Multi-objective optimization energy storage EMS power distribution method considering PCS health degree

Abstract

The invention relates to the technical field of power distribution, and discloses a multi-objective optimization energy storage EMS power distribution method considering PCS health degree, and the method has the remarkable advantages that 1, the power of a low-health PCS is constrained through an SOH score, the service life of the PCS is prolonged, the fault rate of a high-aging risk PCS is reduced, and the average service life is prolonged; secondly, an SOC balance item is implanted, a real-time compensation algorithm is designed, the SOC range is stabilized within 5%, and the problem of SOC unbalance is solved; thirdly, the weight is dynamically adjusted in an emergency scene, the frequency modulation requirement is preferentially guaranteed, the scheduling capability is improved, and the available power maintenance rate is improved according to test and display of a 200MW power station; fourthly, the health assessment model early warns high-risk PCS in advance, the operation and maintenance response time is greatly shortened, the annual maintenance cost is greatly reduced, and the LCOS of the whole life cycle is reduced; and 5, the response time of the optimization algorithm is less than 200ms, and the AGC frequency modulation second-level response requirement is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution, and particularly relates to a multi-objective optimization energy storage EMS power distribution method considering PCS health degree. BACKGROUND

[0002] In the current technical application scenario, the existing energy management system (EMS) of the energy storage power station usually adopts a relatively conventional strategy when performing power distribution tasks, that is, according to the chargeable and dischargeable capacity of the power conversion system (PCS), the power is distributed in a proportional manner. Specifically, during the normal operation of the energy storage power station, the EMS will monitor the upper limit of the chargeable and dischargeable power of each PCS in real time, and then distribute the total power demand to each PCS according to the proportional principle to ensure that each device is relatively balanced in terms of power distribution.

[0003] However, when the state of charge (SOC) of the battery in the energy storage power station is not balanced, the system will manually or automatically switch the power distribution strategy to a distribution scheme based on SOC balancing according to the preset rules. This switching mechanism aims to effectively manage the power modules and battery SOC through the adjustment of power distribution, and to protect the overall performance of the energy storage power station and the service life of the battery.

[0004] Although the existing power distribution strategy can achieve reasonable power distribution and basic management of the battery SOC to some extent, it cannot be ignored that this strategy has obvious defects, that is, it lacks effective consideration of the health management of the PCS. The traditional power distribution scheme only takes the chargeable and dischargeable capacity and the SOC as the basis for distributing the charge and discharge tasks, but completely ignores the health status of the converter itself. In actual operation, the key components in the PCS, such as the insulated gate bipolar transistor (IGBT) module, will gradually age with the increase of the use time, and its performance parameters will change significantly; the capacitance value of the capacitor will also decay due to long-term use and environmental factors; at the same time, the efficiency of the heat dissipation system will gradually decrease with the operation of the device. These factors will have a negative impact on the health status of the PCS, but the traditional power distribution strategy fails to take these factors into account.

[0005] Due to the lack of effective mechanisms for PCS health management, high-load PCSs will accelerate damage under the influence of the above-mentioned adverse factors. As the degree of equipment aging increases, the failure rate of PCSs will also increase significantly, which not only increases the operation and maintenance cost of the energy storage power station, but also affects the reliability and stability of the power station. Especially in the emergency state of frequency modulation, the limitations of the traditional fixed weight algorithm are more prominent. In the process of frequency modulation, the power grid has very high requirements for the response speed and power output of the energy storage power station, and the energy storage power station needs to quickly and accurately adjust the power output to maintain the stable operation of the power grid. However, the traditional fixed weight algorithm cannot dynamically adjust according to the actual health status of the PCS and the real-time demand of the power grid, and cannot meet the needs of equipment protection while ensuring the safety of the power grid. This leads to a situation where the system may have a response delay in the emergency state of frequency modulation, and cannot meet the power regulation demand of the power grid in time; or in order to meet the demand of the power grid, the equipment is overloaded, which further aggravates the damage risk of the equipment, forming a vicious cycle.

[0006] In summary, in view of the many problems existing in the existing power distribution strategy and the potential threat to the safe and stable operation of the energy storage power station, it is urgent to improve and perfect the existing technology to build a more scientific, reasonable and comprehensive power distribution strategy and improve the overall performance and reliability of the energy storage power station.

[0007] The above information is given as background information only to assist with an understanding of the present disclosure, and does not determine or acknowledge whether any of the above is available as prior art with respect to the present disclosure. SUMMARY

[0008] The present application provides a multi-objective optimization energy storage EMS power distribution method considering PCS health degree to solve the problems existing in the prior art.

[0009] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0010] A multi-objective optimization energy storage EMS power distribution method considering PCS health degree, the method comprising:

[0011] S101, collecting the operation parameters of the PCS, the battery state and the power grid dispatching power instruction;

[0012] S102, inputting the collected data and the data in the pre-established health state database into the SOH health model to obtain the SOH score; the SOH health model is established based on three dimensions of efficiency, temperature rise and reliability, and the weight coefficient is determined by using the entropy weight method;

[0013] S103, inputting the SOH score and data in the health state database into a multi-objective optimization model to obtain a power distribution result; the multi-objective optimization model comprises three objectives: power balance, health cost minimization and SOC balance, weight coefficients of which are dynamically adjusted in real time according to system states, and SOC closed-loop compensation is performed.

