A novel energy storage power station operation performance evaluation method and system

Abstract

The application discloses a novel energy storage power station operation performance evaluation method and system, and the method comprises the following steps: obtaining actual monitoring data of each evaluation parameter of a target novel energy storage power station; comparing the actual monitoring data with corresponding reference technical parameters to calculate the degradation degree of each evaluation parameter; combining the degradation degree of the evaluation parameter, and using a mapping function to calculate the membership degree of each evaluation parameter in a preset evaluation level; matrix integrating the membership degrees of all the evaluation parameters to construct an evaluation matrix; deriving a comprehensive evaluation component in cooperation with the evaluation matrix and a pre-determined global index weight; matching the comprehensive evaluation component with a score interval of each evaluation level to obtain a quantitative total score of the operation efficiency of the energy storage power station through conversion; and the application can solve the problem of single evaluation dimension and strong subjectivity of current energy storage operation performance evaluation, and improve the scientificity of evaluation.

Description

Technical Field

[0001] This invention belongs to the field of novel energy storage technology, and in particular relates to a novel method and system for evaluating the operational performance of energy storage power stations. Background Technology

[0002] In the macro context of building a new power system, a renewable energy power system characterized by green and low-carbon features is undergoing rapid expansion. However, constrained by the natural environment, energy forms represented by wind power and photovoltaic power generation exhibit significant spatiotemporal randomness and intermittent output. With the continuous increase in the installed capacity of such energy sources and the large-scale penetration of distributed power sources, the robust operation of the existing power grid and the reliable guarantee of power source-grid coordination are facing severe challenges. To overcome these technical bottlenecks, domestic and foreign research institutions and power companies are actively exploring various regulation paths. Among them, large-capacity electrochemical energy storage technology, with its excellent static response characteristics, can effectively balance the load curve through "peak-valley time shifting," thereby significantly reducing the peak load of the power grid. At the same time, the energy storage system has millisecond-level power response capabilities, enabling precise capture and rapid correction of system frequency fluctuations, and its regulation accuracy and conversion efficiency are significantly better than traditional frequency regulation methods. In regions with a high concentration of new energy sources, deploying battery energy storage stations with efficient active power regulation characteristics can not only comprehensively enhance the grid's flexibility in peak shaving and dynamic frequency regulation margins and increase the local consumption ratio of clean energy, but also generate diversified economic returns through participation in the electricity ancillary services market. Although the energy storage industry is developing rapidly, the current systematic quantitative evaluation mechanism for the operational efficiency of energy storage power stations is still incomplete, and there is a lack of unified and comprehensive evaluation standards. Summary of the Invention

[0003] Purpose of the invention: The purpose of this invention is to provide a new method and system for evaluating the operational performance of energy storage power stations that can effectively improve the evaluation mechanism of energy storage systems.

[0004] Technical solution: The present invention provides a novel method for evaluating the operational performance of an energy storage power station, comprising:

[0005] Obtain actual monitoring data for each evaluation parameter of the target novel energy storage power station; the evaluation parameters are set based on a pre-established hierarchical index system;

[0006] By comparing the actual monitoring data with the corresponding benchmark technical parameters, the degree of degradation of each evaluation parameter is calculated.

[0007] Based on the degradation degree of the evaluation parameters, the membership degree of each evaluation parameter in the preset evaluation level is calculated using a mapping function;

[0008] The membership degrees of all evaluation parameters are matrix-integrated to construct an evaluation matrix;

[0009] By combining the evaluation matrix with the pre-determined global indicator weights, a comprehensive evaluation component is derived;

[0010] By matching the comprehensive evaluation components with the score ranges of each evaluation level, the total quantitative score of the energy storage power station's operational efficiency is obtained.

[0011] Furthermore, the construction process of the hierarchical indicator system includes:

[0012] The core evaluation dimensions were determined based on the operational characteristics of the target new energy storage power station;

[0013] The operational information is categorized according to the core evaluation dimensions, and the evaluation parameters corresponding to each core evaluation dimension are established to complete the construction of the indicator system. Among them, the core evaluation dimensions are the first-level evaluation factors, and the evaluation parameters are the second-level evaluation sub-items.

[0014] Furthermore, the primary evaluation factors include energy efficiency level, operational robustness, grid connection compliance, and response and adjustment capabilities; the secondary evaluation sub-items include comprehensive conversion efficiency, station power loss rate, energy storage energy loss rate, unplanned outage ratio, battery cell failure rate, response delay, adjustment action duration, mode switching time, frequency regulation mileage contribution, peak shaving output depth, and power adjustment rate.

