METHOD AND SYSTEM FOR INTERMEDIATE TO LONG-TERM FORECASTING OF ELECTRIC PRICES AND ENERGY DEMAND FOR INTEGRATED SUPPLY-SIDE ENERGY PLANNING

• US 20110071882A1
• Filed: 09/22/2009
• Published: 03/24/2011
• Est. Priority Date: 09/22/2009
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Jakagnanam, Jayant R., Subramanian, Dharmashankar
• Application Number:
  US12/564,753
• Publication Number:
  US 20110071882A1
• Time in Patent Office: Days
• US Class Current: 705/10
• 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: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

A method of price forecasting in an electrical energy supply network and/or load (energy demand) forecasting of a given consumer of electrical energy, in the context of an electrical energy supply network that is adapted to supply electrical energy to a number consumers connected to the network. The method includes developing a multi-regime, regime switching stochastic model for determining day ahead/spot market energy prices using at least one historical profile and subjective opinion from at least one expert; and the multiple regimes correspond to a number of combinations of physical factors. A regime is identifiable by at least three factors. The method thus facilitates identifying the optimal mix of energy hedge and exposure to day ahead/spot market prices for deriving economic benefits in overall energy expenditure.

Claims

1. A method of price forecasting in an electrical energy supply network and/or load (energy demand) forecasting of a given consumer of electrical energy, in the context of an electrical energy supply network that is adapted to supply electrical energy to a number consumers connected to the network, for identifying the optimal mix of energy hedge and exposure to day ahead/spot market prices for deriving economic benefits in overall energy expenditure, the method comprising;

developing a multi-regime, regime switching stochastic model for determining day ahead/spot market energy prices using at least one historical profile and an experts opinion; and

configuring the multiple regimes to correspond to a number of combinations of key physical factors, each regime is identified by at least three factors,

wherein a program using a processor unit runs one or more of said developing and configuring steps.

2. The method of claim 1 further comprising using at least an interval of time, a season of a year and a season of a year as a factor for identifying a regime.

3. The method of claim 2 wherein said regime has a probability distribution obtained by using a combination of historical behavior and at least one expert opinion.

4. The method of claim 3 further comprising providing real time price/day ahead price data in said probability distribution.

5. The method of claim 2 further comprising using an interval of time which is not less that one month.

6. The method of claim 2 further comprising:

providing regimes having probability distributions of an electrical load during a normal stress peak period, a low stress peak period, a medium stress peak period and a severe stress peak period.

7. The method of claim 6 wherein the probability distribution of electrical load is in MegaWatts per Hour.

8. A system of price forecasting in an electrical energy supply network and/or load (energy demand) forecasting of a given consumer of electrical energy, in the context of an electrical energy supply network that is adapted to supply electrical energy to a number consumers connected to the network, for identifying the optimal mix of energy hedge and exposure to day ahead/spot market prices for deriving economic benefits in overall energy expenditure, the method comprising;

a memory;

a processor in communication with said memory, wherein the computer system is capable of performing a method comprising;

developing a multi-regime, regime switching stochastic model for determining day ahead/spot market energy prices using at least one historical profile and subjective opinion from at least one expert; and

configuring the multiple regimes to correspond to a number of combinations of key physical factors;

wherein a regime is identifiable by at least three factors.

9. The system of claim 8 further comprising using an interval of time as a factor for identifying a regime.

10. The system of claim 9 further comprising using a season of a year as a factor for identifying a regime.

11. The system of claim 10 further comprising using a stress condition as a factor for identifying a regime.

12. The system of claim 11 wherein said regime has a probability distribution obtained by using a combination of historical behavior and at least one expert opinion.

13. The system of claim 12 further comprising providing real time price/day ahead price data in said probability distribution.

14. The system of claim 9 further comprising using an interval of time which is not less that one month.

15. The system of claim 11 further comprising:

providing regimes having probability distributions of an electrical load during a normal stress peak period, a low stress peak period, a medium stress peak period and a severe stress peak period.

16. The system of claim 15 wherein the probability distribution of electrical load is in MegaWatts per Hour.

17. A computer program for price forecasting in an electrical energy supply network and/or load (energy demand) forecasting of a given consumer of electrical energy, in the context of an electrical energy supply network that is adapted to supply electrical energy to a number consumers connected to the network, for identifying the optimal mix of energy hedge and exposure to day ahead/spot market prices for deriving economic benefits in overall energy expenditure, the method comprising:

a storage medium readable by a processing circuit and storing instructions for processing by the processing circuit for performing a method comprising;

providing a multi-regime, regime switching stochastic model for determining day ahead/spot market energy prices using at least one historical profile and subjective opinion from at least one expert; and

configuring the multiple regimes to correspond to a number of combinations of key physical factors;

wherein a regime is identifiable by at least three factors.

18. The computer program product of claim 17 further comprising using an interval of time as a factor for identifying a regime.

19. The computer program product of claim 18 further comprising using a season of a year as a factor for identifying a regime.

20. The computer program product of claim 19 further comprising using a stress condition as a factor for identifying a regime.

21. The computer program product of claim 20 wherein said regime has a probability distribution obtained by using a combination of historical behavior and at least one expert opinion.

22. The computer program product of claim 21 further comprising providing real time price/day ahead price data in said probability distribution.

23. The computer program product of claim 18 further comprising using an interval of time which is not less that one month.

24. The computer program product of claim 20 further comprising:

providing regimes having probability distributions of an electrical load during a normal stress peak period, a low stress peak period, a medium stress peak period and a severe stress peak period.

25. The computer program product of claim 24 wherein the probability distribution of electrical load is in MegaWatts per Hour.

26. A method of price forecasting in an electrical energy supply network and/or load (energy demand) forecasting of a given consumer of electrical energy, in the context of an electrical energy supply network that is adapted to supply electrical energy to a number consumers connected to the network, for identifying the optimal mix of energy hedge and exposure to day ahead/spot market prices for deriving economic benefits in overall energy expenditure, the method comprising;

selecting a time frame of at least one month;

selecting a set of hedge contracts for the time frame of at least one month with purchase price and sell back logic for unused energy;

computing, using the time frame and hedge contract selections, the overall energy expenditure distribution and quantify risk of exceeding a user defined known threshold;

applying numerical and simulation techniques to obtain a solution; and

generating, using said the obtained solution, sample sets of various volatile quantities consistent with the physical understanding and intra-/ inter-variable temporal correlation,

wherein a program using a processor unit runs one or more of said selecting a time frame, selecting a set of hedge contracts, computing, applying and generating steps.

27. A method of price forecasting in an electrical energy supply network and/or load (energy demand) forecasting of a given consumer of electrical energy, in the context of an electrical energy supply network that is adapted to supply electrical energy to a number consumers connected to the network, for identifying the optimal mix of energy hedge and exposure to day ahead/spot market prices for deriving economic benefits in overall energy expenditure, the method comprising;

selecting a time frame of at least one month;

selecting a set of hedge contracts for the time frame of at least one month with purchase price and sell back logic for unused energy;

computing, based on the selections, rate structure details, and candidate set of energy hedge blocks along with minimum block size and minimum duration of purchase;

computing a set of hedge blocks with size and duration of coverage and real time and day ahead exposure using the result of the above computing; and

using stochastic mathematical programming techniques for obtaining a result,

wherein a program using a processor unit runs one or more of said selecting a time frame, selecting a set of hedge contracts, using, computing, and using stochastic mathematical programming techniques steps.