Energy storage power station state evaluation method and system based on real-time demand analysis

Abstract

The invention discloses an energy storage power station state evaluation method and system based on real-time demand analysis, and relates to the technical field of power station state evaluation, and the method comprises the steps: obtaining the real-time operation data of an energy storage power station; the real-time operation data comprises voltage data, current data, temperature data and power data; based on the real-time operation data, establishing an energy storage power station real-time evaluation index system under the demand of a power grid system; and based on the energy storage power station real-time evaluation index system, establishing an energy storage power station performance comprehensive evaluation model, and performing energy storage power station state evaluation according to the energy storage power station performance comprehensive evaluation model. According to the method, operation data are acquired from multiple dimensions of a PCS converter, a battery body and a PCC point, a systematic evaluation index system of four requirements of power grid frequency modulation, peak regulation, voltage regulation and power oscillation suppression is combined, and an evaluation method of combining dynamic fuzzy comprehensive evaluation and adaptive weight dynamic adjustment is adopted; and comprehensive and accurate evaluation of the state of the energy storage power station is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power station status assessment, and in particular to a method and system for assessing the status of an energy storage power station based on real-time demand analysis. Background Art

[0002] As an important regulating resource of the power system, the status assessment of energy storage power stations is of great significance for ensuring the safe and stable operation of the power grid. At present, the status assessment of energy storage power stations mainly revolves around performance indicators at the equipment level, such as the use of status monitoring methods based on SOC and SOH, parameter evaluation methods based on battery management systems, etc. Although these methods can reflect the basic operating status of energy storage equipment, most of them are limited to static evaluation in a single dimension. At the same time, with the increasing diversification of the functions of energy storage power stations in the power grid, traditional evaluation methods are difficult to meet the status assessment requirements of the power grid under the diversified needs of energy storage power stations. Especially when energy storage power stations participate in multiple service scenarios such as frequency regulation, peak regulation, and voltage support at the same time, the existing evaluation methods lack analysis of response characteristics at different time scales, and fail to fully consider the coupling relationship between various power grid demands.

[0003] The existing state assessment methods for energy storage power stations have the following deficiencies: first, there is a lack of a systematic assessment index system, which makes it difficult to fully reflect the response capabilities of energy storage power stations under different grid demands; second, the linear weighting method with fixed weights is generally used in the assessment process, which fails to reflect the changes in the importance of different indicators during dynamic operation; third, the assessment results often only reflect the current state, lack analysis of historical data trends and prediction of future states; finally, the existing assessment methods have obvious limitations in dealing with the state assessment problem when multiple grid services coexist, making it difficult to accurately assess the comprehensive service capabilities of energy storage power stations. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention provides a method and system for evaluating the state of an energy storage power station based on real-time demand analysis, which can solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for evaluating the state of an energy storage power station based on real-time demand analysis, comprising:

[0007] Acquire real-time operation data of the energy storage power station; the real-time operation data includes voltage data, current data, temperature data, and power data;

[0008] Based on the real-time operation data, a real-time evaluation index system for energy storage power stations under the needs of the power grid system is established;

[0009] Based on the real-time evaluation index system, a comprehensive performance evaluation model for an energy storage power station is established, and the state of the energy storage power station is evaluated according to the comprehensive performance evaluation model for the energy storage power station.

[0010] As a preferred solution of the energy storage power station status assessment method based on real-time demand analysis described in the present invention, wherein: obtaining real-time operation data of the energy storage power station includes the following steps: collecting voltage data and current data from the PCS converter, collecting temperature data from the battery body, and collecting power data from the common connection point PCC; detecting and filtering the collected real-time operation data to within a preset operation parameter range; and classifying and storing the filtered real-time operation data in a database.

[0011] As a preferred solution of the energy storage power station state assessment method based on real-time demand analysis described in the present invention, wherein: the power grid system demand includes power grid frequency regulation demand, power grid peak regulation and energy transfer demand, power grid voltage regulation demand, and power grid power oscillation suppression demand; the energy storage power station real-time assessment index system includes: real-time assessment indicators of power grid frequency regulation demand, including the rated power, ramp rate, rated power of the energy storage power station, and the frequency regulation contribution factor λ of the conventional unit; real-time assessment indicators of power grid peak regulation and energy transfer demand, including upper and lower limits of charging and discharging power, upper and lower limits of state of charge SOC, load standard deviation minimization objective function value, load peak-valley coefficient, and load rate; real-time assessment indicators of power grid voltage regulation demand, including grid connection point voltage deviation value ΔU(t), voltage deviation change rate Δo(t), voltage cumulative change ΔE(t), voltage state perception index γ(t), and voltage qualification rate λu; real-time assessment indicators of power grid power oscillation suppression demand, including the equivalent inertia coefficient, damping coefficient, and synchronization coefficient of the energy storage power station to the power system.

[0012] As a preferred solution of the energy storage power station status assessment method based on real-time demand analysis described in the present invention, the real-time assessment of the grid frequency regulation demand includes obtaining the power matching value of the rated power of the energy storage power station and the system reference power, the following value of the climbing rate of the energy storage power station and the grid frequency change, and the rated power frequency regulation duration value of the energy storage power station; if the power matching value, the following value and the frequency regulation duration value all meet the preset assessment requirements, then enter the deep assessment stage: based on the change trend of the frequency regulation contribution factor λ, a hierarchical assessment is performed; when the frequency regulation contribution factor λ is greater than a first preset threshold and is on an upward trend, the frequency regulation weight of the energy storage power station is increased, and the frequency regulation output of the conventional unit is reduced; when the frequency regulation contribution factor λ is on a downward trend, the energy storage power station is set to a standby frequency regulation mode; when the frequency regulation contribution factor λ is less than a second preset threshold, a frequency regulation warning signal is generated.

