Energy storage optimization configuration method for flexible charging and discharging

Abstract

The invention discloses an energy storage optimization configuration method for flexible charging and discharging, and relates to the technical field of power systems. According to the method, historical data and real-time data of the power system are processed through data acquisition and preprocessing operation, so that an energy storage analysis result based on the historical data and the real-time data is obtained, the energy storage supply and demand balance state of the power system is determined, and a basic strategy is provided for flexible charging and discharging of the energy storage system; then, based on an optimization algorithm, strategy scheme data in the prior art are substituted, an energy storage system optimization configuration model is established, through iteration processing of the model, the flexibility and accuracy of measurement of charging and discharging of the energy storage system are improved, and various real-time data in the energy storage system are input into the energy storage system optimization configuration model, so that the energy storage system is optimized. And accurate and flexible charging and discharging control is realized on the energy storage system based on the output strategy, so that the charging and discharging efficiency and the service life of the energy storage system can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for optimizing energy storage configuration with flexible charging and discharging. Background Art

[0002] Energy storage system refers to a system that stores energy in the form of electricity through different media and releases it when needed. According to the different storage methods, it can be divided into chemical energy storage and physical energy storage. Chemical energy storage includes hydrogen, carbon hydrogen, carbon hydrogen oxygen and electrochemical energy storage. Electrochemical energy storage includes lithium-ion batteries, flow batteries, lead-acid batteries and sodium-based batteries. Physical energy storage includes pumped storage, compressed air storage, flywheel storage and energy storage.

[0003] At present, the configuration of traditional energy storage systems is mainly based on static capacity requirements and single functionality, and lacks multi-dimensional optimization of flexible charging and discharging modes. As a result, the energy storage systems in the prior art have low energy resource utilization. When the grid load fluctuates greatly, the energy storage system cannot fully participate in flexible peak regulation, resulting in energy waste or the need to introduce additional electricity purchases; and it is impossible to achieve flexible charging and discharging operations on the energy storage system, which affects the charging and discharging efficiency and life of the batteries in the energy storage system, causing damage to the hardware of the energy storage system, which is not conducive to the long-term and stable use of the energy storage system. Based on the above reasons, the present invention proposes a flexible charging and discharging energy storage optimization configuration method. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies of the prior art, the present invention realizes processing operations on the historical data and real-time data of the power system through data acquisition and preprocessing operations, thereby obtaining energy storage analysis results based on the historical data and real-time data, and determining the energy storage supply and demand balance state of the power system, providing a basic strategy for the flexible charging and discharging of the energy storage system; then, based on the optimization algorithm, the strategy scheme data of the prior art is substituted to establish an energy storage system optimization configuration model, and through iterative processing of the model, the measurement flexibility and accuracy of the charging and discharging of the energy storage system are improved, and then by inputting various real-time data in the energy storage system into the energy storage system optimization configuration model, and based on the output strategy, the energy storage system is accurately and flexibly controlled to charge and discharge, which can effectively improve the charging and discharging efficiency and life of the energy storage system.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a method for optimizing energy storage configuration with flexible charging and discharging, which is implemented based on a power system and an energy storage system, and the method for optimizing energy storage configuration comprises the following steps:

[0008] S1: Data collection and preprocessing operations, collecting historical data and real-time data of the power system, including power load data, power generation data and energy storage system data, and preprocessing the collected data;

[0009] S2: Demand forecasting and analysis: Analyze the preprocessed data to obtain energy storage analysis results based on historical data and real-time data, and determine the energy storage supply and demand balance status of the power system;

[0010] S3: Establish an energy storage system optimization configuration model and formulate a charging and discharging strategy for the energy storage system based on the supply and demand balance of the power system and energy storage requirements;

[0011] S4: Real-time control and optimization: Based on the charging and discharging strategy generated by the optimization configuration model, the energy storage system is automatically charged and discharged during the operation of the power system.

[0012] S5: Benefit evaluation and optimization: regularly evaluate the operating effect of the energy storage system, and feed the collected data back to the energy storage system optimization configuration model to further optimize the energy storage system charging and discharging strategy and improve the flexibility of the energy storage system charging and discharging.

[0013] Preferably, the data preprocessing operation in step S1 includes data cleaning and data transformation, wherein the data cleaning includes processing missing values ​​and abnormal values ​​of power data and data denoising, and the data transformation converts the format, unifies the units and performs time calibration operations on data from different power data acquisition devices.

