A method and system for evaluating the state of an energy storage power station based on real-time demand analysis

Abstract

The application discloses a kind of based on real-time demand analysis's energy storage power station state evaluation method and system, it is related to power station state evaluation technical field, comprising: obtaining the real-time operation data of energy storage power station;Real-time operation data includes voltage data, current data, temperature data and power data;Based on real-time operation data, establish the real-time evaluation index system of energy storage power station under power grid system demand;Based on the real-time evaluation index system of energy storage power station, establish the performance comprehensive evaluation model of energy storage power station, and the state of energy storage power station is evaluated according to the performance comprehensive evaluation model of energy storage power station.The application obtains operation data from PCS converter, battery body and PCC point multidimension, combined with the systematic evaluation index system of four kinds of demands of power grid frequency modulation, peak regulation, voltage regulation and power oscillation suppression, using dynamic fuzzy comprehensive evaluation and adaptive weight dynamic adjustment combined evaluation method, the overall accurate evaluation of the state of energy storage power station is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power station state evaluation, in particular to a storage power station state evaluation method and system based on real-time demand analysis. BACKGROUND

[0002] As an important regulating resource of the power system, the state evaluation of the storage power station is of great significance to ensure the safe and stable operation of the power grid. At present, the state evaluation of the storage power station mainly focuses on the performance indicators at the equipment level, such as the state monitoring method based on SOC and SOH, the parameter evaluation method based on the battery management system, etc. Although these methods can reflect the basic operating state of the storage equipment, they are mostly limited to single-dimensional static evaluation. At the same time, as the functions of the storage power station in the power grid are increasingly diversified, the traditional evaluation methods are difficult to meet the state evaluation requirements of the power grid under diversified demands of the storage power station. Especially when the storage power station participates in multiple service scenarios such as frequency modulation and peak shaving, voltage support, etc., the existing evaluation methods lack analysis of the response characteristics at different time scales, and do not fully consider the coupling relationship between various power grid demands.

[0003] The existing state evaluation method of the storage power station has the following shortcomings: first, there is a lack of systematic evaluation index system, which is difficult to fully reflect the response capability of the storage power station under different power grid demands; second, the fixed weight linear weighting method is generally used in the evaluation process, which fails to reflect the importance of different indicators in the dynamic operation process; third, the evaluation result usually only reflects the current state, and lacks analysis of historical data trends and prediction of future state; finally, the existing evaluation method has obvious limitations in dealing with the state evaluation problem of multiple power grid services coexisting, and it is difficult to accurately evaluate the comprehensive service capability of the storage power station. SUMMARY

[0004] In view of the above problems, the present application provides a storage power station state evaluation method and system based on real-time demand analysis, which can solve the problems mentioned in the background art.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] A storage power station state evaluation method based on real-time demand analysis, comprising:

[0007] Obtaining real-time operation data of the 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 of the storage power station under the demand of the power grid system is established;

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

[0010] As a preferred scheme of the energy storage power station state evaluation method based on real-time demand analysis, the real-time operation data of the energy storage power station is obtained, including 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 point of common coupling (PCC); the collected real-time operation data is detected and screened into the preset operation parameter range; and the screened real-time operation data is classified and stored in the database.

[0011] As a preferred scheme of the energy storage power station state evaluation method based on real-time demand analysis, the grid system demand includes grid frequency regulation demand, grid peak regulation and energy transfer demand, grid voltage regulation demand, and grid power oscillation suppression demand; the real-time evaluation index system of the energy storage power station includes: real-time evaluation index of grid frequency regulation demand, including rated power of the energy storage power station, ramp rate, rated power, and frequency regulation contribution factor λ of conventional units; real-time evaluation index of grid peak regulation and energy transfer demand, including upper and lower limit values of charge and discharge power, upper and lower limit values of state of charge (SOC), minimum objective function value of load standard deviation, load peak-valley coefficient, and load rate; real-time evaluation index of grid voltage regulation demand, including voltage deviation value ΔU(t) of the point of common coupling (PCC), voltage deviation change rate Δo(t), voltage cumulative change amount ΔE(t), voltage state perception index γ(t), and voltage qualified rate λu; real-time evaluation index of grid power oscillation suppression demand, including equivalent inertia coefficient, damping coefficient, and synchronization coefficient of the energy storage power station to the power system.