[0015] Furthermore, the degree of degradation of each evaluation parameter is calculated by comparing the actual monitoring data with the corresponding benchmark technical parameters, specifically as follows:

[0016] Based on the permissible fluctuation limits and safety limits in the aforementioned benchmark technical parameters, the degree of degradation is calculated using a difference mapping model in conjunction with actual monitoring data;

[0017] Specifically, when the evaluation parameter is of the "smaller is better" type, then the degree of degradation... Determined by the following formula:

[0018]

[0019] in, Indicates the sequence number of the evaluation parameter. Indicates the first Actual monitoring data for each evaluation parameter Indicates the permissible fluctuation limit. Indicates the maximum value. The coefficient is constant.

[0020] When the evaluation parameter is of the type where larger is better, then the degree of degradation Determined by the following formula:

[0021]

[0022] in, This represents the minimum value.

[0023] Furthermore, the step of combining the degradation degree of the evaluation parameters and using a mapping function to calculate the membership degree of each evaluation parameter in the preset evaluation level is as follows:

[0024] Four evaluation levels are set: excellent, good, average, and qualified. The mapping rules corresponding to each evaluation level are pre-defined. The mapping rules adopt a membership function composed of a half-ridge distribution and a triangular distribution.

[0025] For each evaluation parameter, its degree of degradation is used to calculate the membership degree of the evaluation parameter under the evaluation level according to the mapping rule corresponding to each evaluation level.

[0026] The membership function expressions for each evaluation level are shown in the following formula:

[0027]

[0028]

[0029]

[0030]

[0031] in, , , and These represent the membership functions of the evaluation parameters under the four evaluation levels of Excellent, Good, Average, and Pass, respectively. Indicates the degree of degradation.

[0032] Furthermore, the step of matrix-integrating the membership degrees of all evaluation parameters to construct an evaluation matrix is ​​as follows:

[0033] Summarize the membership degrees of a single evaluation parameter under different evaluation levels to generate a membership vector;

[0034] The membership vectors corresponding to each evaluation parameter are stacked in rows or columns to form an evaluation matrix;

[0035] Specifically, the membership vector is represented as , Indicates the first One evaluation parameter, and The order is number 1 The membership degree of each evaluation parameter at each evaluation level is then used to form the evaluation matrix. Represented as:

[0036] in, This indicates the number of evaluation parameters.

[0037] Furthermore, the determination of the global indicator weights includes:

[0038] Collect weight analysis materials for primary evaluation factors and secondary evaluation sub-items separately;

[0039] Based on the weighted analysis materials, a judgment matrix is ​​constructed using the comparison scaling rule;

[0040] Perform extreme value removal and arithmetic mean processing on the judgment matrix to derive the weight distribution vector;

[0041] The independent weights of each evaluation parameter are derived based on the weight distribution vector, and then the composite global weight of each secondary evaluation item is synthesized.

[0042] Based on the same inventive concept, the present invention also provides a novel energy storage power station operation performance evaluation system, comprising:

[0043] The data acquisition module is used to acquire actual monitoring data of various evaluation parameters of the target new energy storage power station; the evaluation parameters are set based on a pre-established hierarchical index system.

[0044] The degradation estimation module is used to estimate the degree of degradation of each evaluation parameter by comparing actual monitoring data with the corresponding benchmark technical parameters.

[0045] The membership module is used to calculate the membership degree of each evaluation parameter in the preset evaluation level by combining the degradation degree of the evaluation parameters and using a mapping function.

[0046] The evaluation matrix module is used to matrix-integrate the membership degrees of all evaluation parameters to construct the evaluation matrix.

[0047] The evaluation component module is used to coordinate the evaluation matrix with the pre-determined global indicator weights to derive the comprehensive evaluation components;

[0048] The evaluation module is used to match the comprehensive evaluation components with the score range of each evaluation level, and calculate the total quantitative score of the energy storage power station's operating efficiency.

[0049] Based on the same inventive concept, the present invention also provides a computing device, comprising: one or more processors, one or more memories, and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor, and when the programs are loaded onto the processor, they implement the steps of the novel energy storage power station operation performance evaluation method according to any of the preceding claims.

