Energy storage dynamic evaluation method and system based on scene joint debugging and risk early warning

Abstract

The present application relates to the technical field of power system planning evaluation, and more particularly to a method and system for dynamic evaluation of energy storage based on scenario joint debugging and risk early warning, the method comprising: constructing an evaluation index system; obtaining original index data of the energy storage technology to be evaluated under standard working conditions and performing dimensionless processing; calculating the first weight of each evaluation index using a grey correlation degree analysis method, calculating the second weight of each evaluation index using an entropy weight method, and obtaining a basic fusion weight by fusion; determining a scenario correction coefficient based on the demand proportion of grid frequency modulation and ramp service, and performing a first dynamic correction on the original index data; determining an environmental risk correction coefficient based on an extreme temperature environment, and performing a second dynamic correction on the original index data; and performing weighted calculation on the original index data and the basic fusion weight, and outputting a comprehensive utility score. Through the present application, the problems of lack of scene adaptability and environmental risk early warning and the problem of fuzzy data quantization in the existing energy storage evaluation method are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of power system planning and evaluation technology, and in particular to a dynamic evaluation method and system for energy storage based on scenario-based joint commissioning and risk early warning. Background Technology

[0002] To address the continued growth in demand for renewable energy sources such as wind and solar power driven by global climate change, the randomness and volatility of these energy sources pose a severe challenge to grid load balancing. Therefore, large-scale, highly reliable new energy storage technologies, such as electrochemical energy storage, compressed air energy storage, flywheel energy storage, and gravity energy storage, have become key solutions. Energy storage technologies differ significantly in response speed, energy density, and risk characteristics, necessitating comprehensive and objective evaluation methods to optimize technology selection and deployment.

[0003] Existing energy storage technology evaluation systems are typically based on rated operating conditions or historical average data, focusing on performance indicators under steady-state scenarios. However, these methods have fundamental flaws: First, they ignore the dynamic differences in grid service demands, such as the changing ratio of frequency regulation and ramp services, making unified horizontal comparisons and synergistic utility analysis across technology scenarios impossible. Second, they neglect the potential impact of extreme operating environments, failing to consider the threats posed by "long-tail" scenarios such as high and low temperatures and high-frequency regulation to the physical mechanisms of various energy storage systems, such as thermal runaway or mechanical degradation, thus failing to identify failure risks and utility degradation. Third, they lack data processing capabilities, making it difficult to quantify uncertainties based on fuzzy indicators such as environmental impact and scarce small-sample data. These flaws directly lead to a lack of accuracy, reliability, and foresight in evaluation results under real and variable scenarios, urgently requiring a new framework to achieve unified optimization of fuzzy information fusion, full-scenario coverage, and risk warning.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for dynamic evaluation of energy storage based on scenario-based joint commissioning and risk early warning, which can effectively solve the problems in the background technology.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A dynamic assessment method for energy storage based on scenario-based commissioning and risk early warning, the method comprising: Construct an evaluation index system that includes evaluation indicators in four dimensions: technical performance, economic efficiency, reliability and environmental performance, and scenario adaptability; Obtain the raw index data of the energy storage technology to be evaluated under standard operating conditions and perform dimensionless processing; The first weight of each evaluation index is calculated using the grey relational analysis method, and the second weight of each evaluation index is calculated using the entropy weight method. The first weight and the second weight are then combined to obtain the basic fusion weight. The scenario correction coefficient is determined based on the ratio of power grid frequency regulation and ramp service demand, and the original index data is dynamically corrected for the first time using the scenario correction coefficient. Based on the extreme temperature environment, an environmental risk correction coefficient is determined, and the original index data is then dynamically corrected using the environmental risk correction coefficient. The original indicator data after the second dynamic correction is weighted and calculated with the basic fusion weight to output the comprehensive utility score.

[0007] Furthermore, the evaluation indicators include: Response speed, power regulation accuracy, continuous power output capability, charge and discharge efficiency, cycle life, cost per kilowatt-hour, capacity confidence, system availability, environmental impact index, ramp service utility matching degree, and frequency modulation service utility matching degree.

[0008] Furthermore, the calculation formula for the basic fusion weights includes: ; in, The basic fusion weight for the j-th indicator, For the first weight, This is the second weight. This represents the weighted preference coefficient.

[0009] Furthermore, the method for determining the scene correction coefficient includes: When the ratio of power grid frequency regulation to ramping service is 5:5, the scenario correction coefficient is taken as the base value of 1. When the grid frequency regulation and ramping service ratio is 3:7, which is a ramping-dominated type, the continuous power output capability and the utility value of the levelized cost of electricity index of energy storage are improved, while the ramping service utility matching degree and the continuous power output capability index utility value of power storage are reduced. When the grid frequency regulation and ramp-up service ratio is 7:3, which is a frequency regulation-dominated type, the response speed and cycle life index utility value of power-type energy storage are improved, while the frequency regulation service utility matching degree and cycle life index utility value of mechanical energy storage are reduced.

[0010] Furthermore, the method for determining the environmental risk correction coefficient includes: When the operating environment is in a stable state at room temperature, the environmental risk correction factor is taken as 1; When the operating environment is at an extreme high temperature, reduce the environmental impact index and capacity confidence index performance values ​​of electrochemical energy storage. The extreme high temperature is defined as greater than 40°C. When the operating environment is at an extreme low temperature, the performance values ​​of the capacity confidence index of electrochemical energy storage and the performance values ​​of the charge and discharge efficiency index of mechanical energy storage are reduced. The extreme low temperature is defined as less than -10℃.