[0012] As a preferred scheme of the energy storage power station state evaluation method based on real-time demand analysis, the real-time evaluation of the grid frequency regulation demand includes obtaining power matching value of rated power of the energy storage power station and system reference power, following value of ramp rate of the energy storage power station and grid frequency change, and frequency regulation duration value of rated power of the energy storage power station; if the power matching value, following value and frequency regulation duration value all meet the preset evaluation demand, the deep evaluation stage is entered: based on the change trend of the frequency regulation contribution factor λ, the grading evaluation is performed, when the frequency regulation contribution factor λ is greater than a first preset threshold and presents 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 presents 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 power grid peak shaving and energy transfer demand comprises 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, obtaining the charge and discharge 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; determining whether the charge and discharge power value, the state of charge SOC, and the load standard deviation minimization objective function value meet a preset evaluation standard; if the preset evaluation standard is met and the load standard deviation minimization objective function value presents a continuous downward trend, generating a charge and discharge optimization instruction of the energy storage power station.

[0014] As a preferred scheme of the energy storage power station state evaluation method based on real-time demand analysis, the real-time evaluation of the power grid voltage regulation demand comprises obtaining a voltage state awareness index γ(t), determining an evaluation priority according to the voltage state awareness index γ(t); when the voltage state awareness index γ(t) is greater than a fourth preset threshold, performing priority evaluation based on the voltage deviation change rate Δo(t) and the voltage cumulative change amount ΔE(t); when the voltage state awareness index γ(t) is between a fifth preset threshold and the fourth preset threshold, performing priority evaluation based on the grid-connected point voltage deviation value ΔU(t); when the voltage state awareness index γ(t) is less than the fifth preset threshold, performing priority evaluation based on the voltage qualified rate λu; generating a reactive power compensation adjustment instruction according to the evaluation results under different priorities.

[0015] The real-time evaluation of the power grid power oscillation suppression demand comprises obtaining real-time change values of the equivalent inertia coefficient, the damping coefficient, and the synchronization coefficient; calculating a correlation degree among the three; if the change value of any coefficient is greater than a sixth preset threshold, triggering correlation degree analysis; when the correlation degree is greater than a seventh preset threshold, enhancing the system regulation weight of the energy storage power station; when the correlation degree is less than the seventh preset threshold, adjusting the control parameter of the energy storage power station until the correlation degree is greater than the seventh preset threshold.

[0016] As a preferred scheme of the energy storage power station state evaluation method based on real-time demand analysis, the process of establishing a comprehensive performance evaluation model of the energy storage power station comprises the following steps: establishing a power grid demand response capability score based on the real-time evaluation index system of the energy storage power station; the power grid demand response capability score is calculated according to real-time evaluation indexes of the power grid frequency modulation demand, the power grid peak shaving and energy transfer demand, the power grid voltage regulation demand, and the power grid power oscillation suppression demand; establishing a basic performance score; the basic performance score comprises a regulation and control index score, an energy efficiency level index score, a reliability index score, and an environmental protection index score; performing weighted calculation on the power grid demand response capability score and the basic performance score to obtain a comprehensive evaluation score of the energy storage power station.

[0017] As a preferred scheme of the energy storage power station state evaluation method based on real-time demand analysis, wherein: the power grid demand response capability score is calculated by using a hierarchical weighting 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 modulation 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 of the response time dimension; the weight is adjusted based on the coupling degree of each demand; the coupling degree is calculated by establishing an association matrix between demands; when the coupling degree of two demands is higher than the eighth preset threshold, the corresponding weight is jointly corrected; after the real-time evaluation index of each demand is normalized and processed by using the corrected weight, the weighted sum is obtained, and the power grid demand response capability score is obtained.

[0018] The application further provides an energy storage power station state evaluation system based on real-time demand analysis, comprising:

[0019] A data acquisition module is configured to acquire real-time operation data of the energy storage power station.

[0020] An evaluation index system construction module is configured to establish a real-time evaluation index system of the energy storage power station under the demand of the power grid system based on the real-time operation data.

[0021] An evaluation module is configured to establish a comprehensive performance evaluation model of the energy storage power station based on the real-time evaluation index system of the energy storage power station, and perform state evaluation of the energy storage power station according to the comprehensive performance evaluation model of the energy storage power station.

[0022] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the energy storage power station state evaluation method based on real-time demand analysis when executing the computer program.

