Method, device and computer device for comprehensively evaluating the performance of a battery energy storage system

Abstract

The present invention discloses a method, device and computer device for comprehensively evaluating the performance of a battery energy storage system. The method uses a group order relationship evaluation algorithm to calculate the comprehensive evaluation value of multiple different physical performance indexes of the BESS based on the capacity decay function of the battery energy storage system, the total daily operating cost target function and the power balance constraint condition, energy storage power constraint condition and control plan constraint condition of the battery energy storage system (BESS) after linearization processing. In multiple scheduling scenarios, such as combining the load side with the energy side and the upper grid side, the operating range of the charge state of the BESS is controlled in real time based on the control plan constraint condition, and the BESS continuous charge and discharge time plan is established according to various scheduling requirements. By repeating the six different physical performance index parameters of the BESS to each constraint condition, the comprehensive evaluation value of the six different physical performance indexes can finally be obtained, and the type selection of the BESS and the differentiation of various different physical performance evaluations can be fully analyzed, making the analysis of the BESS more comprehensive.

Description

[Technical field]

[0001] The present invention relates to the technical field of performance evaluation of a battery energy storage system (BESS), and in particular to a method, an apparatus and a computer device for comprehensively evaluating the performance of a battery energy storage system. [Background technology]

[0002] In order to overcome the intermittency and probability of renewable energy generation such as wind power and solar power generation, and to solve the problem of time coordination between the renewable energy generation side and the power consumer load side, a BESS is needed in the power supply system to improve the stability and reliability of the power supply system.

[0003] At present, the combined operation of large-scale BESS and wind and solar renewable energy generation has become a research direction highly regarded at all levels of the country, including technical indicators such as energy storage type selection, operational applicability, and performance evaluation in specific or multiple scenarios, as well as evaluation of safety, reliability, economics, and environmental benefits. In recent years, a lot of research has been conducted at home and abroad on the optimal allocation of energy storage capacity, control planning, and evaluation of economic benefits. However, energy storage with different physical characteristics has different performance in terms of technicality, safety, reliability, economics, etc., so research on the performance evaluation of energy storage is of great significance in terms of selecting the type of energy storage with different physical characteristics and promoting its business model.

[0004] The related technology has conducted a preliminary search for the application possibility of energy storage, the type selection and performance evaluation of energy storage, and established an analysis of energy storage based on different physical characteristics such as technical evaluation index, whole life cycle cost evaluation index, whole life cycle profit evaluation index and integration evaluation index. The related technology analyzes the performance of the load side energy storage system for a specific execution plan based on a specific scheduling scenario, usually in a peak shaving plan or an energy storage optimal distribution plan. In addition, since there are many factors related to the type selection and evaluation index of energy storage, the different physical performance of the energy storage battery cannot be known in the current performance analysis method of the battery energy storage system, and in multiple scheduling scenarios such as combining the load side with the energy side and the upper grid side, the performance analysis of the battery energy storage system is not comprehensive enough and the flexibility is reduced due to the differentiation of the type selection and performance evaluation. Summary of the Invention [Problem to be solved by the invention]

[0005] Therefore, the technical problem that the present invention aims to solve is to overcome the problem that the current performance analysis methods of battery energy storage systems in the prior art cannot know the different physical performances of the energy storage batteries, and in multiple scheduling scenarios, such as combining the load side with the energy side and the upper grid side, the performance analysis of the battery energy storage system is not comprehensive enough and flexibility is reduced due to the differentiation of its type selection and performance evaluation. To provide a method, device and computer device for comprehensively evaluating the performance of a battery energy storage system. [Means for solving the problem]

[0006] According to a first aspect, an embodiment of the present invention provides a method for comprehensively assessing performance of a battery energy storage system, comprising: Obtaining power consumption data generated on the load side by a target microgrid user, power purchase data in which the target microgrid user purchases power from an upper grid side, output data on the energy side for the target microgrid user, and energy storage data of the battery energy storage system; Developing a total daily operating cost objective function for the target microgrid user, and a power balance constraint, an energy storage power constraint, and a control plan constraint for the battery energy storage system based on the power consumption data, the power purchase data, the output data, and the energy storage data; Linearizing the power balance constraint, the energy storage power constraint and the control plan constraint of the battery energy storage system according to a preset linear processing algorithm; According to the capacity decay function of the battery energy storage system, the total daily operating cost target function, and the power balance constraint, energy storage power constraint, and control plan constraint of the battery energy storage system after linearization processing, a group order relationship evaluation algorithm is used to calculate a comprehensive evaluation value of a plurality of different physical performance indexes of the battery energy storage system. A method for comprehensively evaluating the performance of a battery energy storage system is disclosed.

