A method and system for flexible coordination optimization of wind, photovoltaic and energy storage

Abstract

The present invention relates to the field of energy management and control technology, and in particular to a method and system for flexible coordinated optimization of wind, solar and energy storage, including improving the stability of energy supply by predicting the operating status of wind and solar power generation equipment; improving the utilization rate of energy storage equipment by analyzing the charging and discharging requirements of energy storage equipment and formulating energy storage charging and discharging strategies, and effectively balancing the supply and demand of the power grid; optimizing the charging and discharging power of energy storage equipment and reducing energy consumption costs by finely controlling the charging and discharging process of energy storage equipment, dynamically adjusting the energy storage charging and discharging strategies, and evaluating the performance of energy storage equipment to create a fault judgment mechanism, timely discover potential faults, and avoid fault expansion. Accurate maintenance strategies can be formulated according to fault levels and types to improve maintenance efficiency. The present invention is used to solve the technical problems of insufficient energy management and dispatch optimization of smart grid management in existing solutions.

Description

Technical Field

[0001] The present invention relates to the field of energy management and control technology, and in particular to a method and system for flexible coordination optimization of wind, solar and electricity storage. Background Art

[0002] Energy management and control technology refers to a technology that achieves energy conservation and emission reduction and improves energy efficiency through monitoring, control and management of the energy use process. It covers a series of management activities from planning, coordination and control of energy production, distribution, conversion and other links. It is of great significance to promote energy sustainable development and meet energy challenges.

[0003] However, in the field of energy management and control, especially in smart grid management, there are still multiple challenges, as follows: energy is unstable. Since renewable energy power generation equipment such as wind power and solar energy are greatly affected by natural conditions, energy supply is unstable; the system is difficult to connect to the grid. When renewable energy power generation equipment is connected to the grid, it may be difficult to connect to the grid due to changes in grid demand and the instability of power generation equipment output; insufficient energy coordination management and energy storage optimization scheduling. The coordination management and energy storage optimization scheduling between renewable energy power generation equipment, energy storage equipment and the grid are insufficient, resulting in low energy utilization efficiency and inability to fully utilize the potential of energy storage equipment to balance grid supply and demand; fault discovery and processing are not timely. The equipment faults managed by the current smart grid may be difficult to discover and process in time, affecting the continuity and safety of power supply. To this end, the present invention proposes a wind, photovoltaic and energy storage flexible coordination optimization method and system. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose a wind, photovoltaic and energy storage flexible coordination optimization method and system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A wind, photovoltaic and energy storage flexible coordination optimization method, comprising:

[0007] Step 1: Use the prediction algorithm to predict the operating status of the wind and solar power generation equipment, and compare the actual output of the wind and solar power generation equipment monitored in real time with the prediction result;

[0008] Step 2: Analyze the charging and discharging requirements of energy storage equipment based on the predicted operating status of wind and solar power generation equipment, and formulate energy storage charging and discharging strategies;

[0009] Step 3: Finely control the charging and discharging process of the energy storage equipment and dynamically adjust the energy storage charging and discharging strategy;

[0010] Step 4: Evaluate the performance of energy storage equipment, create a fault determination mechanism, and take measures to respond and handle it.

[0011] It should be noted that the application object of a wind, photovoltaic and energy storage flexible coordination optimization method and system in the embodiment of the present invention can be smart grid management in the field of energy management and control. Specifically, it can be to conduct in-depth analysis of meteorological data and operating status data of wind and photovoltaic power generation equipment through high-precision sensors, Internet of Things technology and prediction algorithms, monitor the actual output of wind and photovoltaic power generation equipment in real time and compare it with the prediction results, and promptly discover and warn of potential energy supply shortages or surpluses; according to the predicted operating status data of wind and photovoltaic power generation equipment, combined with the power grid demand, dynamically analyze the charging and discharging needs of energy storage equipment, and formulate refined energy storage charging and discharging strategies; through refined control of the charging and discharging process of energy storage equipment, dynamically adjust the energy storage charging and discharging strategy to achieve efficient operation of the energy storage system; at the same time, evaluate the performance of energy storage equipment, create a fault judgment mechanism, and take timely measures to deal with and handle low-performance or faulty energy storage equipment, thereby improving the stability and reliability of the entire wind and photovoltaic energy storage system, which is conducive to optimizing resource allocation and reducing the occurrence of faults, and further improving the intelligence level and operational efficiency of smart grid management.

