Smarter-Grid: Method to Forecast Electric Energy Production and Utilization Subject to Uncertain Environmental Variables

• US 20110307109A1
• Filed: 05/27/2010
• Published: 12/15/2011
• Est. Priority Date: 05/27/2010
• Status: Active Grant

• Current Assignee: International Business Machines Corporation, ServiceNow Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Sri-Jayantha, Sri M.
• Granted Patent: US 8,600,572 B2
• Time in Patent Office: Days
• US Class Current: 700/291
• 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 50/06 Energy or water supply
• 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

Abstract

A method to forecast the energy sources and energy sinks to facilitate continuous capacity planning, regulation and control of energy state of an entity under variable weather condition is established. Energy sources of specific focus are related to renewable energy forms from wind, solar and wave that are highly dependent on prevailing weather conditions.

Claims

1. A method of establishing a power forecasting service model for use in conjunction with a system, said method comprising estimating energy production, forecasting utilization of said energy and storing said energy, for use in a system by maintaining a balance between a plurality of sources of energy and a plurality of users sinks thereof, said method utilizing:

at least a first renewable source of said energy sources having an energy output which fluctuates due to environmental energy generation factors, at least a second non-renewable source of said energy sources having an energy output which is accurately controllable through correspondingly accurate control of fuel consumption,

at least a first environmentally dependent sink of said energy sinks which is subject to an energy consumption factor which fluctuates due to environmental energy consumption factors, and

at least a second directly controllable sink of said energy sinks which is subject to an energy consumption factor which is accurately controllable through correspondingly accurate regulation of controllable energy consumption factors,

said method further comprising;

a) taking measurements to obtain a first renewable energy source forecast for each said environmental energy generation factor based on said measurements taken thereof;

b) based on said first renewable energy source forecast, estimating an energy output for said first renewable energy source at a selected point in time;

c) obtaining a sink forecast for each said environmental energy consumption factor based on measurements thereof;

d) based on said sink forecast, estimating an energy consumption value for said first sink at said selected point in time;

e) regulating said fuel consumption by said second non-renewable source of energy, and regulating said energy consumption by said second sink in order to maintain said balance between said plurality of sources of energy and said plurality of usersinks thereof.

2. The method as set forth in claim 1, wherein said first source is selected from a wind generator, a photovoltaic solar cell, or wave-energy generator and where said environmental energy generation factors comprise wind velocity, cloud cover and wave height during selected time periods.

3. The method as set forth in claim 1, wherein said environmental energy consumption factors comprise outside air temperature, humidity, wind speed and precipitation.

4. The method as set forth in claim 1, wherein said controllable energy consumption factors are selected from thermostat temperature or rate of production.

5. The method set forth in claim 1, wherein said source forecast employs a multiplicity of geographically distributed sensors to provide voltage, current and power data corresponding to electric energy producers, consumers, distributors and transmitters at periodic time intervals.

6. The method set forth in claim 5 wherein said source forecast is transmitted through a wired or wireless link to a communication network.

7. The method set forth in claim 6 wherein a computer receives data from said communication network and generates a unified estimate of an electric state of an electric grid system in a given locality using an intelligent algorithm, wherein said computer receives:

a.) present weather forecast data including cloud cover, wind streamline and wave height/period to estimate the expected energy output from renewable sources and upgrade the model parameters to match the sensor-based power data;

b.) combines weather forecast data, whereby a renewable energy model computes projected energy generation by the renewable energy sources;

c.) uses energy producer capacity information and projected renewable energy forecasts and creates therefrom an algorithm which constructs maximum, most likely and minimum energy generation forecasts for the selected grid system in a given locality.

8. The method set forth in claim 7, wherein said computer develops historical and present consumer sensor data and evaluates:

consumer power usage model;

consumer power storage model;

[−

] consumer seasonal and random usage;

characteristics consumer weather-dependent energy usage behavior.

