Compressed air energy storage and electrochemical energy storage cooperative scheduling method for power grid regulation

Abstract

The invention relates to the technical field of smart power grids, in particular to a compressed air energy storage and electrochemical energy storage cooperative scheduling method for power grid regulation, which comprises the following steps: acquiring load power of a power grid load side at different moments in each time period under each monitoring period, obtaining the charging and discharging power of each energy storage device in the hybrid energy storage system at each moment in each time period; the random fluctuation degree, the supply and demand deterioration degree and the unsteady state coefficient of each time period are calculated, and the unsteady state coefficient of each time period in the next monitoring period is predicted; extracting all asynchronous time periods; and the asynchronous coefficient of each time period is calculated, the adjustment coefficient of each time period in the next monitoring period is determined, the proportion parameters of the voltage loop and the current loop of the double-closed-loop PI control strategy are respectively adjusted, and cooperative scheduling is carried out on the energy storage equipment in the hybrid energy storage system. According to the invention, the energy storage system can timely respond to the rapid change of the load, and the fluctuation of the power grid frequency and voltage is reduced.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, specifically to a method for coordinated scheduling of compressed air energy storage and electrochemical energy storage for grid regulation. Background Technology

[0002] With the high proportion of renewable energy sources, such as wind power and solar power, being connected to the grid, the volatility and randomness of the power system have significantly increased, posing a severe challenge to the real-time power balance and frequency stability of the grid. By integrating the complementary advantages of electrochemical energy storage and compressed air energy storage to form a hybrid energy storage system for coordinated dispatch, the advantages of different energy storage technologies can be fully utilized, becoming a key means to smooth grid fluctuations and provide flexible adjustment services.

[0003] Traditional methods using dual-loop PI control strategies for coordinated dispatch typically employ fixed proportional parameters. However, due to the complex dynamic characteristics of load-side power fluctuations, which exhibit both short-term high-frequency randomness and long-term trends, and the differences in dynamic response characteristics among different types of energy storage devices, asynchronous charging and discharging behaviors are prone to occur during coordinated operation. Fixed proportional parameters cannot adapt to this complex scenario of dual uncertainty—complex and variable external demand and uncoordinated internal device behavior—leading to insufficient response to sharp fluctuations or over-adjustment during stable periods. This restricts the overall regulation efficiency and accuracy of the hybrid energy storage system, affecting the operational stability of the power grid. Summary of the Invention

[0004] To address the aforementioned technical issues, a method for coordinated scheduling of compressed air energy storage and electrochemical energy storage for power grid regulation is provided to resolve existing problems.

[0005] The solution to the technical problem presented in this application is to provide a method for the coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation, comprising the following steps: The load power of the grid load side at different times in each time period under each monitoring cycle is obtained, and the charging and discharging power of each energy storage device in the hybrid energy storage system at each time in each time period is obtained. Analyze the volatility of the rate of change of load power and the randomness of load power fluctuations in different time periods, and calculate the random volatility of each time period. Based on the changing trends of the charging and discharging power and load power of all energy storage devices in each time period, the degree of supply and demand deterioration in each time period is calculated. Combined with the random fluctuation, the unsteady-state coefficient of each time period is obtained, and the unsteady-state coefficient of each time period in the next monitoring cycle is predicted. Based on the charging and discharging power of energy storage devices in each time period, the incoordination of charging and discharging behavior of different energy storage devices is evaluated, and all asynchronous time periods are extracted. By calculating the proportion of charging and discharging power of different energy storage devices in asynchronous time periods, the asynchronous coefficient of each time period is calculated. Combined with the predicted value, the adjustment coefficient of each time period in the next monitoring cycle is determined. The proportional parameters of the voltage loop and current loop of the dual closed-loop PI control strategy are adjusted respectively. Using the adjusted proportional parameters, the dual closed-loop PI control strategy is adopted to coordinate the scheduling of energy storage devices in the hybrid energy storage system.

[0006] Preferably, the energy storage devices in the hybrid energy storage system include compressed air energy storage devices and electrochemical energy storage devices.

[0007] Preferably, the calculation of the random fluctuation of each time period includes: Curve fitting is performed on the load power at all times within each time period, the derivative of the fitted curve at each time is calculated, and the dispersion of all derivatives within each time period is calculated. Obtain all extreme points of load power at all times within each time period; take the difference in load power between two adjacent extreme points within each time period as the first difference, and calculate the fractal dimension of all first differences within each time period. The random volatility is positively correlated with the degree of dispersion and the fractal dimension.