[0011] Furthermore, the dynamic correction process includes: ; in, Let be the correction value of the j-th indicator for the i-th energy storage technology in scenario s. The original indicator data, This refers to the corresponding scenario correction factor or the environmental risk correction factor.

[0012] Furthermore, after standardizing the modified original indicator data, a weighted sum is performed based on the basic fusion weights to obtain the comprehensive utility score.

[0013] Furthermore, the energy storage technology to be evaluated includes at least two heterogeneous technologies selected from electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage.

[0014] A dynamic energy storage assessment system based on scenario-based commissioning and risk early warning, the system comprising: The system establishment module constructs an evaluation index system that includes evaluation indicators in four dimensions: technical performance, economy, reliability and environmental performance, and scenario adaptability. The data acquisition module acquires the raw index data of the energy storage technology to be evaluated under standard operating conditions and performs dimensionless processing. The weight fusion module uses grey relational analysis to calculate the first weight of each evaluation indicator, uses entropy weight to calculate the second weight of each evaluation indicator, and merges the first weight and the second weight to obtain the basic fusion weight. The initial correction module determines the scenario correction coefficient based on the ratio of power grid frequency regulation and ramp service demand, and uses the scenario correction coefficient to perform the initial dynamic correction on the original indicator data. The secondary correction module determines the environmental risk correction coefficient based on extreme temperature environments and uses the environmental risk correction coefficient to perform secondary dynamic correction on the original indicator data. The scoring module performs a weighted calculation on the original indicator data after secondary dynamic correction and the basic fusion weights, and outputs a comprehensive utility score.

[0015] Furthermore, the secondary correction module includes: For units operating at room temperature, when the operating environment is at room temperature and stable, the environmental risk correction factor is set to 1. Extreme high temperature operation unit, when the operating environment is extremely high temperature, reduces the environmental impact index and capacity confidence index performance value of electrochemical energy storage; Extreme low temperature operation unit, when the operating environment is extremely low temperature, reduces the performance value of the capacity confidence index of electrochemical energy storage and the performance value of the charge and discharge efficiency index of mechanical energy storage.

[0016] The technical solution of this invention can achieve the following technical effects: By constructing a four-dimensional assessment system for response characteristics and environmental risks, and combining grey system theory to handle the fuzziness of indicators; introducing the ratio of grid frequency regulation and ramp demand to dynamically correct the initial weight database, and simultaneously establishing a correlation model between extreme environments and physical failure mechanisms; and finally outputting a comprehensive energy storage performance score that adapts to all scenarios through weighted aggregation, the system effectively solves the problems of lack of scenario adaptability and environmental risk early warning, as well as the quantification defects of fuzzy data in existing energy storage assessment methods.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a dynamic evaluation method for energy storage based on scenario-based joint commissioning and risk early warning. Figure 2 A flowchart illustrating the methodology for dynamic assessment of energy storage; Figure 3 A schematic diagram illustrating the process for determining the scene correction coefficient; Figure 4 This is a schematic diagram illustrating the process for determining the environmental risk correction factor. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Example 1; like Figure 1 and Figure 2 As shown, this application provides a dynamic assessment method for energy storage based on scenario-based commissioning and risk early warning. The method includes: S10: Construct an evaluation index system that includes evaluation indicators in four dimensions: technical performance, economy, reliability and environmental performance, and scenario adaptability. S20: Obtain the raw index data of the energy storage technology to be evaluated under standard operating conditions and perform dimensionless processing; S30: The grey relational analysis method is used to calculate the first weight of each evaluation indicator, the entropy weight method is used to calculate the second weight of each evaluation indicator, and the first and second weights are combined to obtain the basic fusion weight. S40: Determine the scenario correction coefficient based on the ratio of power grid frequency regulation and ramp service demand, and use the scenario correction coefficient to perform the first dynamic correction on the original indicator data; S50: Determine the environmental risk correction coefficient based on extreme temperature environments, and use the environmental risk correction coefficient to perform secondary dynamic correction on the original indicator data; S60: The original indicator data after the second dynamic correction is weighted and calculated with the basic fusion weight to output the comprehensive utility score.

