Hybrid energy storage configuration method and device, electronic equipment and storage medium

Abstract

The invention discloses a hybrid energy storage configuration method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an automatic power generation control signal of a power grid in a preset time period of a target region, carrying out the decomposition processing of the automatic power generation control signal based on adaptive noise complete set empirical mode decomposition, so as to determine a high-frequency component and a low-frequency component, correspondingly distributing the high-frequency component and the low-frequency component to the energy storage battery to determine a power grid energy storage output demand, configuring a battery rate and a battery capacity of the energy storage battery based on the power grid energy storage output demand, and constructing an energy storage configuration constraint model based on a constraint condition of the energy storage battery, and establishing an energy storage battery life cycle cost efficiency model and a regional power grid frequency modulation policy model, and determining the capacity configuration of hybrid energy storage in the energy storage battery when the frequency modulation benefit of the energy storage battery life cycle cost efficiency model is optimal in combination with the battery rate, the battery capacity, the energy storage configuration constraint model and the regional power grid frequency modulation policy model. And the optimal capacity configuration can be accurately selected.

Description

Technical Field

[0001] The present application relates to the technical field of power system energy storage and frequency regulation, and specifically to a hybrid energy storage configuration method, device, electronic equipment, and storage medium. Background Art

[0002] In recent years, clean energy sources such as wind and solar have rapidly emerged. The proportion of renewable energy generation in the power system has continued to increase, while the proportion of traditional thermal power units has decreased. This has led to a continuous decrease in the grid's available frequency regulation resources, highlighting the problem of insufficient frequency regulation capacity. Furthermore, the heavy frequency regulation workload can impact the lifespan of generator sets. Furthermore, the output power of renewable energy generation is significantly affected by meteorological changes, exhibiting volatility and uncertainty. The large-scale integration of renewable energy generation into the grid can significantly impact the grid, increasing the pressure on grid frequency regulation. Relying solely on traditional thermal power units for secondary frequency regulation can no longer meet the grid's increasing frequency regulation needs. Therefore, finding more effective auxiliary grid frequency regulation methods is a pressing issue in grid frequency regulation applications.

[0003] Currently, most domestic energy storage and frequency regulation scenarios use lithium iron phosphate batteries as the main solution. However, the cycle life of lithium iron phosphate batteries is relatively short. According to the frequency regulation requirements, frequent charging and discharging are required, and the battery life is short. It needs to be replaced later, which increases the later maintenance costs. There are also a few hybrid energy storage configurations that use flywheel batteries, supercapacitors and lithium iron phosphate batteries, but there are problems such as low energy density and high maintenance costs. Lithium titanate has excellent rate characteristics, which meets the needs of power system frequency regulation scenarios.

[0004] At the same time, existing methods fail to fully consider the benefits of grid frequency regulation, such as policy subsidies, and fail to establish an accurate full-lifecycle economic model, which affects the optimal capacity configuration of hybrid energy storage. It is imperative to find more efficient and economical methods for configuring hybrid energy storage frequency regulation capacity. Summary of the Invention

[0005] In view of the above problems, the present application provides a hybrid energy storage configuration method, device, electronic device and storage medium to solve the problem of power system frequency fluctuation caused by new energy sources such as wind and light and load uncertainty, so that the system can operate more efficiently and safely and achieve better economic benefits.

[0006] In a first aspect, an embodiment of the present application provides a hybrid energy storage configuration method, comprising:

[0007] Obtaining automatic power generation control signals for the power grid in the target area during a preset time period;

[0008] Decomposing the automatic power generation control signal based on adaptive noise complete set empirical mode decomposition to determine high-frequency components and low-frequency components;

[0009] Allocating the high-frequency component to a first battery in the energy storage battery and allocating the low-frequency component to a second battery in the energy storage battery to determine the energy storage output demand of the power grid;

[0010] Configuring the battery rate and battery capacity of the energy storage battery based on the energy storage output demand of the power grid;

[0011] Constructing an energy storage configuration constraint model based on the constraint conditions of the energy storage battery;

[0012] Establish a full life cycle cost efficiency model for energy storage batteries and a regional power grid frequency regulation policy model, and combine the battery rate, battery capacity, energy storage configuration constraint model, and regional power grid frequency regulation policy model to determine the capacity configuration of hybrid energy storage in the energy storage battery when the frequency regulation benefit of the full life cycle cost efficiency model of the energy storage battery is optimal.

