Energy storage frequency modulation strategy optimization method and system based on artificial bee colony algorithm

Abstract

The invention relates to the technical field of artificial bee colony algorithms, provides an energy storage frequency modulation strategy optimization method and system based on an artificial bee colony algorithm, and solves the problems of low suppression speed and low stability of power grid frequency fluctuation. The method comprises the following steps: acquiring power grid frequency deviation data under a load sudden change working condition, extracting frequency change characteristics through sliding time window processing, and performing extreme value normalization to form a standard fluctuation sequence representing frequency dynamic characteristics; and after the sequence is converted into an initial power compensation demand signal, phase compensation preprocessing is carried out to obtain an optimized power compensation signal. Establishing a parameter optimization model taking virtual inertia control parameters as optimization variables, performing iterative solution by adopting an artificial bee colony algorithm to obtain an optimal parameter combination, and constructing a mapping function of frequency and power according to the optimal parameter combination. And forming a self-adaptive virtual inertia control strategy based on the mapping function, and generating an energy storage output instruction after processing the power compensation signal. According to the invention, the suppression speed and stability of power grid frequency fluctuation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial bee colony algorithm, and particularly relates to an energy storage frequency modulation strategy optimization method and system based on an artificial bee colony algorithm. BACKGROUND

[0002] In the operation of a power system, load mutation can cause rapid fluctuations in the frequency of the power grid, and the energy storage system needs to provide instantaneous power support to maintain frequency stability. This requires a frequency modulation control strategy that not only responds quickly to frequency changes, but also needs to adaptively adjust the output according to the dynamic characteristics of the power grid, while suppressing frequency overshoot and ensuring the safe operation of the energy storage device.

[0003] The existing scheme uses a virtual inertia control method based on fixed parameters. By measuring the frequency deviation of the power grid, the power compensation instruction is calculated through a pre-set differential element and an inertia element, and then combined with the operating state constraints of the energy storage system to generate the final frequency modulation instruction.

[0004] When dealing with frequency fluctuations of different amplitudes and rates of change, the adaptability of the control parameters is limited, which may result in a mismatch between the response speed of power compensation and the actual needs of the power grid. At the same time, fixed control parameters may cause fluctuations in the suppression effect of frequency fluctuations when facing complex and variable power grid operating conditions, affecting the stable operation of the system. SUMMARY

[0005] The present application provides an energy storage frequency modulation strategy optimization method and system based on an artificial bee colony algorithm to solve the problem of slow suppression speed and low stability of power grid frequency fluctuations in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides an energy storage frequency modulation strategy optimization method based on an artificial bee colony algorithm, comprising:

[0007] Obtaining frequency deviation data generated by an energy storage frequency modulation strategy under load mutation conditions of a power grid;

[0008] Performing sliding time window processing on the frequency deviation data to extract frequency change characteristics, and performing extreme value normalization processing on the frequency change characteristics corresponding to different sliding windows, respectively, to form a standard fluctuation sequence representing the dynamic characteristics of the power grid frequency;

[0009] Converting the standard fluctuation sequence into an initial power compensation demand signal, and performing phase compensation preprocessing on the initial power compensation demand signal to obtain a preprocessed power compensation demand signal;

[0010] A parameter optimization model is established with the differential gain coefficient and the response delay time of the virtual inertia control as optimization variables, and an artificial bee colony algorithm is used to solve the parameter optimization model in multiple rounds of iteration to obtain an optimal parameter combination, and based on the optimal parameter combination, a mapping function between frequency and power is constructed;

[0011] Based on the mapping function, an adaptive virtual inertia control strategy is formed, and the pre-processed power compensation demand signal is processed based on the adaptive virtual inertia control strategy to generate a energy storage output instruction, and the energy storage frequency modulation strategy of the energy storage system is optimized according to the energy storage output instruction to complete the optimized suppression of the power grid frequency fluctuation.

[0012] Optionally, the parameter optimization model is established with the differential gain coefficient and the response delay time of the virtual inertia control as optimization variables, and an artificial bee colony algorithm is used to solve the parameter optimization model in multiple rounds of iteration to obtain an optimal parameter combination, and based on the optimal parameter combination, a mapping function between frequency and power is constructed, comprising:

[0013] A parameter optimization model is established, the differential gain coefficient of the virtual inertia control is set as a first optimization variable, and the response delay time of the virtual inertia control is set as a second optimization variable;

[0014] Based on the first optimization variable and the second optimization variable, a target function is defined with the target of minimizing the frequency deviation integral and the power change rate;

[0015] According to the characteristics of the target function, search parameters of the artificial bee colony algorithm are set, the search parameters including the number of employed bees, the number of onlooker bees, and the maximum number of iterations;

[0016] Based on the search parameters, the artificial bee colony algorithm is used to solve in multiple rounds of iteration to determine the optimal parameter combination;

[0017] Based on the differential gain coefficient and the response delay time in the optimal parameter combination, a mapping function containing a frequency differential item and a delay compensation item is constructed.

[0018] Optionally, based on the search parameters, the artificial bee colony algorithm is used to solve in multiple rounds of iteration to determine the optimal parameter combination, comprising:

[0019] Based on the search parameters, an initial population containing multiple parameter combinations is initialized, each parameter combination containing the values of the first optimization variable and the second optimization variable;

[0020] In the employed bee stage of the artificial bee colony algorithm, each parameter combination in the initial population is subjected to neighborhood search to obtain an updated parameter combination, and based on the target function, the target function value corresponding to each updated parameter combination is calculated;

[0021] The onlooker bee stage of the artificial bee colony algorithm selects a high-quality parameter combination with a target function value greater than a preset threshold for deep search, and obtains an optimized parameter combination;

[0022] The scout bee stage of the artificial bee colony algorithm replaces parameters that fall into local optimization in the optimized parameter combination, obtains a new parameter combination, and takes the new parameter combination as an initial population;

[0023] The iterative search process of the employed bee stage, the onlooker bee stage and the scout bee stage is repeated until a preset termination condition is met, and a parameter combination with the optimal target function value is output as an optimal parameter combination.

[0024] Optionally, the mapping function including a frequency differential item and a delay compensation item is constructed based on the differential gain coefficient and the response delay time in the optimal parameter combination, including:

[0025] The frequency deviation data is differentiated to obtain a frequency differential signal;

[0026] The multiplication result of the frequency differential signal and the differential gain coefficient is taken as the frequency differential item;

[0027] The preprocessed power compensation demand signal is time-delayed to obtain a delay power signal;

[0028] The difference between the delay power signal and the preprocessed power compensation demand signal is calculated as a delay compensation item;

[0029] The frequency differential item and the delay compensation item are combined in a weighted manner to construct a mapping function.

[0030] Optionally, the standard fluctuation sequence is converted into an initial power compensation demand signal, and the initial power compensation demand signal is phase-compensated and preprocessed to obtain a preprocessed power compensation demand signal, including:

[0031] The standard fluctuation sequence is decomposed into a plurality of frequency sub-band signals, and power conversion coefficients corresponding to each frequency sub-band signal are determined according to grid impedance parameters and energy storage system response characteristics;

[0032] Each frequency sub-band signal is multiplied by the corresponding power conversion coefficient to obtain a power compensation signal of each frequency sub-band signal, and the power compensation signals of each frequency sub-band signal are synthesized to generate an initial power compensation demand signal;

[0033] An adaptive filtering algorithm is used to calculate the phase offset of the initial power compensation demand signal with respect to the grid voltage signal, and a real-time phase compensation value is calculated according to the phase offset and in combination with a pre-established phase compensation model.

[0034] applying the real-time phase compensation value to the initial power compensation demand signal to obtain a phase-compensated power compensation demand signal;

[0035] performing amplitude limiting processing on the phase-compensated power compensation demand signal to obtain a preprocessed power compensation demand signal.

[0036] Optionally, the frequency deviation data is subjected to sliding time window processing to extract frequency variation characteristics, and the frequency variation characteristics corresponding to different sliding windows are subjected to extreme value normalization processing respectively to form a standard fluctuation sequence representing power grid frequency dynamic characteristics, including:

[0037] a plurality of sliding time windows of different time lengths are set, the sliding time windows including a first window for capturing fluctuation characteristics and a second window for identifying variation trends;

[0038] the first window and the second window are moved in sequence according to a preset time interval, and frequency data segments of corresponding time periods are intercepted from the frequency deviation data respectively;

[0039] a speed characteristic quantity reflecting instantaneous variation is generated by performing difference calculation on the frequency data segment of the time period corresponding to each first window;

[0040] a slope characteristic quantity reflecting a sustained variation trend is generated by performing linear fitting on the frequency data segment of the time period corresponding to each second window;

[0041] the speed characteristic quantity and the slope characteristic quantity are combined as corresponding frequency variation characteristics to form a frequency variation characteristic set, and the maximum value and the minimum value of each characteristic quantity in the frequency variation characteristic set are identified;

[0042] Based on the maximum value and the minimum value, each characteristic quantity in the frequency variation characteristic set is subjected to extreme value normalization processing, and the frequency variation characteristic set subjected to extreme value normalization processing is rearranged in time sequence to form a standard fluctuation sequence.

[0043] Optionally, based on the mapping function, an adaptive virtual inertia control strategy is formed, and the preprocessed power compensation demand signal is processed based on the adaptive virtual inertia control strategy to generate a energy storage output instruction, including:

[0044] Based on the frequency differential term and the delay compensation term in the mapping function, a feedforward control channel and a feedback correction channel are constructed respectively;

[0045] The feedforward control channel and the feedback correction channel are connected in parallel to form an adaptive virtual inertia control strategy;

[0046] Based on the adaptive virtual inertia control strategy, the pre-processed power compensation demand signal is subjected to a differential gain process to generate a feedforward power component, and the frequency deviation data is subjected to a delay compensation process to generate a feedback correction component;

[0047] The feedforward power component and the feedback correction component are subjected to a weighted fusion process according to corresponding dynamic weight coefficients to generate an unregulated control signal;

[0048] The unregulated control signal is subjected to a power change rate limiting process to obtain a rate limited signal, and the rate limited signal is subjected to a power upper and lower limit truncation process to obtain a storage output instruction.