[0013] The real-time evaluation of the peak-shaving and energy transfer requirements of the power grid includes calculating the characteristic product of the load rate and the load peak-valley coefficient; if the characteristic product is greater than a third preset threshold, obtaining the charging and discharging power value of the energy storage power station, the state of charge SOC of the energy storage power station and the load standard deviation minimization objective function value; judging whether the charging and discharging power value, the state of charge SOC and the load standard deviation minimization objective function value meet the preset evaluation criteria; if the preset evaluation criteria are met and the load standard deviation minimization objective function value shows a continuous downward trend, generating charging and discharging optimization instructions for the energy storage power station.

[0014] As a preferred solution of the energy storage power station state assessment method based on real-time demand analysis described in the present invention, wherein: the real-time assessment of the grid voltage regulation demand includes obtaining the voltage state perception index γ(t), and determining the assessment priority according to the voltage state perception index γ(t); when the voltage state perception index γ(t) is greater than the fourth preset threshold, priority assessment is performed based on the voltage deviation change rate Δo(t) and the voltage cumulative change ΔE(t); when the voltage state perception index γ(t) is between the fifth preset threshold and the fourth preset threshold, priority assessment is performed based on the grid connection point voltage deviation value ΔU(t); when the voltage state perception index γ(t) is less than the fifth preset threshold, priority assessment is performed based on the voltage qualification rate λu; and reactive compensation adjustment instructions are generated according to the assessment results under different priorities.

[0015] The real-time evaluation of the power oscillation suppression demand of the power grid includes obtaining the real-time change values ​​of the equivalent inertia coefficient, the damping coefficient and the synchronization coefficient; calculating the correlation between the three; if the change value of any coefficient is greater than the sixth preset threshold, the correlation analysis is triggered; when the correlation is greater than the seventh preset threshold, the system regulation weight of the energy storage power station is enhanced; when the correlation is less than the seventh preset threshold, the control parameters of the energy storage power station are adjusted until the correlation is greater than the seventh preset threshold.

[0016] As a preferred solution of the energy storage power station status assessment method based on real-time demand analysis described in the present invention, the process of establishing a comprehensive performance assessment model of the energy storage power station includes the following steps: establishing a grid demand response capability score based on the real-time assessment index system of the energy storage power station; the grid demand response capability score is calculated based on the real-time assessment indicators of the grid frequency regulation demand, grid peak regulation and energy transfer demand, grid voltage regulation demand, and grid power oscillation suppression demand; establishing a basic performance score; the basic performance score includes a controllable index score, an energy efficiency level index score, a reliability index score, and an environmental protection index score; and weighting the grid demand response capability score and the basic performance score to obtain a comprehensive assessment score of the energy storage power station.

[0017] As a preferred solution of the energy storage power station status assessment method based on real-time demand analysis described in the present invention, wherein: the power grid demand response capability score is calculated using a hierarchical weighted method: the basic weight is determined according to the importance of each demand; the time dimension of each demand response is analyzed; the time dimension includes second-level frequency regulation response, minute-level voltage regulation response, hour-level peak regulation response and day-level oscillation suppression response; the basic weight is corrected according to the difference in response time dimension; the weight is adjusted based on the coupling degree of each demand; the coupling degree is calculated by establishing a correlation matrix between the demands; when the coupling degree of two demands is higher than the eighth preset threshold, the corresponding weights are jointly corrected; according to the corrected weights, the real-time evaluation indicators of each demand are normalized and weighted summed to obtain the power grid demand response capability score.

[0018] The present invention also provides a system for evaluating the state of an energy storage power station based on real-time demand analysis, comprising:

[0019] A data acquisition module is used to obtain real-time operation data of the energy storage power station;

[0020] An evaluation index system building module, which establishes a real-time evaluation index system for energy storage power stations under the requirements of the power grid system based on the real-time operation data;

[0021] The evaluation module establishes a comprehensive evaluation model for the performance of an energy storage power station based on the real-time evaluation index system of the energy storage power station, and performs an energy storage power station status evaluation according to the comprehensive evaluation model for the performance of the energy storage power station.

[0022] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the energy storage power station status assessment method based on real-time demand analysis as described above are implemented.

[0023] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the energy storage power station status assessment method based on real-time demand analysis as described above.

