Multi-point energy storage scheduling method and system in region

Abstract

The invention relates to an intra-region multi-point energy storage scheduling method, system, device and medium, and the method comprises the steps: obtaining the state of charge, health degree and available cycle life related real-time data of each new energy station energy storage system in a region; according to the fuzzy reasoning model and the real-time data, a preset membership function is adopted, and the charge state, the health degree and the membership degree of the available cycle life are obtained. And determining a rule base in the fuzzy inference model based on the state of charge, the health degree, the membership degree of available cycle life, the operation condition of the energy storage system and related technical standards, and classifying the energy storage systems of the new energy stations in the region according to the rule base in the fuzzy inference model to obtain a classification result. And in response to the received scheduling instruction, scheduling each new energy station energy storage system in the region according to a preset scheduling rule based on the classification result, and obtaining a scheduling result. The efficiency of the system is improved, and the problem of low utilization rate of multi-point energy storage in the existing region is solved.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage scheduling, and in particular to a method, system, device and medium for scheduling multi-point energy storage in a region. Background Art

[0002] The development of new energy storage technology is crucial to improving grid flexibility, reducing power abandonment rates, and enhancing the regulation capabilities of the power system.

[0003] The energy storage control technology of new energy stations mainly includes related control strategies such as energy storage frequency regulation and peak regulation. However, these energy storage control technologies are all for a single energy storage system and use conventional control algorithms and strategies.

[0004] At present, there are many new energy storage sites in the region, most of which are idle and scattered, making it difficult to fully play the role of energy storage in supporting the power grid. In other words, there is a problem of low utilization rate of multi-point energy storage in the existing region. Summary of the invention

[0005] The embodiments of the present application provide a method, system, device and medium for scheduling multi-point energy storage within a region, so as to at least solve the problem of low scheduling accuracy of multi-point energy storage within a region in the related art.

[0006] In a first aspect, an embodiment of the present application provides a method for dispatching multi-point energy storage in a region, comprising:

[0007] Obtain real-time data on the state of charge, health, and available cycle life of energy storage systems at each renewable energy station in the region;

[0008] According to the fuzzy inference model and the real-time data, a preset membership function is used to obtain the membership of the three important energy storage parameter states of the state of charge, the health degree and the available cycle life;

[0009] Based on the operation status of the energy storage system and relevant technical standards, combined with the membership of three important parameters, the rule base in the fuzzy reasoning model is determined, and the energy storage systems of each new energy station in the region are classified according to the rule base in the fuzzy reasoning model to obtain the classification results;

[0010] In response to receiving a dispatch instruction, based on the classification result, each new energy station energy storage system in the area is dispatched according to a preset dispatch rule to obtain a dispatch result.

[0011] In one embodiment, the fuzzy inference model and the real-time data are used to obtain the membership of the three important energy storage parameters of the state of charge, the health and the available cycle life by using a preset membership function, including:

[0012] According to the change value of the state of charge, a Z-type function and an S-type function are selected as membership functions, and according to the membership functions, a membership degree of a high state of charge is determined;

[0013] According to the deviation value of the health degree, a Z-type function and an S-type function are selected as membership functions, and according to the membership functions, a membership degree with a high health degree is determined;

[0014] According to the deviation value of the available cycle life, a Z-type function, a triangular function and an S-type function are selected as membership functions to determine the membership degrees of long, relatively long and relatively short available cycle life.

[0015] In one embodiment, selecting a Z-type function and an S-type function as a membership function according to the change value of the state of charge, and determining a high degree of membership of the state of charge according to the membership function includes:

[0016] Obtaining a difference between a maximum value and a minimum value of the state of charge, and obtaining a change value of the state of charge according to the difference;

[0017] Respectively obtaining calculation results of the Z-type function and the S-type function of the state of charge, wherein a minimum state of charge membership reference value and a maximum state of charge membership reference value are determined from the calculation results of the Z-type function and the calculation results of the S-type function;

[0018] When the change value is less than the minimum state of charge membership reference value, the high state of charge membership is determined to be 1; when the change value is between the minimum state of charge membership reference value and the maximum state of charge membership reference value, the high state of charge membership is determined to be 0-1; when the change value is greater than the maximum state of charge membership reference value, the high state of charge membership is determined to be 0.

[0019] In one embodiment, selecting a Z-type function and an S-type function as a membership function according to the deviation value of the health degree, and determining the membership degree of the health degree according to the membership function includes:

[0020] Obtain the health of the energy storage system of the new energy station according to the open circuit voltage and the operating parameters of the energy storage system of the new energy station, and obtain the deviation value of the health according to the health;

[0021] Respectively obtaining calculation results of the Z-type function and the S-type function of the health, wherein a minimum health membership reference value and a maximum health membership reference value are determined from the calculation results of the Z-type function and the calculation results of the S-type function;

[0022] When the health deviation value is less than the minimum health membership reference value, the high health membership is determined to be 1; when the health deviation value is between the minimum health membership reference value and the maximum health membership reference value, the high health membership is determined to be 0-1; when the health deviation value is greater than the maximum health membership reference value, the high health membership is determined to be 0.

[0023] In one embodiment, according to the deviation value of the available cycle life, a Z-type function, a trigonometric function and an S-type function are selected as membership functions to determine the membership degree of the available cycle life being long, relatively long and relatively short, including:

[0024] According to the operating parameters of the new energy station, the expected life of the lithium battery at different temperatures is tested; the expected life is compared with the design life of the lithium battery to obtain the deviation value of the available cycle life;

[0025] Respectively obtaining calculation results of the Z-type function, the trigonometric function and the S-type function of the available cycle life, wherein a minimum available cycle life membership reference value and a maximum available cycle life membership reference value are determined from the calculation results of the Z-type function, the trigonometric function and the S-type function;

[0026] When the deviation value of the available cycle life is less than the minimum available cycle life membership reference value, the membership of the long available cycle life is determined to be 1; when the deviation value of the available cycle life is between the minimum available cycle life membership reference value and the maximum available cycle life membership reference value, the membership of the longer available cycle life is determined to be 1; when the deviation value of the available cycle life is greater than the maximum available cycle life membership reference value, the membership of the shorter available cycle life is determined to be 1.

[0027] In one embodiment, the calculation result of the Z-type function of the state of charge includes a=0.08 and b=0.15, and the calculation result of the S-type function of the state of charge includes a=0.01 and b=0.2;

[0028] The calculation results of the Z-type function of the health degree include a=0.02 and b=0.05, and the calculation results of the S-type function of the health degree include a=0.04 and b=0.06;

[0029] The calculation results of the Z-type function of the available cycle life include a=0.1 and b=0.3, the calculation results of the S-type function of the available cycle life include a=0.3 and b=0.6, and the calculation results of the triangular function include a=0.1, b=0.3, and c=0.6.