[0013] In some embodiments, the decomposing the automatic power generation control signal based on the adaptive noise complete set empirical mode decomposition to determine the high-frequency component and the low-frequency component includes:

[0014] Adding a preset expected Gaussian distribution white noise to the automatic power generation control signal to obtain a new signal;

[0015] Performing empirical mode decomposition on the new signal to obtain eigenmode components;

[0016] Performing empirical mode decomposition processing on the remainder of the eigenmode component until the remainder cannot be subjected to empirical mode decomposition, so as to determine a high-frequency component and a low-frequency component.

[0017] In some embodiments, configuring the battery rate and battery capacity of the energy storage battery based on the grid energy storage output demand includes:

[0018] Determining energy storage demand operating parameters and basic battery parameters of the energy storage battery based on the energy storage output demand of the power grid;

[0019] Convert the actual durations of the first battery and the second battery at different outputs at different times to the same reference rate according to the rate characteristic curve, and calculate the theoretical duration of the energy storage battery;

[0020] When the theoretical duration is greater than the actual duration threshold, the battery rate and battery capacity of the energy storage battery are determined.

[0021] In some embodiments, constructing an energy storage configuration constraint model based on the constraint conditions of the energy storage battery includes:

[0022] Determining a constraint condition corresponding to the energy storage operating condition, wherein the constraint condition adopts at least one of a power constraint, a SOC constraint, a charge / discharge power constraint, and a rated power constraint;

[0023] Construct an energy storage configuration constraint model corresponding to the constraint conditions.

[0024] In some embodiments, establishing a life cycle cost efficiency model for an energy storage battery includes:

[0025] The energy storage battery investment cost model, energy storage battery operation and maintenance cost model, energy storage battery replacement times model within the service life, energy storage battery replacement cost model and energy storage battery total cost model are constructed.

[0026] In some embodiments, establishing a regional power grid frequency regulation policy model includes:

[0027] Construct frequency regulation mileage compensation model, frequency regulation capacity compensation model and frequency regulation market compensation cost model.

[0028] In some embodiments, the first battery is a lithium titanate battery, the second battery is a lithium iron phosphate battery, and hybrid energy storage of lithium titanate batteries and lithium iron phosphate batteries is used for automatic power generation control signal adjustment and frequency modulation capacity configuration.

[0029] In a second aspect, an embodiment of the present application provides a hybrid energy storage configuration device, comprising:

[0030] An acquisition module, used to obtain the automatic power generation control signal of the power grid in the target area during a preset time period;

[0031] a decomposition module, configured to decompose the automatic power generation control signal based on adaptive noise complete set empirical mode decomposition to determine a high-frequency component and a low-frequency component;

[0032] an allocation module, configured to allocate the high-frequency component to a first battery in the energy storage battery and allocate the low-frequency component to a second battery in the energy storage battery, so as to determine the energy storage output demand of the power grid;

[0033] A configuration module, configured to configure the battery rate and battery capacity of the energy storage battery based on the energy storage output demand of the power grid;

[0034] A construction module, configured to construct an energy storage configuration constraint model based on the constraint conditions of the energy storage battery;

[0035] A determination module is used to establish a full life cycle cost efficiency model for energy storage batteries and a regional power grid frequency regulation policy model, and to determine the capacity configuration of hybrid energy storage in the energy storage battery when the frequency regulation benefit is optimal according to the full life cycle cost efficiency model for energy storage batteries, in combination with the battery rate, battery capacity, energy storage configuration constraint model, and regional power grid frequency regulation policy model.

[0036] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores program code that can be run on the processor, and when the program code is executed by the processor, the hybrid energy storage configuration method described in any embodiment of the first aspect is implemented.

[0037] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing one or more programs, which can be executed by the electronic device as described in the third aspect to implement the hybrid energy storage configuration method as described in any embodiment of the first aspect.

[0038] The embodiments of the present application provide a hybrid energy storage configuration method, device, electronic device, and storage medium. The method obtains an automatic power generation control signal from a power grid in a target area during a preset time period, decomposes the automatic power generation control signal based on adaptive noise complete set empirical mode decomposition to determine high-frequency and low-frequency components. The method allocates the high-frequency component to a first battery in the energy storage battery and allocates the low-frequency component to a second battery in the energy storage battery to determine the power grid's energy storage output demand. The method configures the battery rate and battery capacity of the energy storage battery based on the power grid's energy storage output demand. The method constructs an energy storage configuration constraint model based on the constraints of the energy storage battery. The method establishes a full life cycle cost efficiency model for the energy storage battery and a regional power grid frequency regulation policy model. The method combines the battery rate, battery capacity, energy storage configuration constraint model, and regional power grid frequency regulation policy model to determine the capacity configuration of the hybrid energy storage in the energy storage battery when the frequency regulation benefit is optimal based on the full life cycle cost efficiency model for the energy storage battery. The method effectively optimizes the required configured energy storage capacity, improves the frequency regulation quality of the automatic power generation control signal, and takes the full life cycle cost-benefit model of the power grid frequency regulation details as the optimization target. The method more accurately selects the optimal capacity configuration and improves the economic benefits of hybrid energy storage frequency regulation.