[0007] According to the first aspect, in one embodiment of the first aspect, the total daily operating cost target function is implemented according to the following formula:

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[0008] According to the first aspect, in another embodiment of the first aspect, the power balance constraint is implemented by the following equation:

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[0009] According to the first aspect, in another embodiment of the first aspect, the linearization of the power balance constraint, the energy storage power constraint, and the control plan constraint of the battery energy storage system based on a preset linear processing algorithm is performed by the following formula:

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[0010] According to the first aspect, in another embodiment of the first aspect, the capacity fade function of the battery energy storage system is performed according to the following formula:

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[0011] According to the first aspect, in another embodiment of the first aspect, the step of calculating a comprehensive evaluation value of a plurality of different physical performance indexes of the battery energy storage system using the group order relationship evaluation algorithm includes: Calculating a first weight of each different physical performance index of the battery energy storage system using an initial order relationship evaluation algorithm according to a preset group evaluation condition; calculating a second weight for each different physical performance indicator based on the importance ordering value for each different physical performance indicator assigned by each evaluator; obtaining an initial weight for each assessor based on a first weight for each different physical performance indicator and a second weight for each different physical performance indicator; calculating an overall weight for each rater based on the second weights for each different physical performance index and the initial weights for each rater; determining a contribution ratio judgment matrix to which each evaluator contributes each of the different physical performance indicators; Calculating a third weight for each of the different physical performance indicators using the initial order relationship evaluation algorithm according to a first weight for each of the different physical performance indicators, a total weight for each of the assessors, and an element of a contribution ratio determination matrix to which each assessor contributes each of the different physical performance indicators; and calculating a comprehensive evaluation value of each of the different physical performance indexes based on a third weight of each of the different physical performance indexes.

[0012] According to the first aspect, in another embodiment of the first aspect, calculating a first weight of each different physical performance index of the battery energy storage system using an initial order relationship evaluation algorithm based on a preset group evaluation condition includes: Each evaluator

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[0013] According to the first aspect, in another embodiment of the first aspect, a plurality of different physical performance indicators of the battery energy storage system are provided. The mark is , including technical evaluation indicators and / or capacity setting evaluation indicators and / or cost evaluation indicators and / or environmental profit evaluation indicators and / or economic profit evaluation indicators and / or concentration evaluation indicators.

[0014] According to a second aspect, an embodiment of the present invention further discloses an apparatus for comprehensively evaluating the performance of a battery energy storage system, comprising: a data acquisition module for acquiring power consumption data generated on the load side by a target microgrid user, power purchase data in which the target microgrid user purchases power from an upper grid side, energy output data for the target microgrid user, and energy storage data of the battery energy storage system; a generating module for generating a total daily operating cost objective function of the target microgrid user, and a power balance constraint, an energy storage power constraint, and a control plan constraint of the battery energy storage system based on the power consumption data, the power purchase data, the output data, and the energy storage data; a linearization module for linearizing the power balance constraint, the energy storage power constraint, and the control plan constraint of the battery energy storage system based on a preset linearization algorithm; and and a comprehensive evaluation value calculation module for calculating a comprehensive evaluation value of multiple different physical performance indexes of the battery energy storage system using a group order relationship evaluation algorithm based on the capacity decay function of the battery energy storage system, the total daily operating cost target function, and the power balance constraints, energy storage power constraints, and control plan constraints of the battery energy storage system after linearization processing.

[0015] According to a third aspect, an embodiment of the present invention further discloses a computer readable storage medium, the computer readable storage medium storing computer instructions, the computer instructions comprising: ,Ko The method for comprehensively assessing the performance of a battery energy storage system according to the first aspect or any embodiment of the first aspect may be used to cause a computer to carry out the method.

[0016] According to a fourth aspect, an embodiment of the present invention further discloses a computer device including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for comprehensively evaluating performance of a battery energy storage system as set forth in the first aspect or any of the embodiments of the first aspect.

[0017] The technical solution of the present invention has the following advantages: The present invention discloses a method, device and computer device for comprehensively evaluating the performance of a battery energy storage system. The method uses a group order relationship evaluation algorithm to calculate the comprehensive evaluation value of multiple different physical performance indexes of a battery energy storage system based on the capacity decay function of the battery energy storage system, the total daily operating cost target function and the power balance constraint condition, energy storage power constraint condition and control plan constraint condition of the battery energy storage system (BESS) after linearization processing. In multiple scheduling scenarios, such as combining the load side with the energy side and the upper grid side, the operating range of the charge state of the BESS is controlled in real time based on the control plan constraint condition, and a BESS continuous charge and discharge time plan according to various scheduling requirements is established. By repeating the six different physical performance index parameters of the BESS to each constraint condition, the comprehensive evaluation value of the six different physical performance indexes can finally be obtained. The type selection of the BESS and the differentiation of various different physical performance evaluations can be fully analyzed, making the analysis of the BESS more comprehensive.

