Comprehensive evaluation method and device for performance of battery energy storage system and computer equipment

Abstract

The application discloses a kind of comprehensive evaluation method, device and computer equipment of battery energy storage system performance, wherein method, based on battery energy storage system capacity attenuation function, daily operating cost total objective function and linearization processing after battery energy storage system (Battery Energy Storage System, BESS) power balance constraint condition, energy storage power constraint condition, control strategy constraint condition, the comprehensive evaluation value of multiple different physical performance indexes of BESS is calculated using group order relation evaluation algorithm, can be combined in load side energy side and multiple different scheduling scenarios of upper grid side, based on control strategy constraint condition, the operating range of BESS state of charge is controlled in real time, and according to different scheduling requirements, BESS continuous charge-discharge time strategy is established, 6 different physical performance index parameters of BESS are iterated into each constraint condition, finally the comprehensive evaluation value of 6 different physical performance indexes can be obtained, the difference of the selection of BESS and each different physical performance evaluation can be fully analyzed, so that BESS analysis is more comprehensive.

Description

Technical Field

[0001] This invention relates to the field of performance evaluation technology for Battery Energy Storage Systems (BESS), specifically to a comprehensive evaluation method, apparatus, and computer equipment for the performance of battery energy storage systems. Background Technology

[0002] To overcome the intermittency and randomness of renewable energy generation such as wind and solar power, and to solve the time coordination problem between renewable energy generation and power user load, BESS needs to be set up in the power supply system to improve the stability and reliability of the power supply system.

[0003] Currently, large-scale BESS (Building Emergency Shielding) combined with wind and solar renewable energy generation has become a research direction highly valued at all levels of government. This involves technical indicators such as energy storage selection, operational applicability, and performance evaluation in specific or multi-scenario applications, as well as evaluations of safety, reliability, economy, and environmental benefits. In recent years, numerous studies have been conducted both domestically and internationally on energy storage capacity optimization, control strategies, and economic benefit assessment. However, different physical properties of energy storage exhibit varying performance in terms of technology, safety, reliability, and economy. Therefore, research on energy storage performance evaluation is of great significance for the selection of energy storage with different physical properties and the promotion of its business models.

[0004] Preliminary explorations have been conducted on related technologies regarding energy storage applicability, energy storage selection, and performance evaluation. Analysis of energy storage based on different physical performance indicators, including technical evaluation indicators, life-cycle cost evaluation indicators, life-cycle benefit evaluation indicators, and integration degree evaluation indicators, has been established. These technologies typically analyze energy storage system performance on the load side using peak-shaving and valley-filling strategies or energy storage optimization configuration strategies, based on specific scheduling scenarios and operating strategies. Furthermore, numerous factors influence energy storage selection and evaluation indicators. Therefore, current performance analysis methods for battery energy storage systems cannot fully understand the various physical properties of energy storage batteries. The differences in selection and performance evaluation across various scheduling scenarios, including the load side, energy side, and upstream grid side, result in incomplete and inflexible performance analysis of battery energy storage systems. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem that the current performance analysis methods of battery energy storage systems in the prior art cannot know the various physical properties of the energy storage battery, and the differences in its selection and performance evaluation under various scheduling scenarios such as load side, energy side and upper-level grid side, which leads to the problem that the performance analysis of battery energy storage systems is not comprehensive and has poor flexibility. Therefore, the present invention provides a comprehensive evaluation method, device and computer equipment for the performance of battery energy storage systems.

[0006] According to a first aspect, embodiments of the present invention disclose a comprehensive evaluation method for the performance of a battery energy storage system, comprising the following steps:

[0007] Acquire the electricity consumption data generated by the target microgrid user on the load side, the electricity purchase data of the target microgrid user from the upstream grid side, the power output data output from the energy side to the target microgrid user, and the energy storage data of the battery energy storage system;

[0008] Based on the electricity consumption data, the electricity purchase data, the power output data, and the energy storage data, create the total objective function for the daily operating cost of the target microgrid user, and the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system;

[0009] The power balance constraint, energy storage power constraint, and control strategy constraint of the battery energy storage system are linearized based on a preset linear processing algorithm.

[0010] Based on the battery energy storage system capacity decay function, the daily operating cost objective function, and the linearized power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system, a comprehensive evaluation value of multiple different physical performance indicators of the battery energy storage system is calculated using a group order relation evaluation algorithm.

[0011] In conjunction with the first aspect, in one embodiment of the first aspect, the total objective function for daily operating costs is executed by the following formula:

[0012]

[0013] Specifically, when the output data is greater than the power consumption data, the battery energy storage system stores electrical energy; when the output data is less than or equal to the power consumption data, the battery energy storage system releases electrical energy.

[0014] The daily electricity cost for the target microgrid user i; For the target microgrid user i, after connecting to the battery energy storage system, the power purchased from the upstream grid side during time period t; λ t load λ is the electricity purchase price λ represents for the user operator of the battery energy storage system to provide ancillary services to the microgrid users during time period t. t g The electricity price in the area where the target user is located. The charging power of the battery energy storage system when the target microgrid user i uses the battery energy storage system to store electrical energy in time period t. Let Δt be the discharge power of the battery energy storage system used by the target microgrid user i during time period t, where Δt is the scheduling time interval, T is the total number of time periods, and Z is the total number of microgrid users.