[0030] In one embodiment, determining the rule base in the fuzzy reasoning model based on the membership degree includes:

[0031] When the state of charge is high, the health is high, and the available cycle life is long, the category of the new energy station energy storage system is determined to be a type of resource;

[0032] When the memberships of the high state of charge and the high health are both 1, and the membership of the relatively long available cycle life is 1, the category of the new energy station energy storage system is determined to be a second-class resource;

[0033] When the memberships of the high state of charge and the high health are both 1, and the membership of the short available cycle life is 1, the category of the new energy station energy storage system is determined to be a third type of resource;

[0034] When the membership degrees of the high state of charge and the high available cycle life are both 1, the category of the new energy station energy storage system is determined to be a second-class resource;

[0035] When the membership of the high state of charge is 1, and the membership of the relatively long available cycle life is 1, the category of the new energy station energy storage system is determined to be the third category of resources;

[0036] When the membership degrees of the high health degree and the long available cycle life are both 1, the category of the new energy station energy storage system is determined to be a type of resource;

[0037] When the membership degree of the high health degree is 1, and the membership degree of the long available cycle life is 1, the category of the new energy station energy storage system is determined to be a second-class resource;

[0038] When the membership degree of the available cycle life being longer is 1, the category of the energy storage system of the new energy station is determined to be the third category of resources;

[0039] When the membership degree of the shorter available cycle life is 1, the category of the new energy station energy storage system is determined to be the fourth category of resources.

[0040] In one embodiment, whether the output power of the resource meets the total power requirement remaining after outputting the power of the first type of resource, if it meets, the first type of resource outputs the maximum power, the second type of resource outputs the remaining power requirement of the total power requirement, and the output power of the third type of resource and the fourth type of resource is 0; if it does not meet, the total power requirement remaining after outputting the power of the first type of resource and the power of the second type of resource is calculated;

[0041] Determine whether the output power of the three types of resources meets the total power requirement remaining after outputting the power of the first type of resources and the power of the second type of resources. If so, the first type of resources outputs the maximum power, the second type of resources outputs the maximum power, the third type of resources outputs the remaining power requirement of the total power requirement, and the output power of the fourth type of resources is 0; if not, calculate the remaining power requirement after outputting the power of the first type of resources, the power of the second type of resources and the third type of resources;

[0042] Determine whether the output power of the four categories of resources meets the remaining power requirements after outputting the power of the first category of resources, the second category of resources and the third category of resources. If so, the first category of resources outputs the maximum power, the second category of resources outputs the maximum power, the third category of resources outputs the maximum power, and the fourth category of resources outputs the remaining power requirement of the total power requirement; if not, the first category of resources outputs the maximum power, the second category of resources outputs the maximum power, the third category of resources outputs the maximum power and the fourth category of resources outputs the maximum power.

[0043] In a second aspect, an embodiment of the present application provides a regional multi-point energy storage scheduling system, the system comprising a real-time data acquisition module, a membership acquisition module, a classification module and a scheduling module, wherein:

[0044] The real-time data acquisition module is used to acquire real-time data on the state of charge, health and available cycle life of energy storage systems of various new energy stations in the region;

[0045] The membership acquisition module is used to acquire the membership of the state of charge, the health and the available cycle life by using a preset membership function according to the fuzzy inference model and the real-time data;

[0046] The classification module determines a rule base in the fuzzy reasoning model based on the state of charge, the health, the membership of the available cycle life and the operating condition of the energy storage system, and classifies the energy storage systems of each new energy station in the region according to the rule base in the fuzzy reasoning model to obtain a classification result;

[0047] The scheduling module is used to respond to receiving a scheduling instruction, schedule each new energy station energy storage system in the area based on the classification result and according to a preset scheduling rule to obtain a scheduling result.

[0048] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for scheduling multi-point energy storage within a region as described in the first aspect above is implemented.

[0049] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for scheduling multi-point energy storage within a region as described in the first aspect above.

[0050] The embodiments of the present application provide a method, system, device and medium for scheduling multi-point energy storage in a region, which have at least the following technical effects.

[0051] Obtain relevant real-time data on the state of charge, health and available cycle life of each new energy station energy storage system in the area. According to the fuzzy reasoning model and the real-time data of each new energy station energy storage system in the area, a preset membership function is used to obtain the membership of the three important parameters of the energy storage system, namely the state of charge, the health and the available cycle life. Based on the operating status of the energy storage system and relevant technical standards, combined with the membership of the three important parameters, the rule base in the fuzzy reasoning model is determined, and the energy storage systems of each new energy station in the area are classified according to the rule base in the fuzzy reasoning model to obtain the classification results. In response to receiving a scheduling instruction, based on the classification result, the energy storage systems of each new energy station in the area are scheduled according to the preset scheduling rules to obtain the scheduling results. This application uses the Mamdani fuzzy reasoning model to classify resources according to the characteristic quantities of various energy storage systems, providing a basis for efficient collaborative scheduling of energy storage aggregation. The fuzzy reasoning model dynamically corrects the resource classification results according to the changes in energy storage resources (real-time data), thereby achieving efficient and reliable operation of energy storage resources under constantly changing energy storage resources, making the best use of available resources, improving system efficiency, and solving the problem of low utilization of multi-point energy storage in the existing area.

[0052] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0054] Figure 1 is a flow chart of a method for dispatching multi-point energy storage in a region according to an embodiment of the present application;

[0055] Figure 2 is a flowchart of step S102 according to an exemplary embodiment;

[0056] Figure 3 is a flowchart of step S104 according to an exemplary embodiment;

[0057] Figure 4 is a flowchart of energy storage resource scheduling according to an exemplary embodiment.

[0058] Figure 5 A block diagram of a system for multi-point energy storage scheduling in a region according to an exemplary embodiment

[0059] Figure 6 is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0061] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0062] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0063] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0064] At present, the increasing depletion of fossil energy has made it an inevitable trend for energy consumption to transform to clean and low-carbon. Most renewable energy generation is characterized by randomness and uncontrollability, which limits the power grid's ability to absorb it. The energy storage systems configured by new energy sites in the region are independently built by each power plant. Therefore, there are differences in the status and configuration of the energy storage systems, equipment utilization, equipment models and parameters. The existing energy storage control strategy does not effectively control the energy storage systems of different sites under multiple application scenarios. Therefore, it is necessary to study the collaborative management technology of massive heterogeneous energy storage resources, and combine it with diversified energy storage application scenarios to reasonably aggregate various energy storage energies and other available resource entities in the region, provide technical support for the access of large-scale new energy power, and realize the complementary collaborative scheduling and efficient optimization operation of distributed energy storage resources in the region. In other words, there is a problem of low utilization of multi-point energy storage in the existing region.

[0065] Therefore, it is necessary to propose an efficient operation and control method suitable for the coordinated aggregation of energy storage in multiple renewable energy stations in the region, so as to integrate, reasonably dispatch and coordinate the energy storage resources of various renewable energy stations in the region, improve the utilization rate of energy storage system resources, and extend the system operation life.

[0066] Based on the above situation, the embodiments of the present application provide a method, system, device and medium for scheduling multi-point energy storage in a region to solve the above problems.

