Task allocation method and device for energy storage power station and storage medium

Abstract

The invention discloses a task allocation method and device for an energy storage power station and a storage medium. The method comprises the steps that positive and negative ideal solutions are regarded as mutually opposite existence, a power grid weak link recognition combination model integrating risks and toughness is provided, and dynamic task allocation is completed through cooperation of three types of energy storage power stations including management, task and report; candidate energy storage power stations are selected by analyzing task requirements, a bidding value is calculated by adopting a step function and an execution time parameter, and energy storage power station task allocation is realized by combining three bid winning strategies of a maximum bidding value, probability selection and a minimum distance. According to the method, the scientificity and efficiency of task allocation of the energy storage power station are remarkably improved by introducing a connection degree theory and an auction mechanism. On one hand, the evaluation result is more practical; and on the other hand, the auction mechanism improves the utilization rate and the task completion rate of the energy storage power station through dynamic task allocation. And the stability and toughness of the power grid are enhanced through the supplementary effect of the energy storage power station.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid, in particular to a task allocation method for energy storage power station, equipment and storage medium. BACKGROUND

[0002] With the continuous growth of global energy demand and the transformation of energy structure, the technical field of power grid is facing unprecedented challenges. Traditional power grid is not capable of coping with peak load, new energy access and emergencies, and new technologies and methods are urgently needed to improve the stability and resilience of power grid. Energy storage power station, as a flexible energy regulation means, has attracted attention because it can quickly respond to power grid demand and balance supply and demand.

[0003] However, there are still many problems in the practical application of energy storage power station, especially in task allocation. Existing technologies often use static or simple allocation strategies, which cannot fully consider the real-time state of power grid and the dynamic characteristics of energy storage power station, resulting in low efficiency of task allocation and even affecting the safe operation of power grid. SUMMARY

[0004] The technical problem solved by the present application is to provide a task allocation method for energy storage power station, which solves the above problems.

[0005] To solve the above technical problems, one technical solution adopted by the present application is to provide a task allocation method for energy storage power station, including the steps of: regarding positive and negative ideal solutions as mutually opposite existence, replacing traditional Euclidean distance with contact vector distance to sort the resilience value of each link of power grid, combining risk matrix analysis to propose a weak link identification combination model of power grid that measures the resilience level and risk degree of power grid, and identifies the weak links in power grid;

[0006] The three types of energy storage power stations, management, task and report, cooperate to complete dynamic task allocation; candidate energy storage power stations are selected by analyzing task demand, and bid value is calculated by using step function and execution time parameter, and three kinds of bidding strategies, maximum bid value, probability selection and minimum distance, are combined to realize task allocation of energy storage power station.

[0007] The present application also provides a computer device, including a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to realize the steps of the method.

[0008] The present application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to realize the steps of the method.

[0009] The beneficial effects of the present application are: the present application introduces the connection degree theory and the auction mechanism, which significantly improves the scientificity and efficiency of the task allocation of the energy storage power station. On the one hand, the connection degree theory overcomes the reverse order problem of the traditional ideal solution method, so that the evaluation result is more in line with the actual situation; on the other hand, the auction mechanism improves the utilization rate and task completion rate of the energy storage power station through dynamic task allocation. Through the supplementary role of the energy storage power station, the stability and resilience of the power grid are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 is a flowchart according to an embodiment of the present application. DETAILED DESCRIPTION

[0011] In order to facilitate the understanding of the present application, the present application will be described in more detail below in combination with the drawings and specific embodiments. The preferred embodiments of the present application are shown in the drawings. However, the present application can be realized in many different forms and is not limited to the embodiments described in the specification. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0012] It should be noted that, unless otherwise defined, all technical and scientific terms used in the specification have the same meaning as commonly understood by those skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application. The term "and / or" used in the specification includes any and all combinations of one or more related listed items.

[0013] Figure 1 Embodiments of the task allocation method of the energy storage power station of the present application are shown, including:

[0014] Step S1: regarding the positive and negative ideal solutions as opposite existence, the traditional Euclidean distance is replaced by the introduction of the connection vector distance to sort the resilience values of each link of the power grid, and a weak link identification combination model of the power grid is proposed by combining risk matrix analysis, which measures the resilience level and risk degree of the power grid at the same time, and identifies the weak link in the power grid;

[0015] Step S2: using the management, task and report three types of energy storage power stations to cooperate to complete dynamic task allocation; by analyzing the task demand to select the candidate energy storage power station, and using the step function and execution time parameter to calculate the bid value, combining the maximum bid value, probability selection and minimum distance three kinds of bidding strategy to realize the task allocation of the energy storage power station.