[0018] In order to more clearly describe the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the accompanying drawings used in the description of the specific embodiments or the prior art. The accompanying drawings in the following description are part of the embodiments of the present invention, and it is obvious to those skilled in the art that they can obtain other accompanying drawings based on these drawings without exerting creative efforts. [Brief description of the drawings]

[0019] [Figure 1] 1 is a flowchart of one specific example of a method for comprehensively evaluating the performance of a battery energy storage system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a principle block diagram of another specific example of a method for comprehensively evaluating the performance of a battery energy storage system according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a structural block diagram of a comprehensive evaluation device for the performance of a battery energy storage system according to an embodiment of the present invention; [Figure 4] FIG. 2 is a schematic diagram of a hardware configuration of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] The technical solutions of the present invention will be described below clearly and completely with reference to the accompanying drawings, and it is clear that the described embodiments are only a part of the embodiments of the present invention, and are not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of the present invention.

[0021] In describing the present invention, it is to be understood that the orientations or positional relationships indicated by the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., used are orientations or positional relationships based on the drawings, and are merely for the purpose of simplifying the description and illustration of the present invention, and do not indicate or imply that a specified location or element must have a particular orientation or be operated in a particular orientation configuration, and therefore should not be understood as limiting the present invention. Furthermore, the terms "first", "second" and "third" are for illustrative purposes only, and should not be understood as indicating or implying relative importance.

[0022] In the present invention, unless otherwise clearly specified and limited, the terms "attach", "connect", "connection", etc. should be understood in a broad sense, for example, it may be a fixed connection, a removable connection, or an integral connection, it may be a mechanical connection, it may be an electrical connection, it may be a direct connection, it may be an indirect connection via an intermediate medium, it may be an internal communication between two elements, it may be a wireless connection, or it may be a wired connection. A person skilled in the art can understand the specific meaning of the above terms in this specification according to the situation.

[0023] Furthermore, the technical features relating to different embodiments of the present invention described below can be combined with each other unless they are inconsistent with each other.

[0024] In the related technology, the battery energy storage system (BESS) participates in the energy optimization scheduling of microgrid users, and analyzes the performance of the load-side energy storage system for a specific execution plan based on a specific scheduling scenario, usually in a peak shaving plan or an energy storage optimal allocation plan. In addition, because there are many factors related to the type selection and evaluation index of energy storage, the current performance analysis method of the battery energy storage system cannot know the different physical characteristics of the energy storage battery, and in multiple scheduling scenarios such as combining the load side with the energy side and the upper grid side, the performance analysis of the battery energy storage system is not comprehensive enough and the flexibility is reduced due to the differentiation of type selection and performance evaluation.

[0025] In view of this, an embodiment of the present invention discloses a method for comprehensively evaluating the performance of a battery energy storage system, which includes steps S11 to S14, as shown in FIG.

[0026] In step S11, power consumption data generated on the load side by the target microgrid user, power purchase data in which the target microgrid user purchases power from the upper grid side, energy side output data for the target microgrid user, and energy storage data of the battery energy storage system are obtained.

[0027] The target microgrid user is a power consuming user that participates in the BESS energy optimization scheduling on the load side. The above power consumption data is the power consumption load actually generated by the target microgrid user on the load side in period t, and the power consumption data can be expressed as P or W, for example, P=100W, W=78kWh. The above power purchase data includes the purchased power unit price and purchased power of the auxiliary service provided by the upper grid side in period t, where the purchased power unit price is

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[0028] Since the above power consumption data, power purchase data, energy storage data and output data relate to energy scheduling in different scenarios on the load side, upper grid side and energy side, the target microgrid users use the BESS to participate in various different scheduling scenarios.

[0029] In step S12, based on the power consumption data, power purchase data, output data and energy storage data, a total daily operating cost objective function of the target microgrid user, and a power balance constraint, an energy storage power constraint and a control plan constraint of the battery energy storage system are generated.

[0030] Here, the total daily operating cost objective function ensures that the load-side target microgrid users use power in the most economical way. The power balance constraint, energy storage power constraint, and control plan constraint of the battery energy storage system ensure that the target microgrid users use the BESS to participate in multiple different scheduling scenarios and execute multiple different execution control plans.