[0067] In a first aspect, an embodiment of the present application provides a method for dispatching multi-point energy storage in a region. Figure 1 It is a flow chart of multi-point energy storage scheduling in a region, such as Figure 1 As shown, a method for dispatching multi-point energy storage in a region includes:

[0068] Step S101, obtaining relevant real-time data on the state of charge, health and available cycle life of energy storage systems of various new energy stations in the region.

[0069] Step S102: According to the fuzzy inference model and real-time data, a preset membership function is used to obtain the membership of the state of charge, health and available cycle life.

[0070] Step S103: Based on the acquired state of charge, health, membership of available cycle life and operating conditions of the energy storage system, determine the rule base in the fuzzy reasoning model, classify the energy storage systems of various new energy stations in the region according to the rule base in the fuzzy reasoning model, and obtain the classification results.

[0071] Step S104: In response to receiving the dispatch instruction, based on the classification result, the energy storage systems of each new energy station in the area are dispatched according to the preset dispatch rules to obtain the dispatch result.

[0072] In summary, the embodiment of the present application provides a method for dispatching multi-point energy storage in a region, which obtains relevant real-time data on the state of charge, health and available cycle life of each new energy station in the region. According to the fuzzy reasoning model and the real-time data of each new energy station in the region, a preset membership function is used to obtain the membership of three important parameters of the energy storage system, namely the state of charge, health and available cycle life. Based on the operating status of the energy storage system and relevant technical standards, combined with the membership of the three important parameters, the rule base in the fuzzy reasoning model is determined, and the energy storage systems of each new energy station in the region are classified according to the rule base in the fuzzy reasoning model to obtain the classification results. In response to receiving the dispatching instruction, based on the classification results, the energy storage systems of each new energy station in the region are dispatched according to the preset dispatching rules to obtain the dispatching results. According to the important characteristic quantities of various energy storage systems, the present application adopts the Mamdani fuzzy reasoning model to classify resources for aggregation, providing a basis for efficient collaborative dispatching of energy storage aggregation. The fuzzy reasoning model dynamically corrects the resource classification results according to the changes in energy storage resources, so as to maintain the efficient and reliable operation of energy storage resources under the ever-changing energy storage resources, maximize the use of available resources, improve the efficiency of the system, and solve the problem of low utilization of multi-point energy storage in the existing area.

[0073] In one embodiment, step S101, obtains relevant real-time data on the state of charge, health and available cycle life of energy storage systems of each new energy station in the region. Specifically includes:

[0074] Since there are differences in the status, configuration, and equipment utilization of multiple energy storage systems in the region, it is necessary to classify and control the distributed multi-point energy storage systems in the region to ensure that the energy storage resources can operate efficiently and reliably under the ever-changing application requirements, and to maximize the use of available resources to improve the efficiency of the aggregation system. Through the analysis of the working principle of the electrochemical energy storage system, it can be seen that the important parameters affecting the performance of the energy storage system are reflected in the state of charge (SOC), health (SOH), and utilizable cycle life (Tn). Therefore, these three characteristic quantities can be used as an important basis for the classification of energy storage resources. The real-time data of the state of charge, health, and utilizable cycle life of each new energy station in the region are collected in real time through sensors and monitoring systems.

[0075] The implementation of step S101 ensures the intelligence and efficiency of energy storage scheduling. Through real-time monitoring and analysis of key parameters, accurate control and optimization of energy storage resources are achieved, thereby improving the performance and economy of the entire energy management.

[0076] In one embodiment, the identification method of the fuzzy reasoning model (Mamdani) is used to classify each energy storage resource. The fuzzy reasoning model refers to first determining a series of reasoning rules, and then establishing a reasoning and calculation relationship between input and output based on these rules to form a reasonable identification system. This reasoning method is essentially a qualitative reasoning rather than a quantitative reasoning. Fuzzy theory is based on fuzzy sets. It is different from ordinary sets. Its elements belong to or do not belong to the set to a certain extent. This degree is called membership.

[0077] The fuzzy reasoning model includes a fuzzy generator, a fuzzy rule base, a fuzzy inference engine and a defuzzifier. The fuzzy generator converts clear input data into fuzzy sets, that is, assigns each input variable one or more fuzzy subsets of membership. Fuzzy rule base: contains a series of IF-THEN statements to describe the relationship between input and output. For example, "If the membership of high SOC is 1, and the membership of long Tn is 1, then the classification result of the energy storage unit is three types of resources with lower priority". Fuzzy inference engine: executes the reasoning process according to the input fuzzy subsets and the rules in the fuzzy rule base, and calculates the output fuzzy subsets. The fuzzy reasoning model is specifically as follows:

[0078] IF x1 is A j1and x2 is A j2 and...and x n is A jn THEN y j =B j ,j=1,2,...,M

[0079] Where: x i (i=1,2,……,n) is the input variable; y j (j=1,2,..,M) is the output variable; A ji and B j represents the fuzzy subset, and M is the total number of fuzzy rules.

[0080] Among them, the Z-type function: is usually a monotonically decreasing function, and the parameters a and b define the turning point where the function drops from 1 to 0. Z-type function:

[0081]

[0082] Trigonometric function: It is defined by three parameters a, b, and c, forming a triangle, b is the location of the peak, and a and c define the base of the triangle. Trigonometric function:

[0083]

[0084] S-type function: Usually a monotonically increasing function, the parameters a and b define the turning point where the function rises from 0 to 1. S-type function:

[0085]

[0086] Among them, a, b, c∈R represent known parameters.

[0087] After obtaining the membership degree according to the above membership function, the fuzzy rules are used for judgment to obtain the corresponding fuzzy result, and finally the result is defuzzified to obtain the specific result. The conventional method of defuzzification is the centroid method, that is, after obtaining the membership function curve, the centroid of the figure surrounded by the fuzzy membership function curve and the horizontal axis is regarded as the result. The actual membership degree is a series of discrete sampling points, and its centroid calculation formula is as follows:

[0088]

[0089] Finally, the most similar value in the fuzzy set is output.

[0090] The performance of energy storage resources changes dynamically with different operating conditions, and it is unknown whether it is affected by a single factor, so it is impossible to establish a reasonable mathematical model, and therefore it is difficult to determine the specific characteristic value. The fuzzy reasoning model is more in line with reality. It does not require a certain threshold, but only represents the characteristic value in a certain range. By selecting a reasonable membership function, the membership of the input value is calculated. The fuzzy reasoning model uses the SOC, SOH and Tn of the energy storage as the input of fuzzy reasoning, and the classification level of the energy storage resources as the output. The fuzzy reasoning model analyzes and compares the simulation results of each characteristic value through a large number of simulation analyses, so as to select the membership function and its corresponding parameters suitable for each characteristic, and determine the value range corresponding to different characteristics of each type of energy storage resource level.

[0091] Using fuzzy reasoning models to identify and classify energy storage resources can effectively deal with ambiguity and uncertainty in the system, improve the accuracy of identification and the robustness of decision-making, and thus optimize the management and scheduling of energy storage resources.