[0024] Beneficial effects of the present invention: First, the present invention obtains the real-time operation data of the energy storage power station from multiple dimensions of the PCS converter, the battery body and the PCC point, so as to realize the accurate collection and screening of the data; secondly, by constructing a real-time evaluation index system including four dimensions of grid frequency regulation, peak regulation, voltage regulation and power oscillation suppression, a systematic evaluation of different grid-side support functions of the energy storage power station is realized; finally, based on the dynamic fuzzy comprehensive evaluation method and the adaptive weight dynamic adjustment method, the grid demand response capability score and the energy storage power station basic performance score are integrated and evaluated, which not only considers the response characteristics under different time scales, but also introduces a double-layer adaptive adjustment mechanism between groups and within groups, thereby improving the accuracy and reliability of the evaluation results, and providing effective support for the optimized operation and scheduling decision-making of the energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0026] Figure 1 This is a schematic diagram of the overall process of a method for evaluating the state of an energy storage power station based on real-time demand analysis proposed by the present invention;

[0027] Figure 2 This is a structural schematic diagram of an energy storage power station status assessment system based on real-time demand analysis proposed by the present invention;

[0028] Figure 3 A diagram of computer equipment in a method for evaluating the state of an energy storage power station based on real-time demand analysis proposed by the present invention. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for evaluating the state of an energy storage power station based on real-time demand analysis.

[0032] The present application provides an effective solution to the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to implement the energy storage power station status assessment method based on real-time demand analysis.

[0033] Figure 1 The overall flow chart of a method for evaluating the state of an energy storage power station based on real-time demand analysis is shown, including:

[0034] S1: Obtain real-time operation data of the energy storage power station.

[0035] Specifically, the real-time operation data includes voltage data, current data, temperature data and power data.

[0036] Collect voltage and current data from the PCS converter, temperature data from the battery body, and power data from the common connection point PCC;

[0037] Detect and filter the collected real-time operation data to within the preset operation parameter range;

[0038] The filtered real-time operation data is classified and stored in the database.

[0039] S2: Based on real-time operation data, establish a real-time evaluation index system for energy storage power stations under the needs of the power grid system.

[0040] Specifically, the grid system requirements include grid frequency regulation requirements, grid peak regulation and energy transfer requirements, grid voltage regulation requirements, and grid power oscillation suppression requirements.

[0041] Real-time assessment of grid frequency regulation needs includes:

[0042] Obtain the power matching value between the rated power of the energy storage power station and the system reference power, the following value between the climbing rate of the energy storage power station and the grid frequency change, and the rated power frequency regulation duration of the energy storage power station;

[0043] If the power matching value, following value and frequency regulation duration value all meet the preset evaluation requirements, the in-depth evaluation stage will be entered: a graded evaluation will be performed based on the changing trend of the frequency regulation contribution factor λ. When the frequency regulation contribution factor λ is greater than the first preset threshold and shows an upward trend, the frequency regulation weight of the energy storage power station will be increased and the frequency regulation output of the conventional units will be reduced; when the frequency regulation contribution factor λ shows a downward trend, the energy storage power station will be set to the standby frequency regulation mode; when the frequency regulation contribution factor λ is less than the second preset threshold, a frequency regulation warning signal will be generated.

[0044] Among them, the power matching value is the ratio of the rated power of the energy storage power station to the system benchmark power; the following value is the ratio of the power change rate of the energy storage power station to the frequency change rate of the grid; the frequency regulation duration value is the length of time the energy storage power station maintains the rated power output; the frequency regulation weight is the output proportion of the energy storage power station in all frequency regulation equipment.

[0045] Preferably, the real-time evaluation of the grid frequency regulation demand expands the traditional single frequency regulation contribution factor evaluation into a dynamic evaluation system by introducing a deep evaluation and hierarchical response mechanism. This evaluation system not only takes into account the basic performance indicators of the energy storage power station (power matching value, following value, frequency regulation duration value), but also realizes the dynamic adjustment of the frequency regulation weight based on the changing trend of the frequency regulation contribution factor, so that the energy storage power station can adaptively participate in the system frequency regulation according to its actual contribution. Compared with the static evaluation method in the prior art, this dynamic evaluation mechanism can more accurately reflect the real-time frequency regulation capability of the energy storage power station and improve the utilization efficiency of the system frequency regulation resources.

[0046] Real-time assessment of grid peak load and energy shifting needs including:

[0047] Calculate the characteristic product of the load rate and the load peak-valley coefficient; if the characteristic product is greater than the third preset threshold, obtain the charging and discharging power value of the energy storage power station, the state of charge SOC of the energy storage power station, and the load standard deviation minimization objective function value; determine whether the charging and discharging power value, the state of charge SOC, and the load standard deviation minimization objective function value meet the preset evaluation criteria; if the preset evaluation criteria are met and the load standard deviation minimization objective function value shows a continuous downward trend, generate charging and discharging optimization instructions for the energy storage power station.

[0048] Among them, the characteristic product is equal to the normalized value of the load rate multiplied by the normalized value of the load peak-valley coefficient; the preset evaluation criteria include: the charging and discharging power values ​​are within the preset power range, the state of charge SOC is within the preset SOC range, and the load standard deviation minimization objective function value is less than the power fluctuation upper limit value; the charging and discharging optimization instructions include the charging start and stop time points and the discharging start and stop time points.

[0049] Preferably, the present invention proposes a two-layer linkage evaluation mechanism of load characteristics and energy storage status in real-time evaluation of power grid peak regulation and energy transfer needs. The design of characteristic product triggered evaluation avoids the waste of computing resources caused by frequent evaluation in traditional methods. At the same time, the objective function of minimizing the charging and discharging power, SOC status and load standard deviation is comprehensively considered, and the charging and discharging strategy is dynamically optimized based on the improvement trend of the load curve. Compared with the existing technology, this method can more accurately evaluate the peak regulation effect of the energy storage power station and improve the accuracy of peak shaving and valley filling.

