A dual-control wind power energy storage method and system

Abstract

The present invention discloses a dual-control wind power energy storage method and system, which relates to the field of wind farm control prediction, including S1, obtaining wind farm operation logs, analyzing the wind farm operation logs to obtain past operation scenarios and operation data of corresponding scenarios; S2, obtaining wind farm deployment status, and building a wind power energy storage virtual framework in combination with the operation data of past operation scenarios and corresponding scenarios; S3, counting past power plant equipment losses, marking past power plant equipment losses into the wind power energy storage virtual framework to obtain a wind power energy storage virtual operation model; S4, predicting future operation conditions based on the wind power energy storage virtual operation model to obtain a dual-control wind power energy storage strategy. The present invention adopts a dual-control wind power energy storage method to monitor and predict wind farm operation conditions, simulate equipment losses, and then regulate the power plant to effectively improve power plant efficiency and maintain power plant safety.

Description

Technical Field

[0001] The present invention relates to the field of wind farm control prediction, and in particular to a dual-control wind power energy storage method and system. Background Art

[0002] Wind power generation uses wind to drive rotors, converting the wind's kinetic energy into mechanical energy, which is then converted into electrical energy by a generator. The problem with wind power is its instability, as wind power varies from time to time, requiring energy storage to balance it.

[0003] Traditional wind power energy storage systems, such as lithium batteries or supercapacitors, are deployed to smooth out wind power fluctuations. Their primary function is to release or store energy during unstable wind conditions, maintaining stable output. Traditional systems focus on a single function, such as power smoothing or frequency regulation. Control strategies may be relatively simple, such as charging and discharging based on state of charge (SOC), without requiring complex coordination.

[0004] Dual-control systems offer both power control and energy management, handling real-time power fluctuations while optimizing the use of stored energy. However, this dual-control process offers limited consideration of equipment wear and tear, preventing the ability to predict and replace equipment, potentially leading to greater economic losses.

[0005] In order to solve these problems, a dual-control wind power energy storage method and system that takes equipment loss into consideration is urgently needed. Summary of the Invention

[0006] To solve the above problems, the present application proposes a dual-control wind power energy storage method and system.

[0007] A dual-control wind power energy storage method comprises the following steps:

[0008] S1. Obtain wind farm operation logs and analyze them to obtain past operation scenarios and operation data of corresponding scenarios;

[0009] S2. Obtain the deployment status of the wind farm and build a wind power energy storage virtual framework by combining the operation data of past operation scenarios and their corresponding scenarios;

[0010] S3. Counting past power plant equipment losses, marking the past power plant equipment losses into a wind power energy storage virtual framework to obtain a wind power energy storage virtual operation model;

[0011] S4. Based on the wind power energy storage virtual operation model, the future operation conditions are predicted to obtain a dual-control wind power energy storage strategy.

[0012] Preferably, the specific contents of the past operation scenarios and the operation data of the corresponding scenarios obtained by analyzing the wind farm operation log in S1 are:

[0013] Analyze the wind farm operation log to obtain past weather conditions, past urban demand conditions, and the correlation between the two;

[0014] Conduct feature analysis on past weather conditions and past urban demand conditions to obtain weather characteristics and urban demand characteristics;

[0015] According to the correlation between past weather conditions and past urban demand conditions, weather characteristics and urban demand characteristics are correlated to obtain correlation groups;

[0016] The wind farm operation logs are divided according to the associated groups to obtain past operation scenarios, and the operation data of the corresponding scenarios are extracted to form scenario data groups.

[0017] Preferably, the specific contents of obtaining the deployment status of the wind farm in S2 and building the wind power energy storage virtual framework in combination with the operation data of the past operation scenarios and their corresponding scenarios are as follows:

[0018] Obtain the deployment status of the wind farm, and obtain the wind farm geographical information, wind turbine side area information, energy storage side area information and equipment information;

[0019] Construct an initial plane block based on the wind farm geographic information, and divide the initial plane block into wind turbine blocks and energy storage blocks based on the wind turbine side area information and energy storage side area information;

[0020] Extracting device information to obtain device location information and device structure information;

[0021] Generate a device virtual model based on the device structure information, and build the device virtual model on the initial plane block based on the device location information to form a wind power energy storage virtual block;

[0022] The operation data of past operation scenarios and their corresponding scenarios are simulated on the wind power energy storage virtual block to obtain the wind power energy storage virtual framework.

