Hybrid energy storage capacity optimal configuration method and system

Abstract

The invention provides a hybrid energy storage capacity optimal configuration method and system. A typical day wind power signal is decomposed into a direct grid-connected component and an energy storage stabilizing component by applying an ensemble empirical mode decomposition method; the energy storage power is decomposed into a high-frequency component stabilized by a super capacitor and a low-frequency component stabilized by an electrochemical energy storage battery by adopting a secondary ensemble empirical mode decomposition method. Aiming at the influence of frequent charging and discharging on the energy storage life, the invention provides a control strategy considering a stabilizing dead zone, so that the service life of electrochemical energy storage equipment is prolonged, and the comprehensive cost and the state of charge of the hybrid energy storage system are obtained through solving in different scenes. And evaluating the charge-discharge depth in combination with a rain-flow counting method, and calculating the equivalent cycle life of electrochemical energy storage.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage, and in particular relates to a hybrid energy storage capacity optimization configuration method and system. Background Art

[0002] The randomness and uncertainty of wind speed lead to fluctuations in wind turbine power, which has a negative impact on the safety and stability of the power system. For this reason, many countries have set requirements for wind turbine ramping power. As the power system faces challenges brought by wind power fluctuations on multiple time scales, a single energy storage method can no longer meet the needs of smoothing such fluctuations. The hybrid energy storage system integrates the advantages of different types of energy storage and can effectively solve the problem of wind power fluctuations, but how to economically and effectively configure the hybrid energy storage capacity needs to be solved urgently. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the present invention provides a hybrid energy storage capacity optimization configuration method, comprising:

[0004] Based on the simulation method, the wind power signal is decomposed using the ensemble empirical mode decomposition technique;

[0005] When the first frequency component is obtained by decomposition, the first frequency component is connected to the grid;

[0006] When the second frequency component is decomposed, the second frequency component is decomposed using a set empirical mode decomposition technique, and the supercapacitor power task and / or the electrochemical energy storage power task is determined based on the decomposition result;

[0007] Determine the supercapacitor configuration parameters and the electrochemical energy storage configuration parameters based on the supercapacitor power task and the electrochemical energy storage power task respectively;

[0008] Among them, the second frequency is greater than the first frequency; the hybrid energy storage includes supercapacitors and electrochemical energy storage.

[0009] Preferably, the step of decomposing the wind power signal by using the ensemble empirical mode decomposition technique includes:

[0010] The wind power signal is decomposed into several IMF components using the ensemble empirical mode decomposition technique;

[0011] The IMF component is reconstructed using the low-frequency reconstruction method to obtain the fluctuation amount;

[0012] The fluctuation amount less than the grid connection limit is the first frequency component; the fluctuation amount greater than or equal to the grid connection limit is the first frequency component.

[0013] Preferably, the low-frequency reconstruction method includes the following calculation formula:

[0014]

[0015] Where: L is the low-order reconstruction component; r is the residual component; F is the intrinsic mode function, n is the number of components, and k is the k-th component.

[0016] Preferably, the grid connection limit is set according to different time scales and installed capacities respectively.

[0017] Preferably, decomposing the second frequency component by using the ensemble empirical mode decomposition technique includes:

[0018] Decomposing the second frequency component of the wind power signal by using the ensemble empirical mode decomposition technique to obtain a number of IMF components;

[0019] Reconstructing the IMF components by using a low-frequency reconstruction method to obtain a second fluctuation quantity.

[0020] Preferably, determining the supercapacitor power and / or the electrochemical energy storage power based on the decomposition result includes:

[0021] When there is a second fluctuation quantity less than the set filtering order, calculating the electrochemical energy storage power based on the second fluctuation quantity less than the set filtering order;

[0022] When there is a second fluctuation quantity greater than or equal to the set filtering order, calculating the supercapacitor power based on the second fluctuation quantity greater than or equal to the set filtering order.

[0023] Preferably, before decomposing the second frequency component by using the ensemble empirical mode decomposition technique and determining the supercapacitor power task and / or the electrochemical energy storage power task based on the decomposition result, it further includes:

[0024] Dividing the second frequency component into a non-response region range and an energy storage response region based on a set power fluctuation suppression dead zone;

[0025] Setting the second frequency component within the non-response region range as the directly connected grid connection power;

[0026] Determining the supercapacitor power task and / or the electrochemical energy storage power task based on the second frequency component within the energy storage response region range.

[0027] Preferably, the supercapacitor configuration parameters include: the rated capacity and the rated power of the supercapacitor;

[0028] The electrochemical energy storage configuration parameters include: the rated capacity and the rated power of the electrochemical energy storage.