[0014] Further, before the S101, the method further comprises:

[0015] S100, when the system is started, preset parameters and historical data are automatically loaded, parameter legality is verified, and a health state database is established.

[0016] Further, the S101 in the multi-objective optimization energy storage EMS power distribution method considering PCS health degree comprises:

[0017] S1011, in a sampling period, GOOSE protocol communication is adopted, PCS operation parameters and battery states are collected in parallel, and IEC104 communication protocol is adopted to collect power grid dispatching instructions;

[0018] S1012, IEEE 1588 clock synchronization technology is used to ensure that all collected data are aligned in time stamp;

[0019] S1013, the collected data are preprocessed.

[0020] Further, the preprocessing in the multi-objective optimization energy storage EMS power distribution method considering PCS health degree comprises Hanning window-based sliding filtering and abnormal value elimination.

[0021] Further, the multi-objective optimization energy storage EMS power distribution method considering PCS health degree further comprises:

[0022] S201, the following SOH health model is established based on three dimensions of efficiency, temperature rise and reliability:

[0023] ;

[0024] wherein, is a weight coefficient of three indexes, i represents a PCS device number, is a current actual efficiency of the i-th PCS, is a maximum allowable temperature rise rate, is a temperature rise rate of the i-th PCS; is an annual / monthly failure frequency of the i-th PCS;

[0025] S202, determine the weight coefficient by using the entropy weight method .

[0026] Further, in the multi-objective optimization energy storage EMS power distribution method considering the health degree of PCS, the S202 comprises:

[0027] S2021, construct an original data matrix for m PCS devices

[0028] ;

[0029] Wherein, i represents the i-th device; j=1, 2, 3, represents the efficiency ratio, temperature rise item and reliability item, and the three indexes are regarded as benefit type indexes and standardized processing, and the formula is:

[0030] ;

[0031] To The j-th index value of the i-th device, that is, to normalize each column, so that the value of each index is between 0 and 1;

[0032] S2022, calculate the proportion:

[0033] ;

[0034] S2023, calculate the entropy value of the j-th index:

[0035] ;

[0036] Wherein, when , it is defined that ;

[0037] S2024, calculate the information utility value, the greater the information utility value, the stronger the importance of the index:

[0038] ;

[0039] S2025, calculate the weight of each index to obtain the weight of the three indexes :

[0040] .

[0041] Further, in the multi-objective optimization energy storage EMS power distribution method considering the health degree of PCS, in the S201, the SOH score adopts a grading system of 0-100 points, and the specific grading standard is:

[0042] ≥80 points, excellent, the decision is to allow full power operation;

[0043] 60-79 points, good, decision is power limiting to 90% of the rated value;

[0044] 40-59 points, early warning, decision is to participate in emergency frequency modulation only;

[0045] ≤40 points, for repair, decision is forced shutdown for repair.

[0046] Further, in the multi-objective optimization energy storage EMS power distribution method considering PCS health degree, the method further comprises:

[0047] S301, a multi-objective optimization model based on power balance, health cost minimization and SOC balance is constructed, which is formalized as a multi-objective optimization problem with constraints:

[0048] ;

[0049] Wherein, the power balance term: is the to-be-optimized power of the i-th PCS, is the total power in the grid dispatching instruction issued by the grid, is the number of PCSs, is the rated power of a single PCS; the target is to make the actual power of each PCS as close as possible to the average allocation value avoiding that part of the equipment is overloaded while the other equipment is idle; the difference is normalized and divided by to convert the power difference into a dimensionless value and eliminate the dimension influence;

[0050] Health cost term: is the health score of the PCS, this term is to punish the high power task of the PCS with low health degree, the health cost generated by the power allocation of the lower health degree is higher, that is, the power is preferentially allocated to the equipment with high health degree, prolonging the service life of the system;

[0051] SOC balance term: is the current state of charge of the i-th battery cluster, is the average SOC of the whole station, the purpose is to minimize the variance of the SOC of each battery cluster and the average SOC, preventing overcharging / overdischarging of part of the batteries due to uneven power distribution;

[0052] S302, design constraint conditions, which need to ensure that the sum of the allocated power of each PCS satisfies that the deviation from the total power in the grid dispatching instruction is ≤1%, and at the same time, the allocated power does not exceed the lower limit and the upper limit of the power of the PCS, the constraint condition is expressed as:

[0053] ;

[0054] Wherein, and are the lower and upper power limits of the ith PCS, which are limited by the device hardware capability and the available capacity of the battery, and satisfy the following conditions:

[0055] ;

[0056] wherein, represents the battery charge / discharge rate, is the ith battery capacity, is the battery health, provided by the BMS;

[0057] S303, the weight coefficient is adjusted in real time according to the system state, and the state recognition logic is:

[0058] ;

[0059] Emergency frequency regulation mode, high , priority: power tracking > SOC balancing > device health, trigger condition: sharp change of grid frequency; SOC balancing mode, high , priority: SOC balancing > device health > power balancing, trigger condition: too large difference in SOC between battery clusters; normal mode, i.e., balancing mode, the three priorities are equivalent, and the default weights are: = 0.3, = 0.4, = 0.3;

[0060] Hysteresis switching: the exit condition is 2% stricter than the entry condition;

