METHOD AND SYSTEM FOR INTERMEDIATE TO LONG-TERM FORECASTING OF ELECTRIC PRICES AND ENERGY DEMAND FOR INTEGRATED SUPPLY-SIDE ENERGY PLANNING
First Claim
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;
- 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.
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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.
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Citations
2 Claims
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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;
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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.
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2. 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;
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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.
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Specification