[0092] Figure 2 is a flowchart of step S102 according to an exemplary embodiment. Figure 2 As shown, step S102, based on the fuzzy inference model and real-time data, a preset membership function is used to obtain the membership of three important characteristic parameters of the energy storage system, namely, state of charge, health and available cycle life. Specifically, the following steps are included:

[0093] Step S1021 : According to the change value of the state of charge, a Z-type function and an S-type function are selected as membership functions, and according to the membership functions, a membership degree of a high state of charge is determined.

[0094] Step S1022: According to the deviation value of the health degree, a Z-type function and an S-type function are selected as the membership function, and according to the membership function, a membership degree with a high health degree is determined.

[0095] Step S1023: According to the deviation value of the available cycle life, a Z-type function, a triangular function and an S-type function are selected as membership functions to determine the membership degrees of long, relatively long and relatively short available cycle life.

[0096] Optionally, an appropriate membership function (Z-type, S-type or triangular type) is selected according to the characteristics of each parameter to express the fuzziness of these parameters in mathematical form. Z-type and S-type functions are used to represent the fuzzy boundaries of parameters below or above a certain threshold, respectively, while triangular functions are used to represent parameters in a fuzzy set within a specific range. Based on the selected membership function and real-time data (such as change values ​​or deviation values), the membership degree of each parameter is calculated. The membership degree reflects the degree of membership of the parameter value in the fuzzy set, and the value range is generally between 0 and 1. The closer the value is to 1, the more the parameter value conforms to the definition of the fuzzy set.

[0097] By quantifying fuzzy concepts, fuzzy reasoning models can provide more sophisticated management strategies and help energy storage system operators make more reasonable scheduling decisions based on the actual status of the battery. Traditional threshold judgments may lead to misjudgments due to sudden fluctuations. Fuzzy reasoning models reduce this risk through continuous membership evaluation and improve the robustness of decision-making. Membership functions and fuzzy rules can be dynamically adjusted according to actual conditions to ensure that the system can respond to external environmental changes in a timely manner, such as grid demand or weather conditions.

[0098] In one embodiment, step S1021, according to the change value of the state of charge, select a Z-type function and an S-type function as a membership function, and determine the membership degree of a high state of charge according to the membership function. Specifically, it includes:

[0099] Obtain the difference between the maximum and minimum values ​​of the state of charge, and obtain the change value of the state of charge according to the difference;

[0100] Obtaining calculation results of a Z-type function and an S-type function of the state of charge respectively, wherein a minimum state of charge membership reference value and a maximum state of charge membership reference value are determined from the calculation results of the Z-type function and the calculation results of the S-type function;

[0101] When the change value is less than the minimum state of charge membership reference value, the high state of charge membership is determined to be 1; when the change value is between the minimum state of charge membership reference value and the maximum state of charge membership reference value, the high state of charge membership is determined to be 0-1; when the change value is greater than the maximum state of charge membership reference value, the high state of charge membership is determined to be 0.

[0102] Optionally, first, monitor and record the maximum and minimum state of charge of the new energy station within a certain time interval, and calculate the difference between the two, that is, the change value of SOC. This difference reflects the fluctuation of the state of charge of the energy storage system during this time period. Then, the membership of the state of charge variation range is calculated by the Z-type function and the S-type function respectively. Based on these two functions, the minimum and maximum state of charge membership reference values ​​can be obtained. Finally, the membership of the SOC variation range is determined based on the relationship between the variation value and the membership reference value. If the absolute value of the difference between the SOC variation range and 100% is less than the minimum reference value, the membership of the larger SOC variation range is 1, that is, the SOC high membership is 1, indicating that the SOC variation range is large, and the state of charge is determined to be high quality; if the absolute value of the difference between the SOC variation range and 100% is between the minimum and maximum reference values, the membership varies between 0 and 1, and the state of charge is determined to be medium; if the absolute value of the difference between the SOC variation range and 100% is greater than the maximum reference value, the SOC large variation range membership is 0, that is, the SOC high membership is 0, indicating that the SOC variation range is small, and the state of charge is determined to be poor.

[0103] Specifically, the upper and lower limits of the detected energy storage SOC values ​​are subtracted, and the absolute value is taken, and then this absolute value is subtracted from 100% to obtain the difference (ΔSOC*), that is, ΔSOC*=100%-|SOC_max-SOC_min|. It can be seen that the smaller ΔSOC* is, the larger the range of SOC variation is, and the better the performance of the energy storage system is. Analysis of a large number of simulation experiments shows that the membership function of the energy storage SOC variation range is selected as follows:

[0104] The calculation results of the Z-type function for the SOC variation range are: a = 0.08, b = 0.15; the calculation results of the S-type function are: a = 0.1, b = 0.2. If ΔSOC* is less than 0.08, the membership degree for a large SOC variation range is 1; if ΔSOC* is between 0.08 and 0.2, the membership degree for a large SOC variation range is between 0 and 1; if ΔSOC* is greater than 0.2, the membership degree for a large SOC variation range is 0.

[0105] The determination of the membership degree in step S1021 provides an objective basis for decision making, which helps to achieve automated and intelligent resource allocation. In addition, low membership indicates that the system is unstable or in poor health, which helps early warning and preventive maintenance.

[0106] In one embodiment, step S1022, according to the deviation value of the health degree, select the Z-type function and the S-type function as the membership function, and determine the membership degree with high health degree according to the membership function. Specifically includes:

[0107] According to the open circuit voltage and the operating parameters of the energy storage system of the new energy station, the internal and external characteristic parameters of the battery are analyzed to obtain the health of the energy storage system of the new energy station. According to the health of the energy storage system, the deviation value of the health is obtained;

[0108] Obtaining calculation results of a Z-type function and an S-type function of health respectively, wherein a minimum health membership reference value and a maximum health membership reference value are determined from the calculation results of the Z-type function and the calculation results of the S-type function;

[0109] When the health deviation value is less than the minimum health membership reference value, the high health membership is determined to be 1; when the health deviation value is between the minimum health membership reference value and the maximum health membership reference value, the high health membership is determined to be between 0 and 1; when the health deviation value is greater than the maximum health membership reference value, the high health membership is determined to be 0.

[0110] Optionally, first, by monitoring the open circuit voltage (OCV) and other operating parameters (such as temperature, current, etc.), combined with historical data and battery models, the current health of the energy storage system of the new energy station is calculated. Then, the current health is compared with the nominal health (usually a new battery is 100% healthy) to obtain the health deviation value, that is, the energy storage SOH value is subtracted from 100%, and the absolute value is taken to obtain the deviation value (ΔSOH*). Based on the Z-type function and the S-type function, the relationship between the deviation value and the membership is determined. If the health deviation value is less than the minimum health membership reference value, the high health membership is 1, indicating that the SOH value is larger; the health state of this energy storage battery is determined to be high quality; if the deviation value is between the minimum and maximum reference values, the high health membership varies between 0 and 1, and the health state is determined to be medium; if the deviation value is greater than the maximum reference value, the high health membership is 0, indicating that the SOH value is small, and the health is determined to be low.