[0049] In a second aspect, the present application provides a storage frequency modulation strategy optimization system based on an artificial bee colony algorithm, comprising:

[0050] An acquisition module is configured to acquire frequency deviation data generated by a storage frequency modulation strategy under a load mutation condition of a power grid;

[0051] An extraction module is configured to perform a sliding time window process on the frequency deviation data to extract frequency change characteristics, and perform extreme value normalization processing on the frequency change characteristics corresponding to different sliding windows to form a standard fluctuation sequence representing dynamic characteristics of a power grid frequency;

[0052] A conversion module is configured to convert the standard fluctuation sequence into an initial power compensation demand signal, and perform phase compensation preprocessing on the initial power compensation demand signal to obtain a pre-processed power compensation demand signal;

[0053] An establishment module is configured to establish a parameter optimization model taking a differential gain coefficient and a response delay time of virtual inertia control as optimization variables, and perform multi-round iterative solving on the parameter optimization model using an artificial bee colony algorithm to obtain an optimal parameter combination, and construct a mapping function between frequency and power based on the optimal parameter combination;

[0054] A generation module is configured to form an adaptive virtual inertia control strategy based on the mapping function, and process the pre-processed power compensation demand signal based on the adaptive virtual inertia control strategy to generate a storage output instruction, and optimize a storage frequency modulation strategy of a storage system according to the storage output instruction to complete the optimized suppression of frequency fluctuation of the power grid.

[0055] In a third aspect, the present application provides an electronic device, comprising:

[0056] A memory is configured to store a computer program;

[0057] The processor is configured to implement the steps of the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization method according to the first aspect.

[0058] In a fourth aspect, the present application provides a computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, and the computer program is configured to implement the steps of the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization method according to the first aspect when executed by a processor.

[0059] In the present application, an artificial bee colony algorithm-based energy storage frequency regulation strategy optimization method is provided, which comprises the following steps: acquiring frequency deviation data generated by an energy storage frequency regulation strategy of a power grid under a load mutation working condition; performing sliding time window processing on the frequency deviation data to extract frequency variation characteristics, and performing extreme value normalization processing on the frequency variation characteristics corresponding to different sliding windows respectively to form a standard fluctuation sequence representing dynamic characteristics of power grid frequency; converting the standard fluctuation sequence into an initial power compensation demand signal, and performing phase compensation preprocessing on the initial power compensation demand signal to obtain a preprocessed power compensation demand signal; establishing a parameter optimization model taking a differential gain coefficient of virtual inertia control and a response delay time as optimization variables, and performing multi-round iteration solving on the parameter optimization model by using an artificial bee colony algorithm to obtain an optimal parameter combination, and constructing a mapping function between frequency and power based on the optimal parameter combination; forming an adaptive virtual inertia control strategy based on the mapping function, and processing the preprocessed power compensation demand signal based on the adaptive virtual inertia control strategy to generate an energy storage output instruction, and optimizing an energy storage frequency regulation strategy of an energy storage system according to the energy storage output instruction to complete optimization and suppression of power grid frequency fluctuation.

[0060] The technical scheme provided by the present application has the following beneficial effects:

[0061] The present application establishes real-time sensing capability for power grid frequency state, and provides accurate data basis for subsequent frequency regulation control. The dynamic characteristics of frequency variation are accurately captured, and fluctuation information of different time scales is unified into standardized representation. The accurate conversion from frequency fluctuation to power demand is realized, and the synchronization of power signal is improved through phase compensation. The control parameters most matched with the current working condition are obtained through intelligent optimization algorithm, and the adaptability of control is improved. The quantitative corresponding relationship between frequency and power is established, and accurate mathematical basis is provided for control strategy. The control mechanism capable of adjusting autonomously according to frequency fluctuation characteristics is formed, and the strain capacity of the system is enhanced. The precise control of energy storage power is realized, and the stability effect of power grid frequency is improved.

[0062] Further, the application further establishes a parameter optimization model taking the virtual inertia control parameter as the optimization object, adopts the artificial bee colony algorithm for multi-round iteration solving to determine the optimal differential gain coefficient and response delay time combination, and constructs a mapping function containing frequency differentiation and delay compensation based on the combination, thereby forming a complete parameter optimization and function construction process.

[0063] Moreover, the automatic matching of the control parameter and the operating condition is realized, the adaptability and accuracy of the virtual inertia control are improved, more accurate control basis is provided for the energy storage frequency modulation, and the ability of the system to cope with different frequency fluctuation conditions is enhanced.

[0064] These aspects or other aspects of the application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0066] Figure 1 A flow chart of an energy storage frequency modulation strategy optimization method based on an artificial bee colony algorithm provided by an embodiment of the application;

[0067] Figure 2 A specific implementation schematic diagram of an energy storage frequency modulation strategy optimization method based on an artificial bee colony algorithm provided by an embodiment of the application;

[0068] Figure 3 A structure schematic diagram of an energy storage frequency modulation strategy optimization system based on an artificial bee colony algorithm provided by an embodiment of the application. DETAILED DESCRIPTION

[0069] The existing virtual inertia control method based on fixed parameters has obvious limitations in dealing with complex and variable operating conditions of the power grid. The method uses preset control parameters to process frequency fluctuations of different characteristics, resulting in a matching deviation between the power compensation response and the actual demand of the power grid. The lack of parameter adaptability causes fluctuations in frequency suppression effect, affecting the stability of system operation, especially when facing frequency disturbances with different amplitudes and rates, the stability of the control effect needs to be improved.

[0070] To solve the above problems, the application provides a frequency regulation strategy optimization method based on an artificial bee colony algorithm, which realizes adaptive setting of key parameters of virtual inertia control by establishing a dynamic parameter optimization mechanism. The method first performs multi-scale analysis and standardization processing on the frequency fluctuation characteristics, then uses an intelligent optimization algorithm to solve the optimal parameter combination according to the current working condition, and constructs an accurate mapping relationship between the frequency and the power. By forming an adaptive virtual inertia control strategy, the method can dynamically adjust the control parameters according to the actual operation state of the power grid, so that the power compensation response better fits the real-time demand of the power grid, thereby effectively improving the stability of the frequency fluctuation suppression effect and solving the problem of insufficient adaptability of the fixed parameter control method in the face of complex working conditions.

[0071] To make the personnel in the technical field better understand the application scheme, the application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the application.

[0072] The core of the application is to provide a frequency regulation strategy optimization method based on an artificial bee colony algorithm, and a specific embodiment of the method is shown in the flowchart as Figure 1 The method comprises the following steps.

[0073] Step 101: Obtain frequency deviation data generated by the frequency regulation strategy based on energy storage under load mutation working condition.

[0074] In step 101, the load mutation working condition refers to the working state in which the power load in the power grid suddenly changes greatly. The frequency regulation strategy based on energy storage refers to a control method for adjusting the frequency of the power grid through charging and discharging of energy storage equipment. The frequency deviation data is the difference sequence between the actual frequency of the power grid and the standard frequency.

[0075] In the embodiment of the application, the power grid frequency measurement device continuously acquires real-time frequency data of the power grid, compares and calculates it with the standard frequency reference value, and obtains the frequency deviation data sequence, which reflects the frequency fluctuation of the power grid under load mutation and provides basic data for subsequent analysis.

[0076] For example, in a certain regional power grid, when a large industrial equipment is suddenly started, the real-time frequency data of the power grid is acquired by the frequency measurement device at a sampling frequency of 100 times per second, the standard frequency reference value is 50 Hz, and the frequency deviation data sequence is calculated, in which the maximum frequency deviation reaches 0.15 Hz. The data sequence will be used for subsequent processing and analysis.

[0077] Step 102: sliding time window processing is performed on the frequency deviation data to extract frequency change characteristics, and the frequency change characteristics corresponding to different sliding windows are respectively subjected to extreme value normalization processing to form a standard fluctuation sequence representing dynamic characteristics of the power grid frequency.

[0078] In step 102, sliding time window processing is an analysis method of segmenting and intercepting continuous data in fixed length time periods. The frequency change characteristics include instantaneous change rate and trend change rate, etc. The extreme value normalization processing is a data processing method of linearly converting data to a specific interval according to the maximum value and the minimum value. The standard fluctuation sequence is a data sequence formed after multi-scale analysis and standardization processing, which uniformly represents the dynamic characteristics of the frequency.

[0079] In the embodiments of the present application, first, different time length sliding windows are set to segment and intercept the frequency deviation data, the instantaneous change rate characteristics are calculated for the data segments in the short time window, and the trend change rate characteristics are calculated for the data segments in the long time window. Then, the characteristic values obtained by each window are respectively subjected to extreme value normalization processing. Finally, all the normalized characteristic values are combined in time sequence to form a standard fluctuation sequence.

[0080] For example, two window lengths of 0.2 seconds and 2 seconds are used to process the frequency deviation data, wherein the 0.2 second window is used to calculate the instantaneous change rate, and the 2 second window is used to calculate the trend change rate, The trend change rate is obtained by linear fitting to obtain a slope value, wherein represents the frequency change amount, represents the time interval. The calculated characteristic values are subjected to normalization processing, and the conversion formula is , wherein represents the normalized characteristic value, is the original characteristic value, and are the minimum value and the maximum value of the characteristic value, respectively. Finally, the normalized characteristic values are arranged in time sequence to form a standard fluctuation sequence.

