System and method for wind generation forecasting
Systems and methods for managing electricity of an electrical supply grid electrically connected to a wind farm are disclosed. Some systems and methods may include identifying relationships among a plurality of turbines in the wind farm with a minimum spanning tree, calculating a power output relationship among the plurality of turbines with the minimum spanning tree, creating a finite state space Markov chain forecast model for the plurality of turbines in the wind farm, predicting a power output of the wind farm with the finite state space Markov chain forecast model, and modifying at least one of a generation of electricity and a distribution of electricity based on the predicted power output of the wind farm. Also disclosed are systems and methods for predicting the power output of a wind farm.
1. A method of managing electricity of an electrical supply grid electrically connected to a wind farm, the method comprising:
identifying relationships among a plurality of turbines in the wind farm with a minimum spanning tree, wherein the minimum spanning tree separates the plurality of wind turbines into a plurality of classes, identifies a parent turbine for each turbine within a particular class, and guarantees that each turbine in a particular class is linked to a root turbine of that class;
calculating a power output relationship among the plurality of turbines with the minimum spanning tree;
creating a finite state space Markov chain forecast model for the plurality of turbines in the wind farm;
predicting a power output of the wind farm with the finite state space Markov chain forecast model; and
modifying at least one of a generation of electricity and a distribution of electricity based on the predicted power output of the wind farm.
2. The method according to claim 1 , further comprising forming a probability distribution of aggregate power generation of the wind farm.
3. The method according to claim 1 , further comprising measuring real-time wind farm electricity generation.
4. The method according to claim 3 , wherein the predicting a power output of the wind farm comprises at least one of a distributional forecast and a point forecast.
5. The method according to claim 1 , wherein the predicted power output of the wind farm is predicted for a period about 10 minutes into the future.
6. The method according to claim 1 , wherein the predicted power output of the wind farm is a period of time about 5 minutes into the future.
7. The method according to claim 1 , wherein the predicted power output of the wind farm is a period of time about one hour into the future.
8. The method according to claim 1 , wherein the creation of the finite state space Markov chain forecast utilizes ramp trend information.
9. The method according to claim 1 , wherein the creation of the finite state space Markov chain forecast model comprises historical data comprising at least one of a historic data of wind turbine power output and a historic data of wind speed.
10. The method according to claim 1 , wherein a plurality of finite state space Markov chains are created for an epoch.
11. The method according to claim 1 , wherein a plurality of finite state space Markov chains are created for each month in a year.
12. A system for managing electricity of an electrical supply grid electrically connected to a wind farm, the system comprising:
a processor configured to be in electrical communication with a wind farm power output sensor, wherein the processor is configured to:
identify relationships among a plurality of turbines in the wind farm with a minimum spanning tree, wherein the minimum spanning tree separates the plurality of wind turbines into a plurality of classes, identifies a parent turbine for each turbine within a particular class, and guarantees that each turbine in a particular class is linked to a root turbine of that class;
calculate a power output relationship among the plurality of turbines with a minimum spanning tree;
create a finite state space Markov chain forecast model for the plurality of turbines in the wind farm;
predict a power output of the wind farm with the finite state space Markov chain forecast model; and
determine, based on the predicted power output of the wind farm, whether at least one of a generation of electricity and a distribution of electricity should be modified.
13. The system according to claim 12 , wherein the system is in electrical communication with a meteorological tower collocated with a turbine of the wind farm.
14. The system according to claim 12 , wherein the system is in electrical communication with a class sensor.
15. The system according to claim 14 , wherein the class sensor signals least one of a turbine height, a turbine manufacturer, and a turbine model.
16. The system according to claim 12 , further comprising a power grid sensor that detects a current electrical power of the electrical grid.