[0039] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Hereinafter, the present application will be described in more detail based on embodiments with reference to the accompanying drawings.

[0041] Figure 1 A schematic flow chart of a hybrid energy storage configuration method proposed in one embodiment of the present application is shown;

[0042] Figure 2 A flow chart of an exemplary hybrid energy storage configuration method based on the frequency modulation characteristics of lithium titanate / lithium iron phosphate proposed in one embodiment of the present application is shown;

[0043] Figure 3 shows an exemplary AGC data of a typical day proposed in one embodiment of the present application;

[0044] Figure 4 1. It shows the components and residues after decomposition of an exemplary AGC signal proposed in one embodiment of the present application;

[0045] Figure 5 A structural block diagram of an exemplary hybrid energy storage configuration device proposed in one embodiment of the present application is shown;

[0046] Figure 6 A structural block diagram of an electronic device for executing the hybrid energy storage configuration method according to an embodiment of the present application is shown;

[0047] Figure 7 A computer-readable storage medium proposed in an embodiment of the present application for storing or carrying a method for implementing a hybrid energy storage configuration according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0049] Existing technologies fail to fully consider benefits such as grid frequency regulation policy subsidies, and lack an accurate full-lifecycle economic model, which impacts the optimal capacity configuration of hybrid energy storage. Finding a more efficient and economical method for configuring hybrid energy storage frequency regulation capacity is crucial.

[0050] The applicant proposes a hybrid energy storage configuration method, device, electronic device, and storage medium. The method obtains the automatic power generation control signal of the power grid in the target area during a preset time period, decomposes the automatic power generation control signal based on the adaptive noise complete set empirical mode decomposition to determine the high-frequency component and the low-frequency component. The high-frequency component and the low-frequency component are allocated to the corresponding batteries in the energy storage battery to determine the power grid energy storage output demand. The battery rate and battery capacity of the energy storage battery are configured based on the power grid energy storage output demand; an energy storage configuration constraint model is constructed based on the constraints of the energy storage battery; a full life cycle cost efficiency model of the energy storage battery and a regional power grid frequency regulation policy model are established, and the battery rate, battery capacity, energy storage configuration constraint model, and regional power grid frequency regulation policy model are combined to determine the capacity configuration of the hybrid energy storage in the energy storage battery when the frequency regulation benefit is optimal according to the full life cycle cost efficiency model of the energy storage battery. This method can effectively optimize the required configured energy storage capacity and improve the economic benefits of hybrid energy storage frequency regulation. The hybrid energy storage configuration method is described in detail in subsequent embodiments.

[0051] The following describes the application scenarios of the hybrid energy storage configuration method provided in the embodiments of the present application:

[0052] See also Figure 1 , Figure 1 A flow chart of a hybrid energy storage configuration method provided in an embodiment of the present application is shown. In this embodiment, the hybrid energy storage configuration method can be applied to Figure 5 The hybrid energy storage configuration device 300 shown is Figure 6 In the electronic device 200 shown, the electronic device may include one or more electronic devices, and information may be transmitted between the multiple electronic devices in a wireless and / or wired manner. The multiple electronic devices may collaborate to complete the hybrid energy storage configuration method. For example, the electronic device may include a computer, a mobile terminal, a tablet, etc., which is not limited in this application. Figure 1 The process shown is described in detail, and the hybrid energy storage configuration method may include S110 to S160.

[0053] S110: Obtaining an automatic power generation control signal of the power grid in the target area during a preset time period.

[0054] S120: Decomposing the automatic power generation control signal based on the adaptive noise complete set empirical mode decomposition to determine the high-frequency component and the low-frequency component.

[0055] In an embodiment of the present application, the automatic generation control (AGC) signal is decomposed by using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the decomposed high-frequency and low-frequency components are respectively given to lithium titanate and lithium iron phosphate to determine the energy storage output demand plan.