[0012] Furthermore, the process of using the prediction algorithm to predict the operating status of the wind and solar power generation equipment and comparing the actual output of the wind and solar power generation equipment monitored in real time with the prediction result includes:

[0013] High-precision sensors and Internet of Things technologies are used to collect meteorological data and operating status data of wind and solar power generation equipment; among which, meteorological data includes wind speed, wind direction, light intensity and temperature, and operating status data of wind and solar power generation equipment includes the power generation of wind power generation equipment and the power generation of solar power generation equipment; it can be understood that a high-precision sensor is a device that can measure and convert physical quantities (such as temperature, pressure, light, wind speed, etc.) into processable and transmittable signals; in meteorological data collection and wind and solar power generation equipment monitoring, the following types of high-precision sensors are used: wind speed and direction sensors, which contain one or more rotating blades or ultrasonic devices to measure wind speed and direction; light intensity sensors, which are used to measure light intensity and light level; temperature sensors, which use components such as thermistors, thermocouples or integrated temperature sensors to measure the temperature of the environment; Internet of Things refers to the technology of connecting various physical devices (such as sensors, actuators, smart devices, etc.) through the network to realize data exchange and intelligent control between devices, such as transmitting the data collected by high-precision sensors to the data collection center or cloud platform by wired or wireless means;

[0014] The collected meteorological data and the operating status data of wind and solar power generation equipment are stored in the form of time series, and defined They are meteorological data and operating status data of wind and solar power generation equipment respectively; among them, Indicates a timestamp;

[0015] Extract all historical meteorological data and wind and solar power equipment operating status data stored in the database, and integrate the historical data sets of meteorological data and wind and solar power equipment operating status data ;in, Indicates the amount of historical data, that is, the total amount of historical data collected so far, the historical data set It grows over time, and new data collected is added to the historical data set synchronously middle;

[0016] Building a prediction function , so that ;in, Indicates based on Weather data at the moment and historical datasets Predicted The operating status data of wind and solar power generation equipment at all times;

[0017] Obtain real-time monitoring of wind and solar power equipment operating status data , used for comparison with predicted data;

[0018] Setting alarm thresholds ,when When an alarm occurs, an alarm is generated.

[0019] Furthermore, based on the prediction results of the operating status of the wind and solar power generation equipment, the process of analyzing the charging and discharging requirements of the energy storage equipment includes:

[0020] Get the Wind and solar power generation equipment Predicted operating status data at the moment ;in, is the time step of the prediction, indicating the difference between the predicted future moment and the current moment The interval between

[0021] Will The grid demand and grid interaction power at the moment are marked as and ;in, Indicates the index of wind and solar power generation equipment, grid interaction power Represents the actual power exchange between the energy storage device and the grid;

[0022] Combined with the predicted operating status data of wind and solar power generation equipment , Grid Demand And grid interaction power Calculate the charging and discharging requirements of energy storage devices ; Among them, the specific analysis formula is as follows:

[0023] , where is the preset proportional coefficient used to adjust the the extent of the impact; Used to traverse all wind and solar power generation equipment, and ; Indicates the total number of wind and solar power generation equipment; Indicates The weight of each wind and solar power generation device is used to reflect its importance in the charging and discharging decision of the energy storage device. These weights may need to be determined based on factors such as the capacity and reliability of the wind and solar power generation devices; Indicates the grid interaction power Moment and The change between moments is used to reflect the dynamic changes in grid demand.

[0024] Furthermore, the process of formulating energy storage charging and discharging strategies includes:

[0025] definition The state vector of the energy storage device at the moment is ;in, Indicates the state of charge, Respectively Charging and discharging power at each moment;

[0026] Limit the state of charge, charging power, and discharging power so that they vary within the permitted range, including:

[0027] , where Represent the minimum and maximum state of charge, respectively. Represent the maximum value of charging and discharging power respectively;

[0028] According to the charging and discharging requirements of energy storage equipment and the energy storage device state vector Formulate energy storage charging and discharging strategies , so that , used to output the state vector of the energy storage device at the next moment;

[0029] It is understandable that the energy storage charging and discharging strategy is a mapping function that converts each moment The energy storage device charging and discharging demand and energy storage device state are mapped to the charging and discharging power of the energy storage device, and the energy storage device state vector is updated by introducing the mapping function.

[0030] Furthermore, the process of finely controlling the charging and discharging process of the energy storage device and dynamically adjusting the energy storage charging and discharging strategy includes:

[0031] Use the objective function to minimize the energy cost required for energy storage:

[0032] , where Represents the total energy cost required for energy storage; express Charging power at all times The corresponding unit cost of charging; express Discharge power at all times The corresponding unit cost of discharge, where the unit cost of discharge is a loss coefficient, indicating the energy loss during the discharge process; represents the total time; it is understandable that due to the energy storage process The system is already in a certain state (e.g., a certain state of charge), so the energy cost over the entire time period starting from this initial state is considered;

[0033] At every moment , analyze the current energy storage charging and discharging strategy based on the real-time monitored operating status data of wind and solar power generation equipment and the current grid demand The charging and discharging power and objective function under the above conditions are used to adjust the energy storage charging and discharging strategy through iterative updating. , to generate new charging power and discharge power , thereby achieving refined control of the charging and discharging process of the energy storage device.