[0008] Preferably, the calculation of the supply and demand deterioration degree for each time period includes: Calculate the sum of the charging and discharging power of all energy storage devices at the same moment as the total charging and discharging power. Calculate the difference between the total charging and discharging power at adjacent moments in each time period as the second difference. Obtain the trend statistics of all second differences in each time period and perform positive mapping on them as the first trend quantity. Obtain the trend statistics of all load power in each time period, and perform positive mapping on them as the second trend quantity; The degree of deterioration in supply and demand is positively correlated with the second trend quantity, but negatively correlated with the first trend quantity.

[0009] Preferably, the non-steady-state coefficient is the result of a positive fusion of random volatility and supply-demand deterioration.

[0010] Preferably, the extraction process of the asynchronous time period is as follows: count the times when the signs of the charging and discharging power of all energy storage devices are inconsistent in each time period, mark them as different times, and form the asynchronous time periods by consecutive different times in each time period.

[0011] Preferably, the calculation of the asynchrony coefficient for each time period includes: Curve fitting is performed on all charging and discharging power of each energy storage device in each time period. The total area of ​​the fitted curve in the corresponding whole time period is calculated, and the sum of the areas of the fitted curve in all asynchronous time periods is calculated. The ratio of the sum to the total area is used as the asynchronous proportion of each energy storage device in each time period. The asynchronous coefficient is the result of positively integrating the asynchronous proportions of all energy storage devices in each time period.

[0012] Preferably, the adjustment coefficient is positively correlated with the predicted value and the asynchronous coefficient.

[0013] Preferably, the voltage loop is in the first... The monitoring cycle under the first The adjusted proportional parameters for each time period The calculation formula is: ,in, This is the preset minimum proportional parameter for the voltage loop. For the first The monitoring cycle under the first Each time period corresponds to a normalized adjustment coefficient.

[0014] Preferably, the current loop is in the first... The monitoring cycle under the first The adjusted proportional parameters for each time period The calculation formula is: ,in, This is the preset minimum proportional parameter for the current loop. This is the preset adjustment range.

[0015] This application has at least the following beneficial effects: This application calculates the random volatility of load power across different time periods by considering the discreteness of the rate of change and the randomness of fluctuations. Its advantages include quantifying the short-term volatility of load power from two dimensions to assess the complex dynamic characteristics of load-side power; calculating the supply-demand deterioration degree of each time period, which takes into account the growth trend of grid load demand and the continuous decline trend of energy storage support capacity, accurately sensing the overall power supply pressure of the grid; obtaining the unsteady-state coefficients for each time period and predicting the unsteady-state coefficients for each time period in the next monitoring cycle, which combines the random volatility characterizing the intensity of short-term disturbances with the supply-demand deterioration degree characterizing the medium- and long-term deterioration trend, comprehensively assessing the overall unsteady-state characteristics of the grid from multiple dimensions; and predicting and quantifying the unsteady-state characteristics of the grid operation in the next monitoring cycle by predicting the unsteady-state coefficients, thereby proactively optimizing the control resource allocation of the hybrid energy storage system and realizing a dispatch transformation from passive disturbance resistance to active defense; and extracting all asynchronous time periods, which accurately identifies the uncoordinated charging and discharging phenomena between energy storage devices by using the time of asynchronous charging and discharging states between energy storage devices. Calculating the asynchrony coefficient for each time period has the benefit of assessing the charging and discharging coordination of energy storage devices in the hybrid energy storage system throughout the entire time period, reflecting the overall regulation efficiency of the hybrid energy storage system. Determining the regulation coefficient for each time period in the next monitoring cycle has the benefit of comprehensively assessing the impact of load fluctuations and the uncertainty of the energy storage system on grid operation from two dimensions: predicted external load fluctuation risk and the internal coordination of energy storage devices. This allows for early assessment of the grid's operating status, quantifying the control strength and response speed of the hybrid energy storage system. Adjusting the proportional parameters of the voltage and current loops of the dual-loop PI control strategy, and using the adjusted proportional parameters, employs the dual-loop PI control strategy to coordinate the scheduling of energy storage devices in the hybrid energy storage system. This has the benefit of increasing the proportional parameters of the voltage and current loops when the external load fluctuation risk is high and internal asynchrony is severe, improving the response speed and adjustment strength to bus voltage and current, optimizing the real-time accuracy and coordination efficiency of power allocation, improving the coordination scheduling level and overall regulation efficiency of the hybrid energy storage system, ensuring that the energy storage system can respond promptly to rapid load changes, and reducing grid frequency and voltage fluctuations. Attached Figure Description

[0016] The following detailed description, in conjunction with the accompanying drawings, provides a further explanation of the method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in this application.