[0023] Preferably, S3, statistics are collected on past power plant equipment losses, and the past power plant equipment losses are marked into the wind power energy storage virtual framework to obtain the specific content of the wind power energy storage virtual operation model:

[0024] Statistics on past power plant equipment losses, and analysis of the operation and maintenance of different equipment to obtain periodic change data for different equipment;

[0025] The periodic change data of different devices are mapped with the scene data groups in the past operation scenes in the wind power energy storage virtual framework to obtain the mapping relationship;

[0026] The periodic change data and scenario data groups of different devices are labeled into the wind power energy storage virtual framework and trained with the mapping relationship to obtain the energy storage SOC safety constraints, converter constraints, wind turbine constraints and equipment loss values ​​under different scenarios.

[0027] Preferably, the specific content of the dual-control wind power energy storage strategy obtained by predicting the future operation status according to the wind power energy storage virtual operation model in S4 is:

[0028] Obtain the city demand and weather conditions for a period of time in the future and extract the city demand characteristics and weather characteristics;

[0029] Based on the city's demand characteristics and weather characteristics, the corresponding association groups are obtained. Based on the association groups, the past operating scenarios and their corresponding energy storage SOC safety constraints, converter constraints, wind turbine constraints, and equipment loss values ​​are defined as target constraints.

[0030] Obtain current operating information and its corresponding constraints, as well as current equipment loss values, and determine whether to optimize scheduling based on target constraints. If not, simulate wind farm operating data in real time and perform equipment monitoring.

[0031] If optimal scheduling is performed, the target constraint is modified by combining the equipment loss value in the target constraint and the current equipment loss value to obtain a dual-control wind power energy storage strategy.

[0032] Preferably, the target constraint is modified by combining the equipment loss value in the target constraint and the current equipment loss value to obtain the specific content of the dual-control wind power energy storage strategy:

[0033] Define the equipment loss value in the target constraint as the target loss value;

[0034] Obtain the current equipment loss value and target loss value. If the current equipment loss value is greater than the target loss value, the current operating status is not suitable for optimized scheduling. Perform equipment inspection and make manual adjustments.

[0035] If the current equipment loss value is less than the target loss value, it is defined as an abnormal device and the cumulative loss value variable is calculated. The conversion factor is configured for the cumulative loss value variable according to the equipment impact weight conversion coefficient to obtain the control function;

[0036] The target constraints are adjusted according to the control function to obtain a dual-control wind power energy storage strategy.

[0037] Preferably, the expression of the objective constraint is:

[0038] ;

[0039] in,

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] in, is the lower limit of the state of charge, is the upper limit of the state of charge, is the state of charge at the current time, is the corrected upper limit of discharge power, is the corrected upper limit of charging power, is the output electrical power, is the lower limit of the fan speed, is the upper limit of the fan speed, is the current fan speed, For charging efficiency, is the charging power, is the discharge power, is the discharge efficiency, is the corrected battery capacity, is the initial battery capacity, is the initial discharge power, is the initial fan speed, Y is the control function, is the fan control function, is the converter control function, It is the energy storage SOC safety control function;

[0045] Among them, energy storage Security control function : ;

[0046] Converter control function : ;

[0047] Fan control function : ;

[0048] Where i is the i-th abnormal device, is the loss value variable of the i-th abnormal device, is the impact weight conversion coefficient of the i-th abnormal device.

[0049] Preferably, the equipment loss value is assessed based on equipment loss conditions.

[0050] A dual-control wind power energy storage system, comprising:

[0051] Data collection unit: obtains wind farm operation logs, analyzes the wind farm operation logs to obtain past operation scenarios and their corresponding operation data;

[0052] Framework construction unit: Obtains the deployment status of wind farms and builds a wind power energy storage virtual framework based on the operation data of past operation scenarios and their corresponding scenarios;

[0053] Model generation unit: Statistics on past power plant equipment losses, marking the past power plant equipment losses into the wind power storage virtual framework to obtain a wind power storage virtual operation model;

[0054] Strategy generation unit: predicts future operation conditions based on the wind power energy storage virtual operation model to obtain a dual-control wind power energy storage strategy.

[0055] In summary, compared with traditional technologies, the dual-control wind power energy storage method and system of the present invention adopts a dual-control wind power energy storage method to monitor and predict the operation of wind farms, simulate equipment losses, and then regulate the power plant to effectively improve power plant efficiency and maintain power plant safety.

[0056] The technical method of the present invention is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a step diagram of a dual-control wind power energy storage method of the present invention;

[0058] Figure 2 This is a module diagram of a dual-control wind power energy storage system of the present invention. DETAILED DESCRIPTION

[0059] The technical method of the present invention is further described below through the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and values ​​described in these embodiments do not limit the scope of this application.