[0061] S304, further SOC closed-loop compensation is performed to dynamically adjust the power instructions of each PCS, so that the SOCs of all battery clusters tend to be consistent, thereby maximizing the available capacity and life of the energy storage system:

[0062] ;

[0063] wherein, , is a positive value indicating that the SOC of the cluster is lower than the average value, and needs to be discharged less / charged more; is a negative value, indicating that the SOC of the cluster is higher than the average value, and needs to be discharged more / charged less; is a compensation gain determined by parameter tuning, and a typical value is 0.005 × rated power; at the same time, the following conditions need to be satisfied:

[0064] Dead zone control: when <2% do not compensate;

[0065] Rate limit: the power adjustment amount per minute does not exceed 20% of the rated power;

[0066] Boundary protection: the power after compensation does not exceed the battery safety interval.

[0067] Further, in the multi-objective optimization energy storage EMS power distribution method considering PCS health, after S103, the method further comprises:

[0068] S104, issuing the power distribution instruction.

[0069] Further, in the multi-objective optimization energy storage EMS power distribution method considering PCS health, in S104, GOOSE protocol communication is adopted.

[0070] Compared with the prior art, the present application has the following beneficial effects:

[0071] (1) Prolong the life of PCS: dynamically constrain the upper limit of low health PCS power SOH<60 through SOH score to avoid over-stress operation. Actual measurement shows that the failure rate of high aging risk PCS (temperature rise>8℃ / s) is reduced by 65%, and the average life is prolonged by more than 3 years.

[0072] (2) Solve SOC imbalance: implant SOC balancing term in the objective function, and design real-time compensation algorithm, close-loop correction every 5 minutes to stabilize the SOC range within 5%, and eliminate capacity waste.

[0073] (3) Improve scheduling ability: dynamic weight mechanism automatically reduces the health cost weight (λ2 from 0.4→0.1) in emergency scenarios (|df / dt|>1Hz / s), and preferentially guarantees the frequency modulation demand. 200MW power station test shows that the available power maintenance rate is improved from 72% to 98%.

[0074] (4) Reduce operation and maintenance cost: the health assessment model early warns high-risk PCS (SOH<40 triggers alarm), and the operation and maintenance response time is shortened by 80%. Statistics show that the annual maintenance cost is reduced by 42%, and the life cycle degree cost (LCOS) is reduced by 15%.

[0075] (5) Millisecond-level real-time response: pure software solution adapts to mainstream hardware platforms, and the response time of the optimized algorithm is less than 200ms (traditional solution>500ms), which meets the AGC frequency modulation second-level response demand.

[0076] The present application has other characteristics and advantages, which will be apparent or will be described in detail in the accompanying drawings and subsequent specific embodiments incorporated herein, which together serve to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0078] Figure 1 is a flowchart of a multi-objective optimization energy storage EMS power distribution method considering PCS health degree provided by an embodiment of the present application;

[0079] Figure 2 is a schematic diagram of a data acquisition mechanism provided by an embodiment of the present application;

[0080] Figure 3 is a schematic diagram of a dynamic weight switching mechanism provided by an embodiment of the present application. DETAILED DESCRIPTION

[0081] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0082] In this paper, the term "embodiment" means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of the present application. The term "embodiment" appearing at various positions in the specification does not necessarily refer to the same embodiment, and does not particularly limit the independence or association between other embodiments. In principle, in this application, as long as there is no technical contradiction or conflict, each technical feature mentioned in each embodiment can be combined in any way to form a corresponding implementable technical solution.

[0083] Unless otherwise defined, the meanings of the technical terms used in this paper are the same as those commonly understood by those skilled in the art to which the present application belongs; the use of related terms in this paper is only for the purpose of describing specific embodiments, and is not intended to limit the present application.

[0084] In the description of the present application, the word "and / or" is a description of the logical relationship between the objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this paper generally represents that the associated objects before and after are a "or" logical relationship.

[0085] In the present application, the terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any actual number, primary or secondary, or order relationship between the entities or operations.

[0086] In the present application, the "include", "contain", "have" or other similar expressions used in the statements are intended to cover non-exclusive inclusion, and these expressions do not exclude the presence of additional elements in the process, method or product comprising the elements, so that the process, method or product comprising a series of elements can not only include those defined elements, but also include other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0087] In the present application, the expressions such as "greater than", "less than", "exceed" and the like are understood as not including the number itself; the expressions such as "above", "below", "within" and the like are understood as including the number itself. In addition, in the description of the embodiments of the present application, the meaning of "multiple" is more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times" and the like, unless otherwise explicitly specified.

[0088] In the description of the embodiments of the present application, the spatial-related expressions used, such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like, indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or the drawings, and are only for the convenience of describing the specific embodiments of the present application or for the reader to understand, and do not indicate or imply that the indicated device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0089] Unless otherwise explicitly specified or limited, in the description of the embodiments of the present application, the terms "mount", "connect", "connect", "fix", "set" and the like should be interpreted broadly. For example, the "connection" can be a fixed connection, or a detachable connection, or an integral setting; it can be a mechanical connection, or an electrical connection, or a communication connection; it can be directly connected, or indirectly connected through an intermediate medium; it can be the internal communication or interaction relationship of two elements. For those skilled in the art to which the present application belongs, the specific meaning of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0090] Please refer to Figure 1A flowchart of a multi-objective optimization energy storage EMS power distribution method considering PCS health degree is provided for the first embodiment of the present application. The method specifically comprises the following steps:

[0091] S101, collect the operation parameters of the PCS, the battery state and the power instruction of the grid dispatch.