[0017] Figure 1 A flowchart illustrating the steps of a method for coordinated scheduling of compressed air energy storage and electrochemical energy storage for power grid regulation, provided in an embodiment of this application. Figure 2A flowchart illustrating the steps of the method for obtaining the asynchronous coefficients for each time period provided in the embodiments of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of the method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation, in conjunction with the accompanying drawings and implementation examples, is provided. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a method for coordinated scheduling of compressed air energy storage and electrochemical energy storage for power grid regulation, according to an embodiment of this application. The method includes the following steps: Step 1: Obtain the load power of the grid load side at different times in each time period under each monitoring cycle, and obtain the charging and discharging power of each energy storage device in the hybrid energy storage system at each time in each time period.

[0021] With the continuous integration of renewable energy into the power grid, the random fluctuations and uncertainties of the power system are increasing dramatically. Hybrid energy storage, as an important energy conversion and storage component, plays a crucial regulatory role. By integrating the characteristics of different types of energy storage technologies, hybrid energy storage can achieve precise regulation of the power grid across multiple time scales and power levels, effectively mitigating power fluctuations from renewable energy sources, improving energy utilization efficiency, and enhancing the stability and reliability of the power grid.

[0022] Taking a hybrid energy storage system combining compressed air energy storage and electrochemical energy storage as an example, this combination fully utilizes the advantages of large-scale, long-term energy storage and low cost of compressed air energy storage, as well as the complementary advantages of rapid response and precise power regulation of electrochemical energy storage, thereby improving the flexibility and cost-effectiveness of the hybrid energy storage system.

[0023] Based on the above analysis, the load power of the load side at different times is obtained through smart meters in the power grid, and the charging and discharging power of each energy storage device at different times is obtained through the hybrid energy storage system. The energy storage devices include compressed air energy storage and electrochemical energy storage. In this embodiment, the load power acquisition time interval is 10s and the charge / discharge power acquisition time interval is 1s. As for other implementation methods, the implementer can set them according to the actual situation.

[0024] It should be noted that a negative charging / discharging power indicates that the energy storage device is releasing electrical energy to the grid and is in a discharging or releasing state; a positive charging / discharging power indicates that the energy storage device is absorbing electrical energy from the grid and is in a charging or storing state. In this case, the energy storage device will not discharge while charging.

[0025] Secondly, the electricity consumption of the power grid system changes constantly throughout the day, exhibiting significant time-varying characteristics and regularity. The different electricity consumption states at different times of the day directly affect the synergistic regulation effect of the hybrid energy storage system.

[0026] Therefore, all moments within a day are considered as a monitoring cycle, and a monitoring cycle is divided into multiple time periods; In this embodiment, the duration of the time period is 4 hours, that is, a day contains 6 time periods. As for other implementation methods, the implementer can set it according to the actual situation.

[0027] Thus, the load power of the power grid at different times in each time period under each monitoring cycle, and the charging and discharging power of each energy storage device at different times in each time period are obtained.

[0028] Step 2: Analyze the volatility of the load power change rate and the randomness of load power fluctuations within each time period, and calculate the random volatility of each time period; based on the charging and discharging power of all energy storage devices and the trend of load power changes within each time period, calculate the supply and demand deterioration degree of each time period, and combine the random volatility to obtain the unsteady-state coefficient of each time period, and predict the unsteady-state coefficient of each time period in the next monitoring cycle.

[0029] The load in the power grid is constantly changing. This change may be caused by the random superposition of electricity consumption behaviors of a large number of users. For example, residents use electrical appliances at different times, and factories adjust equipment operation according to production plans. These random behaviors cause the load to fluctuate frequently and rapidly in a short period of time. Secondly, the performance of energy storage systems in grid regulation may also be uncertain. For example, the charging and discharging efficiency of energy storage devices may decrease over time, or it may perform differently at different temperatures. This uncertainty means that energy storage systems may not work exactly as expected in actual operation, further increasing the complexity of grid regulation. The frequent fluctuations in load and the uncertainty of energy storage system performance will directly affect the stability of grid operation, thereby causing the risk of frequency exceeding limits, voltage fluctuations, and even local power outages.

[0030] Based on the above analysis, the random superposition of electricity consumption behaviors of a large number of users leads to frequent and rapid fluctuations in load within a short period of time, and the rate of load change varies considerably. Therefore, the analysis of load power fluctuations and the calculation of random volatility are as follows: Perform curve fitting on the load power at all times within each time period, and calculate the derivative of the fitted curve at each time. In this embodiment, the least squares method is used for curve fitting. The least squares method and the calculation of the derivative are well-known techniques and will not be described in detail here.