[0050] Based on the same inventive concept, the present invention also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the novel energy storage power station operation performance evaluation method according to any one of the preceding claims.

[0051] Beneficial Effects: Compared with existing technologies, this invention relies on a pre-established, scientifically rigorous evaluation framework to retrieve real-time measured data of various performance indicators of energy storage power stations. This framework can comprehensively and multidimensionally characterize the operational behavior of energy storage power stations, laying a solid data foundation for subsequent in-depth quantitative analysis. In the specific calculation process, this solution collaboratively utilizes monitoring values ​​and corresponding technical benchmark parameters to accurately calculate the operational degradation index of each indicator. Subsequently, a fuzzy membership calculation model is introduced to determine the probability of the degradation index belonging to each preset quality ladder, and these probability values ​​are compiled into an evaluation correlation matrix. Furthermore, through the composite operation of this matrix and pre-determined global weight factors, a multi-dimensional evaluation vector is derived, and it is converted into the final operational performance quantitative score according to the energy level scoring standard. Through this quantitative score, the overall technical level of the new energy storage power station can be intuitively and accurately mapped. This invention effectively improves the evaluation mechanism of energy storage systems and significantly enhances the scientific depth, objectivity, fairness, and system completeness of performance evaluation. Attached Figure Description

[0052] Figure 1 This is a system framework diagram of an embodiment of the present invention; Figure 2 This is a schematic diagram of the membership function distribution shape according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0053] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0054] As described in the background section, new energy storage power stations are playing an increasingly important role in power systems as a crucial regulatory tool for promoting safe, stable, and economical operation. Energy storage systems possess extremely fast response speeds, enabling them to react quickly to changes in grid frequency, resulting in more precise and efficient regulation. The excellent active power regulation capabilities of battery energy storage power stations can comprehensively improve the peak-shaving and frequency regulation capabilities of power grids in large renewable energy-rich areas, thereby increasing the level of renewable energy consumption and bringing significant economic benefits. Researching the operational performance of new energy storage power stations is essential for a thorough understanding of new energy storage technologies. Currently, the technology for evaluating the operational performance of new energy storage power stations is not yet perfect.

[0055] In view of this, this embodiment proposes a novel energy storage power station operation performance evaluation method. It focuses on analyzing the operation performance of the novel energy storage power station from four aspects: power station energy efficiency, reliability, grid connection performance and regulation performance. It constructs a comprehensive evaluation parameter system and proposes a comprehensive evaluation method for the operation performance of the novel energy storage power station, providing a way for the comprehensive evaluation of the operation performance of the novel energy storage power station.

[0056] like Figure 1 As shown, the novel energy storage power station operation performance evaluation method of this embodiment includes:

[0057] Step 1: Obtain actual monitoring data for each evaluation parameter of the target new energy storage power station; the evaluation parameters are set based on a pre-established hierarchical index system;

[0058] Step 2: Compare the actual monitoring data with the corresponding benchmark technical parameters to calculate the degree of degradation of each evaluation parameter;

[0059] Step 3: Combine the degradation degree of the evaluation parameters and use the mapping function to calculate the membership degree of each evaluation parameter in the preset evaluation level;

[0060] Step 4: Integrate the membership degrees of all evaluation parameters into a matrix to construct an evaluation matrix;

[0061] Step 5: Combine the evaluation matrix with the pre-determined global indicator weights to derive the comprehensive evaluation components;

[0062] Step 6: Match the comprehensive evaluation components with the score ranges of each evaluation level to calculate the total quantitative score of the energy storage power station's operating efficiency.

[0063] Specifically, in step 1, a hierarchical indicator system is pre-constructed based on the operational information of the new energy storage power station. This system specifies various evaluation parameters used to assess the operational performance of the new energy storage power station. Before acquiring actual monitoring data, the evaluation parameters to be acquired are determined according to the hierarchical indicator system, and then the actual monitoring data is acquired sequentially based on each evaluation parameter. Each evaluation parameter includes indicators related to power station energy efficiency, reliability, grid connection performance, and regulation performance, allowing for a comprehensive and holistic evaluation of the operational performance of the new energy storage power station.

[0064] In step 2, the degree of degradation of the evaluation parameter is determined based on the actual monitoring data and the preset parameter values ​​of the evaluation parameter.

[0065] Specifically, the degradation degree reflects the degree of degradation by comparing the actual operating state of the system with the state when the device alarms or malfunctions. The degradation degree is a quantitative value, and the value of the degradation degree is different when the degradation degree is different. Its value range is [0,1].

