CN111160653A - Distributed energy storage system wind power consumption capacity monitoring method based on cloud computing - Google Patents

Distributed energy storage system wind power consumption capacity monitoring method based on cloud computing Download PDF

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CN111160653A
CN111160653A CN201911409917.6A CN201911409917A CN111160653A CN 111160653 A CN111160653 A CN 111160653A CN 201911409917 A CN201911409917 A CN 201911409917A CN 111160653 A CN111160653 A CN 111160653A
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孙碣
金国锋
刘宏扬
韩永强
滕云
袁元缘
王泽镝
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State Grid Corp of China SGCC
Shenyang University of Technology
Economic and Technological Research Institute of State Grid Inner Mongolia Electric Power Co Ltd
State Grid Eastern Inner Mongolia Power Co Ltd
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Shenyang University of Technology
Economic and Technological Research Institute of State Grid Inner Mongolia Electric Power Co Ltd
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Abstract

The invention discloses a method for monitoring wind power consumption capacity of a distributed energy storage system based on cloud computing, which belongs to the technical field of new energy based on cloud computing analysis. The method considers environmental factors and transmission loss, improves the prediction precision of wind power output and load, and has both accuracy and real-time performance on the monitoring of the wind power consumption capability.

Description

Distributed energy storage system wind power consumption capacity monitoring method based on cloud computing
Technical Field
The invention relates to the technical field of new energy based on cloud computing analysis, in particular to a method for monitoring wind power consumption capacity of a distributed energy storage system based on cloud computing.
Background
In recent years, the renewable energy power generation technology in China is rapidly developed, the proportion of wind power generation in a power grid is continuously increased, and however, wind power cannot be completely absorbed by the power grid in the 'three north' area with rich wind energy resources due to insufficient system peak regulation capacity and the like. The distributed energy storage system has the characteristics of bidirectional power capability, flexible regulation characteristic and the like, and can effectively improve the wind power consumption capability of a power grid.
The existing wind power absorption capacity research mainly focuses on wind power absorption capacity evaluation, and the main evaluation method comprises the following steps: engineering method, constraint factor method, time domain simulation method. The wind power consumption capacity of the power grid is calculated by the engineering method through estimation according to historical experience, and the method is poor in precision performance. The constraint factor method evaluates the power grid absorption capacity based on the constraint of the power grid wind power absorption capacity, and the time domain simulation method evaluates the power grid absorption capacity by building a regional power grid platform, so that the two methods are poor in real-time performance. The method provided by the invention has the advantages of high accuracy and real-time performance, can well solve the problems, and has high applicability.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a distributed energy storage system wind power consumption capacity monitoring method based on cloud computing.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: the flow of the method for monitoring the wind power consumption capability of the distributed energy storage system based on cloud computing is shown in fig. 1, and the method comprises the following steps:
step 1: respectively collecting environmental parameters of a wind power plant in a power grid at the current T moment and a power grid loss coefficient delta; the environmental parameters comprise wind speed v, air humidity h and environmental temperature t.
Step 2: inputting the environmental parameter variables collected in the step 1, the daily environmental parameter forecast values given by a local meteorological department (day before the current T moment) and the proportion coefficients of the wind speed, humidity and temperature influence factors into a cloud neural network input layer of cloud computing as input signals to perform iterative computation on the input signals to obtain the error coefficients of wind power prediction at the current T moment, so as to calculate the real-time wind power prediction power;
step 2.1: comparing the environmental parameter variables acquired in the step 1 with predicted values given by local meteorological departments in the day ahead, and obtaining an error coefficient gamma of wind power prediction at the current T moment in real time based on cloud computing by combining the percentage coefficients of wind speed, humidity and temperature influence factors:
Figure BDA0002349685270000021
wherein, βv、βh、βtThe ratio coefficient v of the wind speed, humidity and temperature influencing factors*、h*、t*Respectively providing predicted values of wind speed, air humidity and environment temperature for local meteorological departments in the day ahead;
step 2.2: calculating wind power prediction power P at the current T moment according to the error coefficient gammaa：