[0060] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0061] Technologies, systems, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0062] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0063] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0064] like Figure 1 As shown, the present invention provides a dual-control wind power energy storage method, S1, obtaining the wind farm operation log, analyzing the wind farm operation log to obtain past operation scenarios and operation data of corresponding scenarios.

[0065] Furthermore, in S1, the wind farm operation log is analyzed to obtain the specific contents of the past operation scenarios and the operation data of the corresponding scenarios:

[0066] The wind farm operation logs are analyzed to obtain past weather conditions, past urban demand conditions, and the relationship between the two.

[0067] The weather characteristics and urban demand characteristics are obtained by analyzing the characteristics of past weather conditions and past urban demand conditions.

[0068] According to the correlation between past weather conditions and past urban demand conditions, weather characteristics and urban demand characteristics are correlated to obtain correlation groups.

[0069] The wind farm operation logs are divided according to the associated groups to obtain past operation scenarios, and the operation data of the corresponding scenarios are extracted to form scenario data groups.

[0070] S2. Obtain the deployment status of the wind farm and build a wind power energy storage virtual framework by combining the operation data of past operation scenarios and their corresponding scenarios.

[0071] Furthermore, S2 obtains the deployment status of the wind farm and combines the operation data of past operation scenarios and corresponding scenarios to build a wind power energy storage virtual framework. The specific content is:

[0072] Obtain the deployment status of the wind farm, and obtain the wind farm geographical information, wind turbine side area information, energy storage side area information and equipment information.

[0073] An initial plane block is constructed based on the geographical information of the wind farm, and is divided into a wind turbine block and an energy storage block based on the regional information of the wind turbine side and the energy storage side.

[0074] The device information is extracted to obtain the device location information and device structure information.

[0075] A device virtual model is generated according to the device structure information, and the device virtual model is established on the initial plane block according to the device location information to form a wind power energy storage virtual block.

[0076] The operation data of past operation scenarios and their corresponding scenarios are simulated on the wind power energy storage virtual block to obtain the wind power energy storage virtual framework.

[0077] S3. Count the past power plant equipment losses, and mark the past power plant equipment losses into the wind power energy storage virtual framework to obtain a wind power energy storage virtual operation model.

[0078] Furthermore, in S3, the past power plant equipment losses are counted and marked into the wind power energy storage virtual framework to obtain the specific content of the wind power energy storage virtual operation model:

[0079] Statistics on past power plant equipment losses were collected, and the operation and maintenance of different equipment were analyzed to obtain periodic change data of different equipment.

[0080] The periodic change data of different devices are mapped with the scene data groups in the past operation scenes in the wind power energy storage virtual framework to obtain a mapping relationship.

[0081] The periodic change data and scenario data groups of different devices are labeled into the wind power energy storage virtual framework and trained with the mapping relationship to obtain the energy storage SOC safety constraints, converter constraints, wind turbine constraints and equipment loss values ​​under different scenarios.

[0082] S4. Based on the wind power energy storage virtual operation model, the future operation conditions are predicted to obtain a dual-control wind power energy storage strategy.

[0083] Furthermore, in S4, the specific content of the dual-control wind power energy storage strategy is obtained by predicting the future operation status based on the wind power energy storage virtual operation model:

[0084] Obtain the city demand situation and weather conditions for a period of time in the future and extract the city demand characteristics and weather characteristics.

[0085] The corresponding association groups are obtained according to the city demand characteristics and weather characteristics. The past operation scenarios and their corresponding energy storage SOC safety constraints, converter constraints, wind turbine constraints and equipment loss values ​​are defined as target constraints based on the association groups.

[0086] Obtain the current operating information and its corresponding constraints as well as the current equipment loss value, and determine whether to perform optimal scheduling based on the target constraints. If optimal scheduling is not performed, simulate the wind farm operating data in real time and perform equipment monitoring.

[0087] If optimal scheduling is performed, the target constraint is modified by combining the equipment loss value in the target constraint and the current equipment loss value to obtain a dual-control wind power energy storage strategy.

[0088] Furthermore, by combining the equipment loss value in the target constraint with the current equipment loss value, the target constraint is modified to obtain the specific content of the dual-control wind power energy storage strategy:

[0089] Define the equipment loss value in the target constraint as the target loss value.

[0090] Obtain the current equipment loss value and target loss value. If the current equipment loss value is greater than the target loss value, the current operating status is not suitable for optimized scheduling. Perform equipment inspection and make manual adjustments.