[0056] S130: Allocate the high-frequency component to a first battery in the energy storage battery, and allocate the low-frequency component to a second battery in the energy storage battery, so as to determine the energy storage output demand of the power grid.

[0057] S140: configuring the battery rate and battery capacity of the energy storage battery based on the grid energy storage output demand.

[0058] In the embodiment of the present application, based on the above modal decomposition and taking into account the rate characteristics of the lithium battery in the corresponding scenario, the optimal rate selection and battery capacity configuration of the energy storage battery are determined.

[0059] S150: Constructing an energy storage configuration constraint model based on the constraint conditions of the energy storage battery.

[0060] In the embodiment of the present application, based on the above-mentioned frequency modulation output demand and optimal ratio selection, considering the energy storage SOC (state of charge) calculation method and rated power calculation method, multiple groups of reconstruction configuration schemes can be established.

[0061] S160: Establish a full life cycle cost efficiency model for energy storage batteries and a regional power grid frequency regulation policy model, and combine the battery rate, battery capacity, energy storage configuration constraint model, and regional power grid frequency regulation policy model to determine the capacity configuration of hybrid energy storage in the energy storage battery when the frequency regulation benefit is optimal based on the full life cycle cost efficiency model for energy storage batteries.

[0062] In the embodiment of the present application, factors such as construction cost, maintenance cost, replacement cost and policy subsidy benefits may be considered based on the optimal economic efficiency of the energy storage power station over its entire life cycle.

[0063] In this embodiment, the required energy storage capacity can be effectively optimized to improve the quality of AGC frequency regulation. By taking the full life cycle cost-benefit model based on the grid frequency regulation regulations as the optimization target, the optimal capacity configuration can be more accurately selected to improve the economic benefits of hybrid energy storage frequency regulation.

[0064] Furthermore, the first battery is a lithium titanate battery, and the second battery is a lithium iron phosphate battery. Hybrid energy storage of lithium titanate batteries and lithium iron phosphate batteries is used for automatic power generation control signal adjustment and frequency modulation capacity configuration.

[0065] In some embodiments, S120 decomposes the automatic power generation control signal based on the adaptive noise complete set empirical mode decomposition to determine the high-frequency component and the low-frequency component, including S121 to S123, wherein:

[0066] S121: Adding a preset expected Gaussian distribution white noise to the automatic power generation control signal to obtain a new signal.

[0067] S122: Performing empirical mode decomposition on the new signal to obtain eigenmode components.

[0068] S123: Performing empirical mode decomposition on the remainder of the eigenmode component until the remainder cannot be subjected to empirical mode decomposition, so as to determine a high-frequency component and a low-frequency component.

[0069] In the embodiment of the present application, based on the CEEMDAN (Complete Ensemble Empirical Mode Decomposition of Adaptive Noise) decomposition strategy, modal components with different frequency characteristics are established to establish power-type and energy-type energy storage output demand plans; CEEMDAN is used to distribute power between energy storage systems, and the decomposed IMF components are separated by constructing a spatiotemporal filter to distinguish high- and low-frequency components and give them to lithium titanate and lithium iron phosphate. The Completely Adaptive Ensemble Empirical Mode Decomposition of Noise (CEEMDAN) decomposition strategy is as follows:

[0070] The original signal is added with K times Gaussian distributed white noise with an expectation of 0 and a standard deviation of 1 to obtain a set of new signals. The calculation formula is as follows:

[0071] x i (n)=x(n)+ε0ω i (n)

[0072] The new signal is subjected to EMD respectively, and the first eigenmode component is solved. The calculation formula is as follows:

[0073]

[0074] Where K is the number of Gaussian white noise; is the jth IMF component of the EMD decomposition of the i-th signal; is the j-th IMF component decomposed by CEEMDAN.

[0075] The margin of the first eigenmode component is determined using the following calculation formula:

[0076]

[0077] Among them, is the j-th residual and is the original signal.

[0078] The next eigenmode component is calculated as:

[0079]

[0080] Among them, is the j-th component after EMD decomposition.

[0081] Repeat steps 3 and 4 until the remainder of the jth eigenmode component cannot be obtained by EMD, and the jth eigenmode component is obtained as:

[0082]

[0083] Finally, the original signal is decomposed into:

[0084] In some embodiments, configuring the battery rate and battery capacity of the energy storage battery based on the grid energy storage output demand in S140 includes steps S141 to S143, wherein:

[0085] S141: Determine the energy storage demand operating parameters and basic battery parameters of the energy storage battery based on the energy storage output demand of the power grid.