[0111] Specifically, combined with the open circuit voltage (OCV), the dynamic characteristic parameters of the battery cell are obtained, and the relationship model between the internal dynamic parameters of the battery and the external electrical performance characteristic parameters is established. According to the correlation analysis results of the internal and external characteristic parameters of the battery, the SOH of the energy storage is obtained; then the energy storage SOH value is subtracted from 100%, and the absolute value is taken to obtain the deviation value (ΔSOH*). The smaller the ΔSOH* value, the higher the SOH, indicating that the healthier the energy storage system is and the better the performance of the energy storage system. Analysis of a large number of simulation experiments shows that the membership function of SOH should be selected as follows:

[0112] The calculation results of the Z-type function of SOH are a=0.02, b=0.05; the calculation results of the S-type function are a=0.04, b=0.06. If ΔSOH* is less than 0.02, the membership degree for SOH high is 1; if the calculated value is between 0.02 and 0.06, the membership degree for SOH high is between 0 and 1; if the calculated value is greater than 0.06, the membership degree for SOH high is 0.

[0113] Step S1022: The membership degree can objectively reflect the health index, which is convenient for resource allocation and task scheduling. In addition, through the membership degree evaluation of the health deviation value, the trend of battery performance degradation can be discovered early, which is helpful for preventive maintenance.

[0114] In one embodiment, step S1023, according to the deviation value of the available cycle life, a Z-type function, a trigonometric function and an S-type function are selected as the membership function to determine the membership degree of the available cycle life being long, relatively long and relatively short. Specifically, it includes:

[0115] According to the operating parameters of the energy storage system of the new energy station, the expected life of the lithium battery at different temperatures is tested; the expected life is compared with the design life of the lithium battery to obtain the deviation value of the available cycle life;

[0116] Respectively obtaining calculation results of a Z-type function, a trigonometric function, and an S-type function of the available cycle life, wherein a minimum available cycle life membership reference value and a maximum available cycle life membership reference value are determined from the calculation results of the Z-type function, the trigonometric function, and the S-type function;

[0117] When the deviation value of the available cycle life is less than the minimum available cycle life membership reference value, the membership of the long available cycle life is determined to be 1; when the deviation value of the available cycle life is between the minimum available cycle life membership reference value and the maximum available cycle life membership reference value, the membership of the longer available cycle life is determined to be 1; when the deviation value of the available cycle life is greater than the maximum available cycle life membership reference value, the membership of the shorter available cycle life is determined to be 1.

[0118] First, based on the operating parameters of the energy storage system of the new energy station, the expected life of the lithium battery at different temperatures is tested by experiments or simulation. Then, this expected life is compared with the design life of the lithium battery, and the absolute value is taken and divided by the design life of the lithium battery to obtain the deviation value ΔLife-s of the available cycle life. Next, the membership reference value of the available cycle life is determined by the calculation results of the Z-type function, the trigonometric function and the S-type function. The reference value is used to divide the relationship between the deviation value and the membership. According to the relationship between the deviation value and the reference value, the membership of the available cycle life is determined. If the deviation value of the available cycle life is less than the minimum available cycle life membership reference value, the membership of the available cycle life is long is 1, indicating that the value of Tn is large; the available cycle life state is determined to be high quality; if the deviation value is between the minimum and maximum reference values, the membership of the available cycle life is long is 1, and the available cycle life state is determined to be medium; if the deviation value is greater than the maximum reference value, the membership of the available cycle life is short is 1, indicating that the value of Tn is small, and the available cycle life is determined to be poor.

[0119] Specifically, according to the configuration and operating conditions of the energy storage system, the lithium battery is tested at multiple temperature points, and then the expected life of the lithium battery under the current state can be derived through the fitting curve. This value is subtracted from the design life of the lithium battery to obtain the absolute value, and then divided by the design life of the lithium battery, recorded as ΔLife-s. Multiple simulation experiments for different lithium batteries can obtain the membership function of their available cycle life as follows:

[0120] The calculation results of the Z-type function of the cycle life Tn can be used, a = 0.1, b = 0.3; the calculation results of the trigonometric function, a = 0.1, b = 0.3, c = 0.6; the calculation results of the S-type function, a = 0.3, b = 0.6. If the calculated ΔLife-s ≤ 0.1, it belongs to high Tn, that is, the membership degree of high Tn is 1; if the calculated ΔLife-s ≤ 0.3, it belongs to relatively high Tn, that is, the membership degree of relatively high Tn is 1; if the calculated ΔLife-s ≤ 0.6, it belongs to relatively low Tn, that is, the membership degree of relatively low Tn is 1.

[0121] According to the degree of membership of the key characteristic quantities of the energy storage system (real-time data on the state of charge, health and available cycle life of the energy storage system at the new energy station), fuzzy rules are used to make judgments, thereby obtaining corresponding fuzzy results. Finally, the results are defuzzified to obtain specific results and classify the energy storage resources. According to the changes in energy storage resources, the parameters that adapt to changes in working conditions can be dynamically calculated, so that the system can adjust decisions according to real-time conditions, improve resource utilization efficiency and system stability. Resources with higher classification levels can be scheduled first, while resources with lower classification levels can be arranged for subsequent scheduling, thereby optimizing the operation and maintenance strategy of the entire energy storage system.

[0122] In one embodiment, step S103, based on obtaining the state of charge, health, membership of available cycle life and operating conditions of the energy storage system, determine the rule base in the fuzzy reasoning model, and classify the energy storage systems of various new energy stations in the region according to the rule base in the fuzzy reasoning model to obtain the classification results.

[0123] Specifically include:

[0124] When the membership of high state of charge, high health and long available cycle life are all 1, the energy storage system category of the new energy station is determined to be a Class I resource;

[0125] When the memberships of high state of charge and high health are both 1, and the membership of long available cycle life is 1, the energy storage system category of the new energy station is determined to be a Class II resource;

[0126] When the memberships of high state of charge and high health are both 1, and the membership of short available cycle life is 1, the energy storage system category of the new energy station is determined to be Class III resources;

[0127] When the membership of high state of charge and long available cycle life are both 1, the energy storage system category of the new energy station is determined to be a Class II resource;

[0128] When the membership of high state of charge is 1, and the membership of long available cycle life is 1, the energy storage system category of the new energy station is determined to be three types of resources;

[0129] When the membership of high health and high available cycle life are both 1, the category of the new energy station is determined to be a type of resource;

[0130] When the membership of high health is 1, and the membership of long available cycle life is 1, the energy storage system category of the new energy station is determined to be a Class II resource;

[0131] When the membership degree of the available cycle life is longer is 1, the energy storage system category of the new energy station is determined to be the third type of resource;

[0132] When the membership degree of the shorter available cycle life is 1, the energy storage system category of the new energy station is determined to be the fourth type of resource.