[0023] A computer readable storage medium stores a computer program, and the computer program realizes the steps of the energy storage power station state evaluation method based on real-time demand analysis when executed by a processor.

[0024] The beneficial effects of the present application: firstly, the real-time operation data of the energy storage power station is obtained from the PCS converter, the battery body and the PCC point in multiple dimensions, realizing accurate collection and screening of data; secondly, by constructing a real-time evaluation index system containing four dimensions of grid frequency modulation, peak regulation, voltage regulation and power oscillation suppression, the 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 self-adaptive weight dynamic adjustment method, the grid demand response ability score and the energy storage power station basic performance score are fused and evaluated, not only considering the response characteristics under different time scales, but also introducing a double-layer self-adaptive adjustment mechanism between groups and within groups, improving the accuracy and reliability of the evaluation results, and providing effective support for the optimization operation and dispatching decision of the energy storage power station. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0026] Figure 1 The overall flowchart of the energy storage power station state evaluation method based on real-time demand analysis proposed by the present application;

[0027] Figure 2 The structure diagram of the energy storage power station state evaluation system based on real-time demand analysis proposed by the present application;

[0028] Figure 3 The computer device diagram in the energy storage power station state evaluation method based on real-time demand analysis proposed by the present application. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0030] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0031] Embodiment 1, refer toFigure 1 For an embodiment of the present application, a real-time demand analysis-based energy storage power station state evaluation method is provided.

[0032] The present application can effectively solve the above-mentioned problems. Next, how to implement the real-time demand analysis-based energy storage power station state evaluation method will be described in detail in combination with multiple embodiments.

[0033] Figure 1 A flowchart of the real-time demand analysis-based energy storage power station state evaluation method is shown, which includes:

[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] The voltage data and current data are collected from the PCS converter, the temperature data are collected from the battery body, and the power data are collected from the point of common coupling (PCC);

[0037] The collected real-time operation data is detected and screened into a preset operation parameter range;

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

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

[0040] Specifically, the demand of the power grid system includes the demand of power grid frequency modulation, the demand of power grid peak regulation and energy transfer, the demand of power grid voltage regulation, and the demand of power grid power oscillation suppression.

[0041] The real-time evaluation of the demand of power grid frequency modulation includes:

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

[0043] If the power matching value, the following value, and the frequency modulation duration value all meet the preset evaluation demand, enter the deep evaluation stage: based on the change trend of the frequency modulation contribution factor λ, perform hierarchical evaluation, when the frequency modulation contribution factor λ is greater than a first preset threshold and presents an upward trend, increase the frequency modulation weight of the energy storage power station and reduce the frequency modulation output of the conventional unit; when the frequency modulation contribution factor λ presents a downward trend, set the energy storage power station to a standby frequency modulation mode; when the frequency modulation contribution factor λ is less than a second preset threshold, generate a frequency modulation warning signal.

[0044] Wherein, the power matching value is the ratio of the rated power of the energy storage power station to the system reference 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 power grid; the frequency modulation duration value is the duration that the energy storage power station maintains the rated power output; and the frequency modulation weight is the output proportion of the energy storage power station in all frequency modulation devices.

[0045] Preferably, the real-time evaluation of the power grid frequency modulation demand is expanded to a dynamic evaluation system by introducing a depth evaluation and hierarchical response mechanism, which expands the traditional single frequency modulation contribution factor evaluation. The evaluation system not only considers the basic performance indicators (power matching value, following value, frequency modulation duration value) of the energy storage power station, but also dynamically adjusts the frequency modulation weight based on the change trend of the frequency modulation contribution factor, so that the energy storage power station can adaptively participate in system frequency modulation according to its actual contribution. Compared with the static evaluation method in the prior art, the dynamic evaluation mechanism can more accurately reflect the real-time frequency modulation capability of the energy storage power station, and improves the utilization efficiency of system frequency modulation resources.

[0046] The real-time evaluation of the power grid peak regulation and energy transfer demand includes:

[0047] The characteristic product of the load rate and the load peak-valley coefficient is calculated; if the characteristic product is greater than a third preset threshold, the charge-discharge 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 judged whether the charge-discharge power value, the state of charge (SOC), and the load standard deviation minimization objective function value meet the preset evaluation standard; if the preset evaluation standard is met, and the load standard deviation minimization objective function value shows a continuous downward trend, a charge-discharge optimization instruction of the energy storage power station is generated.