[0050] Real-time assessment of grid voltage regulation needs includes:

[0051] Obtain a voltage state perception index γ(t), and determine an evaluation priority based on the voltage state perception index γ(t); when the voltage state perception index γ(t) is greater than a fourth preset threshold, perform a priority evaluation based on the voltage deviation change rate Δo(t) and the voltage cumulative change ΔE(t); when the voltage state perception index γ(t) is between the fifth preset threshold and the fourth preset threshold, perform a priority evaluation based on the grid connection point voltage deviation value ΔU(t); when the voltage state perception index γ(t) is less than the fifth preset threshold, perform a priority evaluation based on the voltage qualification rate λu; generate a reactive compensation adjustment instruction based on the evaluation results under different priorities.

[0052] It should be noted that the evaluation priorities are divided into three levels: high, medium and low; priority evaluation refers to the key assessment indicators under each priority level. The high priority level focuses on the voltage deviation change rate and the cumulative voltage change. The medium priority level focuses on the voltage deviation value of the grid connection point. The low priority level focuses on the voltage qualification rate. The reactive compensation adjustment instructions include the switching order and compensation capacity of the reactive compensation equipment. The evaluation priority is determined in the following way: the calculation of the voltage state perception index is divided into the time dimension severity index and the amplitude dimension severity index. The time dimension severity index is calculated based on the voltage drop duration, and the amplitude dimension severity index is calculated based on the voltage drop amplitude. The voltage state perception index is equal to the time dimension severity index multiplied by the amplitude dimension severity index; different basic reactive power control strategies are used for priority evaluation: under high priority, the reactive power compensation adjustment command is calculated based on the system reactive power shortage, and the system reactive power shortage is equal to the available reactive power capacity of the energy storage power station minus the product of the inverter rated capacity and the dominant scenario active power output; under medium priority, the reactive power compensation adjustment command is obtained by weighting the voltage deviation term, the voltage change rate term and the integral term; under low priority, the reactive power compensation adjustment command is determined only by the voltage deviation term; the specific value of the reactive power compensation adjustment command is composed of the reactive power theoretically required to restore the voltage to the rated value, the output amount that quickly responds to voltage mutations, and the energy storage output amount that eliminates static errors.

[0053] Preferably, the present invention constructs a multi-dimensional evaluation system based on the voltage state perception index for the real-time evaluation of the grid voltage regulation demand, and realizes the comprehensive perception of the voltage state through the combination of the time dimension severity index and the amplitude dimension severity index. The evaluation system adopts differentiated basic reactive power control strategies according to different priorities, and subdivides the reactive power compensation adjustment instructions into three components: theoretical required amount, rapid response amount, and static error elimination amount. Compared with the existing technology, this refined evaluation and control strategy can more accurately identify abnormal voltage states and provide more targeted reactive power compensation support.

[0054] Real-time assessment of the need for grid power oscillation suppression includes:

[0055] Obtain the real-time change values ​​of the equivalent inertia coefficient, damping coefficient and synchronization coefficient; calculate the correlation between the three; if the change value of any coefficient is greater than the sixth preset threshold, trigger the correlation analysis; when the correlation is greater than the seventh preset threshold, enhance the system regulation weight of the energy storage power station; when the correlation is less than the seventh preset threshold, adjust the control parameters of the energy storage power station until the correlation is greater than the seventh preset threshold.

[0056] It should be noted that the physical meanings of the equivalent inertia coefficient, damping coefficient and synchronization coefficient correspond to the inertia effect, damping capacity and synchronization characteristics in the electrical torque analysis model respectively. The real-time change value is obtained by normalizing each coefficient with its corresponding rated value; the correlation degree adopts a three-dimensional correlation degree calculation method (this calculation method is a prior art and can be found on Baidu): first calculate the correlation coefficient R1 between the equivalent inertia coefficient and the damping coefficient, the correlation coefficient R2 between the damping coefficient and the synchronization coefficient, and the correlation coefficient R3 between the synchronization coefficient and the equivalent inertia coefficient. The correlation degree is equal to the weighted average of the correlation coefficients R1, R2 and R3; the system adjustment weight is based on the energy storage battery The mechanical dynamic process model of the station is determined, wherein the mechanical dynamic process is described by standard dynamic equations, including the moment of inertia term and the damping coefficient term; the feedback gain coefficient in the control parameter is determined based on the influence of the energy storage power station on the dynamic characteristics of the power system, and the control period is determined based on the electromechanical time scale of the power oscillation; wherein, the control parameter adjustment strategy of the energy storage power station includes: when the power angle or electromagnetic power is fed back, proportional-differential control is used to change the synchronization ability and damping level of the synchronous machine network, and when the speed or grid frequency is fed back, proportional-integral-differential control (existing technology, available on Baidu) is used to adjust the damping level, synchronization ability and inertia effect of the synchronous machine power system.

[0057] Preferably, the present invention proposes an evaluation method based on three-dimensional correlation, and establishes a quantitative evaluation standard for the oscillation suppression capability of the energy storage power station by calculating the correlation coefficient between the equivalent inertia coefficient, the damping coefficient and the synchronization coefficient. This method not only considers the individual changes of each parameter, but also pays attention to the coupling relationship between the parameters, and dynamically adjusts the control strategy according to the results of the correlation. Compared with the existing technology, this evaluation method can more comprehensively evaluate the contribution of the energy storage power station to the system stability and improve the effect of power oscillation suppression.