[0023] Specifically, firstly, an evaluation index system covering four dimensions—technical performance, economy, reliability and environmental performance, and scenario adaptability—is constructed. The index layer is preferably set as a quantitative set that simultaneously reflects rapid adjustment capability, long-term support capability, lifecycle economy, availability, reliability and environmental risk, and business compatibility. More preferably, eleven indicators are used, including response speed, power regulation accuracy, continuous power output capability, charge / discharge efficiency, cycle life, levelized cost of electricity (LCOE), capacity confidence, system availability, environmental impact index, ramp-up service utility matching degree, and frequency modulation service utility matching degree, to form a unified comprehensive utility evaluation framework. This ensures that, under the same index... Under the same standard and scoring rules, technologies with significantly different principles, such as electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage, are compared fairly. Then, raw indicator data of the energy storage technologies to be evaluated are obtained under standard operating conditions. Standard operating conditions can be representative parameters obtained from stable operation statistics of industry testing specifications or demonstration projects at room temperature. The raw data is then dimensionless to eliminate differences in dimensions and orders of magnitude. Dimensionless processing preferably distinguishes between benefit-type and cost-type indicators: for indicators where larger values ​​represent better performance, they are mapped to comparable utility values ​​using a uniform scale; for indicators where smaller values ​​represent higher cost, they are mapped to comparable utility values. Indicators with lower cost or risk, and therefore better, such as cost per kilowatt-hour, response time, and environmental impact index, are mapped to comparable utility values ​​in the opposite direction of preference, ensuring consistent utility direction for each indicator in subsequent weighted calculations. Regarding weight determination, grey relational analysis is preferred to obtain the first weight reflecting the closeness of each technical indicator sequence to the ideal optimal sequence, adapting to the characteristics of small sample size, limited information, and uneven distribution in energy storage assessment data. Simultaneously, entropy weighting is used to obtain the second weight reflecting the dispersion and information content of the indicators. Preferably, for indicators with uncertainty, fuzzy scoring is applied before calculating their entropy weights to avoid the bias caused by a single weight. The deviation is then analyzed, and the first weight and the second weight are fused to obtain the basic fused weight. The fusion process preferably uses an adjustable weight preference coefficient to achieve a linear compromise. For example, in the absence of clear subjective preferences, equal preferences can be adopted to take into account both trend similarity and information content differences, thereby forming a reproducible basic weight vector. In terms of dynamic updates, in order to reflect the technical effect of scenario joint commissioning, it is preferable to determine the scenario correction coefficient based on the ratio of power grid frequency regulation and ramp service demand and to perform the first dynamic correction on the dimensionless index data. It is preferable to set the benchmark demand structure as a certain proportion of frequency regulation and ramp service and set the corresponding correction coefficient as the benchmark value, so as to serve as a unified comparison.When the operating scenario is dominated by ramp-up services, the priority should be given to highlighting the importance of long-term energy support capabilities. This means emphasizing the utility contribution of energy storage technologies to indicators strongly correlated with long-term support, such as improving their continuous power output capability. Simultaneously, it should reflect their unit energy cost advantage in long-term scenarios from an economic perspective. For power storage technologies, the priority should be given to reducing their utility contribution to ramp-up service matching and long-term support-related indicators, ensuring that their overall score in this scenario reflects the real advantage transfer brought about by changes in service demand structure. When the operating scenario is dominated by frequency regulation services, the priority should be given to highlighting rapid response and high-frequency cycling tolerance. The priority is to improve the performance of power-type energy storage technologies by enhancing their response speed and cycle life, which are strongly correlated with high-frequency regulation. For mechanical energy storage technologies, which experience more significant performance degradation under frequent start-stop cycles or high-frequency mechanical wear, the priority is to reduce their contribution to frequency regulation service matching and lifespan-related indicators. This allows for dynamic re-evaluation of the same technology under different business demand structures. Regarding risk warning, to reflect the safety and effectiveness degradation in extreme temperature environments, a secondary dynamic correction is performed on the indicator data after the initial dynamic correction, based on an environmental risk correction coefficient determined for extreme temperature environments. The operating environment is preferably divided into three conditions: stable ambient temperature, extreme high temperature, and extreme low temperature. Under stable ambient temperature, the environmental risk correction coefficient can be set as a baseline value to maintain consistency with standard operating conditions. Under extreme high temperature, a punitive correction consistent with thermal runaway risk and thermal management costs is preferred for electrochemical energy storage. This reduces the contribution of the environmental impact index and capacity confidence level, among other safety-related indicators, and simultaneously reflects the efficiency degradation caused by additional energy consumption at high temperatures, thus quantifying the decline in efficiency under high-temperature, high-risk scenarios. Under extreme low temperature, a punitive correction consistent with thermal runaway risk and thermal management costs is preferred for electrochemical energy storage. A capacity confidence penalty consistent with capacity decay and low-temperature lithium plating risk is applied. For mechanical energy storage, an efficiency penalty consistent with efficiency decreases caused by mechanical brittleness and changes in lubricating medium viscosity is preferred, allowing for differentiated reflection of performance degradation of energy storage technologies with different physical mechanisms under low-temperature conditions. Finally, the index data after secondary dynamic correction is weighted and calculated with the basic fusion weights to output a comprehensive utility score. The weighted calculation preferably uses a method of summarizing the utility values ​​of each index according to the fusion weights to obtain a single comparable score, which can be used to rank, select, or perform adaptability analysis on different energy storage technologies.

[0024] The technical solution of this invention constructs a four-dimensional assessment system for response characteristics and environmental risks, and combines grey system theory to handle the fuzziness of indicators; it introduces the ratio of grid frequency regulation and ramp demand to dynamically correct the initial weight database, and simultaneously establishes a correlation model between extreme environments and physical failure mechanisms; finally, it outputs a comprehensive energy storage performance score that adapts to all scenarios through weighted aggregation, effectively solving the problems of lack of scenario adaptability and environmental risk early warning and the fuzzy data quantification defects in existing energy storage assessment methods.

[0025] Furthermore, the evaluation indicators include: Response speed, power regulation accuracy, continuous power output capability, charge and discharge efficiency, cycle life, cost per kilowatt-hour, capacity confidence, system availability, environmental impact index, ramp service utility matching degree, and frequency modulation service utility matching degree.