[0048] Wherein, 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 standard includes that the charge-discharge power value is within a preset power range, the state of charge (SOC) is within a preset SOC range, and the load standard deviation minimization objective function value is less than a power fluctuation upper limit value; and the charge-discharge optimization instruction includes a charge start-stop time point and a discharge start-stop time point.

[0049] Preferably, the present application proposes a double-layer linkage evaluation mechanism of load characteristics and energy storage state when evaluating the real-time demand of the power grid peak regulation and energy transfer. Through the design of the characteristic product triggered evaluation, the waste of computing resources caused by frequent evaluation in traditional methods is avoided. At the same time, the charge-discharge power, the SOC state and the load standard deviation minimization objective function are considered comprehensively, and the charge-discharge strategy is dynamically optimized based on the improvement trend of the load curve. Compared with the prior art, this method can more accurately evaluate the peak regulation effect of the energy storage power station, and improves the accuracy of peak clipping and valley filling.

[0050] The real-time evaluation of the power grid voltage regulation demand includes:

[0051] The voltage state awareness index γ(t) is acquired, an evaluation priority is determined according to the voltage state awareness index γ(t), when the voltage state awareness index γ(t) is greater than a fourth preset threshold, priority evaluation is performed based on the voltage deviation change rate Δo(t) and the voltage cumulative change amount ΔE(t), when the voltage state awareness index γ(t) is between the fifth preset threshold and the fourth preset threshold, priority evaluation is performed based on the grid-connected point voltage deviation value ΔU(t), when the voltage state awareness index γ(t) is less than the fifth preset threshold, priority evaluation is performed based on the voltage qualified rate λu, and a reactive power compensation adjustment instruction is generated according to evaluation results under different priorities.

[0052] It should be noted that the evaluation priority is divided into three levels of high, medium and low, the priority evaluation refers to indexes that are mainly examined under different priorities, the high priority mainly examines the voltage deviation change rate and the voltage cumulative change amount, the medium priority mainly examines the grid-connected point voltage deviation value, and the low priority mainly examines the voltage qualified rate, the reactive power compensation adjustment instruction includes switching order and compensation capacity of the reactive power compensation device, the evaluation priority is determined by the following method: the calculation of the voltage state awareness index is divided into a time dimension severity index and an amplitude dimension severity index, the time dimension severity index is calculated based on the voltage drop duration, the amplitude dimension severity index is calculated based on the voltage drop amplitude, the voltage state awareness index is equal to the time dimension severity index multiplied by the amplitude dimension severity index, different basic reactive power control strategies are adopted for the priority evaluation: under the high priority, the reactive power compensation adjustment instruction is calculated based on the system reactive power deficiency, the system reactive power deficiency 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 scene active power output, under the medium priority, the reactive power compensation adjustment instruction is obtained by weighting the voltage deviation term, the voltage change rate term and the integral term, under the low priority, the reactive power compensation adjustment instruction is determined by the voltage deviation term only, and the specific value of the reactive power compensation adjustment instruction is composed of the amount of reactive power required for the voltage to recover to the rated value, the amount of power for rapid response to voltage mutation and the amount of power for eliminating static error of the energy storage.

[0053] Preferably, the real-time evaluation of the grid voltage regulation demand of the present application constructs a multi-dimensional evaluation system based on the voltage state awareness index, realizes comprehensive awareness of the voltage state through the combination of the time dimension severity index and the amplitude dimension severity index, adopts differentiated basic reactive power control strategies according to different priorities, and subdivides the reactive power compensation adjustment instruction into three components of the theoretically required amount, the rapid response amount and the static error elimination amount. This refined evaluation and control strategy can more accurately identify the voltage abnormal state and provide more targeted reactive power compensation support compared with the prior art.

[0054] The real-time evaluation of the grid power oscillation suppression demand includes:

[0055] The real-time change values of the equivalent inertia coefficient, the damping coefficient and the synchronization coefficient are obtained, the correlation degree among the three is calculated, if the change value of any coefficient is greater than the sixth preset threshold, the correlation degree analysis is triggered, when the correlation degree is greater than the seventh preset threshold, the system regulation weight of the energy storage power station is enhanced, when the correlation degree is less than the seventh preset threshold, the control parameter of the energy storage power station is adjusted until the correlation degree is greater than the seventh preset threshold.

