Lithium battery energy storage system safe operation intelligent decision-making method based on multilateral cooperative game

Abstract

The invention discloses an intelligent decision-making method for safe operation of a lithium battery energy storage system based on a multilateral cooperative game. The method comprises the following steps: calculating user interruption time cmi according to a power failure record of the lithium battery energy storage system; evaluating maintenance effectiveness factors eta f and eta d according to the maintenance record; calculating a user interruption time change delta cmi; evaluating an expected cost [delta] pm of the maintenance activity; calculating the expected comprehensive efficiency delta E of the lithium battery energy storage system; according to the user interruption time change delta cmi, the expected cost delta pm and the expected comprehensive efficiency delta E, calculating characteristic functions of a game participant power grid company, an energy storage operation and maintenance provider and an energy storage user; configuring an excitation coefficient alpha, and standardizing a characteristic function through a Sigmoid function; and calculating a global decision score according to the standardized feature function, and evaluating the optimal date of implementation of the maintenance activity. According to the method, the system comprehensive efficiency, the cost consumption and the user interruption time are taken as multi-objective functions, and the weight is allocated by configuring the excitation coefficient, so that the game relationship of multiple participants can be comprehensively considered, and the optimal operation and maintenance time is determined.

Description

Technical Field

[0001] The present invention belongs to the field of operation and maintenance of electrochemical energy storage systems, and specifically relates to an intelligent decision-making method for safe operation of a lithium battery energy storage system based on multilateral cooperative game. Background Art

[0002] Under the severe energy situation and huge pressure of emission reduction, new energy and renewable technologies have developed rapidly. Due to the characteristics of high energy density, high power density and low self-discharge rate, lithium-ion batteries are widely used in energy storage systems. At present, the safety and reliability research of lithium-ion batteries has become a research hotspot in this field. Engineering application scenarios have put forward higher requirements on the performance of lithium-ion battery energy storage systems. Its reliability and economy cover the multi-level game relationship of multiple stakeholders such as power grid companies, energy storage operators and energy storage users. Therefore, during the life cycle of lithium-ion batteries, the adoption of intelligent safety operation strategies is a necessary way to improve the health management level of lithium-ion battery energy storage systems. Summary of the invention

[0003] Purpose of the invention: In order to overcome the shortcomings of the prior art, an intelligent decision-making method for the safe operation of a lithium battery energy storage system based on multilateral cooperative game is provided, which takes into account the interactive relationship between multiple stakeholders such as power grid companies, energy storage operators and energy storage users, and takes system comprehensive efficiency, cost consumption and user interruption time as multi-objective functions. By configuring incentive coefficients and allocating weights, it is possible to comprehensively consider the game relationship among multiple participants and determine the optimal operation and maintenance time.

[0004] Technical solution: To achieve the above purpose, the present invention provides an intelligent decision-making method for safe operation of a lithium battery energy storage system based on multilateral cooperative game, comprising the following steps:

[0005] S1: Calculate the user interruption time cmi based on the power outage record of the lithium battery energy storage system;

[0006] S2: Evaluate the maintenance effectiveness factor η based on maintenance records f and η d ;

[0007] S3: Based on the effectiveness factor η f and η d , calculate the user interruption time change Δcmi;

[0008] S4: Evaluate the expected cost Δpm of maintenance activities;

[0009] S5: Calculate the expected overall efficiency ΔE of the lithium battery energy storage system;

[0010] S6: Calculate the characteristic functions of the game participants, the power grid company, the energy storage operator and the energy storage user, based on the user interruption time change Δcmi, the expected cost Δpm and the expected comprehensive efficiency ΔE;

[0011] S7: configure the excitation coefficient α, and standardize the feature function of step S6 by using the Sigmoid function;

[0012] S8: Calculate the global decision score based on the standardized characteristic function and evaluate the optimal date for implementing the maintenance activity.

[0013] Furthermore, the calculation formula of the user interruption time cmi in step S1 is:

[0014] cmi=N×n×d(1)

[0015] Where cmi represents the user interruption time of the lithium battery energy storage system, N represents the number of interrupted batteries, n represents the number of interruptions, and d represents the average interruption duration.