[0016] The application significantly improves the scientificity and efficiency of the energy storage power station task allocation by introducing the connection degree theory and the auction mechanism. On the one hand, the connection degree theory overcomes the reverse order problem of the traditional ideal solution method, so that the evaluation result is more in line with the actual situation; on the other hand, the auction mechanism improves the utilization rate and task completion rate of the energy storage power station through dynamic allocation of tasks. Through the supplementary role of the energy storage power station, the stability and resilience of the power grid are enhanced.

[0017] Resilience value and connection vector distance of positive and negative ideal points

[0018] The weak link of the power grid can be determined by the ideal solution method. The basic idea of the ideal solution method is to construct the positive ideal solution and the negative ideal solution of the multi-factor problem, and to use the closeness to the positive ideal solution and the negative ideal solution as the basis for evaluation to sort the pros and cons of each scheme. The ideal solution method has the characteristics of simple calculation, flexible application and reasonable evaluation, and is an effective multi-attribute decision-making method. With the wide application of the traditional ideal solution method evaluation method, its disadvantages in decision-making have also emerged, that is, the points closer to the Euclidean distance of the positive ideal solution are also closer to the Euclidean distance of the negative ideal solution, resulting in reverse order phenomenon of the decision result.

[0019] To solve the reverse order problem of the traditional ideal solution method in the evaluation process, the application regards the positive and negative ideal solutions as opposite existence in the system, and introduces the connection vector distance to replace the Euclidean distance to sort the resilience value of each link of the power grid. This method considers the correlation between distances in the decision-making process, and is more in line with the actual situation when evaluating uncertain problems.

[0020] The application regards the uncertain problem as an uncertain system, and uses the connection degree to uniformly quantify, so as to convert the dialectical understanding of uncertainty into specific mathematical operation.

[0021] The analysis of the weak link of the power grid is similar to the analysis of whether the link of the power grid is safe and stable, so the identification of the weak link of the power grid needs to measure the resilience level of the power grid and consider the risk degree of the link. The application integrates the risk assessment and resilience assessment of the power grid by using the risk matrix analysis and the connection degree, and proposes a combined model for identifying the weak link of the power grid based on the comprehensive risk and resilience, and the expression is as follows:

[0022]

[0023] Wherein, P is the weakness degree of the power grid link, the larger the value is, the weaker the power grid link is, respectively, the risk coefficient and the resilience coefficient, respectively, the connection vector distance of the resilience value of the power grid link and the positive and negative ideal points, is the risk level of the power grid link.

[0024] The distance between two sets can be determined as follows: Then by set The set pairs formed by combining Comparison of There are 10 corresponding terms, of which s terms differ only slightly in number. The items differed greatly in quantity, the rest The items may differ in number, but the difference is not significant. If the corresponding items in two sets differ only slightly, they are considered the same; if the difference is significant, they are considered opposites; if there is a certain difference, they are considered dissimilar. Thus, based on sets... The set pair relationship is transformed into a relationship of similarity, dissimilarity, and inversion. Similarity, dissimilarity, and inversion describe the relationship between the two sets from different perspectives. Therefore, the degree of connection of an uncertain quantitative relationship between sets can be expressed as...

[0025]

[0026] in, For set The degree of connection, with a value range of Within the range. Let a, b, and c represent the degree of identity, difference, and opposition between the two sets, respectively, where a = s / n, b = f / n, and c = p / n. The closer the value is to 1, the higher the degree of identity between the two sets. The closer the value is to 1, the higher the degree of opposition between the two sets. The coefficient of variation is defined as follows: The values ​​can be selected based on different situations. The degree of opposition coefficient is taken as a constant value of -1.

[0027] Let set The degree of connection is Then, the vector composed of the set's identity, difference, and opposition is called the vector. Let the link vector between two sets be denoted as . Let set The connection vector is The connection vector is Then set and The connection vector distance is

[0028]

[0029] When set and When they are equal, Then the set and The connection vector distance is

[0030]

[0031] The positive and negative ideal points are regarded as opposite sets in the system. The existence of opposite sets is fully considered when calculating the connection degree between the evaluation scheme and the positive and negative ideal solutions, which overcomes the shortcomings of the traditional ideal solution method.

[0032] The specific steps of the ideal solution method based on connection degree are as follows:

[0033] (1) Determine the positive ideal solution set and the negative ideal solution set of the driving factors, denoted as and According to the set pair analysis thought, the positive ideal solution set and the negative ideal solution set are opposite to each other in the system.