[0014] Preferably, the data transformation uses a standardized method to convert the data to the same dimension, including the following steps:

[0015] S11: Calculate mean and standard deviation: The mean (μ) and standard deviation (σ) of the data set need to be calculated;

[0016] S12: Apply the standardization formula: Then, the data is converted into a standard distribution using the standardization formula, which is: x′=fracx-musigma, where x is the original data and x′ is the standardized data;

[0017] S13: Handling special cases: If the standard deviation of the data set is 0, that is, all values ​​are the same, then normalization cannot be performed. In this case, you can choose a normalization method or reconsider the validity of the data.

[0018] Preferably, the establishment of the energy storage system optimization configuration model in step S3 includes the following steps:

[0019] S21: Determine the optimization direction of the energy storage system, so as to determine the constraint conditions of the energy storage system optimization configuration model for charge and discharge optimization;

[0020] S22: Initialize the particle swarm for the preprocessed, predicted and analyzed data;

[0021] S23: Apply the initialization and evaluation processed data to

[0022] vi,j(t+1)=wcdotvi,j(t)+c1cdotr1cdot(pi,j-xi,j(t))+c2cdotr2cdot(gj-xi,j(t))

[0023] xi,j(t+1)=xi,j(t)+vi,j(t+1)

[0024] Where vi,j(t) and xi,j(t) are the velocity and position of the i-th particle in the j-th dimension, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, pi,j is the j-th dimension of the individual optimal solution of the i-th particle, and gj is the j-th dimension of the global optimal solution;

[0025] S24: repeat the above steps S22 and S23 until the maximum number of iterations and the constraint threshold are reached, and then stop iterating;

[0026] S25: Outputting the energy storage charging and discharging strategy after the optimized configuration, and applying the optimized configuration strategy.

[0027] Preferably, the constraint condition in step S21 is the service life of the energy storage system, thereby ensuring the use of the energy storage system throughout its entire life cycle.

[0028] Preferably, the initialized particle swarm in step S22 includes particle positions, particle velocities, individual optimal solutions and global optimal solutions;

[0029] Particle position: The position of each particle represents an energy storage optimization configuration plan, including the capacity, initial power, and charging and discharging strategy of the energy storage system;

[0030] Particle speed: The speed of a particle is used to adjust its position, that is, the adjustment direction and step size of the optimization scheme;

[0031] Individual optimal solution: Each particle records the optimal solution it has found;

[0032] Global optimal solution: the optimal solution found by the entire particle swarm.

[0033] Preferably, in step S4, during the operation of the energy storage system, the state of the energy storage system is monitored in real time, so as to collect and feedback the charging and discharging data.

[0034] (III) Beneficial effects

[0035] Compared with the prior art, the present invention provides a method for optimizing the configuration of energy storage with flexible charging and discharging, which has the following beneficial effects: the present invention realizes processing operations on the historical data and real-time data of the power system through data acquisition and preprocessing operations, thereby obtaining energy storage analysis results based on historical data and real-time data, and determining the energy storage supply and demand balance state of the power system, providing a basic strategy for the flexible charging and discharging of the energy storage system; then, based on the optimization algorithm, the strategy scheme data of the prior art is substituted to establish an energy storage system optimization configuration model, and through iterative processing of the model, the measurement flexibility and accuracy of the charging and discharging of the energy storage system are improved, and then by inputting various real-time data in the energy storage system into the energy storage system optimization configuration model, and based on the output strategy, the energy storage system is accurately and flexibly controlled to charge and discharge, which can effectively improve the charging and discharging efficiency and life of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of the energy storage optimization configuration method of the present invention. DETAILED DESCRIPTION

[0037] In order to better understand the purpose, structure and function of the present invention, and to realize the charge and discharge control of the energy storage system, the present invention realizes the processing operation of the historical data and real-time data of the power system through data acquisition and preprocessing operations, thereby obtaining the energy storage analysis results based on the historical data and real-time data, and determining the energy storage supply and demand balance state of the power system, and providing a basic strategy for the flexible charge and discharge of the energy storage system; then, based on the optimization algorithm, the strategy scheme data of the prior art is substituted to establish an energy storage system optimization configuration model, and after iterative processing of the model, the measurement flexibility and accuracy of the charge and discharge of the energy storage system are improved, and then by inputting various real-time data in the energy storage system into the energy storage system optimization configuration model, and realizing accurate and flexible charge and discharge control of the energy storage system based on the output strategy, the charge and discharge efficiency and life of the energy storage system can be effectively improved. A flexible charge and discharge energy storage optimization configuration method of the present invention is further described in detail.