[0081] Step 103: converting the standard fluctuation sequence into an initial power compensation demand signal, and performing phase compensation preprocessing on the initial power compensation demand signal to obtain a preprocessed power compensation demand signal.

[0082] In step 103, the initial power compensation demand signal is a preliminary power regulation instruction calculated according to the frequency fluctuation characteristics. The phase compensation preprocessing is a data processing method of adjusting the phase of the signal to eliminate the time delay effect. The preprocessed power compensation demand signal is a power instruction signal that can be used for control execution after phase optimization.

[0083] In the embodiment of the present application, the standard fluctuation sequence is decomposed into multiple sub-band signals according to different frequency components, the power conversion coefficients corresponding to each sub-band are determined according to the impedance characteristics of the power grid and the response capability of the energy storage system, the sub-band signals are multiplied by the corresponding coefficients to synthesize the initial power compensation demand signal, then the phase shift of the signal relative to the grid voltage is detected, the compensation value is calculated through the phase compensation model and the signal is phase adjusted, and finally the amplitude limiting processing is performed to obtain the preprocessed power compensation demand signal.

[0084] For example, the standard fluctuation sequence is decomposed into three frequency sub-bands of 0.1-0.5 Hz, 0.5-2 Hz and 2-5 Hz, and the power conversion coefficients are taken as 0.8, 1.2 and 1.5 respectively, and the initial power compensation demand signal is obtained after synthesis. It is detected that the signal has a phase lag of 0.1 radian relative to the grid voltage, the compensation value is calculated through the phase compensation model, the phase compensation is performed by complex multiplication, and finally the signal amplitude is limited to the range of ±10 megawatts, and the preprocessed power compensation demand signal is obtained.

[0085] Step 104: Establish a parameter optimization model taking the differential gain coefficient and the response delay time of the virtual inertia control as optimization variables, and perform multiple rounds of iterative solutions on the parameter optimization model by using the artificial bee colony algorithm to obtain an optimal parameter combination, and based on the optimal parameter combination, a mapping function between frequency and power is constructed.

[0086] In step 104, the parameter optimization model is a deterministic optimization model based on mathematical programming theory, and the model structure includes a decision variable layer, a target function layer and a constraint condition layer. The decision variable layer is composed of the differential gain coefficient and the response delay time, the target function layer is composed of the frequency deviation integral term and the power change rate term, and the constraint condition layer includes the parameter value range constraint and the system stability constraint. The training process is realized by iterative search of the artificial bee colony algorithm, including five stages of initialization of population, calculation of fitness, selection operation, crossover mutation and update of population, and finally converging to the optimal solution satisfying the constraint condition. The artificial bee colony algorithm is a swarm intelligence optimization algorithm simulating the foraging behavior of a bee colony. The optimal parameter combination is the parameter value combination that makes the target function optimal. The mapping function is a function expression that establishes the mathematical relationship between the frequency input and the power output.

[0087] In the embodiment of the present application, a parameter optimization model taking the differential gain coefficient and the response delay time as optimization variables is established, a target function including the frequency deviation and the power change rate is defined, the search parameters of the artificial bee colony algorithm are set and the population is initialized, the neighborhood search is performed in the employed bee stage, the high-quality solution is selected for deep search in the onlooker bee stage, the inferior solution is replaced in the scout bee stage, the optimal parameter combination is obtained by iterative solution, and a mapping function including the frequency differential term and the delay compensation term is constructed based on the combination.

[0088] For example, a parameter optimization model is established, and a target function is wherein represents a frequency deviation, represents a power change rate. The artificial bee colony parameters are set as 20 employed bees, 20 onlooker bees, and 100 maximum iterations. After 85 iterations, the optimal parameter combination is obtained as a differential gain coefficient of 3.5 and a response delay time of 0.15 seconds. A mapping function is constructed based on this wherein df / dt represents a frequency change rate, represents a delay power compensation amount.

[0089] Step 105: Based on the mapping function, an adaptive virtual inertia control strategy is formed, and the preprocessed power compensation demand signal is processed based on the adaptive virtual inertia control strategy to generate a energy storage output instruction. The energy storage output instruction is used to optimize the energy storage frequency modulation strategy of the energy storage system, so as to complete the optimized suppression of the power grid frequency fluctuation.

[0090] In step 105, the adaptive virtual inertia control strategy is a control method that automatically adjusts the control parameters according to the system state. The energy storage output instruction is a control instruction sent to the energy storage system to execute power output. The energy storage system refers to an energy storage device connected to the power grid through a power electronic conversion device. It dynamically adjusts the charge and discharge power by receiving the energy storage output instruction generated by the real-time frequency deviation data and the frequency change rate data optimized by the artificial bee colony algorithm, so as to realize the rapid compensation and stable control of the power grid frequency fluctuation. The power grid provides the frequency deviation signal as the control basis for the energy storage system, and the energy storage system provides frequency support for the power grid through power output, forming a closed-loop frequency modulation control loop.

[0091] In the embodiments of the present application, the adaptive virtual inertia control strategy containing feedforward control and feedback correction is constructed based on the mapping function. The preprocessed power compensation demand signal is input into the feedforward channel to generate a feedforward power component, and the frequency deviation data is input into the feedback channel to generate a feedback correction component. The two components are fused according to a dynamic weight coefficient to generate a control signal. After the change rate limitation and amplitude limitation, the energy storage output instruction is formed. The power output of the energy storage system is adjusted according to the instruction to realize the frequency fluctuation suppression.

[0092] For example, based on the mapping function, a control strategy is constructed, the pre-processed power compensation demand signal is differentiated to obtain a feedforward power component, and the frequency deviation data is time-delay compensated to obtain a feedback correction component. When the frequency change rate is greater than 0.5 Hz / s, the feedforward weight is 0.7, and the feedback weight is 0.3; otherwise, the feedforward weight is 0.4, and the feedback weight is 0.6. The fused control signal is subjected to a change rate limit of ±20 MW / s and an amplitude limit of ±10 MW to generate a storage output instruction, and the power output of the energy storage system is adjusted to make the frequency deviation recover to within ±0.05 Hz within 2 seconds.

[0093] The method realizes accurate suppression of power grid frequency fluctuations through multi-scale frequency feature analysis, intelligent parameter optimization, and adaptive control strategy. The method can automatically adjust the control parameters according to the actual operation state of the power grid, so that the power output of the energy storage system better matches the frequency regulation demand of the power grid, improving the adaptability and stability of frequency control and effectively improving the frequency quality of the power grid under load mutation conditions.

[0094] To solve the problem of insufficient adaptability caused by fixed virtual inertia control parameters, in some embodiments, step 104: a parameter optimization model is established with the differential gain coefficient and the response delay time of the virtual inertia control as optimization variables, and an artificial bee colony algorithm is used to solve the parameter optimization model for multiple rounds to obtain an optimal parameter combination. Based on the optimal parameter combination, a mapping function between frequency and power is constructed, as shown in Figure 2 The mapping function includes:

[0095] Step 201: A parameter optimization model is established, and the differential gain coefficient of the virtual inertia control is set as a first optimization variable, and the response delay time of the virtual inertia control is set as a second optimization variable.

[0096] In step 201, the first optimization variable refers to the differential gain coefficient of the virtual inertia control that adjusts the response strength of the frequency change, and the second optimization variable refers to the response delay time from detection to execution in the control system.

[0097] In the embodiments of the present application, first, the framework structure of the parameter optimization model is constructed, and the two key parameters that affect the control performance, i.e., the differential gain coefficient and the response delay time, are set as variables that need to be optimized, and an optimization problem mathematical model containing the two variables is established to lay a foundation for subsequent optimization calculation.

[0098] Step 202: Based on the first optimization variable and the second optimization variable, a target function is defined with the minimum frequency deviation integral and power change rate as the target.

[0099] In step 202, the objective function is a single-objective function instead of a multi-objective function, which is formed by fusing the frequency deviation integral term and the power rate of change term into a single comprehensive index through weighted summation, and the weight coefficient is 0.05 for balancing the contribution degrees of the two optimization objectives, and finally a unified scalar evaluation function is formed for guiding the parameter optimization process. The frequency deviation integral is the cumulative amount of the frequency deviation from the standard value, and the power rate of change is the amplitude of the power change per unit time.

[0100] In the embodiment of the present application, the objective function is constructed based on the two optimization variables set, which includes the frequency deviation integral term and the power rate of change term, and by fusing the two indicators reflecting the control quality into a unified evaluation standard, a clear direction and target are provided for parameter optimization.

[0101] Step 203: According to the characteristics of the objective function, set the search parameters of the artificial bee colony algorithm, including the number of employed bees, the number of onlooker bees and the maximum number of iterations.

[0102] In step 203, the characteristics of the objective function refer to the mathematical characteristics exhibited by the function in the parameter optimization process, including multi-peak, nonlinearity and complexity of the constraint conditions, which are derived from the dynamic coupling relationship between the frequency deviation and the power output in the virtual inertia control and the safety boundary limit of the power grid operation. The employed bees are responsible for exploring new solutions, the onlooker bees are responsible for following the best, and the maximum number of iterations is the maximum number of rounds limit of the algorithm running.

[0103] In the embodiment of the present application, the running parameters of the artificial bee colony algorithm are configured according to the mathematical characteristics of the objective function, including determining the number of employed bees and onlooker bees, and setting a reasonable maximum number of iterations, and the setting of these parameters directly affects the search efficiency and solution quality of the algorithm.

[0104] Step 204: Based on the search parameters, use the artificial bee colony algorithm to perform multiple rounds of iteration to determine the optimal parameter combination.

