Power distribution network energy storage multi-objective optimization method, system, device, product and medium

Abstract

The present application relates to the field of energy storage optimization, and provides a power distribution network energy storage multi-objective optimization method, system, device, product and medium, comprising obtaining battery thermal parameters of an energy storage device to establish a battery energy storage thermal coupling model; establishing a battery charging and discharging cycle model according to battery fitting parameters and battery allowed power; establishing an energy storage converter efficiency and loss model through converter loss and converter efficiency; establishing energy storage device constraint conditions including energy storage parameter balance relationship and device parameter constraint conditions; determining the working condition of the energy storage device, determining the model weight according to the working condition, constructing a transient target function and a steady target function, solving the multi-objective function according to the model weight and the energy storage device constraint conditions to obtain operating parameters; optimizing the operating parameters to obtain target operating parameters, and controlling the operation of the energy storage device according to the target operating parameters. The present application can improve the operation stability of the energy storage device.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization technology, and in particular to multi-objective optimization methods, systems, equipment, products and media for energy storage in distribution networks. Background Technology

[0002] With the integration of numerous new energy power generation devices into the power system, the inertia support capacity of the power grid is declining, and system frequency and voltage stability issues are becoming increasingly prominent. The intermittency and volatility of new energy power generation devices easily lead to power imbalances. Traditional synchronous generators have slow frequency regulation response speeds, making it difficult to meet the demands of rapid frequency regulation. Energy storage systems, due to their rapid power regulation capabilities, have become a key technological means to improve grid stability. However, traditional energy storage optimization methods have significant limitations, with their core problems concentrated in insufficient consideration of complex system characteristics and one-sided optimization dimensions. Specifically, traditional energy storage optimization strategies are often limited to the scheduling needs of a single stage or a single-dimensional optimization objective, failing to consider the different optimization objectives under different operating conditions and stages. Single-objective optimization models, such as those that only minimize operating costs, ignore power quality objectives such as voltage deviation and harmonic suppression, failing to comprehensively consider the needs of power quality, operating costs, and other aspects. Furthermore, existing technologies also rarely consider factors such as converter losses, converter efficiency, and battery heating. The process of optimizing and obtaining operating parameters still only looks at the result of finding the optimal solution to the objective function, ignoring the consideration of these factors. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a multi-objective optimization method for energy storage in distribution networks, which constructs multiple objective functions and assigns model weights to these objective functions, thereby providing different optimization schemes adapted to different operating conditions.

[0004] This invention provides a multi-objective optimization method for energy storage in distribution networks, comprising:

[0005] S1: Set up the energy storage device, obtain the battery thermal parameters of the energy storage device, and establish a battery energy storage thermal coupling model based on the battery thermal parameters;

[0006] S2: Determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity.

[0007] S3: Calculate the converter loss parameters of the energy storage device, calculate the converter loss and converter efficiency through the converter loss parameters, and establish the energy storage converter efficiency and loss model through the converter loss and converter efficiency.

[0008] S4: Establish energy storage device constraints, including energy storage parameter balance relationships and device parameter constraints;

[0009] S5: Determine the operating conditions of the energy storage equipment, determine the model weights based on the operating conditions, construct the transient objective function and the steady-state objective function, and solve the transient objective function and the steady-state objective function for multi-objective function based on the model weights and the constraints of the energy storage equipment to obtain the operating parameters;

[0010] S6: Optimize the operating parameters based on the battery energy storage thermal coupling model, battery charge-discharge cycle model, and energy storage converter efficiency and loss model to obtain the target operating parameters, and control the operation of the energy storage device according to the target operating parameters.

[0011] According to the multi-objective optimization method for energy storage in distribution networks provided by the present invention, step S1 further includes:

[0012] S11: Set up an energy storage device including a battery, and acquire the battery thermal parameters including battery specific heat capacity, surface heat transfer coefficient, battery mass, and battery surface area;

[0013] S12: Calculate the battery heat dissipation power using the battery thermal parameters, calculate the battery thermal power, and establish the battery energy storage thermal coupling model based on the battery thermal power and battery heat dissipation power.

