Method for configuring capacity of hybrid energy storage system of converter station

Abstract

The application discloses a method for configuring capacity of a hybrid energy storage system of a converter station, and aims to solve how to configure a reasonable capacity of a hybrid energy storage system of a converter station, effectively guide configuration and operation of the hybrid energy storage system of the converter station, and improve energy-source-load imbalance of the converter station. The method comprises the following steps: obtaining an energy source structure of the converter station and a load curve of the hybrid energy storage system; based on the energy source structure and the load curve, combining a preset frequency division mode to establish decision variables considering capacity configuration and power distribution; constructing a multi-objective function of the hybrid energy storage system and energy storage operation constraints, and based on the decision variables, the multi-objective function and the energy storage operation constraints, constructing a multi-objective optimization model; combining the load curve, and through optimization solving of the multi-objective optimization model based on self-adaptive load segmentation, an optimal capacity configuration scheme of the hybrid energy storage system is obtained.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to a method for configuring the capacity of a hybrid energy storage system in a converter station, a device for configuring the capacity of a hybrid energy storage system in a converter station, an electronic device, and a storage medium. Background Technology

[0002] Energy is the foundation upon which humanity depends for survival and development. With the continuous development of the economy and society, energy production and consumption patterns are undergoing significant transformations. The energy industry shoulders new missions such as improving energy efficiency, ensuring energy security, and promoting the consumption of new energy sources. Converter stations play a crucial role in AC / DC grid interconnection, long-distance power transmission, and the consumption of new energy sources.

[0003] On the one hand, with the continuous increase in the proportion of new energy sources connected to the grid, their power output exhibits strong fluctuation characteristics. On the other hand, the internal power demand of DC transmission converter stations is large, exhibiting different energy consumption timing characteristics and random load demand, which often leads to power-load imbalance problems within the converter stations. This results in DC bus voltage fluctuations, converter equipment overload, and even affects the stability of the entire power grid.

[0004] In recent years, many energy storage technologies have been widely developed and applied in power systems. For example, lithium battery-supercapacitor hybrid energy storage systems, which combine energy-type and power-type energy storage elements, fully utilize the different discharge characteristics of these two types of energy storage to effectively mitigate renewable energy fluctuations and provide steady-state power support. How to configure hybrid energy storage systems of appropriate capacity for converter stations is a core technical problem that urgently needs to be solved. Summary of the Invention

[0005] This invention provides a method for configuring the capacity of a hybrid energy storage system in a converter station, a device for configuring the capacity of a hybrid energy storage system in a converter station, an electronic device, and a storage medium, which are used to solve or partially solve how to configure a hybrid energy storage system with a reasonable capacity for a converter station, so as to effectively guide the configuration and operation of the hybrid energy storage system in the converter station and improve the problem of power-load imbalance in the converter station.

[0006] This invention provides a method for configuring the capacity of a hybrid energy storage system in a converter station, the method comprising:

[0007] Obtain the energy structure of the converter station and the load curve of the hybrid energy storage system;

[0008] Based on the energy structure and the load curve, decision variables that simultaneously consider capacity configuration and power allocation are established in conjunction with a preset frequency division method.

[0009] Construct the multi-objective function and energy storage operation constraints of the hybrid energy storage system, and construct a multi-objective optimization model based on the decision variables, the multi-objective function and the energy storage operation constraints;

[0010] By combining the load curve, the optimal capacity configuration scheme of the hybrid energy storage system is obtained by performing adaptive load segmentation optimization on the multi-objective optimization model.

[0011] Optionally, the types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the decision variables established based on the energy structure and the load curve, combined with a preset frequency division method, that simultaneously consider capacity configuration and power allocation, include:

[0012] Based on the energy structure, the load curve is analyzed for operating conditions.

[0013] Based on the operational condition analysis results and combined with the preset frequency division method, the operational condition parameter set and power distribution parameter set of the hybrid energy storage system are constructed.

[0014] Based on the operating condition parameter set and the power allocation parameter set, an energy management parameter set is constructed;

[0015] Simultaneously considering the number of batteries connected in series, the number of batteries connected in parallel, and the battery model, as well as the number of supercapacitors connected in series, the number of supercapacitors connected in parallel, and the supercapacitor model, a set of capacity configuration parameters is constructed.

[0016] Decision variables are constructed based on the set of capacity configuration parameters and the set of energy management parameters.

[0017] Optionally, the step of constructing the operating condition parameter set and power allocation parameter set of the hybrid energy storage system based on the operating condition analysis results and in combination with a preset frequency division method includes:

[0018] Using the time points when the operating conditions on the load curve change as the operating condition segment points of the load curve, all the operating condition segment points are integrated to construct an operating condition parameter set;

[0019] Based on all the aforementioned operating condition segment points and in conjunction with the preset frequency division method, the frequency division strategy for each load segment curve on the load curve is determined, and the frequency division strategies of all the aforementioned load segment curves are integrated to construct a power allocation parameter set.

[0020] Optionally, the types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the multi-objective function includes an investment cost function and a system lifetime degradation function; the construction process of the multi-objective function of the hybrid energy storage system includes:

[0021] Simultaneously considering the unit price and total quantity of the batteries, as well as the unit price and total quantity of the supercapacitors, an investment cost function is constructed;

[0022] Simultaneously considering the battery's output power, rated capacity, and total number of charge-discharge cycles, a system lifetime degradation function is constructed;

[0023] Based on the investment cost function and the system lifetime degradation function, a multi-objective function for the hybrid energy storage system is constructed.