[0092] It should be noted that this step is the starting point of the whole method. Through various sensors and communication interfaces, parameters related to the operation of the PCS are collected, such as efficiency / temperature rise rate / failure frequency, etc. At the same time, the state information of the battery is obtained, such as the SOC (State Of Charge) of the battery, the SOH (State of Health), etc. In addition, the power instruction issued by the power grid dispatching department is also received, which specifies the size and direction of the power output or absorbed by the energy storage system. These collected data will provide basic information for subsequent health assessment and power distribution.

[0093] S102, input the collected data and the data in the pre-established health state database into the SOH health model to obtain the SOH score; the SOH health model is established based on three dimensions of efficiency, temperature rise and reliability, and the weight coefficient is determined by using the entropy weight method.

[0094] It should be noted that this step is to integrate the PCS operation parameters, battery state and other data collected in the previous step with the data in the pre-constructed health state database, and input them into the SOH health model together. The health state database contains a large amount of historical data, constant parameters of the equipment, etc., which are used to assist health assessment.

[0095] The SOH health model evaluates the health status of the PCS from three key dimensions of efficiency, temperature rise and reliability. Efficiency reflects the loss in the energy conversion process of the PCS; temperature rise reflects the heating degree of the PCS during operation, and excessive temperature rise may affect the service life of the equipment; reliability considers the probability of failure of the PCS in long-term operation and other factors.

[0096] The entropy weight method is an objective weighting method, which determines the weight according to the information entropy of each dimension data. The smaller the information entropy, the smaller the uncertainty of the dimension data, and the greater the influence on the overall evaluation, so the weight given is also greater. Through this method, the weights of efficiency, temperature rise and reliability in the SOH score can be determined more scientifically and reasonably, so as to obtain more accurate SOH score results.

[0097] S103, input the SOH score and data in the health state database into a multi-objective optimization model to obtain a power allocation result; the multi-objective optimization model includes three objectives: power balance, health cost minimization, and SOC balance, and the weight coefficients of the three objectives are dynamically adjusted in real time according to the system state, and SOC closed-loop compensation is performed.

[0098] It should be noted that this step is to input the SOH score calculated in the previous step and the related data in the health state database into a multi-objective optimization model. These data will provide the basis for the health state of the model for power allocation.

[0099] The multi-objective optimization model sets three optimization objectives.

[0100] Power balance: aims to make the power distribution of each PCS in the energy storage system more uniform, avoid the situation that some PCSs are overloaded and some PCSs are lightly loaded, and improve the stability and reliability of the whole system.

[0101] Health cost minimization: considering the health state of the PCS, try to reduce the adverse effects on the health of the PCS during power distribution, reduce the risk of accelerated aging and damage of the PCS due to unreasonable power distribution, and thus reduce the health cost of the equipment.

[0102] SOC balance: ensure that the SOC of each battery in the energy storage system is as consistent as possible, avoid the situation that some batteries are overcharged or overdischarged, prolong the service life of the battery, and improve the overall performance of the battery pack.

[0103] The weight coefficients of the three objectives in the multi-objective optimization model are not fixed, but are dynamically adjusted according to the real-time state of the system. For example, under different operating conditions, grid demand, etc., a higher priority may be given to a certain objective. By dynamically adjusting the weight coefficients, the system can better adapt to changes and achieve better power distribution.

[0104] In addition, a real-time compensation algorithm is designed to perform closed-loop control on the SOC. Every interval (such as every 5 minutes), the range of the SOC is detected and corrected, so that the SOC range is stabilized within a certain range (such as within 5%), thereby effectively solving the problem of SOC imbalance and eliminating the waste of capacity caused by SOC imbalance.

[0105] In one embodiment of the embodiment, before S101, the method further comprises:

[0106] S100, when the system starts, automatically load the preset parameters and historical data, perform parameter legality verification, and establish a health state database.

[0107] It should be noted that the preset parameters and historical data include key parameters such as rated power, maximum temperature rise, compensation coefficient and battery capacity, and the data reliability is ensured through parameter persistent storage and abnormal recovery mechanism.

[0108] These preset parameters are the basis for the normal operation of the system and the implementation of various functions, which provide the system with initial setting values or historical cumulative values. For example, the rated power parameter defines the maximum power that the PCS can output under normal working conditions, which is an important reference for subsequent power distribution; the battery capacity parameter is used to accurately assess the energy storage state and charging and discharging capacity of the battery.

[0109] After loading the preset parameters and historical data, the system will perform legality check on these parameters. This is to ensure that the parameter values read are within a reasonable range and meet the system's operation requirements and physical laws. For example, the maximum temperature rise parameter should be a positive number and within a reasonable range that the device can withstand. If the read maximum temperature rise value is negative or exceeds the device's specified limit, the system will determine that the parameter is illegal. Through parameter legality verification, problems such as system operation abnormalities, calculation errors or device damage caused by incorrect parameters can be avoided, ensuring the stability and reliability of the system.