Pa＝(1-γ)Pt
Wherein, PtAnd the power is predicted for the original wind power of the current day obtained by the wind power plant according to the meteorological data given by the meteorological department in the current day.
And step 3: predicting the load of the power grid according to the annual single-day maximum load of the power grid, environmental parameters and weather conditions;
step 3.1: obtaining a temperature influence coefficient psi according to the environment temperature T at the current T moment:
Figure BDA0002349685270000022
step 3.2: obtaining a weather influence coefficient omega according to weather conditions:
Figure BDA0002349685270000023
step 3.3: calculating the load of the power grid at the current T moment according to the annual single-day maximum load of the power grid, the ambient temperature T, the temperature influence coefficient psi obtained in the step 3.1 and the weather influence coefficient omega obtained in the step 3.2
Figure BDA0002349685270000024
Figure BDA0002349685270000025
Wherein the content of the first and second substances,
Figure BDA0002349685270000026
for the annual single-day maximum load of the power grid, U is a coefficient matrix, and the calculation process is as follows:
Figure BDA0002349685270000031
wherein, A is a prediction constant matrix, and B is a matrix of influence of weather and temperature on load;
U＝A*B
and 4, step 4: according to the wind power generation capacity of the power grid wind field obtained in real time in the step 2 and the predicted power utilization load value obtained in real time in the step 3, a daily power generation plan of a conventional generator set is integrated, and the air abandoning amount at the current T moment is calculated as follows:
Figure BDA0002349685270000032
wherein, PrThe daily generation planned power of the conventional generator set of the power grid is represented, and delta is a grid loss coefficient.
And 5: and calculating the wind power absorption capacity according to the real-time wind abandoning rate by combining the charging and discharging power of the battery energy storage system at the current T moment.
Step 5.1: by calculating the abandoned wind rate during wind power consumption in real time at the cloud, the formula is as follows:
Figure BDA0002349685270000033
wherein the content of the first and second substances,
Figure BDA0002349685270000034
for the charging power of the battery energy storage system at the moment T,
Figure BDA0002349685270000035
the discharge power of the battery energy storage system at the moment T.
Step 5.2: calculating a real-time monitoring value zeta of the wind power consumption capacity of the distributed energy storage system:
ζ＝1-ε1。
the cloud computing realizes real-time computing processing of a large amount of data, a specific cloud computing process is shown in fig. 2, when a computing request is made, a corresponding data processing task table containing task information is created, after the data processing task table is created, the data processing task table is distributed to n servers connected with a cloud system and requests to be connected with the servers, after the connection with the servers is established, tasks in the task table are sent to the servers connected with the servers, the servers can process the tasks, when the tasks are processed, the servers can return corresponding completion information to the cloud system, the cloud system can delete the completed tasks from the task table after receiving the information and feed corresponding computing processing results back to a computing request end, and the whole cloud computing process is finished.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
1. according to the invention, environmental factors influencing wind power generation power are fully considered, and the loss of electric energy generated in the transmission process is considered, so that the accuracy and reliability of a calculation result are better guaranteed;
2. according to the method, the forecast value given by a meteorological department is compared with the measured value, the weight proportion setting is carried out on the influence factors, the error is expressed through an error coefficient and is substituted into the subsequent calculation, and the fitting effect of the obtained wind power prediction power and the actual wind power is good;
3. according to the invention, the influence of weather and temperature on the load is considered by establishing the corresponding temperature influence coefficient and weather influence coefficient, and the load predicted value can be intuitively and conveniently calculated by solving the matrix norm of '1';
4. the method and the device collect real-time data, realize the processing of the real-time data based on cloud computing, and provide guarantee for the real-time monitoring of the wind power consumption capability.
Drawings
FIG. 1 is a flow chart of a method for monitoring wind power consumption of a distributed energy storage system based on cloud computing according to the present invention;
FIG. 2 is a flow chart of cloud computing according to the present invention;