[0034] Furthermore, the process of evaluating the performance of energy storage equipment includes:

[0035] Use energy storage monitoring software to obtain performance data of energy storage equipment; the performance data specifically includes: basic electrical data (such as battery voltage, current, etc., used to reflect the basic electrical characteristics of energy storage equipment), energy and capacity parameters (such as battery capacity, remaining capacity, available capacity, etc., used to reflect the ability of energy storage equipment to store energy) and efficiency parameters (such as charging and discharging efficiency, cycle efficiency, etc., used to reflect the energy conversion efficiency of energy storage equipment during charging and discharging);

[0036] The raw data of energy storage device performance is marked as ;

[0037] Use exponential smoothing method to smooth the raw data of energy storage equipment performance: , where express The smoothed value at time, express The raw data of energy storage equipment performance at each moment, represents the smoothing coefficient, and ;

[0038] The original performance data of energy storage equipment after smoothing is defined as ;

[0039] Based on the raw data of energy storage equipment performance after smoothing , extract the energy storage device performance characteristic vector , as the input feature vector of the energy storage device performance evaluation model;

[0040] Use support vector machines to establish an energy storage equipment performance evaluation model for evaluation:

[0041] , where is the decision function of the support vector machine, which is used to evaluate the performance feature vector of the energy storage device for each new input. Classification is used to determine the performance characteristic vector of each new input energy storage device Category of; Represents the performance feature vector of the training sample energy storage device; is the performance feature vector of the training sample energy storage device The label indicates the category to which the performance feature vector of the training sample energy storage device belongs, and The value of is +1 or -1, representing two different categories, namely low performance or high performance; The coefficient of the support vector is used to measure the influence of the support vector on the decision boundary; Represents the kernel function, which is used to calculate the performance characteristic vector of the energy storage device for each new input The performance feature vector of energy storage equipment with training samples The similarity or distance between is a bias term used to adjust the position of the decision boundary; Is a sign function, used to output +1 or -1 according to the classification result. Output +1 indicates that the energy storage device has high performance. Output -1 indicates that the performance of the energy storage device is low.

[0042] Furthermore, the process of creating a fault determination mechanism and taking measures to deal with and handle the fault includes:

[0043] Count all the evaluation results of the energy storage device performance evaluation model, filter out low-performance energy storage devices, and extract the energy storage device performance feature vector corresponding to the low-performance energy storage device ;

[0044] Setting the fault judgment threshold , determine the fault level of the energy storage device;

[0045] Will and Compare and analyze to obtain different fault labels, namely, common fault labels, serious fault labels and emergency fault labels; among them, if Less than , then the first fault signal is generated and The corresponding energy storage device is associated with a common fault tag; if Not less than and not greater than of , is a real number greater than one hundred, a second fault signal is generated and The corresponding energy storage device is associated with a serious fault label; if Greater than of , a third fault signal is generated and The corresponding energy storage device is associated with an emergency fault tag;

[0046] Corresponding maintenance strategies are adopted for different fault tags obtained, including: formulating corresponding maintenance plans according to the severity and type of the fault tags; for common faults, only simple debugging or replacement of some parts are required, while for serious or emergency faults, comprehensive inspection or replacement of the entire equipment is required. For equipment that frequently has a certain fault tag, the cause of the fault can be analyzed, and corresponding preventive maintenance measures can be taken, such as regular inspection, replacement of wearing parts, optimization of operating parameters, etc., to reduce the occurrence of faults; at the same time, according to the distribution of fault tags, the operation and maintenance plan of the equipment is optimized, that is, the inspection frequency is adjusted.

[0047] A wind, photovoltaic and energy storage flexible coordination optimization system, comprising:

[0048] The prediction and comparison module is used to predict the operating status of wind and solar power generation equipment using a prediction algorithm, and compare the actual output of wind and solar power generation equipment monitored in real time with the prediction results;

[0049] The energy storage strategy formulation module is used to analyze the charging and discharging requirements of energy storage equipment and formulate energy storage charging and discharging strategies based on the operating status prediction results of wind and solar power generation equipment;

[0050] The energy storage control optimization module is used to finely control the charging and discharging process of energy storage equipment and dynamically adjust the energy storage charging and discharging strategy;

[0051] The performance evaluation and fault handling module is used to evaluate the performance of energy storage equipment, create a fault judgment mechanism, and take measures to respond and handle it.