[0031] Calculate the degree of dispersion of all derivatives within each time period; In this embodiment, the degree of dispersion is measured by calculating the coefficient of variation of all derivatives in each time period. The calculation of the coefficient of variation is a well-known technique and will not be described in detail here. As other implementation methods, implementers may use other methods of the prior art, such as standard deviation, etc. This embodiment does not impose any special restrictions on this.

[0032] Obtain all extreme points of load power at all times within each time period; In this embodiment, the AMPD (Automatic multiscale-based peak detection) algorithm is used to obtain the extreme points. The AMPD algorithm is a well-known technology and will not be described in detail here.

[0033] The difference between the load power corresponding to each maximum point and the load power corresponding to its adjacent minimum point within each time period is taken as the first difference. Calculate the fractal dimension of all first differences within each time period; In this embodiment, the Higuchi algorithm is used to calculate the fractal dimension, and the maximum time interval is set to 20. As for other implementation methods, the implementer can set it according to the actual situation. The Higuchi algorithm is a well-known technology and will not be described in detail here.

[0034] The random volatility, dispersion, and fractal dimension of each time period are all positively correlated. It should be noted that a positive correlation means that the dependent variable increases as the independent variable increases and decreases as the independent variable decreases.

[0035] In this embodiment, the product of the degree of dispersion and the fractal dimension is used as the random fluctuation of each time period. In other implementations, the implementer can use the sum of the degree of dispersion and the fractal dimension as the random fluctuation of each time period.

[0036] It should be noted that the derivative reflects the rate of change of load power, i.e., whether the load power is rising or falling. The greater the dispersion, the greater the fluctuation of the rate of change of load power during this period, and the more unstable the load change. The first difference reflects the fluctuation range of load power between adjacent extreme points. The greater the fractal dimension, the higher the complexity and randomness of the fluctuation of load power during this period. The higher the random volatility, the worse the load is in this period. Not only does the rate of change fluctuate, but the fluctuation pattern is also completely random. This reflects the more significant the random characteristics of the rate of change and short-term fluctuation of load power during this period, and the stronger the random interference caused by new energy output, instantaneous load changes, etc.

[0037] Furthermore, since the load power has a certain trend of rise and fall, the trend of load change affects the charging and discharging power of the hybrid energy storage system to a certain extent. When the load reaches its peak or is in a rapid rising phase, the hybrid energy storage system discharges to smooth the grid pressure; while when the load is in a falling or low phase, the hybrid energy storage system can charge to store excess electrical energy, thereby improving the stability of grid operation.

[0038] In hybrid energy storage systems, different energy storage devices need to work collaboratively. However, one energy storage device may be charging while another is discharging. Therefore, the smaller the overall charging and discharging power of the hybrid energy storage system, and the more pronounced the decreasing trend of the overall charging and discharging power (i.e., the more the discharge gradually increases), while the load power shows an upward trend, the greater the supply pressure on the grid's power supply side. Therefore, analyzing the changing trends of the charging and discharging power of different energy storage devices and the changing trends of load power is crucial for calculating the degree of supply-demand deterioration. Specifically: Calculate the sum of the charging and discharging power of all energy storage devices at the same moment, and use it as the total charging and discharging power; It should be noted that the larger the absolute value of the negative total charging and discharging power, the greater the output power of the hybrid energy storage system to the grid, that is, the more the hybrid energy storage system discharges to the grid. Conversely, the larger the positive total charging and discharging power, the smaller the output power of the hybrid energy storage system to the grid.

[0039] Calculate the difference in total charging and discharging power between adjacent time points within each time period, and use this as the second difference; In this embodiment, the difference between the total charging and discharging power at each moment within each time period and the previous moment is calculated as the second difference.

[0040] Obtain the trend statistics of all second differences within each time period, and positively map them to serve as the first trend quantity. In this embodiment, the MK (Mann-Kendall) trend test algorithm is used to obtain trend statistics. The MK trend test algorithm is a well-known technique and will not be described in detail here. Secondly, the positive mapping process is as follows: the sigmoid function is used to perform positive mapping on the trend statistics. The sigmoid function is a well-known technique and will not be described in detail here. Through the positive mapping process, the result of the positive mapping is made to be greater than 0.