9. The method set forth in claim 7, wherein combining said weather forecast and said consumer power consumption usage models, said algorithm generates projected demand for said given locality.

10. The method set forth in claim 7, wherein Power generation and demand estimates and forecasts are replicated to adjoining regions of interest, and eventually an aggregate meso-scale model for a whole nation or other power-sharing area is established.

11. The method set forth in claim 7, wherein the grid-state estimation and forecast is shared 24/7 with business entities.

12. (canceled)

13. A method to forecast the power and energy state of an electric system comprising:

estimating an electric power production renewable energy source, said energy source selected from the group consisting of solar, wind and wave energy, said estimate being based upon technology-dependent parameters and weather-driven variables, said parameters and said variables derived from distributed power sensors, satellite-based cloud cover images, ground-based wind velocity data and ocean-based wave height/period data respectively;

forecasting national power utilization sink levels wherein the impact of weather including temperature, humidity and wind on consumption by a system is correlated to establish a model from historical data and applied for enhanced accuracy of consumption projection;

estimating stored energy available for time delayed consumption;

integrating said production, utilization and storage, to establish a power-forecasting service model to provide an “

energy map”

displaying the power generation or consumption forecast to detailed databases of an “

energy-state”

of a geographic entity.

14. The method to forecast the power and energy state of an electric system defined in claim 13 wherein said energy source is solar and wind energy.

15. The method to forecast the power and energy state of an electric system defined in claim 14 wherein said solar energy is a composite of solar irradiance measured directly through the atmosphere, diffused through a system of clouds and reflected from objects on the terrain, so that the solar power produced is calculated using the formula:

P_solar=η

(A_diffusedE_diffused+A_directE_direct+A_reflectedE_reflected)

wherein η

represents the conversion efficient of the solar array, A is the effective area presented to each irradiance component and E is power density falling on the respective area.

16. The method to forecast the power and energy state of an electric system defined in claim 15 wherein the value of P_solar is generalized to

P_solar=f(C(x,y,t),θ

_Solar)

wherein C(x,y,t) represents cloud related geometric parameters and θ

_Solar

is a parameter vector representing properties in the system prone to error;

estimating the value of θ

_solar to determine θ

_solar hat and based upon said formula, the amount of solar power converted to electricity power is calculated by taking measurements

P_solar(t)=f(C(x,y,t),θ

_Solar hat)

comparing the initial estimate of said parameter vector with the actual value and determining that the values are substantially equal, the forecast for solar power in a future time is calculated according to;

P_solar(t)=f(C(x,y,t+Δ

t),θ

_Solar hat)

wherein θ

_Solar hat represents a parameter set that may require refinement based upon measured solar data.

17. The method to forecast the power and energy state of an electric system defined in claim 13 wherein the energy source is wind power, wherein a collection of variables representing vector V(x,y,t) is obtained using a sensor measuring speed, density, temperature, and direction of said wind, with the result that the wind power available to a wind turbine is computed as:

P_wind-input(t)=[AV(t)][(½

)ρ

(t)V²(t)]

wherein the velocity V(t) is the vector normal to the plane of rotation of a wind turbine with an area A and ρ

is the wind turbine conversion efficiency.

18. The method to forecast the power and energy state of an electric system defined in claim 17 wherein said collection of variables are measured and corresponding electric power output due to wind is written as:

P_wind=η

[P_wind-input]=η

f(V(x,y,t),θ

_Wind)

wherein η

represents the conversion factor for the system, and based upon said results, the estimated electric power P_wind (t) is computed;

P_wind=f(V(x,y,t), θ

_Wind hat) wherein θ

_Wind hat represents a parameter set that may require refinement based upon measured power data.