[0110] The health status database is an important data warehouse for storing health status information of PCS and battery and other related devices. This database is established at system startup and is used for subsequent recording and querying of device health data. These data can include device operation history records, fault information, health assessment results, etc. By establishing the health status database, the system can comprehensively and systematically manage and analyze the health status of the device, providing rich data support for the SOH health model and multi-objective optimization model, thereby achieving more accurate health assessment and more reasonable power distribution.

[0111] In an embodiment of the embodiment, S101 comprises:

[0112] S1011, according to the sampling period, using GOOSE protocol communication, collecting the running parameters of PCS and the state of battery in parallel, and using IEC104 communication protocol to collect power grid dispatching instructions.

[0113] It should be noted that the sampling period is a pre-set time interval, and the system will periodically collect data according to this fixed time interval. This can ensure that the collected data has regularity and continuity, and can accurately reflect the state changes of PCS, battery and power grid at different times, providing comprehensive information for subsequent analysis and decision-making. For example, if the sampling period is set to 100ms, the system will collect related data every 100ms.

[0114] GOOSE (Generic Object Oriented Substation Event) protocol is a fast message transmission protocol for smart substations, with the characteristics of fast transmission speed and high reliability. In the energy storage system, using GOOSE protocol for communication can quickly and accurately transmit the operating parameters of PCS (such as current, voltage, power, etc.), battery status (such as SOC, SOH, etc.), and grid dispatching instructions from various data sources to the data acquisition module, ensuring the real-time and integrity of the data.

[0115] Parallel acquisition means that the system can collect the operating parameters of PCS, battery status and grid dispatching instructions at the same time, rather than sequentially. This way greatly improves the efficiency of data acquisition, reduces the acquisition time, and can quickly obtain all the required data to meet the real-time requirements of the energy storage system. For example, at the same sampling time, the system can simultaneously obtain the output power of PCS, the SOC value of the battery, and the dispatching power instruction of the grid.

[0116] S1012, using IEEE 1588 clock synchronization technology to ensure that all collected data is aligned in time stamp.

[0117] It should be noted that IEEE 1588 is a precise time protocol (PTP) that can achieve high-precision time synchronization in distributed systems, with a precision of sub-microseconds. In the energy storage system, since it involves data collection from multiple devices (such as PCS, battery management system, data acquisition module, etc.), these devices may have their own clocks, and if not synchronized, the collected data will have a time deviation, leading to errors in subsequent analysis and decision-making.

[0118] By using IEEE 1588 clock synchronization technology, the time of all collection devices will be synchronized to a unified time reference. In this way, each collected data will be marked with an accurate time stamp, and in subsequent processing, different devices and different time data can be accurately corresponded and associated according to the time stamp, ensuring the spatiotemporal consistency of the data. For example, when analyzing the relationship between the power change of PCS and the SOC change of the battery, accurate time stamping can ensure that the two are corresponding in time, thus obtaining the correct conclusion.

[0119] S1013, pre-processing the collected data.

[0120] It should be noted that the raw data collected may have noise, interference or outliers, which will affect the accuracy and reliability of subsequent health assessment and power allocation. Therefore, the collected data needs to be pre-processed to improve the quality of the data.

[0121] Optionally, the preprocessing includes Hanning window-based sliding filtering and outlier rejection.

[0122] The Hanning window is a commonly used window function with the characteristic of smoothing signals. The Hanning window-based sliding filtering method is to apply the Hanning window to the collected data sequence and perform weighted average processing on the data in a sliding window manner. In this way, high-frequency noise and random interference in the data can be effectively filtered out, making the data smoother and highlighting the trend and characteristics of the data. For example, for the power data of the PCS, after Hanning window sliding filtering, the noise component in the power fluctuation can be removed, and the actual change of the power can be more accurately reflected.

[0123] In the data collection process, some abnormal values may be collected due to equipment failure, sensor error, etc. These abnormal values differ greatly from normal data, and if not handled, they will have a serious impact on subsequent analysis and decision-making. The outlier rejection method usually identifies and rejects outliers by setting a certain threshold or using statistical methods (such as a method based on standard deviation). For example, for the SOC data of the battery, if the difference between a certain data point and the previous and subsequent data points exceeds the set threshold, it can be considered that the data point is an outlier, which is rejected and replaced by the average value of the previous and subsequent data or other reasonable methods. The data collection mechanism is shown in FIG. Figure 2

[0124] In an embodiment of the present embodiment, the multi-objective optimization energy storage EMS power distribution method considering the health of the PCS further comprises:

[0125] S201, establish the SOH health model as follows based on the three dimensions of efficiency, temperature rise, and reliability:

[0126] ;

[0127] wherein, is the weight coefficient of the three indicators, i represents the number of the PCS, is the current actual efficiency of the i-th PCS, is the maximum allowable temperature rise rate, is the temperature rise rate (10s window) of the i-th PCS; is the annual / monthly failure frequency of the i-th PCS.

[0128] ​It should be noted that the efficiency decay of PCS leads to an increase in loss, which in turn leads to an increase in temperature rise, which further leads to accelerated material thermal fatigue, and finally leads to a decrease in reliability, forming a positive feedback degradation chain in the three dimensions of efficiency, temperature rise and reliability. The PCS health score model established based on the three dimensions is shown above, and risk adaptive regulation is realized through dynamic weight allocation (such as increasing the temperature rise weight in the emergency frequency modulation mode).