[0041] It should be noted that the larger the first trend quantity, the more likely the overall charging and discharging power of the hybrid energy storage system is to gradually increase, indicating that the overall discharging power is decreasing or the overall charging power is increasing. That is, the hybrid energy storage system is gradually and rapidly changing towards a larger charging state or a smaller discharging state, reflecting that the discharge of the hybrid energy storage system is gradually decreasing. Conversely, the smaller the first trend quantity, the more likely the overall charging and discharging power of the hybrid energy storage system is to gradually decrease, indicating that the overall discharging power is increasing.

[0042] Obtain the trend statistics of all load power in each time period, and perform positive mapping on them as the second trend quantity; In this embodiment, the MK (Mann-Kendall) trend test algorithm is used to obtain trend statistics. The MK trend test algorithm is a well-known technique and will not be described in detail here. Secondly, the positive mapping process is as follows: the sigmoid function is used to perform positive mapping on the trend statistics. The sigmoid function is a well-known technique and will not be described in detail here. Through the positive mapping process, the result of the positive mapping is made to be greater than 0.

[0043] It should be noted that the larger the second trend value, the more obvious the increasing trend of load power during that period.

[0044] The degree of deterioration in supply and demand over different time periods is positively correlated with the second trend quantity, but negatively correlated with the first trend quantity. It should be noted that a positive correlation means that the dependent variable increases as the independent variable increases and decreases as the independent variable decreases; a negative correlation means that the dependent variable decreases as the independent variable increases and increases as the independent variable decreases.

[0045] In this embodiment, the ratio of the second trend quantity to the first trend quantity is used as the degree of supply and demand deterioration in each time period. In other implementation methods, the implementer can also calculate the difference between the second trend quantity and the first trend quantity. In order to avoid the difference being less than 0, the difference is used as the result of an exponential function with the natural constant as the base and the difference as the exponent, as the degree of supply and demand deterioration in each time period.

[0046] It should be noted that the greater the degree of supply and demand deterioration, the greater the load response demand of the power grid during that period, and the greater the discharge of the hybrid energy storage system. This reflects that the power grid is facing a severe situation of continuous load growth and continuous decline in energy storage support capacity during that period. The safety margin of the energy storage system in maintaining voltage and frequency stability is being rapidly consumed, and the power supply pressure on the power grid is greater.

[0047] Furthermore, the characteristics of load power and hybrid energy storage system changes vary over different time periods. Both frequent short-term fluctuations and overall upward and downward trends will affect the regulation efficiency of hybrid energy storage system. Therefore, accurately identifying and predicting these trend characteristics is the key to optimizing energy storage dispatch strategies.

[0048] First, based on the random volatility and the degree of supply and demand deterioration, the non-steady-state coefficient is determined as follows: By positively fusing random volatility and supply-demand deterioration, the unsteady-state coefficients for each time period are obtained; In this embodiment, the specific process of forward fusion is as follows: the random volatility and the supply-demand deterioration are normalized using the maximum-minimum normalization method, and the product of the normalized random volatility and the normalized supply-demand deterioration is used as the non-steady-state coefficient for each time period. In other implementation methods, the implementer can also calculate the mean of the normalized random volatility and the normalized supply-demand deterioration as the non-steady-state coefficient for each time period. The maximum-minimum normalization method is a well-known technique and will not be described in detail here.

[0049] It should be noted that the larger the non-steady-state coefficient, the more severe the load fluctuation of the power grid and the greater the degree of decay of the overall dynamic stability margin of the power grid during that period. In order to cope with the high regulation demand, a faster response speed is needed to adjust the charging and discharging power of the energy storage device in order to maintain the stable operation of the power grid.

[0050] Furthermore, since the load change patterns are similar across different monitoring cycles, predicting user electricity consumption behavior at different times allows for advance assessment of the demand intensity for energy storage regulation under different load changes. This helps to more rationally allocate the output of energy storage devices and optimize the overall performance of the hybrid energy storage system. Therefore, based on the unsteady-state coefficients of each monitoring cycle and the same time period in previous monitoring cycles, the unsteady-state coefficients for the next monitoring cycle are predicted, specifically as follows: The unsteady-state coefficients for the same time period in each monitoring cycle and multiple previous monitoring cycles are predicted to obtain the predicted values ​​for each time period in the next monitoring cycle. In this embodiment, a first exponential smoothing algorithm is used to predict the unsteady-state coefficients for the same time period in each monitoring cycle and the 20 monitoring cycles prior to it. As other implementation methods, implementers can set their own methods according to actual conditions. The first exponential smoothing algorithm is a well-known technology and will not be described in detail here. As other implementation methods, implementers can use other methods of existing technology, such as the ARIMA model. This embodiment does not impose any special restrictions on this.