[0105] In step 204, multiple rounds of iteration are the process of gradually approaching the optimal solution by repeated improvement.

[0106] In the embodiment of the present application, multiple rounds of optimization calculation are performed using the artificial bee colony algorithm set, in each round of iteration, the employed bees perform neighborhood search on the current solution, the onlooker bees develop high-quality solutions in depth based on fitness selection, and the scout bees replace stagnant solutions, and finally the optimal parameter combination is obtained by continuously updating the population.

[0107] Step 205: Based on the differential gain coefficient and the response delay time in the optimal parameter combination, a mapping function including the frequency differential term and the delay compensation term is constructed.

[0108] In step 205, the differential gain coefficient in the optimal parameter combination represents the degree of influence of the frequency change rate on the power compensation amount, and the response delay time represents the time delay from frequency detection to power output. Both parameters are the numerical solutions that make the objective function optimal, which are obtained by evaluating and selecting each parameter combination based on the objective function value in the multi-round iterative search process of the artificial bee colony algorithm.

[0109] In the embodiments of the present application, a complete mapping function is constructed based on the obtained optimal parameter combination. The function includes a frequency differential item weighted by the differential gain coefficient and a delay compensation item determined by the response delay time, forming a complete mathematical relationship from frequency input to power output.

[0110] The following is a specific example:

[0111] In the frequency modulation scenario of the regional power grid responding to the sudden load of large industrial equipment, based on the obtained preprocessed power compensation demand signal and frequency deviation data sequence, the differential gain coefficient of virtual inertia control is set as the first optimization variable when establishing the parameter optimization model , and the response delay time of virtual inertia control is set as the second optimization variable , wherein The value range of is set to 0.1 to 10.0, The value range of is set to 0.01 seconds to 1.0 seconds, which is determined according to the regulation capacity of the energy storage system and the safety requirements of the power grid; and the objective function is defined based on the two optimization variables , wherein represents the frequency deviation in hertz, represents the power change rate in megawatts per second, is a weight coefficient with a value of 0.05, which is used to balance the two optimization objectives of frequency stability and power fluctuation; the search parameters of the artificial bee colony algorithm are set according to the nonlinear multi-peak characteristics of the objective function, the number of employed bees is set to 20, the number of onlooker bees is set to 20, and the maximum number of iterations is set to 100. These parameters are determined through preliminary experiments to balance the calculation efficiency and optimization effect; based on these search parameters, the artificial bee colony algorithm is used for multi-round iterative solution, 40 parameter combinations are initialized to generate a population, in each round of iteration, the employed bees perform neighborhood search on the current parameter combination, the onlooker bees select high-quality parameter combinations for deep search according to the fitness value, and the scout bees replace the parameter combinations that have not been improved for 10 consecutive times. After 85 iterations, the iteration is stopped when the improvement of the best objective function value is less than 0.001 for 5 consecutive times, and the optimal parameter combination is finally obtained as and Td=0.15 seconds, wherein the objective function value is reduced from the initial 2.56 to 0.85; and the mapping function is constructed based on the differential gain coefficient 3.5 and the response delay time 0.15 seconds in the optimal parameter combination wherein denotes the rate of change of frequency in hertz per second, denotes the power compensation amount after a 0.15 second delay in megawatts, is a compensation coefficient with a value of 0.8, which is obtained by regression analysis of historical operation data, and the finally formed mapping function will be used to generate an adaptive virtual inertia control strategy.

[0112] In the embodiments of the present application, the optimal control parameters are automatically obtained through an intelligent optimization algorithm, an accurate frequency-power mapping relationship is established, and the adaptability and accuracy of virtual inertia control are improved, so that the energy storage system can provide more matched power support according to the actual state of the power grid, and the stability effect of frequency control is enhanced.

[0113] In order to further improve the efficiency and accuracy of parameter optimization, in some embodiments, step 204: based on the search parameters, a multi-round iteration is solved by using an artificial bee colony algorithm to determine the optimal parameter combination, comprising:

[0114] Step 301: based on the search parameters, an initial population containing multiple parameter combinations is initialized and generated, each parameter combination containing the values of the first optimization variable and the second optimization variable.

[0115] In step 301, the initial population is an initial solution set composed of multiple parameter combinations, each parameter combination containing specific values of the first optimization variable differential gain coefficient and the second optimization variable response delay time.

[0116] In the embodiments of the present application, according to the preset search parameter scale, an initial population containing multiple parameter combinations is randomly generated, and the differential gain coefficient and the response delay time in each parameter combination are randomly generated within their allowed value range, forming an initial search solution set.

[0117] Step 302: through the employed bee stage of the artificial bee colony algorithm, each parameter combination in the initial population is subjected to neighborhood search to obtain an updated parameter combination, and based on the objective function, the objective function value corresponding to each updated parameter combination is calculated.

[0118] In step 302, the employed bee stage is the stage responsible for exploring new solutions in the artificial bee colony algorithm, neighborhood search is a method of generating new solutions by small-range perturbation near the current solution, and the objective function value is a numerical index for evaluating the pros and cons of the parameter combination.

[0119] In the embodiment of the present application, the hired bees perform neighborhood search on each parameter combination in the initial population, generate a new parameter combination by adding a random disturbance to the current parameter value, then calculate the objective function value corresponding to each new parameter combination based on the objective function, and update the solution in the population. The specific process is: different parameter combinations directly affect the dynamic response characteristics of virtual inertia control, which is specifically manifested as the influence on the frequency deviation integral value and the power change rate value, and the expression used to calculate the objective function value is The expression is the defined objective function, and the optimal parameter combination is selected from multiple parameter combinations based on the ascending order of the objective function value, that is, the parameter combination with the minimum objective function value is selected as the optimal solution.

[0120] Step 303: In the observation bee stage of the artificial bee colony algorithm, the high-quality parameter combination with the target function value greater than the preset threshold is selected for deep search to obtain the optimized parameter combination.

[0121] In step 303, the observation bee stage is the stage responsible for selection and follow-up in the artificial bee colony algorithm, the high-quality parameter combination refers to the parameter combination with a good target function value, and the deep search is a more detailed search process near the high-quality solution.

[0122] In the embodiment of the present application, the observation bee selects the high-quality parameter combination according to the target function value of each parameter combination, uses the probability selection mechanism to make the parameter combination with a smaller target function value have a higher selection probability, and performs deep search on the selected high-quality parameter combination to obtain a further optimized parameter combination.

[0123] Step 304: In the scout bee stage of the artificial bee colony algorithm, the parameters trapped in local optimization in the optimized parameter combination are replaced to obtain a new parameter combination, and the new parameter combination is used as the initial population.

[0124] In step 304, the scout bee stage is the stage responsible for jumping out of local optimization in the artificial bee colony algorithm, and the local optimization refers to the state that the search is trapped in a local area and cannot continue to improve. The parameter replacement is to replace the parameter combination with poor effect with a newly generated parameter combination.

[0125] In the embodiment of the present application, the scout bee detects the parameter combinations that have not been improved for a plurality of rounds in the optimized parameter combination, replaces these parameter combinations trapped in local optimization with newly generated parameter combinations, and maintains the diversity of the population.

[0126] Step 305: Repeat the iterative search process of the hired bee stage, the observation bee stage and the scout bee stage until the preset termination condition is met, and output the parameter combination with the optimal target function value as the optimal parameter combination.

[0127] The specific content of the termination condition includes reaching a preset maximum number of iterations, the improvement of the objective function value in continuous multiple iterations being less than a set threshold, or the distribution of the optimal solution in the search population reaching a predetermined convergence criterion.

[0128] In the embodiments of the present application, the search processes of the employed bee stage, the onlooker bee stage, and the scout bee stage are repeatedly performed, and the search is stopped when the maximum number of iterations is reached or the optimal solution is not improved for continuous multiple rounds, and the parameter combination with the minimum objective function value in the entire search process is output as the optimal parameter combination.

[0129] The following is a specific example:

[0130] In the frequency modulation scenario of the regional power grid coping with the sudden load of large industrial equipment, based on the set number of employed bees 20, the number of onlooker bees 20, and the maximum number of iterations 100, an initial population containing 40 parameter combinations is initialized and generated, each parameter combination contains the values of the first optimization variable differential gain coefficient and the second optimization variable response delay time , which are uniformly generated by a random number generator within the value range of 0.1 to 10.0 of and the value range of 0.01 seconds to 1.0 seconds of , wherein the first parameter combination is = 2.1, = 0.08 seconds], the second parameter combination is = 5.7, = 0.25 seconds], and so on to generate 40 different parameter combinations; the neighborhood search of each parameter combination in the initial population is performed through the employed bee stage of the artificial bee colony algorithm, and the new parameter combination calculation formula is , , wherein is the search step value of the differential gain coefficient, which is 0.2, is the search step value of the response delay time, which is 0.05, and are uniformly distributed random numbers between 0 and 1, the objective function value corresponding to each updated parameter combination is calculated, and the best objective function value in the initial population is 2.56; the high-quality parameter combination with the objective function value greater than the preset threshold is selected for deep search through the onlooker bee stage of the artificial bee colony algorithm, the preset threshold is set to 1.50, and the selection probability calculation formula is , wherein represents the fitness value of the th parameter combination, which is equal to , represents the fitness value of the The target function value of the selected parameter combination is searched in depth to obtain an optimized parameter combination; the parameters in the optimized parameter combination that fall into local optimization are replaced through the scout bee stage of the artificial bee colony algorithm, parameter combinations with an improvement amount of the target function value of less than 0.001 in 10 continuous iterations are marked as falling into local optimization, the parameter combinations are replaced with newly generated parameter combinations, and the new parameter combinations are used as initial populations in the next iteration; the iteration search process of the employed bee stage, the onlooker bee stage and the scout bee stage is repeated 85 times until a preset termination condition is met, that is, the improvement amount of the best target function value in 5 continuous iterations is less than 0.001, at this time, the best target function value is reduced from the initial 2.56 to 0.85, and the parameter combination with the optimal target function value is output as the optimal parameter combination, wherein the differential gain coefficient is equal to 3.5, and the response delay time is equal to 0.15 seconds.