[0014] According to the multi-objective optimization method for energy storage in distribution networks provided by the present invention, step S2 further includes:

[0015] S21: Determine the battery's allowable capacity, including the upper limit and lower limit of the battery's allowable capacity;

[0016] S22: Obtain the battery fitting parameters of the energy storage device, calculate the maximum number of cycles using the battery fitting parameters, and establish the battery charge-discharge cycle model using the maximum number of cycles and the battery's allowable capacity.

[0017] According to the multi-objective optimization method for energy storage in distribution networks provided by the present invention, step S3 further includes:

[0018] S31: Calculate the converter loss parameters of the energy storage device, including conduction loss, reverse recovery loss and converter loss;

[0019] S32: Determine the operating environment coefficient, and calculate the converter loss based on the operating environment coefficient and the converter loss parameters;

[0020] S33: Determine the operating state coefficient, calculate the converter efficiency using the converter loss and the operating state coefficient, and establish an energy storage converter efficiency and loss model using the converter loss and the converter efficiency.

[0021] According to the multi-objective optimization method for energy storage in distribution networks provided by the present invention, step S4 further includes:

[0022] S41: Establish the energy storage parameter balance relationship, including the active power balance relationship, reactive power balance relationship, and line power flow balance relationship;

[0023] S42: Establish the equipment parameter constraint conditions, including energy storage capacity constraints, branch power constraints, and node voltage constraints, thereby establishing energy storage equipment constraint conditions that include energy storage parameter balance relationships and equipment parameter constraint conditions.

[0024] According to the multi-objective optimization method for energy storage in distribution networks provided by the present invention, in step S5, after determining the model weights, transient performance objectives including total harmonic distortion, frequency deviation and transient voltage deviation are determined, and the transient objective function is constructed through the transient performance objectives;

[0025] A steady-state performance objective is determined, including peak-valley arbitrage revenue, battery power fluctuation, and curtailment loss. The steady-state objective function is then constructed based on the steady-state performance objective.

[0026] An objective function is constructed based on the model weights, the transient objective function, and the steady-state objective function. Under the constraints of the energy storage device, the objective function is solved using a multi-objective function to obtain the operating parameters.

[0027] This invention also provides a multi-objective optimization system for energy storage in power distribution networks, comprising:

[0028] Battery Energy Storage Thermal Coupling Model Module: Used to set up energy storage devices, obtain battery thermal parameters of energy storage devices, and establish a battery energy storage thermal coupling model based on battery thermal parameters;

[0029] Battery charge-discharge cycle model module: used to determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity;

[0030] Energy storage converter efficiency and loss model module: used to calculate the converter loss parameters of energy storage equipment, calculate converter loss and converter efficiency through the converter loss parameters, and establish the energy storage converter efficiency and loss model through converter loss and converter efficiency.

[0031] Energy storage device constraint module: used to establish energy storage device constraint conditions, including energy storage parameter balance relationships and device parameter constraints;

[0032] Operating parameters module: used to determine the operating conditions of energy storage equipment, determine model weights based on the operating conditions, construct transient and steady-state objective functions, and solve multi-objective functions based on model weights and energy storage equipment constraints to obtain operating parameters;

[0033] The target operating parameter module includes optimizing the operating parameters based on the battery energy storage thermal coupling model, the battery charge-discharge cycle model, and the energy storage converter efficiency and loss model to obtain the target operating parameters, and controlling the operation of the energy storage device based on the target operating parameters.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described multi-objective optimization methods for distribution network energy storage.

[0035] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-objective optimization method for distribution network energy storage as described above.

[0036] The present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer is able to perform the steps of any of the above-described distribution network energy storage multi-objective optimization methods.

[0037] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0038] The multi-objective optimization method, system, equipment, products, and media for distribution network energy storage provided by this invention constructs multiple objective functions and assigns model weights to these objective functions, thereby providing more suitable and comprehensive operating parameters from both transient and steady-state indices according to different operating conditions. Furthermore, the operating parameters can be optimized based on battery energy storage thermal coupling models, battery charge-discharge cycle models, and energy storage converter efficiency and loss models to further improve the stability of energy storage equipment operation and reduce operating costs.

[0039] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating the multi-objective optimization method for energy storage in power distribution networks provided by the present invention.

[0042] Figure 2 This is a schematic diagram of the battery heat transfer model of the multi-objective optimization method for energy storage in power distribution networks provided by this invention.

