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US 2018/0019595 A1Published application
System and method of determining forecast error for renewable energy fluctuations
AssigneeOkinawa Institute of Science and Technology Graduate U
InventorMahesh M. BANDI
Life of this patent
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Abstract
A system for determining (a) forecast error and/or (b) scaling error for wind power generation is provided, the system utilizing generated and forecast time series for power generation derived from wind and analyzing temporal correlations in the wind fluctuations to quantify: (a) the forecast error defined by deviations between the high frequency components of the forecast and generated time series, and (b) a scaling error defined by a degree that temporal correlations fail to be predicted for an accurate predictor of wind fluctuations. Wind fluctuations may exhibit multi-fractal behavior at the turbine level and/or may be rectified to a fractal structure at the grid level. A memory kernel may be used to reduce the forecast and scaling errors.
Claims24
A computer-readable storage medium storing a program to manage energy production in an energy grid based on a physical quantity, the program causing a computer to execute at least following processes,
- a first process including:
- a (1-1)-th step that fixes n to an integer equal to 1 or greater;
- a (1-2)-th step that makes a calculation shown in a formula (1) regarding the physical quantity:
- PG(t) represented as a function of a time t and a predicted value of the physical quantity:
- PF (t+τ) at a time t+τ when a fixed time τ passes from the time t, (|PF(t+τ)−PG(t)|)n . . . (1);
- a (1-3)-th step that calculates a first average value as an average value of the formula (1) when the time t is varied for a fixed period;
- a (1-4)-th step that calculates a first average value when the T is varied;
- a (1-5)-th step that performs the (1-1)-th step to the (1-4)-th step at least once for the integer that is different from the n in the (1-1)-th step;
- and
- a (1-6)-th step that calculates a time scaling error in accordance with a numeric of the n from the first average value calculated in the (1-4)-th step and the first average value calculated in the (1-5)-the step;
- a second process including steps below:
- a (2-1)-th step that fixes n to the integer equal to 1 or greater;
- a (2-2)-th step that makes a calculation shown in a formula (2) regarding the physical quantity:
- PG (t) represented as the function of the time t and the physical quantity:
- PG (t+τ) at the time t+τ when the fixed time τ passes from the time t, (|PG(t+τ)−PG(t)|)n . . . (2);
- a (2-3)-th step that calculates a second average value as the average value of the formula (2) when the time t is varied for the fixed period;
- a (2-4)-th step that calculates a second average value when the τ is varied;
- a (2-5)-th step that makes an evaluation to determine whether the second average value is proportional to τA and calculates A when proportional;
- a (2-6)-th step that performs the (2-1)-th step to the (2-5)-th step at least once for the integer that is different from the n in the (2-1)-th step;
- and
- a (2-7)-th step that calculates a slope:
- Aslope when a relationship between the A calculated in the (2-5)-th step and the (2-6)-th step and the n is approximated as a linear line;
- a third process including:
- a (3-1)-th step that performs the (1-1)-th step to the (1-4)-th step;
- a (3-2)-th step that determines whether the first average value is proportional to τB and calculates B when proportional;
- a (3-3)-th step that performs the (3-1)-th step and the (3-2)-th step at least once for the integer that is different from the n in the (3-1)-th step;
- and
- a (3-4)-th step that calculates a Bslope when the relationship between the B calculated in the (3-2)-th step and the (3-3)-th step and the n is approximated as a linear line;
- a fourth process including:
- a (4-1)-th step that calculates a scaling error from a difference between the Aslope calculated in the (2-7)-th step and the Bslope calculated in the (3-4)-th step;
- and;
- a fifth process including:
- analyzing the time-scaling error and the scaling error to determine a predicted value of physical quantities PF(t) and PG(t) used in the energy grid.
30 more elements
A method of determining time-dependent forecast error in an energy grid, the method comprising:
- introducing a data set defined by an identified generated power trend and an identified forecast power trend of the energy grid across a time period, each of the identified generated and forecast power trends being based on a physical process;
- producing a statistical structure by comparing the identified generated and forecast power trends and analyzing the data set to identify temporal error variations defined by temporal correlated fluctuations between the identified generated power trend and the forecast power trend;
- calculating the time-dependent forecast error comprised of a time-scaling error and a scaling error based on the statistical structure derived from the physical process;
- and
- analyzing the time-dependent forecast error to determine a predicted value of energy fluctuations of the physical process associated with the identified generated and forecast power trends used to manage the energy grid.
2 more elements
A system for determining forecast error from a renewable energy source, the system comprising:
- a generated power time series for power generation derived from the renewable energy source;
- a forecast power time series for power forecasted to be derived from the renewable energy source;
- a computing system comprising:
- a computer-readable storage medium comprising memory and a program executed in memory for causing a computer to execute at least the following steps:
- introducing a data set defined by an identified generated power trend and an identified forecast power trend of an energy grid across a time period, each of the identified generated and forecast power trends being based on a physical process associated with the renewable energy source;
- producing a statistical structure by comparing the identified generated and forecast power trends and analyzing the data set to identify temporal error variations defined by temporal correlated fluctuations between the identified generated power trend and the forecast power trend;
- and
- calculating a time-dependent forecast error comprised of a time-scaling error and a scaling error based on the statistical structure derived from the physical process;
- and
- a processor operatively coupled to the computer-readable storage medium for executing the program;
- wherein the temporal correlated fluctuations are analyzed to quantify the time-dependent forecast error of energy fluctuations of the physical process defined by deviations between (a) high frequency components of the forecast and generated power time series and (b) the scaling error.
8 more elements
Dependencies are inferred from the claim text.