[0034] (2) Normalize the initial evaluation matrix to obtain the characteristic matrix which is the data of the power grid resilience driving factors.

[0035] (3) Calculate the connection degree and between the characteristic matrix and the positive ideal point and the negative ideal point

[0036]

[0037]

[0038]

[0039] wherein, is the weight of the resilience driving factor: ;

[0040] When , ;

[0041] When , ;

[0042]

[0043]

[0044]

[0045] wherein, ;

[0046] When , ;

[0047] When and , ;

[0048] When , it is provided ;

[0049] When , it is provided .

[0050] (4) Calculate the connection vector distance of each resilience driving factor and the positive ideal point The connection vector of the positive ideal point is , and the connection vector of the resilience driving factor is The connection vector distance of the resilience driving factor and the positive ideal point is

[0051]

[0052] (5) Calculate the connection vector distance of each resilience driving factor and the positive ideal point The connection vector of the negative ideal point is , and the connection vector of the resilience driving factor is The connection vector distance of the resilience driving factor and the negative ideal point is

[0053]

[0054] (6) Calculate the relative closeness c of the resilience driving factor and the positive ideal point The greater c is, the closer to the ideal point.

[0055]

[0056] The purpose of the present application is to construct a weak link identification model of power grid from the perspective of risk resilience dual dimension, to quantify the risk factors of power grid by risk matrix theory, and to calculate the risk quantization grade by linear interpolation method; to quantify the driving factors of power grid resilience, and to introduce the connection degree theory into the ideal point method to solve the reverse sequence problem of traditional model: to integrate the risk assessment and resilience assessment of power grid, and to propose a combined model of weak link identification of power grid based on comprehensive risk and resilience. Thus, the weak links in the power grid can be accurately judged, and accurate judgment data can be provided for the power grid distribution task based on energy storage power station.

[0057] After identifying the weak links of the power grid, the energy storage power station is assigned a supplementary task, and the energy storage power station is used to supplement the power at the weak link of the power grid, and the weak link of the power grid is supplemented and adjusted.

[0058] Use set to represent the replenishment task, where the number of tasks is unknown. For each task , it is modeled as where represent the task type, replenishment time, deadline, weak link position and replenishment information, respectively. Among them, the task type can include emergency energy replenishment, daily adjustment, etc., and the task replenishment information is mainly to better replenish the description to adapt to the needs of different tasks.

[0059] represent the set of energy storage power stations of different capacity types. For a single energy storage power station , it is described by the model , where , represent the replenishment speed, capacity type, replenishment time, completion time of the last task (the last task in the current task list of the energy storage power station), position of the last task, remaining energy and total replenishment time of . Among them, the capacity type of the energy storage power station includes peak shaving type energy storage for balancing the peak and valley of power grid load. Frequency modulation type energy storage quickly responds to frequency fluctuations to maintain grid stability. Standby power type energy storage provides emergency power support when the grid fails. Energy transfer type energy storage realizes energy time transfer through low storage and high release. Voltage support type energy storage improves the voltage quality of local power grid. Black start type energy storage helps restore power supply after the power grid is completely stopped, etc. The replenishment time mainly restricts the ability of the energy storage power station to execute tasks.

[0060] where and represent the completion time and position of the last task in the current task list of the energy storage power station . If a new task is assigned to , the completion time and position of the last task of need to be updated according to the parameters of . Use to represent the remaining energy, when the value of is 0, it means that the energy of the energy storage power station has been exhausted and cannot undertake any replenishment task. is the preparation time required for the energy storage power station to execute the task . and represent the start time of the preparation process and the start time of the execution process of the energy storage power station if it executes the task . refers to undertaking a task The allocation matrix defines the allocation relationship between tasks and energy storage power stations, element takes value or 0, respectively indicating that the task is allocated or not allocated to the energy storage power station .

[0061] Some basic constraint conditions in the task allocation process will be summarized and a mathematical model will be constructed. The first constraint condition is:

[0062]

[0063] For the preparation start time the execution start time and the end time , they are determined by the supplementary time of and the last task end time of . The preparation start time is determined by the following formula.

[0064]

[0065] Where, represents the time spent in the auction process. For the execution start time , it is determined by the following formula,

[0066]

[0067] The execution end time is calculated by the following formula.