[0024] Optionally, the energy storage operation constraints include minimum rated energy storage power constraints, minimum energy storage capacity constraints, hybrid energy storage system voltage limit constraints, and energy storage device state of charge constraints.

[0025] Optionally, the decision variables include a set of capacity configuration parameters and a set of energy management parameters; the step of combining the load curve and obtaining the optimal capacity configuration scheme of the hybrid energy storage system by performing adaptive load segmentation-based optimization on the multi-objective optimization model includes:

[0026] Based on the energy management parameter set, a frequency-divided energy storage response based on adaptive load segmentation is performed on the load curve to construct a power allocation strategy;

[0027] The power allocation strategy is used as a power allocation constraint in the optimization solution. The multi-objective optimization model is optimized and solved by intelligent optimization algorithm to obtain the optimal capacity configuration parameter set.

[0028] The energy storage capacity of the hybrid energy storage system is configured according to the optimal capacity configuration parameter set to generate an optimal capacity configuration scheme.

[0029] Optionally, the types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the energy management parameter set includes an operating condition parameter set and a power allocation parameter set; the step of constructing a power allocation strategy by performing frequency-division energy storage response based on adaptive load segmentation of the load curve according to the energy management parameter set includes:

[0030] Based on the set of operating parameters, the load curve is adaptively segmented to obtain a multi-segment load curve.

[0031] For each segment of the load curve, a target frequency division strategy corresponding to the load curve is matched from the power allocation parameter set, and the target frequency division strategy is used to perform frequency division processing on the load curve to determine the low-frequency load and the high-frequency load.

[0032] The low-frequency load is allocated to the battery for response, and the high-frequency load is allocated to the supercapacitor for response;

[0033] Complete the frequency-division energy storage response of all the load segment curves to form a power allocation strategy.

[0034] The present invention also provides a capacity configuration device for a hybrid energy storage system in a converter station, the device comprising:

[0035] The data acquisition unit is used to acquire the energy structure of the converter station and the load curve of the hybrid energy storage system;

[0036] The decision variable construction unit is used to establish decision variables that simultaneously consider capacity configuration and power allocation based on the energy structure and the load curve, combined with a preset frequency division method.

[0037] The multi-objective optimization model construction unit is used to construct the multi-objective function and energy storage operation constraints of the hybrid energy storage system, and to construct a multi-objective optimization model based on the decision variables, the multi-objective function and the energy storage operation constraints.

[0038] The optimization solution unit is used to combine the load curve and perform adaptive load segmentation-based optimization solution on the multi-objective optimization model to obtain the optimal capacity configuration scheme of the hybrid energy storage system.

[0039] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0040] The memory is used to store program code and transmit the program code to the processor;

[0041] The processor is used to execute the hybrid energy storage system capacity configuration method for the converter station as described above, according to the instructions in the program code.

[0042] The present invention also provides a computer-readable storage medium for storing program code for executing the hybrid energy storage system capacity configuration method for converter stations as described in any of the preceding claims.

[0043] As can be seen from the above technical solutions, the present invention has the following advantages:

[0044] This paper presents a method for capacity configuration of a hybrid energy storage system in a converter station. First, the energy structure of the converter station and the load curve of the hybrid energy storage system are obtained for subsequent modeling and capacity configuration optimization calculations. Next, based on the energy structure and load curve, and combined with a preset frequency division method, decision variables considering both capacity configuration and power allocation are established. This allows for the inclusion of both capacity configuration and operating parameters as optimization decision variables in the subsequent optimization model. Based on an adaptive piecewise power allocation strategy adapted to load operation, the optimal capacity configuration design is obtained through capacity configuration optimization, improving the DC bus voltage stability within the converter station and effectively mitigating the adverse effects of renewable energy fluctuations and load uncertainties. Then, a multi-objective function and energy storage operation constraints for the hybrid energy storage system are constructed. Based on the decision variables, the multi-objective function, and the energy storage operation constraints, a multi-objective optimization model is built. Finally, by combining the load curve and performing adaptive load piecewise optimization on the multi-objective optimization model, the optimal capacity configuration scheme of the hybrid energy storage system is obtained. By analyzing the energy structure of converter stations and establishing corresponding capacity configuration optimization models for hybrid energy storage systems, the configuration and operation of hybrid energy storage systems in converter stations can be effectively guided, thereby effectively improving the problem of power-load imbalance in converter stations. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating the steps of a hybrid energy storage system capacity configuration method for a converter station;

[0047] Figure 2 A schematic diagram of the overall process for configuring the capacity of a hybrid energy storage system in a converter station;

[0048] Figure 3 This is a schematic diagram of the DC system topology within a converter station, as shown in a specific example.

[0049] Figure 4 This is a schematic diagram of a typical load curve for a hybrid energy storage system in a specific example;

[0050] Figure 5 This is a schematic diagram of the adaptive segmentation mechanism;

[0051] Figure 6 A schematic diagram is constructed for the power allocation strategy;

[0052] Figure 7This is a structural block diagram of a hybrid energy storage system capacity configuration device for a converter station. Detailed Implementation

[0053] This invention provides a method for configuring the capacity of a hybrid energy storage system in a converter station, a device for configuring the capacity of a hybrid energy storage system in a converter station, an electronic device, and a storage medium. These methods are used to solve or partially solve how to configure a hybrid energy storage system with a reasonable capacity for a converter station, so as to effectively guide the configuration and operation of the hybrid energy storage system in the converter station and improve the problem of power-load imbalance in the converter station.