[0052] Compared with the existing technology, the advantages of the flexible coordination optimization method and system based on wind, photovoltaic and energy storage provided by the present invention are:

[0053] 1. The present invention predicts the operating status of wind and solar power generation equipment by using a prediction algorithm, and compares the actual output of wind and solar power generation equipment monitored in real time with the prediction result, so as to accurately predict the operating status of wind and solar power generation equipment, thereby effectively coping with the instability of energy supply, so as to make more reasonable arrangements in resource scheduling;

[0054] 2. The present invention analyzes the charging and discharging requirements of the energy storage device according to the prediction results of the operating status of the wind and solar power generation equipment, and formulates the energy storage charging and discharging strategy, which can reduce the interactive pressure of the power grid to a certain extent, make the operation of the system more stable, and help improve the operating efficiency of the entire system; by finely controlling the charging and discharging process of the energy storage device and dynamically adjusting the energy storage charging and discharging strategy, it helps to improve the utilization rate of the energy storage device and reduce the energy waste caused by unreasonable charging and discharging;

[0055] 3. The present invention can timely discover performance degradation or potential fault hazards by evaluating the performance of energy storage equipment, creating a fault judgment mechanism, and taking measures to respond and handle them, thereby ensuring the continuity and safety of power supply.

[0056] In summary, the present invention significantly improves the stability of energy supply, reduces energy consumption costs, and improves system efficiency and reliability by collecting data, accurately predicting, formulating strategies, and evaluating performance, thereby ensuring the normal operation of a subsequent wind, solar, and energy storage flexible coordination optimization method and system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a wind, photovoltaic and energy storage flexible coordination optimization method proposed in the present invention.

[0058] Figure 2 This is a module diagram of a wind, photovoltaic and energy storage flexible coordination optimization system proposed by the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] See also Figure 1 The present invention provides a method for flexible coordination optimization of wind, photovoltaic and energy storage, comprising:

[0061] Step 1: Use the prediction algorithm to predict the operating status of the wind and solar power generation equipment, and compare the actual output of the wind and solar power generation equipment monitored in real time with the prediction result;

[0062] Step 2: Analyze the charging and discharging requirements of energy storage equipment based on the predicted operating status of wind and solar power generation equipment, and formulate energy storage charging and discharging strategies;

[0063] Step 3: Finely control the charging and discharging process of the energy storage equipment and dynamically adjust the energy storage charging and discharging strategy;

[0064] Step 4: Evaluate the performance of energy storage equipment, create a fault determination mechanism, and take measures to respond and handle it.

[0065] It should be noted that the application object of a wind, photovoltaic and energy storage flexible coordination optimization method and system in the embodiment of the present invention can be smart grid management in the field of energy management and control. Specifically, it can be to conduct in-depth analysis of meteorological data and operating status data of wind and photovoltaic power generation equipment through high-precision sensors, Internet of Things technology and prediction algorithms, monitor the actual output of wind and photovoltaic power generation equipment in real time and compare it with the prediction results, and promptly discover and warn of potential energy supply shortages or surpluses; according to the predicted operating status data of wind and photovoltaic power generation equipment, combined with the power grid demand, dynamically analyze the charging and discharging needs of energy storage equipment, and formulate refined energy storage charging and discharging strategies; through refined control of the charging and discharging process of energy storage equipment, dynamically adjust the energy storage charging and discharging strategy to achieve efficient operation of the energy storage system; at the same time, evaluate the performance of energy storage equipment, create a fault judgment mechanism, and take timely measures to deal with and handle low-performance or faulty energy storage equipment, thereby improving the stability and reliability of the entire wind and photovoltaic energy storage system, which is conducive to optimizing resource allocation and reducing the occurrence of faults, and further improving the intelligence level and operational efficiency of smart grid management.

[0066] See also Figure 1 The present invention provides a method for flexible coordinated optimization of wind, solar and energy storage. In step 1, the operation status of wind and solar power generation equipment is predicted by using a prediction algorithm, and the actual output of the wind and solar power generation equipment is compared with the prediction result in real time monitoring, which includes:

[0067] Step 101: Use high-precision sensors and Internet of Things technology to collect meteorological data and operating status data of wind and solar power generation equipment;

[0068] In step 101, the meteorological data includes wind speed, wind direction, light intensity and temperature, and the operating status data of the wind and solar power generation equipment includes the power generation of the wind power generation equipment and the power generation of the solar power generation equipment; wherein, the high-precision sensor is a device that can measure and convert physical quantities (such as temperature, pressure, light, wind speed, etc.) into processable and transmittable signals; in meteorological data collection and wind and solar power generation equipment monitoring, the following types of high-precision sensors are used: wind speed and direction sensors, which contain one or more rotating blades or ultrasonic devices, which can measure the speed and direction of the wind; light intensity sensors, which are used to measure the intensity and light level of light; temperature sensors, which use components such as thermistors, thermocouples or integrated temperature sensors to measure the temperature of the environment; the Internet of Things refers to a technology that connects various physical devices (such as sensors, actuators, smart devices, etc.) through a network to achieve data exchange and intelligent control between devices, such as transmitting the data collected by high-precision sensors to a data collection center or cloud platform by wired or wireless means;