[0068]

[0069] For a certain task , whether it can be allocated to the energy storage power station is mainly determined by the time constraint and the capacity constraint. The constraint model is constructed as follows:

[0070]

[0071] Where represents the required capacity type of the task . The time constraint mainly refers to the completion time of the task must meet the time limit requirement, and the capacity constraint mainly refers to the energy storage power station must have the required capacity type of the task.

[0072] Another constraint requires the remaining energy of the candidate energy storage power station not to be exhausted at the time of task allocation. This constraint is modeled as:

[0073]

[0074] where the constraints only need to be considered in the process of task allocation, because after the task is executed, it can be equal to 0.

[0075] For the energy storage power station, it should improve the completion rate of dynamic tasks as much as possible, that is, as many dynamic tasks as possible should be successfully allocated to suitable energy storage power stations for execution. For the entire task allocation problem, the task success rate and execution time are taken as two main allocation objectives. The task success rate:

[0076]

[0077] The execution time proportion:

[0078]

[0079] The above formula shows that the task allocation method based on the energy storage power station aims to allocate more tasks to the energy storage power station successfully under complex constraints, while also taking into account the execution efficiency of a single energy storage power station, so as to obtain the overall efficiency of the energy storage power station.

[0080] In order to realize the task allocation based on the energy storage power station, the energy storage power station is divided into three types, the management energy storage power station, the task energy storage power station and the reporting energy storage power station. All energy storage power stations operate and cooperate based on their own rules to complete the auction process. A triple is used to represent.

[0081] The energy storage power station used in the present application, wherein is the management energy storage power station, is the task energy storage power station, is the reporting energy storage power station. In the following, for each type of energy storage power station, a detailed description will be given. is responsible for managing all task energy storage power stations and reporting energy storage power stations.

[0082] is a set of task energy storage power stations, wherein represents the energy storage power station of the task . The task energy storage power station is mainly responsible for the auction and execution of the task. For each task energy storage power station, it will be constructed with the arrival of the corresponding task, and will be recycled with the completion of the task execution. will be constructed on the energy storage power station responsible for the task .

[0083] is a set of reporting energy storage power stations, wherein represents the energy storage power station The reporting energy storage station is mainly responsible for resource management and task bidding of the energy storage station. For each reporting energy storage station, it will exist on the corresponding energy storage station. In addition, each reporting energy storage station will update the state information and report to the management energy storage station in the following cases: 1) the reporting energy storage station is constructed; 2) there is a new strike task assigned to the energy storage station; 3) the energy storage station encounters an abnormal situation.

[0084] In an auction process, there are mainly two roles, namely the tenderer and the bidder. The work of the tenderer is mainly completed by the task energy storage station, and the work of the bidder is mainly completed by the reporting energy storage station.

[0085] Since the key of the problem of energy storage station task assignment is to assign each dynamically arriving task to the appropriate energy storage station.

[0086] In this application, an auction mechanism is used to determine the appropriate arrival task. The first step is to select potential bidders, and in the task assignment process, the task demand is analyzed and the candidate energy storage station is selected, which is called the preparation stage. Next is the tendering stage, the bidding stage, the winning stage and the execution stage.

[0087] Preparation stage. In this stage, there are mainly three types of work. The first type of work is that when the conditions mentioned above occur, the reporting energy storage station will report the basic state information to the management energy storage station. The second type is to send the basic information to the management energy storage station when the task arrives at the energy storage station. The third type is that the management energy storage station completes the matching process and sends the information of the candidate energy storage station to the requesting task energy storage station.

[0088] Tendering stage. The main work of this stage is to issue a tender invitation to the candidate energy storage station, and the task energy storage station will receive the information of the candidate energy storage station and send the basic task information to these matched energy storage stations for invitation. As for the reporting energy storage station, they will receive the tender invitation and the basic task information.

[0089] Bidding stage. In this stage, each reporting energy storage station will calculate the bid value according to its state parameters (including location, earliest completion time, replenishment time, etc.) and task basic information. The bid value calculation is based on the globally unified rules defined in advance. After obtaining the bid value, the reporting energy storage station will send these values to the task energy storage station for bidding. It should be noted that the bidding process does not conflict with the task execution process, because the energy storage station still bids for new tasks according to its own state information when executing the assigned task.

[0090] Bidding stage. After the task storage station receives the bid values from the many bidding reporting storage stations, it selects the appropriate storage station to determine the winner according to the pre-defined selection strategy. The complete task information will be sent by the task storage station to the winning reporting storage station. The complete task information here is distinguished from the basic task information before, because only the basic information is needed in the bidding process, and the complete task information is sent after the winner is determined. In this way, unnecessary communication overhead can be minimized.