[0043] Figure 3 This is a schematic diagram of the structure of the multi-objective optimization device for distribution network energy storage provided by the present invention.

[0044] Figure 4 This is a schematic diagram of the multi-objective optimization device for distribution network energy storage provided by the present invention. (Figure reference numerals:)

[0045] 100. Battery energy storage thermal coupling model module; 200. Battery charge-discharge cycle model module; 300. Energy storage converter efficiency and loss model module; 400. Energy storage equipment constraint condition module; 500. Operating parameter module; 600. Target operating parameter module; 810. Processor; 820. Communication interface; 830. Memory; 840. Communication bus. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0047] In the description of the embodiments of the present invention, it should be noted that the terms "first", "second" and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0048] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" 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. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention based on the specific circumstances.

[0049] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0050] The following is combined with Figures 1 to 4 Specific embodiments of the present invention are described below:

[0051] Figure 1 This is a flowchart illustrating the multi-objective optimization method for energy storage in distribution networks provided by this invention. The multi-objective optimization method for energy storage in distribution networks provided by this invention includes:

[0052] S1: Set up the energy storage device, obtain the battery thermal parameters of the energy storage device, and establish a battery energy storage thermal coupling model based on the battery thermal parameters;

[0053] Furthermore, the objective of this stage is to obtain the battery thermal parameters of the energy storage device and establish a battery energy storage thermal coupling model. Specifically, step S1 further includes:

[0054] S11: Set up an energy storage device including a battery, and acquire the battery thermal parameters including battery specific heat capacity, surface heat transfer coefficient, battery mass, and battery surface area;

[0055] S12: Calculate the battery heat dissipation power using the battery thermal parameters, calculate the battery thermal power, and establish the battery energy storage thermal coupling model based on the battery thermal power and battery heat dissipation power.

[0056] The specific implementation method for the above steps in this embodiment is as follows:

[0057] First, energy storage equipment needs to be constructed. In this embodiment, the energy storage equipment consists of batteries and is connected to the external power distribution network to stabilize the network's frequency and voltage. During operation, the batteries generate heat due to factors such as internal resistance; the battery's thermal power... The expression is:

[0058]

[0059] Where I is the battery current, U is the battery open-circuit voltage, U is the battery load terminal voltage, and T is the measured battery temperature.

[0060] Subsequently, the battery thermal parameters, including the battery specific heat capacity c, surface heat transfer coefficient h, battery mass m, and battery surface area S, are obtained, and the battery heat dissipation power is calculated using these parameters. :

[0061]

[0062]

[0063]

[0064] in, For battery heat capacity, For battery thermal resistance, Let t represent the ambient temperature, and t be the time interval between two sampling calculations of the battery's heat dissipation power. A schematic diagram of the battery heat transfer model is shown below. Figure 2 As shown, the battery heat dissipation power is calculated in this way. The battery energy storage thermal coupling model can be established by calculating the difference between the battery thermal power and the battery heat dissipation power. It can be seen that the battery heat dissipation power is a constant value under certain conditions. By changing the battery current, the battery thermal power can be effectively reduced, thereby reducing the difference between the battery heat dissipation power and the battery thermal power, reducing heat accumulation in the battery, and improving the battery's operating environment. The battery energy storage thermal coupling model can calculate the difference between the battery heat dissipation power and the battery thermal power, and requires that these two differences be as small as possible.

[0065] S2: Determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity.

[0066] Furthermore, the objective of this stage is to establish a battery charge-discharge cycle model using battery fitting parameters and the battery's allowable capacity. Specifically, step S2 further includes:

[0067] S21: Determine the battery's allowable capacity, including the upper limit and lower limit of the battery's allowable capacity;

[0068] S22: Obtain the battery fitting parameters of the energy storage device, calculate the maximum number of cycles using the battery fitting parameters, and establish the battery charge-discharge cycle model using the maximum number of cycles and the battery's allowable capacity.

[0069] The specific implementation method for the above steps in this embodiment is as follows:

[0070] First, it is necessary to determine the allowable battery capacity, which includes the upper limit and the lower limit of the allowable battery capacity. In this embodiment, the upper limit of the allowable battery capacity is set to 80%, and the lower limit of the allowable battery capacity is set to 20%. This can effectively avoid overcharging and discharging of the battery, thereby affecting its lifespan.