[0086] In the embodiment of the present application, the energy storage demand operating condition parameters are determined, and the basic battery parameters, including its allowable charge / discharge rate, etc., are determined. The energy storage output is taken as the operating condition requirement, and the rated power Prate and rated capacity Erate of the lithium titanate battery are:

[0087] P rate =P b / N k

[0088]

[0089] Where Nk is the maximum charge and discharge rate of the selected lithium battery, Pb is the maximum output of the lithium battery in a certain period of time, and ΔPi is the real-time output of the energy storage battery in the corresponding sampling time period i.

[0090] S142: Convert the actual durations of the first battery and the second battery at different outputs at different times to the same reference rate according to the rate characteristic curve, and calculate the theoretical duration of the energy storage battery.

[0091] In the embodiment of the present application, the actual duration at different outputs at different times is converted to the same reference rate according to the rate characteristic curve as follows:

[0092]

[0093] Where α is the time equivalent coefficient of lithium battery, T(N i ) is the real-time charge / discharge rate N of the lithium battery i Corresponding sustainable charge / discharge time; T(N n ) is the maximum charge / discharge rate N corresponding to the selected reference value n Related sustainable charge / discharge time; α(t) is the charge / discharge rate N i The charge / discharge process is converted to a rate of N n Equivalent time conversion factor.

[0094] S143: When the theoretical duration is greater than the actual duration threshold, determine the battery rate and battery capacity of the energy storage battery.

[0095] In the embodiment of the present application, the theoretical duration T and the actual duration t, when T>t, the rate meets the working condition requirements, and the rated power and rated capacity of the battery are determined; otherwise, the selected rate of the battery is reduced, and the above steps are executed again until T>t is satisfied, and the capacity optimization configuration is completed.

[0096] In some embodiments, constructing an energy storage configuration constraint model based on the constraints of the energy storage battery in S150 includes:

[0097] Determine the constraint conditions corresponding to the energy storage operating condition, wherein the constraint conditions adopt at least one of power constraint, SOC constraint, charge and discharge power constraint, and rated power constraint.

[0098] In this embodiment, based on the above frequency modulation output requirements and optimal rate selection, multiple sets of reconstruction configuration schemes are established taking into account the energy storage SOC (state of charge) calculation method and rated power calculation method; the energy storage configuration constraint model is:

[0099] Power Constraints:

[0100]

[0101] Wherein, is the output power of lithium iron phosphate battery; is the output power of lithium titanate battery.

[0102] SOC constraints:

[0103]

[0104] soc t，min ≤soc t ≤soc t，max

[0105] Where, P t,cha is the charging power at time t; P t,dis is the discharge power at time t; E rat is the rated capacity of energy storage; SOC t,min The maximum SOC value allowed for energy storage; SOC t,min It is the minimum SOC value allowed by energy storage.

[0106] Charge and discharge power constraints:

[0107]

[0108] Rated power constraints:

[0109] Based on the above content, an energy storage configuration constraint model corresponding to the constraint conditions is constructed.

[0110] In some embodiments, establishing a life cycle cost efficiency model for energy storage batteries includes:

[0111] Construct energy storage battery investment cost model, energy storage battery operation and maintenance cost model, energy storage battery replacement times model within the use cycle, energy storage battery replacement cost model and energy storage battery total cost model.

[0112] in:

[0113] Energy storage battery investment cost model:

[0114] C binv =C bsinv E rate

[0115] Where C bsom is the unit power operation and maintenance cost of energy storage, Wb (K) is the annual charge and discharge capacity of energy storage, r is the benchmark discount rate, T is the total service life of battery energy storage, and m is the number of replacements within T years.

[0116] Energy storage battery operation and maintenance cost model:

[0117]

[0118] Where C bsom is the unit power operation and maintenance cost of energy storage, W b (K) is the annual charge and discharge capacity of energy storage, r is the benchmark discount rate, T is the total service life of battery energy storage, and m is the number of replacements within T years.

[0119] Use the model of the number of replacements within a period:

[0120] m=T / (Nu / Nu_a)

[0121] Where N u N is the number of cycles in the battery life cycle at the corresponding rate. u-a The number of battery cycles per year.

[0122] Energy storage battery replacement cost model:

[0123]

[0124] Energy storage battery total cost model:

[0125] C tot =C binv +C bom +C bre

[0126] In some embodiments, establishing a regional power grid frequency regulation policy model includes:

[0127] Construct frequency regulation mileage compensation model, frequency regulation capacity compensation model and frequency regulation market compensation cost model.