[0091] Execution stage. In this stage, the winning storage station adds the task to the task list, waits for execution, and reports the result to the task storage station after execution.

[0092] An example of a task allocation process based on an auction mechanism is given below. In this example, a task is to be allocated to the appropriate storage station for execution. It is assumed that there are three reporting storage stations: , The task first sends its basic task information to the management storage station . Then, selects the storage stations that meet the basic requirements as candidate storage stations, in this example, all meet the basic task requirements. After the preparation stage, sends the matched storage station basic information to . Next, invites bids and the necessary task information to , and the three reporting storage stations calculate the bid values for the task on their local platforms after receiving the basic task information. After the storage stations calculate the bid values, they send their bid values to . After receiving the bid values from each storage station, selects as the winning storage station according to the bid values and the bidding selection strategy. Finally, sends the winning result and the complete information of the task to , completing the task allocation. After the task allocation, the task execution and the situation report will be completed by .

[0093] In the auction mechanism designed above, the task storage station selects the appropriate storage station according to the bid values. Therefore, the calculation of the bid values is crucial for efficiency. The most important factor in the bidding process is whether the candidate storage station can meet the deadline, and a step function is used to describe this influence.

[0094] ​

[0095] Another factor that should be considered in the calculation of the bid value is the execution start time, which should be as early as possible. For the preparation time, it should be as short as possible. In addition, the execution time should also be as short as possible. Therefore, the energy storage power station is calculated by the following formula The bid value of the task .

[0096]

[0097] In the auction process, if there is at least one reported energy storage power station whose bid value is not equal to 0, the task energy storage power station will select one of the reported energy storage power stations with a bid value greater than 0 as the winning bidder.

[0098] The selection strategy is a very important influencing factor. In this application, three selection strategies are proposed, namely the maximum bid value strategy, the probability selection strategy and the minimum distance strategy.

[0099] When the task arrives, a task will be generated to be responsible for its bidding work, which will be deployed in the energy storage power station responsible for the task. As for the energy storage power station that bids for the task , it is represented by the set . It should be noted that when the bid value is 0, the corresponding energy storage power station will not be considered as a valid bidder, so it will not be added to the set . Let represent the bid value set, where represents the bid value of the bidder to .

[0100] Maximum bid value strategy. Under this strategy, the bidder with the maximum bid value will be selected. That is, if is selected as the winner, the following constraints must be met:

[0101]

[0102] Minimum distance strategy. Under this strategy, the bidder is selected according to the probability strategy. The winning probability of the bidder is determined by the following formula:

[0103]

[0104] In order to realize the selection of bidders according to probability, let Represents a random number, and Without losing generality, it can make .when When the value satisfies the following formula, the bidder will be selected. As the successful bidder.

[0105]

[0106] Probabilistic selection strategy. Considering that the spatial distance between the mission and the energy storage power station is a key factor affecting execution efficiency, the probabilistic selection strategy attempts to select the bidder with the shortest relocation distance. To implement this distance-based selection strategy, bidders are required to send their bids, including distance values, to the tendering party. For task location and bidder The distance between them. If selected To be awarded the contract, the following constraints must be met:

[0107]

[0108] This application combines the auction mechanism with the multi-energy storage power station method, and proposes a distributed autonomous task allocation method with the task allocation among energy storage power stations as a representative scenario. By designing a local value calculation mode, the amount of communication required for distributed negotiation is greatly reduced, and the constraint of low communication bandwidth is met.

[0109] The above are merely embodiments of this application and do not limit the scope of this patent application. Any equivalent structural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of this application.

Claims

1. A method for task allocation in an energy storage power plant, characterized in that, The method comprises the steps of: The positive and negative ideal solutions are regarded as opposite sets, the traditional Euclidean distance is replaced by the connection vector distance, the resilience values of each link of the power grid are sorted, and a combined model for identifying weak links of the power grid based on the comprehensive risk and resilience is proposed by combining risk matrix analysis, which measures the resilience level and risk degree of the power grid and identifies the weak links in the power grid; The three types of energy storage power stations are used to cooperate to complete dynamic task allocation; candidate energy storage power stations are selected by analyzing task requirements, and bid values are calculated using step functions and execution time parameters, and three types of winning strategies, i.e., maximum bid value, probability selection, and minimum distance, are combined to realize task allocation of energy storage power stations.