[0015] In conjunction with the first aspect, in another embodiment of the first aspect, the power balance constraint condition is performed by the following formula:

[0016]

[0017] in, For the target microgrid user i, obtain the power generation of wind renewable energy on the energy side during time period t. To obtain the power generation of photovoltaic renewable energy on the energy side of the target microgrid user i during time period t, The power consumption of the target microgrid user i during time period t;

[0018] The energy storage power constraint condition is executed by the following formula:

[0019]

[0020] Where, η c η represents the charging conversion efficiency of the battery energy storage system. d ψ is the discharge conversion efficiency of the battery energy storage system. c,t ψ represents the charging state of the battery energy storage system during time period t. d,t This indicates the discharge state of the battery energy storage system during time period t, when ψ c,t =1, ψ d,t When ψ = 0, the battery energy storage system is in a charging state; when ψ = 0, the battery energy storage system is in a charging state. d,t =1, ψ c,t When P = 0, the battery energy storage system is in a discharging state. rate P is the rated power of the battery energy storage system. d,t P is the discharge power of the battery energy storage system during time period t. c,t The charging power of the battery energy storage system during time period t;

[0021] The control strategy constraints are executed using the following formula:

[0022]

[0023] Among them, E t E represents the state of charge of the battery energy storage system during time period t. t-1 E represents the state of charge of the battery energy storage system during time period t-1. min E represents the minimum electrical capacity of the energy storage system. max E represents the maximum capacity of the battery energy storage system.rate The rated capacity of the battery energy storage system is given by SOC. t Let SOC be the state of charge of the battery energy storage system at time t. t-1 The state of charge (SOC) of the battery energy storage system at time t-1 is given by [reference needed]. min The minimum state of charge (SOC) of the battery energy storage system. max The maximum state of charge (SOC) of the battery energy storage system; SOC0 is the initial state of charge (SOC) of the battery energy storage system in the current scheduling cycle. T+1 The initial state of charge (SOC) for the next scheduling cycle adjacent to the current scheduling cycle is determined by the battery energy storage system to ensure continuous charging and discharging during the scheduling cycle. T+1 =SOC0; γ is the charging and discharging duration when the battery energy storage system participates in scheduling; τ1 and τ2 are the state of charge coefficients of the control strategy of the battery energy storage system.

[0024] In conjunction with the first aspect, in another embodiment of the first aspect, the power balance constraint, energy storage power constraint, and control strategy constraint of the battery energy storage system are linearized based on a preset linear processing algorithm by the following formula:

[0025]

[0026] Wherein, M is the maximum coefficient in the preset linear processing algorithm, and the preset linear processing algorithm is the maximum M algorithm.

[0027] In conjunction with the first aspect, in another embodiment of the first aspect, the capacity decay function of the battery energy storage system is performed by the following formula:

[0028]

[0029] Among them, D t D represents the depth of discharge of the battery energy storage system during time period t. r For the standard depth of discharge, N r D r The corresponding number of cycles, α1 and α2 are the fitting parameters of the battery energy storage system, when the capacity decay rate of the battery energy storage system... At that time, the battery energy storage system reaches the end of its service life.

[0030] In conjunction with the first aspect, in another embodiment of the first aspect, the calculation of the comprehensive evaluation value of multiple different physical performance indicators of the battery energy storage system using the group order relation evaluation algorithm includes:

[0031] Based on preset group evaluation conditions, the first weights of various physical performance indicators of the battery energy storage system are calculated using the initial order relation evaluation algorithm.

[0032] Based on the importance ranking values ​​of various physical performance indicators given by each evaluator, the second weight of each different physical performance indicator is calculated.

[0033] Based on the first weight and the second weight of each different physical performance index, the initial weight of each evaluator is determined.

[0034] Based on the second weights of the different physical performance indicators and the initial weights of each evaluator, the comprehensive weight of each evaluator is calculated.

[0035] Determine the contribution ratio judgment matrix of each evaluator for the different physical performance indicators;

[0036] Based on the first weight of each different physical performance index, the comprehensive weight of each evaluator, and the elements of the contribution ratio judgment matrix of each different physical performance index given by each evaluator, the third weight of each different physical performance index is calculated using the initial order relation evaluation algorithm.

[0037] Based on the third weight of each of the different physical performance indicators, the comprehensive evaluation value of each of the different physical performance indicators is calculated.

[0038] In conjunction with the first aspect, in another embodiment of the first aspect, the step of calculating the first weights of various physical performance indicators of the battery energy storage system using an initial order relation evaluation algorithm based on preset group evaluation conditions includes:

[0039] Let each evaluator be e1, e2, ..., e k Given various physical performance indicators J1, J2, ..., J m The order relation is The ratio of the contributions of each different physical performance index is r. c k The initial order relation evaluation algorithm is used to calculate the first weight of each different physical performance index using the following formula.

[0040]

[0041] in, The weights of each different physical performance index based on the c-1 value, The weights of each different physical performance index based on the c value;

[0042] Based on the importance ranking values ​​of the various physical performance indicators given by each evaluator, the second weight of each physical performance indicator is calculated using the following formula:

[0043]

[0044] For the evaluator e k For each different physical performance index J1, J2, ..., J m A set of sorted value vectors; For various physical performance indicators J1, J2, ..., J m The comprehensive sorted value vector set, where This is the second weight for each of the different physical performance indicators;

[0045] Based on the first weight and the second weight of each of the different physical performance indicators, the initial weight of each evaluator is determined using the following formula:

[0046]

[0047] in, For the evaluator e k For each different physical performance index J1, J2, ..., J m The first weight set of each different physical performance index obtained according to the initial order relation evaluation algorithm; For the various physical performance indicators J1, J2, ..., J m The second weight set of various physical performance indicators, where, v m For the various physical performance indicators J1, J2, ..., J m The comprehensive weight vector; The initial weights for each evaluator;

[0048] Based on the second weights of the various physical performance indicators and the initial weights of each evaluator, the comprehensive weight of each evaluator is calculated using the following formula:

[0049]

[0050] Where, ξ k The combined weight of each evaluator. The initial weights for each evaluator, This is the second weight for each of the different physical performance indicators;

[0051] The contribution ratio judgment matrix for each of the different physical performance indicators given by each evaluator is determined by the following formula:

[0052]

[0053]

[0054]

[0055] in, β k For the evaluator e k The different physical performance indices J1, J2, ..., J are given. m A matrix of importance ranking values ​​between items and the contribution ratio of their adjacent physical performance indicators; For the evaluator e k The ratio of the importance ranking set of each physical performance indicator to the contribution set of adjacent evaluation indicators is given, where β is the value of β. k The resulting matrix is ​​a judgment matrix of the contribution ratios of the various physical performance indicators; a lj The elements of the matrix are used to determine the contribution ratio of various physical performance indicators;

[0056] Based on the elements of the judgment matrix of the comprehensive weight of each evaluator and the contribution ratio of each different physical performance index, the third weight of each different physical performance index is calculated by the initial order relation evaluation algorithm.