[0071] Next, the battery fitting parameters of the energy storage device are obtained. These parameters include a first battery fitting parameter α and a second battery fitting parameter β, determined based on the battery's performance. Then, the maximum number of cycles is calculated. :

[0072]

[0073]

[0074] Here, DOD represents the depth of charge / discharge, and SOC represents the remaining battery capacity. The maximum number of cycles can be changed by altering the remaining battery capacity. Combining this with the remaining battery capacity allows for the establishment of a battery charge / discharge cycle model. This model requires adjusting the battery capacity to maximize the maximum number of cycles while maintaining the battery within its allowable capacity.

[0075] S3: Calculate the converter loss parameters of the energy storage device, calculate the converter loss and converter efficiency through the converter loss parameters, and establish the energy storage converter efficiency and loss model through the converter loss and converter efficiency.

[0076] Furthermore, the objective of this stage is to establish an efficiency and loss model for the energy storage converter by analyzing converter losses and converter efficiency. Specifically, step S3 further includes:

[0077] S31: Calculate the converter loss parameters of the energy storage device, including conduction loss, reverse recovery loss and converter loss;

[0078] S32: Determine the operating environment coefficient, and calculate the converter loss based on the operating environment coefficient and the converter loss parameters;

[0079] S33: Determine the operating state coefficient, calculate the converter efficiency using the converter loss and the operating state coefficient, and establish an energy storage converter efficiency and loss model using the converter loss and the converter efficiency.

[0080] The specific implementation method for the above steps in this embodiment is as follows:

[0081] First, calculate the conduction loss of the energy storage device. :

[0082]

[0083] in, This refers to the gate voltage of the insulated-gate bipolar transistor in the converter of the energy storage device. This refers to the gate voltage of the diode in the converter of the energy storage device. To conduct current, The on-resistance of the insulated-gate bipolar transistor in the converter of the energy storage device. This refers to the on-resistance of the diode in the converter of the energy storage device.

[0084] Next, calculate the reverse recovery loss. :

[0085]

[0086] in, This is the collector-emitter voltage of an insulated-gate bipolar transistor. This is the first initial operating environment coefficient. This is the second initial operating environment coefficient. This is the third initial operating environment coefficient.

[0087] Calculate converter losses :

[0088]

[0089] in, This is the fourth initial operating environment coefficient. This is the fifth initial operating environment coefficient. This is the sixth initial operating environment factor. All the above initial operating environment factors are determined based on the specific parameters of the converter of the energy storage device.

[0090] Then the converter losses were calculated. :

[0091]

[0092] in, Let the resistance be the converter's resistance. Since each of the following terms—conduction loss, reverse recovery loss, and converter loss—has a conduction current or the square of that current, and the other values ​​are constants, the coefficients of the converter loss expression can be rearranged to determine the first operating environment coefficient. Second operating environment coefficient and the third operating environment coefficient The operating environment coefficient is obtained, and the converter loss is calculated.

[0093] Subsequently, since the collector-emitter voltage of the insulated-gate bipolar transistor is a constant, the power transmission P of the converter is proportional to the conduction current, that is... Therefore, the operating environment coefficient is always related to Dividing them yields the first operating state coefficient. Second operating state coefficient and the third operating state coefficient The operating state coefficients allow us to transform the expression for converter losses into:

[0094]

[0095] The converter efficiency can then be calculated. :

[0096]

[0097] This allows for the establishment of an efficiency and loss model for energy storage converters using converter losses and efficiency. The energy storage converter efficiency and loss model calculates converter efficiency and losses by changing the power output, aiming for the highest possible converter efficiency and the lowest possible converter losses.

[0098] S4: Establish energy storage device constraints, including energy storage parameter balance relationships and device parameter constraints;

[0099] Furthermore, the objective of this stage is to establish constraints for the energy storage device. Specifically, step S4 further includes:

[0100] S41: Establish the energy storage parameter balance relationship, including the active power balance relationship, reactive power balance relationship, and line power flow balance relationship;

[0101] S42: Establish the equipment parameter constraint conditions, including energy storage capacity constraints, branch power constraints, and node voltage constraints, thereby establishing energy storage equipment constraint conditions that include energy storage parameter balance relationships and equipment parameter constraint conditions.