FIG. 3 is a schematic diagram of input variables for calculating a wind power prediction error coefficient according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a cloud computing model in the embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In this embodiment, taking a wind farm in a certain area as an example, the wind speed of the weather department forecasted day before is 6m/s, the air humidity is 30%, the ambient temperature is 22 ℃, the actual wind speed at the time T is 5m/s, the air humidity is 28%, the ambient temperature is 25 ℃, the weather condition is clear, and the maximum load P of the power grid is one day per yearL mIs 220MW, PtIs 26MW, PrThe power grid loss coefficient delta is 0.1 at 198MW, the total capacity of the battery energy storage system is 120MWh, the rated power is 30MW, and the conversion efficiency is 80%; and the state of the battery energy storage system at the moment T is obtained and is shown in table 1:
TABLE 1 Battery energy storage System State at time T
Figure RE-GDA0002384191910000042
As shown in fig. 1, the method of the present embodiment is as follows.
Step 1: collecting environmental parameters of a wind power plant in a power grid at T moment: the wind speed v is 5m/s, the air humidity h is 28%, the ambient temperature t is 25 ℃ and the grid loss coefficient delta is 0.1.
Step 2: inputting the environmental parameter variables collected in the step 1, environmental parameter forecast values given by local meteorological departments day ahead, and the proportion coefficients of wind speed, humidity and temperature influence factors as input signals into an input layer of a cloud computing cloud neural network, and performing iterative computation on the input signals by using the neural network based on a cloud to obtain an error coefficient of wind power prediction at the current T moment so as to calculate real-time wind power prediction power;
step 2.1: as shown in fig. 3, v is 5, h is 28%, t is 25, v*＝6，h*＝30％，t*＝22，βv＝0.63、βh＝0.18、βtSubstituting 0.19 into the following equation, cloud was usedCalculating an error coefficient gamma of the wind power prediction at the current T moment in real time:
Figure BDA0002349685270000051
wherein, βv、βh、βtThe ratio coefficient v of the wind speed, humidity and temperature influencing factors*、h*、t*Respectively obtaining the predicted values of the wind speed, the air humidity and the environmental temperature given by the local meteorological department in the day-ahead, and solving the gamma to be 0.132;
in the embodiment, a cloud computing model shown in fig. 4 is adopted, raw data required by various computations enters a neural network input layer based on cloud computing through an input layer of the cloud computing model, iterative computation is performed on the input data through iteration in a hidden layer to obtain a required computation result, and output feedback is performed on an output layer.
Step 2.2: calculating wind power prediction power P at the current T moment according to the error coefficient gammaa＝22.568MW：
Pa＝(1-γ)Pt
And step 3: predicting the load of the power grid according to the annual single-day maximum load of the power grid, environmental parameters and weather conditions;
step 3.1: obtaining a temperature influence coefficient psi according to the environment temperature T at the current T moment:
Figure BDA0002349685270000052
step 3.2: obtaining a weather influence coefficient omega according to weather conditions:
Figure BDA0002349685270000053
this embodiment can obtain: ψ is 0.6 and ω is 1.
Step 3.3: calculating the current T moment according to the annual single-day maximum load of the power grid, the ambient temperature T, the temperature influence coefficient psi obtained in the step 3.1 and the weather influence coefficient omega obtained in the step 3.2Load of the electric network
Figure BDA0002349685270000054
Figure BDA0002349685270000055
Wherein the content of the first and second substances,
Figure BDA0002349685270000056
for the annual single-day maximum load of the power grid, U is a coefficient matrix, and the calculation process is as follows:
Figure BDA0002349685270000061
wherein, A is a prediction constant matrix, and B is a matrix of influence of weather and temperature on load;
U＝A*B
the load of the power grid at the time T is calculated and obtained in the embodiment
Figure BDA0002349685270000062
And 4, step 4: according to the wind power generation capacity of the power grid wind field obtained in real time in the step 2 and the predicted power utilization load value obtained in real time in the step 3, a daily power generation plan of a conventional generator set is integrated, and the air abandoning amount at the current T moment is calculated as follows:
Figure BDA0002349685270000063
wherein, PrThe daily generation planned power of the conventional generator set of the power grid is represented, delta is a grid loss coefficient, and the air volume of the power grid at the time T is abandoned
Figure BDA0002349685270000064
And 5: and calculating the real-time wind abandoning rate during wind power consumption by combining the charge and discharge power of the battery energy storage system at the current T moment, as follows:
Figure BDA0002349685270000065
wherein the content of the first and second substances,
Figure BDA0002349685270000066
for the charging power of the battery energy storage system at the moment T,
Figure BDA0002349685270000067
the discharge power of the battery energy storage system at the moment T.
This example, in conjunction with Table 1, will
Figure BDA0002349685270000068
PaSubstituting 22.568MW into the above equation to calculate the air abandon rate ε at time T12.2%. The monitored wind curtailment and absorption capacity of the distributed energy storage is zeta 1-epsilon1＝97.8％。