[0026] As a preferred embodiment, the above eleven indicators are preferably used as a unified indicator layer set under the same evaluation caliber to achieve comparability and feasible dynamic correction of heterogeneous energy storage technologies such as electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage under different service demand structures and extreme temperature environments; wherein, the response speed is preferably defined as the time interval from when the energy storage system receives a scheduling or AGC command to when the output power reaches the target power or a certain percentage threshold of the target power, which can be obtained through project measurement records, controller logs, or standard test procedures, and is preferably measured in seconds or milliseconds to reflect the fast response advantage of power-type energy storage; power regulation Precision is preferred to characterize the magnitude of the deviation between the output power and the target power of the energy storage during the tracking command process. It can be described by the statistics of steady-state error or tracking error to reflect the accuracy capability in frequency regulation scenarios. Continuous power output capability is preferred to be characterized by the duration of stable output at rated or specified power levels or the continuous supply capability that meets the ramp-up support window, highlighting the long-term support advantage of energy storage in ramp-up dominated scenarios. Charge-discharge efficiency is preferred to be the round-trip energy efficiency under standard operating conditions, reflecting the energy loss level, and serving as one of the sensitive indicators of the efficiency decline of mechanical energy storage at extreme low temperatures for subsequent environmental risk correction. Cycle life is preferred to be... The equivalent number of cycles or the amount of usable cycles within a lifetime that can be completed under a specified capacity decay threshold is used to characterize the difference in durability under high-frequency regulation or frequent start-stop conditions, and is preferred as one of the key advantage indicators of power-type energy storage in frequency regulation-dominated scenarios; the levelized cost of electricity (LCOE) preferably adopts the caliber of unit electricity cost over the entire life cycle, so as to establish an economically comparable benchmark between different technical routes, and to reflect the unit energy cost advantage of energy-type technologies in ramp-up-dominated scenarios with equal-length call durations; the capacity confidence level is preferably used to describe the probability or confidence level of energy storage providing definite usable capacity at a specific moment, and can be derived from measured statistics or from standard parameters and degradation models. Engineering estimation is used, and the system is used as a key penalty target for electrochemical energy storage under extreme high or low temperature environments to reflect the risk of thermal runaway or low temperature capacity decay. The system availability rate is preferably the proportion of available time within a certain statistical period in which energy storage can be dispatched and meets performance constraints, which is used to reflect the reliable operation capability of the equipment and support the comparability of different technologies in the long-term operation. The environmental impact index is preferably used to quantify the emissions of waste gas, wastewater, and solid waste, eco-friendliness, and environmental burden level related to safety risks. More preferably, it is associated with the environmental or safety externalities brought about by the thermal runaway risk of electrochemical energy storage under extreme high temperatures, so as to carry out risk warning reduction of electrochemical technology in environmental risk correction.The matching degree of ramp service utility and the matching degree of frequency regulation service utility are preferably used to characterize the degree of matching between energy storage technology and the grid ramp support demand window and frequency regulation high-frequency tracking demand, respectively. The matching degree is preferably determined by mapping grid demand characteristics, such as the continuous supply window required for ramp support and the rapid response and high-frequency regulation tolerance required for frequency regulation, to the response speed, continuous output, cycle life, and regulation accuracy of energy storage technology, under the same caliber, to form a comparable quantitative score. This ensures that the evaluation compares not only the intrinsic performance but also whether it can truly exert its utility in the specific business scenario.

[0027] Furthermore, the formula for calculating the basic fusion weights includes: ; in, The basic fusion weight for the j-th indicator, As the first weight, As the second weight, This represents the weighted preference coefficient.

[0028] As a preferred embodiment of the above, firstly, after the original index data of each energy storage technology under standard operating conditions are dimensionless, a comparable data sequence is formed for each evaluation index, and grey relational analysis is performed with the ideal optimal sequence as a reference to obtain the first weight reflecting the degree of closeness to the ideal optimal and the similarity of geometric trends for each index. Simultaneously, entropy weight calculation is performed on the same batch of dimensionless index data to obtain the second weight reflecting the dispersion and information contribution of each index. When some indicators are ambiguous or originate from graded evaluations, it is preferable to first convert the indicator into a comparable numerical score under the same standard before participating in the entropy weight calculation to ensure the reproducibility and comparability of the second weight. After obtaining the first and second weights, it is preferable to introduce a weight preference coefficient to linearly compromise and fuse the two types of weights, thereby obtaining the basic fused weight corresponding to each index. The weight preference coefficient is preferably limited to between 0 and 1 to express the evaluator's preference for trend similarity (grey relational analysis). The relative preferences of the two weighting logics (linkage) and information content difference (entropy weight) are as follows: When the preference coefficient is closer to 1, the basic fusion weight emphasizes the small sample trend pattern reflected by the gray relational degree, which is suitable for scenarios with small sample size and poor data information but where it is desirable to maintain the relative superiority or inferiority trend of the technical sequence; when the preference coefficient is closer to 0, the basic fusion weight emphasizes the contribution of the index variability reflected by the entropy weight, which is suitable for scenarios with relatively sufficient samples or large index differences and where it is desirable to highlight the dominant role of information content; when there is a lack of clear preference or it is necessary to take into account both types of information, it is preferable to adopt an equal preference setting so that the contributions of the two types of weights to the basic fusion weight are relatively balanced; furthermore, in order to ensure the stability and interpretability of the subsequent comprehensive utility score calculation, it is preferable to unify the basic fusion weights of all indicators after fusion, so that they are non-negative, comparable in the same direction, and form a unified weight set, for example, keeping the sum of all indicator weights at a preset total amount, thereby avoiding scale drift caused by different weighting standards.