2. The method of task allocation for an energy storage plant of claim 1, wherein, The connection degree of uncertain quantitative relationship between sets is represented as wherein is the degree of association of the set of contacts; Let set The contact degree of set is The contact vector of two sets is , denoted as The contact vector of set is The contact vector distance of set and is When the set is equal to , then the set is equal to and the connection vector distance of is 。 3. The method of task assignment for an energy storage plant of claim 2, wherein, The positive and negative ideal points are regarded as opposite sets in the system, and the existence of opposite sets is fully considered when calculating the connection degree between the evaluation scheme and the positive and negative ideal solutions, and the specific steps are as follows: (1) Determine the positive ideal solution set and the negative ideal solution set of driving factors, respectively denoted as and According to the set pair analysis thought, the positive ideal solution set and the negative ideal solution set are opposite to each other in the system; (2) The initial evaluation matrix is normalized to obtain the feature matrix Data for the enterprise resilience drivers;​ (3) Computing the degree of association of the feature matrix with positive ideal points and negative ideal points and ​ wherein, is the toughness driver weight; (4) The linkage vector distance between each resilience driver and the ideal point The linkage vector of the ideal point is The linkage vector of the resilience driver is The linkage vector distance between the resilience driver and the ideal point is (5) Calculate the relationship between each toughness driving factor and the positive ideal point. The connection vector distance; negative ideal point The connection vector is The relationship vector of resilience driving factors is Then the driving factors of resilience and the negative ideal point The connection vector distance is (6) Calculate the toughness driving factors and the positive ideal point Relative closeness The larger it is, the closer it is to the ideal point; 。 4. The method of task assignment for an energy storage plant of claim 3, wherein, The risk assessment and resilience assessment of the power grid are integrated, and a combined model for identifying weak links of the power grid based on the comprehensive risk and resilience is proposed, and the expression is as follows: Wherein, P is the weakness degree of the power grid link, the greater the value, the more weak the power grid link, respectively are the risk coefficient and the resilience coefficient, respectively are the resilience value of the power grid link and the connection vector distance of the positive and negative ideal points, is the risk level of the power grid link.

5. The method of task assignment for an energy storage plant of claim 4, wherein, Each energy storage power station can only undertake one task at any time; the first constraint condition is: Preparation start time Execution start time and end time They are determined by Supplementary time and Last task end time; Preparation start time Is determined by the following formula; wherein represents the time spent in the auction process; for the execution start time is determined by the formula, The end time of execution is calculated by the following formula; For a certain task whether to be allocated to the energy storage power station is mainly determined by time constraints and capacity constraints; The constraint model is constructed as follows: wherein representing a task type of capability required; Another constraint requires that the remaining energy of the candidate energy storage power station not be exhausted during task allocation; this constraint is modeled as: 。 6. The method of task assignment for an energy storage plant of claim 5, wherein, For the entire task allocation problem, the task success rate and execution time are taken as the two main allocation objectives; the task success rate is: The execution time ratio is: The task allocation method based on energy storage power stations aims to allocate more successful tasks to energy storage power stations under complex constraint conditions, while also considering the execution efficiency of individual energy storage power stations to obtain the overall efficiency of energy storage power stations.

7. The method of task assignment for an energy storage plant of claim 6, wherein, Whether the candidate energy storage power station can meet the deadline is described by using a step function; The energy storage plant is calculated by the following formula The bid value for the task ; In the auction process, if there is at least one reporting energy storage power station whose bid value is not equal to 0, the task energy storage power station will select a reporting energy storage power station with a bid value greater than 0 as the winner.

8. The method of task assignment for an energy storage plant of claim 7, wherein, The selection strategies include the maximum bid value strategy, the probability selection strategy, and the minimum distance strategy; When a task arrives, a task will be generated , which is responsible for its bidding work, The energy storage power station responsible for the task will be deployed; as for the energy storage power stations bidding for the task , a set is used to represent; the bidding value set is represented by , where represents the bidder 's bidding value maximum bid value strategy; the bidder with the maximum bid value will be selected; That is, if the winning bid, the following constraints must be met: minimum distance strategy selecting bidders according to a probability strategy; bidders probability of winning is determined by the equation: To select the bidder according to the probability, let be a random number, and ; without loss of generality, let ; when the value of satisfies the following formula, the bidder will be selected as the winning bidder; The probability selection strategy; The bidders are required to send their bid information with distance values to the tenderer; let be the distance between the task location and the bidder ; if the bid is selected, the following constraints are satisfied: ​ 。 9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the method of any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 8.

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