[0057] Based on the third weight of each of the different physical performance indicators, the comprehensive evaluation value of each of the different physical performance indicators is calculated using the following formula:

[0058]

[0059] Among them, Y b Let be the comprehensive evaluation value of various physical performance indicators of the battery energy storage system type b (b∈B), where B is the set of battery energy storage system types. J is the third weight of the i-th physical performance index. i Let be the i-th physical performance index.

[0060] In conjunction with the first aspect, in another embodiment of the first aspect, the various physical performance indicators of the battery energy storage system include: technical evaluation indicators and / or capacity configuration evaluation indicators and / or cost evaluation indicators and / or environmental benefit evaluation indicators and / or economic benefit evaluation indicators and / or integration degree evaluation indicators.

[0061] According to a second aspect, embodiments of the present invention also disclose a comprehensive evaluation device for the performance of a battery energy storage system, comprising the following modules:

[0062] The data acquisition module is used to acquire the electricity consumption data generated by the target microgrid user on the load side, the electricity purchase data of the target microgrid user from the upstream grid side, the power output data output from the energy side to the target microgrid user, and the energy storage data of the battery energy storage system.

[0063] A creation module is used to create, based on the electricity consumption data, the electricity purchase data, the power output data, and the energy storage data, the total objective function for the daily operating cost of the target microgrid user, and the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system;

[0064] The linearization processing module is used to linearize the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system based on a preset linearization processing algorithm.

[0065] The comprehensive evaluation value calculation module is used to calculate the comprehensive evaluation value of multiple physical performance indicators of the battery energy storage system based on the group order relation evaluation algorithm, according to the battery energy storage system capacity decay function, the daily operating cost total objective function, and the linearized power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system.

[0066] According to a third aspect, embodiments of the present invention also disclose a computer-readable storage medium storing computer instructions for causing the computer to execute the comprehensive evaluation method for the performance of the battery energy storage system described in the first aspect or any embodiment of the first aspect.

[0067] According to a fourth aspect, embodiments of the present invention also disclose a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the comprehensive evaluation method for the performance of the battery energy storage system as described in the first aspect or any embodiment of the first aspect.

[0068] The technical solution of this invention has the following advantages:

[0069] This invention discloses a comprehensive evaluation method, apparatus, and computer equipment for the performance of a battery energy storage system. The method, based on the battery energy storage system's capacity decay function, daily operating cost objective function, and linearized power balance constraints, energy storage power constraints, and control strategy constraints, utilizes a group order relation evaluation algorithm to calculate the comprehensive evaluation value of multiple physical performance indicators of the battery energy storage system. This allows for real-time control of the BESS's state of charge (SOC) operating range and the establishment of continuous charge / discharge time strategies based on different scheduling requirements, under various dispatch scenarios combining the load side, energy side, and upper-level grid side, and based on control strategy constraints. It iterates the six different physical performance indicator parameters of the BESS into each constraint condition, ultimately obtaining the comprehensive evaluation value of the six different physical performance indicators. This method can fully analyze the selection of the BESS and the differences in various physical performance evaluations, making the BESS analysis more comprehensive. Attached Figure Description

[0070] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0071] Figure 1 This is a flowchart illustrating a specific example of a comprehensive evaluation method for the performance of a battery energy storage system in an embodiment of the present invention.

[0072] Figure 2 This is a principle block diagram of another specific example of the comprehensive evaluation method for the performance of a battery energy storage system in this invention.

[0073] Figure 3 This is a structural block diagram of the comprehensive performance evaluation device for battery energy storage systems in an embodiment of the present invention;

[0074] Figure 4 This is a schematic diagram of the hardware structure of a computer device in an embodiment of the present invention. Detailed Implementation

[0075] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0077] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0078] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0079] In related technologies, battery energy storage systems (BESS) participate in the energy optimization and scheduling of microgrid users. Typically, peak shaving and valley filling strategies or energy storage optimization configuration strategies are used to analyze the performance of energy storage systems on the load side based on specific scheduling scenarios and specific operating strategies. In addition, there are many factors related to energy storage selection and evaluation indicators. Therefore, the current performance analysis methods of battery energy storage systems cannot know the various physical properties of energy storage batteries. The differences in selection and performance evaluation under various scheduling scenarios, such as the load side, power output side, and upstream grid side, result in an incomplete and inflexible performance analysis of battery energy storage systems.

[0080] In view of this, embodiments of the present invention disclose a comprehensive evaluation method for the performance of a battery energy storage system, such as... Figure 1 As shown, it includes the following steps:

[0081] Step S11: Obtain the electricity consumption data generated by the target microgrid user on the load side, the electricity purchase data of the target microgrid user from the upstream grid side, the power output data of the energy side to the target microgrid user, and the energy storage data of the battery energy storage system.

[0082] The target microgrid users are those participating in BESS energy optimization dispatch on the load side. The electricity consumption data mentioned above represents the actual electricity load generated by the target microgrid users on the load side during time period t. This data can be represented by P or W, for example: P = 100W, W = 78kWh. The electricity purchase data mentioned above includes the purchase price and power of ancillary services provided by the upstream grid on the time period t. The purchase price can be represented by λ. t load It means that the purchased power capacity can be used This indicates that the energy source can be renewable energy, with wind and solar energy being preferred. The output data refers to the power output of the renewable energy source. The energy storage data mentioned above includes the charging power and discharging power of the BESS when storing electrical energy during time period t. The charging power can be expressed as... This indicates that the discharge power can be expressed as... express.

[0083] The electricity consumption data, electricity purchase data, energy storage data, and power output data mentioned above involve energy dispatching in different scenarios on the load side, the upstream grid side, and the energy side, enabling the target microgrid users to participate in a variety of different dispatching scenarios using BESS.

[0084] Step S12: Based on electricity consumption data, electricity purchase data, power output data, and energy storage data, create the total objective function for the daily operating cost of the target microgrid user, and the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system.

[0085] Among them, the daily operating cost objective function is to ensure the most economical electricity consumption for the target microgrid users on the load side; the power balance constraint, energy storage power constraint, and control strategy constraint of the battery energy storage system are to ensure that the target microgrid users can reliably operate under various operating control strategies when using BESS to participate in various scheduling scenarios.