[0091] If the current equipment loss value is less than the target loss value, it is defined as an abnormal device and the cumulative loss value variable is calculated. The conversion factor is configured for the cumulative loss value variable according to the equipment impact weight conversion coefficient to obtain the control function;

[0092] The target constraints are adjusted according to the control function to obtain a dual-control wind power energy storage strategy.

[0093] Furthermore, the expression of the objective constraint is:

[0094] .

[0095] in, It can be seen that in the process of adjusting the target constraint according to the control function, the energy storage The safety constraint is to adjust the upper limit of the state of charge. A further step is to adjust the calibrated battery capacity. The lower limit of the state of charge can be set to a fixed value.

[0096] .

[0097] .

[0098] .

[0099] It can be seen that in the process of adjusting the target constraint according to the control function, the fan constraint is to adjust the upper limit of the fan speed, and a fixed value can be set.

[0100] in, is the lower limit of the state of charge, is the upper limit of the state of charge, is the state of charge at the current time, is the corrected upper limit of discharge power, is the corrected upper limit of charging power, is the output electrical power, is the lower limit of the fan speed, is the upper limit of the fan speed, is the current fan speed, For charging efficiency, is the charging power, is the discharge power, is the discharge efficiency, is the corrected battery capacity, is the initial battery capacity, is the initial discharge power, is the initial fan speed, Y is the control function, is the fan control function, is the converter control function, It is the energy storage SOC safety control function;

[0101] Among them, energy storage Security control function : .

[0102] Converter control function : .

[0103] Fan control function : .

[0104] Where i is the i-th abnormal device, is the loss value variable of the i-th abnormal device, is the impact weight conversion coefficient of the i-th abnormal device.

[0105] Furthermore, the equipment loss value is evaluated according to the equipment loss situation.

[0106] like Figure 2 As shown, a dual-control wind power energy storage system includes:

[0107] Data collection unit: obtains wind farm operation logs, analyzes the wind farm operation logs to obtain past operation scenarios and their corresponding operation data.

[0108] Framework construction unit: Obtain the deployment status of the wind farm and build a wind power energy storage virtual framework based on the operation data of past operation scenarios and their corresponding scenarios.

[0109] Model generation unit: statistics on past power plant equipment losses, marks the past power plant equipment losses into the wind power energy storage virtual framework to obtain a wind power energy storage virtual operation model.

[0110] Strategy generation unit: predicts future operation conditions based on the wind power energy storage virtual operation model to obtain a dual-control wind power energy storage strategy.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical method of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical method to deviate from the spirit and scope of the technical method of the present invention.

Claims

1. A dual-control wind power energy storage method, characterized in that: The following steps are involved: S1. Obtain wind farm operation logs and analyze them to obtain past operation scenarios and operation data of corresponding scenarios; S2. Obtain the deployment status of the wind farm and build a wind power energy storage virtual framework by combining the operation data of past operation scenarios and their corresponding scenarios; In step S2, the specific contents are: Obtain the deployment status of the wind farm, and obtain the wind farm geographical information, wind turbine side area information, energy storage side area information and equipment information; Construct an initial plane block based on the wind farm geographic information, and divide the initial plane block into wind turbine blocks and energy storage blocks based on the wind turbine side area information and energy storage side area information; Extracting device information to obtain device location information and device structure information; Generate a device virtual model based on the device structure information, and build the device virtual model on the initial plane block based on the device location information to form a wind power energy storage virtual block; Simulate the operating data of past operating scenarios and their corresponding scenarios on the wind power energy storage virtual block to obtain a wind power energy storage virtual framework; S3. Counting past power plant equipment losses, marking the past power plant equipment losses into a wind power energy storage virtual framework to obtain a wind power energy storage virtual operation model; The specific contents in step S3 are: Statistics on past power plant equipment losses, and analysis of the operation and maintenance of different equipment to obtain periodic change data for different equipment; The periodic change data of different devices are mapped with the scene data groups in the past operation scenes in the wind power energy storage virtual framework to obtain the mapping relationship; The periodic change data and scenario data groups of different devices are labeled into the wind power energy storage virtual framework and trained with the mapping relationship to obtain the energy storage SOC safety constraints, converter constraints, wind turbine constraints and equipment loss values ​​under different scenarios; S4. Based on the wind power energy storage virtual operation model, the future operation conditions are predicted to obtain a dual-control wind power energy storage strategy.