[0066] In step 3, based on the degree of degradation and the preset evaluation level, the membership degree of the evaluation parameter in each evaluation level is calculated using a membership function.

[0067] Specifically, the preset evaluation levels in this embodiment include four types: Excellent, Good, Average, and Pass. Correspondingly, the evaluation set is V = {V1, V2, V3, V4} = {Excellent, Good, Average, Pass}. Different preset evaluation levels correspond to different membership functions. For each evaluation parameter, the membership degree under each evaluation level is calculated. The closer the membership degree is to 1, the higher the degree to which the evaluation parameter belongs to that evaluation level; the closer the membership degree is to 0, the lower the degree to which the evaluation parameter belongs to that evaluation level.

[0068] In step 4, the evaluation matrix is ​​obtained by combining all the membership degrees corresponding to all evaluation parameters.

[0069] Specifically, all membership degrees corresponding to each evaluation parameter are combined into a membership degree vector, and then all membership degree vectors are combined to form an evaluation matrix, so as to calculate the operational performance evaluation score of the new energy storage power station.

[0070] In step 5, a comprehensive evaluation component is calculated based on the evaluation matrix and the global indicator weights pre-calculated according to the hierarchical indicator system.

[0071] Specifically, the product of the evaluation matrix and the global index weights is used as the comprehensive evaluation component, laying the foundation for calculating the operational performance evaluation score.

[0072] In step 6, the operational performance evaluation score of the new energy storage power station is calculated based on the comprehensive evaluation components and the scoring range of the preset evaluation level.

[0073] In this embodiment, the preset evaluation levels include excellent, good, average, and qualified. The scoring range and description for each evaluation level are shown in Table 1.

[0074] The scoring vector is determined based on the scoring range, as shown in Table 1. The determined scoring vector is Y, where Y = {100 70 50 30}. The comprehensive evaluation result is multiplied by the scoring vector to obtain the operational performance evaluation score.

[0075] It should be noted that the evaluation levels and the scoring range of each evaluation level in this embodiment can be set according to the real-time evaluation situation. The above is only an example and has no limiting effect.

[0076] Based on steps 1 to 6 above, the operational performance evaluation method for a novel energy storage power station provided in this embodiment includes collecting actual monitoring data of various evaluation parameters of the novel energy storage power station. Each evaluation parameter is determined based on a pre-constructed hierarchical indicator system. This hierarchical indicator system can comprehensively and deeply reflect the operational performance of the novel energy storage power station, laying the foundation for further evaluation work. The degradation degree of the evaluation parameter is determined based on the actual monitoring data and the preset parameter values. Based on the degradation degree and the preset evaluation level, the membership degree of the evaluation parameter in each evaluation level is calculated using a membership function. All membership degrees corresponding to all evaluation parameters are combined to obtain an evaluation matrix. Based on the evaluation matrix and the comprehensive indicator weights pre-calculated according to the indicator system, a comprehensive evaluation component is calculated. Based on the comprehensive evaluation component and the scoring range of the preset evaluation level, the operational performance evaluation score of the novel energy storage power station is calculated. The operational performance evaluation score reflects the overall performance of the novel energy storage power station, improving the evaluation method for novel energy storage power stations and enhancing the objectivity and comprehensiveness of the evaluation.

[0077] In some embodiments, determining the degree of degradation of the evaluation parameter based on the actual monitoring data and the preset parameter value of the evaluation parameter includes:

[0078] The degree of degradation is calculated based on the permissible and limit values ​​in the preset parameter values ​​and the actual monitoring data.

[0079] Specifically, the calculation method for the degree of degradation varies depending on the numerical type of the evaluation parameter. When the evaluation parameter is of the type where smaller is better, the degree of degradation... Determined by the following formula (1):

[0080] (1)

[0081] in, Indicates the sequence number of the evaluation parameter. This represents the actual monitoring data for the i-th evaluation parameter. Indicates the permitted value. The value represents the limit (maximum value), and k is a constant coefficient, usually taken as 1. The smaller the better type means that the smaller the value of the evaluation parameter, the better the performance. In this case, the limit value of the evaluation parameter is the maximum value. The permissible value represents the value under normal conditions. For example, if the evaluation parameter is the energy storage loss rate, the value type of the index is the smaller the better type, the permissible value is 0.5, the limit value, that is, the maximum value is 0, the actual monitoring data is 0.25, and the degree of degradation calculated according to formula (1) is 0.5.