[0031] In a specific embodiment, the total daily operating cost objective function is implemented according to the following equation (1):

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[0032] The total objective function of the target microgrid user is a total objective function that minimizes the usage cost of the BESS and the cost of purchasing power from the upper grid. The target microgrid user i may use the BESS to store power when the wind or solar renewable energy power output of the target microgrid user i is higher than the load demand, and may use the BESS to release power when the load is higher than the renewable energy power output of the target microgrid user i. In addition, the target microgrid user i may consider using the BESS to store or release power, and may also consider fluctuations in the power price in the area. If the cost of using the BESS is higher than the cost of purchasing power from the upper grid, the target microgrid user i must purchase power from the upper grid during this period, so the daily operating cost of the target microgrid user i is calculated according to the above formula (1).

[0033] In the above formula (1), when the output data is greater than the power consumption data, the battery energy storage system stores power, and when the output data is equal to or less than the power consumption data, the battery energy storage system releases power.

[0034]

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[0035] In another specific embodiment, the power balance constraint is implemented according to the following equation (2):

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[0036] The economic benefit of BESS is calculated by the following equation (3).

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[0037] The carbon emissions due to the use of thermal generator units for power generation output before and after the target microgrid user is connected to the BESS are carried out by the following equations (4) and (5):

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[0038] In a specific embodiment, the energy storage power constraint is implemented according to the following equation (6):

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[0039] In a specific embodiment, the control scheme constraint is implemented according to the following equation (7):

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[0040] According to the above formula (7), two different control strategies can be established. One is to control the operation range of the real-time charge state of the BESS, and the other is to establish the BESS continuous charge and discharge time strategy according to different scheduling requirements. In formula (7), where:

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[0041] In step S13, the power balance constraint condition, the energy storage power constraint condition, and the control plan constraint condition of the battery energy storage system are linearized according to a preset linear processing algorithm.

[0042] The preset linear processing algorithm may be a big M algorithm, and the preset linear processing algorithm converts the nonlinear constraint into a linear constraint, and the linear constraint appears in the form of an integer, which is easy to participate in the calculation of the subsequent group order relationship evaluation algorithm.

[0043] In a specific embodiment, the linearization processing of the power balance constraint, the energy storage power constraint, and the control plan constraint of the battery energy storage system based on a preset linear processing algorithm is performed according to the following equation (8).

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[0044] The nonlinear constraint is converted into a linear constraint by equation (8), where M is the maximum coefficient in the preset linear processing algorithm, and the preset linear processing algorithm is the big M algorithm.

[0045] In step S14, based on the capacity decay function, the total daily operating cost target function, and the power balance constraints, energy storage power constraints, and control plan constraints of the battery energy storage system after linearization processing, a group order relationship evaluation algorithm is used to calculate the comprehensive evaluation value of multiple different physical performance indexes of the battery energy storage system.

[0046] In a specific embodiment, the capacity fade function of the battery energy storage system is implemented by the following equation (9):

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[0047] The main factors of capacity fade of BESS include depth of discharge (DOD), discharge power, rated capacity, cycle number, etc. In the above equation (9),

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[0048]

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[0049] In one embodiment, a number of different physical performance indicators of a battery energy storage system are used. The mark is , technical evaluation index and / or capacity setting evaluation index and / or cost evaluation index and / or environmental profit evaluation index and / or economic profit evaluation index and / or integration evaluation index, and may further include other physical performance indexes. However, in the embodiment of the present invention, mainly six different physical performance indexes are studied and analyzed, namely, technical evaluation index, capacity setting evaluation index, cost evaluation index, environmental profit evaluation index, economic profit evaluation index and integration evaluation index.

[0050] Here, the above technical evaluation index is a technical evaluation index consisting of the service life of the BESS, the root mean square of the DOD within the scheduling cycle, and the overall conversion efficiency of the battery, and is specifically implemented by the following equation (11).

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[0051] In the formula (11),

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[0052] In the above formula (13),

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[0053] Here, the above cost evaluation index is still one of the very important considerations in the initial construction of BESS. BESS operators in the investment and construction stage need to consider the construction costs brought by batteries with different physical performances. Therefore, in the embodiment of the present invention, a cost evaluation index for BESS is established, which is specifically implemented by the following equation (14).

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[0054] In the formula (14),

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[0055] Here, with regard to the above environmental revenue evaluation index, in order to reduce the environmental pollution caused when the coal-fired power generation unit or the thermal power generation unit supplies power to the target microgrid user after being connected to the BESS, the environmental revenue evaluation index of the BESS is implemented by the following equation (15).

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[0056] In the above formula (15),

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[0057] Here, regarding the above integration degree evaluation index, BESSs with different physical properties have different physical property characteristics due to differences in the internal chemical materials and manufacturing methods. In the embodiment of the present invention, an integration degree evaluation index for BESSs is established, which is specifically implemented by the following formula (16).