[0102] The specific implementation method for the above steps in this embodiment is as follows:

[0103] For energy storage devices, the active power balance relationship must be satisfied:

[0104]

[0105] in, The active power injected into the energy storage device from the distribution network. The power output of the energy storage device. The power generation capacity of the power generation equipment in the distribution network to which the energy storage device is connected. The active power output from the output end of the distribution network. The power supplied by the power distribution network to charge energy storage devices. This refers to the active power consumed within the energy storage device.

[0106] Next, establish the reactive power balance relationship:

[0107]

[0108] in, This refers to the reactive power injected into the energy storage device by the power distribution network. The reactive power in the power distribution network to which the energy storage device is connected. The reactive power output of energy storage devices. This refers to the reactive power consumed within the energy storage device. When the energy storage device is divided into multiple nodes, the active power balance relationship and the reactive power balance relationship also hold for each node, and the energy storage device cannot be charged and discharged simultaneously.

[0109] Next, establish the power flow balance relationship of the line:

[0110]

[0111] in, The active power at the starting end of the distribution network. The reactive power at the starting end of the distribution network. The active power at the end of the distribution network. Reactive power at the end of the distribution network R is the voltage at the end of the distribution network, R is the resistance of the distribution network, and X is the reactance of the distribution network. This completes the construction of the energy storage parameter balance relationship.

[0112] Subsequently, energy storage capacity constraints are established. First, the maximum amount of electricity required by the distribution network to stabilize frequency and voltage is estimated, and the energy storage device must be able to store an amount greater than or equal to this maximum. Branch power constraints require that the power of each branch in the distribution network must not exceed its upper power limit. Node voltage constraints require that the voltage of each node in the distribution network must not be lower than an empirically determined lower limit, nor higher than an empirically determined upper limit. This completes the construction of equipment parameter constraints, allowing the establishment of energy storage device constraints that include energy storage parameter balance relationships and equipment parameter constraints. Both the target operating parameters and the operating parameters must ensure that the energy storage device constraints are met in both the transmission network and the energy storage device itself.

[0113] S5: Determine the operating conditions of the energy storage equipment, determine the model weights based on the operating conditions, construct the transient objective function and the steady-state objective function, and solve the transient objective function and the steady-state objective function for multi-objective function based on the model weights and the constraints of the energy storage equipment to obtain the operating parameters;

[0114] Furthermore, the objective of this stage is to determine the model weights and, based on the model weights and the constraints of the energy storage device, solve for the transient objective function and the steady-state objective function to obtain the operating parameters. Specifically, in step S5, after determining the model weights, transient performance targets including total harmonic distortion, frequency deviation, and transient voltage deviation are determined, and the transient objective function is constructed using these transient performance targets.

[0115] A steady-state performance objective is determined, including peak-valley arbitrage revenue, battery power fluctuation, and curtailment loss. The steady-state objective function is then constructed based on the steady-state performance objective.

[0116] An objective function is constructed based on the model weights, the transient objective function, and the steady-state objective function. Under the constraints of the energy storage device, the objective function is solved using a multi-objective function to obtain the operating parameters.

[0117] The specific implementation method for the above steps in this embodiment is as follows:

[0118] First, the operating conditions of the energy storage device need to be determined, and the model weights are then determined based on these conditions. In this embodiment, the operating conditions are divided into stable operation and failure. When operating stably, the first model weight y is set to 0.2, and the second model weight (1-y) is set to 0.8. When a failure occurs, the first model weight y is set to 0.8, and the second model weight (1-y) is set to 0.2. After determining the model weights, the total harmonic distortion (THD) can be determined. Frequency deviation and transient voltage deviation The transient performance objective is determined, thereby constructing the transient objective function. :

[0119]

[0120] in, The first transient coefficient is determined based on experience. The second transient coefficient is determined based on experience. This is the third transient coefficient determined empirically. The transient objective function requires that the transient voltage deviation be less than or equal to 3%, the frequency deviation be less than or equal to 0.1 Hz, and the total harmonic distortion be less than or equal to 4%.

