System and method for correlating point-to-point sky clearness for use in photovoltaic fleet output estimation with the aid of a digital computer

• US 10,436,942 B2
• Filed: 03/14/2016
• Issued: 10/08/2019
• Est. Priority Date: 07/25/2011
• Status: Active Grant

• Current Assignee: Clean Power Research LLC
• Original Assignee: Clean Power Research LLC
• Inventors: Hoff, Thomas E.
• Primary Examiner(s): Park, Hyun D
• Application Number:
  US15/069,889
• Publication Number:
  US 20160195639A1
• Time in Patent Office: 1,303 Days
• Field of Search: 702 2, 702 3, 702 36, 702 40, 702 60, 702189, 702194, 2502011, 2502521
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09
• CPC Class Codes: G01W 1/12 Sunshine duration recorders measuring intensity of sunshine G01J
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• 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 50/04 Manufacturing
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2300/24 of photovoltaic origin
• CPC Class Codes: H02J 3/004 Generation forecast, e.g. methods or systems for forecasting future energy generation
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
• CPC Class Codes: Y02B 10/10 Photovoltaic [PV]
• CPC Class Codes: Y02E 10/56 Power conversion systems, e.g. maximum power point trackers
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y02P 90/30 Computing systems specially adapted for manufacturing
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

Statistically representing point-to-point photovoltaic power estimation and area-to-point conversion of satellite pixel irradiance data are described. Accuracy on correlated overhead sky clearness is bounded by evaluating a mean and standard deviation between recorded irradiance measures and the forecast irradiance measures. Sky clearness over the two locations is related with a correlation coefficient by solving an empirically-derived exponential function of the temporal distance. Each forecast clearness index is weighted by the correlation coefficient to form an output set of forecast clearness indexes and the mean and standard deviation are proportioned. Additionally, accuracy on correlated satellite imagery is bounded by converting collective irradiance into point clearness indexes. A mean and standard deviation for the point clearness indexes is evaluated. The mean is set as an area clearness index for the bounded area. For each point, a variance of the point clearness index is determined and the mean and standard deviation are proportioned.

Claims

1. A method for managing photovoltaic fleet production with the aid of a digital computer, comprising the steps of:

regularly measuring a set of irradiance observations with an irradiance sensor for a first physical location in a geographic region within which a photovoltaic power production fleet is situated, the photovoltaic power production fleet being connected to a power grid and comprising a plurality of photovoltaic power production plants deployed at different physical locations within the geographic region, the power grid further comprising a plurality of power generators other than the photovoltaic power production plants in the photovoltaic fleet, a transmission infrastructure, and a power distribution infrastructure for distributing power from the photovoltaic system and the power generators to consumers;

regularly measuring cloud speed with a wind sensor for the first physical location; and

centrally operating with a digital computer comprising steps of;

selecting a second physical location in the geographic region that is different than the first physical location at which one of the photovoltaic power production plants is located;

regularly providing to the computer the set of irradiance observations from the irradiance sensor and the cloud speed from the wind sensor, plus clear sky global horizontal irradiance for the geographic region;

determining, by the computer, a set of input sky clearness indexes as a ratio of each of the irradiance observations in the set of irradiance observations and the clear sky global horizontal irradiance;

determining, by the computer, a temporal distance as a physical distance between the first and the second physical locations in proportion to the cloud speed;

calculating, by the computer, a clearness index correlation coefficient between the first and the second physical locations as an empirically-derived function of the temporal distance and bounding statistical error between the first and the second physical locations by evaluating a mean and standard deviation;

weighting, by the computer, the set of input sky clearness indexes by the clearness index correlation coefficient to form a set of output sky clearness indexes, which indicates the sky clearness for the second physical location, and proportioning the mean and standard deviation to the output set of sky clearness indexes;

regularly estimating photovoltaic power production of the photovoltaic power production plant located at the second physical location in proportion to the output set of clear sky indexes as bounded by the proportioned mean and standard deviation; and

regularly adjusting a forecast of aggregate photovoltaic power production for the photovoltaic power production fleet comprising the estimated power production of the photovoltaic power production plant located at the second physical location and estimated power production of the other photovoltaic power production plants comprised in the photovoltaic power production fleet.