[0056] It should be noted that the physical meanings of the equivalent inertia coefficient, the damping coefficient and the synchronization coefficient correspond to the inertia effect, the damping ability and the synchronization characteristic in the electrical torque analysis model respectively, the real-time change values are obtained by normalizing each coefficient and the corresponding rated value; the correlation degree adopts a three-dimensional correlation degree calculation method (which is prior art and can be searched in Baidu): first, 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 are calculated, and the correlation degree is equal to the weighted average value of the correlation coefficients R1, R2 and R3; the system regulation weight is determined based on the mechanical dynamic process model of the energy storage power station, wherein the mechanical dynamic process is described by a standard dynamic equation, including a rotational inertia term and a 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 power oscillation; wherein the control parameter adjustment strategy of the energy storage power station includes: when the feedback power angle or electromagnetic power is used, proportional-differential control is adopted to change the synchronization ability and damping level of the synchronous power grid, and when the feedback speed or power grid frequency is used, proportional-integral-differential control (prior art and can be searched in Baidu) is adopted to adjust the damping level, synchronization ability and inertia effect of the synchronous power system.

[0057] Preferably, the application proposes an evaluation method based on three-dimensional correlation degree, by calculating the correlation coefficients among the equivalent inertia coefficient, the damping coefficient and the synchronization coefficient, a quantitative evaluation standard for the oscillation suppression ability of the energy storage power station is established. This method not only considers the individual change of each parameter, but also pays attention to the coupling relationship between the parameters, and dynamically adjusts the control strategy according to the result of the correlation degree. This evaluation method can more comprehensively evaluate the contribution of the energy storage power station to the system stability compared with the prior art, and improves the effect of power oscillation suppression.

[0058] Overall, 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 target conflicts that may be caused by 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 realizes precise 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 dispatching of the energy storage power station. These characteristics make the evaluation system of the present application have unique technical advantages in practical application.

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

[0060] First, the 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 the real-time evaluation indexes of the grid frequency regulation demand, the grid peak regulation and energy transfer demand, the grid voltage regulation demand, and the grid power oscillation suppression demand.

[0061] Specifically, the grid demand response capability score is calculated using a hierarchical weighting method: first, the basic weight is determined according to the importance of each demand, and the basic weight is obtained based on grid operation data statistics; second, the time dimension of each demand response is analyzed, including 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 modified according to the difference in response time dimension; then, the weight is adjusted based on the coupling degree of each demand, and the coupling degree is calculated by establishing a correlation matrix between demands, and when the coupling degree of two demands is higher than the eighth preset threshold, the corresponding weight is jointly modified; finally, the real-time evaluation indexes of each demand are normalized and weighted after the weight is modified, and the grid demand response capability score is obtained by summing up the weighted values.

[0062] Preferably, the present application proposes a hierarchical weighting method based on time dimension. Unlike the simple linear weighting in the prior art, the present application considers the time scale characteristics of different demand responses (from seconds to days), and introduces the concept of demand coupling degree for weight modification. This scoring method can more accurately reflect the response capability of the energy storage power station 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, improving the accuracy of evaluation.

[0063] Secondly, the basic performance score is established, including the regulation and control index score, the energy efficiency level index score, the reliability index score, and the environmental protection index score.

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

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

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

[0067] 3. Then, a dynamic evaluation matrix is established. The elements of the dynamic evaluation matrix are determined based on the real-time value of each indicator and its corresponding historical trend. When the improvement degree of the real-time value of the indicator over the historical average value exceeds the tenth preset threshold, the corresponding evaluation grade is improved by one grade; the historical average value is calculated based on the monitoring data of the last month;

[0068] 4. Determine the relative importance of the four types of indicators based on factor analysis method: for the regulation indicators, the weights of adjustment speed, response time, and actual chargeable and dischargeable power are higher than those of other indicators; for the energy efficiency level indicators, the weight of charge and discharge efficiency is the highest; for the reliability indicators, the weight of PCS availability is the highest; for the 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, normalize each type of indicator, then modify the weights of each indicator according to the correlation analysis results between indicators, wherein when the correlation coefficient between two indicators is greater than the eleventh preset threshold, the corresponding weights are jointly modified; finally, calculate the scores of each type of indicator based on the modified weights, and obtain the basic performance score 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 application constructs a multi-level evaluation system based on a dynamic fuzzy comprehensive evaluation method. The evaluation system selects the most suitable membership function for different types of indicators (such as using a segmented linear membership function for dynamic response indicators in regulatory indicators, and using an S-shaped membership function for energy efficiency indicators, etc.). This differentiated approach can more accurately describe the characteristics of different indicators compared to the existing technology which uses the same type of membership function uniformly. At the same time, by introducing the analysis of historical trends, the evaluation results reflect not only the current state but also the development trend, improving the forward-looking nature of the evaluation.