[0086] In one specific implementation, the total objective function for daily operating costs is executed using the following formula (1):

[0087]

[0088] The overall objective function for the target microgrid user is to minimize the cost of using BESS and purchasing electricity from the upstream grid. Target microgrid user i can use BESS to store electricity when the output of its self-built wind and solar renewable energy exceeds the load demand, and use BESS to release electricity when the load exceeds the output of its self-built renewable energy. In addition, while considering the use of BESS to store or release electricity, target microgrid user i also considers the fluctuation of electricity prices in the region. If the cost of using BESS is higher than the cost of purchasing electricity from the upstream grid, target microgrid user i should purchase electricity from the upstream grid during that period. Therefore, the daily operating cost of target microgrid user i is calculated using the above formula (1).

[0089] In the above formula (1), when the output data is greater than the power consumption data, the battery energy storage system stores electrical energy; when the output data is less than or equal to the power consumption data, the battery energy storage system releases electrical energy.

[0090] The daily electricity cost for target microgrid user i; λ represents the power purchased from the upstream grid by target microgrid user i after it connects to the battery energy storage system during time period t; t load The unit price of electricity purchased by the user operator of the battery energy storage system for providing ancillary services to the target microgrid users during time period t, λ t g The electricity price in the target user's area. The discharge power of the battery energy storage system when the target microgrid user i uses the battery energy storage system to store electrical energy during time period t. Let Δt be the discharge power of the battery energy storage system used by the target microgrid user i during time period t, where Δt is the scheduling time interval, T is the total number of time periods, and Z is the total number of users in the microgrid.

[0091] In another specific implementation, the power balance constraint is performed by the following formula (2):

[0092]

[0093] in, To obtain the power generation capacity of wind renewable energy on the energy side for target microgrid user i during time period t, To obtain the power generation of photovoltaic renewable energy on the energy side of the target microgrid user i during time period t, Let be the power consumption of target microgrid user i during time period t.

[0094] The economic benefits of BESS are achieved through the following formula (3):

[0095]

[0096] The carbon emissions generated by thermal power units before and after the target microgrid users are connected to the BESS are calculated using the following formulas (4) and (5):

[0097]

[0098]

[0099] Where α represents the carbon emissions generated by the target microgrid user using thermal power units before configuring BESS, and β represents the carbon emissions generated by the target microgrid user using thermal power units. b This represents the carbon emissions generated by thermal power units for the target microgrid users after implementing BESS. Before the target microgrid user i connects to the BESS, power is purchased from the upstream grid during time period t, π co2 Carbon emissions per unit of electricity.

[0100] In one specific implementation, the energy storage power constraint is enforced by the following formula (6):

[0101]

[0102] Where, η c η is the charging conversion efficiency of a battery energy storage system. d ψ represents the discharge conversion efficiency of a battery energy storage system. c,t ψ represents the state of charge of the battery energy storage system during time period t. d,t This represents the discharge state of the battery energy storage system during time period t, when ψ c,t =1, ψ d,t When ψ = 0, the battery energy storage system is in a charging state. d,t =1, ψ c,t When P = 0, the battery energy storage system is in a discharging state. rate P is the rated power of the battery energy storage system. d,t P represents the discharge power of the battery energy storage system during time period t. c,t The charging power of the battery energy storage system during time period t;

[0103] In one specific implementation, the control strategy constraints are enforced by the following formula (7):

[0104]

[0105] Based on the above formula (7), two different control strategies can be established: one is to control the operating range of the real-time state of charge of the BESS, and the other is to establish a continuous charging and discharging time strategy for the BESS according to different scheduling requirements. In formula (7), where E t E represents the state of charge of the battery energy storage system during time period t.t-1 E represents the state of charge of the battery energy storage system during time period t-1. min E represents the minimum energy capacity of the energy storage system. max E represents the maximum capacity of the battery energy storage system. rate SOC is the rated capacity of the battery energy storage system. t Let SOC be the state of charge of the battery energy storage system at time t. t-1 The State of Charge (SOC) of the battery energy storage system at time t-1. min Minimum State of Charge (SOC) of a battery energy storage system max SOC0 represents the maximum state of charge (SOC) of the battery energy storage system; SOC0 represents the initial state of charge (SOC) of the battery energy storage system during the current scheduling cycle. T+1 To determine the initial state of charge (SOC) for the next scheduling cycle adjacent to the current one, and to ensure the battery storage system can continuously charge and discharge during the scheduling cycle, the SOC... T+1 =SOC0; γ is the charging and discharging duration when the battery energy storage system participates in the scheduling; τ1 and τ2 are the state-of-charge coefficients of the battery energy storage system control strategy.

[0106] Step S13: Linearize the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system based on a preset linear processing algorithm.

[0107] The preset linear processing algorithm can be the maximum M method. The preset linear processing algorithm transforms nonlinear constraints into linear constraints. The linear constraints appear in integer form, which is convenient for participation in the subsequent group order relation evaluation algorithm calculation.

[0108] In one specific implementation, the power balance constraint, energy storage power constraint, and control strategy constraint of the battery energy storage system are linearized based on a preset linear processing algorithm by the following formula (8):

[0109]

[0110] Formula (8) transforms nonlinear constraints into linear constraints, where M is the maximum coefficient in the preset linear processing algorithm, and the preset linear processing algorithm is the maximum M algorithm.

[0111] Step S14: Based on the battery energy storage system capacity decay function, daily operating cost objective function, and the linearized power balance constraints, energy storage power constraints, and control strategy constraints, calculate the comprehensive evaluation value of multiple different physical performance indicators of the battery energy storage system using the group order relation evaluation algorithm.

[0112] In one specific implementation, the capacity decay function of the battery energy storage system is performed by the following formula (9):

[0113]

[0114] The main factors affecting BESS capacity decay include depth of discharge (DOD), discharge power, rated capacity, and cycle count. In equation (9) above, D... t D represents the depth of discharge of the battery energy storage system during time period t. r For the standard depth of discharge, N r D r The corresponding number of loops, P d,t Let α1 and α2 be the discharge power of the battery energy storage system during time period t, and α1 and α2 be the fitting parameters of the battery energy storage system. When the capacity decay rate of the battery energy storage system... When the battery energy storage system reaches the end of its lifespan, the BESS capacity decay rate... At this point, BESS reaches the end of its service life.