19. The method to forecast the power and energy state of an electric system defined in claim 13 wherein the energy source is wave power, wherein a collection of variables representing vector W(x,y,t) is obtained using a sensor measuring wave height, wave velocity and wave periodicity, with the result that the wave power available to a mechanism that converts wave motion into electromechanical means for delivering power is computed as:

P_wave=f(V(x,y,t),θ

_Wave hat)

wherein W(x,y,t) represents wave related properties at time “

t” and

θ

_Wave hat corresponds to parameters requiring estimation to minimize forecast error.

20. A method of forecasting the supply and demand condition of an electric grid using renewable and non-renewable sources of electric power comprising:

forecasting total electrical power capacity which is the sum of renewable and non-renewable energy sources;

forecasting the total renewable source of electric power using a sensor network to;

obtain cloud cover data according to the relationship;

P_solar(t)=f(C(x,y,t+Δ

t),θ

_hat)

obtain wind streamline data according to the relationship;

P_wind(t)=f(V(x,y,t+Δ

t),θ

_Wave hat)

obtain wave data according to the relationship;

P_wave(t)=f(W(x,y,t+Δ

t),θ

_Wave hat)

wherein (x,y) in each instance above is location, t is time dependent and

θ

_Wave hat is an estimate of a selected group of parameters from each power source, not yet known, which are estimated using sensor data and equations governing basic physics of power conversion;

determining the non-renewable source of electric power by;

obtaining known non-renewable energy power capacity;

combining said data obtained from said renewable sources with said known non-renewable energy user capacity to result in information that comprises a total forecast capacity;

said combined data is composed into an electric state vector X which defines location (x,y) and is time (t) dependent and incorporates transmission line voltage (V), current (I), power (P), energy (E) parameters therein;

using the parameters in said X state, estimating stored energy from resources that are used in forming direct and indirect estimates, that are based upon historic trend data from home consumption data and from industry data;

combining said estimates obtained from said X state to result in information that comprises a total forecast demand;

evaluating forecast capacity and forecast demand, in a planning and control stage, and then balancing supply and demand limitations within an electric system.

21. The method of forecasting the supply and demand condition of an electric grid using renewable and non-renewable sources of electric power defined in claim 20 wherein:

collecting said data emanating from said sensor network relating to renewable wind, solar and wave energy sources are transmitted by wireless and transmission line means;

collecting weather data transmitted by satellite means; and

collecting data from said non-renewable energy source at locations along its transmission path between a power generating facility and an ultimate user and storing same in a data network, combining and supplying said data collections to a data network planning and control stage which comprises an entity that receives multi-channel measurements derived from said sensor network and detailed characteristics of present and future weather conditions.

22. An article of manufacture comprising a computer usable medium having computer readable program code means embodied therein for causing a forecasting of electrical energy production and utilization subject to uncertain environmental variables, the computer readable program code means in said article of manufacture comprising computer readable program code means for causing a computer to effect:

estimating an electric power production renewable energy source, said energy source selected from the group consisting of solar, wind and wave energy, said estimate being based upon technology-dependent parameters and weather-driven variables, said parameters and said variables derived from distributed power sensors, satellite-based cloud cover images, ground-based wind velocity data and ocean-based wave height/period data respectively;

forecasting national power utilization sink levels wherein the impact of weather including temperature, humidity and wind on consumption by a system is correlated to establish a model from historical data and applied for enhanced accuracy of consumption projection;

estimating stored energy available for time delayed consumption;

integrating said production, utilization and storage, to establish a power-forecasting service model to provide an “

energy map”

displaying the power generation or consumption forecast to detailed databases of an “

energy-state”

of a geographic entity.