[0029] Preferably, determining the supercapacitor configuration parameters and the electrochemical energy storage configuration parameters respectively based on the supercapacitor power task and the electrochemical energy storage power task includes:

[0030] Taking the minimum comprehensive cost of the wind energy storage system as the objective function;

[0031] Taking the rated capacity and rated power of the electrochemical energy storage, the rated capacity and rated power of the supercapacitor, the power task of the electrochemical energy storage, the power task of the supercapacitor, and the charge and discharge power of the electrochemical energy storage as the constraint conditions;

[0032] Through an optimization solver, iteratively solve the objective function and the constraint conditions to determine the rated capacity and rated power of the electrochemical energy storage, and the rated capacity and rated power of the supercapacitor;

[0033] Based on the rated capacity and rated power of the electrochemical energy storage and the rated capacity and rated power of the supercapacitor, determine the optimal configuration of the hybrid energy storage capacity.

[0034] Preferably, after determining the rated capacity and rated power of the electrochemical energy storage, it further includes:

[0035] Based on the depth of discharge and the number of charge and discharge cycles, use the rain flow counting method to determine the cycle life of the electrochemical energy storage;

[0036] According to the calculated cycle life, determine whether the life of the electrochemical energy storage reaches the deadline.

[0037] Based on the same inventive concept, the present invention also provides a hybrid energy storage capacity optimization configuration system, including:

[0038] A signal decomposition module, configured to decompose the wind power signal by using the ensemble empirical mode decomposition technique based on a simulation method;

[0039] A direct grid connection module, configured to connect the first frequency component to the grid when the first frequency component is obtained by decomposition;

[0040] A hybrid energy storage smoothing module, configured to decompose the second frequency component by using the ensemble empirical mode decomposition technique when the second frequency component is obtained by decomposition, and determine the supercapacitor power task and / or the electrochemical energy storage power task based on the decomposition result;

[0041] A capacity configuration module, configured to determine the supercapacitor configuration parameters and the electrochemical energy storage configuration parameters based on the supercapacitor power task and the electrochemical energy storage power task respectively;

[0042] Wherein, the second frequency is greater than the first frequency; the hybrid energy storage includes a supercapacitor and an electrochemical energy storage.

[0043] Based on the same inventive concept, the present invention also provides an electronic device, including: at least one processor and a memory; the memory and the processor are connected by a bus;

[0044] The memory is used to store one or more programs;

[0045] When the one or more programs are executed by the at least one processor, a method for optimizing the configuration of the hybrid energy storage capacity provided by the present invention is implemented.

[0046] Based on the same inventive concept, the present invention also provides a readable storage medium with an execution program stored thereon. When the execution program is executed, a method for optimizing the configuration of the hybrid energy storage capacity provided by the present invention is implemented.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] The present invention provides a method and system for optimizing the configuration of the hybrid energy storage capacity. Based on the simulation method, the wind power signal is decomposed by using the ensemble empirical mode decomposition technique; when the first frequency component is obtained by decomposition, the first frequency component is connected to the grid; when the second frequency component is obtained by decomposition, the second frequency component is decomposed by using the ensemble empirical mode decomposition technique, and the supercapacitor power task and / or the electrochemical energy storage power task are determined based on the decomposition result; the supercapacitor configuration parameters and the electrochemical energy storage configuration parameters are determined respectively based on the supercapacitor power task and the electrochemical energy storage power task; the present invention can suppress the wind power fluctuation through the cooperation of the supercapacitor and the electrochemical energy storage to improve the suppression effect.

[0049] By using the method and system for optimizing the configuration of the hybrid energy storage capacity provided by the present invention, the wind power fluctuation is suppressed through the cooperation of the supercapacitor and the electrochemical energy storage, providing an operation framework for the application of the hybrid energy storage technology, providing development suggestions and theoretical support for decision-makers to carry out the construction and planning of the hybrid energy storage system, thereby promoting the wide application of the hybrid energy storage, reducing energy waste, and reducing the environmental impact. Description of the Drawings

[0050] Figure 1 It is a schematic flow chart of a method for optimizing the configuration of a hybrid energy storage system provided by the present invention;

[0051] Figure 2 It is a supercapacitor - electrochemical hybrid energy storage system architecture model;

[0052] Figure 3 It is a decomposition flow chart of EEMD;

[0053] Figure 4 It is a wind power distribution flow chart based on EEMD;

[0054] Figure 5 It is a schematic diagram of the energy storage power for suppressing the dead zone;

[0055] Figure 6 It is a flow chart of the hybrid energy storage power task allocation based on the secondary EEMD;