[0069] Step 102: Store the collected meteorological data and the operating status data of the wind and solar power generation equipment in the form of time series, and define They are meteorological data and operating status data of wind and solar power generation equipment respectively; among them, Indicates a timestamp;

[0070] Step 103: extract all the historical meteorological data and the operating status data of the wind and solar power generation equipment stored in the database, and integrate the historical data sets of the meteorological data and the operating status data of the wind and solar power generation equipment. ;in, Indicates the amount of historical data, that is, the total amount of historical data collected so far, the historical data set It grows over time, and new data collected is added to the historical data set synchronously middle;

[0071] Step 104: Construct prediction function , so that ;in, Indicates based on Weather data at the moment and historical datasets Predicted The operating status data of wind and solar power generation equipment at all times;

[0072] Step 105: Obtaining real-time monitored operating status data of wind and solar power generation equipment , used for comparison with predicted data;

[0073] Step 106: Set the alarm threshold ,when When an alarm occurs, an alarm is generated.

[0074] See also Figure 1 The present invention provides a method for flexible coordination optimization of wind, solar and energy storage. In step 2, the steps of analyzing the charging and discharging requirements of the energy storage device and formulating the energy storage charging and discharging strategy according to the prediction results of the operating status of the wind and solar power generation equipment include:

[0075] Step 201: Get Wind and solar power generation equipment Predicted operating status data at the moment ;in, Indicates the index of wind and solar power generation equipment. is the time step of the prediction, indicating the difference between the predicted future moment and the current moment The interval between

[0076] Step 202: The grid demand and grid interaction power at the moment are marked as and ; Among them, the grid interaction power Represents the actual power exchange between the energy storage device and the grid;

[0077] Step 203: Combine the predicted operating status data of the wind and solar power generation equipment , Grid Demand And grid interaction power Calculate the charging and discharging requirements of energy storage devices ; Among them, the specific analysis formula is as follows:

[0078] , where is the preset proportional coefficient used to adjust the the extent of the impact; Used to traverse all wind and solar power generation equipment, and ; Indicates the total number of wind and solar power generation equipment; Indicates The weight of each wind and solar power generation device is used to reflect its importance in the charging and discharging decision of the energy storage device. These weights may need to be determined based on factors such as the capacity and reliability of the wind and solar power generation devices; Indicates the grid interaction power Moment and The change between moments is used to reflect the dynamic changes in grid demand;

[0079] Step 204: Definition The state vector of the energy storage device at the moment is ;in, Indicates the state of charge, Respectively Charging and discharging power at each moment;

[0080] Step 205: limiting the state of charge, charging power, and discharging power so that they vary within an allowable range, specifically including:

[0081] , where They represent the minimum and maximum state of charge, respectively. Represent the maximum value of charging and discharging power respectively;

[0082] Step 206: Charge and discharge according to the energy storage device and the energy storage device state vector Formulate energy storage charging and discharging strategies , so that , used to output the state vector of the energy storage device at the next moment;

[0083] In step 206, the energy storage charging and discharging strategy is a mapping function that converts each moment The energy storage device charging and discharging demand and energy storage device state are mapped to the charging and discharging power of the energy storage device, and the energy storage device state vector is updated by introducing the mapping function.

[0084] See also Figure 1 The present invention provides a method for flexible coordination and optimization of wind, photovoltaic and energy storage. In step three, the step of finely controlling the charging and discharging process of the energy storage device and dynamically adjusting the energy storage charging and discharging strategy includes:

[0085] Step 301: Use the objective function to minimize the energy consumption cost required for energy storage:

[0086] , where Represents the total energy cost required for energy storage; express Charging power at all times The corresponding unit cost of charging; express Discharge power at all times The corresponding unit cost of discharge, where the unit cost of discharge is a loss coefficient, indicating the energy loss during the discharge process; Indicates the total time;

[0087] In step 301, due to the energy storage process The system is already in a certain state (e.g., a certain state of charge), so the energy cost over the entire time period starting from this initial state is considered;

[0088] Step 302: At each moment , analyze the current energy storage charging and discharging strategy based on the real-time monitored operating status data of wind and solar power generation equipment and the current grid demand The charging and discharging power and objective function under the above conditions are used to adjust the energy storage charging and discharging strategy through iterative updating. , to generate new charging power and discharge power , thereby achieving refined control of the charging and discharging process of the energy storage device.