[0131] In the embodiments of the present application, the multi-stage cooperative search mechanism of the artificial bee colony algorithm is used to realize efficient optimization of the parameter combination, avoid the search process from falling into local optimization, ensure that the global optimal or approximate optimal parameter configuration is obtained, and improve the overall performance of the virtual inertia control system.

[0132] To further improve the accuracy and practicability of the mapping function construction, in some embodiments, step 205: constructing a mapping function containing a frequency differential item and a delay compensation item based on the differential gain coefficient and the response delay time in the optimal parameter combination, comprises:

[0133] Step 401: performing differential processing on the frequency deviation data to obtain a frequency differential signal.

[0134] In step 401, the frequency differential signal is a signal reflecting the rate of frequency change, which is obtained by differential operation on the frequency deviation data and represents the speed of the change of the power grid frequency.

[0135] In the embodiments of the present application, the differential calculation method is used to process the real-time collected frequency deviation data, extract the instantaneous change characteristics of the frequency change, and obtain the frequency differential signal reflecting the rate of frequency change.

[0136] Step 402: multiplying the frequency differential signal by the differential gain coefficient to obtain a frequency differential item.

[0137] In step 402, the frequency differential item is a power adjustment component formed by multiplying the frequency differential signal by the differential gain coefficient, which reflects the influence degree of the rate of frequency change on power compensation.

[0138] In the embodiment of the present application, the frequency differential signal is multiplied by the differential gain coefficient in the optimal parameter combination, the effect of frequency change on power adjustment is amplified, and the frequency differential term is generated as an important component of the mapping function.

[0139] Step 403: Time delay processing is performed on the preprocessed power compensation demand signal to obtain a delay power signal.

[0140] In step 403, the delay power signal is a signal obtained by performing time delay processing on the power compensation demand signal, reflecting the influence of system response delay on power output.

[0141] In the embodiment of the present application, the preprocessed power compensation demand signal is processed by a time delay module, and the delay time adopts the response delay time in the optimal parameter combination, to obtain a delay power signal considering system delay.

[0142] Step 404: The difference between the delay power signal and the preprocessed power compensation demand signal is calculated as a delay compensation term.

[0143] In step 404, the delay compensation term is the difference between the delay power signal and the original power compensation demand signal, used to compensate for the power deviation caused by system delay.

[0144] In the embodiment of the present application, the difference between the delay power signal and the preprocessed power compensation demand signal is calculated, and the difference reflects the power compensation error caused by system delay, which is used as a delay compensation term to participate in the construction of the mapping function.

[0145] Step 405: The frequency differential term and the delay compensation term are combined in a weighted manner to construct a mapping function.

[0146] In step 405, the weighted manner combination is a calculation method for fusing different components according to a specific weight ratio, used to balance the contribution degree of each component in the final function.

[0147] In the embodiment of the present application, the frequency differential term and the delay compensation term are weighted and summed according to the preset weight coefficient, to construct a complete mapping function mathematical expression, forming a complete conversion relationship from frequency input to power output.

[0148] The following is a specific example:

[0149] In the frequency modulation scene of the regional power grid coping with the sudden increase of large industrial equipment load, based on the differential gain coefficient 3.5 and the response delay time 0.15 seconds in the obtained optimal parameter combination, the frequency differential signal is first obtained by differentiating the frequency deviation data, and the central difference method calculation formula is , wherein represents the frequency change rate (frequency differential signal), represents the current time frequency value in hertz, represents the previous time frequency value in hertz, represents the sampling time interval value of 0.01 seconds in seconds, when the frequency is detected to change from 49.92 Hz to 49.90 Hz, the frequency differential signal value is calculated to be negative 2.0 Hz per second; the frequency differential signal is multiplied by the differential gain coefficient 3.5 to obtain the frequency differential term, the calculation process is 3.5 times negative 2.0 equal to negative 7.0, and the unit is converted to megawatts to obtain the frequency differential term value of negative 7.0 megawatts; at the same time, the preprocessed power compensation demand signal is subjected to time delay processing, and the delay time is 0.15 seconds, when the original power compensation demand signal is 8.5 megawatts, the time delay power signal value is 8.5 megawatts after 0.15 seconds delay; the difference between the time delay power signal and the preprocessed power compensation demand signal is calculated as the time delay compensation term, since the signal values before and after delay are the same, the time delay compensation term calculation result is 0 megawatts; finally, the frequency differential term and the time delay compensation term are combined in a weighted manner to construct a mapping function, and the weight coefficient and , the mapping function expression is , substituting the numerical value calculation obtains P equal to 0.7 times negative 7.0 plus 0.3 times 0 equal to negative 4.9 megawatts, wherein represents the target power value output by the mapping function in megawatts, and the complete mapping function expression finally constructed is .

[0150] In the embodiments of the present application, by constructing a mapping function containing frequency differential and time delay compensation, an accurate frequency-power conversion relationship is established, the accuracy and response characteristics of control are improved, and reliable technical support is provided for the participation of energy storage systems in grid frequency modulation.

[0151] In order to further improve the accuracy and practicability of the power compensation signal, in some embodiments, step 103: the standard fluctuation sequence is converted into an initial power compensation demand signal, and the initial power compensation demand signal is subjected to phase compensation preprocessing to obtain a preprocessed power compensation demand signal, comprising:

[0152] Step 501: decompose the standard fluctuation sequence into a plurality of frequency sub-band signals, and determine the power conversion coefficients corresponding to each frequency sub-band signal according to the grid impedance parameters and the response characteristics of the energy storage system.

[0153] In step 501, the frequency sub-band signal is a signal component obtained by decomposing a standard fluctuation sequence according to different frequency ranges. The grid impedance parameter is obtained by frequency domain impedance scanning test on the grid node, and represents the impedance characteristics of the grid at different frequencies. The energy storage system response characteristic is obtained by step response test on the energy storage converter, and represents the dynamic response capability of the energy storage system under different working conditions. The power conversion coefficient is the conversion ratio of each frequency band signal to the power signal determined according to the grid impedance characteristics and the response capability of the energy storage system. The decomposition of the standard fluctuation sequence is processed by using a digital filter bank, and the decomposition is based on the main oscillation mode characteristics of the grid frequency fluctuation. Specifically, the signal is separated by setting three characteristic frequency bands of 0.1-0.5Hz, 0.5-2Hz and 2-5Hz. These frequency band ranges are determined according to the typical low-frequency oscillation frequency distribution of the power system.

[0154] In the embodiment of the present application, the standard fluctuation sequence is decomposed into a plurality of sub-band signals of different frequency intervals by a filter bank, and appropriate power conversion coefficients are allocated to each frequency sub-band according to the grid impedance frequency characteristics and the response capability of the energy storage system in different frequency bands.

[0155] Step 502: multiplying each frequency sub-band signal by the corresponding power conversion coefficient to obtain the power compensation signal of each frequency sub-band signal, and synthesizing the power compensation signals of the frequency sub-band signals to generate an initial power compensation demand signal.

[0156] In step 502, the power compensation signal is a power adjustment component obtained by power conversion of each frequency sub-band signal, and the initial power compensation demand signal is a total power demand signal synthesized by each frequency band power compensation signal.

[0157] In the embodiment of the present application, each frequency sub-band signal is multiplied by the corresponding power conversion coefficient to obtain the power compensation signal of each sub-band, and then the power compensation signals of all sub-bands are superimposed and synthesized according to the time point to generate a complete initial power compensation demand signal.

[0158] Step 503: calculating the phase offset of the initial power compensation demand signal to the grid voltage signal by using an adaptive filtering algorithm, and calculating a real-time phase compensation value according to the phase offset and combining a pre-established phase compensation model.

[0159] In step 503, the grid voltage signal is a three-phase voltage instantaneous value signal collected directly from the grid connection point. The phase shift amount is the time lag amount of the initial power compensation demand signal relative to the grid voltage signal. The construction process of the phase compensation model includes: establishing a switching delay function based on the switching characteristics of the energy storage system power device, constructing a signal transmission delay function combined with the grid transmission line parameters, determining the sampling delay function through the sampling period of the grid frequency measurement unit, and superimposing the three types of delay functions in the time domain to form a complete phase compensation model. The model is a deterministic mathematical model based on grid parameters and device characteristics. The model structure includes a series combination of switching delay modules, transmission delay modules, and sampling delay modules. The specific process of model training includes: collecting actual phase deviation data under different operating conditions as training samples, taking the grid operating parameters and power instructions as model inputs, adjusting the delay parameters in the model through the gradient descent algorithm, using the root mean square error as the loss function to evaluate the degree of agreement between the model output and the actual phase deviation, repeating iterative optimization until the model accuracy meets the predetermined requirements, and finally obtaining a trained model that can accurately predict the phase compensation value. The real-time phase compensation value is the phase value that needs to be compensated according to the current phase shift amount.

[0160] In the embodiments of the present application, the adaptive filtering algorithm is used to compare the waveforms of the initial power compensation demand signal and the grid voltage signal, calculate the phase shift amount between the two, and then calculate the real-time phase compensation value according to the current phase shift amount through the pre-established phase compensation model.

[0161] Step 504: Apply the real-time phase compensation value to the initial power compensation demand signal to obtain the phase-compensated power compensation demand signal.