[0056] Figure 7 Structural diagram of an optimized configuration system for a hybrid energy storage system provided by the present invention;

[0057] Figure 8 Schematic diagram of the structure of an electronic device provided by the present invention. Detailed implementation manners

[0058] The present invention proposes a method and system for optimizing the configuration of a hybrid energy storage system. The empirical mode decomposition method is used to decompose the typical daily wind power signal into a directly grid-connected component and an energy storage smoothing component; the second empirical mode decomposition method is used to decompose the energy storage power into a high-frequency component smoothed by a supercapacitor and a low-frequency component smoothed by an electrochemical energy storage battery. Aiming at the influence of frequent charge and discharge on the energy storage life, the present invention proposes a control strategy considering a smoothing dead zone, aiming to extend the life of the electrochemical energy storage device, and obtaining the comprehensive cost and state of charge of the hybrid energy storage system by solving in different scenarios. The charge and discharge depth is evaluated by combining the rain flow counting method, and the equivalent cycle life of the electrochemical energy storage is calculated. Finally, the model is evaluated by comparing the charge and discharge times and the energy storage life. To better understand the present invention, the content of the present invention will be further described below with reference to the accompanying drawings of the specification and examples.

[0059] Example 1:

[0060] As Figure 1 shown, the present invention provides a method for optimizing the configuration of a hybrid energy storage capacity, including:

[0061] S1. Based on a simulation method, the wind power signal is decomposed by using the empirical mode decomposition technique;

[0062] S2. When the first frequency component is obtained by decomposition, the first frequency component is grid-connected;

[0063] When the second frequency component is obtained by decomposition, the second frequency component is decomposed by using the empirical mode decomposition technique, and the supercapacitor power task and / or the electrochemical energy storage power task are determined based on the decomposition result;

[0064] S3. The supercapacitor configuration parameters and the electrochemical energy storage configuration parameters are determined respectively based on the supercapacitor power task and the electrochemical energy storage power task;

[0065] Here, the first frequency is a low frequency, and the second frequency is a high frequency; the first frequency component is a high-frequency component, and the second frequency component is a low-frequency component.

[0066] The hybrid energy storage of the present application is a supercapacitor-electrochemical hybrid energy storage, including a supercapacitor and an electrochemical energy storage. The chemical energy storage of the present application can be a lithium battery. The architecture model of the supercapacitor-electrochemical hybrid energy storage system of the present invention is as Figure 2 shown. In the figure, P W,tThe wind power at time t is P HESS,t The hybrid energy storage power at time t is P CRID,t The direct grid-connected power at time t.

[0067] Specifically, step S1 includes:

[0068] S101: Using an electrochemical energy storage (Battery Energy System Storage, BESS), such as a lithium battery, although it can suppress wind power fluctuations, there may be a situation where the energy storage power and capacity utilization rate are low. In addition, the BESS has a low cycle life, and frequent charging and discharging will accelerate its aging. To solve this problem, a complementary solution with a supercapacitor (Supercapacitor, SC) with a long cycle life and a fast charge and discharge rate is proposed to suppress the wind power fluctuations at the minute level.

[0069] First, taking the wind power signal of a typical day as the original signal, using EEMD decomposition for processing to obtain the low-frequency component and the high-frequency component. The low-frequency component is directly grid-connected, and the high-frequency component is used as the hybrid energy storage power task; secondly, aiming at the problem of allocating a single energy storage power task for the hybrid energy storage power, it is proposed to use secondary EEMD decomposition to decompose the hybrid energy storage task, and according to the characteristics of the SC and BESS in suppressing power fluctuations, the corresponding Intrinsic Mode Function (IMF) is reconstructed for high and low frequencies, as Figure 3 shown, and then the energy storage power tasks of the SC and BESS are obtained.

[0070] S102: In order to reduce the impact of large-scale wind power access to the power grid on the safe operation of the power grid, the maximum limits of the output power changes of the wind farm in 1 minute and 10 minutes are now clearly specified. The wind power grid connection limit is shown in Table 1. P iwpc is the installed capacity of the wind farm.

[0071] Table 1 Wind power grid connection limit

[0072]

[0073] Based on the above table, the collaborative suppression of wind power fluctuations by the supercapacitor and electrochemical energy storage of the present invention can provide the suppression of wind power fluctuations at the minute level.

[0074] Figure 4 The wind power distribution flowchart is given. High frequency reconstruction (Hfr) and low-frequency reconstruction (Low-frequency reconstruction, Lfr) are two ways of reconstruction. The present invention reconstructs through the low-frequency component, and the reconstruction method is shown in Equation (1).