Claims (6)

1. A method for monitoring wind power consumption capability of a distributed energy storage system based on cloud computing is characterized by comprising the following steps:
step 1: respectively collecting environmental parameters of a wind power plant in the power grid at the current T moment, including wind speed v, air humidity h, environmental temperature T and a power grid loss coefficient delta;
step 2: inputting the environmental parameter variables collected in the step 1, environmental parameter forecast values given by local meteorological departments day ahead, and the proportion coefficients of wind speed, humidity and temperature influence factors as input signals into a cloud neural network input layer of cloud computing to perform iterative computation on the input signals to obtain error coefficients of wind power prediction at the current T moment, so as to calculate real-time wind power prediction power;
and step 3: predicting the load of the power grid according to the annual single-day maximum load of the power grid, environmental parameters and weather conditions;
and 4, step 4: according to the wind power generation amount of the power grid wind field obtained in real time in the step 2 and the predicted power utilization load amount value obtained in real time in the step 3, a daily power generation plan of a conventional generator set is integrated, and the air abandoning amount at the current T moment is calculated;
and 5: and calculating the wind power absorption capacity according to the real-time wind abandoning rate by combining the charging and discharging power of the battery energy storage system at the current T moment.
2. The method for monitoring wind power consumption capability of the distributed energy storage system based on cloud computing according to claim 1, wherein the process of the step 2 is as follows:
step 2.1: comparing the environmental parameter variables acquired in the step 1 with predicted values given by local meteorological departments in the day ahead, and obtaining an error coefficient gamma of wind power prediction at the current T moment in real time based on cloud computing by combining the percentage coefficients of wind speed, humidity and temperature influence factors:
Figure FDA0002349685260000011
wherein, βv、βh、βtThe ratio coefficient v of the wind speed, humidity and temperature influencing factors*、h*、t*Respectively providing predicted values of wind speed, air humidity and environment temperature for local meteorological departments in the day ahead;
step 2.2: calculating wind power prediction power P at the current T moment according to the error coefficient gammaa：
Pa＝(1-γ)Pt
Wherein, PtAnd obtaining the original wind power predicted power of the current day for the wind power plant according to the meteorological data given by the meteorological department in the current day.
3. The method for monitoring wind power consumption capability of the distributed energy storage system based on cloud computing according to claim 1, wherein the process of the step 3 is as follows:
step 3.1: obtaining a temperature influence coefficient psi according to the environment temperature T at the current T moment:
Figure FDA0002349685260000021
step 3.2: obtaining a weather influence coefficient omega according to weather conditions:
Figure FDA0002349685260000022
step 3.3: calculating the load of the power grid at the current T moment according to the annual single-day maximum load of the power grid, the ambient temperature T, the temperature influence coefficient psi obtained in the step 3.1 and the weather influence coefficient omega obtained in the step 3.2
Figure FDA0002349685260000023
Figure FDA0002349685260000024
Wherein the content of the first and second substances,
Figure FDA0002349685260000025
the maximum load of the power grid on a single day in a year is U, and the U is a coefficient matrix.
4. The method for monitoring wind power consumption capability of the distributed energy storage system based on cloud computing according to claim 3, wherein the coefficient matrix U in step 3.3 is calculated as follows:
Figure FDA0002349685260000026
wherein, A is a prediction constant matrix, and B is a matrix of influence of weather and temperature on load;
U＝A*B。
5. the method for monitoring wind power consumption capability of the distributed energy storage system based on cloud computing according to claim 1, wherein the formula for calculating the wind curtailment amount at the current moment in the step 4 is as follows:
Figure FDA0002349685260000027
wherein, PrThe daily generation planned power of the conventional generator set of the power grid is represented, and delta is a grid loss coefficient.
6. The method for monitoring wind power consumption capability of the distributed energy storage system based on cloud computing according to claim 1, wherein the process of the step 5 is as follows:
step 5.1: by calculating the abandoned wind rate during wind power consumption in real time at the cloud, the formula is as follows:
Figure FDA0002349685260000031
wherein the content of the first and second substances,
Figure FDA0002349685260000032
for the charging power of the battery energy storage system at the moment T,
Figure FDA0002349685260000033
the discharge power of the battery energy storage system at the T moment;
step 5.2: calculating a real-time monitoring value zeta of the wind power consumption capacity of the distributed energy storage system:
ζ＝1-ε1。
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