[0128] Frequency modulation mileage compensation model:

[0129]

[0130] Where: n is the total number of trading cycles in the daily FM market; D i The frequency regulation mileage (MW) provided by the power generation unit in the i-th trading cycle; Q i K is the mileage calculation price for the i-th transaction cycle (yuan / MW); i is the average value of the comprehensive frequency regulation performance index of the power generation unit in the i-th trading cycle.

[0131] Frequency regulation capacity compensation model:

[0132]

[0133] Where: m is the total number of daily dispatch periods; C j is the AGC capacity of the power generation unit in the jth scheduling period (MW); T j is the frequency regulation service duration of the power generation unit in the jth scheduling period (h); s is the AGC capacity compensation standard (yuan / MWh).

[0134] Frequency modulation market compensation fee model:

[0135] R=R 日调频里程补偿 +R 日AGC容量收益

[0136] According to the above capacity configuration plan, the regional auxiliary frequency regulation benefits are incorporated into the full life cycle net benefit model of energy storage frequency regulation. Combined with the energy storage frequency regulation performance indicators, an optimal benefit capacity configuration plan considering the frequency regulation characteristics of lithium batteries is constructed.

[0137] In summary, combined with the above scheme, the present invention can effectively optimize the required energy storage capacity and improve the quality of AGC frequency regulation; taking the full life cycle cost-benefit model based on the grid frequency regulation details as the optimization target, it can more accurately select the optimal capacity configuration and improve the economic benefits of hybrid energy storage frequency regulation.

[0138] A hybrid energy storage configuration method may include:

[0139] S1, based on the CEEMDAN decomposition strategy, establishes modal components with different frequency characteristics and establishes power-type and energy-type energy storage output demand plans;

[0140] S2, based on the above modal decomposition, considering the rate characteristics of lithium batteries in the corresponding scenario, determines the optimal rate selection and capacity configuration of the energy storage battery;

[0141] S3: Based on the above frequency modulation output requirements and optimal rate selection, multiple reconstruction configuration schemes are established taking into account the energy storage SOC (state of charge) calculation method and rated power calculation method;

[0142] S4, considering the regional grid frequency regulation policy, builds a full life cycle net benefit model of hybrid energy storage frequency regulation.

[0143] A typical day data of AGC signal of a regional power grid company in one year is used for simulation analysis. Figure 2 The flowchart of an exemplary hybrid energy storage configuration method based on the frequency modulation characteristics of lithium titanate / lithium iron phosphate is shown, in which the AGC signal is decomposed into IMF components of different frequencies and a residual through CEEMDAN.

[0144] Corresponding reference Figure 3The AGC data of a typical day is shown in FIG. IMFI to IMF9 are the natural mode components of each order obtained by decomposing the ACE signal, and RES is the remainder. Figure 4 The decomposed components and residuals of the AGC signal are shown in the figure. Analysis of the figure shows that between IMF1 and IMF9, the frequency decreases as the order increases. IMF1 changes the fastest and represents the highest-frequency component, while IMF9 changes the least and represents the lowest-frequency component. Lithium titanate batteries have a shorter response time and can charge and discharge at high frequencies, while lithium iron phosphate batteries have a longer response time and cannot track high-frequency signals. Therefore, by setting different reconstruction coefficients d1 and d2, the AGC signal is decomposed and reconstructed into high-frequency and low-frequency components, which are allocated to the lithium titanate and lithium iron phosphate batteries, respectively.

[0145] Among them, the relevant parameters of lithium titanate and lithium iron phosphate batteries can be found in Table 1:

[0146] Table 1 Related parameters of lithium titanate and lithium iron phosphate batteries

[0147]

[0148]

[0149] Based on the above decomposition strategy, the optimal selection of lithium titanate battery rate is made. Combined with the full life cycle net benefit model of hybrid energy storage and frequency regulation considering regional battery compensation details, the corresponding configuration scheme of the above decomposition and reconstruction strategy is calculated.

[0150] The configuration is shown in Table 2:

[0151] Table 2 Full life cycle configuration under different reconstruction strategies

[0152]

[0153] Under this scheme, the lithium titanate battery is planned to have a power of 75.6MW, a capacity of 22.1MWh, and a battery rate of 2C. The lithium iron phosphate battery is planned to have a power of 0.6MW and a capacity of 167.4MWh. This hybrid energy storage configuration method enables the lithium titanate / lithium iron phosphate hybrid energy storage frequency modulation to achieve better frequency modulation performance and economic benefits.