[0075]

[0076] In the formula: L——low-order reconstruction component; r——residual component; F——intrinsic mode function.

[0077] The fluctuation amount is defined in the text as the limit value of the active power change required for the grid connection of the wind farm. The constraint of the wind power fluctuation amount is shown in Equation (2).

[0078]

[0079] In the formula: P Lfr(n),m ——the power of the nth-order reconstructed component within m time periods; Δt is the time interval, which is 1 min here; ΔP limit ——the grid connection limit of the wind power.

[0080] First, sort Lrf(k) from small to large (k is any value from 1 to n); when all the values of Lrf(k) are less than the grid connection limit, it is in the low-frequency case and is directly connected to the grid; when the smallest value of Lrf(1) is greater than the grid connection limit, it is in the high-frequency case and then the hybrid energy storage needs to be executed and then connected to the grid; when there are high-frequency components and low-frequency components, the low-frequency components are directly connected to the grid, and the high-frequency components need to be connected to the grid after performing the hybrid energy storage task.

[0081] Step S2 includes:

[0082] S201: After the decomposition and reconstruction, the hybrid energy storage system will have high-frequency fluctuating power. The frequent charge and discharge and the increase of the energy storage power task will weaken the energy storage life. Therefore, in the present invention, by setting a power fluctuation suppression dead zone, the suppression response stage of the energy storage system is divided into a non-response area and an energy storage response area to suppress the power fluctuation of the energy storage task. The power suppression target value considering the power suppression dead zone is shown in Equation (3). When there is no response, the energy storage power task is directly connected to the grid-connected power, thereby reducing the capacity configuration and weakening the influence of the frequent charge and discharge of the energy storage on the life of the energy storage device.

[0083]

[0084] In the formula: P * (m) is the hybrid energy storage power task within the mth time period considering the suppression dead zone; P (m)

[0085] is the hybrid energy storage power task within the mth time period without adding the suppression dead zone; P sq is the set suppression dead zone limit value. Here, the setting of the dead zone is related to the installed capacity of the wind farm and the influence of the dead zone fluctuation on the energy storage system, that is, the power fluctuation in the non-response interval cannot cause severe frequency fluctuations; Δt is the time interval, which is set to 1 min here. Figure 5The schematic diagram of suppressing the power of the energy storage in the dead zone within 20 minutes is given.

[0086] To evaluate the economy and reliability of the hybrid energy storage under different scenarios, the present invention constructs three capacity optimization configuration models considering the suppression dead zone, and obtains the comprehensive cost and state of charge of the energy storage system by solving under different scenarios. Now, a comparative analysis is carried out on the capacity configuration results of the hybrid energy storage in different scenarios with different dead zone settings. The capacity configuration of the hybrid energy storage is mainly divided into 3 scenarios. The simulation analysis part will discuss the differences in the charge and discharge times and capacity configuration of the hybrid energy storage under different dead zone settings.

[0087] Scenario 1: Traditional hybrid energy storage optimization configuration;

[0088] Scenario 2: Hybrid energy storage optimization configuration considering that the suppression dead zone of the energy storage is 1.5 MW;

[0089] Scenario 3: Hybrid energy storage optimization configuration considering that the suppression dead zone of the energy storage is 3 MW.

[0090] The capacity configuration results of the energy storage under different scenarios are shown in Table 2. Compared with Scenario 1, the capacity configuration scheme in Scenario 2 reduces the comprehensive cost by 2.132 million yuan on a typical day, a decrease of 16.3%; at the same time, the capacity configuration scheme in Scenario 3 of the present invention reduces the comprehensive cost by 2.512 million yuan compared with Scenario 1 on a typical day, that is, a decrease of 19.2%. Thus, it can be seen that the comprehensive cost of the energy storage configuration in Scenario 3 is the lowest. The main reason is that the existence of the suppression dead zone reduces the power task of the hybrid energy storage, making the power task burden of the hybrid energy storage lighter, resulting in a reduction in the optimized configuration of the hybrid energy storage capacity. At the same time, the comprehensive cost of Scenario 3 is 0.38 million yuan less than that of Scenario 2, a year-on-year decrease of 0.4%, which further illustrates the effectiveness of the strategy proposed in the present invention.

[0091] Table 2 Capacity configuration results of hybrid energy storage under different scenarios

[0092]

[0093] In the table is the investment and construction cost of the hybrid energy storage; is the operation and maintenance cost of the hybrid energy storage; is the wind power fluctuation compensation cost.