[0162] In step 504, the phase-compensated power compensation demand signal is a power demand signal that is synchronized with the grid voltage after phase adjustment.

[0163] In the embodiments of the present application, the calculated real-time phase compensation value is applied to the initial power compensation demand signal through phase rotation operation, so that the power demand signal is synchronized with the grid voltage signal, and the phase-compensated power compensation demand signal is obtained. The specific process is as follows: the specific implementation process of applying the real-time phase compensation value to the initial power compensation demand signal is completed through complex multiplication operation. First, the initial power compensation demand signal is converted to complex form, and then multiplied by the phase compensation factor, where is the real-time phase compensation value, and finally the real part of the product is taken as the phase-compensated signal. In the embodiments, when the phase shift amount is detected to be 0.1 radian, the initial power compensation demand signal is output in the form of

[0164] Step 505: Perform amplitude limiting processing on the phase-compensated power compensation demand signal to obtain a preprocessed power compensation demand signal.

[0165] In step 505, amplitude limiting processing is a signal processing method that limits the signal amplitude to an allowable range. The preprocessed power compensation demand signal is the final power command signal obtained after a complete preprocessing process.

[0166] In this embodiment, the amplitude of the phase-compensated power compensation demand signal is checked, and the signal amplitude exceeding the allowable range of the energy storage system is limited to between the maximum and minimum values, so as to obtain a pre-processed power compensation demand signal that meets the system safety requirements.

[0167] Here is a specific example:

[0168] In a frequency regulation scenario for a regional power grid to cope with a sudden increase in load from large industrial equipment, based on an established standard fluctuation sequence, the standard fluctuation sequence is first decomposed into three frequency sub-band signals using a digital filter bank: a low-frequency sub-band signal of 0.1-0.5 Hz, a mid-frequency sub-band signal of 0.5-2 Hz, and a high-frequency sub-band signal of 2-5 Hz. These frequency band divisions are determined based on the characteristics of the main oscillation modes of the power grid. Based on the test results of the power grid impedance parameters and the dynamic response characteristics of the energy storage system, the power conversion coefficients corresponding to each frequency sub-band signal are determined to be 0.8 for low frequency, 1.2 for mid-frequency, and 1.5 for high frequency. These coefficients are obtained by fitting the impedance frequency characteristic curve. The power compensation signal for each frequency sub-band signal is obtained by multiplying each frequency sub-band signal by its corresponding power conversion coefficient. The calculation formula is as follows: ,in Indicates the first The power compensation signal unit for each sub-band is megawatts. Indicates the first The amplitude of each sub-band, Indicates the first The power conversion coefficients corresponding to each sub-band are used. The power compensation signals from the three sub-bands are synthesized to generate the initial power compensation demand signal. The synthesis method uses weighted summation, with weighting coefficients of 0.4, 0.35, and 0.25 based on the importance of each frequency band. The calculated initial power compensation demand signal value is 8.5 MW. An adaptive filtering algorithm is used to calculate the phase shift of the initial power compensation demand signal relative to the grid voltage signal. The phase difference is obtained by comparing the zero-crossing time difference between the two signals. The calculation formula is as follows: ,in The phase offset is expressed in radians. The fundamental frequency of the power grid is 50 Hz. The time difference of zero-crossing points is 0.000318 seconds, and the phase offset is calculated to be 0.1 radian. According to the phase offset and the pre-established phase compensation model wherein represents the real-time phase compensation value in radian, and the real-time phase compensation value is calculated to be -0.085 radian. The real-time phase compensation value is applied to the initial power compensation demand signal through complex multiplication, and the calculation formula is wherein represents the power compensation demand signal after phase compensation, represents the initial power compensation demand signal, and the power compensation demand signal after phase compensation is obtained. The amplitude limiting processing is performed on the signal, the amplitude limiting range is set to be -10 MW to 10 MW according to the capacity of the energy storage system, the amplitude of the signal is limited in the range, and finally the preprocessed power compensation demand signal value is 8.5 MW.

[0169] In the embodiments of the present application, through the complete preprocessing process of frequency sub-band decomposition, power conversion, phase compensation and amplitude limitation, the power compensation signal synchronized with the power grid and meeting the system safety requirements is obtained, and accurate and reliable input is provided for the subsequent control strategy.

[0170] In order to further improve the comprehensiveness and accuracy of frequency feature extraction, in some embodiments, step 102: the frequency deviation data is processed by a sliding time window to extract frequency change characteristics, and the frequency change characteristics corresponding to different sliding windows are respectively processed by extreme value normalization to form a standard fluctuation sequence representing the dynamic characteristics of the power grid frequency, including:

[0171] Step 601: setting a plurality of sliding time windows with different time lengths, the sliding time windows including a first window for capturing fluctuation characteristics and a second window for identifying change trend.

[0172] In step 601, the sliding time window is a fixed length time period sliding on the time axis, the first window is a short time window for capturing fast fluctuation characteristics, and the second window is a long time window for identifying slow change trend. The time length of the first window is less than that of the second window, the first window adopts 0.2 seconds for capturing fast fluctuation, and the second window adopts 2 seconds for identifying slow trend, which ensures that the instantaneous change and long-term evolution characteristics of the frequency can be captured at the same time.

[0173] In the embodiments of the present application, two sliding time windows with different time lengths are set according to the characteristics of the power grid frequency fluctuation, wherein the shorter first window is used to capture instantaneous fluctuation characteristics, and the longer second window is used to identify long-term change trend.

[0174] Step 602: moving the first window and the second window in turn according to a preset time interval, and cutting out a frequency data segment of a corresponding time period from the frequency deviation data.

[0175] In step 602, the preset time interval is a time step of window movement, and the frequency data segment is a data segment of a continuous time period cut out from the frequency deviation data.

[0176] In the embodiment of the present application, the two windows are moved in turn according to a fixed time interval, and a data segment of a corresponding time period is cut out from the frequency deviation data after each movement to form a sequence of continuously covered data segments. Specifically, the two windows are moved in turn according to a preset time interval, that is, the two windows are slid on the time axis at a fixed step of 0.01 seconds, and a data segment of a corresponding time period is cut out after each movement to form an analysis sequence that continuously covers all frequency data.

[0177] Step 603: performing difference calculation on the frequency data segment of the corresponding time period of each first window to generate a speed feature quantity reflecting instantaneous change.

[0178] In step 603, difference calculation is a mathematical method of calculating the difference value of adjacent data, and the speed feature quantity is a characteristic index reflecting the speed of frequency instantaneous change.

[0179] In the embodiment of the present application, difference calculation is performed on each frequency data segment cut out by the first window to obtain a speed feature quantity reflecting the rate of frequency instantaneous change.

[0180] Step 604: performing linear fitting on the frequency data segment of the corresponding time period of each second window to generate a slope feature quantity reflecting a trend of continuous change.

[0181] In step 604, linear fitting is a method of approximating the trend of data change with a straight line, and the slope feature quantity is a trend index reflecting the direction of frequency continuous change.

[0182] In the embodiment of the present application, linear fitting is performed on each frequency data segment cut out by the second window to obtain a slope feature quantity reflecting the trend of frequency change.

[0183] Step 605: combining the speed feature quantity and the slope feature quantity as corresponding frequency change features to form a frequency change feature set, and identifying the maximum value and the minimum value of each feature quantity in the frequency change feature set.

[0184] In step 605, the frequency change feature set is a complete set containing all feature quantities, and the maximum value and the minimum value are the limit values of the feature quantities in the statistical range.

[0185] In this embodiment of the application, the calculated velocity and slope features are combined to form a complete feature set, and the maximum and minimum values ​​of each feature are found throughout the entire statistical period.

[0186] Step 606: Based on the maximum value and the minimum value, perform extreme value normalization processing on each feature quantity in the frequency change feature set, and rearrange the frequency change feature set after extreme value normalization processing in chronological order to form a standard fluctuation sequence.

[0187] In step 606, extreme value normalization is a standardization method that linearly transforms the data to a specific interval based on its maximum and minimum values. The standard fluctuation sequence representation is achieved by arranging the normalized values ​​of velocity and slope characteristics in chronological order. The velocity characteristic reflects the rate of frequency change, while the slope characteristic reflects the direction of frequency change. Together, they comprehensively describe the dynamic behavior of the power grid frequency in both time and amplitude dimensions.

[0188] In this embodiment, each feature is normalized based on the found maximum and minimum values, and then the processed feature values ​​are rearranged in chronological order to form a standard fluctuation sequence.