[0115]

[0116] In equation (10), L is the service life of BESS.

[0117] In one embodiment, the various physical performance indicators of a battery energy storage system include: technical evaluation indicators and / or capacity configuration evaluation indicators and / or cost evaluation indicators and / or environmental benefit evaluation indicators and / or economic benefit evaluation indicators and / or integration degree evaluation indicators, and may also include other physical performance indicators. However, in this embodiment of the invention, six different physical performance indicators are mainly studied and analyzed, namely, technical evaluation indicators, capacity configuration evaluation indicators, cost evaluation indicators, environmental performance evaluation indicators, economic benefit evaluation indicators, and integration degree evaluation indicators.

[0118] Among them, the technical evaluation indicators mentioned above are composed of BESS lifespan, root mean square of DOD during the scheduling cycle, and battery overall conversion efficiency, and are specifically implemented by the following formula (11):

[0119]

[0120] In equation (11): D av The root mean square of real-time DOD during the BESS participation in the target microgrid user scheduling cycle; L max The longest service life among all BESS types; η represents the overall conversion efficiency of the BESS; η max This represents the maximum overall conversion efficiency among all BESS components.

[0121]

[0122] Among them, the capacity configuration evaluation index mentioned above is important because different physical performance BESS have different characteristics such as energy density, state of charge constraints, and conversion efficiency, resulting in different required capacity configurations when BESS is connected to the power system for dispatch. As the capacity of the BESS is larger, the land area and battery purchase cost are also larger. Therefore, in specific power system scenarios, the smaller the capacity configuration of the BESS, the better. Therefore, this embodiment of the invention establishes a BESS capacity configuration evaluation index, which is specifically implemented by the following formula (13):

[0123]

[0124] In the above equation (13), E rate,max This represents the maximum rated capacity of each BESS.

[0125] Among them, the above-mentioned cost evaluation indicators are still one of the most important considerations in the initial construction of BESS. During the construction phase, BESS operators should consider the construction costs brought about by batteries with different physical performance. Therefore, this embodiment of the invention establishes BESS cost evaluation indicators, which are specifically implemented through the following formula (14):

[0126]

[0127] In equation (14), C e C is the unit cost of capacity for BESS. p C is the power cost per unit of BESS; e,max C represents the highest capacity cost per unit among all BESS products. p,max P represents the highest power cost per unit among all BESS products. rate Rated power for BESS configuration; P rate,max This is the highest rated power among all BESS.

[0128] Among them, regarding the environmental benefit evaluation indicators mentioned above, after connecting to BESS, the environmental pollution generated when coal-fired power units and thermal power units supply power to the target microgrid users is reduced. Therefore, the environmental benefit evaluation indicators of BESS are implemented by the following formula (15).

[0129]

[0130] In equation (15) above, α represents the carbon emissions generated by the target microgrid user using thermal power units before configuring BESS, and β represents the carbon emissions generated by the target microgrid user using thermal power units. b This represents the carbon emissions generated by thermal power units for target microgrid users after implementing BESS.

[0131] Among them, for the above-mentioned integration evaluation index, different physical properties of BESS vary due to differences in internal chemical materials and manufacturing methods. This embodiment of the invention designs a BESS integration evaluation index, specifically implemented through the following formula (16):

[0132]

[0133] In the above formula (16), S ar To construct the BESS site, S ar,max This is the largest land area among all BESS projects.

[0134] In one specific implementation, such as Figure 2 As shown, step S14 above: calculating the comprehensive evaluation value of multiple different physical performance indicators of the battery energy storage system based on the group order relation evaluation algorithm, includes the following steps:

[0135] Step S21: Based on the preset group evaluation conditions, calculate the first weight of each different physical performance index of the battery energy storage system using the initial order relation evaluation algorithm.

[0136] The initial order relation evaluation algorithm is a traditional order relation method. In one specific implementation, step S21 above, based on preset group evaluation conditions, uses the initial order relation evaluation algorithm to calculate the first weights of various physical performance indicators of the battery energy storage system, including:

[0137] Let each evaluator be e1, e2, ..., e k Given various physical performance indicators J1, J2, ..., J m The order relation is The ratio of the contributions of each different physical performance index is r. c k The initial order relation evaluation algorithm is used to calculate the first weight of each different physical performance index using the following formula (17).

[0138]

[0139] in, The weights of each different physical performance index based on the c-1 value, The weights of each different physical performance index based on the c value;

[0140] Step S22: Based on the importance ranking values ​​of the different physical performance indicators given by each evaluator, calculate the second weight of each different physical performance indicator.

[0141] After each evaluator gives the order of importance of various physical performance indicators, the ranking value of the importance of various physical performance indicators can be obtained. Then, the second weight of each physical performance indicator is calculated, which is carried out by the following formula (18):

[0142]

[0143] For the evaluator e k For each different physical performance index J1, J2, ..., J m A set of sorted value vectors; For various physical performance indicators J1, J2, ..., J m The comprehensive sorted value vector set, where It serves as the second weight for each of the various physical performance indicators.

[0144] when At that time, it indicates that each evaluator e k The information regarding the importance of each of the different physical performance indicators of BESS is the same; when This indicates that the evaluators have different levels of information regarding the importance of the BESS evaluation indicators. The larger the value, the more evaluators there are. k We have a relatively comprehensive understanding of the importance of the BESS evaluation indicators.

[0145] Step S23: Based on the first weight of each different physical performance index and the second weight of each different physical performance index, mine the initial weight of each evaluator.

[0146] Provided by each evaluator Then, the first weight of each different physical performance index is calculated using the initial order relation evaluation algorithm. Then calculate the initial weights for each evaluator.