[0054] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. 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.

[0055] As an example, converter stations play a crucial role in AC / DC grid interconnection, long-distance power transmission, and renewable energy consumption. In recent years, many energy storage technologies have been widely developed and applied in power systems. For instance, lithium-ion battery-supercapacitor hybrid energy storage systems, combining energy-type and power-type energy storage elements, fully utilize the different discharge characteristics of these two types of components, effectively mitigating renewable energy fluctuations and providing steady-state power support. How to configure a hybrid energy storage system with an appropriate capacity for converter stations is a core technical problem that urgently needs to be solved.

[0056] Therefore, one of the core inventive points of this invention is to provide an optimal capacity configuration method for a hybrid energy storage system in a converter station. By analyzing the energy structure of the converter station, a corresponding capacity configuration optimization model for the hybrid energy storage system is established to effectively guide the configuration and operation of the hybrid energy storage system in the converter station, thereby effectively improving the power-load imbalance problem in the converter station. On the one hand, by establishing a multi-objective optimization model that considers system operational safety, service life, and economy, its operational reliability can be effectively improved, and the investment cost of the converter station can be effectively reduced. On the other hand, by incorporating configuration parameters and operating parameters into the decision variables, the optimal capacity configuration design scheme is obtained through optimization, which can improve the stability of the DC bus voltage in the converter station and effectively mitigate the adverse effects of new energy fluctuations and load uncertainties.

[0057] Reference Figure 1 This document illustrates a flowchart of a method for configuring the capacity of a hybrid energy storage system in a converter station according to an embodiment of the present invention. Specifically, the method may include the following steps:

[0058] Step 101: Obtain the energy structure of the converter station and the load curve of the hybrid energy storage system;

[0059] In practical implementation, it is first necessary to obtain the typical load curve of the hybrid energy storage system and the energy structure of the converter station for subsequent related modeling and capacity configuration optimization calculations.

[0060] Step 102: Based on the energy structure and the load curve, establish decision variables that simultaneously consider capacity configuration and power allocation, combined with a preset frequency division method;

[0061] This step is mainly based on the energy structure and load curve obtained in the previous steps, combined with a preset frequency division method (such as low-pass filtering, or a three-level frequency division method combining band-pass filtering and low-pass and high-pass filtering, or wavelet packet decomposition, etc. In this embodiment of the invention, low-pass filtering is used as the frequency division method) to establish decision variables that simultaneously consider capacity configuration and power allocation.

[0062] The decision variables that simultaneously consider capacity allocation and power distribution can be established using the following formula:

[0063] ;

[0064] In the formula, For decision variables (i.e. decision vectors); and These represent the number of batteries connected in series and in parallel, respectively. and These represent the number of supercapacitors connected in series and in parallel, respectively. and These are the model numbers for the battery and the supercapacitor, respectively. , , , , , This constitutes a set of capacity configuration parameters; The energy management parameter set is expressed as follows:

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] In the formula, For the set of operating parameters; For power allocation parameter set; The maximum possible value for the number of parameters is determined by the load curve. This indicates the point on the load curve where the operating conditions change; The first corresponding load curve Frequency division strategy for segmented load curves; The value is determined by the specific frequency division strategy.

[0070] Understandable Section load requires Each segment point (operating condition parameter), and the power distribution strategy needs to be... There is one load (one for each load segment). The load start point, as the implicit "condition 0", is not recorded in the condition parameter set. When the value is 1, it corresponds to the first segmentation point, which is the starting point of the second working condition, forming a misaligned correspondence of "one less segmentation point, one more strategy", realizing the collaborative optimization of adaptive segmentation and dynamic frequency division.

[0071] Based on the preceding discussion, the main types of energy storage devices in a hybrid energy storage system can include batteries and supercapacitors. In practical implementation, the process of establishing decision variables that simultaneously consider capacity configuration and power allocation, based on the energy structure and load curve, and combined with a preset frequency division method, can include the following steps S01 to S05:

[0072] Step S01: Analyze the operating conditions of the load curve in conjunction with the energy structure;

[0073] Step S02: Based on the operational condition analysis results and combined with the preset frequency division method, construct the operational parameter set and power distribution parameter set of the hybrid energy storage system;

[0074] Furthermore, the specific implementation process of constructing the operating condition parameter set and power allocation parameter set of the hybrid energy storage system based on the operating condition analysis results and in combination with the preset frequency division method in step S02 may include: taking the time point when the operating condition changes on the load curve as the operating condition segment point of the load curve, integrating all operating condition segment points to construct the operating condition parameter set; based on all operating condition segment points and in combination with the preset frequency division method, determining the frequency division strategy of each load segment curve on the load curve, and integrating the frequency division strategies of all load segment curves to construct the power allocation parameter set.