23. An article of manufacture comprising a computer usable medium having computer readable program code means embodied therein for causing a forecasting of electrical energy production and utilization subject to uncertain environmental variables, the computer readable program code means in said article of manufacture comprising computer readable program code means for causing a computer to effect:

forecasting total electrical power capacity which is the sum of renewable and non-renewable energy sources;

forecasting the total renewable source of electric power using a sensor network to;

obtain cloud cover data according to the relationship;

P_solar(t)=f(C(x,y,t+Δ

t),θ

_Solar hat)

obtain wind streamline data according to the relationship;

P_wind(t)=f(V(x,y,t+Δ

t),θ

_Wind hat)

obtain wave data according to the relationship;

P_wave(t)=f(W(x,y,t+Δ

t),θ

_Wave hat)

wherein (x,y) in each instance above is location, t is time dependent and

θ

_Wave hat is an estimate of a selected group of parameters from each power source, not yet known, which are estimated using sensor data and equations governing basic physics of power conversion;

determining the non-renewable source of electric power by;

obtaining known non-renewable energy power capacity;

combining said data obtained from said renewable sources with said known non-renewable energy user capacity to result in information that comprises a total forecast capacity;

said combined data is composed into an electric state vector X which defines location (x,y) and is time (t) dependent and incorporates transmission line voltage (V), current (I), power (P), energy (E) parameters therein;

using the parameters in said X state, estimating stored energy from resources that are used in forming direct and indirect estimates, that are based upon historic trend data from home consumption data and from industry data;

combining said estimates obtained from said X state to result in information that comprises a total forecast demand;

evaluating forecast capacity and forecast demand, in a planning and control stage, and then balancing supply and demand limitations within an electric system.

24. A computer program product comprising a computer usable medium having computer readable program code means embodied therein for causing a forecasting of electrical energy production and utilization subject to uncertain environmental variables, the computer readable program code means in said computer program product comprising computer readable program code means for causing a computer to effect:

estimating an electric power production renewable energy source, said energy source selected from the group consisting of solar, wind and wave energy, said estimate being based upon technology-dependent parameters and weather-driven variables, said parameters and said variables derived from distributed power sensors, satellite-based cloud cover images, ground-based wind velocity data and ocean-based wave height/period data respectively;

forecasting national power utilization sink levels wherein the impact of weather including temperature, humidity and wind on consumption by a system is correlated to establish a model from historical data and applied for enhanced accuracy of consumption projection;

estimating stored energy available for time delayed consumption;

integrating said production, utilization and storage, to establish a power-forecasting service model to provide an “

energy map”

displaying the power generation or consumption forecast to detailed databases of an “

energy-state”

of a geographic entity.

25. A computer program product comprising a computer usable medium having computer readable program code means embodied therein for causing a forecasting electrical energy production and utilization subject to uncertain environmental variables, said computer readable program code means in said computer program product comprising computer readable program code means for causing a computer to effect:

forecasting total electrical power capacity which is the sum of renewable and non-renewable energy sources;

forecasting the total renewable source of electric power using a sensor network to;

obtain cloud cover data according to the relationship;

P_solar(t)=f(C(x,y,t+Δ

t),θ

_Solar hat)

obtain wind streamline data according to the relationship;

P_wind(t)=f(V(x,y,t+Δ

t),θ

_Wind hat)

obtain wave data according to the relationship;

P_wave(t)=f(W(x,y,t+Δ

t),θ

_Wave hat)

wherein (x,y) in each instance above is location, t is time dependent and

θ

_Wave hat is an estimate of a selected group of parameters from each power source, not yet known, which are estimated using sensor data and equations governing basic physics of power conversion;

determining the non-renewable source of electric power by;

obtaining known non-renewable energy power capacity;

combining said data obtained from said renewable sources with said known non-renewable energy user capacity to result in information that comprises a total forecast capacity;

said combined data is composed into an electric state vector X which defines location (x,y) and is time (t) dependent and incorporates transmission line voltage (V), current (I), power (P), energy (E) parameters therein;

using the parameters in said X state, estimating stored energy from resources that are used in forming direct and indirect estimates, that are based upon historic trend data from home consumption data and from industry data;

combining said estimates obtained from said X state to result in information that comprises a total forecast demand;

evaluating forecast capacity and forecast demand, in a planning and control stage, and then balancing supply and demand limitations within an electric system.