System for generating a resultant model for a power system

• US 20040225486A1
• Filed: 05/08/2003
• Published: 11/11/2004
• Est. Priority Date: 05/08/2003
• Status: Abandoned Application

• Current Assignee: Mirant Intellectual Asset Management and Marketing
• Original Assignee: Mirant Intellectual Asset Management and Marketing
• Inventors: Moharari, Nader Shariat, Mullis, Vance Condary III
• Application Number:
  US10/434,026
• Publication Number:
  US 20040225486A1
• Time in Patent Office: Days
• US Class Current: 703/18
• CPC Class Codes: G06F 2119/06 Power analysis or power optimisation
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• 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

Abstract

A system for generating a resultant model for a particular generation or transmission power system is described, illustrated and claimed. The system involves a new and novel method that combines scenarios and their probabilities to estimate the areas of possible congestion, the expected values of congestion contracts, locational marginal prices, and scenario specific generation and transmission expansion plans as well as other relevant outcomes. One embodiment of the present invention is a model that develops multiple generation and transmission scenarios.

Claims

1. A method for generating a resultant model for a particular generation or transmission power system, the method comprising the steps of:

receiving scenario parameters related to the power system;

generating a plurality of scenarios and probabilities representing cost solutions for operating said power system given said scenario parameters;

optimizing each of said plurality of scenario for least cost solutions; and

generating the resultant model that identifies expected values within said power system based on the optimization step and that is associated with said scenarios and their probabilities.

2. The method of claim 1, wherein said scenario parameters comprise data representing economic growth.

3. The method of claim 1, wherein said scenario parameters comprise data representing environmental legislation.

4. The method of claim 1, wherein said scenario parameters comprise data representing fuel prices.

5. The method of claim 1, wherein said scenario parameters comprise data representing economic growth, environmental legislation and fuel prices.

6. The method of claim 1, wherein said optimizing step further comprises optimizing each of said plurality of scenario for minimizing total cost.

7. The method of claim 1, wherein said optimizing step further comprises optimizing each of said plurality of scenario for maximizing profit.

8. The method of claim 1, wherein said optimizing step further comprises optimizing each of said plurality of scenario for maximizing societal benefit.

9. The method of claim 1, wherein said optimizing step further comprises optimizing each of said plurality of scenario for minimizing total cost, maximizing profit and maximizing societal benefit.

10. The method of claim 1, further comprising the step of generating an optimum expansion plan based on the optimization step.

11. The method of claim 10, wherein said generating an optimum expansion plan comprises data that represents each scenario.

12. The method of claim 10, wherein said generating an optimum expansion plan comprises data that represents associated locational marginal prices.

13. The method of claim 10, wherein said generating an optimum expansion plan comprises data that represents related contract values.

14. The method of claim 10, wherein said generating an optimum expansion plan comprises data that represents each scenario, associated locational marginal prices and related contract values.

15. A system for generating a resultant model for a particular generation or transmission power system, the system comprising the components of:

a scenario and probabilities model, the scenario and probabilities model being operative to;

receive scenario parameters related to the power system, wherein said scenario parameters comprise data representing economic growth, environmental legislation and fuel prices; and

generate a plurality of scenarios and probabilities representing cost solutions for operating said power system given said scenario parameters;

an optimization model, the optimization model being operative to;

optimize each of said plurality of scenarios for minimizing total cost, maximizing profit and maximizing societal benefit; and

generate an optimized expansion plan by each of said plurality of scenarios, associated locational management price and related contract values; and

a value estimator model, the value estimator model being operative to;

generate the resultant model that identifies expected values within said power system based on the optimization step and that is associated with said scenarios and their probabilities.

16. The system of claim 15, wherein said scenario parameters comprise data of both a current and a predicted nature.

17. A system for generating a resultant model for a particular generation or transmission power system, the system comprising the components of:

a scenario and probabilities model, the scenario and probabilities model being operative to;

receive scenario parameters related to the power system, wherein said scenario parameters comprise data representing compliance technology, economic growth, environmental legislation, market conditions, fuel prices and transportation costs; and

generate a plurality of scenarios and probabilities representing cost solutions for operating said power system given said scenario parameters;

an optimization model, said optimization model comprising the components of;

an optimal generation expansion model, said optimal generation expansion model being operative to;

receive said plurality of scenarios and probabilities;

receive hourly loads related to said scenario parameters;

select a number of power units; and

generate a plurality of power curves describing a plurality of least cost configurations to operate said power system;

an optimal power flow model, said optimal power flow model being operative to;

identify a group of said plurality of curves, wherein said group is identified to minimize societal costs and maintain reliability; and

generate a plurality of locational marginal prices based on said group of said plurality of supply curves and associated with a power unit at a point of injection or withdrawal for a particular power system;

an optimal power flow regression extension model, said optimal power flow regression extension model being operative to;

create a plurality of expected locational marginal prices from said plurality of locational marginal prices and said hourly loads; and

create a plurality of shift factors from said plurality of locational marginal prices and said hourly loads; and

an optimum expansion model, said optimum expansion model being operative to;

determine power system infrastructure data based on data associated with the said expected locational marginal prices and said shift factors;

determine a set of power system additions to maximize societal value by said expected locational marginal prices and said shift factors;

feed said infrastructure data into said optimal power flow model, whereby said infrastructure data is used to determine a new plurality of locational marginal prices and a new plurality of shift factors; and

optimize power system based upon said new plurality of locational marginal prices; and

a value estimator model, the value estimator model being operative to;

generate the resultant model that identifies expected values within said power system based on the optimization model and that is associated with said scenarios and their probabilities.

18. The system of claim 17, wherein said optimal power flow regression extension model operation of creating a plurality of expected locational marginal prices from said plurality of locational marginal prices and said hourly loads, further comprises using regression analysis to create said expected locational marginal prices.