[0075] Step S03: Construct an energy management parameter set based on the operating condition parameter set and the power allocation parameter set;

[0076] Step S04: Simultaneously consider the number of batteries connected in series, the number of batteries connected in parallel, and the battery type, as well as the number of supercapacitors connected in series, the number of supercapacitors connected in parallel, and the supercapacitor type, to construct a set of capacity configuration parameters;

[0077] Step S05: Construct decision variables based on the capacity configuration parameter set and the energy management parameter set.

[0078] Step 103: Construct the multi-objective function and energy storage operation constraints of the hybrid energy storage system, and construct a multi-objective optimization model based on the decision variables, the multi-objective function and the energy storage operation constraints;

[0079] This step mainly establishes energy storage operation constraints that consider the safety and reliability of converter station operation, as well as a multi-objective function that considers the economy and durability of hybrid energy storage system, and further combines decision variables to construct a multi-objective optimization model.

[0080] Specifically, the multi-objective function constructed in the embodiments of the present invention mainly includes a system investment cost function and a system lifetime degradation function.

[0081] The system investment cost function can be constructed using the following formula:

[0082] ;

[0083] In the formula, Let the objective function be 1, representing the investment cost; and These are the unit prices of batteries and supercapacitors, respectively. and These represent the total number of batteries and supercapacitors, respectively.

[0084] The system lifetime degradation function is constructed using the following formula:

[0085] ;

[0086] In the formula, Objective function 2 represents battery degradation; To generate power for the battery; This refers to the battery's rated capacity. This represents the total number of charge-discharge cycles for the battery.

[0087] Based on this, the process of constructing the multi-objective function of the hybrid energy storage system can include: simultaneously considering the unit price and total quantity of batteries, as well as the unit price and total quantity of supercapacitors, to construct an investment cost function; simultaneously considering the battery's output power, rated capacity, and total number of charge-discharge cycles, to construct a system lifetime degradation function; and based on the investment cost function and the system lifetime degradation function, to construct the multi-objective function of the hybrid energy storage system.

[0088] In some embodiments, energy storage operation constraints may mainly include minimum rated energy storage power constraints, minimum energy storage capacity constraints, voltage limit constraints for hybrid energy storage systems, and state of charge constraints for energy storage devices.

[0089] Specifically, the minimum energy storage rated power constraint is constructed using the following formula:

[0090] ;

[0091] In the formula, This refers to the rated power of the hybrid energy storage system. This represents the power deficit that needs to be met at each moment; For the discharge efficiency of the energy storage system; Improve the charging efficiency of energy storage systems; For converter efficiency; and These are the upper and lower limits of the state of charge, respectively.

[0092] The minimum energy storage capacity constraint is constructed using the following formula:

[0093] ;

[0094] In the formula, This refers to the rated capacity of the energy storage system. This refers to the energy deficit that needs to be met for ships at any given time.

[0095] The voltage constraint of the energy storage system is constructed using the following formula:

[0096] ;

[0097] In the formula, and These are the minimum and maximum operating voltages of the converter, respectively. This refers to the series branch voltage of the hybrid energy storage system.

[0098] The state of charge constraints for energy storage devices are constructed using the following formula:

[0099] ;

[0100] In the formula, and These represent the states of charge of batteries and supercapacitors, respectively. and These are the minimum and maximum allowable values ​​for the battery's state of charge, respectively. and These are the minimum and maximum allowable values ​​for the state of charge of a supercapacitor, respectively.

[0101] Based on this, a multi-objective optimization model is established using the following formula:

[0102] ;

[0103] ;

[0104] In the formula, For multi-objective optimization models; express An optimization objective function model (in this embodiment of the invention, it includes the previously constructed investment cost function model and system lifetime degradation function model); To optimize the total number of objective function models; For decision variables; To optimize the total cycle time; For the first One decision variable; The total number of decision variables; For the first One inequality constraint condition; This represents the total number of inequality constraints. This is the feasible region.

[0105] It should be noted that, although only the investment cost function has been constructed so far... and system lifetime degradation function There are two objective functions, but the general multi-objective optimization model framework established above... Expansion space has been clearly reserved. To optimize the total number of objectives, new objective functions (such as reliability, environmental protection, etc.) can be added later. Simply add the new objective to the vector. That's all.

[0106] Indicates the first The standardized mathematical expression of the inequality constraints, in this embodiment of the invention, specifically corresponds to the mathematical forms of the converter station operation safety and reliability conditions, such as minimum rated power constraint, minimum energy storage capacity constraint, energy storage system voltage limit constraint, and energy storage device state of charge constraint. It requires that the constraint function values ​​be non-positive to ensure the decision variables... Satisfy feasible region Boundary requirements.

[0107] Step 104: Combining the load curve, the optimal capacity configuration scheme of the hybrid energy storage system is obtained by performing adaptive load segmentation optimization on the multi-objective optimization model.

[0108] This step mainly involves adaptively segmenting the load curve (which can be seen as the load) and forming a power allocation strategy based on a preset frequency division method. On this basis, a multi-objective optimization model is solved through an intelligent optimization algorithm to obtain the capacity configuration scheme of the hybrid energy storage system.

[0109] The main implementation points of adaptive load curve segmentation include: adaptively segmenting the load curve based on the load condition parameter set to obtain multiple segmented load curves. Since the load condition parameter set participates in the construction of the multi-objective optimization model as part of the decision variables, this step can also be understood as: first, based on the multi-objective optimization model, obtaining a scale of... The set of operating condition parameters (at this time, the scale corresponding to each operating condition parameter in the set can be obtained synchronously) is 1. The power distribution parameter set is then used to segment the load curve at the corresponding positions to obtain the following: Segmented load curve.