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[0058] In the above formula (16),

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[0059] In a specific embodiment, as shown in FIG. 2, in the above step S14, calculating the comprehensive evaluation value of multiple different physical performance indexes of the battery energy storage system based on the group order relationship evaluation algorithm includes steps S21 to S27.

[0060] In step S21, based on the preset group evaluation condition, an initial order relationship evaluation algorithm is used to calculate a first weight of each different physical performance index of the battery energy storage system.

[0061] The initial order relationship evaluation algorithm is a conventional order relationship method. In a specific embodiment, the above step S21 is to calculate the first weights of each different physical performance index of the battery energy storage system according to the preset group evaluation conditions using the initial order relationship evaluation algorithm; Each evaluator

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[0062] Where:

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[0063] In step S22, a second weight for each different physical performance index is calculated based on the importance ordering value of each different physical performance index given by each evaluator.

[0064] After each evaluator gives the importance order of each different physical performance index, the importance ordering value of each different physical performance index can be obtained, and then the second weight of each different physical performance index is calculated, which is specifically performed by the following equation (18):

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[0065]

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[0066]

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[0067] In step S23, obtain an initial weight for each evaluator based on the first weight for each different physical performance index and the second weight for each different physical performance index.

[0068] Each evaluator

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[0069] In a specific embodiment, obtaining an initial weight for each evaluator based on the first weight for each different physical performance index and the second weight for each different physical performance index is performed according to the following formula (19):

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[0070] Where:

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[0071]

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[0072] In step S24, calculate a total weight for each evaluator based on the second weight for each different physical performance index and the initial weight for each evaluator.

[0073] The weighting of each evaluator is determined mainly from two perspectives: calculating the weighting of each evaluation value from each different physical performance index of the BESS, and obtaining the weighting of each evaluator from the weighting of each different physical performance index of the BESS. 。

[0074] In a specific embodiment, in the above step S24, calculating the overall weight of each evaluator based on the second weight of each different physical performance index and the initial weight of each evaluator is performed according to the following formula (20):

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[0075] Where:

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[0076] In step S25, a contribution ratio judgment matrix for each evaluator that gives each different physical performance index is determined.

[0077] The contribution ratio determination matrix of each different physical performance index is implemented by the following equation (21).

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[0078] Where:

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[0079] In step S26, based on the first weight of each different physical performance index, the overall weight of each evaluator, and the elements of the contribution ratio judgment matrix of each different physical performance index given by each evaluator, further calculate a third weight of each different physical performance index using an initial order relationship evaluation algorithm.

[0080] Specifically, according to equations (20) and (23), each different physical performance index

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[0081] In step S27, the comprehensive evaluation value of each different physical performance index is calculated according to the third weight of each different physical performance index, which is specifically performed according to the following formula (24):

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[0082] Where:

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[0083] The comprehensive evaluation value of each different physical performance index of the BESS is used to reflect the comprehensive evaluation of the different physical performances of the battery participating in one specific scene or multiple scenes;

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[0084] The method for comprehensively evaluating the performance of the battery energy storage system according to the embodiment of the present invention performs the above steps S11 to S14, and based on the capacity decay function of the battery energy storage system, the daily operating cost total target function, and the power balance constraint condition, energy storage power constraint condition, and control plan constraint condition of the battery energy storage system after linearization processing, a group order relationship evaluation algorithm is used to calculate the comprehensive evaluation value of multiple different physical performance indexes of the battery energy storage system, and in multiple scheduling scenarios, such as combining the load side with the energy side and the upper grid side, based on the control plan constraint condition, the operating range of the charge state of the BESS is controlled in real time, and a BESS continuous charge and discharge time plan is established according to various scheduling requirements, and the six different physical performance index parameters of the BESS are repeated for each constraint condition, and finally the comprehensive evaluation value of the six different physical performance indexes can be obtained, and the type selection of the BESS and the differentiation of various different physical performance evaluations can be fully analyzed, making the analysis of the BESS more comprehensive.

[0085] Based on the same concept, an embodiment of the present invention further discloses an apparatus for comprehensively evaluating the performance of a battery energy storage system, as shown in FIG. a data acquisition module 31 for acquiring power consumption data generated on the load side by the target microgrid user, power purchase data in which the target microgrid user purchases power from the upper grid side, output data on the energy side for the target microgrid user, and energy storage data of the battery energy storage system; a generating module 32 for generating a total daily operating cost objective function of the target microgrid user, and a power balance constraint, an energy storage power constraint, and a control plan constraint of the battery energy storage system based on the power consumption data, the power purchase data, the output data, and the energy storage data; a linearization module 33 for linearizing the power balance constraint, the energy storage power constraint, and the control plan constraint of the battery energy storage system based on a preset linearization algorithm; and The battery energy storage system includes a comprehensive evaluation value calculation module 34 for calculating a comprehensive evaluation value of multiple different physical performance indexes of the battery energy storage system using a group order relationship evaluation algorithm based on the capacity decay function of the battery energy storage system, the total daily operating cost target function, and the power balance constraints, energy storage power constraints, and control plan constraints of the battery energy storage system after linearization processing.