[0129] The three expressions contained in the model are efficiency, temperature rise and reliability, respectively:

[0130] Efficiency: PCS efficiency is the core indicator of energy conversion capability decay, and is normalized to quantify the degree of device aging, directly reflecting the degradation phenomena such as increased IGBT chip conduction loss and decreased capacitor capacitance;

[0131] Temperature rise: based on linear proportion of the temperature rise parameter to evaluate the performance of the heat dissipation system to prevent thermal runaway risk, when the temperature rise approaches the maximum allowable value, the model will significantly reduce the score weight, which is directly related to the IGBT module thermal fatigue failure mechanism;

[0132] Reliability: through the logarithmic function to suppress the influence of high-frequency faults, highlighting the long-term operation stability, the logarithmic transformation can compress the numerical fluctuations of extreme failure frequency, more sensitive to early potential failure, and at the same time conforming to the electronic device life characteristics described by Weibull distribution.

[0133] S202, the weight coefficient is determined by entropy weight method .

[0134] It should be noted that based on the measured historical data, the weight is determined by entropy weight method, based on information entropy theory, the weight is determined by the data dispersion degree, the greater the data dispersion degree, the greater the information quantity, and the higher the weight.

[0135] In one embodiment of the present embodiment, the S202 can be further refined to include the following steps:

[0136] S2021, for m PCS devices, construct an original data matrix:

[0137] ;

[0138] Where i represents the ith device; j=1,2,3, represents the efficiency ratio, temperature rise and reliability, and the three indicators are all considered as benefit type indicators (the larger the better) for standardization processing, the formula is:

[0139] ;

[0140] to The jth index value of the ith device, i.e., normalize each column (each index) so that the value of each index is between 0 and 1;

[0141] S2022, calculate the proportion:

[0142] ;

[0143] S2023, calculate the entropy value of the jth index:

[0144] ;

[0145] When , it is defined that ;

[0146] S2024, calculate the information utility value, the greater the information utility value, the stronger the importance of the index:

[0147] ;

[0148] S2025, calculate the weight of each index to obtain the weight of the three indexes :

[0149] .

[0150] It should be noted that through the above steps, the weight of the three indexes can be obtained. The weight calculated by the actual measurement data (100 sets of historical running data of the device) is about [0.42, 0.31, 0.27], and after rounding, it is [0.4, 0.3, 0.3] for convenience of calculation.

[0151] In an embodiment of the embodiment, in the S201, the SOH score adopts a grading system of 0-100 points, and the specific grading standards are:

[0152] ≥80 points, excellent, decision is to allow full power operation;

[0153] 60-79 points, good, decision is to limit the power to 90% of the rated value;

[0154] 40-59 points, early warning, decision is to participate in emergency frequency modulation only;

[0155] ≤40 points, for repair, decision is to force shutdown for repair.

[0156] It should be noted that the system adopts a 0-100 point system to evaluate SOH, and when the actual calculation value exceeds the range, it is automatically truncated. The grading mechanism controls the strategy through quantitative indexes to ensure that the device operates within the safety threshold

[0157] In an embodiment of the present embodiment, the multi-objective optimization energy storage EMS power distribution method considering PCS health degree further comprises:

[0158] S301, a multi-objective optimization model based on power balance, health cost minimization and SOC balance is constructed, which is formalized as a multi-objective optimization problem with constraints:

[0159] ;

[0160] Wherein, the power balance term: is the to-be-optimized power (decision variable) of the i-th PCS, is the total power in the grid dispatch instruction issued by the grid (known quantity), is the number of PCSs, is the rated power of a single PCS; the objective is to make the actual power of each PCS as close as possible to the average allocation value avoiding that part of the equipment is overloaded while the other equipment is idle; the difference is normalized, divided by to convert the power difference into a dimensionless value and eliminate the dimension influence;

[0161] Health cost term: is the health score of the PCS, this term is to punish the high-power task of the PCS with low health degree, the health cost generated by the power allocation of the lower health degree is higher, that is, the power is preferentially allocated to the high health degree equipment, prolonging the system life;

[0162] SOC balance term: is the current state of charge of the i-th battery cluster, is the average SOC of the whole station, the objective is to minimize the variance of the SOC of each battery cluster and the average SOC, preventing overcharging / overdischarging of part of the batteries due to uneven power distribution;

[0163] S302, design constraint conditions, which need to ensure that the sum of the allocated power of each PCS meets the deviation ≤1% of the total power in the grid dispatch instruction, and at the same time meets the condition that the allocated power does not exceed the lower limit and upper limit of the power of the PCS, the constraint condition is expressed as:

[0164] ;

[0165] Wherein, and are the lower limit and upper limit of the power of the i-th PCS; the power upper limit is limited by the hardware capability of the equipment and the available capacity of the battery, which meets the following conditions:

[0166] ;

[0167] wherein, represents the battery charge / discharge rate (e.g. 1C represents 1 hour full charge), is the capacity of the i-th battery, is the battery health (0-1), provided by the BMS;

[0168] S303, the weight coefficient is adjusted in real time according to the system state, and the state recognition logic is:

[0169]

[0170] Emergency frequency regulation mode, high , priority: power tracking > SOC balancing > device health, trigger condition: sharp change of grid frequency; SOC balancing mode, high , priority: SOC balancing > device health > power balancing, trigger condition: too large difference in SOC between battery clusters; normal mode, i.e. balancing mode, the three priorities are equivalent, and the default weight is: = 0.3, = 0.4, = 0.3; the dynamic weight switching mechanism is shown in Figure 3 .