[0133] Optionally, Table 1 is a schematic diagram of a rule base, as shown in Table 1:

[0134] Table 1: Rule base

[0135]

[0136] Using the rule base in the fuzzy reasoning model, the energy storage resources of the new energy station are classified according to the membership of high state of charge (SOC), high health (SOH) and long, relatively long and relatively short available cycle life (Tn) and the operating conditions of the energy storage system. When the membership of high SOC, high SOH and long Tn are all 1, the energy storage resources of the new energy station are classified as Class I resources (high quality). This means that the energy storage system of the station performs best in terms of state of charge, health and available life, and is the top priority for scheduling. When the membership of high SOC and high SOH is 1, the membership of long Tn is 1 or only the membership of high SOC and long Tn is 1, the membership of high SOH is 1 and the membership of long Tn is 1, the energy storage resources of the new energy station are classified as Class II resources. The station performs well in some key parameters, but is slightly inferior to Class I resources in other parameters. When the membership of high SOC and high SOH is 1, the membership of long Tn is 1, or the membership of high SOC is 1 and the membership of long Tn is 1, or only the membership of long Tn is 1, the energy storage resources of new energy stations are classified as Class III resources. These stations have obvious deficiencies in at least one key parameter, but still have certain use value. When the membership of short Tn is 1, the energy storage resources of new energy stations are classified as Class IV resources. The energy storage systems of such stations may be approaching the end of their service life.

[0137] The rule base provides decision makers with a clear classification basis, which helps to make scheduling decisions quickly and accurately, and improve the operating efficiency of the entire system. Through the classification of fuzzy reasoning, it is possible to optimize the scheduling of resources, ensure that high-quality resources are used first, and reasonably arrange maintenance or replacement of resources that are about to reach the end of their service life.

[0138] Figure 3 is a flowchart of step S104 according to an exemplary embodiment. Figure 3 As shown, step S104, in response to receiving the dispatch instruction, dispatch each new energy station in the area according to the preset dispatch rule based on the classification result to obtain the dispatch result. Specifically, it includes the following steps:

[0139] Step S1041: determine whether the output power of the first-class resources meets the total power requirement. If so, the first-class resources output power according to the total power requirement, and the output power of the second-class resources, the third-class resources and the fourth-class resources is 0; if not, calculate the remaining total power requirement after outputting the power of the first-class resources;

[0140] Step S1042: determine whether the output power of the second-class resources meets the total power requirement remaining after outputting the power of the first-class resources. If so, the first-class resources output the maximum power, the second-class resources output the remaining power requirement of the total power requirement, and the output power of the third-class resources and the fourth-class resources is 0; if not, calculate the total power requirement remaining after outputting the power of the first-class resources and the power of the second-class resources;

[0141] Step S1043: determine whether the output power of the three types of resources meets the total power demand remaining after outputting the power of the first type of resources and the power of the second type of resources. If so, the first type of resources outputs the maximum power, the second type of resources outputs the maximum power, the third type of resources outputs the remaining power demand of the total power demand, and the output power of the fourth type of resources is 0; if not, calculate the remaining power demand after outputting the power of the first type of resources, the power of the second type of resources and the power of the third type of resources;

[0142] Step S1044, determine whether the output power of the four categories of resources meets the remaining power requirements after outputting the power of the first category of resources, the second category of resources and the third category of resources. If so, the first category of resources outputs the maximum power, the second category of resources outputs the maximum power, the third category of resources outputs the maximum power, and the fourth category of resources outputs the remaining power requirements of the total power requirements; if not, the first category of resources outputs the maximum power, the second category of resources outputs the maximum power, the third category of resources outputs the maximum power and the fourth category of resources outputs the maximum power.

[0143] Optionally, Figure 4 is a flow chart of energy storage resource scheduling according to an exemplary embodiment. Figure 4Therefore, it is determined whether the remaining capacity of the first category of energy storage resources can meet the total dispatch instruction (PM). If so, the first category of energy storage resources outputs according to the PM power, and the output power of the remaining categories of energy storage resources is 0; if the first category of energy storage resources cannot meet the PM, the remaining demand (PM-P1) is calculated, and then it is determined whether the remaining capacity of the second category of energy storage resources can meet the remaining demand; if the second category of energy storage resources cannot meet the remaining demand either, the remaining demand (PM-P1-P2) is continued to be calculated, and it is determined whether the remaining capacity of the third category of energy storage resources can meet the remaining demand; if the third category of energy storage resources still cannot meet the remaining demand, the remaining demand (PM-P1-P2-P3) is continued to be calculated, and it is determined whether the remaining capacity of the fourth category of energy storage resources can meet the remaining demand; if all categories of energy storage resources participate in the dispatch but still cannot meet the PM, all energy storage resources will output at their respective maximum power until the PM is met or their respective power limits are reached;

[0144] Specifically, based on the classification of energy storage aggregation resources, the energy storage system resources are dispatched, and the dispatch priority is Class I → Class II → Class III → Class IV. First, try to use Class I resources (i.e., high-quality resources) to meet the total power demand. If the output power of Class I resources is sufficient to meet the total demand, then Class II, Class III, and Class IV resources do not need to output power. This reflects the priority use of high-quality resources to ensure high efficiency and long life. If the power of Class I resources is not enough to meet the total demand, the remaining power demand will be calculated, and Class II resources (secondary quality resources) will be tried to make up the difference. The same logic applies to Class III resources, that is, when Class I and Class II resources cannot meet the demand, Class III resources will be tried to make up for it. Class IV resources (i.e., the least quality resources) will be dispatched only when all higher-class resources have reached their maximum output power but still fail to meet the total demand. This helps to extend the service life of high-quality resources throughout the system. If the output power of all resources reaches the maximum, but still cannot meet the total power demand, then all resources will output their maximum power until their respective limits are reached.

[0145] In the process of energy storage aggregation and collaborative optimization, the characteristic quantities of energy storage resources change in real time. The fuzzy reasoning model will dynamically correct the resource classification results according to the changes in energy storage resources, so as to achieve efficient and reliable operation of energy storage resources under constantly changing energy storage resources, maximize the use of available resources, and improve system efficiency.

[0146] In summary, the embodiment of the present application provides a method for dispatching multi-point energy storage in a region, which obtains relevant real-time data on the state of charge, health and available cycle life of the energy storage systems of each new energy station in the region. According to the fuzzy reasoning model and the real-time data of the energy storage resources of each new energy station in the region, a preset membership function is used to obtain the membership of the three important energy storage parameter states of state of charge, health and available cycle life. Based on the operation status of the energy storage system and relevant technical standards, combined with the membership status of the three important parameter states, the rule base in the fuzzy reasoning model is determined, and the various new energy stations in the region are classified according to the rule base in the fuzzy reasoning model to obtain the classification results. In response to receiving the dispatching instruction, based on the classification results, the energy storage systems of each new energy station in the region are dispatched according to the preset dispatching rules to obtain the dispatching results. According to the characteristic quantities of various energy storage systems, the present application adopts the Mamdani fuzzy reasoning model to classify resources for resource aggregation, providing a basis for efficient collaborative dispatching of energy storage aggregation. The fuzzy reasoning model dynamically corrects the resource classification results according to the changes in energy storage resources, so as to maintain the efficient and reliable operation of energy storage resources under the ever-changing energy storage resources, maximize the use of available resources, improve the efficiency of the system, and solve the problem of low utilization of multi-point energy storage in the existing area.

