CN112507619A - Centralized wind power prediction method and system based on cloud computing - Google Patents
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Abstract
The invention relates to the technical field of new energy regulation and control, and discloses a centralized wind power prediction method and system based on cloud computing to improve effectiveness, overall planning and accuracy of new energy regulation and control. The method comprises the following steps: the method comprises the steps that a station side server transmits live meteorological data and wind power plant output data of a paired wind power plant within a period of time T2 to a cloud server; the cloud server searches meteorological prediction data corresponding to the wind power plant in a future period of time T1, inputs the meteorological prediction data, live meteorological data in T2 and wind power plant output data into a wind power prediction model to obtain wind power prediction data of the wind power plant in time T1, and sends the wind power prediction data of the wind power plant in time T1 to a station end server; the method comprises the steps that a station side server sends scheduling data including wind power prediction data in a time T1 of a wind power plant to a scheduling side server; and the dispatching terminal server performs grid-connected regulation and control on the wind power plant according to the received wind power prediction data within the time of the wind power plant T1.
Description
Technical Field
The invention relates to the technical field of new energy regulation and control, in particular to a centralized wind power prediction method and system based on cloud computing.
Background
With the rapid development of new energy, the proportion of new energy grid-connected power generation is larger and larger. At present, the total scale of new energy installation in China is the first global. However, the new energy power generation is easily influenced by environmental factors, has the characteristics of intermittence and volatility, and is difficult to predict output. The wind power generation also has strong uncertainty and uncontrollable property, the wind speed fluctuates continuously, the large fluctuation of the generated power of the fan is caused, the power supply of the wind turbine generator connected with the power grid cannot meet the requirements of system stability, continuity and the like, and meanwhile, the wind power output power which changes continuously easily brings large impact to the power grid, and the load of peak shaving operation of the power grid is aggravated.
The deviation of the new energy output prediction brings adverse effects to power grid power generation planning arrangement and real-time monitoring, so that a new energy consumption scheme cannot be reasonably arranged, and the phenomena of light abandonment, wind abandonment and water abandonment occur. The average wind abandon rate of new energy power generation in the whole country in 2017 reaches more than 15.6%. In the end of 2017, the national reform committee and the energy agency have issued an implementation scheme for solving the problems of water abandonment, wind abandonment and light abandonment, and the requirements are that the proportion of the electric quantity and the electricity limit of the water abandonment, the wind abandonment and the light abandonment of each power grid enterprise is reduced year by year, and the problems of water abandonment, wind abandonment and light abandonment are effectively solved nationwide by 2020.
But current wind power prediction has difficulties at site side, scheduling side, etc. At a site end, two servers are deployed in a regulation and control center and used for operating a wind power prediction system, wherein one server is used as a data server, receives meteorological element data such as wind speed, wind direction, temperature and the like transmitted from an external network, transmits the data to an application server, and calculates according to a wind power prediction model deployed on the application server. Due to the limitation of the network speed and the computing capacity, the data transmission quantity cannot be too large, and the prediction model is simple, so that the wind power prediction accuracy cannot be improved. At a dispatching end, although each wind power plant transmits the predicted data to dispatching, the systems developed by different manufacturers have different prediction capabilities, unstable transmission time and missing data during dispatching, and the new energy dispatching work cannot be effectively supported.
Disclosure of Invention
The invention aims to disclose a centralized wind power prediction method and system based on cloud computing so as to improve effectiveness, overall planning and accuracy of new energy regulation.
In order to achieve the above object, the present invention discloses a centralized wind power prediction method based on cloud computing, which includes:
the method comprises the steps that a station side server transmits live meteorological data and wind power plant output data of a paired wind power plant within a period of time T2 to a cloud server;
the cloud server searches meteorological prediction data corresponding to the wind power plant in a future period of time T1, inputs the meteorological prediction data in T1, live meteorological data in T2 and wind power plant output data into a wind power prediction model to obtain wind power prediction data of the wind power plant in time T1, and sends the wind power prediction data of the wind power plant in time T1 to the station end server;
the station side server sends scheduling supply data to a scheduling side server, wherein the scheduling supply data comprise wind power prediction data in the time T1 of the wind power plant;
and the dispatching terminal server performs grid-connected regulation and control on the wind power plant according to the received wind power prediction data within the time of the wind power plant T1.
Preferably, the meteorological predicted data comprises wind speed, wind direction, temperature, humidity and barometric pressure data for different altitudes.
Preferably, the method of the present invention further comprises: and configuring unique identification marks for each wind power plant governed by the cloud server respectively, and establishing mapping between the identification marks and the wind power plant parameters and storing the mapping in a database server associated with the cloud server.
Preferably, the method of the present invention further comprises: and constructing a meteorological prediction model in the cloud server to respectively predict meteorological prediction data of each wind farm in a future period of time T1.