[0051] It should be noted that the forecast values ​​reflect the expected pre-stress state of the power grid during this period in the next monitoring cycle, characterized by high regulation demand and high operational risk.

[0052] Thus, the predicted values ​​for each time period in the next monitoring cycle are obtained.

[0053] Step 3: Based on the charging and discharging power of energy storage devices in each time period, assess the incoordination of charging and discharging behavior of different energy storage devices and extract all asynchronous time periods; calculate the asynchronous coefficient of each time period by the proportion of charging and discharging power of different energy storage devices in the asynchronous time periods, and determine the adjustment coefficient of each time period in the next monitoring cycle by combining the predicted value; adjust the proportional parameters of the voltage loop and current loop of the dual closed-loop PI control strategy respectively, and use the adjusted proportional parameters to perform coordinated scheduling of energy storage devices in the hybrid energy storage system using the dual closed-loop PI control strategy.

[0054] Furthermore, different energy storage devices have different charging and discharging modes or performance characteristics, and the parameters of their internal components change with temperature and aging, leading to uncertainties in the performance of energy storage systems. For example, electrochemical energy storage has a fast charging and discharging response, making it very suitable for providing rapid frequency regulation and power support, but its energy density and cycle life are limited by chemical materials, and it exhibits a certain degree of self-discharge. Compressed air energy storage, on the other hand, involves complex thermodynamic processes of air compression and expansion during large-scale energy storage, and its system efficiency is significantly affected by thermal management; its response speed is also slower than that of electrochemical energy storage. This results in a severe imbalance between charging and discharging in hybrid energy storage systems. The asynchronous charging and discharging characteristics between energy storage devices reduce the overall operating efficiency of the power grid and increase the difficulty of power coordination among the various energy storage devices.

[0055] Based on the above analysis, by analyzing the asynchronous charging and discharging states of different energy storage devices, asynchronous time periods are extracted, and the proportion of charging and discharging amounts of energy storage devices during asynchronous time periods is analyzed to calculate the asynchronous coefficient. The flowchart of the method for obtaining the asynchronous coefficient for each time period provided in this application embodiment is shown below. Figure 2 As shown, it specifically includes: The moments when the signs of the charging and discharging power of all energy storage devices are inconsistent within each time period are marked as moments of dissimilarity. Different consecutive moments within each time period are considered asynchronous time periods. Curve fitting is performed on all charging and discharging power of each energy storage device in each time period. The total area of ​​the fitted curve in the corresponding whole time period is calculated, and the sum of the areas of the fitted curve in all asynchronous time periods is calculated. In this embodiment, the least squares method is used for curve fitting. The least squares method and the calculation of the integral area are well-known techniques and will not be described in detail here.

[0056] The ratio of the summation to the total area of ​​the integration is used as the proportion of asynchrony of each energy storage device in each time period; It should be noted that, to avoid the denominator being zero when calculating the ratio, a parameter tuning factor is added to the denominator. The range of values ​​for the parameter tuning factor is [range missing]. In this embodiment, the parameter tuning factor is set to 1. In other implementation methods, the implementer can set it according to the actual situation.

[0057] The asynchronous proportion of all energy storage devices in each time period is positively integrated and used as the asynchronous coefficient for each time period. In this embodiment, the specific process of forward fusion is as follows: the average of the asynchronous proportions of all energy storage devices in each time period is used as the asynchronous coefficient for each time period; as another implementation method, the implementer can also calculate the sum of the asynchronous proportions of all energy storage devices in each time period as the asynchronous coefficient for each time period.

[0058] It should be noted that if the charging and discharging states of compressed air energy storage and electrochemical energy storage systems are inconsistent, the signs of their corresponding charging and discharging power will be opposite. The total integral area reflects the total energy storage or release of the energy storage device during this time period, and the sum reflects the energy storage or release of the energy storage device during the asynchronous period. The larger the asynchronous proportion, the more significant the charging and discharging asynchrony between the energy storage devices during this time period, reflecting a higher proportion of charging and discharging in the asynchronous period in the entire time period. The larger the obtained asynchronous coefficient, the worse the coordination of the entire hybrid energy storage system during this time period, the more severe the internal friction between energy storage devices, and the lower the overall regulation efficiency.

[0059] Furthermore, when coordinating the scheduling of hybrid energy storage systems, dual-loop PI control is a commonly used control strategy, which typically includes two control loops: an inner current loop and an outer voltage loop. The inner current loop is responsible for responding quickly to changes in current, while the outer voltage loop is used to stabilize the system's output voltage. Through this structure, dual-loop PI control can effectively suppress system interference and improve system stability and response speed.