[0029] Furthermore, the methods for determining the scene correction coefficient include: When the ratio of power grid frequency regulation to ramping service is 5:5, the scenario correction coefficient is taken as the baseline value of 1. When the ratio of grid frequency regulation to ramping service is 3:7, which is a ramp-dominated type, the continuous power output capability and the utility value of the levelized cost of electricity index of energy storage should be improved, while the matching degree of ramping service utility and the utility value of continuous power output capability index of power storage should be reduced. When the ratio of grid frequency regulation to ramp service is 7:3, which is a frequency regulation-dominated type, the response speed and cycle life index utility value of power-type energy storage should be improved, while the frequency regulation service utility matching degree and cycle life index utility value of mechanical energy storage should be reduced.

[0030] As a preferred embodiment of the above, the grid service demand structure is used as the input for scenario-based joint commissioning. First, the dispatching side statistically analyzes or the planning side sets the ratio of frequency regulation service to ramp service within the evaluation period, and this ratio is used as the criterion for triggering scenario correction rules. Preferably, a frequency regulation:ramp ratio of 5:5 is defined as the baseline scenario. Under the baseline scenario, no biased amplification or penalty is introduced for any indicators, and the scenario correction coefficient is set to a baseline value of 1 to ensure the comparability of various energy storage technologies under standard operating conditions and the consistency of subsequent cross-scenario comparisons. Based on this, the present invention preferably follows the principle of performance exclusivity in scenario correction, that is, when grid demand is significantly biased towards a certain type of service, the capability indicator that can form actual utility in that type of service should be... Enhancement should be prioritized, while performance indicators that are inherently incompatible with or difficult to utilize for this type of service should be suppressed, so that the overall score truly reflects the utility migration of the same technology under different demand structures. Specifically, when the frequency regulation:ramp ratio is 3:7, which is ramp-dominant, it is preferable to classify energy storage technologies into two categories—energy storage and power storage—according to their mechanisms and service characteristics, and process them separately. Energy storage preferably refers to technologies with strong long-term support and continuous supply capabilities, such as compressed air energy storage and gravity energy storage. Power storage preferably refers to technologies with extremely fast response, suitable for high-frequency short-term regulation, but with relatively short continuous power supply time, such as flywheel energy storage. In ramp-dominant scenarios, it is preferable to prioritize the continuous power of energy storage. The utility value of the output capacity indicator is increased to reflect its advantage of continuous supply within the ramp-up support window. Simultaneously, the utility value of the levelized cost of electricity (LCOE) indicator is improved to reflect its unit energy cost competitiveness under long-term dispatch conditions. Given that LCOE is a cost-based indicator, it is preferable to enhance its corresponding utility value to express that the cost advantage can be more effectively translated into actual utility in this scenario. This can be achieved by amplifying the dimensionless utility value or by adjusting the converted value of the cost-based indicator in a favorable direction, ensuring that the adjusted result still maintains the consistent standard that a larger value represents better utility. Conversely, in ramp-up-dominated scenarios, it is preferable to optimize the matching degree of ramp-up service utility and continuous power output capacity of power-type energy storage. The utility value of the force index is lowered, causing it to show a disadvantage in the overall utility due to its inability to provide long-term ramping support. Furthermore, the above-mentioned upward or downward adjustments are corrected using a preset coefficient range. For example, an enhancement coefficient greater than 1 is set for the ramping capability index of energy storage, and an inhibition coefficient less than 1 is set for the ramping matching degree of power storage. This allows for dynamic re-evaluation across scenarios without changing the index system. When frequency regulation: ramping = 7:3 is the frequency regulation-dominated type, the preferred approach is to make differentiated adjustments based on the service demand structure: for power storage, the preferred approach is to increase the utility values ​​of its response speed and cycle life indexes, so that its advantages in fast response and durability under high-frequency regulation and frequent cycling conditions can be amplified.Meanwhile, considering that mechanical energy storage may experience wear accumulation and reduced lifespan under frequent start-stop or high-frequency operation conditions, which are detrimental to frequency regulation requirements, mechanical energy storage, such as flywheels and compressed air, is preferentially classified as a category requiring additional constraints. In frequency regulation-dominated scenarios, its frequency regulation service utility matching degree and cycle life utility value are lowered to reflect its adaptability differences and potential performance degradation risks under this demand structure.

[0031] Furthermore, the methods for determining environmental risk correction coefficients include: When the operating environment is at a stable normal temperature, the environmental risk correction factor is taken as 1. When the operating environment is extremely high temperature, reduce the performance values ​​of the environmental impact index and capacity confidence index of electrochemical energy storage. Extreme high temperature is defined as greater than 40℃. When the operating environment is at an extreme low temperature, the performance values ​​of the capacity confidence index of electrochemical energy storage and the charge and discharge efficiency index of mechanical energy storage are reduced. Extreme low temperature is defined as less than -10℃.