[0082] When the evaluation parameter is of the type where larger is better, then the degree of degradation Determined by the following formula (2):

[0083] (2)

[0084] in, This represents the limit value (minimum value). The smaller the better type means that the larger the value of the evaluation parameter, the better the performance. In this case, the limit value of the evaluation parameter is the minimum value. For example, if the evaluation parameter is the power plant's overall efficiency, the value type of the index is the larger the better type, the permissible value is 100, the limit value, that is, the minimum value is 80, the actual monitoring data is 93, and the degree of degradation calculated according to formula (2) is 0.65.

[0085] In some embodiments, the step of calculating the membership degree of the evaluation parameter in each evaluation level based on the degradation degree and a preset evaluation level using a membership function includes:

[0086] Determine the membership function corresponding to each evaluation level, wherein the membership function is a combination of semi-ridge and triangular membership functions;

[0087] For each evaluation parameter, based on the degree of degradation, the membership degree of the evaluation parameter under the evaluation level is calculated using the membership function corresponding to each evaluation level.

[0088] Specifically, in this embodiment, the membership function is a combination of a half-ridge and a triangle membership function. The ridge distribution has a gentle transition and a wide range of principal values, accurately reflecting the relationship between system factors and states. Therefore, this embodiment combines the half-ridge and triangle membership functions to form the membership function. Figure 2A schematic diagram of the membership function distribution shape in this embodiment is shown. The membership function expressions corresponding to different evaluation levels are shown in equations (3)-(6) below:

[0089] (3)

[0090] (4)

[0091] (5)

[0092] (6)

[0093] in, , , and These represent the membership functions of the evaluation parameters under the four evaluation levels of Excellent, Good, Average, and Pass, respectively. The degree of degradation is indicated. The membership degree of each evaluation parameter under different evaluation levels can be calculated according to equations (3) to (6).

[0094] In some embodiments, the step of combining all membership degrees corresponding to all evaluation parameters to obtain an evaluation matrix includes:

[0095] The membership vector is obtained based on the membership degree of the evaluation parameters at each evaluation level;

[0096] The evaluation matrix is ​​obtained by combining the membership vectors corresponding to all evaluation parameters.

[0097] Specifically, the membership vector can be represented as , Indicates the first One evaluation parameter, , , , The order is number 1 The membership degree of each evaluation parameter at each evaluation level. Evaluation matrix. It can be represented as:

[0098] Where n represents the number of evaluation parameters.

[0099] In some embodiments, the method for constructing the hierarchical indicator system includes:

[0100] The core evaluation dimensions of the target are determined based on the operational information of the new energy storage power station.

[0101] The operation information of the new energy storage power station is divided according to the core evaluation dimensions of the target, and the evaluation parameters corresponding to the core evaluation dimensions and the target operating conditions are determined to complete the construction of the indicator system. The core evaluation dimensions are used as primary indicators, and the evaluation parameters are used as secondary indicators.

[0102] Specifically, the indicator system in this application is a two-level indicator system. The description of the new energy storage power station mainly focuses on energy efficiency, operational robustness, grid connection compliance, and response and regulation capabilities. The target indicator types include power station energy efficiency, reliability, grid connection performance, and regulation performance. Therefore, the primary indicators are determined as power station energy efficiency indicators, reliability indicators, grid connection performance indicators, and regulation performance indicators. Under the primary indicators, secondary indicators are established to comprehensively decompose the primary indicators. Based on the primary indicators, various evaluation parameters of the new energy storage power station are collected. For power station energy efficiency, three indicators are selected: overall power station efficiency, power consumption rate, and energy storage loss rate. For reliability, two indicators are selected: unplanned outage coefficient and battery failure rate. For grid connection performance, three indicators are selected: charge / discharge response time, charge / discharge regulation time, and charge / discharge conversion time. For regulation performance, three indicators are selected: frequency regulation mileage, peak regulation amplitude, and regulation speed. The completed indicator system is shown in Table 2.

[0103]

[0104] In some embodiments, the method for calculating the global indicator weight includes:

[0105] Obtain the weight analysis data of the primary and secondary indicators respectively; determine the judgment matrix using the scaling method based on the weight analysis data; calculate the weight matrix using the pruned arithmetic mean method based on the judgment matrix; calculate the indicator weights based on the weight matrices; and calculate the comprehensive indicator weight of each secondary indicator based on the indicator weights of each level of indicators.