[0089] See also Figure 1 The present invention provides a method for flexible coordination and optimization of wind, photovoltaic and energy storage. In step 4, the steps of evaluating the performance of energy storage equipment, creating a fault determination mechanism, and taking measures to deal with and handle the faults include:

[0090] Step 401: Obtain performance data of energy storage equipment using energy storage monitoring software;

[0091] In step 401, the performance data specifically includes: basic electrical data (such as battery voltage, current, etc., used to reflect the basic electrical characteristics of the energy storage device), energy and capacity parameters (such as battery capacity, remaining capacity, available capacity, etc., used to reflect the ability of the energy storage device to store energy) and efficiency parameters (such as charging and discharging efficiency, cycle efficiency, etc., used to reflect the energy conversion efficiency of the energy storage device during the charging and discharging process);

[0092] Step 402: Mark the original data of the energy storage device performance as ;

[0093] Step 403: Use exponential smoothing method to smooth the original data of energy storage device performance: , where express The smoothed value at time, express The raw data of energy storage equipment performance at each moment, represents the smoothing coefficient, and ;

[0094] Step 404: define the original performance data of the energy storage device after smoothing as ;

[0095] Step 405: Original performance data of energy storage device after smoothing , extract the energy storage device performance characteristic vector , as the input feature vector of the energy storage device performance evaluation model;

[0096] Step 406: Use a support vector machine to establish an energy storage device performance evaluation model for evaluation:

[0097] , where is the decision function of the support vector machine, which is used to evaluate the performance feature vector of the energy storage device for each new input. Classification is used to determine the performance characteristic vector of each new input energy storage device Category of; Represents the performance feature vector of the training sample energy storage device; is the performance feature vector of the training sample energy storage device The label indicates the category to which the performance feature vector of the training sample energy storage device belongs, and The value of is +1 or -1, representing two different categories, namely low performance or high performance; The coefficient of the support vector is used to measure the influence of the support vector on the decision boundary; Represents the kernel function, which is used to calculate the performance characteristic vector of the energy storage device for each new input The performance feature vector of energy storage equipment with training samples The similarity or distance between is a bias term used to adjust the position of the decision boundary; Is a sign function, used to output +1 or -1 according to the classification result. Output +1 indicates that the energy storage device has high performance. If the output is -1, it means that the performance of the energy storage device is low;

[0098] Step 407: Count all evaluation results of the energy storage device performance evaluation model, filter out low-performance energy storage devices, and extract the energy storage device performance feature vector corresponding to the low-performance energy storage device. ;

[0099] Step 408: Setting the fault determination threshold , determine the fault level of the energy storage device;

[0100] Step 409: and Compare and analyze to obtain different fault labels, namely, common fault labels, serious fault labels and emergency fault labels; among them, if Less than , then the first fault signal is generated and The corresponding energy storage device is associated with a common fault tag; if Not less than and not greater than of , is a real number greater than one hundred, a second fault signal is generated and The corresponding energy storage device is associated with a serious fault label; if Greater than of , a third fault signal is generated and The corresponding energy storage device is associated with an emergency fault tag;

[0101] Step 410: Adopt corresponding maintenance strategies for different acquired fault labels;

[0102] In step 410, the maintenance strategy specifically includes: formulating a corresponding maintenance plan according to the severity and type of the fault tag; for ordinary faults, only simple debugging or replacement of some parts is required, while for serious or emergency faults, comprehensive inspection or replacement of the entire equipment is required. For equipment that frequently has a certain fault tag, the cause of the fault can be analyzed and corresponding preventive maintenance measures can be taken, such as regular inspection, replacement of wearing parts, optimization of operating parameters, etc., to reduce the occurrence of faults; at the same time, according to the distribution of fault tags, the operation and maintenance plan of the equipment is optimized, that is, the inspection frequency is adjusted.

[0103] In the embodiment of the present invention, meteorological data and operating status data of wind and solar power generation equipment are collected through high-precision sensors and Internet of Things technology, and prediction functions are constructed using these data, so that the operating status of wind and solar power generation equipment can be predicted more accurately. By real-time monitoring of the actual output of wind and solar power generation equipment and comparing it with the prediction results, deviations can be discovered in time, providing a reliable basis for the subsequent formulation of energy storage charging and discharging strategies. By combining the predicted operating status data of wind and solar power generation equipment with the grid demand and grid interaction power, the charging and discharging requirements of the energy storage equipment can be accurately calculated, thereby improving the utilization rate of the energy storage equipment. By formulating an energy storage charging and discharging strategy, the supply and demand of the grid can be effectively balanced to ensure the stable operation of the grid. By finely controlling the charging and discharging process of the energy storage equipment, the charging and discharging power of the energy storage equipment can be optimized to reduce energy consumption costs. By dynamically adjusting the energy storage charging and discharging strategy, it can adapt to the characteristics of wind and solar power generation in different time periods to ensure that the energy storage equipment is always in the best working state. By evaluating the performance of the energy storage equipment, potential faults can be discovered in time to avoid fault expansion. By creating a fault judgment mechanism, accurate maintenance strategies can be formulated according to the fault level and type to improve maintenance efficiency. In summary, the examples of the present invention involve data collection and analysis, result generation and performance evaluation decisions, solving the technical problems of insufficient energy control and scheduling optimization in smart grid management in existing solutions. In actual situations, more data and contextual information may be needed to make specific decisions and optimization solutions.