[0032] As a preferred embodiment of the above, the ambient temperature is used as the risk warning input. Ambient temperature data is collected at the deployment area or operating point of the energy storage system to be evaluated at the start of the assessment. This data can come from on-site temperature sensors, operation logs, or meteorological or station monitoring records. Based on this, the operating environment is identified as a stable ambient temperature state, an extreme high-temperature state, or an extreme low-temperature state. An environmental risk correction coefficient is determined based on this identification result and applied to the performance values ​​of the corresponding indicators. First, when identified as a stable ambient temperature state, to maintain consistency with the standard operating condition assessment standard, the environmental risk correction coefficient is preferably set to 1, meaning no additional penalty or amplification is applied to the performance values ​​of each indicator, ensuring that the comparability of different technologies under ambient temperature conditions is not compromised. Second, when identified as an extreme high-temperature state, a risk warning correction is preferably performed targeting the physical failure and safety risk characteristics of electrochemical energy storage. This involves reducing the performance values ​​of the environmental impact index and capacity confidence index of electrochemical energy storage to reflect the risk of thermal runaway at high temperatures and the resulting consequences. Increased environmental or safety externalities and greater uncertainty in available capacity enable comprehensive utility assessments to explicitly reflect the safety and utility degradation of electrochemical energy storage in high-temperature scenarios. Thirdly, when identified as an extreme low-temperature state, it is preferable to implement differentiated penalties for energy storage technologies with different physical mechanisms: for electrochemical energy storage, it is preferable to reduce the performance value of its capacity confidence index to reflect risk factors such as capacity degradation and decreased availability confidence at low temperatures; for mechanical energy storage, it is preferable to reduce the performance value of its charge / discharge efficiency index to reflect the efficiency decrease caused by mechanical brittleness and changes in lubricant viscosity due to low temperatures, so that the increased losses of mechanical energy storage in low-temperature scenarios can be quantified in the assessment results. In engineering implementation, the reduction is preferably achieved by pre-configuring a set of callable correction coefficient rules for different environmental states, allowing direct reduction of the corresponding index performance values ​​without changing the index system itself, and ensuring that the reduction still follows a unified comparison standard where a larger value represents better utility or better performance.

[0033] Furthermore, the dynamic correction process includes: ; in, Let be the correction value of the j-th indicator for the i-th energy storage technology in scenario s. The original indicator data, This refers to the corresponding scenario correction factor or environmental risk correction factor.

[0034] As a preferred embodiment of the above, firstly, for the j-th evaluation index of the i-th energy storage technology, a scenario correction coefficient is determined based on the ratio of grid frequency regulation to ramp service demand. This scenario correction coefficient is then directly applied to the baseline data of the index, resulting in the first corrected index value under the selected scenario s. Subsequently, an environmental risk correction coefficient is determined based on the operating environment identification results, and this coefficient is further applied to the first corrected index value to obtain the final corrected value of the index for the technology under scenario s. For indices unaffected by the current scenario or environment, it is preferable to set the corresponding correction coefficient to a baseline value of 1 to ensure that only indices strongly correlated with scenario-based joint commissioning or risk warning undergo directional changes without compromising the comparability of the overall index system. In terms of implementation details, the correction coefficient is preferably applied to the index data using multiplicative reduction or multiplicative amplification. The method is as follows: when it is necessary to reflect the amplified advantages of a certain technology in a particular scenario, the corresponding correction coefficient is set to be greater than 1, causing the corrected value of the indicator to be adjusted upward relative to the benchmark value; when it is necessary to reflect mismatch or risk penalty, the corresponding correction coefficient is set to be less than 1, causing the corrected value of the indicator to be adjusted downward relative to the benchmark value. This achieves a quantitative expression of enhanced advantages or reduced risks using the same standard, and ensures that the corrected data can still be directly included in subsequent standardization and comprehensive utility weighted calculations. At the same time, to avoid ambiguity in the correction direction of cost-type indicators such as cost per kilowatt-hour, response time, and environmental impact index, it is preferable to uniformly map each indicator to a utility direction where larger values ​​represent better utility or performance during the dimensionless stage. On this basis, the above multiplicative correction is then implemented, ensuring that the rule of upward adjustment representing utility enhancement and downward adjustment representing utility penalty is consistent across all indicators.

[0035] Furthermore, after standardizing the corrected original indicator data, a weighted sum is obtained by combining the basic fusion weights to obtain the comprehensive utility score.

[0036] As a preferred embodiment of the above, after completing scenario correction and environmental risk correction, the corrected data of each indicator obtained by each energy storage technology under the same scenario combination are used as a unified input, and standardized processing of the same scenario and standard is performed before entering the comprehensive calculation to eliminate the incomparability caused by different indicator dimensions, orders of magnitude, and data spans between different technologies. Specifically, the standardization processing is preferably performed on an indicator-by-indicator, cross-technology basis. That is, for each evaluation indicator, the corrected values ​​of all energy storage technologies to be evaluated under the current scenario are summarized to determine the relative range and distribution of the indicator in this evaluation sample. Then, the corrected value of the indicator is mapped to a standardized utility value of the same scale, so that it falls into a preset comparable range, such as unified to 0 to 1 or unified to a percentage range. This ensures that indicators with significantly different dimensions, such as response speed, levelized cost of electricity, and environmental impact index, can directly participate in the weighted summation under the same comprehensive framework as indicators such as continuous power output capability, cycle life, and system availability. To avoid introducing ambiguity in the standardization direction of cost-type indicators, it is preferred to follow the methods described in the disclosure content. Consistent Standards: In the preceding dimensionless and dynamic correction stages, all indicators have been uniformly processed so that larger values ​​represent better utility or performance. Therefore, standardization only unifies the scale and does not change the direction of superiority or inferiority, thus ensuring consistency in the interpretation that a larger weight in the subsequent weighted summation indicates a more significant contribution of the indicator to the overall score. After standardization, the basic fusion weights are preferably used as fixed weight benchmarks to perform weighted summation on the standardized utility values ​​of each indicator, thereby obtaining the overall utility score of each energy storage technology under the current scenario combination. Based on this, ranking results, optimization conclusions, or adaptability analysis conclusions can be output. The weighted summation optimization reflects two points: First, the weights are derived from the fusion of grey relational analysis and entropy weight method, which can take into account the similarity of trends in small samples and the differences in the amount of information in the indicators, making the overall score both stable and highlighting key difference indicators; Second, the standardized data has been superimposed with scenario correction and environmental risk correction, so the overall score naturally carries the linkage effect of changes in service demand structure and extreme temperature risk warnings, which can truly reflect the utility migration of the same technology under different scenarios.