[0110] The main implementation points of forming a power allocation strategy include: based on the above load segmentation results, combined with the frequency division strategy in the corresponding power allocation parameter set, the frequency division method is used to perform frequency division processing on each load segment curve to obtain the high-frequency part load and the low-frequency part load; then the high-frequency part load and the low-frequency part load are respectively allocated to the corresponding energy storage devices for response (low-frequency load → battery, high-frequency load → supercapacitor), thereby forming a power allocation strategy.

[0111] In some embodiments, a multi-objective optimization model is solved using an intelligent optimization algorithm. The intelligent optimization algorithms that may be used include, but are not limited to:

[0112] (1) Improved NSGA-Ⅱ algorithm (Improved Non-dominated Sorting Genetic AlgorithmⅡ). Its improvements include chaotic mapping population initialization and adaptive crossover operator.

[0113] (2) Improved MOPSO algorithm (Improved Multi-Objective Particle Swarm Optimization). Its improvements include quantum behavior mechanism and Levy flight mutation strategy.

[0114] (3) Improved differential evolution algorithm. Improvements include initialization of the optimal point set and Gaussian-Cauchy hybrid mutation.

[0115] In practical applications, one or a combination of these parameters can be selected based on the dimensionality of the decision variables and the complexity of the constraints to obtain the optimal capacity configuration parameter set for the hybrid energy storage system. Then, the energy storage capacity of the hybrid energy storage system is configured according to the obtained optimal capacity configuration parameter set to generate the optimal capacity configuration scheme.

[0116] Based on the preceding discussion, decision variables mainly include the capacity configuration parameter set and the energy management parameter set. In specific implementation, the process of obtaining the optimal capacity configuration scheme for the hybrid energy storage system by combining the load curve and optimizing the multi-objective optimization model based on adaptive load segmentation can include: first, constructing a power allocation strategy by performing frequency-based energy storage response on the load curve based on adaptive load segmentation according to the energy management parameter set; then, using the power allocation strategy as the power allocation constraint during optimization, optimizing the multi-objective optimization model through an intelligent optimization algorithm to obtain the optimal capacity configuration parameter set; finally, configuring the energy storage capacity of the hybrid energy storage system according to the optimal capacity configuration parameter set to generate the optimal capacity configuration scheme.

[0117] Furthermore, the energy management parameter set can include an operating condition parameter set and a power allocation parameter set. The specific implementation process for constructing a power allocation strategy based on adaptive load segmentation and frequency-division energy storage response of the load curve according to the energy management parameter set can include: adaptively segmenting the load curve according to the operating condition parameter set to obtain multiple load segment curves; for each load segment curve, matching the target frequency division strategy corresponding to the load segment curve from the power allocation parameter set, and using the target frequency division strategy to perform frequency division processing on the load segment curve to determine the low-frequency load and high-frequency load; for this load segment curve, allocating the low-frequency load to the battery for response and allocating the high-frequency load to the supercapacitor for response; completing the frequency-division energy storage response for all load segment curves to form a power allocation strategy.

[0118] This invention provides a method for configuring the capacity of a hybrid energy storage system in a converter station. First, the energy structure of the converter station and the typical load curve of the hybrid energy storage system are obtained, and decision variables are established based on these variables and a preset frequency division method. These decision variables include a capacity configuration parameter set and an energy management parameter set, whereby the energy management parameter set includes an operating condition parameter set and a power allocation parameter set. Next, energy storage operation constraints considering the safety and reliability of the converter station's operation and a multi-objective function considering the economy and durability of the hybrid energy storage system are established, and a multi-objective optimization model is further constructed based on the decision variables. Finally, the load curve is adaptively segmented according to the operating condition parameter set, and a power allocation strategy is formed based on the preset frequency division method and the power allocation parameter set. On this basis, an intelligent optimization algorithm is used to optimize and solve the multi-objective optimization model to obtain the capacity configuration scheme of the hybrid energy storage system. By implementing this invention, the comprehensive energy utilization rate of the converter station can be effectively improved, and the selection and capacity calculation of various energy storage systems can be achieved. Simultaneously, the system's service life is extended, and investment costs are reduced.

[0119] For better illustration, refer to Figure 2This diagram illustrates the overall flow of a hybrid energy storage system capacity configuration method for a converter station according to an embodiment of the present invention. It should be noted that this embodiment only provides a brief description of the general flow of hybrid energy storage system capacity configuration for a converter station. The specific implementation process of each step can be understood by referring to the relevant content in the foregoing embodiments, and will not be elaborated upon here. It is understood that the present invention does not impose any limitations on this.

[0120] Step 201: Obtain the energy structure of the converter station and the load curve of the hybrid energy storage system;

[0121] Step 202: Combine the energy structure to perform operating condition analysis on the load curve. Based on the operating condition analysis results and the preset frequency division method, construct the operating condition parameter set and power distribution parameter set of the hybrid energy storage system. Based on the operating condition parameter set and power distribution parameter set, construct the energy management parameter set.