[0094] S202: Decompose the power task of the hybrid energy storage through secondary EEMD, and the power task allocation process is as Figure 6As shown. This process extracts the low-frequency components and high-frequency components separately, and then inputs the low-frequency components into the lithium battery energy storage system for frequency regulation. Since lithium batteries have good energy density and fast response capabilities, they are suitable for handling medium and low-frequency power fluctuations; while the high-frequency components are input into the supercapacitor, and the high power density and fast charge and discharge characteristics of the supercapacitor are used to quickly respond to short-term fluctuations in frequency. In this way, the hybrid energy storage system can efficiently achieve a reasonable distribution of high and low-frequency power, thereby enhancing the stability of the system.

[0095] Step S3 includes:

[0096] S301: To avoid uneven power distribution during the suppression process of the hybrid energy storage system, the present invention uses a charge and discharge command distribution strategy for SC and BESS based on the filtering order j. With the goal of minimizing the comprehensive cost of the wind energy storage system, considering system power, capacity, and charge and discharge power constraints, and finally iteratively solving through the CPLEX optimization solver to determine the rated capacities of BESS and SC and the rated power Furthermore, the optimal configuration of the energy storage capacity is obtained.

[0097] 1) Comprehensive cost of the wind energy storage system

[0098]

[0099] C BESS is the total cost of the electrochemical energy storage, C sc is the total cost of the supercapacitor, is the cost of compensating for wind power fluctuations.

[0100] C BESS and C sc are shown in Equations (5) and (6) respectively.

[0101]

[0102] In the formula: and are the construction cost and operation and maintenance cost of BESS respectively; and are the construction cost and operation and maintenance cost of SC respectively.

[0103] 2) Construction investment cost

[0104] The construction investment costs of BESS and SC can be expressed as:

[0105]

[0106] In the formula: and are the investment cost coefficients of the power and capacity of the BESS, respectively; and are the investment cost coefficients of the power and capacity of the SC, respectively; is the rated power of the BESS; is the rated power of the SC; r is the discount rate, which is set to 5% in this embodiment; Y1 is the operation cycle of the BESS, which is set to 5 years in this embodiment; Y2 is the operation cycle of the SC, which is set to 15 years in this embodiment.

[0107] 3) Operation and maintenance cost

[0108] The operation and maintenance cost can be solved according to the proportion of the construction cost, as shown in Equations (9) and (10) respectively.

[0109]

[0110] In the formula: a is the proportion of the operation and maintenance cost of the BESS in its investment cost; b is the proportion of the operation and maintenance cost of the SC in its investment cost.

[0111] 4) Wind power fluctuation compensation cost

[0112]

[0113] In the formula: c com is the compensation coefficient; P zheng_com,t and P fu_com,t are the positive and negative compensation amounts at time t respectively; T is the wind power fluctuation compensation time, which is set to 1440 min here.

[0114] S302: Calculation of the equivalent cycle life of the BESS based on the rainflow counting method

[0115] The cycle life of the SC is affected by factors such as temperature and discharge rate. At the same time, its service life is more than 15 years, and the cycle life is as high as 500,000 to 1 million times. Therefore, the present invention mainly considers the equivalent cycle life of the BESS through the rainflow counting method. The life of the BESS is related to factors such as the depth of discharge and the number of charge and discharge cycles. Overcharge and over-discharge caused by frequent fluctuations of wind power will shorten the service life of the BESS.

[0116] The depth of discharge of the BESS in its i-th cycle is D i , and the equivalent cycle life N(D i ) is as shown in Equation (12).

[0117]

[0118] In the formula: N ctf (D1) - the cycle life corresponding to a discharge depth of 1; N ctf (D i) —— The cycle life when the depth of discharge is D i The cycle life when the depth of discharge is D

[0119] The equivalent cycle life N of the BESS during the working cycle is shown in Equation (13).

[0120]

[0121] Where N is the battery cycle life (times), and D oD is the depth of discharge of the battery;

[0122] The life loss of the BESS is defined as shown in Equation (14).

[0123]

[0124] When N = N ctf (D1) or T = 1, it indicates that the service life of the BESS has reached its limit and needs to be replaced.

[0125] Therefore, by constructing a capacity optimization configuration model for a hybrid energy storage system considering the dead zone suppression, the comprehensive cost and state of charge of the energy storage system are obtained by solving under different scenarios. Combining the rain flow counting method to evaluate the charge and discharge depth and calculating the equivalent cycle life can utilize electric energy more efficiently, reduce the adverse impact of wind power fluctuations on the power grid, reduce carbon emissions, and improve the consumption of new energy.

[0126] Finally, the effectiveness of the invention is verified by comparing the charge and discharge times and the energy storage life.