2. A method according to claim 1, further comprising the steps of at least one of:

evaluating, by the computer, a variance of the output set of sky clearness indexes; and

evaluating in the computer a change in the variance of the output set of sky clearness indexes.

3. A method according to claim 2, wherein the variance σ

_Kt² of the sky clearness index Kt from a measured time interval Δ

t ref to a desired output time interval Δ

t is determined in accordance with;

4. A method according to claim 2, wherein the variance σ

<sub>Δ

Kt</sub>² of the change in the sky clearness index Δ

Kt from a measured time interval Δ

t ref to a desired output time interval Δ

t is determined in accordance with;

5. A method according to claim 1, further comprising the steps of:

selecting, by the computer, a time period comprising a time resolution by which the each irradiance observations in the set of irradiance observations was regularly measured;

dividing, by the computer, the set of irradiance observations by increments of the time period into a time series set of irradiance observations; and

forming, by the computer, a time series set of the output sky clearness indexes over each time period increment in the time series set of irradiance observations and bounding statistical error at each physical location in the geographic region by evaluating a mean and standard deviation.

6. A method according to claim 5, further comprising the steps of

forming, by the computer, a full set of the output sky clearness indexes for a plurality of additional physical locations, which are each within the geographic region;

forming, by the computer, a full time series set of the full set of the output sky clearness indexes over each time period increment in the time series;

determining, by the computer, irradiance statistics from the mean and standard deviation at each additional physical location in the geographic region for the full time series set of the output sky clearness indexes; and

building, by the computer, power statistics for the photovoltaic fleet as a function of the fleet irradiance statistics and a power rating of the photovoltaic fleet.

7. A method according to claim 6, further comprising the step of:

generating, by the computer, a time series of the power statistics for the photovoltaic fleet by applying a time lag correlation coefficient for an output time interval to the power statistics over each of the time period increments.

8. A method according to claim 1, wherein the clearness index correlation coefficient ρ

^Kti,Ktj for the first and the second physical locations i and j is determined in accordance with;

ρ

^Kti,Ktj=exp(C₁×

TemporalDistance)^{ClearnessPower}

where C₁ equals 10<sup>−

3</sup> seconds<sup>−

1</sup> and;

Clearness Power=In(C₂Δ

t)−

k

such that Δ

t is a measured time interval in seconds, C₂ equals 1 seconds<sup>−

1</sup>, and 5 ≤

k≤

15.

9. A method according to claim 8 wherein the change in the clearness index correlation coefficientρ

<sup>Δ

Kti,Δ

Ktj</sup> for the first and the second physical locations i and j is determined in accordance with;

ρ

<sup>Δ

Kti,Δ

Ktj</sup>=(ρ

^Kti,Ktj)<sup>Δ

ClearnessPower</sup>

where;

10. A system for managing photovoltaic fleet production with the aid of a digital computer, comprising:

an irradiance sensor for a first physical location in a geographic region within which a photovoltaic power production fleet comprised in a power grid is situated, the photovoltaic power production fleet comprising a plurality of photovoltaic power production plants deployed at different physical locations within the geographic region, the irradiance sensor configured to regularly measure a set of irradiance observations, the power grid further comprising a plurality of power generators other than the photovoltaic power production plants in the photovoltaic fleet, a transmission infrastructure, and a power distribution infrastructure for distributing power from the photovoltaic system and the power generators to consumers;

a wind sensor for the first physical location, the wind sensor configured to regularly measure cloud speed;

a non-transitory computer readable storage medium comprising program code; and

a computer processor and memory on a digital computer configured to centrally operate the photovoltaic power production fleet with the computer processor coupled to the storage medium, wherein the computer processor is configured to execute the program code to perform steps to;

select a second physical location in the geographic region that is different than the first physical location at which one of the photovoltaic power production plants is located;

regularly provide the set of irradiance observations from the irradiance sensor and the cloud speed from the wind sensor, plus clear sky global horizontal irradiance for the geographic region;