[0016] Furthermore, the effectiveness factor η is maintained in step S2. f and η d It is obtained by calculating the ratio of the number of interruptions of the lithium battery energy storage system before and after maintenance and the average interruption duration.

[0017] Furthermore, the calculation method of the user interruption time change Δcmi in step S3 is:

[0018] Calculate the expected change in the number of outages and average outage duration after implementing the maintenance activity:

[0019] Δn=-(n×η f ) (2)

[0020] Δd=-(d×η d ) (3)

[0021] Where η f The maintenance effectiveness factor representing the number of interruptions of the lithium battery energy storage system, η d The maintenance effectiveness factor representing the average interruption duration of the lithium battery energy storage system;

[0022] Calculate the change in user interruption time Δcmi:

[0023] Δcmi=N×(ΔnΔd+dΔn+nΔd) (4)

[0024] Where Δcmi represents the expected change in user interruption time after the maintenance activity is implemented.

[0025] Furthermore, the expected cost Δpm of the maintenance activity in step S4 includes labor costs, maintenance costs, and replacement costs of some energy storage devices.

[0026] Furthermore, the expected overall efficiency ΔE in step S5 is equal to the ratio of the input power to the output power of the lithium battery energy storage system.

[0027] Furthermore, the characteristic functions of the game participants in step S6, namely, the power grid company, the energy storage operation and maintenance provider, and the energy storage user, are:

[0028]

[0029] In the formula, represents the characteristic function of the power grid company, represents the characteristic function of energy storage operation and maintenance providers, represents the energy storage user characteristic function, ΔE j Represents the expected overall efficiency of the lithium battery energy storage system, Δpm j It represents the expected cost of implementing maintenance activities, s k represents the expected maintenance date, and j=1,2,...,q represents the number of users.

[0030] Furthermore, in step S7, the different feature functions among the participants are standardized by the Sigmoid function, and the standardized function is:

[0031]

[0032] In the formula, u i (s k ) represents the standardized characteristic function, s0 represents the initial strategy value of the game, and u of all participants i (s0) is 0.5, α represents the incentive coefficient, which is used to allocate the weights of participants and is selected between 0.5 and 2.0. The larger the value, the more inclined the decision is to the participant.

[0033] Furthermore, the expression of the global decision score in step S8 is:

[0034]

[0035] In the formula, u G (s k ) represents the global decision score, and the highest score represents the optimal game strategy.

[0036] Beneficial effects: Compared with the prior art, the present invention takes into account the game interaction relationship among multiple stakeholders such as power grid companies, energy storage operators, and energy storage users, takes system comprehensive efficiency, cost consumption, and user interruption time as multi-objective functions, and allocates weights by configuring incentive coefficients, thereby being able to comprehensively consider the game relationship among multiple participants and determine the optimal operation and maintenance time, thereby improving the reliability of the lithium-ion battery energy storage system. On the basis of ensuring the operating effect of the lithium-ion battery energy storage system, the additional cost of predictive maintenance is reduced, which is conducive to fault prediction and health management through predictive maintenance technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of the present invention;

[0038] Figure 2 The line chart is a global decision score under different expected maintenance dates. DETAILED DESCRIPTION

[0039] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0040] The present invention provides an intelligent decision-making method for safe operation of a lithium battery energy storage system based on multilateral cooperative game, such as Figure 1 As shown, it includes the following steps:

[0041] S1: Calculate the user interruption time cmi based on the power outage record of the lithium battery energy storage system;

[0042] The calculation formula for user interruption time cmi is:

[0043] cmi=N×n×d (1)

[0044] Where cmi represents the user interruption time of the lithium battery energy storage system, N represents the number of interrupted batteries, n represents the number of interruptions, and d represents the average interruption duration.

[0045] S2: Evaluate the maintenance effectiveness factor η based on maintenance records f and η d ;

[0046] Maintenance effectiveness factor η f and η d It is obtained by calculating the ratio of the number of interruptions of the lithium battery energy storage system before and after maintenance and the average interruption duration.