[0086] The embodiment of the present invention further discloses a computer device, and as shown in Fig. 4, the computer device may include a processor 41 and a memory 42. Here, the processor 41 and the memory 42 may be connected by a bus or other method, and Fig. 4 takes the example of connecting them by a bus.

[0087] Processor 41 may be a Central Processing Unit (CPU), or may be other general purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or other chips, or combinations of these various chips.

[0088] The memory 42 can be used as a non-transitory computer-readable storage medium to store non-transitory software programs, non-transitory computer-executable programs and modules. The processor 41 executes the non-transitory software programs, instructions and modules stored in the memory 42 to execute various functions of the processor and data processing, that is, to realize the method for comprehensively evaluating the performance of the battery energy storage system in the above embodiment.

[0089] The memory 42 may include a program storage area and a data storage area, where the program storage area may store an operating system, application programs necessary for at least one function, and the data storage area may store data created by the processor 41, etc. Additionally, the memory 42 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk memory device, flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 42 may optionally include memory configured remotely with respect to the processor 41, and these remote memories may be connected to the processor 41 via a network. Examples of such networks include, but are not limited to, a power grid, the Internet, a corporate intranet, a local area network, a mobile communication network, and combinations thereof.

[0090] The one or more modules, when stored in the memory 42 and executed by the processor 41, implement a method for comprehensively assessing the performance of a battery energy storage system in the embodiment shown in the figures.

[0091] Specific details of the above computer device can be understood in accordance with the relevant descriptions and effects corresponding to the embodiments shown with reference to the drawings, and will not be repeated in this specification.

[0092] It can be understood by those skilled in the art that the implementation of all or part of the steps in the above-mentioned embodiment of the method can be completed by instructing relevant hardware by a computer program, and the program may be stored in a computer-readable storage medium, and when the program is executed, it may include the steps of each of the above-mentioned embodiment of the method. Here, the storage medium may be a magnetic disk, a compact disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (abbreviated as HDD), or a solid-state drive (SSD). The storage medium may further include a combination of the above-mentioned types of memory.

[0093] Obviously, the above examples are merely illustrative for clear explanation, and do not limit the embodiments. Those skilled in the art can make other different forms of modifications or changes based on the above description. It is not necessary and cannot be possible to cover all of the embodiments in this specification. Any obvious modifications or changes derived therefrom still remain within the protection scope of the present invention.

[0094] This application claims priority to a Chinese patent application filed with the China Patent Office on December 7, 2022, bearing application number 202211563728.6 and entitled "Method, apparatus and computer device for comprehensively assessing the performance of battery energy storage system," the entire contents of which are incorporated herein by reference.

Claims

1. A method for comprehensively evaluating the performance of a battery energy storage system, comprising: Obtaining power consumption data generated on the load side by a target microgrid user, power purchase data in which the target microgrid user purchases power from an upper grid side, output data on the energy side for the target microgrid user, and energy storage data of the battery energy storage system; Developing a total daily operating cost objective function for the target microgrid user, and a power balance constraint, an energy storage power constraint, and a control plan constraint for the battery energy storage system based on the power consumption data, the power purchase data, the output data, and the energy storage data; Linearizing the power balance constraint, the energy storage power constraint and the control plan constraint of the battery energy storage system according to a preset linear processing algorithm; According to the capacity decay function of the battery energy storage system, the total daily operating cost target function, and the power balance constraint, energy storage power constraint, and control plan constraint of the battery energy storage system after linearization processing, a group order relationship evaluation algorithm is used to calculate a comprehensive evaluation value of a plurality of different physical performance indexes of the battery energy storage system. A method for comprehensively evaluating the performance of a battery energy storage system, comprising:

2. The total daily operating cost target function is implemented according to the following formula: [0010] Wherein, when the output data is greater than the power consumption data, the battery energy storage system stores power; and when the output data is equal to or less than the power consumption data, the battery energy storage system discharges power; [0025] is the daily power consumption cost of the target microgrid user i; [0030] is the purchased power that the target microgrid user i purchases from the upper grid side in a period t after connecting to the battery energy storage system; [0045] is the purchased electricity unit price of the auxiliary service provided by the user operator of the battery energy storage system to the microgrid user in the period t; [0050] is the electricity price in the area where the target user is located; [006] is the charging power that the target microgrid user i stores using the battery energy storage system in a period t; [0070] is the discharge power that the target microgrid user i discharges using the battery energy storage system in a period t; [0080] is the scheduling time interval, T is the total number of periods, and Z is the total number of microgrid users.