[0122] Step 203: Simultaneously consider the number of batteries connected in series, the number of batteries connected in parallel, and the battery type in the hybrid energy storage system, as well as the number of supercapacitors connected in series, the number of supercapacitors connected in parallel, and the supercapacitor type, to construct a capacity configuration parameter set. Then, based on the capacity configuration parameter set and the energy management parameter set, construct decision variables.

[0123] Step 204: Construct the multi-objective function and energy storage operation constraints of the hybrid energy storage system, and construct a multi-objective optimization model based on decision variables, multi-objective function and energy storage operation constraints;

[0124] Step 205: Based on the energy management parameter set, perform frequency-division energy storage response based on adaptive load segmentation on the load curve, construct a power allocation strategy, and use the power allocation strategy as the power allocation constraint in the optimization solution. Then, use an intelligent optimization algorithm to optimize and solve the multi-objective optimization model to obtain the optimal capacity configuration parameter set.

[0125] Step 206: Configure the energy storage capacity of the hybrid energy storage system according to the optimal capacity configuration parameter set to generate the optimal capacity configuration scheme.

[0126] To enable those skilled in the art to better understand the technical solutions of the present invention, the following specific example is used to illustrate the embodiments of the present invention.

[0127] This explanation will use a converter station as an example. The topology of the DC system within this converter station is as follows: Figure 3 As shown. A typical electrical load curve for the hybrid energy storage system within this converter station is shown below. Figure 4 As shown in the example, low-pass filtering is selected as the frequency division method.

[0128] In another example, a three-stage frequency division method combining bandpass filtering with low-pass and high-pass filtering can be used. Specifically, a Butterworth bandpass filter is selected to extract the mid-frequency power component (0.01Hz–0.1Hz), which is responded to by a sodium-sulfur battery or flywheel energy storage system. Low-frequency components below 0.01Hz are responded to by a lead-acid battery, while high-frequency components above 0.1Hz are responded to by a supercapacitor.

[0129] When using a bandpass filter for frequency division, the bandpass filter can achieve three-way frequency division, that is, extracting the mid-frequency power and distributing it to the third type of energy storage device (such as a flywheel), forming a multi-layer response structure of "high frequency → supercapacitor, mid-frequency → hybrid energy storage, low frequency → battery".

[0130] In another example, wavelet packet decomposition can be used as a frequency division method. Specifically, the load curve is decomposed into eight frequency bands using the db4 wavelet basis function ([0~f / 16], [f / 16~f / 8], [f / 8~f / 4], [f / 4~f / 2], and their corresponding high-frequency mirror bands). Then, the energy entropy of each frequency band is calculated and merged into a low-frequency group ([0~f / 4]) and a high-frequency group ([f / 4~f / 2]) with a threshold of 0.1, and allocated to the battery and supercapacitor respectively.

[0131] Specifically, when wavelet packet decomposition is used as the frequency division method, the adaptive segmentation parameter ω and the wavelet decomposition level j can be optimized in tandem. Specifically, when load fluctuations are severe (variance greater than a threshold)... When j=4, the number of decomposition layers is automatically increased, and the number of segments is adjusted accordingly. When the load is stable, reduce the number of decomposition layers to j=2, at which point ω=3. The frequency division parameter k in wavelet decomposition is represented as the energy entropy threshold of each frequency band, and together with ω, it participates in multi-objective optimization as a decision variable, thereby avoiding power allocation failure caused by mode mixing.

[0132] The explanation will continue with the use of low-pass filtering as the frequency division method. First, decision variables considering both capacity configuration and power allocation are established, referring to the previous embodiments. Next, energy storage operation constraints considering the safety and reliability of converter station operation, and a multi-objective function considering the economy and durability of the hybrid energy storage system are established, referring to the previous embodiments.

[0133] In the system lifetime degradation function of the multi-objective function, the total number of battery charge-discharge cycles is set to 5000. In the voltage constraint of the hybrid energy storage system, the minimum and maximum operating voltages of the converter are set to 600V and 800V, respectively. In the state of charge constraint of the energy storage device, the minimum and maximum allowable values ​​for the battery's state of charge are set to 0.2 and 0.8, respectively, and the minimum and maximum allowable values ​​for the supercapacitor's state of charge are set to 0.1 and 0.9, respectively.

[0134] Referring to the aforementioned embodiments, the load curve is adaptively segmented and a power allocation strategy is formed based on a preset frequency division method. On this basis, a multi-objective optimization model is solved through an intelligent optimization algorithm to obtain a capacity configuration scheme for the hybrid energy storage system. Figure 5 A schematic diagram of an adaptive segmentation mechanism for adaptive segmentation of the load curve is shown. Figure 6 This shows a schematic diagram of the power allocation strategy.

[0135] Reference Figure 7 The diagram illustrates a structural block diagram of a hybrid energy storage system capacity configuration device for a converter station according to an embodiment of the present invention, which may specifically include:

[0136] The data acquisition unit 701 is used to acquire the energy structure of the converter station and the load curve of the hybrid energy storage system;

[0137] The decision variable construction unit 702 is used to establish decision variables that simultaneously consider capacity configuration and power allocation based on the energy structure and the load curve, combined with a preset frequency division method.