2. A dual-control wind power energy storage method according to claim 1, characterized in that: In S1, the specific contents of the past operation scenarios and the operation data of the corresponding scenarios obtained by analyzing the wind farm operation log are as follows: Analyze the wind farm operation log to obtain past weather conditions, past urban demand conditions, and the correlation between the two; Conduct feature analysis on past weather conditions and past urban demand conditions to obtain weather characteristics and urban demand characteristics; According to the correlation between past weather conditions and past urban demand conditions, weather characteristics and urban demand characteristics are correlated to obtain correlation groups; The wind farm operation logs are divided according to the associated groups to obtain past operation scenarios, and the operation data of the corresponding scenarios are extracted to form scenario data groups.

3. A dual-control wind power energy storage method according to claim 1, characterized in that: In S4, the specific content of the dual-control wind power energy storage strategy is obtained by predicting the future operation status based on the wind power energy storage virtual operation model: Obtain the city demand and weather conditions for a period of time in the future and extract the city demand characteristics and weather characteristics; Based on the city's demand characteristics and weather characteristics, the corresponding association groups are obtained. Based on the association groups, the past operating scenarios and their corresponding energy storage SOC safety constraints, converter constraints, wind turbine constraints, and equipment loss values ​​are defined as target constraints. Obtain current operating information and its corresponding constraints, as well as current equipment loss values, and determine whether to optimize scheduling based on target constraints. If not, simulate wind farm operating data in real time and perform equipment monitoring. If optimal scheduling is performed, the target constraint is modified by combining the equipment loss value in the target constraint and the current equipment loss value to obtain a dual-control wind power energy storage strategy.

4. A dual-control wind power energy storage method according to claim 3, characterized in that: The specific content of the dual-control wind power energy storage strategy is obtained by correcting the target constraint by combining the equipment loss value in the target constraint and the current equipment loss value: Define the equipment loss value in the target constraint as the target loss value; Obtain the current equipment loss value and target loss value. If the current equipment loss value is greater than the target loss value, the current operating status is not suitable for optimized scheduling. Perform equipment inspection and make manual adjustments. If the current equipment loss value is less than the target loss value, it is defined as an abnormal device and the cumulative loss value variable is calculated. The conversion factor is configured for the cumulative loss value variable according to the equipment impact weight conversion coefficient to obtain the control function; The target constraints are adjusted according to the control function to obtain a dual-control wind power energy storage strategy.

5. A dual-control wind power energy storage method according to claim 3, characterized in that: The expression of the goal constraint is: ； in, ； ； ； ； in, is the lower limit of the state of charge, is the upper limit of the state of charge, is the state of charge at the current time, is the corrected upper limit of discharge power, is the corrected upper limit of charging power, is the output electrical power, is the lower limit of the fan speed, is the upper limit of the fan speed, is the current fan speed, For charging efficiency, is the charging power, is the discharge power, is the discharge efficiency, is the corrected battery capacity, is the initial battery capacity, is the initial discharge power, is the initial fan speed, Y is the control function, for Control function, for Control function, for Control function; in, Control function : ; Control function : ; Control function : ; Where i is the i-th abnormal device, is the loss value variable of the i-th abnormal device, is the impact weight conversion coefficient of the i-th abnormal device.

6. A dual-control wind power energy storage system, characterized in that: include: Data collection unit: obtains wind farm operation logs, analyzes the wind farm operation logs to obtain past operation scenarios and their corresponding operation data; Framework construction unit: Obtains the deployment status of wind farms and builds a wind power energy storage virtual framework based on the operation data of past operation scenarios and their corresponding scenarios; Obtain the deployment status of the wind farm, and obtain the wind farm geographical information, wind turbine side area information, energy storage side area information and equipment information; Construct an initial plane block based on the wind farm geographic information, and divide the initial plane block into wind turbine blocks and energy storage blocks based on the wind turbine side area information and energy storage side area information; Extracting device information to obtain device location information and device structure information; Generate a device virtual model based on the device structure information, and build the device virtual model on the initial plane block based on the device location information to form a wind power energy storage virtual block; Simulate the operating data of past operating scenarios and their corresponding scenarios on the wind power energy storage virtual block to obtain a wind power energy storage virtual framework; Model generation unit: statistics on past power plant equipment losses and analysis of periodic change data of different equipment; Mapping equipment periodic change data with scenario data sets in the wind power energy storage virtual framework to obtain a mapping relationship. Training is performed based on this mapping relationship to obtain energy storage SOC safety constraints, converter constraints, wind turbine constraints, and equipment loss values ​​under different scenarios, forming a wind power energy storage virtual operation model. Strategy generation unit: predicts future operation conditions based on the wind power energy storage virtual operation model to obtain a dual-control wind power energy storage strategy.

Images