[0127] Embodiment 2:

[0128] Based on the same inventive concept, the present invention also provides a hybrid energy storage capacity optimization configuration system, as Figure 7 shown, including:

[0129] A signal decomposition module, configured to decompose the wind power signal by using the ensemble empirical mode decomposition technique based on a simulation method;

[0130] A direct grid connection module, configured to connect the first frequency component to the grid when the first frequency component is obtained by decomposition;

[0131] A hybrid energy storage dead zone suppression module, configured to decompose the second frequency component by using the ensemble empirical mode decomposition technique when the second frequency component is obtained by decomposition, and determine the supercapacitor power task and / or the electrochemical energy storage power task based on the decomposition result;

[0132] A capacity configuration module, configured to determine the supercapacitor configuration parameters and the electrochemical energy storage configuration parameters based on the supercapacitor power task and the electrochemical energy storage power task respectively;

[0133] Among them, the second frequency is greater than the first frequency; the hybrid energy storage includes a super capacitor and an electrochemical energy storage.

[0134] The hybrid energy storage capacity optimization configuration system of the present invention may further include: a life determination module, configured to:

[0135] Determine the cycle life of the electrochemical energy storage based on the depth of discharge and the number of charge and discharge cycles using the rain flow counting method;

[0136] Determine whether the life of the electrochemical energy storage reaches the deadline according to the calculated cycle life.

[0137] Specifically, the signal decomposition module of the present application includes: an IMF component generation sub-module, a low-frequency reconstruction sub-module, and a frequency decomposition sub-module.

[0138] The IMF component generation sub-module is configured to decompose the wind power signal using the ensemble empirical mode decomposition technique to obtain a plurality of IMF components;

[0139] The low-frequency reconstruction sub-module is configured to reconstruct the IMF components using a low-frequency reconstruction method to obtain a fluctuation amount;

[0140] The low-frequency reconstruction method includes the following calculation formula:

[0141]

[0142] In the formula: L is the low-order reconstruction component; r is the residual component; F is the intrinsic mode function, n is the number of components, and k is the k-th component.

[0143] The frequency decomposition sub-module is configured to: the fluctuation amount less than the grid connection limit is the first frequency component; the fluctuation amount greater than or equal to the grid connection limit is the first frequency component.

[0144] Specifically, the hybrid energy storage smoothing module of the present application includes: a hybrid energy storage decomposition sub-module, a hybrid energy storage power determination sub-module, a dead zone division sub-module, and a configuration parameter determination sub-module.

[0145] The hybrid energy storage decomposition sub-module is configured to:

[0146] Decompose the second frequency component of the wind power signal using the ensemble empirical mode decomposition technique to obtain a plurality of IMF components;

[0147] Reconstruct the IMF components using a low-frequency reconstruction method to obtain a second fluctuation amount.

[0148] The hybrid energy storage power determination sub-module is configured to:

[0149] When there is a second fluctuation amount less than the set filtering order, calculate the electrochemical energy storage power based on the second fluctuation amount less than the set filtering order;

[0150] When there is a second fluctuation amount greater than or equal to the set filtering order, calculate the supercapacitor power based on the second fluctuation amount greater than or equal to the set filtering order.

[0151] The dead zone division sub-module is used for:

[0152] Divide the second frequency component into a non-response area range and an energy storage response area based on the set power fluctuation suppression dead zone;

[0153] Set the second frequency component within the non-response area range as the directly connected grid-connected power;

[0154] Determine the supercapacitor power task and / or the electrochemical energy storage power task based on the second frequency component within the energy storage response area range.

[0155] The supercapacitor configuration parameters include: the rated capacity and rated power of the supercapacitor;

[0156] The electrochemical energy storage configuration parameters include: the rated capacity and rated power of the electrochemical energy storage.

[0157] The configuration parameter determination sub-module is used for:

[0158] Take the minimum comprehensive cost of the wind energy storage system as the objective function;

[0159] Take the rated capacity and rated power of the electrochemical energy storage, the rated capacity and rated power of the supercapacitor, the electrochemical energy storage power task, the supercapacitor power task, and the charge and discharge power of the electrochemical energy storage as constraints;

[0160] Iteratively solve the objective function and constraints through an optimization solver to determine the rated capacity and rated power of the electrochemical energy storage, and the rated capacity and rated power of the supercapacitor;

[0161] Determine the optimal configuration of the hybrid energy storage capacity based on the rated capacity and rated power of the electrochemical energy storage and the rated capacity and rated power of the supercapacitor.

[0162] A hybrid energy storage capacity optimization configuration system provided by the present application is to implement a hybrid energy storage capacity optimization configuration method in the above embodiment. Therefore, the naming of each module in this embodiment is exemplary.