[0138] The multi-objective optimization model construction unit 703 is used to construct the multi-objective function and energy storage operation constraints of the hybrid energy storage system, and to construct a multi-objective optimization model based on the decision variables, the multi-objective function and the energy storage operation constraints;

[0139] The optimization solution unit 704 is used to combine the load curve and perform adaptive load segmentation-based optimization solution on the multi-objective optimization model to obtain the optimal capacity configuration scheme of the hybrid energy storage system.

[0140] In one optional embodiment, the types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the decision variable construction unit 702 includes:

[0141] The operating condition analysis unit is used to perform operating condition analysis on the load curve in conjunction with the energy structure.

[0142] The operating condition and power distribution parameter set construction unit is used to construct the operating condition parameter set and power distribution parameter set of the hybrid energy storage system based on the operating condition analysis results and in combination with the preset frequency division method.

[0143] An energy management parameter set construction unit is used to construct an energy management parameter set based on the operating condition parameter set and the power allocation parameter set.

[0144] A capacity configuration parameter set construction unit is used to construct a capacity configuration parameter set by simultaneously considering the number of series-connected batteries, the number of parallel-connected batteries, and the battery model, as well as the number of series-connected batteries, the number of parallel-connected batteries, and the supercapacitor model.

[0145] The decision variable construction subunit is used to construct decision variables based on the capacity configuration parameter set and the energy management parameter set.

[0146] In one optional embodiment, the operating condition and power allocation parameter set construction unit includes:

[0147] The operating condition parameter set construction unit is used to construct an operating condition parameter set by taking the time point when the operating condition changes on the load curve as the operating condition segment point of the load curve, integrating all the operating condition segment points.

[0148] The power allocation parameter set construction unit is used to determine the frequency division strategy of each load segment curve on the load curve based on all the operating condition segment points and in combination with the preset frequency division method, and to integrate the frequency division strategies of all the load segment curves to construct a power allocation parameter set.

[0149] In one optional embodiment, the types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the multi-objective function includes an investment cost function and a system lifetime degradation function; the multi-objective optimization model construction unit 703 includes:

[0150] The investment cost function construction unit is used to construct an investment cost function by simultaneously considering the unit price and total quantity of the battery, as well as the unit price and total quantity of the supercapacitor.

[0151] The system lifetime degradation function construction unit is used to construct the system lifetime degradation function by simultaneously considering the battery's output power, rated capacity, and total number of charge and discharge cycles.

[0152] A multi-objective function construction unit is used to construct a multi-objective function for the hybrid energy storage system based on the investment cost function and the system lifetime degradation function.

[0153] In one optional embodiment, the energy storage operation constraints include minimum rated energy storage power constraints, minimum energy storage capacity constraints, hybrid energy storage system voltage limit constraints, and energy storage device state of charge constraints.

[0154] In one optional embodiment, the decision variables include a capacity configuration parameter set and an energy management parameter set; the optimization solution unit 704 includes:

[0155] A power allocation strategy construction unit is used to construct a power allocation strategy by performing frequency-divided energy storage response based on adaptive load segmentation on the load curve according to the energy management parameter set.

[0156] The optimal capacity configuration parameter set solving unit is used to use the power allocation strategy as the power allocation constraint during optimization, and to optimize the multi-objective optimization model through intelligent optimization algorithm to obtain the optimal capacity configuration parameter set.

[0157] An energy storage capacity configuration unit is used to configure the energy storage capacity of the hybrid energy storage system according to the optimal capacity configuration parameter set, and generate an optimal capacity configuration scheme.

[0158] In one optional embodiment, the types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the energy management parameter set includes an operating condition parameter set and a power allocation parameter set; the power allocation strategy construction unit includes:

[0159] An adaptive load segmentation unit is used to adaptively segment the load curve according to the set of operating parameters to obtain multiple load segmentation curves.

[0160] The frequency division processing unit is used to match a target frequency division strategy corresponding to the load segment curve from the power allocation parameter set for each segment of the load segment curve, and to perform frequency division processing on the load segment curve using the target frequency division strategy to determine the low-frequency load and the high-frequency load.

[0161] The frequency division energy storage response unit is used to distribute the low-frequency load to the battery for response and the high-frequency load to the supercapacitor for response;

[0162] The power allocation strategy forming unit is used to complete the frequency-division energy storage response of all the load segment curves and form a power allocation strategy.

[0163] As the device embodiment is basically similar to the method embodiment, it is described in a relatively simple way. For relevant details, please refer to the description of the method embodiment above.

[0164] This invention also provides an electronic device, which includes a processor and a memory:

[0165] The memory is used to store program code and transfer the program code to the processor;

[0166] The processor is used to execute the capacity configuration method of the hybrid energy storage system of the converter station according to the instructions in the program code of any embodiment of the present invention.

[0167] This invention also provides a computer-readable storage medium for storing program code for executing the capacity configuration method of the hybrid energy storage system for converter stations according to any embodiment of this invention.

[0168] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0169] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0170] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0171] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0172] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0173] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or 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.

[0174] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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. Such 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 method for configuring the capacity of a hybrid energy storage system in a converter station, characterized in that, include: Obtain the energy structure of the converter station and the load curve of the hybrid energy storage system; Based on the energy structure and the load curve, decision variables that simultaneously consider capacity configuration and power allocation are established in conjunction with a preset frequency division method. Construct the multi-objective function and energy storage operation constraints of the hybrid energy storage system, and construct a multi-objective optimization model based on the decision variables, the multi-objective function and the energy storage operation constraints; By combining the load curve, the optimal capacity configuration scheme of the hybrid energy storage system is obtained by performing adaptive load segmentation optimization on the multi-objective optimization model.