[0106] In this embodiment, the calculation of the weights of primary indicators is used for explanation. The calculation method for the weights of secondary indicators is the same as that for primary indicators. For example, primary indicators include power plant energy efficiency indicators, reliability indicators, grid connection performance indicators, and regulation performance indicators. To reduce the subjectivity of the scoring, 10 users are used to score the importance of the primary indicators, resulting in weight analysis data. A total score of 10 points is used, with higher scores indicating higher importance. The average score is calculated by removing the highest and lowest scores and then averaging the results. The weight analysis data is shown in Table 3 below.

[0107]

[0108] Based on the weight analysis data in Table 3, the scaling method is used to compare each of the four primary indicators pairwise according to their importance, and the judgment matrix is ​​determined. The scaling method can be used to determine the importance of one indicator to another. The judgment matrix data is shown in Table 4 below.

[0109]

[0110] Using the arithmetic mean method, the weight matrix of the primary indicators was calculated by normalizing the column data in Table 4, as shown in Table 5 below. For the data in the first row and first column of Table 5, 0.223 = 1 / (1 + 1.091 + 1.114 + 1.273), and the data in the second row and first column, 0.244 = 1.091 / (1 + 1.091 + 1.114 + 1.273), and so on, to calculate the other data in Table 5. ω represents the average value, which is the weight of the primary indicator. The weight of indicator 1 is 0.223, the weight of indicator 2 is 0.244, the weight of indicator 3 is 0.249, and the weight of indicator 4 is 0.284.

[0111]

[0112] Similarly, the calculation method for the weights of secondary indicators is the same as that for primary indicators, and will not be repeated here. The calculation results for the weights of primary and secondary indicators are shown in Table 6. The comprehensive weight of each secondary indicator is equal to the product of the corresponding weight of the primary indicator and the weight of the secondary indicator. For example, for secondary indicator X... 11 The overall indicator weight is 0.080 = 0.223 * 0.357. The global indicator weight of all secondary indicators can be denoted as W.

[0113]

[0114]

[0115] The following specific examples illustrate the calculation method for the performance evaluation score of the new energy storage power station. First, an indicator system as shown in Table 2 is constructed. Actual monitoring data corresponding to each evaluation parameter (secondary indicator) is collected according to the indicator system. Based on the actual monitoring data and preset parameter values ​​(permissible values ​​and limit values), the degree of degradation of each evaluation parameter is calculated, and the calculation results are shown in Table 7.

[0116]

[0117] Based on the degree of deterioration and four evaluation levels, the membership degree of the evaluation parameters in each evaluation level was calculated using a membership function. The calculation results are shown in Table 8.

[0118]

[0119] Based on the membership degree, determine the membership degree vector for each evaluation parameter, such as R(X). 11 ) = (0,0,0,1), and the evaluation matrix is ​​represented as R.

[0120]

[0121] Based on the global index weight W calculated in the aforementioned embodiments, the comprehensive evaluation component N is calculated.

[0122]

[0123] Based on the comprehensive evaluation component N and the scoring vector Y determined by the scoring range in Table 1, the performance evaluation score Z of the new energy storage power station is calculated.

[0124]

[0125] According to the calculated performance evaluation score Z of the new energy storage power station, Z is within the scoring range of (30, 50), which indicates that the performance evaluation of the new energy storage power station is medium. The individual characteristic state quantity or the overall evaluation result exceeds the standard requirements, and there is room for optimization in various indicators.

[0126] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0127] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] Based on the same inventive concept, and corresponding to any of the above embodiments, this application also provides a novel energy storage power station operation performance evaluation device.

[0129] refer to Figure 3 The novel energy storage power station operation performance evaluation device includes:

[0130] The acquisition module 202 is configured to acquire actual monitoring data of various evaluation parameters of the target new energy storage power station; the evaluation parameters are set based on a pre-established hierarchical index system.

[0131] Module 204 is configured to calculate the degree of degradation of each evaluation parameter by comparing actual monitoring data with corresponding benchmark technical parameters.

[0132] The first calculation module 206 is configured to combine the degradation degree of the evaluation parameters and use a mapping function to calculate the membership degree of each evaluation parameter in the preset evaluation level.