[0121] Next, determine the peak-valley arbitrage profits. Battery power fluctuation and power curtailment losses The steady-state performance objective is determined, thereby constructing the steady-state objective function. :

[0122]

[0123] Among them, the peak-valley arbitrage profit is the economic profit minus the total life cycle cost of energy storage. The power curtailment loss coefficient is determined based on experience. This is the battery power fluctuation coefficient determined based on experience.

[0124] Thus, the objective function F can be constructed based on the model weights, transient objective function, and steady-state objective function:

[0125]

[0126] Then, under the constraints of the energy storage device, the improved multi-objective evolutionary algorithm can be used to solve the objective function to obtain the operating parameters of the energy storage device, including battery current, battery capacity, and the transmission power of the converter of the energy storage device. The operating parameters can enable the energy storage device to compensate for the frequency and voltage fluctuations of the distribution network, so that the objective function takes the minimum value.

[0127] S6: Optimize the operating parameters based on the battery energy storage thermal coupling model, battery charge-discharge cycle model, and energy storage converter efficiency and loss model to obtain the target operating parameters, and control the operation of the energy storage device according to the target operating parameters.

[0128] Furthermore, the objective of this stage is to obtain target operating parameters and control the operation of the energy storage device based on these parameters. Specifically, the operating parameters, while meeting the constraints of the energy storage device, also need to minimize the difference between the battery heat dissipation power and the battery thermal power, maximize the maximum number of cycles, minimize the converter losses, and maximize the efficiency. Therefore, the operating parameters can be optimized based on the battery energy storage thermal coupling model, the battery charge-discharge cycle model, and the energy storage converter efficiency and loss model. This involves solving the objective function multiple times to obtain multiple operating parameters. These parameters are then substituted into the battery energy storage thermal coupling model, the battery charge-discharge cycle model, and the energy storage converter efficiency and loss model to find the operating parameters that best meet the requirements of these models. These parameters are then used as the target operating parameters, and the energy storage device is controlled based on these parameters to compensate for voltage and frequency fluctuations in the distribution network.

[0129] This invention can provide more suitable and comprehensive operating parameters from both transient and steady-state aspects according to different operating conditions. It can also optimize operating parameters based on battery energy storage thermal coupling model, battery charge-discharge cycle model and energy storage converter efficiency and loss model, thereby improving the stability of energy storage equipment operation and reducing operating costs.

[0130] The multi-objective optimization device for energy storage in distribution networks provided by the present invention is described below. The multi-objective optimization device for energy storage in distribution networks described below can be referred to in correspondence with the multi-objective optimization method for energy storage in distribution networks described above.

[0131] Figure 3 A schematic diagram of a multi-objective optimization system for energy storage in a distribution network is shown in the example. Figure 3 As shown, the method for implementing the multi-objective optimization method for distribution network energy storage as described above includes:

[0132] Battery energy storage thermal coupling model module 100: used to set up energy storage devices, obtain battery thermal parameters of energy storage devices, and establish a battery energy storage thermal coupling model based on battery thermal parameters;

[0133] Battery charge-discharge cycle model module 200: used to determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity;

[0134] Energy storage converter efficiency and loss model module 300: used to calculate the converter loss parameters of energy storage equipment, calculate converter loss and converter efficiency through the converter loss parameters, and establish an energy storage converter efficiency and loss model through converter loss and converter efficiency.

[0135] Energy storage device constraint module 400: Used to establish energy storage device constraint conditions, including energy storage parameter balance relationships and device parameter constraint conditions;

[0136] Operating Parameter Module 500: Used to determine the operating conditions of the energy storage device, determine the model weights based on the operating conditions, construct transient and steady-state objective functions, and solve the transient and steady-state objective functions for multi-objective functions based on the model weights and energy storage device constraints to obtain the operating parameters;

[0137] Target operating parameter module 600: This module optimizes operating parameters based on battery energy storage thermal coupling model, battery charge-discharge cycle model, and energy storage converter efficiency and loss model to obtain target operating parameters, and controls the operation of energy storage equipment based on the target operating parameters.

[0138] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a multi-objective optimization method for energy storage in the distribution network, which includes:

[0139] S1: Set up the energy storage device, obtain the battery thermal parameters of the energy storage device, and establish a battery energy storage thermal coupling model based on the battery thermal parameters;

[0140] S2: Determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity.