In order to achieve the above object, the present invention further discloses a centralized wind power prediction system based on cloud computing, including:
the station end server is used for transmitting live meteorological data and wind power plant output data of the paired wind power plants within a period of time T2 to the cloud end server;
the cloud server is used for searching meteorological predicted data in a period of time T1 corresponding to the wind power plant in the future, inputting the meteorological predicted data in T1, live meteorological data in T2 and wind power plant output data into a wind power prediction model to obtain wind power predicted data in the time T1 of the wind power plant, and sending the wind power predicted data in the time T1 of the wind power plant to the station end server;
the station end server is further configured to send scheduling data to a scheduling end server, where the scheduling data includes wind power prediction data of the wind farm within T1 time;
and the scheduling end server is used for carrying out grid-connected regulation and control on the wind power plant according to the received wind power prediction data within the time of the wind power plant T1.
In the same way, preferably, the system of the present invention further includes a database server associated with the cloud server, and is configured to configure unique identification identifiers for each wind farm governed by the cloud server, and establish mapping between the identification identifiers and the wind farm parameters for pairing storage.
Preferably, the cloud server is further configured to construct a meteorological prediction model to respectively predict meteorological prediction data of each wind farm in a future period of time T1.
In conclusion, the effectiveness, the overall arrangement and the accuracy of new energy regulation are improved; and also has the following beneficial effects:
1. for the station terminal, the operation and maintenance cost is reduced, the prediction accuracy is improved, the assessment strength is reduced, and the increase of the wind power plant benefit is facilitated.
2. For the dispatching end, all station prediction data can be displayed in real time, operation and maintenance are convenient and simple, and data quality is reliable.
3. The method has the advantages of good universality and strong expandability, and a unified management and control platform can be constructed on the basis of the data of the method for the new energy enterprises with a plurality of wind power plants, so that the cost is saved.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a centralized wind power prediction method based on cloud computing according to a preferred embodiment of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1
The embodiment discloses a centralized wind power prediction method based on cloud computing, as shown in fig. 1, including:
and S1, the station terminal server transmits live meteorological data and wind farm output data of the paired wind power plants within a past time T2 to the cloud server.
And S2, the cloud server searches weather prediction data corresponding to the wind power plant in a period of time T1 in the future, inputs the weather prediction data in T1, live weather data in T2 and wind power plant output data into a wind power prediction model to obtain wind power prediction data of the wind power plant in time T1, and sends the wind power prediction data of the wind power plant in time T1 to the station server.
In this step, preferably, the meteorological predicted data includes wind speed, wind direction, temperature, humidity and barometric pressure data for different altitudes.
And S3, the station side server sends scheduling data to a scheduling side server, wherein the scheduling data comprises wind power prediction data of the wind power plant in T1 time.
And S4, the scheduling end server performs grid-connected regulation and control on the wind power plant according to the received wind power prediction data within the time T1 of the wind power plant.
In this embodiment, in order to facilitate networking deployment and save cost, unique identification identifiers are respectively configured for each wind farm governed by the cloud server, and the identification identifiers and the wind farm parameters are mapped and stored in a database server associated with the cloud server. Further, a meteorological prediction model is directly built in the cloud server to respectively predict meteorological prediction data of each wind farm in a future period of time T1.
Example 2
Corresponding to the above method, the present embodiment discloses a centralized wind power prediction system based on cloud computing, including:
and the station end server is used for transmitting the live meteorological data and the wind power plant output data in the past time T2 of the paired wind power plant to the cloud end server.
The cloud server is used for searching meteorological prediction data (the meteorological prediction data comprise wind speed, wind direction, temperature, humidity, air pressure data and the like of different height layers) corresponding to the wind power plant in a future period of time T1, inputting the meteorological prediction data in T1, live meteorological data in T2 and wind power plant output data into a wind power prediction model to obtain wind power prediction data of the wind power plant in T1 time, and sending the wind power prediction data of the wind power plant in T1 time to the station server. Preferably, the cloud server is further configured to construct a meteorological prediction model to respectively predict meteorological prediction data of each wind farm in a future period of time T1.
The station end server is further configured to send scheduling data to a scheduling end server, where the scheduling data includes wind power prediction data of the wind farm within T1 time.
And the scheduling end server is used for carrying out grid-connected regulation and control on the wind power plant according to the received wind power prediction data within the time of the wind power plant T1.
Further, the system of this embodiment further includes: and the database server is associated with the cloud server and is used for configuring unique identification marks for each wind power plant governed by the cloud server respectively and establishing mapping between the identification marks and the wind power plant parameters for matching storage.
The specific implementation corresponding to this embodiment includes the following steps:
step (1), deploying a mesoscale meteorological model at the cloud end, and conducting weather prediction for a period of time T1. The period of time can be adjusted according to specific requirements, and the general requirement is 7 days. Meteorological elements of meteorological forecast data include, but are not limited to: wind speed, direction, temperature, humidity, air pressure, etc. for different height levels (e.g., 10 meters, 30 meters, 70 meters, 90 meters, 120 meters, etc.).