[0058] In general, the evaluation index system constructed by S2 has the following outstanding advantages: First, the index system systematically integrates the various grid-side support functions of the energy storage power station, avoiding the optimization goal conflicts that may be caused by the traditional single function evaluation; second, each evaluation index is based on quantifiable physical quantities, ensuring the objectivity and repeatability of the evaluation results; most importantly, the index system achieves accurate evaluation of the state of the energy storage power station by introducing innovative mechanisms such as dynamic evaluation and multi-dimensional evaluation, providing a reliable basis for the optimal scheduling of the energy storage power station. These characteristics make the evaluation system of the present invention have unique technical advantages in practical applications.

[0059] S3: Based on the real-time evaluation index system of energy storage power stations, a comprehensive performance evaluation model of energy storage power stations is established, and the status of energy storage power stations is evaluated according to the comprehensive performance evaluation model of energy storage power stations.

[0060] Firstly, a grid demand response capability score is established based on the real-time evaluation index system of energy storage power stations. The grid demand response capability score is calculated based on the real-time evaluation indicators of grid frequency regulation demand, grid peak regulation and energy transfer demand, grid voltage regulation demand and grid power oscillation suppression demand.

[0061] Specifically, the grid demand response capability score is calculated using a hierarchical weighted method: first, the basic weight is determined according to the importance of each demand, and the basic weight is obtained based on the statistics of the grid operation data; secondly, the time dimension of each demand response is analyzed, and the time dimension includes second-level frequency regulation response, minute-level voltage regulation response, hour-level peak regulation response and day-level oscillation suppression response, and the basic weight is corrected according to the difference in the response time dimension; then, the weight is adjusted based on the degree of coupling of each demand, and the degree of coupling is calculated by establishing a correlation matrix between the demands. When the degree of coupling of two demands is higher than the eighth preset threshold, the corresponding weights are jointly corrected; finally, according to the corrected weights, the real-time evaluation indicators of each demand are normalized and weighted summed to obtain the grid demand response capability score.

[0062] Preferably, the present invention proposes a hierarchical weighting method based on the time dimension. Different from the simple linear weighting in the prior art, the present invention takes into account the time scale characteristics of different demand responses (from seconds to days), and introduces the concept of demand coupling to correct the weights. This scoring method can more accurately reflect the response capabilities of energy storage power stations at different time scales, avoiding the problem of ignoring the mutual influence between demands in traditional methods. Especially when the energy storage power station participates in multiple grid services at the same time, this method can effectively balance the weight distribution of various demands and improve the accuracy of the evaluation.

[0063] Secondly, establish basic performance scores, including regulatory index scores, energy efficiency level index scores, reliability index scores and environmental protection index scores.

[0064] Furthermore, the basic performance score of the energy storage power station is calculated based on the dynamic fuzzy comprehensive evaluation method, including the following steps:

[0065] 1. Determine four types of basic performance indicators. Regulation indicators include regulation speed, regulation accuracy, response time, frequency regulation mileage, peak regulation amplitude, actual charge and discharge power of the power station and actual discharge capacity; energy efficiency level indicators include charge and discharge efficiency, energy storage conversion efficiency and system self-consumption rate; reliability indicators include energy storage unit availability, PCS availability and dispatch execution rate; environmental protection indicators include renewable energy consumption contribution, pollutant emission reduction benefits, unit area and energy storage medium pollution;

[0066] 2. Construct the membership functions of various indicators. For the dynamic response indicators in the regulation indicators, including regulation speed, regulation accuracy and response time, the piecewise linear membership function is used; for the capacity indicators in the regulation indicators, including frequency regulation mileage, peak regulation amplitude, actual chargeable and dischargeable power of the power station and actual dischargeable capacity, the Gaussian membership function is used; for the energy efficiency level indicators and reliability indicators, the S-type membership function is used; for the environmental protection indicators, the trapezoidal membership function is used;

[0067] 3. Then establish a dynamic evaluation matrix. The elements of the dynamic evaluation matrix are determined based on the real-time value of each indicator and its corresponding historical change trend. When the improvement of the real-time value of the indicator exceeds the tenth preset threshold compared with the historical average value, the corresponding evaluation level is increased by one level; the historical average value is calculated based on the monitoring data of the most recent month;

[0068] 4. Determine the relative importance of the four types of indicators based on factor analysis: For regulatory indicators, the weights of adjustment speed, response time and actual chargeable and dischargeable power are higher than other indicators; for energy efficiency level indicators, the weight of charge and discharge efficiency is the highest; for reliability indicators, the weight of PCS availability is the highest; for environmental protection indicators, the weight of renewable energy consumption contribution is the highest;

[0069] 5. The basic performance score is calculated using the weighted average-maximum membership method: first, the various indicators are normalized, and then the weights of each indicator are corrected according to the correlation analysis results between the indicators. When the correlation coefficient between two indicators is greater than the eleventh preset threshold, the corresponding weights are jointly corrected; finally, the scores of various indicators are calculated based on the corrected weights, and the basic performance score is obtained by weighted summation. Finally, the grid demand response capability score and the basic performance score are weighted to obtain the comprehensive evaluation score of the energy storage power station.