[0047] S3: Based on the effectiveness factor η f and η d, calculate the user interruption time change Δcmi:

[0048] Calculate the expected change in the number of outages and average outage duration after implementing the maintenance activity:

[0049] Δn=-(n×η f ) (2)

[0050] Δd=-(d×η d ) (3)

[0051] Where η f The maintenance effectiveness factor representing the number of interruptions of the lithium battery energy storage system, η d The maintenance effectiveness factor representing the average interruption duration of the lithium battery energy storage system;

[0052] Calculate the change in user interruption time Δcmi:

[0053] Δcmi=N×(ΔnΔd+dΔn+nΔd) (4)

[0054] Where Δcmi represents the expected change in user interruption time after the maintenance activity is implemented.

[0055] S4: Evaluate the expected cost Δpm of maintenance activities;

[0056] The expected cost Δpm of maintenance activities includes labor costs, maintenance costs, and the replacement costs of some energy storage devices.

[0057] S5: Calculate the expected overall efficiency ΔE of the lithium battery energy storage system;

[0058] The expected overall efficiency ΔE is equal to the ratio of the input power to the output power of the lithium battery energy storage system.

[0059] S6: Calculate the characteristic functions of the game participants, the power grid company, the energy storage operator and the energy storage user, based on the user interruption time change Δcmi, the expected cost Δpm and the expected comprehensive efficiency ΔE;

[0060] The optimal safe operation and maintenance plan is obtained through multilateral cooperative game theory, that is, while maximizing the overall efficiency of the lithium battery energy storage system, minimizing user interruption time and maintenance costs. Game optimization refers to finding a solution that can achieve Pareto optimality and meet the requirements of all participants.

[0061] The characteristic functions of the game participants, power grid companies, energy storage operators and energy storage users, are:

[0062]

[0063] In the formula, represents the characteristic function of the power grid company, represents the characteristic function of energy storage operation and maintenance providers, represents the energy storage user characteristic function, ΔE j Represents the expected overall efficiency of the lithium battery energy storage system, Δpm j It represents the expected cost of implementing maintenance activities, s k represents the expected maintenance date, and j=1,2,...,q represents the number of users.

[0064] S7: configure the excitation coefficient α, and standardize the feature function of step S6 by using the Sigmoid function;

[0065] The different feature functions between participants are standardized by the Sigmoid function. The standardization function is:

[0066]

[0067] In the formula, u i (s k ) represents the standardized characteristic function, s0 represents the initial strategy value of the game, and u of all participants i (s0) is 0.5, α represents the incentive coefficient, which is used to allocate the weights of participants and is selected between 0.5 and 2.0. The larger the value, the more inclined the decision is to the participant.

[0068] S8: Calculate the global decision score based on the standardized characteristic function and evaluate the best date for implementing the maintenance activity;

[0069] When the game starts, the characteristic function of each player is calculated. The best decision is evaluated by the global utility, and the expression of the global decision score is:

[0070]

[0071] In the formula, u G (s k ) represents the global decision score, and the highest score represents the optimal game strategy.

[0072] In order to verify the effectiveness of the method of the present invention, the method of the present invention is applied in this embodiment, as follows:

[0073] The method of the present invention is performed on a lithium battery energy storage system, and the power outage record and maintenance record of the lithium battery energy storage system are as follows:

[0074] The number of interrupted batteries N is 9117;

[0075] The number of interruptions n is 3;

[0076] The average interruption duration d was 42 minutes;

[0077] Maintenance effectiveness factor η of the number of interruptions f is 0.7;

[0078] Maintenance effectiveness factor η of average interruption duration d is 0.

[0079] In order to prioritize the reliability of power system operation, the incentive coefficient α of the power grid company is selected as 2.0, and that of the energy storage operation and maintenance provider and the energy storage user is 1.0.

[0080] The intelligent decision-making method for safe operation of lithium battery energy storage system based on multilateral cooperative game proposed in the present invention is used to calculate the global decision score line graph of the lithium battery energy storage system under different expected maintenance dates. Figure 2 As shown, it can be seen that the optimal predicted maintenance date is June, and the global decision score is the highest score of 0.52205.