[0038] refer to Figure 1 The present invention provides a method for optimizing the configuration of energy storage with flexible charging and discharging, which is implemented based on a power system and an energy storage system. The method for optimizing the configuration of energy storage includes the following steps:

[0039] S1: Data collection and preprocessing operations, collecting historical data and real-time data of the power system, including power load data, power generation data and energy storage system data, and preprocessing the collected data;

[0040] In the present technical solution, the data preprocessing operation includes data cleaning and data transformation. The data cleaning includes processing missing values ​​and abnormal values ​​of power data and data denoising. The data transformation converts the format of data from different power data acquisition devices, unifies the units and performs time calibration operations, which can ensure the accuracy and uniformity of power system data, thereby facilitating unified processing of power data in the later stage and improving the accuracy and efficiency of the model in processing power data.

[0041] Specifically, the data transformation uses a standardized method to convert the data to the same dimension, including the following steps:

[0042] S11: Calculate mean and standard deviation: The mean (μ) and standard deviation (σ) of the data set need to be calculated;

[0043] S12: Apply the standardization formula: Then, the data is converted into a standard distribution using the standardization formula, which is: x′=fracx-musigma, where x is the original data and x′ is the standardized data;

[0044] S13: Handling special cases: If the standard deviation of the data set is 0, that is, all values ​​are the same, then normalization cannot be performed. In this case, you can choose a normalization method or reconsider the validity of the data.

[0045] By standardizing the data, the differences in power data distribution can be eliminated, making it easier to perform statistical analysis and processing operations on the model.

[0046] S2: Demand forecasting and analysis: Analyze the preprocessed data to obtain energy storage analysis results based on historical data and real-time data, and determine the energy storage supply and demand balance status of the power system;

[0047] S3: Establish an energy storage system optimization configuration model and formulate a charging and discharging strategy for the energy storage system based on the supply and demand balance of the power system and energy storage requirements;

[0048] In this technical solution, the establishment of the energy storage system optimization configuration model includes the following steps:

[0049] S21: Determine the optimization direction of the energy storage system, so as to determine the constraint conditions of the energy storage system optimization configuration model for charge and discharge optimization, where the constraint conditions are the service life of the energy storage system, so as to ensure the use of the energy storage system throughout its life cycle;

[0050] S22: Initialize the particle swarm for the preprocessed, predicted and analyzed data;

[0051] Specifically, the initialization of the particle swarm includes particle positions, particle velocities, individual optimal solutions, and global optimal solutions;

[0052] Particle position: The position of each particle represents an energy storage optimization configuration plan, including the capacity, initial power, and charging and discharging strategy of the energy storage system;

[0053] Particle speed: The speed of a particle is used to adjust its position, that is, the adjustment direction and step size of the optimization scheme;

[0054] Individual optimal solution: Each particle records the optimal solution it has found;

[0055] Global optimal solution: the optimal solution found by the entire particle swarm.

[0056] S23: Apply the initialization and evaluation processed data to

[0057] vi,j(t+1)=wcdotvi,j(t)+c1cdotr1cdot(pi,j-xi,j(t))+c2cdotr2cdot(gj-xi,j(t))

[0058] xi,j(t+1)=xi,j(t)+vi,j(t+1)

[0059] Where vi,j(t) and xi,j(t) are the velocity and position of the i-th particle in the j-th dimension, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, pi,j is the j-th dimension of the individual optimal solution of the i-th particle, and gj is the j-th dimension of the global optimal solution;

[0060] S24: repeat the above steps S22 and S23 until the maximum number of iterations and the constraint threshold are reached, and then stop iterating;

[0061] S25: Outputting the energy storage charging and discharging strategy after the optimized configuration, and applying the optimized configuration strategy.

[0062] S4: Real-time control and optimization: Based on the charging and discharging strategy generated by the optimization configuration model, the energy storage system is automatically charged and discharged during the operation of the power system.

[0063] Specifically, through a flexible charging and discharging mechanism, the charging and discharging strategy of the energy storage system is dynamically adjusted according to the actual needs of the power system to achieve efficient utilization of energy storage resources. During the operation of the energy storage system, the status of the energy storage system is monitored in real time to collect and feedback the charging and discharging data.