[0037] Furthermore, the energy storage technologies to be evaluated include at least two heterogeneous technologies among electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage.

[0038] As a preferred embodiment of the above, the evaluation object is set as a combination of at least two types of energy storage technologies with significantly different working mechanisms and complementary utility performance in grid service. The same evaluation standard is used for unified input, unified correction, and unified output to ensure the fairness and reproducibility of the horizontal comparison. Specifically, the heterogeneous technologies preferably include at least one of electrochemical energy storage and mechanical energy storage. Electrochemical energy storage is preferably represented by lithium battery energy storage, which is characterized by fast response and mature engineering applications but is sensitive to temperature environment and safety risks. Mechanical energy storage preferably includes one or both of compressed air energy storage and flywheel energy storage. Compressed air energy storage typically exhibits strong long-term energy support capabilities but relatively slow response; flywheel energy storage typically exhibits millisecond-level fast response and is suitable for high-frequency regulation but has a relatively short continuous energy supply time; gravity energy storage is preferred as another type of physical mechanism technology, characterized by relatively simple structure, strong temperature robustness, and suitability as a long-term support or scenario robustness comparison object; preferably, raw data of the same set of evaluation indicators should be collected for each technology to be evaluated under standard operating conditions. The data sources preferably include actual measurement records of demonstration projects, operation log statistics, and industry standard parameters, where the response speed can be obtained from control and measurement records. The continuous power output capability can be obtained from the statistical analysis of the continuous and stable output time at rated power; the cost per kilowatt-hour can be given by the life-cycle cost accounting standard; the capacity confidence level can be obtained from the statistical analysis of available capacity or the engineering reliability assessment; the environmental impact index can be given by the unified environmental or safety impact standard; and the matching degree of ramp-up or frequency regulation service utility can be formed by mapping the demand window and technical capabilities to form a comparable score, thus providing a directly comparable input basis for at least two heterogeneous technologies under the same indicator system. Furthermore, it is preferable to make differentiated modifications to different mechanism technologies during subsequent scenario commissioning and risk warning: when the proportion of frequency regulation demand increases... The fast response and high-frequency cycling resistance of power-type technologies such as flywheel or electrochemical energy storage are more easily converted into utility. When the proportion of ramp-up demand increases, the continuous supply capacity of energy-type technologies such as compressed air or gravity is more easily converted into utility. When the environment enters extreme high or low temperatures, electrochemical energy storage is more sensitive to capacity confidence and environmental impact indicators due to thermal risks or low-temperature capacity decay. In contrast, physical or mechanical mechanism technologies such as flywheel, compressed air, and gravity are usually affected by temperature in different ways or with smaller amplitudes. Therefore, the risk warning effect of the difference in utility decay of heterogeneous mechanisms under extreme environments can be reflected by the same set of correction rules.

[0039] Example 2; Based on the same inventive concept as the energy storage dynamic assessment method based on scenario-based commissioning and risk warning in the foregoing embodiments, the present invention also provides an energy storage dynamic assessment system based on scenario-based commissioning and risk warning, the system comprising: The system establishment module constructs an evaluation index system that includes evaluation indicators in four dimensions: technical performance, economy, reliability and environmental performance, and scenario adaptability. The data acquisition module acquires the raw index data of the energy storage technology to be evaluated under standard operating conditions and performs dimensionless processing. The weight fusion module uses grey relational analysis to calculate the first weight of each evaluation indicator, uses entropy weight to calculate the second weight of each evaluation indicator, and merges the first weight and the second weight to obtain the basic fusion weight. The initial correction module determines the scenario correction coefficient based on the ratio of power grid frequency regulation and ramp service demand, and uses the scenario correction coefficient to perform the initial dynamic correction on the original indicator data. The secondary correction module determines the environmental risk correction coefficient based on extreme temperature environments and uses the environmental risk correction coefficient to perform secondary dynamic correction on the original indicator data. The scoring module performs a weighted calculation on the original indicator data after secondary dynamic correction and the basic fusion weights, and outputs a comprehensive utility score.

[0040] The adjustment system described above in this invention can effectively realize a dynamic energy storage assessment method based on scenario-based joint commissioning and risk warning. The technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0041] Furthermore, the secondary correction module includes: For units operating at room temperature, when the operating environment is at room temperature and stable, the environmental risk correction factor is set to 1. Extreme high temperature operation unit, when the operating environment is extremely high temperature, reduces the environmental impact index and capacity confidence index performance value of electrochemical energy storage; Extreme low temperature operation unit, when the operating environment is extremely low temperature, reduces the performance value of the capacity confidence index of electrochemical energy storage and the performance value of the charge and discharge efficiency index of mechanical energy storage.