[0154] See also Figure 5 , Figure 5 This is a structural block diagram of a hybrid energy storage configuration device provided by the present application. The hybrid energy storage configuration device 300 includes: an acquisition module 310, a decomposition module 320, an allocation module 330, a configuration module 340, a construction module 350, and a confirmation module 360, wherein:

[0155] The acquisition module 310 is used to acquire the automatic power generation control signal of the power grid in the target area during the preset time period.

[0156] The decomposition module 320 is configured to decompose the automatic power generation control signal based on the adaptive noise complete set empirical mode decomposition to determine a high-frequency component and a low-frequency component.

[0157] The allocation module 330 is configured to allocate the high-frequency component to the first battery in the energy storage battery and allocate the low-frequency component to the second battery in the energy storage battery to determine the energy storage output demand of the power grid.

[0158] The configuration module 340 is used to configure the battery rate and battery capacity of the energy storage battery based on the energy storage output demand of the power grid.

[0159] The construction module 350 is used to construct an energy storage configuration constraint model based on the constraint conditions of the energy storage battery.

[0160] Confirmation module 360 is used to establish a full life cycle cost efficiency model for energy storage batteries and a regional power grid frequency regulation policy model, and combine battery rate, battery capacity, energy storage configuration constraint model, and regional power grid frequency regulation policy model to determine the capacity configuration of hybrid energy storage in the energy storage battery when the frequency regulation benefit is optimal based on the full life cycle cost efficiency model for energy storage batteries.

[0161] The device embodiment in this application may also include other modules, which specifically correspond to part of the content of the above method.

[0162] It should be noted that the device embodiments in this application correspond to the aforementioned method embodiments. The specific principles in the device embodiments can be found in the contents of the aforementioned method embodiments and will not be repeated here.

[0163] In several embodiments provided in this embodiment, the coupling between modules may be electrical, mechanical or other forms of coupling.

[0164] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0165] See also Figure 6 , Figure 6 The present invention provides a structural block diagram of an electronic device 200 that can execute the above-mentioned hybrid energy storage configuration method. The electronic device 200 can be a smart phone, tablet computer, computer or portable computer.

[0166] The electronic device 200 further includes a processor 202 and a memory 204 . The memory 204 stores a program that can execute the contents of the aforementioned embodiments, and the processor 202 can execute the program stored in the memory 204 .

[0167] The processor 202 may include one or more cores for processing data and a message matrix unit. The processor 202 utilizes various interfaces and circuits to connect various components within the electronic device 200. It executes instructions, programs, code sets, or instruction sets stored in the memory 204, and accesses data stored in the memory 204 to perform various functions and process data within the electronic device 200. Optionally, the processor 202 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 202 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem (decoder). The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. It is understood that the modem (decoder) may not be integrated into the processor and may be implemented separately via a communications chip.

[0168] The memory 204 may include a random access memory (RAM) or a read-only memory (ROM). The memory 204 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 204 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., instructions for a user to obtain a random number), instructions for implementing the various method embodiments described below, and the like. The data storage area may also store data (e.g., random numbers) created by the terminal during use.

[0169] The electronic device 200 may also include a network module and a screen. The network module is used to receive and transmit electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices, such as communicating with an audio playback device. The network module may include various existing circuit components for performing these functions, such as an antenna, a radio frequency transceiver, a digital signal processor, an encryption / decryption chip, a user identity module (SIM) card, a memory, etc. The network module can communicate with various networks such as the Internet, an intranet, a wireless network, or communicate with other devices via a wireless network. The above-mentioned wireless network may include a cellular telephone network, a wireless local area network, or a metropolitan area network. The screen can display interface content and perform data interaction.

[0170] Please refer to Figure 7 , Figure 7 The computer-readable storage medium 400 stores program code 410, which can be called by a processor to execute the method described in the above method embodiment.

[0171] Computer-readable storage medium 400 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Alternatively, the computer-readable storage medium includes a non-transitory computer-readable storage medium. Computer-readable storage medium 400 has storage space for program code 410 for executing any of the method steps in the above method. These program codes 410 can be read from or written to one or more computer program products. Program code 410 can be compressed, for example, in a suitable form.

[0172] The present application also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the hybrid energy storage configuration method described in the various optional implementations described above.

[0173] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements 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 application.