[0189] Here is a specific example:

[0190] In a frequency regulation scenario for a regional power grid to cope with a sudden increase in load from large industrial equipment, based on the obtained frequency deviation data sequence, two sliding time windows of different lengths are first set. The first window has a length of 0.2 seconds to capture fluctuation characteristics, and the second window has a length of 2 seconds to identify changing trends. These window lengths are determined based on the main time constants of power grid frequency fluctuations. The first and second windows are moved sequentially at preset time intervals of 0.01 seconds, respectively extracting frequency data segments corresponding to the time period from the frequency deviation data. The first window extracts 21 sampling points each time, and the second window extracts 201 sampling points each time. Differential calculation is performed on the frequency data segments corresponding to the time period of each first window, using the following formula: ,in Indicates the first The velocity characteristic of each sampling point is measured in Hertz per second. Indicates the first The frequency value of each sampling point is in Hertz. Indicates the first The frequency value of each sampling point is in Hertz. The sampling time interval is 0.01 seconds. When the frequency changes from 49.95 Hz to 49.93 Hz, the calculated velocity characteristic is -2.0 Hz per second. Linear fitting is performed on the frequency data segments corresponding to each second window time period, using the least squares method to fit the straight line. wherein represents the frequency prediction value after linear fitting, represents the intercept of the linear fitting line, represents the slope characteristic quantity unit is hertz per second, represents the time variable, when the slope is -1.5 hertz per second, it indicates that the frequency is in a downward trend. The speed characteristic quantity and the slope characteristic quantity are combined as the corresponding frequency change characteristics to form a frequency change characteristic set. Through analysis of the past 24 hours of historical data, it is identified that the maximum value of the speed characteristic quantity is 15 hertz per second and the minimum value is -15 hertz per second, and the maximum value of the slope characteristic quantity is 8 hertz per second and the minimum value is -8 hertz per second. Based on these maximum and minimum values, each characteristic quantity in the frequency change characteristic set is subjected to extreme value normalization processing, and the conversion formula is The speed characteristic quantity -2.0 hertz per second is substituted into the formula to obtain a normalized value of 0.433, and the slope characteristic quantity -1.5 hertz per second is substituted into the formula to obtain a normalized value of 0.406. The frequency change characteristic set after extreme value normalization processing is rearranged in time sequence, and a standard fluctuation sequence [0.433, 0.406, 0.450, 0.388, …] is formed by combining in an alternating arrangement. The odd positions are the normalized values of the speed characteristic quantity, and the even positions are the normalized values of the slope characteristic quantity. The sequence completely characterizes the dynamic characteristics of the power grid frequency.

[0191] In the embodiments of the present application, through multi-scale window analysis and feature normalization processing, a standard sequence capable of comprehensively characterizing the dynamic characteristics of the power grid frequency is formed, providing accurate feature input for subsequent power compensation.

[0192] In order to further improve the adaptability and response performance of the control strategy, in some embodiments, step 105: based on the mapping function, an adaptive virtual inertia control strategy is formed, and the preprocessed power compensation demand signal is processed based on the adaptive virtual inertia control strategy to generate a storage output instruction, including:

[0193] Step 701: based on the frequency differential term and the delay compensation term in the mapping function, a feedforward control channel and a feedback correction channel are respectively constructed.

[0194] In step 701, the feedforward control channel is a fast response channel constructed based on the frequency differential term, and the feedback correction channel is an error correction channel constructed based on the delay compensation term.

[0195] In the embodiment of the present application, the frequency differential term in the mapping function is directly converted into a control channel, that is, a differential gain module is established to perform differential amplification processing on the input signal; the delay compensation term in the mapping function is converted into a feedback loop, that is, a delay compensation module is established to perform delay processing on the frequency deviation signal and then participate in control.

[0196] Step 702: connecting the feedforward control channel and the feedback correction channel in parallel to form an adaptive virtual inertia control strategy.

[0197] In the embodiment of the present application, the feedforward control channel and the feedback correction channel are connected in parallel to form a complete adaptive virtual inertia control strategy framework.

[0198] Step 703: based on the adaptive virtual inertia control strategy, performing differential gain processing on the preprocessed power compensation demand signal to generate a feedforward power component, and performing delay compensation processing on the frequency deviation data to generate a feedback correction component.

[0199] In step 703, the differential gain processing is a process of performing differential amplification on a signal to obtain a feedforward component, and the delay compensation processing is a process of performing delay correction on a signal to obtain a feedback component.

[0200] In the embodiment of the present application, the preprocessed power compensation demand signal is input into the feedforward channel for differential gain processing to obtain a feedforward power component, and the frequency deviation data is input into the feedback channel for delay compensation processing to obtain a feedback correction component.

[0201] Step 704: weighting and fusing the feedforward power component and the feedback correction component according to corresponding dynamic weight coefficients to generate an unregulated control signal.

[0202] In step 704, the dynamic weight coefficient refers to a group of proportional parameters automatically adjusted according to the real-time operation state of the power grid, including a feedforward weight coefficient and a feedback weight coefficient, which are respectively used to adjust the relative proportion of the feedforward power component and the feedback correction component in the fusion process. The two coefficients are complementary to each other and the sum is kept as 1. When a rapid frequency change is detected, the feedforward weight coefficient is automatically increased to 0.7 and the feedback weight coefficient is correspondingly reduced to 0.3, and when the frequency change is slow, the feedforward weight coefficient is reduced to 0.4 and the feedback weight coefficient is increased to 0.6.

[0203] In the embodiment of the present application, the dynamic weight coefficient is determined according to the real-time operation state of the power grid, and the feedforward power component and the feedback correction component are weighted and fused according to the corresponding weight to generate an unregulated control signal.

[0204] Step 705: performing power rate limiting processing on the unregulated control signal to obtain a rate limited signal, and performing power upper and lower limit truncation processing on the rate limited signal to obtain the energy storage output instruction.

[0205] In step 705, the power rate limiting processing is a process of limiting the signal change speed, and the power upper and lower limit truncation processing is a process of limiting the signal amplitude in the allowed range.

[0206] In the embodiments of the present application, the power rate limiting processing on the unregulated control signal ensures smooth change, and the power upper and lower limit truncation processing ensures safe operation, and finally the energy storage output instruction is obtained.

[0207] The following is a specific example:

[0208] In the frequency modulation scene of a certain regional power grid responding to the sudden increase of large industrial equipment load, based on the constructed mapping function , first, based on the frequency differential item in the mapping function , a feedforward control channel is constructed, and based on the time delay compensation item , a feedback correction channel is constructed. The feedforward control channel and the feedback correction channel are connected in parallel to form an adaptive virtual inertia control strategy. Based on the adaptive virtual inertia control strategy, the preprocessed power compensation demand signal 8.5 MW is subjected to differential gain processing, and the calculation formula is that the feedforward power component is equal to , wherein represents the change rate of the preprocessed power compensation demand signal, when the power signal change rate is detected to be negative 2.0 MW per second, the calculated feedforward power component is negative 7.0 MW. At the same time, the frequency deviation data is subjected to time delay compensation processing, the time delay time is 0.15 seconds, and the calculation formula is that the feedback correction component is equal to , wherein represents the frequency deviation after 0.15 seconds of time delay, when the frequency deviation before and after time delay is both negative 0.1 Hz, the calculated feedback correction component is negative 0.08 MW. The feedforward power component and the feedback correction component are subjected to weighted fusion processing according to the corresponding dynamic weight coefficients, when the frequency change rate is detected to be negative 2.0 Hz per second greater than the set threshold 0.5 Hz per second, the feedforward weight coefficient is 0.7 and the feedback weight coefficient is 0.3, and the calculation formula is that the unregulated control signal is equal to , wherein represents the unregulated control signal, represents that the feedforward weight coefficient is 0.7, The inverse weight coefficient is represented as 0.3, and the unregulated control signal is equal to 0.7 times negative 7.0 plus 0.3 times negative 0.08, which is equal to negative 4.924 megawatts, obtained by substituting the numerical value into the calculation. The power change rate limiting process is performed on the unregulated control signal, and the limiting rate is positive or negative 20 megawatts per second. Since the signal change rate is within the allowed range, the rate-limited signal value is negative 4.924 megawatts. Finally, the power upper and lower limit truncation process is performed on the rate-limited signal, and the upper limit is set to 10 megawatts and the lower limit is set to negative 10 megawatts according to the rated capacity of the energy storage system. Since negative 4.924 megawatts is within the allowed range, the final energy storage output instruction is negative 4.9 megawatts, which will be sent to the energy storage system to execute power output.

[0209] In the embodiments of the present application, through the parallel control of feedforward and feedback and dynamic weight adjustment, a fast response and stable control strategy is formed, the accurate control of power compensation is realized, and the regulation quality of the power grid frequency is effectively improved.

[0210] Figure 3 The structure schematic diagram of the energy storage frequency modulation strategy optimization system based on the artificial bee colony algorithm provided in the embodiments of the present application is shown in FIG. 1, and the specific implementation manner is described in the specific implementation manner part.

[0211] The acquisition module 31 is configured to acquire frequency deviation data generated by the energy storage frequency modulation strategy under the load mutation working condition of the power grid.

[0212] The extraction module 32 is configured to perform sliding time window processing on the frequency deviation data to extract frequency change characteristics, and perform extreme value normalization processing on the frequency change characteristics corresponding to different sliding windows respectively to form a standard fluctuation sequence representing the dynamic characteristics of the power grid frequency.

[0213] The conversion module 33 is configured to convert the standard fluctuation sequence into an initial power compensation demand signal, and perform phase compensation preprocessing on the initial power compensation demand signal to obtain a preprocessed power compensation demand signal.

[0214] The establishment module 34 is configured to establish a parameter optimization model taking the differential gain coefficient and the response delay time of the virtual inertia control as optimization variables, and perform multi-round iterative solving on the parameter optimization model by using the artificial bee colony algorithm to obtain an optimal parameter combination, and construct a mapping function between the frequency and the power based on the optimal parameter combination.

[0215] The generation module 35 is configured to form an adaptive virtual inertia control strategy based on the mapping function, process the preprocessed power compensation demand signal based on the adaptive virtual inertia control strategy, generate an energy storage output instruction, and optimize the energy storage frequency modulation strategy of the energy storage system according to the energy storage output instruction to complete the optimized suppression of the power grid frequency fluctuation.

[0216] The artificial bee colony algorithm-based energy storage frequency regulation strategy optimization system of the embodiments of the present application is used to implement the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization method described above, and thus the specific embodiments in the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization system can be seen from the embodiment part of the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization method described above, and the specific embodiments can be referred to the description of the corresponding embodiment part, which will not be repeated here.

[0217] The present application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization methods described above.

[0218] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of any of the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization methods described above.

[0219] In an exemplary embodiment, the computer readable storage medium described above can include, but is not limited to, a U disk, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store computer programs.