And (2) training a wind power prediction model by the cloud. The strong computing power of the cloud is utilized to model the wind power prediction model, and the model can adopt machine learning algorithms such as neural network and deep learning to improve the accuracy. The model input data includes: the time resolution of the meteorological element prediction data in a future period T1, the live meteorological data in a past period T2 and the wind power field output data is not lower than the time resolution required by wind power prediction. Once the model is trained, the model can be kept stable for a period of time, and the model parameters are updated regularly according to the data accumulation condition; or a reinforcement learning method is adopted, new data is substituted into the model along with the continuous increase of the data, and a more accurate model is obtained through training.
And (3) transmitting live meteorological data and live output data of the station terminal within the time T2 to a cloud end, participating in calculation of the output prediction data of the wind power plant, and obtaining wind power prediction data within the time T1 of the wind power plant, wherein the time resolution meets the scheduling requirement.
And (4) transmitting the wind power plant prediction data to a station end of the wind power plant. And deploying the server at the site end, and carrying out visual display after transmitting the data to the server. The data meets the requirements of the station terminal.
And (5) repeating the steps (2) - (4) after adding a new wind power plant.
And (6) transmitting the predicted data to a scheduling end. And deploying the server at the dispatching end, uniformly transmitting the data to the server, and performing visual display. The data meets the requirements of the dispatching end.
In summary, the centralized wind power prediction method and system based on cloud computing disclosed in the embodiments of the present invention improve effectiveness, overall planning and accuracy of new energy regulation; and also has the following beneficial effects:
1. for the station terminal, the operation and maintenance cost is reduced, the prediction accuracy is improved, the assessment strength is reduced, and the increase of the wind power plant benefit is facilitated.
2. For the dispatching end, all station prediction data can be displayed in real time, operation and maintenance are convenient and simple, and data quality is reliable.
3. The method has the advantages of good universality and strong expandability, and a unified management and control platform can be constructed on the basis of the data of the method for the new energy enterprises with a plurality of wind power plants, so that the cost is saved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A centralized wind power prediction method based on cloud computing is characterized by comprising the following steps:
the method comprises the steps that a station side server transmits live meteorological data and wind power plant output data of a paired wind power plant within a period of time T2 to a cloud server;
the cloud server searches meteorological prediction data corresponding to the wind power plant in a future period of time T1, inputs the meteorological prediction data in T1, live meteorological data in T2 and wind power plant output data into a wind power prediction model to obtain wind power prediction data of the wind power plant in time T1, and sends the wind power prediction data of the wind power plant in time T1 to the station end server;
the station side server sends scheduling supply data to a scheduling side server, wherein the scheduling supply data comprise wind power prediction data in the time T1 of the wind power plant;
and the dispatching terminal server performs grid-connected regulation and control on the wind power plant according to the received wind power prediction data within the time of the wind power plant T1.
2. The method of claim 1, wherein the meteorological predicted data includes wind speed, wind direction, temperature, humidity, and barometric pressure data for different altitudes.
3. The method of claim 1 or 2, further comprising:
and configuring unique identification marks for each wind power plant governed by the cloud server respectively, and establishing mapping between the identification marks and the wind power plant parameters and storing the mapping in a database server associated with the cloud server.
4. The method of claim 1 or 2, further comprising:
and constructing a meteorological prediction model in the cloud server to respectively predict meteorological prediction data of each wind farm in a future period of time T1.
5. The method of claim 3, further comprising:
and constructing a meteorological prediction model in the cloud server to respectively predict meteorological prediction data of each wind farm in a future period of time T1.
6. A centralized wind power prediction system based on cloud computing, comprising:
the station end server is used for transmitting live meteorological data and wind power plant output data of the paired wind power plants within a period of time T2 to the cloud end server;
the cloud server is used for searching meteorological predicted data in a period of time T1 corresponding to the wind power plant in the future, inputting the meteorological predicted data in T1, live meteorological data in T2 and wind power plant output data into a wind power prediction model to obtain wind power predicted data in the time T1 of the wind power plant, and sending the wind power predicted data in the time T1 of the wind power plant to the station end server;
the station end server is further configured to send scheduling data to a scheduling end server, where the scheduling data includes wind power prediction data of the wind farm within T1 time;
and the scheduling end server is used for carrying out grid-connected regulation and control on the wind power plant according to the received wind power prediction data within the time of the wind power plant T1.
7. The system of claim 6, wherein the meteorological predicted data includes wind speed, wind direction, temperature, humidity, and barometric pressure data for different altitudes.
8. The system according to claim 6 or 7, wherein the database server associated with the cloud server is configured to configure unique identification identifiers for each wind farm governed by the cloud server, and establish mapping between the identification identifiers and the wind farm parameters for pairing storage.
9. The system of claim 8, wherein the cloud server is further configured to construct a meteorological prediction model to predict meteorological prediction data for a future period of time T1 for each of the wind farms, respectively.
10. The system of claim 6 or 7, wherein the cloud server is further configured to construct a weather prediction model to predict weather prediction data of each of the wind farms within a future period of time T1.
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