[0141] S3: Calculate the converter loss parameters of the energy storage device, calculate the converter loss and converter efficiency through the converter loss parameters, and establish the energy storage converter efficiency and loss model through the converter loss and converter efficiency.

[0142] S4: Establish energy storage device constraints, including energy storage parameter balance relationships and device parameter constraints;

[0143] S5: Determine the operating conditions of the energy storage equipment, determine the model weights based on the operating conditions, construct the transient objective function and the steady-state objective function, and solve the transient objective function and the steady-state objective function for multi-objective function based on the model weights and the constraints of the energy storage equipment to obtain the operating parameters;

[0144] S6: Optimize the operating parameters based on the battery energy storage thermal coupling model, battery charge-discharge cycle model, and energy storage converter efficiency and loss model to obtain the target operating parameters, and control the operation of the energy storage device according to the target operating parameters.

[0145] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0146] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the multi-objective optimization method for distribution network energy storage provided by the above methods, the method comprising:

[0147] S1: Set up the energy storage device, obtain the battery thermal parameters of the energy storage device, and establish a battery energy storage thermal coupling model based on the battery thermal parameters;

[0148] S2: Determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity.

[0149] S3: Calculate the converter loss parameters of the energy storage device, calculate the converter loss and converter efficiency through the converter loss parameters, and establish the energy storage converter efficiency and loss model through the converter loss and converter efficiency.

[0150] S4: Establish energy storage device constraints, including energy storage parameter balance relationships and device parameter constraints;

[0151] S5: Determine the operating conditions of the energy storage equipment, determine the model weights based on the operating conditions, construct the transient objective function and the steady-state objective function, and solve the transient objective function and the steady-state objective function for multi-objective function based on the model weights and the constraints of the energy storage equipment to obtain the operating parameters;

[0152] S6: Optimize the operating parameters based on the battery energy storage thermal coupling model, battery charge-discharge cycle model, and energy storage converter efficiency and loss model to obtain the target operating parameters, and control the operation of the energy storage device according to the target operating parameters.

[0153] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the aforementioned multi-objective optimization methods for distribution network energy storage, the method comprising:

[0154] S1: Set up the energy storage device, obtain the battery thermal parameters of the energy storage device, and establish a battery energy storage thermal coupling model based on the battery thermal parameters;

[0155] S2: Determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity.

[0156] S3: Calculate the converter loss parameters of the energy storage device, calculate the converter loss and converter efficiency through the converter loss parameters, and establish the energy storage converter efficiency and loss model through the converter loss and converter efficiency.

[0157] S4: Establish energy storage device constraints, including energy storage parameter balance relationships and device parameter constraints;

[0158] S5: Determine the operating conditions of the energy storage equipment, determine the model weights based on the operating conditions, construct the transient objective function and the steady-state objective function, and solve the transient objective function and the steady-state objective function for multi-objective function based on the model weights and the constraints of the energy storage equipment to obtain the operating parameters;

[0159] S6: Optimize the operating parameters based on the battery energy storage thermal coupling model, battery charge-discharge cycle model, and energy storage converter efficiency and loss model to obtain the target operating parameters, and control the operation of the energy storage device according to the target operating parameters.

[0160] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0161] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-objective optimization method for energy storage in distribution networks, characterized in that, include: S1: Set up the energy storage device, obtain the battery thermal parameters of the energy storage device, and establish a battery energy storage thermal coupling model based on the battery thermal parameters; S2: Determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity. S3: Calculate the converter loss parameters of the energy storage device, calculate the converter loss and converter efficiency through the converter loss parameters, and establish the energy storage converter efficiency and loss model through the converter loss and converter efficiency. S4: Establish energy storage device constraints, including energy storage parameter balance relationships and device parameter constraints; S5: Determine the operating conditions of the energy storage equipment, determine the model weights based on the operating conditions, construct the transient objective function and the steady-state objective function, and solve the transient objective function and the steady-state objective function for multi-objective function based on the model weights and the constraints of the energy storage equipment to obtain the operating parameters; In step S5, after determining the model weights, a transient performance objective including total harmonic distortion, frequency deviation, and transient voltage deviation is determined, and the transient objective function is constructed through the transient performance objective. A steady-state performance objective is determined, including peak-valley arbitrage revenue, battery power fluctuation, and curtailment loss. The steady-state objective function is then constructed based on the steady-state performance objective. An objective function is constructed based on the model weights, the transient objective function, and the steady-state objective function. Under the constraints of the energy storage device, the objective function is solved using a multi-objective function to obtain the operating parameters. S6: Optimize the operating parameters based on the battery energy storage thermal coupling model, battery charge-discharge cycle model, and energy storage converter efficiency and loss model to obtain the target operating parameters, and control the operation of the energy storage device according to the target operating parameters.