[0104] See also Figure 2 The present invention provides a wind and solar energy storage flexible coordination optimization system, comprising:

[0105] The prediction and comparison module is used to predict the operating status of wind and solar power generation equipment using a prediction algorithm, and compare the actual output of wind and solar power generation equipment monitored in real time with the prediction results;

[0106] The energy storage strategy formulation module is used to analyze the charging and discharging requirements of energy storage equipment and formulate energy storage charging and discharging strategies based on the operating status prediction results of wind and solar power generation equipment;

[0107] The energy storage control optimization module is used to finely control the charging and discharging process of energy storage equipment and dynamically adjust the energy storage charging and discharging strategy;

[0108] The performance evaluation and fault handling module is used to evaluate the performance of energy storage equipment, create a fault judgment mechanism, and take measures to respond and handle it.

[0109] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The proportional coefficient in the formula and the various preset thresholds in the analysis process are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data; the size of the proportional coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the proportional coefficient depends on the amount of sample data and the preliminary setting of the corresponding processing coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0110] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically based on the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0111] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0112] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0116] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0117] Finally: The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for flexible coordination and optimization of wind, solar and energy storage, characterized by: Step 1: Use the prediction algorithm to predict the operating status of the wind and solar power generation equipment, and compare the actual output of the wind and solar power generation equipment monitored in real time with the prediction result; Step 2: Analyze the charging and discharging requirements of energy storage equipment based on the predicted operating status of wind and solar power generation equipment, and formulate energy storage charging and discharging strategies; Step 3: Finely control the charging and discharging process of the energy storage equipment and dynamically adjust the energy storage charging and discharging strategy; Step 4: Evaluate the performance of energy storage equipment, create a fault determination mechanism, and take measures to respond and handle it; The process of analyzing the charging and discharging requirements of the energy storage device according to the predicted results of the operation status of the wind and solar power generation equipment in step 2 includes: Get the Wind and solar power generation equipment Predicted operating status data at the moment ;in, Indicates the index of wind and solar power generation equipment. is the time step of the prediction, indicating the difference between the predicted future moment and the current moment The interval between Will The grid demand and grid interaction power at the moment are marked as and ; Among them, the grid interaction power Represents the actual power exchange between the energy storage device and the grid; Combined with the predicted operating status data of wind and solar power generation equipment , Grid Demand And grid interaction power Calculate the charging and discharging requirements of energy storage devices ; Among them, the specific analysis formula is as follows: , where is the preset proportional coefficient; Used to traverse all wind and solar power generation equipment, and ; Indicates the total number of wind and solar power generation equipment; Indicates The weight of each wind and solar power generation equipment; Indicates the grid interaction power Moment and The change between moments is used to reflect the dynamic changes in grid demand.

2. A wind, photovoltaic and energy storage flexible coordination optimization method according to claim 1, characterized in that: In the step 1, the process of using the prediction algorithm to predict the operating status of the wind and solar power generation equipment and comparing the actual output of the wind and solar power generation equipment monitored in real time with the prediction result includes: Use high-precision sensors and Internet of Things technology to collect meteorological data and operating status data of wind and solar power generation equipment; the meteorological data includes wind speed, wind direction, light intensity and temperature, and the operating status data of wind and solar power generation equipment includes the power generation of wind power generation equipment and the power generation of solar power generation equipment; The collected meteorological data and the operating status data of wind and solar power generation equipment are stored in the form of time series, and defined They are meteorological data and operating status data of wind and solar power generation equipment respectively; among them, Indicates a timestamp; Extract all historical meteorological data and wind and solar power equipment operating status data stored in the database, and integrate the historical data sets of meteorological data and wind and solar power equipment operating status data ;in, Indicates the amount of historical data; Building a prediction function , so that ;in, Indicates based on Weather data at the moment and historical datasets Predicted The operating status data of wind and solar power generation equipment at all times; Obtain real-time monitoring of wind and solar power equipment operating status data , used for comparison with predicted data; Setting alarm thresholds ,when When an alarm occurs, an alarm is generated.