[0147] In one specific implementation, the initial weights of each evaluator are determined based on the first weight and the second weight of each different physical performance index, and are executed using the following formula (19):

[0148]

[0149] in, For the evaluator e k For each different physical performance index J1, J2, ..., J m The first weight set of various physical performance indicators obtained by the initial order relation evaluation algorithm; For various physical performance indicators J1, J2, ..., J mThe second weight set of various physical performance indicators, where, v m For various physical performance indicators J1, J2, ..., J m The comprehensive weight vector; The initial weights for each evaluator.

[0150] when At that time, it indicates that each evaluator e k The information regarding the weights of different physical performance indicators of BESS is the same; when When the information regarding the weights of different physical performance indicators on the BESS evaluation indicators differs, then... The larger the value, the more evaluators there are. k We have comprehensive information on the weights of the BESS evaluation indicators.

[0151] Step S24: Calculate the comprehensive weight of each evaluator based on the second weight of each different physical performance index and the initial weight of each evaluator.

[0152] The weights of each evaluator are determined primarily from two perspectives: first, by calculating the weight of each evaluation value based on the various physical performance indicators of BESS; and second, by extracting the weights of each evaluator from the weights of the various physical performance indicators of BESS. Therefore,

[0153] In one specific implementation, step S24 above calculates the comprehensive weight of each evaluator based on the second weight of each different physical performance index and the initial weight of each evaluator, and performs this calculation using the following formula (20):

[0154]

[0155] Where, ξ k The overall weight of each evaluator, Assigning initial weights to each evaluator. ξ is the second weight for each different physical performance index; in equation (20): k The larger the value, the more evaluators there are. k We have a comprehensive understanding of the order relationships and weights of various physical performance indicators of BESS, and vice versa.

[0156] Step S25: Determine the contribution ratio judgment matrix of each evaluator for each different physical performance index.

[0157] The contribution ratio judgment matrix of each different physical performance index is performed by the following formula (21):

[0158]

[0159]

[0160]

[0161] in, β k For the evaluator e k Give the various physical performance indicators J1, J2, ..., J m A matrix of importance ranking values ​​between items and the contribution ratio of their adjacent physical performance indicators; For the evaluator e k The given set of importance ranking values ​​among various physical performance indicators and their corresponding values.

[0162] The ratio of the contribution levels of the evaluation indicators, where β is the β value. k The resulting matrix is ​​a judgment matrix of the contribution ratios of various physical neighboring energy indices; a lj The elements of the contribution ratio judgment matrix for each different physical performance index are: , and the above formula (22) is the transformation matrix of the contribution ratio judgment matrix for each different physical performance index.

[0163] Step S26: Based on the first weight of each different physical performance index, the comprehensive weight of each evaluator, and the elements of the judgment matrix of the contribution ratio of each different physical performance index given by each evaluator, the third weight of each different physical performance index is calculated using the initial order relation evaluation algorithm.

[0164] Specifically, based on equations (20) and (23), the ratio of the comprehensive contribution of each different physical performance index J1, J2, ..., J6 can be determined as r. c Then, the final weights of each different physical performance index are calculated using equation (17). That is, the third weight.

[0165] Step S27: Calculate the comprehensive evaluation value of each physical performance index based on its third weight. This is specifically performed using the following formula (24):

[0166]

[0167] Among them, Y b Let be the comprehensive evaluation value of various physical performance indicators of battery energy storage system type b (b∈B), where B is the set of battery energy storage system types. J is the third weight of the i-th physical performance index. i Let be the i-th physical performance index.

[0168] The comprehensive evaluation value of various physical performance indicators in BESS is used to reflect the overall evaluation of batteries with different physical performance in a specific or multiple scenarios. b The higher the value, the better the performance evaluation of the battery. In the early stages of building a BESS power station, the battery should be given priority as the main facility of the power station.

[0169] The comprehensive evaluation method for the performance of the battery energy storage system in this embodiment of the invention, by executing the above steps S11-S14, calculates the comprehensive evaluation value of multiple different physical performance indicators of the battery energy storage system using the group order relation evaluation algorithm, based on the battery energy storage system capacity decay function, daily operating cost total objective function, and linearized power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system, combined with the load side, energy side, and upper-level grid side under various different scheduling scenarios. This allows for real-time control of the BESS state of charge operating range and the establishment of BESS continuous charging and discharging time strategies according to different scheduling requirements, based on control strategy constraints. The method iterates the six different physical performance indicator parameters of the BESS into each constraint condition, ultimately obtaining the comprehensive evaluation value of the six different physical performance indicators. This method can fully analyze the selection of the BESS and the differences in various physical performance evaluations, making the BESS analysis more comprehensive.

[0170] Based on the same concept, embodiments of the present invention also disclose a comprehensive evaluation device for the performance of a battery energy storage system, such as... Figure 3 As shown, it includes the following modules:

[0171] The data acquisition module 31 is used to acquire the electricity consumption data generated by the target microgrid user on the load side, the electricity purchase data of the target microgrid user from the upstream grid side, the power output data output from the energy side to the target microgrid user, and the energy storage data of the battery energy storage system.

[0172] Create module 32 to create the total objective function for the daily operating cost of the target microgrid user, and the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system, based on electricity consumption data, electricity purchase data, power output data, and energy storage data.

[0173] Linearization module 33 is used to linearize the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system based on a preset linearization algorithm.

[0174] The comprehensive evaluation value calculation module 34 is used to calculate the comprehensive evaluation value of multiple physical performance indicators of the battery energy storage system based on the battery energy storage system capacity decay function, daily operating cost total objective function, and power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system after linearization, using a group order relation evaluation algorithm.

[0175] This invention also provides a computer device, such as... Figure 4 As shown, the computer device may include a processor 41 and a memory 42, wherein the processor 41 and the memory 42 may be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0176] Processor 41 can be a central processing unit (CPU). Processor 41 can also 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 combinations of the above types of chips.

[0177] The memory 42, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 41 executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 42, thereby realizing the comprehensive evaluation method for the performance of the battery energy storage system in the above embodiments.

[0178] The memory 42 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 41, etc. Furthermore, the memory 42 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 42 may optionally include memory remotely located relative 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, power grids, the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.

[0179] The one or more modules are stored in the memory 42, and when executed by the processor 41, they perform the comprehensive evaluation method for the performance of the battery energy storage system in the embodiment shown in the figure.