[0147] In a second aspect, an embodiment of the present application provides a system for multi-point energy storage scheduling within a region. Figure 5 FIG. 1 is a block diagram of a system for multi-point energy storage scheduling in a region according to an exemplary embodiment. Figure 5 As shown, the system includes a real-time data acquisition module 510, a membership acquisition module 520, a classification module 530 and a scheduling module 540, wherein:

[0148] The real-time data acquisition module 510 is used to acquire the real-time data of the state of charge, health and available cycle life of the energy storage system of each new energy station in the region;

[0149] A membership acquisition module 520 is used to acquire the membership of the state of charge, health and available cycle life according to the fuzzy inference model and real-time data using a preset membership function;

[0150] The classification module 530 is used to determine the rule base in the fuzzy reasoning model based on the state of charge, health, membership of the available cycle life and the operating conditions of the energy storage system, and classify the energy storage systems of various new energy stations in the region according to the rule base in the fuzzy reasoning model to obtain the classification results;

[0151] The scheduling module 540 is used to respond to receiving the scheduling instruction, schedule each new energy station energy storage system in the area according to the classification result and the preset scheduling rule, and obtain the scheduling result.

[0152] In summary, the embodiment of the present application provides a multi-point energy storage scheduling system in a region, which constructs a high-precision multi-point energy storage scheduling model in a region by acquiring a real-time data module 510, acquiring a membership module 520, a classification module 530 and a scheduling module 540, and solves the problem of low utilization of multi-point energy storage in the existing region. Specifically, the real-time data of the state of charge, health and available cycle life of each new energy station in the region are obtained. According to the fuzzy reasoning model and the real-time data of each new energy station in the region, the membership of the three important parameter states of the energy storage system, namely, the state of charge, health and available cycle life, is obtained by using a preset membership function. Based on the operation status of the energy storage system and relevant technical standards, combined with the membership status of the three important parameter states, the rule base in the fuzzy reasoning model is determined, and the various new energy stations in the region are classified according to the rule base in the fuzzy reasoning model to obtain the classification results. In response to receiving the scheduling instruction, based on the classification results, the energy storage systems of each new energy station in the region are scheduled according to the preset scheduling rules to obtain the scheduling results. This application uses the Mamdani fuzzy reasoning model to aggregate and classify resources based on the characteristic quantities of various energy storage systems, providing a basis for efficient coordinated scheduling of energy storage aggregation. The fuzzy reasoning model dynamically corrects the resource classification results according to the changes in energy storage resources, thereby achieving efficient and reliable operation of energy storage resources under constantly changing energy storage resources, making maximum use of available resources, improving system efficiency, and solving the problem of low utilization of multi-point energy storage in existing areas.

[0153] It should be noted that the system for multi-point energy storage scheduling in a region provided in this embodiment is used to implement the above-mentioned implementation mode, and the descriptions that have been made will not be repeated. As used above, the terms "module", "unit", "subunit", etc. can implement a combination of software and / or hardware for a predetermined function. Although the device described in the above embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0154] In a third aspect, an embodiment of the present application provides an electronic device, Figure 6 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 6 As shown, the electronic device may include a processor 61 and a memory 62 storing computer program instructions.

[0155] Specifically, the processor 61 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0156] Among them, the memory 62 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 62 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 62 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 62 may be inside or outside the data processing device. In a specific embodiment, the memory 62 is a non-volatile memory. In a specific embodiment, the memory 62 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable ProgrammableRead-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable ProgrammableRead-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0157] The memory 62 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 61 .

[0158] The processor 61 reads and executes computer program instructions stored in the memory 62 to implement any one of the methods for scheduling multi-point energy storage within a region in the above-mentioned embodiments.

[0159] In one embodiment, a device for multi-point energy storage scheduling in a region may further include a communication interface 63 and a bus 60. Figure 6 As shown, the processor 61, the memory 62, and the communication interface 63 are connected via a bus 60 and communicate with each other.

[0160] The communication interface 63 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication port 63 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0161] The bus 60 includes hardware, software or both, and couples the components of a device for multi-point energy storage scheduling in a region to each other. The bus 60 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 60 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of the above. Where appropriate, bus 60 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0162] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements a method for scheduling multi-point energy storage within a region provided in the first aspect.

[0163] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0164] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps of a method for multi-point energy storage scheduling within a region provided in the first aspect.

[0165] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0166] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0167] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for dispatching multi-point energy storage in a region, characterized in that: The method comprises: Obtain real-time data on the state of charge, health, and available cycle life of energy storage systems at each renewable energy station in the region; According to the fuzzy inference model and the real-time data, a preset membership function is used to obtain the membership of the state of charge, the health and the available cycle life; Based on obtaining the state of charge, the health, the membership degree of the available cycle life and the operating condition of the energy storage system, determining the rule base in the fuzzy reasoning model, and classifying the energy storage systems of each new energy station in the region according to the rule base in the fuzzy reasoning model to obtain a classification result; In response to receiving a dispatch instruction, based on the classification result, each new energy station energy storage system in the area is dispatched according to a preset dispatch rule to obtain a dispatch result.

2. The method according to claim 1, characterized in that The method of obtaining the membership of the state of charge, the health and the available cycle life by using a preset membership function according to the fuzzy inference model and the real-time data includes: According to the change value of the state of charge, a Z-type function and an S-type function are selected as membership functions, and according to the membership functions, a membership degree of a high state of charge is determined; According to the deviation value of the health degree, a Z-type function and an S-type function are selected as membership functions, and according to the membership functions, a membership degree with a high health degree is determined; According to the deviation value of the available cycle life, a Z-type function, a triangular function and an S-type function are selected as membership functions to determine the membership degrees of long, relatively long and relatively short available cycle life.

3. The method according to claim 2, characterized in that The step of selecting a Z-type function and an S-type function as a membership function according to the change value of the state of charge, and determining a high degree of membership of the state of charge according to the membership function, comprises: Obtaining a difference between a maximum value and a minimum value of the state of charge, and obtaining a change value of the state of charge according to the difference; Respectively obtaining calculation results of the Z-type function and the S-type function of the state of charge, wherein a minimum state of charge membership reference value and a maximum state of charge membership reference value are determined from the calculation results of the Z-type function and the calculation results of the S-type function; When the change value is less than the minimum state of charge membership reference value, the high state of charge membership is determined to be 1; when the change value is between the minimum state of charge membership reference value and the maximum state of charge membership reference value, the high state of charge membership is determined to be 0-1; when the change value is greater than the maximum state of charge membership reference value, the high state of charge membership is determined to be 0.