2. The method for comprehensively evaluating the performance of a battery energy storage system according to claim 1.

3. The power balance constraint is implemented by the following equation: [0090] Where: [0089] is the power generation of the wind renewable energy on the energy side acquired by the target microgrid user i in the period t; ##EQU00011## is the power generation of solar renewable energy on the energy side acquired by the target microgrid user i in a period t; ##EQU00012## is the power consumption of the target microgrid user i in time period t; The energy storage power constraint is implemented by the following formula: ##EQU00013## Where: ##EQU14## is the charge conversion efficiency of the battery energy storage system; ##EQU00015## is the discharge conversion efficiency of the battery energy storage system; ##EQU00016## is the state of charge of the battery energy storage system at time period t; ##EQU00017## is the discharge state of the battery energy storage system at time period t; [0018] the battery energy storage system is in a charging state when [0019] the battery energy storage system is in a discharged state when [0020] is the rated power of the battery energy storage system; ##EQU00021## is the discharge power of the battery energy storage system in time period t; [0022] is the charging power of the battery energy storage system at time period t; The control plan constraints are implemented according to the following equation: [0023] Where: [0024] is the energy state of the battery energy storage system at time period t; [0025] is the energy state of the battery energy storage system in time period t-1, [0026] is the minimum power budget of the energy storage system; [0027] is the maximum power capacity of the battery energy storage system; [0028] is the rated capacity of the battery energy storage system; [0029] is the state of charge of the battery energy storage system at time t; [0030] is the charge state of the battery energy storage system at time t-1, [0031] is the minimum charge state of the battery energy storage system; [0032] is the maximum charge state of the battery energy storage system; [Equation 33] is the initial charge state of the battery energy storage system in the current scheduling cycle; [0034] is an initial charge state in the next scheduling cycle adjacent to the current scheduling cycle, and in order to enable the battery energy storage system to be continuously charged and discharged in the scheduling waiting cycle, [Equation 35] year, [0036] is the duration of charging and discharging when the battery energy storage system participates in scheduling; [Equation 37] is the state of charge coefficient of the control scheme of the battery energy storage system; 2. The method for comprehensively evaluating the performance of a battery energy storage system according to claim 1.

4. According to a preset linear processing algorithm, the linearization of the power balance constraint, the energy storage power constraint, and the control plan constraint of the battery energy storage system is performed according to the following formula: [Equation 38] where M is the maximum coefficient in the preset linear processing algorithm, and the preset linear processing algorithm is a big M algorithm.

2. The method for comprehensively evaluating the performance of a battery energy storage system according to claim 1.

5. The capacity fade function of the battery energy storage system is implemented by the following equation: [0039] Where: [0040] is the depth of discharge value of the battery energy storage system at time period t; [0041] is the standard depth of discharge value, [0042] teeth, [0043] is the number of cycles corresponding to [0044] is a fitting parameter of the battery energy storage system, and the capacity fade rate of the battery energy storage system [0045] the useful life of the battery energy storage system ends; 2. The method for comprehensively evaluating the performance of a battery energy storage system according to claim 1.

6. The step of calculating a comprehensive evaluation value of a plurality of different physical performance indexes of the battery energy storage system using the group order relationship evaluation algorithm includes: Calculating a first weight of each different physical performance index of the battery energy storage system using an initial order relationship evaluation algorithm according to a preset group evaluation condition; calculating a second weight for each different physical performance indicator based on the importance ordering value for each different physical performance indicator assigned by each evaluator; obtaining an initial weight for each evaluator based on a first weight for each different physical performance indicator and a second weight for each different physical performance indicator; calculating an overall weight for each evaluator based on the second weights for each different physical performance index and the initial weight for each evaluator; determining a contribution ratio judgment matrix to which each evaluator contributes each of the different physical performance indicators; According to the first weight of each different physical performance index, the overall weight of each evaluator, and the elements of the contribution ratio judgment matrix of each different physical performance index, further using the initial order relationship evaluation algorithm to calculate a third weight of each different physical performance index; and calculating a comprehensive evaluation value of each of the different physical performance indexes based on a third weight of each of the different physical performance indexes.