Claims

1. A hybrid energy storage configuration method, characterized in that: The method comprises: Obtaining automatic power generation control signals for the power grid in the target area during a preset time period; Decomposing the automatic power generation control signal based on adaptive noise complete set empirical mode decomposition to determine high-frequency components and low-frequency components; Allocating the high-frequency component to a first battery in the energy storage battery and allocating the low-frequency component to a second battery in the energy storage battery to determine the energy storage output demand of the power grid; Configuring the battery rate and battery capacity of the energy storage battery based on the energy storage output demand of the power grid; Constructing an energy storage configuration constraint model based on the constraint conditions of the energy storage battery; Establish a full life cycle cost efficiency model for energy storage batteries and a regional power grid frequency regulation policy model, and combine the battery rate, battery capacity, energy storage configuration constraint model, and regional power grid frequency regulation policy model to determine the capacity configuration of hybrid energy storage in the energy storage battery when the frequency regulation benefit of the full life cycle cost efficiency model of the energy storage battery is optimal.

2. A hybrid energy storage configuration method according to claim 1, characterized in that: The step of decomposing the automatic power generation control signal based on the adaptive noise complete set empirical mode decomposition to determine a high-frequency component and a low-frequency component includes: Adding a preset expected Gaussian distribution white noise to the automatic power generation control signal to obtain a new signal; Performing empirical mode decomposition on the new signal to obtain eigenmode components; Performing empirical mode decomposition processing on the remainder of the eigenmode component until the remainder cannot be subjected to empirical mode decomposition, so as to determine a high-frequency component and a low-frequency component.

3. A hybrid energy storage configuration method according to claim 1, characterized in that: The configuring the battery rate and battery capacity of the energy storage battery based on the grid energy storage output demand includes: Determining energy storage demand operating parameters and basic battery parameters of the energy storage battery based on the energy storage output demand of the power grid; Convert the actual durations of the first battery and the second battery at different outputs at different times to the same reference rate according to the rate characteristic curve, and calculate the theoretical duration of the energy storage battery; When the theoretical duration is greater than the actual duration threshold, the battery rate and battery capacity of the energy storage battery are determined.

4. A hybrid energy storage configuration method according to claim 1, characterized in that: The energy storage configuration constraint model is constructed based on the constraint conditions of the energy storage battery, including: Determining a constraint condition corresponding to the energy storage operating condition, wherein the constraint condition adopts at least one of a power constraint, a SOC constraint, a charge / discharge power constraint, and a rated power constraint; Construct an energy storage configuration constraint model corresponding to the constraint conditions.

5. A hybrid energy storage configuration method according to claim 1, characterized in that: The establishment of a full life cycle cost efficiency model for energy storage batteries includes: The energy storage battery investment cost model, energy storage battery operation and maintenance cost model, energy storage battery replacement times model within the service life, energy storage battery replacement cost model and energy storage battery total cost model are constructed.

6. A hybrid energy storage configuration method according to claim 1, characterized in that: The establishment of a regional power grid frequency regulation policy model includes: Construct frequency regulation mileage compensation model, frequency regulation capacity compensation model and frequency regulation market compensation cost model.

7. A hybrid energy storage configuration method according to claim 1, characterized in that: The first battery is a lithium titanate battery, and the second battery is a lithium iron phosphate battery. Hybrid energy storage of lithium titanate batteries and lithium iron phosphate batteries is used for automatic power generation control signal adjustment and frequency modulation capacity configuration.

8. A hybrid energy storage configuration device, characterized in that: The device comprises: An acquisition module, used to obtain the automatic power generation control signal of the power grid in the target area during a preset time period; a decomposition module, configured to decompose the automatic power generation control signal based on adaptive noise complete set empirical mode decomposition to determine a high-frequency component and a low-frequency component; an allocation module, configured to allocate the high-frequency component to a first battery in the energy storage battery and allocate the low-frequency component to a second battery in the energy storage battery, so as to determine the energy storage output demand of the power grid; A configuration module, configured to configure the battery rate and battery capacity of the energy storage battery based on the energy storage output demand of the power grid; A construction module, configured to construct an energy storage configuration constraint model based on the constraint conditions of the energy storage battery; A determination module is used to establish a full life cycle cost efficiency model for energy storage batteries and a regional power grid frequency regulation policy model, and to determine the capacity configuration of hybrid energy storage in the energy storage battery when the frequency regulation benefit is optimal according to the full life cycle cost efficiency model for energy storage batteries, in combination with the battery rate, battery capacity, energy storage configuration constraint model, and regional power grid frequency regulation policy model.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores program code that can be run on the processor, and when the program code is executed by the processor, a hybrid energy storage configuration method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, and the program code can be called by one or more processors to execute a hybrid energy storage configuration method according to any one of claims 1 to 7.

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