[0133] Combination module 208 is configured to matrix-integrate the membership degrees of all evaluation parameters to construct an evaluation matrix;

[0134] The second calculation module 210 is configured to coordinate the evaluation matrix with the predetermined global index weights to derive a comprehensive evaluation component.

[0135] The third calculation module 212 is configured to match the comprehensive evaluation components with the score range of each evaluation level to calculate the total quantitative score of the energy storage power station's operating efficiency.

[0136] In some embodiments, the determining module 204 is further configured to calculate the degree of degradation based on the permissible and limit values ​​in the preset parameter values ​​and the actual monitoring data.

[0137] In some embodiments, the first calculation module 206 is further configured to determine the membership function corresponding to each evaluation level, wherein the membership function is a membership function combining a semi-ridge and a triangle; for each evaluation parameter, based on the degree of degradation, the membership degree of the evaluation parameter under the evaluation level is calculated using the membership function corresponding to each evaluation level.

[0138] In some embodiments, the combination module 208 is further configured to obtain a membership vector based on the membership degree of the evaluation parameters at each evaluation level; and combine the membership vectors corresponding to all evaluation parameters to obtain the evaluation matrix.

[0139] In some embodiments, a construction module is further included, which is configured to determine the target indicator type based on the operation information of the new energy storage power station; divide the operation information of the new energy storage power station according to the target indicator type, and determine the evaluation parameters corresponding to the target indicator type to complete the construction of the indicator system, wherein the target indicator type is used as a primary indicator and the evaluation parameters are used as secondary indicators.

[0140] In some embodiments, the second calculation module 210 is further configured to acquire weight analysis data of the primary indicator and the secondary indicator respectively; determine a judgment matrix using a scaling method based on the weight analysis data; calculate a weight matrix using a pruned arithmetic mean method based on the judgment matrix; calculate the indicator weights based on the weight matrix; and calculate the comprehensive indicator weight of each secondary indicator based on the indicator weights of each level of indicator.

[0141] In some embodiments, the primary indicators include power plant efficiency indicators, reliability indicators, and grid connection performance indicators.

[0142] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0143] The apparatus described above is used to implement the corresponding novel energy storage power station operation performance evaluation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0144] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the novel energy storage power station operation performance evaluation method described in any of the above embodiments.

[0145] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0146] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0147] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0148] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0149] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0150] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0151] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0152] The electronic devices described above are used to implement the corresponding novel energy storage power station operation performance evaluation method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0153] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions, which are used to cause the computer to execute the novel energy storage power station operation performance evaluation method as described in any of the above embodiments.

[0154] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0155] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the novel energy storage power station operation performance evaluation method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0156] Based on the same concept, corresponding to any of the above embodiments, this application also provides a computer program product, including computer program instructions, which, when run on a computer, cause the computer to perform the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0157] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0158] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0159] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0160] The embodiments of this application are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

[0161] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

Claims

1. A novel method for evaluating the operational performance of an energy storage power station, characterized in that, include: Obtain actual monitoring data of each evaluation parameter of the target new energy storage power station; The evaluation parameters are set based on a pre-established hierarchical indicator system; By comparing the actual monitoring data with the corresponding benchmark technical parameters, the degree of degradation of each evaluation parameter is calculated. Based on the degradation degree of the evaluation parameters, the membership degree of each evaluation parameter in the preset evaluation level is calculated using a mapping function; The membership degrees of all evaluation parameters are matrix-integrated to construct an evaluation matrix; By combining the evaluation matrix with the pre-determined global indicator weights, a comprehensive evaluation component is derived; By matching the comprehensive evaluation components with the score ranges of each evaluation level, the total quantitative score of the energy storage power station's operational efficiency is obtained.

2. The novel energy storage power station operation performance evaluation method according to claim 1, characterized in that, The process of constructing the hierarchical indicator system includes: The core evaluation dimensions were determined based on the operational characteristics of the target new energy storage power station; The operational information is categorized according to the core evaluation dimensions, and the evaluation parameters corresponding to each core evaluation dimension are established to complete the construction of the indicator system. Among them, the core evaluation dimensions are the first-level evaluation factors, and the evaluation parameters are the second-level evaluation sub-items.

3. The novel energy storage power station operation performance evaluation method according to claim 2, characterized in that, The primary evaluation factors include energy efficiency level, operational robustness, grid connection compliance, and response and adjustment capabilities; the secondary evaluation sub-items include comprehensive conversion efficiency, station power loss rate, energy storage energy loss rate, unplanned outage ratio, battery cell failure rate, response delay, adjustment action duration, mode switching time, frequency regulation mileage contribution, peak shaving output depth, and power adjustment rate.