4. The method according to claim 2, characterized in that: The step of selecting a Z-type function and an S-type function as a membership function according to the deviation value of the health degree, and determining a membership degree with a high health degree according to the membership function includes: Obtain the health of the energy storage battery of the new energy station according to the open circuit voltage and the operating parameters of the energy storage system of the new energy station, and obtain the deviation value of the health according to the health; Respectively obtaining calculation results of the Z-type function and the S-type function of the health, wherein a minimum health membership reference value and a maximum health membership reference value are determined from the calculation results of the Z-type function and the calculation results of the S-type function; When the health deviation value is less than the minimum health membership reference value, the high health membership is determined to be 1; when the health deviation value is between the minimum health membership reference value and the maximum health membership reference value, the high health membership is determined to be 0-1; when the health deviation value is greater than the maximum health membership reference value, the high health membership is determined to be 0.

5. The method according to claim 2, characterized in that: The method comprises: selecting a Z-type function, a triangular function and an S-type function as a membership function according to the deviation value of the available cycle life, and determining the membership degree of the available cycle life being long, relatively long and relatively short, including: According to the operating parameters of the energy storage system of the new energy station, the expected life of the lithium battery at different temperatures is tested; the expected life is compared with the design life of the lithium battery to obtain the deviation value of the available cycle life; Respectively obtaining calculation results of the Z-type function, the trigonometric function and the S-type function of the available cycle life, wherein a minimum available cycle life membership reference value and a maximum available cycle life membership reference value are determined from the calculation results of the Z-type function, the trigonometric function and the S-type function; When the deviation value of the available cycle life is less than the minimum available cycle life membership reference value, the membership of the long available cycle life is determined to be 1; when the deviation value of the available cycle life is between the minimum available cycle life membership reference value and the maximum available cycle life membership reference value, the membership of the longer available cycle life is determined to be 1; when the deviation value of the available cycle life is greater than the maximum available cycle life membership reference value, the membership of the shorter available cycle life is determined to be 1.

6. The method according to any one of claims 3 to 5, characterized in that: The calculation results of the Z-type function of the state of charge include a=0.08 and b=0.15, and the calculation results of the S-type function of the state of charge include a=0.1 and b=0.2; The calculation results of the Z-type function of the health degree include a=0.02 and b=0.05, and the calculation results of the S-type function of the health degree include a=0.04 and b=0.06; The calculation results of the Z-type function of the available cycle life include a=0.1 and b=0.3, the calculation results of the S-type function of the available cycle life include a=0.3 and b=0.6, and the calculation results of the triangular function include a=0.1, b=0.3, and c=0.

6.

7. The method according to any one of claims 3 to 5, characterized in that: The method of determining a rule base in the fuzzy reasoning model based on obtaining the state of charge, the health, the membership degree of the available cycle life and the operating condition of the energy storage system, and classifying the energy storage systems of each new energy station in the region according to the rule base in the fuzzy reasoning model to obtain the classification results includes: When the memberships of the high state of charge, the high health and the long available cycle life are all 1, the energy storage system category of the new energy station is determined to be a type-one resource; When the memberships of the high state of charge and the high health are both 1, and the membership of the relatively long available cycle life is 1, the energy storage system category of the new energy station is determined to be a second-class resource; When the memberships of the high state of charge and the high health are both 1, and the membership of the short available cycle life is 1, the energy storage system category of the new energy station is determined to be a third-category resource; When the membership of the high state of charge and the long available cycle life are both 1, the energy storage system category of the new energy station is determined to be a second-class resource; When the membership of the high state of charge is 1, and the membership of the long available cycle life is 1, the energy storage system category of the new energy station is determined to be a third type of resource; When the membership degrees of the high health degree and the long available cycle life are both 1, the energy storage system category of the new energy station is determined to be a type-one resource; When the membership degree of the high health degree is 1, and the membership degree of the long available cycle life is 1, the energy storage system category of the new energy station is determined to be a second-class resource; When the membership degree of the available cycle life being longer is 1, the energy storage system category of the new energy station is determined to be the third type of resource; When the membership degree of the shorter available cycle life is 1, the energy storage system category of the new energy station is determined to be the fourth type of resource.

8. The method according to claim 7, characterized in that include: In response to receiving the dispatch instruction, based on the classification result, dispatching each new energy station energy storage system in the area according to a preset dispatch rule, and obtaining a dispatch result, including: Determine whether the output power of the first category of resources meets the total power requirement. If so, the first category of resources outputs power according to the total power requirement, and the output power of the second category of resources, the third category of resources, and the fourth category of resources is 0; if not, calculate the remaining total power requirement after outputting the power of the first category of resources. Determine whether the output power of the second category of resources meets the total power requirement remaining after outputting the power of the first category of resources. If so, the first category of resources outputs the maximum power, the second category of resources outputs the remaining power requirement of the total power requirement, and the output power of the third category of resources and the fourth category of resources is 0; if not, calculate the total power requirement remaining after outputting the power of the first category of resources and the power of the second category of resources; Determine whether the output power of the three types of resources meets the total power requirement remaining after outputting the power of the first type of resources and the power of the second type of resources. If so, the first type of resources outputs the maximum power, the second type of resources outputs the maximum power, the third type of resources outputs the remaining power requirement of the total power requirement, and the output power of the fourth type of resources is 0; if not, calculate the remaining power requirement after outputting the power of the first type of resources, the power of the second type of resources and the third type of resources; Determine whether the output power of the four categories of resources meets the remaining power requirements after outputting the power of the first category of resources, the second category of resources and the third category of resources. If so, the first category of resources outputs the maximum power, the second category of resources outputs the maximum power, the third category of resources outputs the maximum power, and the fourth category of resources outputs the remaining power requirement of the total power requirement; if not, the first category of resources outputs the maximum power, the second category of resources outputs the maximum power, the third category of resources outputs the maximum power and the fourth category of resources outputs the maximum power.

9. A regional multi-point energy storage dispatching system, characterized in that: The system includes a real-time data acquisition module, a membership acquisition module, a classification module and a scheduling module, wherein: The real-time data acquisition module is used to acquire real-time data on the state of charge, health and available cycle life of energy storage systems of various new energy stations in the region; The membership acquisition module is used to acquire the membership of the state of charge, the health and the available cycle life by using a preset membership function according to the fuzzy inference model and the real-time data; The classification module is used to determine the rule base in the fuzzy reasoning model based on the state of charge, the health, the membership of the available cycle life and the operating condition of the energy storage system, and classify the energy storage systems of each new energy station in the region according to the rule base in the fuzzy reasoning model to obtain the classification result; The scheduling module is used to respond to receiving a scheduling instruction, schedule each new energy station energy storage system in the area based on the classification result and according to a preset scheduling rule to obtain a scheduling result.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a method for scheduling multi-point energy storage within a region as described in any one of claims 1 to 8 is implemented.

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