[0171] Hysteresis switching: the exit condition is 2% stricter than the entry condition (e.g. the SOC difference needs to be <8% to exit the balancing mode);

[0172] S304, further SOC closed-loop compensation is performed to dynamically adjust the power instructions of the PCSs, so that the SOCs of all battery clusters tend to be consistent, thereby maximizing the available capacity and life of the energy storage system:

[0173]

[0174] wherein, , is a positive value indicating that the SOC of the cluster is lower than the average value, and needs to be discharged less / charged more; is a negative value, indicating that the SOC of the cluster is higher than the average value, and needs to be discharged more / charged less; is a compensation gain determined by parameter tuning, and a typical value is 0.005x rated power; at the same time, the following conditions need to be met:

[0175] Dead zone control: no compensation when <2% (avoid frequent disturbance);

[0176] Rate limit: the power adjustment amount per minute does not exceed 20% of the rated power;

[0177] Boundary protection: the power after compensation does not exceed the safe interval of the battery.

[0178] ​​In an implementation form of the embodiment, after the S103, the method further comprises:

[0179] S104, issuing the power distribution instruction.

[0180] Optionally, in the S104, a GOOSE protocol is used for communication.

[0181] It should be noted that, in the multi-objective optimization energy storage EMS power distribution method considering PCS health, the S103 completes multi-objective optimization power distribution calculation based on multiple factors (such as PCS health, battery state, grid demand, etc.), and obtains specific power distribution instructions. The S104, which implements the issuing of the power distribution instruction, is to actually deliver the calculated instructions to the execution equipment (PCS, etc.), so that the entire power distribution scheme can be implemented, which is a key link connecting theoretical calculation and actual operation, and ensures that the energy storage system can output or absorb power according to the optimized strategy, and realizes efficient and stable operation of the energy storage system and effective support for the power grid.

[0182] Although the terms such as PCS and power distribution are used more in this application, the possibility of using other terms is not excluded. The use of these terms is only to facilitate the description and explanation of the essence of the application; any additional limitation is contrary to the spirit of the application.

[0183] The multi-objective optimization energy storage EMS power distribution method considering PCS health provided by the embodiment has the following advantages compared with the prior art: first, the PCS life is extended, the SOH score is used to dynamically constrain the upper limit of the power of the low health PCS (SOH<60), over-stress operation is avoided, the failure rate of the high aging risk PCS (temperature rise>8℃ / s) is reduced by 65%, and the average life is extended by more than 3 years. Second, the SOC imbalance problem is solved, the SOC balancing term is implanted and a real-time compensation algorithm is designed, the closed-loop correction is performed every 5 minutes, the SOC range is stabilized within 5%, and the capacity waste is eliminated. Third, the dispatching capability is improved, the dynamic weight mechanism automatically reduces the health cost weight (λ2 from 0.4 to 0.1) in the emergency scene (|df / dt|>1Hz / s), the frequency modulation demand is preferentially guaranteed, and the test of a 200MW power station shows that the available power maintenance rate is increased from 72% to 98%. Fourth, the operation and maintenance cost is reduced, the health assessment model early warns the high risk PCS (SOH<40 triggers an alarm), the operation and maintenance response time is shortened by 80%, the annual maintenance cost is reduced by 42%, and the life cycle degree cost (LCOS) is reduced by 15%. Fifth, millisecond-level real-time response is realized, a pure software scheme is adapted to a mainstream hardware platform, the response time of the optimization algorithm is less than 200ms (the response time of the traditional scheme is greater than 500ms), and the AGC frequency modulation second-level response demand is met.

[0184] Finally, it should be noted that the above embodiments have been described in the specification and drawings of the application, but this does not limit the patent protection scope of the application. Any equivalent structure or equivalent process replacement or modification based on the essential concept of the application, using the content described in the specification and drawings of the application, and directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, etc., are all included in the patent protection scope of the application.

Claims

1. A multi-objective optimization method for energy storage EMS power allocation considering PCS health, characterized in that, The method includes: S101. Collect PCS operating parameters, battery status, and grid dispatch power commands; S102. Input the collected data and the data in the pre-established health status database into the SOH health model to obtain the SOH score; the SOH health model is established based on three dimensions: efficiency, temperature rise, and reliability, and the weight coefficients are determined by the entropy weight method. S103. Input the SOH score and the data in the health status database into the multi-objective optimization model to obtain the power allocation result. The multi-objective optimization model includes three objectives: power balance, health cost minimization and SOC balance. Its weight coefficients are dynamically adjusted in real time according to the system status, and SOC closed-loop compensation is performed.

2. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 1, characterized in that, Prior to S101, the method further includes: S100. When the system starts, it automatically loads preset parameters and historical data, performs parameter validity verification, and establishes a health status database.

3. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 1, characterized in that, S101 includes: S1011. According to the sampling period, the GOOSE protocol is used to communicate and collect the operating parameters of the PCS and the battery status in parallel, and the IEC104 communication protocol is used to collect the power grid dispatching instructions. S1012. Uses IEEE 1588 clock synchronization technology to ensure that all collected data are aligned in timestamps; S1013. Preprocess the collected data.

4. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 3, characterized in that, The preprocessing includes sliding filtering based on the Hanning window and outlier removal.

5. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 1, characterized in that, The method further includes: S201. Based on the three dimensions of efficiency, temperature rise, and reliability, the following SOH health model is established: ； in, Here are the weighting coefficients for the three indicators, where i represents the PCS device number. Let be the current actual efficiency of the i-th PCS. For the maximum allowable rate of temperature rise, Let be the temperature rise rate of the i-th PCS; For the i-th PCS, the annual / monthly number of failures; S202. Determine the weighting coefficients using the entropy weighting method. .

6. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 5, characterized in that, S202 includes: S2021. Construct the original data matrix for m PCS devices: ； Where i represents the i-th device; j=1,2,3, representing the three indicators: efficiency ratio, temperature rise, and reliability. These three indicators are standardized as benefit-type indicators, and the formula is: ； right The j-th index value of the i-th device, that is, normalizing each column so that the value of each index is between 0 and 1; S2022, Calculate the proportion: ； S2023. Calculate the entropy value of the j-th index: ； Among them, when At that time, it was stipulated ; S2024. Calculate the information utility value. The higher the information utility value, the stronger the importance of the indicator. ； S2025. Calculate the weights of each indicator to obtain the weights of the three indicators. : 。 7. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 1, characterized in that, In S201, the SOH score adopts a 0-100 point grading system, and the specific grading criteria are as follows: A score of ≥80 is considered excellent, and the decision is to allow full-power operation. A score of 60-79 is considered good; the decision is to limit the power to 90% of the rated value. The interval between 40 and 59 minutes is an early warning; the decision is to only participate in emergency frequency adjustments. If the score is ≤40, maintenance is required, and the decision is to force a shutdown for maintenance.

8. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 1, characterized in that, The method further includes: S301. Construct a multi-objective optimization model based on the three objectives of power balance, health cost minimization, and SOC balance, and formalize it into a constrained multi-objective optimization problem: ； Among them, the power equalization term: Let i be the power to be optimized for the i-th PCS. The total power in the power grid dispatch instructions issued by the power grid. For the number of PCS, The rated power of a single PCS; the goal is to maximize the actual power of each PCS. As close as possible to the average distribution value To avoid overloading some equipment while leaving others idle; the difference is normalized and divided by Convert the power difference to a per-unit value to eliminate the influence of dimensions; Health cost item: The project assigns a health score to the PCS, which penalizes high-power tasks to PCS with low health. The lower the health score, the higher the health cost when allocating power. In other words, power is prioritized for allocation to devices with high health to extend system life. SOC equilibrium term: The current state of charge of the i-th cluster of cells. The SOC is the average value of the entire station. The purpose is to minimize the variance between the SOC of each battery cluster and the average SOC, and to prevent some batteries from being overcharged / over-discharged due to uneven power distribution. S302. Design constraints require ensuring that the sum of the power allocated to each PCS deviates from the total power in the grid dispatch command by ≤1%, while also ensuring that the allocated power does not exceed the lower and upper limits of the PCS's power. These constraints are expressed as follows: ； in, and These are the lower and upper power limits for the i-th PCS; the upper power limit is constrained by the device hardware capabilities and the available battery capacity, which satisfy the following conditions: ； in, Indicates the battery charge / discharge rate. Let i be the capacity of the i-th battery. Battery health information is provided by the BMS. S303, The weighting coefficients are adjusted in real time according to the system status. The status identification logic is as follows: ； Emergency frequency tuning mode, high The priority order is: Power Point Tracking > State of Charge (SOC) Equalization > Device Health, triggered by a sudden change in grid frequency; in SOC equalization mode, high priority is given. In the following order, priority is: SOC balancing > Device health > Power balancing, triggered by excessive SOC difference between battery clusters; in normal mode (equilibrium mode), the three priorities are equal, with default weights: =0.3, =0.4, =0.3; Hysteresis switching: Exit conditions are 2% stricter than entry conditions; S304. Further perform SOC closed-loop compensation to dynamically adjust the power command of each PCS, so that the SOC of all battery clusters tends to be consistent, thereby maximizing the available capacity and lifespan of the energy storage system: ； in, , A positive value indicates that the SOC of this cluster is below the average, requiring less discharge / more charging; A negative value indicates that the SOC of this cluster is higher than the average, requiring more discharge / less charging; The compensation gain is determined through parameter tuning, with a typical value of 0.005 × rated power; at the same time, the following conditions must be met: Dead zone control: when No compensation is given if the percentage is less than 2%. Change rate limit: Power adjustment per minute shall not exceed 20% of rated power; Boundary protection: The power after compensation does not exceed the battery's safe range.

9. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 1, characterized in that, Following S103, the method further includes: S104, Issue power allocation commands.

10. The multi-objective optimized energy storage EMS power allocation method considering PCS health as described in claim 9, characterized in that, In S104, the GOOSE protocol is used for communication.

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