[0042] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0043] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A dynamic assessment method for energy storage based on scenario-based joint commissioning and risk early warning, characterized in that, The method includes: Construct an evaluation index system that includes evaluation indicators in four dimensions: technical performance, economic efficiency, reliability and environmental performance, and scenario adaptability; Obtain the raw index data of the energy storage technology to be evaluated under standard operating conditions and perform dimensionless processing; The first weight of each evaluation index is calculated using the grey relational analysis method, and the second weight of each evaluation index is calculated using the entropy weight method. The first weight and the second weight are then combined to obtain the basic fusion weight. The scenario correction coefficient is determined based on the ratio of power grid frequency regulation and ramp service demand, and the original index data is dynamically corrected for the first time using the scenario correction coefficient. Based on the extreme temperature environment, an environmental risk correction coefficient is determined, and the original index data is then dynamically corrected using the environmental risk correction coefficient. The original indicator data after the second dynamic correction is weighted and calculated with the basic fusion weight to output the comprehensive utility score.

2. The energy storage dynamic assessment method based on scenario-based joint commissioning and risk early warning as described in claim 1, characterized in that, The evaluation indicators include: Response speed, power regulation accuracy, continuous power output capability, charge and discharge efficiency, cycle life, cost per kilowatt-hour, capacity confidence, system availability, environmental impact index, ramp service utility matching degree, and frequency modulation service utility matching degree.

3. The energy storage dynamic assessment method based on scenario-based joint commissioning and risk early warning as described in claim 1, characterized in that, The calculation formula for the basic fusion weight includes: ； in, The basic fusion weight for the j-th indicator, For the first weight, This is the second weight. This represents the weighted preference coefficient.

4. The energy storage dynamic assessment method based on scenario-based joint commissioning and risk early warning as described in claim 2, characterized in that, The method for determining the scene correction coefficient includes: When the ratio of power grid frequency regulation to ramping service is 5:5, the scenario correction coefficient is taken as the base value of 1. When the grid frequency regulation and ramping service ratio is 3:7, which is a ramping-dominated type, the continuous power output capability and the utility value of the levelized cost of electricity index of energy storage are improved, while the ramping service utility matching degree and the continuous power output capability index utility value of power storage are reduced. When the grid frequency regulation and ramp-up service ratio is 7:3, which is a frequency regulation-dominated type, the response speed and cycle life index utility value of power-type energy storage are improved, while the frequency regulation service utility matching degree and cycle life index utility value of mechanical energy storage are reduced.

5. The energy storage dynamic assessment method based on scenario-based joint commissioning and risk early warning as described in claim 2, characterized in that, The method for determining the environmental risk correction coefficient includes: When the operating environment is in a stable state at room temperature, the environmental risk correction factor is taken as 1; When the operating environment is at an extreme high temperature, reduce the environmental impact index and capacity confidence index performance values ​​of electrochemical energy storage. The extreme high temperature is defined as greater than 40°C. When the operating environment is at an extreme low temperature, the performance values ​​of the capacity confidence index of electrochemical energy storage and the performance values ​​of the charge and discharge efficiency index of mechanical energy storage are reduced. The extreme low temperature is defined as less than -10℃.

6. The energy storage dynamic assessment method based on scenario-based joint commissioning and risk early warning as described in claim 1, characterized in that, The dynamic correction process includes: ； in, Let be the correction value of the j-th indicator for the i-th energy storage technology in scenario s. The original indicator data, This refers to the corresponding scenario correction factor or the environmental risk correction factor.

7. The energy storage dynamic assessment method based on scenario-based joint commissioning and risk early warning according to claim 1, characterized in that, After standardizing the modified original indicator data, a weighted sum is performed based on the basic fusion weights to obtain the comprehensive utility score.

8. The energy storage dynamic assessment method based on scenario-based joint commissioning and risk early warning as described in claim 1, characterized in that, The energy storage technologies to be evaluated include at least two heterogeneous technologies among electrochemical energy storage, compressed air energy storage, gravity energy storage, and flywheel energy storage.

9. A dynamic energy storage assessment system based on scenario-based joint commissioning and risk early warning, characterized in that, The system includes: The system establishment module constructs an evaluation index system that includes evaluation indicators in four dimensions: technical performance, economy, reliability and environmental performance, and scenario adaptability. The data acquisition module acquires the raw index data of the energy storage technology to be evaluated under standard operating conditions and performs dimensionless processing. The weight fusion module uses grey relational analysis to calculate the first weight of each evaluation indicator, uses entropy weight to calculate the second weight of each evaluation indicator, and merges the first weight and the second weight to obtain the basic fusion weight. The initial correction module determines the scenario correction coefficient based on the ratio of power grid frequency regulation and ramp service demand, and uses the scenario correction coefficient to perform the initial dynamic correction on the original indicator data. The secondary correction module determines the environmental risk correction coefficient based on extreme temperature environments and uses the environmental risk correction coefficient to perform secondary dynamic correction on the original indicator data. The scoring module performs a weighted calculation on the original indicator data after secondary dynamic correction and the basic fusion weights, and outputs a comprehensive utility score.

10. The energy storage dynamic assessment system based on scenario-based joint commissioning and risk early warning according to claim 9, characterized in that, The secondary correction module includes: For units operating at room temperature, when the operating environment is at room temperature and stable, the environmental risk correction factor is set to 1. Extreme high temperature operation unit, when the operating environment is extremely high temperature, reduces the environmental impact index and capacity confidence index performance value of electrochemical energy storage; Extreme low temperature operation unit, when the operating environment is extremely low temperature, reduces the performance value of the capacity confidence index of electrochemical energy storage and the performance value of the charge and discharge efficiency index of mechanical energy storage.

Images