[0220] The embodiments of the present application also provide a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps in any of the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization method embodiments described above.

[0221] The skilled person can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0222] The above describes in detail the energy storage frequency modulation strategy optimization method and system based on the artificial bee colony algorithm, the electronic device, and the storage medium provided by the present application. In this paper, specific examples are used to explain the principles and implementation modes of the present application. The above description of the embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary skilled persons in the technical field, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. An artificial bee colony algorithm-based energy storage frequency regulation strategy optimization method, characterized in that, The method comprises the following steps: acquiring frequency deviation data generated by a frequency regulation strategy of energy storage under a load mutation condition of a power grid; performing sliding time window processing on the frequency deviation data to extract frequency variation characteristics, and performing extreme value normalization processing on the frequency variation characteristics corresponding to different sliding windows to form a standard fluctuation sequence representing dynamic characteristics of the frequency of the power grid; converting the standard fluctuation sequence into an initial power compensation demand signal, and performing phase compensation preprocessing on the initial power compensation demand signal to obtain a preprocessed power compensation demand signal; establishing a parameter optimization model with a differential gain coefficient and a response delay time of virtual inertia control as optimization variables, performing multi-round iterative solving on the parameter optimization model by using an artificial bee colony algorithm to obtain an optimal parameter combination, and constructing a mapping function between the frequency and the power based on the optimal parameter combination; forming an adaptive virtual inertia control strategy based on the mapping function, processing the preprocessed power compensation demand signal based on the adaptive virtual inertia control strategy to generate an energy storage output instruction, and optimizing the frequency regulation strategy of the energy storage system based on the energy storage output instruction to complete the optimized suppression of the frequency fluctuation of the power grid.

2. The artificial bee colony algorithm-based optimization method of energy storage frequency modulation strategy according to claim 1, characterized in that, The method of establishing a parameter optimization model with a differential gain coefficient and a response delay time of virtual inertia control as optimization variables, performing multi-round iterative solving on the parameter optimization model by using an artificial bee colony algorithm to obtain an optimal parameter combination, and constructing a mapping function between the frequency and the power based on the optimal parameter combination comprises the following steps: establishing the parameter optimization model, setting the differential gain coefficient of virtual inertia control as a first optimization variable, and setting the response delay time of virtual inertia control as a second optimization variable; defining a target function with the minimum frequency deviation integral and power change rate as the target based on the first optimization variable and the second optimization variable; setting search parameters of the artificial bee colony algorithm according to the characteristics of the target function, wherein the search parameters comprise the number of employed bees, the number of onlooker bees, and the maximum number of iterations; performing multi-round iterative solving by using the artificial bee colony algorithm based on the search parameters to determine the optimal parameter combination; constructing the mapping function containing a frequency differential item and a delay compensation item based on the differential gain coefficient and the response delay time in the optimal parameter combination.

3. The artificial bee colony algorithm based optimization method of energy storage frequency regulation strategy according to claim 2, characterized in that, The method of performing multi-round iterative solving by using the artificial bee colony algorithm based on the search parameters to determine the optimal parameter combination comprises the following steps: initializing to generate an initial population containing a plurality of parameter combinations based on the search parameters, wherein each parameter combination contains the values of the first optimization variable and the second optimization variable; performing neighborhood search on each parameter combination in the initial population by the employed bee stage of the artificial bee colony algorithm to obtain updated parameter combinations, and calculating the target function values corresponding to the updated parameter combinations based on the target function; performing deep search on the high-quality parameter combinations with the target function values greater than a preset threshold by the onlooker bee stage of the artificial bee colony algorithm to obtain optimized parameter combinations. The scout bee stage of the artificial bee colony algorithm replaces the parameters in the optimized parameter combination that fall into local optimization to obtain a new parameter combination, and the new parameter combination is used as an initial population; The iteration search process of the employed bee stage, the onlooker bee stage and the scout bee stage is repeated until a preset termination condition is met, and a parameter combination with an optimal target function value is output as an optimal parameter combination.

4. The artificial bee colony algorithm based optimization method of energy storage frequency regulation strategy according to claim 2, characterized in that, The mapping function including a frequency differential item and a delay compensation item is constructed based on the differential gain coefficient and the response delay time in the optimal parameter combination, including: The frequency differential signal is obtained by differentiating the frequency deviation data; The frequency differential item is obtained by multiplying the frequency differential signal by the differential gain coefficient; The delay power signal is obtained by performing time delay processing on the preprocessed power compensation demand signal; The delay compensation item is obtained by calculating the difference between the delay power signal and the preprocessed power compensation demand signal; The frequency differential item and the delay compensation item are combined in a weighted manner to construct the mapping function.

5. The artificial bee colony algorithm based optimization method of energy storage frequency regulation strategy according to claim 1, characterized in that, The standard fluctuation sequence is converted into an initial power compensation demand signal, and the initial power compensation demand signal is phase compensated and preprocessed to obtain a preprocessed power compensation demand signal, including: The standard fluctuation sequence is decomposed into a plurality of frequency sub-band signals, and the power conversion coefficients corresponding to the frequency sub-band signals are determined based on the grid impedance parameters and the energy storage system response characteristics; The power compensation signals of the frequency sub-band signals are obtained by multiplying the frequency sub-band signals by the corresponding power conversion coefficients, and the power compensation signals of the frequency sub-band signals are synthesized to generate an initial power compensation demand signal; An adaptive filtering algorithm is used to calculate the phase offset of the initial power compensation demand signal with respect to the grid voltage signal, and a real-time phase compensation value is calculated based on the phase offset and a pre-established phase compensation model; The real-time phase compensation value is applied to the initial power compensation demand signal to obtain a phase-compensated power compensation demand signal; The phase-compensated power compensation demand signal is subjected to amplitude limiting processing to obtain a preprocessed power compensation demand signal.

6. The artificial bee colony algorithm based optimization method of energy storage frequency regulation strategy according to claim 1, characterized in that, The frequency change characteristics are extracted by performing sliding time window processing on the frequency deviation data, and the frequency change characteristics corresponding to different sliding windows are subjected to extreme value normalization processing to form a standard fluctuation sequence representing the dynamic characteristics of the grid frequency, including: A plurality of sliding time windows with different time lengths are set, including a first window for capturing fluctuation characteristics and a second window for identifying change trends; The first window and the second window are moved in sequence according to a preset time interval to respectively intercept frequency data segments of corresponding time periods from the frequency deviation data; The speed characteristic quantity reflecting instantaneous change is generated by performing difference calculation on the frequency data segment of each first window corresponding time period; The slope characteristic quantity reflecting the trend of continuous change is generated by performing linear fitting on the frequency data segment of each second window corresponding time period; combining the speed feature and the slope feature as corresponding frequency variation features to form a frequency variation feature set, and identifying maximum and minimum values of each feature in the frequency variation feature set; performing extreme value normalization processing on each feature in the frequency variation feature set based on the maximum and minimum values, and rearranging the frequency variation feature set after the extreme value normalization processing in time sequence to form a standard fluctuation sequence.

7. The artificial bee colony algorithm based optimization method of energy storage frequency regulation strategy according to claim 1, characterized in that, The adaptive virtual inertia control strategy is formed based on the mapping function, and the preprocessed power compensation demand signal is processed based on the adaptive virtual inertia control strategy to generate a storage output instruction. Based on the frequency differential term and the delay compensation term in the mapping function, a feedforward control channel and a feedback correction channel are respectively constructed. The feedforward control channel and the feedback correction channel are connected in parallel to form an adaptive virtual inertia control strategy. Based on the adaptive virtual inertia control strategy, a feedforward power component is generated by performing differential gain processing on the preprocessed power compensation demand signal, and a feedback correction component is generated by performing delay compensation processing on the frequency deviation data. The feedforward power component and the feedback correction component are weighted and fused according to corresponding dynamic weight coefficients to generate an unadjusted control signal. The unadjusted control signal is subjected to power change rate limiting processing to obtain a rate limited signal, and power upper and lower limit truncation processing is performed on the rate limited signal to obtain a storage output instruction.

8. An artificial bee colony algorithm-based energy storage frequency regulation strategy optimization system, characterized in that, comprises: The acquisition module is configured to acquire frequency deviation data generated by a frequency regulation strategy of a storage system under a load mutation condition of a power grid. The extraction module is configured to perform sliding time window processing on the frequency deviation data to extract frequency variation features, and perform extreme value normalization processing on the frequency variation features corresponding to different sliding windows to form a standard fluctuation sequence representing dynamic characteristics of a frequency of the power grid. The conversion module is configured to convert the standard fluctuation sequence into an initial power compensation demand signal, and perform phase compensation preprocessing on the initial power compensation demand signal to obtain a preprocessed power compensation demand signal. The establishment module is configured to establish a parameter optimization model taking a differential gain coefficient and a response delay time of virtual inertia control as optimization variables, perform multi-round iteration solving on the parameter optimization model by using an artificial bee colony algorithm to obtain an optimal parameter combination, and construct a mapping function between frequency and power based on the optimal parameter combination. The generation module is configured to form an adaptive virtual inertia control strategy based on the mapping function, process the preprocessed power compensation demand signal based on the adaptive virtual inertia control strategy, and generate a storage output instruction, and optimize a frequency regulation strategy of the storage system according to the storage output instruction to complete optimization and suppression of frequency fluctuation of the power grid.

9. An electronic device, comprising: comprises: A memory is configured to store a computer program. A processor is configured to execute the computer program to implement the steps of the storage frequency regulation strategy optimization method based on the artificial bee colony algorithm according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the artificial bee colony algorithm-based energy storage frequency regulation strategy optimization method in any one of claims 1 to 7.

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