[0070] Preferably, the present invention constructs a multi-level evaluation system based on the dynamic fuzzy comprehensive evaluation method. The evaluation system selects the most suitable membership function for different types of indicators (such as using a piecewise linear membership function for dynamic response indicators in regulatory indicators, and an S-type membership function for energy efficiency level indicators, etc.). This differentiated processing method can more accurately describe the characteristics of different indicators compared to the method of uniformly using the same type of membership function in the prior art. At the same time, by introducing the analysis of historical change trends, the evaluation results reflect both the current state and the development trend, thereby improving the foresight of the evaluation.

[0071] Finally, the grid demand response capability score and the basic performance score are weighted and calculated to obtain the comprehensive evaluation score of the energy storage power station.

[0072] Specifically, the weighted calculation adopts an adaptive weight dynamic adjustment method, which specifically includes the following steps:

[0073] 1. Establish a hierarchical consistency evaluation system. Decompose the grid demand response capability score to obtain the grid frequency regulation response score, grid peak regulation response score, grid voltage regulation response score, and grid oscillation suppression response score; decompose the basic performance score to obtain the regulation index score, energy efficiency level index score, reliability index score, and environmental protection index score; calculate the inter-group consistency coefficient and intra-group consistency coefficient based on each sub-item score;

[0074] 2. Determine the initial weight configuration rules. The initial weight of the grid demand response capability score is w1, and the initial weight of the basic performance score is w2, satisfying w1+w2=1 and w1>w2; when the consistency coefficient between the regulation speed and response time index scores in the controllability index and the grid frequency regulation response score is greater than the twelfth preset threshold, the value of w1 is dynamically increased; when the consistency coefficient between the charging and discharging efficiency score in the energy efficiency level index and the grid peak regulation response score is greater than the thirteenth preset threshold, the value of w1 is dynamically increased;

[0075] 3. Construct a weight adaptive adjustment mechanism. A two-layer adaptive adjustment model is established based on the inter-group consistency coefficient and the intra-group consistency coefficient; at the inter-group level, when the inter-group consistency coefficient of the grid demand response capability score and the basic performance score is less than the fourteenth preset threshold, w1 and w2 are dynamically adjusted according to the standard deviation ratio of the two types of scores; at the intra-group level, the intra-group standard deviations of the four types of grid response scores and the four types of basic performance scores are calculated respectively. When the intra-group standard deviation is greater than the fifteenth preset threshold, the corresponding score weight is reduced;

[0076] 4. Calculate the comprehensive evaluation score. Introduce the time decay factor, which has an exponential decay relationship with the evaluation time interval; multiply the time decay factor, the adjusted weight coefficient and the corresponding score to obtain the weighted score; for the grid demand response capability score, the internal weight is determined based on the contribution of each score to the safe and stable operation of the grid; for the basic performance score, the internal weight is determined based on the importance and relevance of each indicator; finally, the comprehensive evaluation score of the energy storage power station is obtained through weighted summation.

[0077] Preferably, the present invention proposes a weight calculation method based on double-layer adaptive adjustment. This method achieves dynamic adjustment of weights at both the inter-group and intra-group levels by establishing a hierarchical consistency evaluation system, overcoming the limitation of fixed weights in the prior art. In particular, after the introduction of the time attenuation factor, the evaluation results can better reflect the dynamic change characteristics of the performance of the energy storage power station. This adaptive weight adjustment mechanism can automatically adjust the importance of each indicator according to the actual operating conditions, thereby improving the accuracy and reliability of the evaluation results.

[0078] In summary, the present invention first obtains the real-time operation data of the energy storage power station from multiple dimensions of the PCS converter, the battery body and the PCC point to achieve accurate data collection and screening; secondly, by constructing a real-time evaluation index system including four dimensions of grid frequency regulation, peak regulation, voltage regulation and power oscillation suppression, a systematic evaluation of different grid-side support functions of the energy storage power station is achieved; finally, based on the dynamic fuzzy comprehensive evaluation method and the adaptive weight dynamic adjustment method, the grid demand response capability score and the energy storage power station basic performance score are integrated and evaluated, which not only considers the response characteristics under different time scales, but also introduces a double-layer adaptive adjustment mechanism between and within groups, thereby improving the accuracy and reliability of the evaluation results, and providing effective support for the optimized operation and scheduling decision-making of the energy storage power station.

[0079] Embodiment 2 is an embodiment of the present invention, which provides a system for evaluating the state of an energy storage power station based on real-time demand analysis, including: a data acquisition module for acquiring real-time operation data of the energy storage power station; an evaluation index system construction module for establishing a real-time evaluation index system for the energy storage power station under the demand of the power grid system based on the real-time operation data; an evaluation module for establishing a comprehensive evaluation model for the performance of the energy storage power station based on the real-time evaluation index system of the energy storage power station, and performing an energy storage power station state evaluation according to the comprehensive evaluation model for the performance of the energy storage power station.