2. The method for configuring the capacity of a hybrid energy storage system in a converter station according to claim 1, characterized in that, The types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the decision variables established based on the energy structure and the load curve, combined with a preset frequency division method, that simultaneously consider capacity configuration and power allocation, include: Based on the energy structure, the load curve is analyzed for operating conditions. Based on the operational condition analysis results and combined with the preset frequency division method, the operational condition parameter set and power distribution parameter set of the hybrid energy storage system are constructed. Based on the operating condition parameter set and the power allocation parameter set, an energy management parameter set is constructed; Simultaneously considering the number of batteries connected in series, the number of batteries connected in parallel, and the battery model, as well as the number of supercapacitors connected in series, the number of supercapacitors connected in parallel, and the supercapacitor model, a set of capacity configuration parameters is constructed. Decision variables are constructed based on the set of capacity configuration parameters and the set of energy management parameters.

3. The method for configuring the capacity of a hybrid energy storage system in a converter station according to claim 2, characterized in that, The process of constructing the operating condition parameter set and power allocation parameter set of the hybrid energy storage system based on the operational condition analysis results and in conjunction with the preset frequency division method includes: Using the time points when the operating conditions on the load curve change as the operating condition segment points of the load curve, all the operating condition segment points are integrated to construct an operating condition parameter set; Based on all the aforementioned operating condition segment points and in conjunction with the preset frequency division method, the frequency division strategy for each load segment curve on the load curve is determined, and the frequency division strategies of all the aforementioned load segment curves are integrated to construct a power allocation parameter set.

4. The method for configuring the capacity of a hybrid energy storage system in a converter station according to claim 1, characterized in that, The types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the multi-objective function includes an investment cost function and a system lifetime degradation function; the construction process of the multi-objective function of the hybrid energy storage system includes: Simultaneously considering the unit price and total quantity of the batteries, as well as the unit price and total quantity of the supercapacitors, an investment cost function is constructed; Simultaneously considering the battery's output power, rated capacity, and total number of charge-discharge cycles, a system lifetime degradation function is constructed; Based on the investment cost function and the system lifetime degradation function, a multi-objective function for the hybrid energy storage system is constructed.

5. The method for configuring the capacity of a hybrid energy storage system in a converter station according to any one of claims 1 to 4, characterized in that, The energy storage operation constraints include minimum rated energy storage power constraints, minimum energy storage capacity constraints, voltage limit constraints for hybrid energy storage systems, and state of charge constraints for energy storage devices.

6. The method for configuring the capacity of a hybrid energy storage system in a converter station according to claim 1, characterized in that, The decision variables include a set of capacity configuration parameters and a set of energy management parameters; the optimal capacity configuration scheme of the hybrid energy storage system is obtained by combining the load curve and performing adaptive load segmentation-based optimization on the multi-objective optimization model, including: Based on the energy management parameter set, a frequency-divided energy storage response based on adaptive load segmentation is performed on the load curve to construct a power allocation strategy; The power allocation strategy is used as a power allocation constraint in the optimization solution. The multi-objective optimization model is optimized and solved by intelligent optimization algorithm to obtain the optimal capacity configuration parameter set. The energy storage capacity of the hybrid energy storage system is configured according to the optimal capacity configuration parameter set to generate an optimal capacity configuration scheme.

7. The method for configuring the capacity of a hybrid energy storage system in a converter station according to claim 6, characterized in that, The types of energy storage devices in the hybrid energy storage system include batteries and supercapacitors; the energy management parameter set includes an operating condition parameter set and a power distribution parameter set. The step of constructing a power allocation strategy by performing frequency-division energy storage response based on adaptive load segmentation of the load curve according to the energy management parameter set includes: Based on the set of operating parameters, the load curve is adaptively segmented to obtain a multi-segment load curve. For each segment of the load curve, a target frequency division strategy corresponding to the load curve is matched from the power allocation parameter set, and the target frequency division strategy is used to perform frequency division processing on the load curve to determine the low-frequency load and the high-frequency load. The low-frequency load is allocated to the battery for response, and the high-frequency load is allocated to the supercapacitor for response; Complete the frequency-division energy storage response of all the load segment curves to form a power allocation strategy.

8. A capacity configuration device for a hybrid energy storage system in a converter station, characterized in that, include: The data acquisition unit is used to acquire the energy structure of the converter station and the load curve of the hybrid energy storage system; The decision variable construction unit is used to establish decision variables that simultaneously consider capacity configuration and power allocation based on the energy structure and the load curve, combined with a preset frequency division method. The multi-objective optimization model construction unit is used to construct the multi-objective function and energy storage operation constraints of the hybrid energy storage system, and to construct a multi-objective optimization model based on the decision variables, the multi-objective function and the energy storage operation constraints. The optimization solution unit is used to combine the load curve and perform adaptive load segmentation-based optimization solution on the multi-objective optimization model to obtain the optimal capacity configuration scheme of the hybrid energy storage system.

9. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the capacity configuration method of the hybrid energy storage system of the converter station according to any one of the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the capacity configuration method of the hybrid energy storage system of the converter station according to any one of claims 1-7.

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