determine a set of input sky clearness indexes as a ratio of each of the irradiance observations in the set of irradiance observations and the clear sky global horizontal irradiance;

determine a temporal distance as a physical distance between the first and the second physical locations in proportion to the cloud speed;

calculate a clearness index correlation coefficient between the first and the second physical locations as an empirically-derived function of the temporal distance and bounding statistical error between the first and the second physical locations by evaluating a mean and standard deviation;

weight the set of input sky clearness indexes by the clearness index correlation coefficient to form a set of output sky clearness indexes, which indicates the sky clearness for the second physical location, and proportioning the mean and standard deviation to the output set of sky clearness indexes; and

regularly estimate photovoltaic power production of the photovoltaic power production plant located at the second physical location in proportion to the output set of clear sky indexes as bounded by the proportioned mean and standard deviation; and

regularly adjust a forecast of aggregate photovoltaic power production for the photovoltaic power production fleet comprising the estimated power production of the photovoltaic power production plant located at the second physical location and estimated power production of the other photovoltaic power production plants comprised in the photovoltaic power production fleet.

11. A system according to claim 10, wherein the computer processor is further configured to execute the program code to perform at least one of the steps to:

evaluate a variance of the output set of sky clearness indexes; and

evaluate a change in the variance of the output set of sky clearness indexes.

12. A system according to claim 11, wherein the variance σ

_Kt² of the sky clearness index Kt from a measured time interval <sup>Δ

t ref</sup> to a desired output time interval Δ

t is determined in accordance with;

13. A system according to claim 11, wherein the variance σ

<sub>Δ

Kt</sub>²of the change in the sky clearness index <sup>Δ

Kt</sup> from a measured time interval Δ

t ref to a desired output time interval Δ

t is determined in accordance with;

14. A system according to claim 10, wherein the computer processor is further configured to execute the program code to perform the steps to:

select a time period comprising a time resolution by which the each irradiance observations in the set of irradiance observations was regularly measured;

divide the set of irradiance observations by increments of the time period into a time series set of irradiance observations; and

form a time series set of the output sky clearness indexes over each time period increment in the time series set of irradiance observations and bound statistical error at each physical location in the geographic region by evaluating a mean and standard deviation.

15. A system according to claim 14, wherein the computer processor is further configured to execute the program code to perform the steps to:

form a full set of the output sky clearness indexes for a plurality of additional physical locations, which are each within the geographic region;

form a full time series set of the full set of the output sky clearness indexes over each time period increment in the time series; and

determine irradiance statistics from the mean and standard deviation at each additional physical location in the geographic region for the full time series set of the output sky clearness indexes, and build power statistics for the photovoltaic fleet as a function of the fleet irradiance statistics and a power rating of the photovoltaic fleet.

16. A system according to claim 15, wherein the computer processor is further configured to execute the program code to perform the steps to:

generate a time series of the power statistics for the photovoltaic fleet by applying a time lag correlation coefficient for an output time interval to the power statistics over each of the time period increments.

17. A system according to claim 16, wherein the production output controller for the photovoltaic fleet is further adapted to receive the time series of the power statistics for the photovoltaic fleet to a production output controller for the photovoltaic fleet and operate the photovoltaic fleet to generate sufficient power to realize the time series of the power statistics.

18. A system according to claim 10, wherein the clearness index correlation coefficient ρ

^Kti,Ktj for the first and the second physical locations i and j is determined in accordance with;

ρ

^Kti,Ktj=exp(C₁×

TemporalDistance)^{ClearnessPower}

where C₁ equals 10<sup>−

3</sup> seconds<sup>−

1</sup> and;

Clearness Power =In(C₂ Δ

t)−

k

such that Δ

t is a measured time interval in seconds, C₂ equals 1 seconds<sup>−

1</sup>, and 5 ≤

k≤

15.

19. A system according to claim 18 wherein the change in the clearness index correlation coefficient ρ

<sup>Δ

Kti,Δ

Ktj</sup> for the first and the second physical locations i and j is determined in accordance with;

ρ

<sup>Δ

Kti,Δ

Ktj</sup>=(ρ

^Kti,Ktj)<sup>Δ

ClearnessPower</sup>

where;