3. A wind, photovoltaic and energy storage flexible coordination optimization method according to claim 1, characterized in that: In step 2, the process of formulating the energy storage charging and discharging strategy includes: definition The state vector of the energy storage device at the moment is ;in, Indicates the state of charge, Respectively Charging and discharging power at each moment; Limit the state of charge, charging power, and discharging power so that they vary within the permitted range, including: , where They represent the minimum and maximum state of charge, respectively. Represent the maximum value of charging and discharging power respectively; According to the charging and discharging requirements of energy storage equipment and the energy storage device state vector Formulate energy storage charging and discharging strategies , so that , used to output the state vector of the energy storage device at the next moment.

4. A method for flexible coordination and optimization of wind, solar and energy storage according to claim 3, characterized in that: In step 3, the process of finely controlling the charging and discharging process of the energy storage device and dynamically adjusting the energy storage charging and discharging strategy includes: Use the objective function to minimize the energy cost required for energy storage: , where Represents the total energy cost required for energy storage; express Charging power at all times The corresponding unit cost of charging; express Discharge power at all times The corresponding unit cost of discharge; Indicates the total time; At every moment , analyze the current energy storage charging and discharging strategy based on the real-time monitored operating status data of wind and solar power generation equipment and the current grid demand The charging and discharging power and objective function under the above conditions are used to adjust the energy storage charging and discharging strategy through iterative updating. , to generate new charging power and discharge power , thereby achieving refined control of the charging and discharging process of the energy storage device.

5. The method for flexible coordination and optimization of wind, photovoltaic and energy storage according to claim 1, characterized in that: In step 4, the process of evaluating the performance of the energy storage device includes: Use energy storage monitoring software to obtain performance data of energy storage equipment; the performance data specifically includes: basic electrical data, energy and capacity parameters, and efficiency parameters; The raw data of energy storage device performance is marked as ; Use exponential smoothing method to smooth the raw data of energy storage equipment performance: , where express The smoothed value at time, express The raw data of energy storage equipment performance at each moment, represents the smoothing coefficient, and ; The original performance data of energy storage equipment after smoothing is defined as ; Based on the raw data of energy storage equipment performance after smoothing , extract the energy storage device performance characteristic vector , as the input feature vector of the energy storage device performance evaluation model; Use support vector machines to establish an energy storage equipment performance evaluation model for evaluation: , where is the decision function of the support vector machine, which is used to evaluate the performance feature vector of the energy storage device for each new input. Classification is used to determine the performance characteristic vector of each new input energy storage device Category of; Represents the performance feature vector of the training sample energy storage device; is the performance feature vector of the training sample energy storage device The label indicates the category to which the performance feature vector of the training sample energy storage device belongs, and The value of is +1 or -1, representing two different categories, namely low performance or high performance; The coefficient of the support vector is used to measure the influence of the support vector on the decision boundary; Represents the kernel function, which is used to calculate the performance characteristic vector of the energy storage device for each new input The performance feature vector of energy storage equipment with training samples The similarity or distance between is a bias term used to adjust the position of the decision boundary; is a sign function that outputs +1 or -1 depending on the classification result.

6. A method for flexible coordination and optimization of wind, photovoltaic and energy storage according to claim 5, characterized in that: In step 4, the process of creating a fault determination mechanism and taking measures to deal with and handle the fault includes: Count all the evaluation results of the energy storage device performance evaluation model, filter out low-performance energy storage devices, and extract the energy storage device performance feature vector corresponding to the low-performance energy storage device ; Setting the fault judgment threshold , determine the fault level of the energy storage device; Will and Compare and analyze to obtain different fault labels, namely, common fault labels, serious fault labels and emergency fault labels; among them, if Less than , then the first fault signal is generated and The corresponding energy storage device is associated with a common fault tag; if Not less than and not greater than of , is a real number greater than one hundred, a second fault signal is generated and The corresponding energy storage device is associated with a serious fault label; if Greater than of , a third fault signal is generated and The corresponding energy storage device is associated with an emergency fault tag; Adopt corresponding maintenance strategies according to different fault labels obtained.

7. A wind, photovoltaic and energy storage flexible coordination optimization system, characterized in that: A method for flexible coordination and optimization of wind, photovoltaic and energy storage as claimed in any one of claims 1 to 6, the system comprising: The prediction and comparison module is used to predict the operating status of wind and solar power generation equipment using a prediction algorithm, and compare the actual output of wind and solar power generation equipment monitored in real time with the prediction results; The energy storage strategy formulation module is used to analyze the charging and discharging requirements of energy storage equipment and formulate energy storage charging and discharging strategies based on the operating status prediction results of wind and solar power generation equipment; The energy storage control optimization module is used to finely control the charging and discharging process of energy storage equipment and dynamically adjust the energy storage charging and discharging strategy; The performance evaluation and fault handling module is used to evaluate the performance of energy storage equipment, create a fault judgment mechanism, and take measures to respond and handle it.

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