[0060] The proportional parameter of the dual-loop PI control strategy affects the linear relationship between the inner-loop output and the outer-loop error. An excessively large proportional parameter will cause the inner-loop response to be too fast, the controller to overreact to small errors, and it is prone to overshoot and continuous oscillation. This manifests as high-frequency ripples or even instability and divergence in the output voltage or current, excessively fast dynamic response of the energy storage system, and reduced stability margin. On the other hand, an excessively small proportional parameter will cause the inner-loop response to be too slow, the system response to be sluggish, the controller to be unresponsive to errors, and it will be unable to quickly smooth out fluctuations. This manifests as slow voltage recovery, lag in power tracking, and inability to effectively suppress fluctuations. It may directly lead to frequency overruns or voltage collapse, resulting in loss of regulation function.

[0061] During grid operation, the greater the comprehensive unsteady-state variation characteristics of load power (i.e., the larger the unsteady-state coefficient), the more drastic the load demand fluctuations, and the more rapidly the safety margin for maintaining stability of the energy storage system is consumed. At this point, the grid faces a sharply increased risk of instability. To maintain stability, stronger control is needed to counteract disturbances; therefore, the proportional parameter should be increased to enhance the strength and speed of control commands. Simultaneously, the more asynchronous the charging and discharging states of energy storage devices (i.e., the larger the asynchrony coefficient), the more likely internal coordination within the hybrid energy storage system will fail, severely weakening overall control efficiency. More powerful control intervention is needed to restore its regulatory capacity; therefore, the proportional parameter should be increased to enhance the response stiffness and synchronization constraint of the energy storage system. Therefore, based on the predicted values ​​and the asynchrony coefficient, the regulation coefficient is determined as follows: The adjustment coefficients and predicted values, as well as the asynchronous coefficients, are positively correlated in each time period of the next monitoring cycle. In this embodiment, the normalized result of the product between the asynchronous coefficient of each time period under each monitoring cycle and the predicted value of the same time period under the next monitoring cycle is used as the adjustment coefficient of each time period under the next monitoring cycle. The normalization process is performed by the maximum and minimum value normalization method, which is a well-known technique and will not be described in detail here.

[0062] It should be noted that the larger the adjustment coefficient, the more the power grid load will be in a state of high fluctuation and high risk in each time period of the next monitoring cycle. At the same time, there is a serious asynchrony between the charging and discharging states of energy storage devices. This indicates that the power grid operation is more severely affected by load fluctuations and the uncertainty of the energy storage system. It reflects that the power grid will soon face extremely high external adjustment demands and extremely low internal coordination efficiency at the same time, and the risk of power supply instability will increase dramatically. In order to ensure the stability of the power grid operation, it is necessary to strengthen the control and response speed of the hybrid energy storage system.

[0063] Based on the above analysis, the proportional parameter of the dual-closed-loop PI control strategy is adjusted by adjusting the coefficient, specifically as follows: The formula for calculating the adjusted proportional parameters of the voltage loop for each time period in the next monitoring cycle is as follows: The formula for calculating the adjusted proportional parameters of the current loop for each time period in the next monitoring cycle is as follows: in, This represents the adjusted proportional parameter for the voltage loop in the r-th time period during the (t+1)-th monitoring cycle. This is the preset minimum proportional parameter for the voltage loop. This is the normalized adjustment coefficient corresponding to the r-th time period under the (t+1)-th monitoring cycle; This represents the adjusted proportional parameter for the current loop in the r-th time period during the (t+1)-th monitoring cycle. This is the preset minimum proportional parameter for the current loop. Preset adjustment range; In this embodiment, the voltage loop is the outer loop, and its response speed must be lower than that of the current inner loop; otherwise, it will cause system oscillation. Therefore, the value range of the proportional parameter of the voltage loop is generally [value missing]. The range of values ​​for the proportional parameters of the current loop is generally as follows: Therefore, the preset minimum proportional parameter of the voltage loop is set to 1, the preset minimum proportional parameter of the current loop is set to 12, and the preset adjustment range is set to 10. When changing from 0 to 1, Change 1 Change 10 ensures that, under high-risk conditions, the current loop accelerates much faster than the voltage loop, thus maintaining a fast inner loop and a stable outer loop architecture and preventing closed-loop instability.