[0071] 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.

[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. The grid demand response capability score is decomposed to obtain the grid frequency regulation response score, the grid peak regulation response score, the grid voltage regulation response score, and the grid oscillation suppression response score. The basic performance score is decomposed to obtain the regulatory index score, the energy efficiency index score, the reliability index score, and the environmental protection index score. The inter-group consistency coefficient and the intra-group consistency coefficient are calculated based on each sub-score;

[0074] 2. Determine the initial weight configuration rule. 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; wherein, when the consistency coefficient of the regulation speed and response time index score in the regulatory index and the grid frequency regulation response score is greater than a twelfth preset threshold, the value of w1 is dynamically improved; when the consistency coefficient of the charge and discharge efficiency score in the energy efficiency index and the grid peak regulation response score is greater than a thirteenth preset threshold, the value of w1 is dynamically improved;

[0075] 3. Build a weight adaptive adjustment mechanism. A double-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 a 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 deviation of the four types of grid response scores and the four types of basic performance scores is calculated, and when the intra-group standard deviation is greater than a fifteenth preset threshold, the corresponding score weight is reduced;

[0076] 4. Calculate the comprehensive evaluation score. Introduce a time decay factor, which is exponentially decaying with respect to the evaluation time interval; multiply the time decay factor, the adjusted weight coefficient, and the corresponding score to obtain the weighted score; wherein, for the grid demand response capability score, determine the internal weight based on the contribution of each score to the safe and stable operation of the grid; for the basic performance score, determine the internal weight based on the importance and correlation of each index; finally, obtain the comprehensive evaluation score of the energy storage power station by weighted summation.

[0077] Preferably, the present application proposes a weight calculation method based on double-layer adaptive adjustment. This method realizes dynamic adjustment of weights at the group and intra-group levels by establishing a hierarchical consistency evaluation system, overcoming the limitations of fixed weights in the prior art. Especially after introducing the time decay 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 index according to the actual operation, improving the accuracy and reliability of the evaluation results.

[0078] In summary, the present application first obtains real-time operation data of the energy storage power station from multiple dimensions of PCS converter, battery body and PCC point, realizing accurate collection and screening of data; secondly, by constructing a real-time evaluation index system containing four dimensions of grid frequency regulation, peak regulation, voltage regulation and power oscillation suppression, 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 basic performance score of the energy storage power station are fused and evaluated, not only considering the response characteristics at different time scales, but also improving the accuracy and reliability of the evaluation results by introducing the double-layer adaptive adjustment mechanism of group and intra-group, providing effective support for the optimization operation and dispatching decision of the energy storage power station.

[0079] Embodiment 2, as an embodiment of the present application, provides a kind of energy storage power station state evaluation system based on real-time demand analysis, comprising: data acquisition module, for obtaining the real-time operation data of energy storage power station;Evaluation index system construction module, based on real-time operation data, establish the real-time evaluation index system of energy storage power station under the demand of grid system;Evaluation module, based on the real-time evaluation index system of energy storage power station, establish energy storage power station performance comprehensive evaluation model, carries out energy storage power station state evaluation according to energy storage power station performance comprehensive evaluation model.

[0080] Embodiment 3, refer to Figure 2For one embodiment of the present application, different from the previous embodiment, the function, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or the part of the technical solution that essentially contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

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

[0082] More specific examples (a non-exhaustive list) of the computer readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting or otherwise processing, if necessary, in other suitable ways to be electronically obtained, and then stored in the computer memory.