4. The novel energy storage power station operation performance evaluation method according to claim 1, characterized in that, The degradation degree of each evaluation parameter is calculated by comparing the actual monitoring data with the corresponding benchmark technical parameters, specifically as follows: Based on the permissible fluctuation limits and safety limits in the aforementioned benchmark technical parameters, the degree of degradation is calculated using a difference mapping model in conjunction with actual monitoring data; Specifically, when the evaluation parameter is of the "smaller is better" type, then the degree of degradation... Determined by the following formula: ； in, Indicates the sequence number of the evaluation parameter. Indicates the first Actual monitoring data for each evaluation parameter Indicates the permissible fluctuation limit. Indicates the maximum value. The coefficient is constant. When the evaluation parameter is of the type where larger is better, then the degree of degradation Determined by the following formula: ； in, This represents the minimum value.

5. The novel energy storage power station operation performance evaluation method according to claim 1, characterized in that, The degradation degree of the combined evaluation parameters is used to calculate the membership degree of each evaluation parameter in the preset evaluation level using a mapping function, specifically as follows: Four evaluation levels are set: excellent, good, average, and qualified. The mapping rules corresponding to each evaluation level are pre-defined. The mapping rules adopt a membership function composed of a half-ridge distribution and a triangular distribution. For each evaluation parameter, its degree of degradation is used to calculate the membership degree of the evaluation parameter under the evaluation level according to the mapping rule corresponding to each evaluation level. The membership function expressions for each evaluation level are shown in the following formula: ； ； ； ； in, , , and These represent the membership functions of the evaluation parameters under the four evaluation levels of Excellent, Good, Average, and Pass, respectively. Indicates the degree of degradation.

6. The novel energy storage power station operation performance evaluation method according to claim 1, characterized in that, The step of matrix-integrating the membership degrees of all evaluation parameters to construct an evaluation matrix is ​​as follows: Summarize the membership degrees of a single evaluation parameter under different evaluation levels to generate a membership vector; The membership vectors corresponding to each evaluation parameter are stacked in rows or columns to form an evaluation matrix; Specifically, the membership vector is represented as , Indicates the first One evaluation parameter, and The order is number 1 The membership degree of each evaluation parameter at each evaluation level is then used to form the evaluation matrix. Represented as: ; in, This indicates the number of evaluation parameters.

7. The novel energy storage power station operation performance evaluation method according to claim 2, characterized in that, The establishment of the global indicator weights includes: Collect weight analysis materials for primary evaluation factors and secondary evaluation sub-items separately; Based on the weighted analysis materials, a judgment matrix is ​​constructed using the comparison scaling rule; Perform extreme value removal and arithmetic mean processing on the judgment matrix to derive the weight distribution vector; The independent weights of each evaluation parameter are derived based on the weight distribution vector, and then the composite global weight of each secondary evaluation item is synthesized.

8. A novel energy storage power station operation performance evaluation system, characterized in that, include: The data acquisition module is used to obtain actual monitoring data of various evaluation parameters of the target new energy storage power station; The evaluation parameters are set based on a pre-established hierarchical indicator system; The degradation estimation module is used to estimate the degree of degradation of each evaluation parameter by comparing actual monitoring data with the corresponding benchmark technical parameters. The membership module is used to calculate the membership degree of each evaluation parameter in the preset evaluation level by combining the degradation degree of the evaluation parameters and using a mapping function. The evaluation matrix module is used to matrix-integrate the membership degrees of all evaluation parameters to construct the evaluation matrix. The evaluation component module is used to coordinate the evaluation matrix with the pre-determined global indicator weights to derive the comprehensive evaluation components; The evaluation module is used to match the comprehensive evaluation components with the score range of each evaluation level, and calculate the total quantitative score of the energy storage power station's operating efficiency.

9. A computing device, characterized in that, include: One or more processors, one or more memories, and one or more programs, said programs being stored in the memory and configured to be executed by the processor, said programs being loaded onto the processor to implement the steps of the novel energy storage power station operation performance evaluation method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions that, when executed by a processor, cause the processor to perform the steps of the novel energy storage power station operation performance evaluation method according to any one of claims 1 to 7.

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