[0163] Embodiment 3

[0164] As Figure 8As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected through a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and this data can be called and / or modified when the instructions are executed.

[0165] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a hybrid energy storage capacity optimization configuration method in the above embodiment.

[0166] (Application SpecificIntegrated Circuit, ASIC), Field-Programmable GateArray (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a hybrid energy storage capacity optimization configuration method in the above embodiment.

[0167] Embodiment 4

[0168] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device, and is used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device, and of course can also include the extended storage medium supported by the electronic device. The storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory. By loading and executing one or more instructions stored in the storage medium by the processor, the steps of a hybrid energy storage capacity optimization configuration method in the above embodiment can be implemented.

[0169] Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0170] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0171] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0172] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0174] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A hybrid energy storage capacity optimization configuration method, characterized in that: include: Based on the simulation method, the wind power signal is decomposed using the ensemble empirical mode decomposition technique; When the first frequency component is obtained by decomposition, the first frequency component is connected to the grid; When the second frequency component is decomposed, the second frequency component is decomposed using a set empirical mode decomposition technique, and the supercapacitor power task and / or the electrochemical energy storage power task is determined based on the decomposition result; Determine the supercapacitor configuration parameters and the electrochemical energy storage configuration parameters based on the supercapacitor power task and the electrochemical energy storage power task respectively; Among them, the second frequency is greater than the first frequency; the hybrid energy storage includes supercapacitors and electrochemical energy storage.

2. The method according to claim 1, characterized in that The method of decomposing the wind power signal by using the ensemble empirical mode decomposition technology includes: The wind power signal is decomposed into several IMF components using the ensemble empirical mode decomposition technique; The IMF component is reconstructed using the low-frequency reconstruction method to obtain the fluctuation amount; The fluctuation amount less than the grid connection limit is the first frequency component; the fluctuation amount greater than or equal to the grid connection limit is the first frequency component.

3. The method according to claim 1, characterized in that The low-frequency reconstruction method includes the following calculation formula: Where: L is the low-order reconstruction component; r is the residual component; F is the intrinsic mode function, n is the number of components, and k is the kth component.

4. The method according to claim 1, characterized in that The grid connection limit is set according to different time scales and installed capacities.

5. The method according to claim 1, characterized in that The step of decomposing the second frequency component by using the ensemble empirical mode decomposition technique includes: Decomposing the second frequency component by using the ensemble empirical mode decomposition technique to decompose the wind power signal to obtain a plurality of IMF components; The IMF component is reconstructed using a low-frequency reconstruction method to obtain a second wave momentum.

6. The method according to claim 5, characterized in that The method of determining the supercapacitor power and / or electrochemical energy storage power based on the decomposition result includes: When there is a second fluctuation amount less than the set filter order, calculating the electrochemical energy storage power based on the second fluctuation amount less than the set filter order; When there is a second fluctuation amount greater than or equal to the set filter order, the super capacitor power is calculated based on the second fluctuation amount greater than or equal to the set filter order.

7. The method according to claim 6, characterized in that The method of decomposing the second frequency component by using the ensemble empirical mode decomposition technique and determining the supercapacitor power task and / or the electrochemical energy storage power task based on the decomposition result also includes: Dividing the second frequency component into a non-response region and an energy storage response region based on a set power fluctuation smoothing dead zone; Setting the second frequency component within the unresponsive area to directly access the grid-connected power; The supercapacitor power task and / or the electrochemical energy storage power task is determined based on the second frequency component within the energy storage response region.

8. The method according to claim 1, characterized in that The supercapacitor configuration parameters include: rated capacity and rated power of the supercapacitor; The electrochemical energy storage configuration parameters include: rated capacity and rated power of the electrochemical energy storage.

9. The method according to claim 8, characterized in that The determining of the supercapacitor configuration parameters and the electrochemical energy storage configuration parameters based on the supercapacitor power task and the electrochemical energy storage power task respectively includes: The objective function is to minimize the comprehensive cost of the wind-storage system; The rated capacity and rated power of electrochemical energy storage, the rated capacity and rated power of supercapacitor, the power task of electrochemical energy storage, the power task of supercapacitor and the charge and discharge power of electrochemical energy storage are used as constraints; Iteratively solving the objective function and constraint conditions by an optimization solver to determine the rated capacity and rated power of the electrochemical energy storage and the rated capacity and rated power of the supercapacitor; The optimal configuration of the hybrid energy storage capacity is determined based on the rated capacity and rated power of the electrochemical energy storage and the rated capacity and rated power of the supercapacitor.