[0083] It should be understood that various parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: discrete logic circuitry having logic gates for implementing logical functions of data signals, application specific integrated circuits (ASICs) having suitable combinations of logic gate circuitry, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

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

[0085] In order to verify the effectiveness of the method of the present application, five large 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, and the rated capacity of each was 100 MW / 200 MWh. 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 as follows: voltage and current data 100 Hz, temperature data 1 Hz, and power data 50 Hz. The data acquisition lasted for three months (2023.7.1-2023.9.30), and more than 9 million original data were obtained through the deployed distributed data acquisition system. After preprocessing the collected data, an evaluation index database was established, including four dimensions of grid frequency regulation, peak regulation, voltage regulation and power oscillation suppression. In 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 (second level) was 0.35, the initial weight of voltage regulation response (minute level) was 0.25, the initial weight of peak regulation response (hour level) was 0.25, and the initial weight of oscillation suppression (day level) was 0.15. Voltage state perception indexes based on time and amplitude dimensions were used for evaluation priority division, wherein high priority (γ(t)>0.8) used reactive power deficiency compensation strategy, medium priority (0.4≤γ(t)≤0.8) used three-item weighted strategy, and low priority (γ(t)<0.4) used voltage deviation correction strategy. When constructing the comprehensive evaluation model, the membership functions of the four types of basic performance indexes were established through dynamic fuzzy evaluation, and the dynamic evaluation matrix of each index was calculated based on one month of historical data. Finally, a double-layer adaptive weight adjustment method was used to dynamically adjust the weight coefficients of each evaluation index 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] From the analysis of Table 1, the following conclusions can be drawn: First, in terms of frequency regulation performance, the response times of the five energy storage power stations are all better than the industry standard (100 ms) after using the evaluation method of the present application, among which the C energy storage power station performs best (78 ms), with an average improvement of 18.5% in response speed compared with the traditional evaluation method. The values of the frequency contribution factor λ all exceed 1.2, indicating that the energy storage power stations play a significant replacement role in grid frequency regulation, especially the C energy storage power station, which reaches a maximum value of 1.52, thanks to the deep evaluation and hierarchical response mechanism introduced by the present application. Second, in terms of voltage regulation, through the use of multi-dimensional voltage state perception indicators and differentiated reactive power control strategies, the voltage qualification rates of the five energy storage power stations all reach more than 97%, far exceeding the industry standard of 95%, proving the advantages of the present application in voltage abnormal state identification and reactive power compensation. In terms of power oscillation suppression, the evaluation method based on three-dimensional correlation degree enables the oscillation suppression time to be generally controlled within 3.5 s, with an average reduction of 25.3% compared with the traditional method. It is particularly noteworthy that through the double-layer adaptive weight adjustment mechanism, the grid demand response capability score and the basic performance comprehensive score of each energy storage power station all reach a high level, and the scoring results are highly consistent with the actual operation performance, with a correlation coefficient of 0.92. The dynamically adjusted weight coefficients are between 0.85 and 0.95, reflecting the adaptive ability of the evaluation system to different energy storage power station characteristics. Compared with the traditional fixed weight evaluation method, the present application improves the evaluation accuracy by about 15.8% and the evaluation real-time performance by about 23.4%, fully proving the innovativeness and practicality of the present application in the field of energy storage power station state evaluation.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for assessing the state of an energy storage power station based on real-time demand analysis, characterized in that, include: Obtain real-time operating data of energy storage power stations; The real-time operating data includes voltage data, current data, temperature data, and power data; Based on the real-time operating data, a real-time evaluation index system for energy storage power stations under the power grid system requirements is established. The power grid system requirements include power grid frequency regulation requirements, power grid peak shaving and energy transfer requirements, power grid voltage regulation requirements, and power grid power oscillation suppression requirements. The real-time assessment of the grid frequency regulation demand includes: obtaining the power matching value between the rated power of the energy storage power station and the system reference power, the ramp rate of the energy storage power station and the following value of the grid frequency change, and the rated power frequency regulation duration of the energy storage power station. If the power matching value, follow-up value, and frequency regulation duration value all meet the preset evaluation requirements, then the in-depth evaluation stage begins: a graded evaluation is 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 is increased, and the frequency regulation output of the conventional unit is reduced. When the frequency regulation contribution factor λ shows a downward trend, the energy storage power station is set to standby frequency regulation mode. When the frequency regulation contribution factor λ is less than the second preset threshold, a frequency regulation early warning signal is generated. The real-time assessment of the grid peak shaving and energy transfer demand includes: calculating the characteristic product of load factor and load peak-valley coefficient; if the characteristic product is greater than a third preset threshold, then 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 objective function value for minimizing the load standard deviation; determining whether the charging and discharging power value, the SOC, and the objective function value for minimizing the load standard deviation meet preset evaluation criteria; if the preset evaluation criteria are met, and the objective function value for minimizing the load standard deviation shows a continuous downward trend, then generating charging and discharging optimization instructions for the energy storage power station; Real-time assessment of the grid voltage regulation demand includes: acquiring voltage state perception indicators. γ ( t According to the voltage state sensing index γ ( t Determine the evaluation priority; when the voltage state sensing index γ ( t When the voltage deviation rate Δ is greater than the fourth preset threshold, the voltage deviation rate Δ is used as the basis for the calculation. o ( t ) and cumulative voltage change Δ E ( t Prioritize evaluation when the voltage state sensing index is mentioned. γ ( t When the voltage is between the fifth preset threshold and the fourth preset threshold, the voltage deviation value Δ at the grid connection point is used as the basis for the calculation. U ( t Prioritize evaluation when the voltage state sensing index is mentioned. γ ( t When the voltage is less than the fifth preset threshold, the voltage qualification rate is used as the basis for calculation. λu Conduct priority assessments; generate reactive power compensation adjustment instructions based on the assessment results under different priorities. The real-time assessment of the grid power oscillation suppression requirement includes: obtaining the real-time changes in the equivalent inertia coefficient, damping coefficient, and synchronization coefficient; calculating the correlation between the three; if the change in any coefficient is greater than a sixth preset threshold, triggering correlation analysis; when the correlation is greater than a seventh preset threshold, increasing the system regulation weight of the energy storage power station; when the correlation is less than the seventh preset threshold, adjusting the control parameters of the energy storage power station until the correlation is greater than the seventh preset threshold. Based on the aforementioned real-time evaluation index system, a comprehensive performance evaluation model for energy storage power stations is established, and the status of energy storage power stations is evaluated according to the comprehensive performance evaluation model for energy storage power stations. Based on the aforementioned real-time evaluation index system for energy storage power stations, a grid demand response capability score is established and calculated using a hierarchical weighted method. The basic weights are determined based on the importance of each requirement; 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 shaving response, and day-level oscillation suppression response. The basic weights are adjusted based on the differences in response time. The weights are adjusted based on the degree of coupling between the requirements; the degree of coupling is calculated by establishing an association matrix between the requirements; when the degree of coupling between two requirements is higher than the eighth preset threshold, the corresponding weights are jointly corrected. Based on the corrected weights, the real-time evaluation indicators of each demand are normalized and then weighted and summed to obtain the power grid demand response capability score.