[0064] Based on the adjusted proportional parameters of the voltage and current loops, a dual-closed-loop PI control strategy with first-order low-pass filtering is used to coordinate the scheduling of a hybrid energy storage system consisting of compressed air energy storage and electrochemical energy storage. By fully utilizing the advantages of the two energy storage technologies, the overall regulation efficiency of the hybrid energy storage system is improved, thereby achieving dynamic power balance, frequency and voltage stability of the power grid, and enhancing the stable operation of the power grid under the access of a high proportion of renewable energy.

[0065] It should be noted that the dual closed-loop PI control strategy for first-order low-pass filtering is a well-known technique and will not be elaborated upon here.

[0066] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. A method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation, characterized in that, The method includes the following steps: The load power of the grid load side at different times in each time period under each monitoring cycle is obtained, and the charging and discharging power of each energy storage device in the hybrid energy storage system at each time in each time period is obtained. Analyze the volatility of the rate of change of load power and the randomness of load power fluctuations in different time periods, and calculate the random volatility of each time period. Based on the changing trends of the charging and discharging power and load power of all energy storage devices in each time period, the degree of supply and demand deterioration in each time period is calculated. Combined with the random fluctuation, the unsteady-state coefficient of each time period is obtained, and the unsteady-state coefficient of each time period in the next monitoring cycle is predicted. Based on the charging and discharging power of energy storage devices in each time period, the incoordination of charging and discharging behavior of different energy storage devices is evaluated, and all asynchronous time periods are extracted. By calculating the proportion of charging and discharging power of different energy storage devices in asynchronous time periods, the asynchronous coefficient of each time period is calculated. Combined with the predicted value, the adjustment coefficient of each time period in the next monitoring cycle is determined. The proportional parameters of the voltage loop and current loop of the dual closed-loop PI control strategy are adjusted respectively. Using the adjusted proportional parameters, the dual closed-loop PI control strategy is adopted to coordinate the scheduling of energy storage devices in the hybrid energy storage system.

2. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 1, characterized in that, The energy storage devices in the hybrid energy storage system include compressed air energy storage devices and electrochemical energy storage devices.

3. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 1, characterized in that, The calculation of the random fluctuation of each time period includes: Curve fitting is performed on the load power at all times within each time period, the derivative of the fitted curve at each time is calculated, and the dispersion of all derivatives within each time period is calculated. Obtain all extreme points of load power at all times within each time period; take the difference in load power between two adjacent extreme points within each time period as the first difference, and calculate the fractal dimension of all first differences within each time period. The random volatility is positively correlated with the degree of dispersion and the fractal dimension.

4. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 1, characterized in that, The calculation of the degree of supply and demand deterioration in each time period includes: Calculate the sum of the charging and discharging power of all energy storage devices at the same moment as the total charging and discharging power. Calculate the difference between the total charging and discharging power at adjacent moments in each time period as the second difference. Obtain the trend statistics of all second differences in each time period and perform positive mapping on them as the first trend quantity. Obtain the trend statistics of all load power in each time period, and perform positive mapping on them as the second trend quantity; The degree of deterioration in supply and demand is positively correlated with the second trend quantity, but negatively correlated with the first trend quantity.

5. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 1, characterized in that, The unsteady-state coefficient is the result of a positive fusion of random volatility and supply-demand deterioration.

6. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 1, characterized in that, The process of extracting the asynchronous time period is as follows: count the times when the signs of the charging and discharging power of all energy storage devices are inconsistent within each time period, mark them as different times, and form asynchronous time periods by combining consecutive different times within each time period.

7. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 1, characterized in that, The calculation of the asynchrony coefficient for each time period includes: Curve fitting is performed on all charging and discharging power of each energy storage device in each time period. The total area of ​​the fitted curve in the corresponding whole time period is calculated, and the sum of the areas of the fitted curve in all asynchronous time periods is calculated. The ratio of the sum to the total area is used as the asynchronous proportion of each energy storage device in each time period. The asynchronous coefficient is the result of positively integrating the asynchronous proportions of all energy storage devices in each time period.

8. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 1, characterized in that, The adjustment coefficient is positively correlated with the predicted value and the asynchronous coefficient.

9. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 1, characterized in that, Voltage loop in the first The monitoring cycle under the first The adjusted proportional parameters for each time period The calculation formula is: ,in, This is the preset minimum proportional parameter for the voltage loop. For the first The monitoring cycle under the first Each time period corresponds to a normalized adjustment coefficient.

10. The method for coordinated dispatch of compressed air energy storage and electrochemical energy storage for power grid regulation as described in claim 9, characterized in that, The current loop in the first The monitoring cycle under the first The adjusted proportional parameters for each time period The calculation formula is: ,in, This is the preset minimum proportional parameter for the current loop. This is the preset adjustment range.

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