[0064] S5: Benefit evaluation and optimization: regularly evaluate the operating effect of the energy storage system, and feed the collected data back to the energy storage system optimization configuration model to further optimize the energy storage system charging and discharging strategy and improve the flexibility of the energy storage system charging and discharging.

[0065] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A flexible charging and discharging energy storage optimization configuration method based on a power system and an energy storage system, characterized in that: The energy storage optimization configuration method comprises the following steps: S1: Data collection and preprocessing operations, collecting historical data and real-time data of the power system, including power load data, power generation data and energy storage system data, and preprocessing the collected data; S2: Demand forecasting and analysis: Analyze the preprocessed data to obtain energy storage analysis results based on historical data and real-time data, and determine the energy storage supply and demand balance status of the power system; S3: Establish an energy storage system optimization configuration model and formulate a charging and discharging strategy for the energy storage system based on the supply and demand balance of the power system and energy storage requirements; S4: Real-time control and optimization: Based on the charging and discharging strategy generated by the optimization configuration model, the energy storage system is automatically charged and discharged during the operation of the power system. S5: Benefit evaluation and optimization: regularly evaluate the operating effect of the energy storage system, and feed the collected data back to the energy storage system optimization configuration model to further optimize the energy storage system charging and discharging strategy and improve the flexibility of the energy storage system charging and discharging.

2. The method for optimizing the configuration of energy storage with flexible charging and discharging according to claim 1, characterized in that: The data preprocessing operation in step S1 includes data cleaning and data transformation. The data cleaning includes processing missing values ​​and abnormal values ​​of power data and data denoising. The data transformation converts the format, unifies the units and performs time calibration operations on data from different power data acquisition devices.

3. The method for optimizing the configuration of energy storage with flexible charging and discharging according to claim 2, characterized in that: The data transformation uses a standardized method to convert the data to the same dimension, including the following steps: S11: Calculate mean and standard deviation: The mean (μ) and standard deviation (σ) of the data set need to be calculated; S12: Apply the standardization formula: Then, the data is converted into a standard distribution using the standardization formula, which is: x′=fracx-musigma, where x is the original data and x′ is the standardized data; S13: Handling special cases: If the standard deviation of the data set is 0, that is, all values ​​are the same, then normalization cannot be performed. In this case, you can choose a normalization method or reconsider the validity of the data.

4. The method for optimizing the configuration of energy storage with flexible charging and discharging according to claim 1, characterized in that: The establishment of the energy storage system optimization configuration model in step S3 includes the following steps: S21: Determine the optimization direction of the energy storage system, so as to determine the constraint conditions of the energy storage system optimization configuration model for charge and discharge optimization; S22: Initialize the particle swarm for the preprocessed, predicted and analyzed data; S23: Apply the initialization and evaluation processed data to vi,j(t+1)=wcdotvi,j(t)+c1cdotr1cdot(pi,j-xi,j(t))+c2cdotr2cdot(gj-xi,j(t)) xi,j(t+1)=xi,j(t)+vi,j(t+1) Where vi,j(t) and xi,j(t) are the velocity and position of the i-th particle in the j-th dimension, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, pi,j is the j-th dimension of the individual optimal solution of the i-th particle, and gj is the j-th dimension of the global optimal solution; S24: repeat the above steps S22 and S23 until the maximum number of iterations and the constraint threshold are reached, and then stop iterating; S25: Outputting the energy storage charging and discharging strategy after the optimized configuration, and applying the optimized configuration strategy.

5. The method for optimizing the configuration of energy storage with flexible charging and discharging according to claim 4, characterized in that: The constraint condition in step S21 is the service life of the energy storage system, thereby ensuring the use of the energy storage system throughout its entire life cycle.

6. The method for optimizing the configuration of energy storage with flexible charging and discharging according to claim 1, characterized in that: The initialized particle swarm in step S22 includes particle positions, particle velocities, individual optimal solutions and global optimal solutions; Particle position: The position of each particle represents an energy storage optimization configuration plan, including the capacity, initial power, and charging and discharging strategy of the energy storage system; Particle speed: The speed of a particle is used to adjust its position, that is, the adjustment direction and step size of the optimization scheme; Individual optimal solution: Each particle records the optimal solution it has found; Global optimal solution: the optimal solution found by the entire particle swarm.

7. The method for optimizing the configuration of energy storage with flexible charging and discharging according to claim 1, characterized in that: In step S4, during the operation of the energy storage system, the state of the energy storage system is monitored in real time, so as to collect and feedback the charging and discharging data.

Images