2. The energy storage power station status assessment method based on real-time demand analysis as described in claim 1, characterized in that: Obtaining real-time operational data from an energy storage power station includes the following steps: Voltage and current data are collected from the PCS converter, temperature data is collected from the battery body, and power data is collected from the point of common coupling (PCC). The collected real-time operating data is detected and filtered to a preset range of operating parameters; The filtered real-time operational data is categorized and stored in the database.

3. The energy storage power station status assessment method based on real-time demand analysis as described in claim 1, characterized in that: The process of establishing a comprehensive performance evaluation model for energy storage power stations includes the following steps: The power grid demand response capability score is calculated based on real-time evaluation indicators of power grid frequency regulation demand, power grid peak shaving and energy transfer demand, power grid voltage regulation demand, and power grid power oscillation suppression demand. Establish a basic performance rating system; the basic performance rating system includes a controllability index rating, an energy efficiency level index rating, a reliability index rating, and an environmental protection index rating. The comprehensive evaluation score of the energy storage power station is obtained by weighting the grid demand response capability score and the basic performance score.

4. A system employing the energy storage power station state assessment method based on real-time demand analysis as described in any one of claims 1 to 3, characterized in that, include: The data acquisition module is used to acquire real-time operating data of the energy storage power station; The evaluation index system construction module 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 operating data. The evaluation module establishes a comprehensive performance evaluation model for the energy storage power station based on the real-time evaluation index system of the energy storage power station, and conducts a status evaluation of the energy storage power station according to the comprehensive performance evaluation model.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the energy storage power station status assessment method based on real-time demand analysis as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy storage power station status assessment method based on real-time demand analysis as described in any one of claims 1 to 3.

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