[0180] The specific details of the above-mentioned computer equipment can be understood by referring to the relevant descriptions and effects in the embodiments shown in the accompanying drawings, and will not be repeated here.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0182] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A comprehensive evaluation method for the performance of a battery energy storage system, characterized in that, Includes the following steps: Acquire the electricity consumption data generated by the target microgrid user on the load side, the electricity purchase data of the target microgrid user from the upstream grid side, the power output data output from the energy side to the target microgrid user, and the energy storage data of the battery energy storage system; Based on the electricity consumption data, the electricity purchase data, the power output data, and the energy storage data, create the total objective function for the daily operating cost of the target microgrid user, and the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system; The power balance constraint, energy storage power constraint, and control strategy constraint of the battery energy storage system are linearized based on a preset linear processing algorithm. Based on the battery energy storage system capacity decay function, the total objective function of daily operating cost, and the linearized power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system, the comprehensive evaluation value of multiple different physical performance indicators of the battery energy storage system is calculated using the group order relation evaluation algorithm. The group order relation evaluation algorithm is used to calculate the comprehensive evaluation value of multiple different physical performance indicators of the battery energy storage system, including: Based on preset group evaluation conditions, the first weights of various physical performance indicators of the battery energy storage system are calculated using the initial order relation evaluation algorithm. Based on the importance ranking values ​​of various physical performance indicators given by each evaluator, the second weight of each different physical performance indicator is calculated. Based on the first weight and the second weight of each different physical performance index, the initial weight of each evaluator is determined. Based on the second weights of the different physical performance indicators and the initial weights of each evaluator, calculate the comprehensive weight of each evaluator; Determine the contribution ratio judgment matrix of each evaluator for the different physical performance indicators; Based on the first weight of each different physical performance index, the comprehensive weight of each evaluator, and the elements of the contribution ratio judgment matrix of each different physical performance index, the third weight of each different physical performance index is calculated using the initial order relation evaluation algorithm. Based on the third weight of each of the different physical performance indicators, the comprehensive evaluation value of each of the different physical performance indicators is calculated. The calculation of the first weights of various physical performance indicators of the battery energy storage system based on preset group evaluation conditions and using the initial order relation evaluation algorithm includes: Each evaluator Given various physical performance indicators The order relation is The contribution ratio of each different physical performance index is The initial order relation evaluation algorithm is used to calculate the first weight of each different physical performance index using the following formula. ; ； in, For various physical performance indicators based on c-1 Numerical weights For various physical performance indicators based on c Numerical weights; Based on the importance ranking values ​​of the various physical performance indicators given by each evaluator, the second weight of each physical performance indicator is calculated using the following formula: ； For the evaluator For various physical performance indicators A set of sorted value vectors; For various physical performance indicators The comprehensive sorted value vector set, where ; This is the second weight for each of the different physical performance indicators; Based on the first weight and the second weight of each of the different physical performance indicators, the initial weight of each evaluator is determined using the following formula: ； in, For the evaluator For various physical performance indicators The first weight set of each different physical performance index obtained according to the initial order relation evaluation algorithm; For the various physical performance indicators The second weight set of various physical performance indicators, where, , The various physical performance indicators ; The initial weights for each evaluator; Based on the second weights of the various physical performance indicators and the initial weights of each evaluator, the comprehensive weight of each evaluator is calculated using the following formula: ； in, The combined weight of each evaluator. The initial weights for each evaluator, This is the second weight for each of the different physical performance indicators; The contribution ratio judgment matrix for each of the different physical performance indicators given by each evaluator is determined by the following formula: ； ； ； in, , For the evaluator The various physical performance indicators are given. A matrix of importance ranking values ​​between items and the contribution ratio of their adjacent physical performance indicators; For the evaluator The ratio of the set of importance ranking values ​​among the given physical performance indicators to the set of contribution levels of adjacent evaluation indicators, where, for The resulting matrix is ​​a judgment matrix of the contribution ratios of the various physical performance indicators. The elements of the matrix are used to determine the contribution ratio of various physical performance indicators; Based on the third weight of each of the different physical performance indicators, the comprehensive evaluation value of each of the different physical performance indicators is calculated using the following formula: ； in, The battery energy storage system type b ( The comprehensive evaluation value of various physical performance indicators of ) B This is a collection of battery energy storage system types. For the first The third weight of the physical performance index, For the first Physical performance indicators.

2. The comprehensive evaluation method for the performance of a battery energy storage system according to claim 1, characterized in that, The objective function for total daily operating costs is executed using the following formula: ； Specifically, when the output data is greater than the power consumption data, the battery energy storage system stores electrical energy; when the output data is less than or equal to the power consumption data, the battery energy storage system releases electrical energy. For the target microgrid users i Daily electricity costs; For the target microgrid users i After being connected to the battery energy storage system, during the time period t The amount of electricity purchased from the upstream power grid; For the user operators of the battery energy storage system during the time period t The unit price of electricity purchased to provide ancillary services to the microgrid users. The electricity price in the area where the target user is located. For target microgrid users i Using the battery energy storage system during the time period t Charging power when storing electrical energy For target microgrid users i Using the battery energy storage system during the time period t Discharge power when releasing electrical energy △t For scheduling time intervals, T Total number of time periods Z For the total users of the microgrid.

3. The comprehensive evaluation method for the performance of a battery energy storage system according to claim 1, characterized in that, The power balance constraint condition is applied by the following formula: ； in, For the target microgrid users i Obtaining wind renewable energy from the energy side during the time period t Power generation capacity, For target microgrid users i Obtain the power generation of photovoltaic renewable energy on the energy side during time period t. For the target microgrid users i During the period t The power consumption; The energy storage power constraint condition is executed by the following formula: ； in, The charging conversion efficiency of the battery energy storage system. The discharge conversion efficiency of the battery energy storage system is given. This refers to the charging state of the battery energy storage system during time period t. This indicates that the battery energy storage system is in the time period t The discharge state, when , At that time, the battery energy storage system is in a charging state. , At that time, the battery energy storage system is in a discharging state. The rated power of the battery energy storage system is [missing information]. For the battery energy storage system during the time period t The discharge power, For the battery energy storage system during the time period t The charging power; The control strategy constraints are executed using the following formula: ； in, For the battery energy storage system during the time period t The battery status; For the battery energy storage system during the time period t-1 Battery status, This is the minimum charge of the energy storage system. This represents the maximum capacity of the battery energy storage system. The rated capacity of the battery energy storage system is [missing information]. For the battery energy storage system t State of charge at time t, The minimum state of charge of the battery energy storage system and This represents the maximum state of charge of the battery energy storage system. This represents the initial state of charge (SNP) of the battery energy storage system during its current scheduling cycle. This is the initial state of charge for the next scheduling cycle adjacent to the current scheduling cycle. This is to ensure that the battery energy storage system can continuously charge and discharge during the scheduling cycle. = ; The duration of charging and discharging when the battery energy storage system participates in scheduling; , The state of charge coefficient is the control strategy of the battery energy storage system.