Claims

1. An intelligent decision-making method for safe operation of a lithium battery energy storage system based on multilateral cooperative game, characterized in that: The steps include: S1: Calculate the user interruption time cmi based on the power outage record of the lithium battery energy storage system; S2: Evaluate the maintenance effectiveness factor η based on maintenance records f and η d ; S3: Based on the effectiveness factor η f and η d , calculate the user interruption time change Δcmi; S4: Evaluate the expected cost Δpm of maintenance activities; S5: Calculate the expected overall efficiency ΔE of the lithium battery energy storage system; S6: Calculate the characteristic functions of the game participants, the power grid company, the energy storage operator and the energy storage user, based on the user interruption time change Δcmi, the expected cost Δpm and the expected comprehensive efficiency ΔE; S7: configure the excitation coefficient α, and standardize the feature function of step S6 by using the Sigmoid function; S8: Calculate the global decision score based on the standardized characteristic function and evaluate the optimal date for implementing the maintenance activity.

2. According to claim 1, a method for intelligent decision-making for safe operation of a lithium battery energy storage system based on multilateral cooperative game, characterized in that: The calculation formula of the user interruption time cmi in step S1 is: cmi=N×n×d(1) In the formula, cmi represents the user interruption time of the lithium battery energy storage system, N represents the number of interrupted batteries, n represents the number of interruptions, and d represents the average interruption duration.

3. According to claim 1, a method for intelligent decision-making on safe operation of a lithium battery energy storage system based on multilateral cooperative game, characterized in that: In step S2, the effectiveness factor η is maintained f and η d It is obtained by calculating the ratio of the number of interruptions of the lithium battery energy storage system before and after maintenance and the average interruption duration.

4. According to claim 2, a method for intelligent decision-making for safe operation of a lithium battery energy storage system based on multilateral cooperative game, characterized in that: The calculation method of the user interruption time change Δcmi in step S3 is: Calculate the expected change in the number of outages and average outage duration after implementing the maintenance activity: Δn=-(n×η f ) (2) Δd=-(d×η d ) (3) Where η f The maintenance effectiveness factor representing the number of interruptions of the lithium battery energy storage system, η d The maintenance effectiveness factor representing the average interruption duration of the lithium battery energy storage system; Calculate the change in user interruption time Δcmi: Δcmi=N×(ΔnΔd+dΔn+nΔd)(4) where Δcmi represents the expected change in user interruption time after the maintenance activity is implemented.

5. According to claim 1, a method for intelligent decision-making for safe operation of a lithium battery energy storage system based on multilateral cooperative game, characterized in that: The expected cost Δpm of the maintenance activity in step S4 includes labor costs, maintenance costs, and replacement costs of some energy storage devices.

6. According to claim 1, a method for intelligent decision-making for safe operation of a lithium battery energy storage system based on multilateral cooperative game, characterized in that: The expected overall efficiency ΔE in step S5 is equal to the ratio of the input power to the output power of the lithium battery energy storage system.

7. According to claim 1, a method for intelligent decision-making for safe operation of a lithium battery energy storage system based on multilateral cooperative game, characterized in that: The characteristic functions of the game participants, the power grid company, the energy storage operation and maintenance provider, and the energy storage user in step S6 are: In the formula, represents the characteristic function of the power grid company, represents the characteristic function of energy storage operation and maintenance providers, represents the energy storage user characteristic function, ΔE j Represents the expected overall efficiency of the lithium battery energy storage system, Δpm j It represents the expected cost of implementing maintenance activities, s k represents the expected maintenance date, and j=1,2,...,q represents the number of users.

8. According to claim 7, a method for intelligent decision-making for safe operation of a lithium battery energy storage system based on multilateral cooperative game, characterized in that: In step S7, the different feature functions among the participants are standardized by the Sigmoid function, and the standardization function is: In the formula, u i (s k ) represents the standardized characteristic function, s0 represents the initial strategy value of the game, and α represents the incentive coefficient, which is used to allocate the weights of the participants.

9. The intelligent decision-making method for safe operation of a lithium battery energy storage system based on multilateral cooperative game according to claim 8 is characterized in that: The expression of the global decision score in step S8 is: In the formula, u G (s k ) represents the global decision score, and the highest score represents the optimal game strategy.

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