[0080] Example 3, reference Figure 2, is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0081] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0082] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0083] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0084] Example 4 is an embodiment of the present invention, which provides a method for evaluating the state of an energy storage power station based on real-time demand analysis. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0085] In order to verify the effectiveness of the method of the present invention, five large-scale energy storage power stations of a provincial power grid company were selected as test objects, namely A, B, C, D, and E energy storage power stations, with a rated capacity of 100MW / 200MWh. During the test, high-precision data acquisition devices were first installed on the PCS converters, battery bodies, and PCC points of each energy storage power station, and the sampling frequency was set to: 100Hz for voltage and current data, 1Hz for temperature data, and 50Hz for power data. The data collection lasted for 3 months (2023.7.1-2023.9.30), and more than 9 million pieces of raw data were obtained through the deployed distributed data acquisition system. After preprocessing the collected data, an evaluation index database covering four dimensions of grid frequency regulation, peak regulation, voltage regulation, and power oscillation suppression was established. During the evaluation process, a dynamic weight adjustment mechanism was designed for the response characteristics of different time scales: the initial weight of frequency regulation response (seconds) was 0.35, the initial weight of voltage regulation response (minutes) was 0.25, the initial weight of peak regulation response (hours) was 0.25, and the initial weight of oscillation suppression (days) was 0.15. The voltage state perception indicators based on the time dimension and amplitude dimension were used to divide the evaluation priorities. The high priority (γ(t)>0.8) adopted the reactive power shortage compensation strategy, the medium priority (0.4≤γ(t)≤0.8) adopted the three-item weighting strategy, and the low priority (γ(t)<0.4) adopted the voltage deviation correction strategy. When constructing the comprehensive evaluation model, the membership functions of the four basic performance indicators were established through dynamic fuzzy evaluation, and the dynamic evaluation matrix of each indicator was calculated based on one month of historical data. Finally, the double-layer adaptive weight adjustment method was used to dynamically adjust the weight coefficient of each evaluation indicator according to the inter-group consistency coefficient (threshold 0.85) and the intra-group standard deviation (threshold 0.1).

[0086] Table 1 Comparison of energy storage power station performance evaluation data

[0087]

[0088] The following conclusions can be drawn from the analysis of Table 1: First, in terms of frequency regulation performance, after adopting the evaluation method of the present invention, the frequency regulation response time of the five energy storage power stations is better than the industry standard (100ms), among which the C energy storage power station performs best (78ms), and the response speed is increased by 18.5% on average compared with the traditional evaluation method. The values ​​of the frequency regulation contribution factor λ all exceed 1.2, indicating that the energy storage power station has played a significant alternative role in the frequency regulation of the power grid, especially the C energy storage power station has reached the highest value of 1.52, which is due to the deep evaluation and hierarchical response mechanism introduced by the present invention. Secondly, in terms of voltage regulation, by adopting multi-dimensional voltage state perception indicators and differentiated reactive power control strategies, the voltage qualification rate of the five energy storage power stations has reached more than 97%, far exceeding the industry standard of 95%, proving the advantages of the present invention in voltage abnormal state identification and reactive power compensation. In terms of power oscillation suppression, the evaluation method based on three-dimensional correlation makes the oscillation suppression time generally controlled within 3.5s, which is 25.3% shorter than the traditional method on average. It is particularly noteworthy that through the two-layer adaptive weight adjustment mechanism, the grid demand response capability score and basic performance comprehensive score of each energy storage power station have reached a high level, and the scoring results are highly consistent with the actual operating performance, with a correlation coefficient of 0.92. The dynamically adjusted weight coefficient is between 0.85-0.95, reflecting the adaptive ability of the evaluation system to the characteristics of different energy storage power stations. Compared with the traditional fixed weight evaluation method, the present invention improves the evaluation accuracy by about 15.8% and the evaluation real-time by about 23.4%, which fully demonstrates the innovation and practicality of the present invention in the field of energy storage power station status assessment.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the state of an energy storage power station based on real-time demand analysis, characterized in that: include: Obtain real-time operation data of energy storage power stations; The real-time operation data includes voltage data, current data, temperature data, and power data; Based on the real-time operation data, a real-time evaluation index system for energy storage power stations under the needs of the power grid system is established; Based on the real-time evaluation index system, a comprehensive performance evaluation model for an energy storage power station is established, and the state of the energy storage power station is evaluated according to the comprehensive performance evaluation model for the energy storage power station.

2. The energy storage power station status assessment method based on real-time demand analysis according to claim 1, characterized in that: Obtaining real-time operating data of the energy storage power station includes the following steps: Collect voltage and current data from the PCS converter, temperature data from the battery body, and power data from the common connection point PCC; Detecting and screening the collected real-time operation data to be within a preset operation parameter range; The filtered real-time operation data is classified and stored in a database.

3. The energy storage power station status assessment method based on real-time demand analysis according to claim 1, characterized in that: The grid system requirements include grid frequency regulation requirements, grid peak regulation and energy transfer requirements, grid voltage regulation requirements, and grid power oscillation suppression requirements; The real-time evaluation index system of the energy storage power station includes: Real-time evaluation indicators of grid frequency regulation demand, including the rated power, ramp rate, rated capacity of the energy storage power station, and the frequency regulation contribution factor λ to conventional units; Real-time evaluation indicators for grid peak load regulation and energy transfer requirements, including upper and lower limits of charge and discharge power, upper and lower limits of state of charge (SOC), load standard deviation minimization objective function value, load peak-valley coefficient, and load rate; Real-time evaluation indicators of grid voltage regulation demand, including grid connection point voltage deviation value Δ U ( t ), voltage deviation change rate Δ o ( t ), voltage cumulative change Δ E ( t ), voltage status perception indicators γ ( t ), and voltage qualification rate λu ; Real-time evaluation indicators for the power grid power oscillation suppression needs include the equivalent inertia coefficient, damping coefficient, and synchronization coefficient of the energy storage power station to the power system.