10. The method according to claim 8 or 9, characterized in that After determining the rated capacity and rated power of the electrochemical energy storage, the method further includes: Determine the cycle life of electrochemical energy storage based on the depth of discharge and the number of charge and discharge cycles using the rain flow counting method; It is determined whether the life of the electrochemical energy storage has reached its limit according to the calculated cycle life.

11. A hybrid energy storage capacity optimization configuration system, characterized in that: include: A signal decomposition module is used to decompose the wind power signal using the ensemble empirical mode decomposition technology based on a simulation method; A direct grid-connected module, configured to connect the first frequency component to the grid when the first frequency component is decomposed; A hybrid energy storage smoothing module, for decomposing the second frequency component by using a set empirical mode decomposition technique when the second frequency component is decomposed, and determining the supercapacitor power task and / or the electrochemical energy storage power task based on the decomposition result; A capacity configuration module, used to determine supercapacitor configuration parameters and electrochemical energy storage configuration parameters based on supercapacitor power tasks and electrochemical energy storage power tasks respectively; Among them, the second frequency is greater than the first frequency; the hybrid energy storage includes supercapacitors and electrochemical energy storage.

12. The system according to claim 11, characterized in that The signal decomposition module comprises: The IMF component generation submodule is used to decompose the wind power signal using the ensemble empirical mode decomposition technology to obtain several IMF components; A low-frequency reconstruction submodule is used to reconstruct the IMF component using a low-frequency reconstruction method to obtain a fluctuation amount; The frequency decomposition submodule is used to take the fluctuation amount less than the grid connection limit as the first frequency component; and take the fluctuation amount greater than or equal to the grid connection limit as the first frequency component.

13. The system according to claim 11, characterized in that The low-frequency reconstruction method includes the following calculation formula: Where: L is the low-order reconstruction component; r is the residual component; F is the intrinsic mode function, n is the number of components, and k is the kth component.

14. The system according to claim 11, characterized in that The hybrid energy storage leveling module includes a hybrid energy storage decomposition submodule, which is used to: Decomposing the second frequency component by using the ensemble empirical mode decomposition technique to decompose the wind power signal to obtain a plurality of IMF components; The IMF component is reconstructed using a low-frequency reconstruction method to obtain a second wave momentum.

15. The system of claim 14, wherein: The hybrid energy storage leveling module further includes: a hybrid energy storage power determination submodule, which is used to: When there is a second fluctuation amount less than the set filter order, calculating the electrochemical energy storage power based on the second fluctuation amount less than the set filter order; When there is a second fluctuation amount greater than or equal to the set filter order, the super capacitor power is calculated based on the second fluctuation amount greater than or equal to the set filter order.

16. The system of claim 15, wherein: The hybrid energy storage smoothing module also includes a dead zone division submodule, which is used to: Dividing the second frequency component into a non-response region and an energy storage response region based on a set power fluctuation smoothing dead zone; Setting the second frequency component within the unresponsive area to directly access the grid-connected power; The supercapacitor power task and / or the electrochemical energy storage power task is determined based on the second frequency component within the energy storage response region.

17. The system of claim 16, wherein: The supercapacitor configuration parameters include: rated capacity and rated power of the supercapacitor; The electrochemical energy storage configuration parameters include: rated capacity and rated power of the electrochemical energy storage.

18. The system of claim 17, wherein: The capacity configuration module also includes a configuration parameter determination submodule, which is used to: The objective function is to minimize the comprehensive cost of the wind-storage system; The rated capacity and rated power of electrochemical energy storage, the rated capacity and rated power of supercapacitor, the power task of electrochemical energy storage, the power task of supercapacitor and the charge and discharge power of electrochemical energy storage are used as constraints; Iteratively solving the objective function and constraint conditions by an optimization solver to determine the rated capacity and rated power of the electrochemical energy storage and the rated capacity and rated power of the supercapacitor; The optimal configuration of the hybrid energy storage capacity is determined based on the rated capacity and rated power of the electrochemical energy storage and the rated capacity and rated power of the supercapacitor.

19. The system according to claim 17 or 18, characterized in that The hybrid energy storage capacity optimization configuration system further includes a life determination module, which is used to: Determine the cycle life of electrochemical energy storage based on the depth of discharge and the number of charge and discharge cycles using the rain flow counting method; It is determined whether the life of the electrochemical energy storage has reached its limit according to the calculated cycle life.

20. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a hybrid energy storage capacity optimization configuration method as described in any one of claims 1 to 10 is implemented.

21. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a hybrid energy storage capacity optimization configuration method as described in any one of claims 1 to 10 is implemented.

Images