configured to supply real-time data to the potential action valuation model, the supply chain optimizer, the forecasting and planning model, and the risk management model.

7. The method of claim 6, wherein the real-time data includes real-time commodity prices for electricity.

20. The method of claim 1, wherein the production supply chain comprises one of an air component separation facility, an oil field electric pump network, a refinery, and a metal ore production facility.

8. A computer-readable storage medium containing a program which, when executed, performs operations for identifying an excess energy capacity in a production supply chain operated by a supply chain operator, the operation comprising:

identifying, by a supply chain optimizer, a potential production configuration for the production supply chain for a supply chain, wherein

(i) the supply chain operator also operates at least one power generation facility to sustain industrial production by the production supply chain,

(ii) the supply chain operator is capable of both consuming and selling electricity produced by the power generation facility while operating the production supply chain,

(iii) the potential production configuration is related to a target electricity production by the power generation facility, and

(iv) the potential production configuration reduces a production output and energy consumption for at least some portion of the production supply chain or increases electricity production by the power generation facility during a given time period where a contracted price for the electricity exceeds a forecasted price;

determining, using a potential action valuation model, whether to reduce the production output of the production supply chain or increase electricity production by the power generation facility according to the potential production configuration to create the excess energy capacity for the production supply chain during the time period; and

if production output is determined to be reduced or electricity production by the power generation facility is determined to be increased, selling the excess energy capacity created by implementing the potential production configuration during the time period for the production supply chain and the power generation facility.

9. The computer-readable medium of claim 8, wherein the potential action valuation model determines whether to reduce the production output of the production supply chain using a risk management model.

10. The computer-readable medium of claim 9, wherein the risk management model may be configured according to a set of risk tolerance criteria and risk performance criteria.

11. The computer-readable medium of claim 8, wherein the forecasted price for electricity during the time period is determined using a forecasting and planning model utilizing historical and real-time data.

12. The computer-readable medium of claim 8, wherein, if production output is determined to be reduced, prior to the time period, the operations further include increasing the production output of the supply chain to prepare of the reduced production of the supply chain for the time period.

13. The computer-readable medium of claim 8, wherein a data delivery engine is configured to supply real-time data to the potential action valuation model, supply chain optimizer, forecasting and planning model, and the risk management model.

14. The computer-readable medium of claim 13, wherein the real-time data includes real-time commodity prices for electricity.

21. The computer readable storage medium of claim 8, wherein the production supply chain comprises one of an air component separation facility, an oil field electric pump network, a refinery, and a metal ore production facility.

15. A computing device, comprising:

at least one processor; and

a memory, wherein the memory includes a plurality of models, which when executed by the processor, are configured to identify an excess energy capacity in a production supply chain operated by a supply chain operator, including;

a supply chain optimizer configured to identify a potential production configuration for the production supply chain, wherein;

(i) the supply chain operator also operates at least one power generation facility to sustain industrial production by the production supply chain,

(ii) the supply chain operator is capable of both consuming and selling electricity produced by the power generation facility while operating the production supply chain,

(iii) the potential production configuration is related to a target electricity production by the power generation facility, and

(iv) the potential production configuration reduces a production output and energy consumption for at least some portion of the production supply chain or increases electricity production by the power generation facility during a given time period where a contracted price for the electricity exceeds a forecasted price;

a potential action valuation model configured to determine whether to reduce the production output of the production supply chain or increase electricity production by the power generation facility according to the potential production configuration to create the excess energy capacity for the production supply chain during the time period; and

a data delivery engine configured to supply real-time data to the potential action valuation model and to the supply chain optimizer.

16. The computing device of claim 15, wherein the potential action valuation model determines whether to reduce the production output of the production supply chain using a risk management model.

17. The computing device of claim 16, wherein the risk management model may be configured according to a set of risk tolerance criteria and risk performance criteria.

18. The computing device of claim 15, wherein the forecasted price for electricity during the time period is determined using a forecasting and planning model utilizing historical and real-time data.

19. The computing device of claim 15, wherein the real-time data includes real-time commodity prices for electricity.

22. The computing device of claim 15, wherein the production supply chain comprises one of an air component separation facility, an oil field electric pump network, a refinery, and a metal ore production facility.