4. The energy storage power station status assessment method based on real-time demand analysis according to claim 3 is characterized in that: Real-time assessment of the grid frequency regulation requirements includes, Obtain the power matching value between the rated power of the energy storage power station and the system reference power, the following value between the climbing rate of the energy storage power station and the grid frequency change, and the rated power frequency regulation duration of the energy storage power station; If the power matching value, the following value and the frequency modulation duration value all meet the preset evaluation requirements, the in-depth evaluation stage is entered: a hierarchical evaluation is performed based on the change trend of the frequency modulation contribution factor λ. When the frequency modulation contribution factor λ is greater than the first preset threshold and is on an upward trend, the frequency modulation weight of the energy storage power station is increased and the frequency modulation output of the conventional units is reduced; when the frequency modulation contribution factor λ is on a downward trend, the energy storage power station is set to the standby frequency modulation mode; when the frequency modulation contribution factor λ is less than the second preset threshold, a frequency modulation warning signal is generated; Real-time assessment of the grid peak load and energy transfer requirements includes: Calculating a characteristic product of the load rate and the load peak-valley coefficient; If the characteristic product is greater than a third preset threshold, the charging and discharging power value of the energy storage power station, the state of charge SOC of the energy storage power station and the load standard deviation minimization objective function value are obtained; it is determined whether the charging and discharging power value, the state of charge SOC and the load standard deviation minimization objective function value meet the preset evaluation criteria; if the preset evaluation criteria are met and the load standard deviation minimization objective function value shows a continuous downward trend, a charging and discharging optimization instruction for the energy storage power station is generated.

5. The energy storage power station status assessment method based on real-time demand analysis according to claim 4, characterized in that: The real-time assessment of the grid voltage regulation requirements includes: Obtain the voltage status perception indicator γ ( t ), according to the voltage state perception indicator γ ( t ) determine the evaluation priority; when the voltage state perception indicator γ ( t ) is greater than the fourth preset threshold, based on the voltage deviation change rate Δ o ( t ) and the voltage cumulative change Δ E ( t ) for priority assessment; when the voltage state perception indicator γ ( t ) is between the fifth preset threshold and the fourth preset threshold, based on the grid connection point voltage deviation value Δ U ( t ) for priority assessment; when the voltage state perception indicator γ ( t ) is less than the fifth preset threshold, based on the voltage qualification rate λu Conduct priority evaluation; generate reactive power compensation adjustment instructions according to the evaluation results under different priorities; The real-time assessment of the power grid power oscillation suppression requirements includes, Obtaining the real-time change values ​​of the equivalent inertia coefficient, damping coefficient and synchronization coefficient; calculating the correlation between the three; If the change value of any coefficient is greater than the sixth preset threshold, the correlation analysis is triggered; when the correlation is greater than the seventh preset threshold, the system regulation weight of the energy storage power station is enhanced; when the correlation is less than the seventh preset threshold, the control parameters of the energy storage power station are adjusted until the correlation is greater than the seventh preset threshold.

6. The energy storage power station status assessment method based on real-time demand analysis according to claim 1, characterized in that: The process of establishing a comprehensive evaluation model for energy storage power station performance includes the following steps: A grid demand response capability score is established based on the real-time evaluation index system of the energy storage power station; the grid demand response capability score is calculated based on real-time evaluation indicators of grid frequency regulation demand, grid peak regulation and energy transfer demand, grid voltage regulation demand, and grid power oscillation suppression demand; Establishing basic performance scores; the basic performance scores include regulatory index scores, energy efficiency index scores, reliability index scores, and environmental protection index scores; The grid demand response capability score and the basic performance score are weighted and calculated to obtain a comprehensive evaluation score of the energy storage power station.

7. The energy storage power station status assessment method based on real-time demand analysis according to claim 6, characterized in that: The grid demand response capability score is calculated using a hierarchical weighted method: Determine the basic weight according to the importance of each requirement; Analyze the time dimension of each demand response; the time dimension includes second-level frequency regulation response, minute-level voltage regulation response, hour-level peak regulation response and day-level oscillation suppression response; Modifying the basic weight according to the difference in the response time dimension; The weights are adjusted based on the coupling degree of each demand; the coupling degree is calculated by establishing a correlation matrix between the demands; when the coupling degree of two demands is higher than an eighth preset threshold, the corresponding weights are jointly modified; According to the corrected weights, the real-time evaluation indicators of each demand are normalized and weighted summed to obtain the grid demand response capability score.

8. A system using the energy storage power station status assessment method based on real-time demand analysis as described in any one of claims 1 to 7, characterized in that: include: A data acquisition module is used to obtain real-time operation data of the energy storage power station; An evaluation index system building module, which establishes a real-time evaluation index system for energy storage power stations under the requirements of the power grid system based on the real-time operation data; The evaluation module establishes a comprehensive evaluation model for the performance of an energy storage power station based on the real-time evaluation index system of the energy storage power station, and performs an energy storage power station status evaluation according to the comprehensive evaluation model for the performance of the energy storage power station.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the energy storage power station status assessment method based on real-time demand analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy storage power station status assessment method based on real-time demand analysis described in any one of claims 1 to 7 are implemented.

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