2. The multi-objective optimization method for energy storage in distribution networks according to claim 1, characterized in that, Step S1 further includes: S11: Set up an energy storage device including a battery, and acquire the battery thermal parameters including battery specific heat capacity, surface heat transfer coefficient, battery mass, and battery surface area; S12: Calculate the battery heat dissipation power using the battery thermal parameters, calculate the battery thermal power, and establish the battery energy storage thermal coupling model based on the battery thermal power and battery heat dissipation power.

3. The multi-objective optimization method for energy storage in distribution networks according to claim 1, characterized in that, Step S2 further includes: S21: Determine the battery's allowable capacity, including the upper limit and lower limit of the battery's allowable capacity; S22: Obtain the battery fitting parameters of the energy storage device, calculate the maximum number of cycles using the battery fitting parameters, and establish the battery charge-discharge cycle model using the maximum number of cycles and the battery's allowable capacity.

4. The multi-objective optimization method for energy storage in distribution networks according to claim 1, characterized in that, Step S3 further includes: S31: Calculate the converter loss parameters of the energy storage device, including conduction loss, reverse recovery loss and converter loss; S32: Determine the operating environment coefficient, and calculate the converter loss based on the operating environment coefficient and the converter loss parameters; S33: Determine the operating state coefficient, calculate the converter efficiency using the converter loss and the operating state coefficient, and establish an energy storage converter efficiency and loss model using the converter loss and the converter efficiency.

5. The multi-objective optimization method for energy storage in distribution networks according to claim 1, characterized in that, Step S4 further includes: S41: Establish the energy storage parameter balance relationship, including the active power balance relationship, reactive power balance relationship, and line power flow balance relationship; S42: Establish the equipment parameter constraint conditions, including energy storage capacity constraints, branch power constraints, and node voltage constraints, thereby establishing energy storage equipment constraint conditions that include energy storage parameter balance relationships and equipment parameter constraint conditions.

6. A distribution network energy storage multi-objective optimization system, used to execute the distribution network energy storage multi-objective optimization method as described in any one of claims 1 to 5, characterized in that, include: Battery Energy Storage Thermal Coupling Model Module: Used to set up energy storage devices, obtain battery thermal parameters of energy storage devices, and establish a battery energy storage thermal coupling model based on battery thermal parameters; Battery charge-discharge cycle model module: used to determine the allowable battery capacity, obtain the battery fitting parameters of the energy storage device, and establish a battery charge-discharge cycle model based on the battery fitting parameters and the allowable battery capacity; Energy storage converter efficiency and loss model module: used to calculate the converter loss parameters of energy storage equipment, calculate converter loss and converter efficiency through the converter loss parameters, and establish the energy storage converter efficiency and loss model through converter loss and converter efficiency. Energy storage device constraint module: used to establish energy storage device constraint conditions, including energy storage parameter balance relationships and device parameter constraints; Operating parameters module: used to determine the operating conditions of energy storage equipment, determine model weights based on the operating conditions, construct transient and steady-state objective functions, and solve multi-objective functions based on model weights and energy storage equipment constraints to obtain operating parameters; The target operating parameter module includes optimizing the operating parameters based on the battery energy storage thermal coupling model, the battery charge-discharge cycle model, and the energy storage converter efficiency and loss model to obtain the target operating parameters, and controlling the operation of the energy storage device based on the target operating parameters.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the multi-objective optimization method for distribution network energy storage as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-objective optimization method for energy storage in distribution networks as described in any one of claims 1 to 5.

9. A computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, characterized in that, When the program instructions are executed by the computer, the computer is able to perform the steps of the multi-objective optimization method for distribution network energy storage as described in any one of claims 1 to 5.

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