4. The comprehensive evaluation method for the performance of a battery energy storage system according to claim 1, characterized in that, The power balance constraint, energy storage power constraint, and control strategy constraint of the battery energy storage system are linearized based on a preset linear processing algorithm, and are executed using the following formula: ； in, M The maximum coefficient in the preset linear processing algorithm is the maximum coefficient. M algorithm.

5. The comprehensive evaluation method for the performance of a battery energy storage system according to claim 1, characterized in that, The capacity decay function of the battery energy storage system is executed by the following formula: ； in, For the battery energy storage system during the time period t The depth of discharge value, This is the standard depth of discharge value. for The corresponding number of loops, The parameters are the fitting parameters for the battery energy storage system, and the capacity decay rate of the battery energy storage system is... At that time, the battery energy storage system reaches the end of its service life.

6. A comprehensive evaluation device for the performance of a battery energy storage system, characterized in that, Includes the following modules: The data acquisition module is used to acquire the electricity consumption data generated by the target microgrid user on the load side, the electricity purchase data of the target microgrid user from the upstream grid side, the power output data output from the energy side to the target microgrid user, and the energy storage data of the battery energy storage system. A creation module is used to create, based on the electricity consumption data, the electricity purchase data, the power output data, and the energy storage data, the total objective function for the daily operating cost of the target microgrid user, and the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system; The linearization processing module is used to linearize the power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system based on a preset linearization processing algorithm. The comprehensive evaluation value calculation module is used to calculate the comprehensive evaluation value of multiple physical performance indicators of the battery energy storage system based on the group order relation evaluation algorithm, according to the battery energy storage system capacity decay function, the daily operating cost total objective function, and the linearized power balance constraints, energy storage power constraints, and control strategy constraints of the battery energy storage system. The group order relation evaluation algorithm is used to calculate the comprehensive evaluation value of multiple different physical performance indicators of the battery energy storage system, including: Based on preset group evaluation conditions, the first weights of various physical performance indicators of the battery energy storage system are calculated using the initial order relation evaluation algorithm. Based on the importance ranking values ​​of various physical performance indicators given by each evaluator, the second weight of each different physical performance indicator is calculated. Based on the first weight and the second weight of each different physical performance index, the initial weight of each evaluator is determined. Based on the second weights of the different physical performance indicators and the initial weights of each evaluator, calculate the comprehensive weight of each evaluator; Determine the contribution ratio judgment matrix of each evaluator for the different physical performance indicators; Based on the first weight of each different physical performance index, the comprehensive weight of each evaluator, and the elements of the contribution ratio judgment matrix of each different physical performance index, the third weight of each different physical performance index is calculated using the initial order relation evaluation algorithm. Based on the third weight of each of the different physical performance indicators, the comprehensive evaluation value of each of the different physical performance indicators is calculated. The calculation of the first weights of various physical performance indicators of the battery energy storage system based on preset group evaluation conditions and using the initial order relation evaluation algorithm includes: Each evaluator Given various physical performance indicators The order relation is The contribution ratio of each different physical performance index is The initial order relation evaluation algorithm is used to calculate the first weight of each different physical performance index using the following formula. ; ； in, For various physical performance indicators based on c-1 Numerical weights For various physical performance indicators based on c Numerical weights; Based on the importance ranking values ​​of the various physical performance indicators given by each evaluator, the second weight of each physical performance indicator is calculated using the following formula: ； For the evaluator For various physical performance indicators A set of sorted value vectors; For various physical performance indicators The comprehensive sorted value vector set, where ; This is the second weight for each of the different physical performance indicators; Based on the first weight and the second weight of each of the different physical performance indicators, the initial weight of each evaluator is determined using the following formula: ； in, For the evaluator For various physical performance indicators The first weight set of each different physical performance index obtained according to the initial order relation evaluation algorithm; For the various physical performance indicators The second weight set of various physical performance indicators, where, , The various physical performance indicators ; The initial weights for each evaluator; Based on the second weights of the various physical performance indicators and the initial weights of each evaluator, the comprehensive weight of each evaluator is calculated using the following formula: ； in, The combined weight of each evaluator. The initial weights for each evaluator, This is the second weight for each of the different physical performance indicators; The contribution ratio judgment matrix for each of the different physical performance indicators given by each evaluator is determined by the following formula: ； ； ； in, , For the evaluator The various physical performance indicators are given. A matrix of importance ranking values ​​between items and the contribution ratio of their adjacent physical performance indicators; For the evaluator The ratio of the set of importance ranking values ​​among the given physical performance indicators to the set of contribution levels of adjacent evaluation indicators, where, for The resulting matrix is ​​a judgment matrix of the contribution ratios of the various physical performance indicators. The elements of the matrix are used to determine the contribution ratio of various physical performance indicators; Based on the third weight of each of the different physical performance indicators, the comprehensive evaluation value of each of the different physical performance indicators is calculated using the following formula: ； in, The battery energy storage system type b ( The comprehensive evaluation value of various physical performance indicators of ) B This is a collection of battery energy storage system types. For the first The third weight of the physical performance index, For the first Physical performance indicators.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the comprehensive evaluation method for the performance of the battery energy storage system according to any one of claims 1 to 5.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the comprehensive evaluation method for the performance of the battery energy storage system as described in any one of claims 1 to 5.

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