2. The method for comprehensively evaluating the performance of a battery energy storage system according to claim 1.

7. Calculating a first weight of each different physical performance index of the battery energy storage system using an initial order relationship evaluation algorithm based on a preset group evaluation condition; Each evaluator [0046] There are different physical performance indicators [0047] The ordering relation given to [0048] and the contribution ratio of each different physical performance index is [0049] Then, using the initial order relationship evaluation algorithm, the first weights of the different physical performance indexes are calculated according to the following formula: [Number 50] Calculating [0051] Where: [0052] are the weights of each different physical performance index based on the value of c-1, [0,53] is the weight of each different physical performance index based on the value of c, Calculating a second weight for each different physical performance index based on the importance ordering value for each different physical performance index given by each evaluator is performed according to the following formula: [0,54] [0.55] are the different physical performance indices [0,56] Regarding the evaluator [0.57] is the ordered value vector set created by [0,58] are the different physical performance indices [0,59] where: [0.60] year, [0061] is a second weight for each different physical performance index; Obtaining an initial weight for each evaluator based on the first weight for each different physical performance index and the second weight for each different physical performance index is performed according to the following formula: [0062] Where: [0063] are the different physical performance indices [0,64] According to the initial order relation evaluation algorithm, [0.65] a first set of weights for each different physical performance index calculated by [0.66] Each of the different physical performance indicators [0.67] a second set of weights for each distinct physical performance index of [0068] year, [0,69] Each of the different physical performance indicators [Number 70] is the overall weight vector of [Equation 71] is the initial weight of each evaluator, Calculating the overall weight of each evaluator based on the second weight of each different physical performance index and the initial weight of each evaluator is performed according to the following formula: [Equation 72] Where: [Equation 73] is the overall weight of each evaluator, [74] is the initial weight of each evaluator, [Number 75] is a second weight for each different physical performance index; Determining the contribution ratio judgment matrix to which each evaluator gives each different physical performance index is performed by the following formula: [76] [77] [78] Where: [79] year, [Number 80] is the evaluator [Number 81] Each of the different physical performance indices is given by [0082] a matrix of importance ordering values ​​between and the ratio of contributions of adjacent physical performance indicators; [Number 83] is the evaluator [Number 84] The ratio of the importance ordering value set between each physical performance index and the contribution set of adjacent evaluation indexes given by [Number 85] teeth, [Number 86] A contribution ratio determination matrix of each of the different physical performance indicators through transformation; [Number 87] is an element of the contribution ratio determination matrix of each different physical performance index; Calculating the comprehensive evaluation value of each of the different physical performance indexes according to the third weight of each of the different physical performance indexes is performed according to the following formula: [Number 88] Where: [Number 89] is the battery energy storage system type b ( [Number 90] ) is the overall evaluation value of each different physical performance index, B is the battery energy storage system type set; [Number 91] is the third weight of the i-th physical performance index, [Number 92] is the i-th physical performance index, The method for comprehensively evaluating the performance of a battery energy storage system according to claim 6.

8. The plurality of different physical performance indexes of the battery energy storage system include a technical evaluation index and / or a capacity setting evaluation index and / or a cost evaluation index and / or an environmental profit evaluation index and / or an economic profit evaluation index and / or an integration evaluation index; 2. The method for comprehensively evaluating the performance of a battery energy storage system according to claim 1.

9. An apparatus for comprehensively evaluating the performance of a battery energy storage system, comprising: a data acquisition module for acquiring power consumption data generated on the load side by a target microgrid user, power purchase data in which the target microgrid user purchases power from an upper grid side, energy output data for the target microgrid user, and energy storage data of the battery energy storage system; a generating module for generating a total daily operating cost objective function of the target microgrid user, and a power balance constraint, an energy storage power constraint, and a control plan constraint of the battery energy storage system based on the power consumption data, the power purchase data, the output data, and the energy storage data; a linearization module for linearizing the power balance constraint, the energy storage power constraint, and the control plan constraint of the battery energy storage system based on a preset linearization algorithm; and and a comprehensive evaluation value calculation module for calculating a comprehensive evaluation value of a plurality of different physical performance indexes of the battery energy storage system using a group order relationship evaluation algorithm according to a capacity decay function of the battery energy storage system, the total daily operating cost target function, and a power balance constraint condition, an energy storage power constraint condition, and a control plan constraint condition of the battery energy storage system after linearization processing. An apparatus for comprehensively evaluating the performance of a battery energy storage system, comprising:

10. A computer-readable storage medium, the computer-readable storage medium storing computer instructions, the computer instructions being used to cause a computer to execute a method for comprehensively evaluating the performance of a battery energy storage system according to any one of claims 1 to 8.

11. A computer device including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for comprehensively evaluating the performance of a battery energy storage system according to any one of claims 1 to 8.

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