Power distribution method, device, medium and equipment of photovoltaic hybrid energy storage system

Abstract

The application provides a power distribution method, device, medium and equipment of a photovoltaic hybrid energy storage system, the method comprising: acquiring the health state of a storage battery and a super capacitor in real time; determining the power distribution weight coefficient of the storage battery and the super capacitor respectively according to the health state of the storage battery and the super capacitor by using a preset nonlinear weight function; correcting the power distribution weight coefficient respectively by using a life cost factor; determining the first distribution power of the storage battery and the super capacitor respectively according to the real-time state of charge value of the storage battery and the super capacitor; further correcting the first distribution power respectively by using the corrected power distribution weight coefficient, and determining the second distribution power of the storage battery and the super capacitor respectively. Through the application, the life cost factor is introduced in combination with the real-time health state and the real-time state of charge value of the energy storage unit, the power of the energy storage unit is distributed, the aging process of the energy storage unit is delayed, and the maintenance and replacement cost of the device is reduced.

Description

Technical Field

[0001] This application relates to the field of photovoltaic energy storage technology, specifically to a power distribution method, apparatus, medium, and equipment for a photovoltaic hybrid energy storage system. Background Technology

[0002] As the global energy structure transitions towards cleaner and lower-carbon energy, solar energy, as an inexhaustible renewable energy source, has received widespread attention for its development and utilization. Photovoltaic power generation systems can directly convert solar energy into electricity; however, due to natural factors such as sunlight intensity, ambient temperature, and cloud cover, photovoltaic output power exhibits significant intermittency and fluctuations. This not only reduces power quality but may also impact the stable operation of microgrids or distribution networks. To address this issue, configuring energy storage devices within photovoltaic systems has become a key technological approach.

[0003] Hybrid energy storage systems, composed of batteries and supercapacitors, are widely used in photovoltaic systems due to their complementary advantages. Batteries, with their high energy density and relatively low storage cost, are suitable for long-term energy buffering tasks. Supercapacitors, on the other hand, offer high power density, fast charge / discharge response, and long cycle life, enabling them to quickly smooth out instantaneous power fluctuations and provide instantaneous power support to the system. The coordinated operation of these two types of energy storage units depends on a reasonable power allocation strategy, and their performance directly determines the operating efficiency, service life, and economics of the hybrid energy storage system. In existing technologies, the power allocation strategies of photovoltaic hybrid energy storage systems mostly rely on the state of charge (SOC) of the energy storage units as the core control basis. These strategies, by monitoring the SOC values ​​of the batteries and supercapacitors in real time, allocate photovoltaic output power or load demand power to different energy storage units. The aim is to maintain the SOC of each unit within a preset safe range, avoiding overcharging or over-discharging, thereby ensuring the basic operational safety of the energy storage units. For example, in the patent "CN112865067B; A power distribution method, system and electronic device for a hybrid energy storage system", the SOC values ​​of the battery and supercapacitor in the hybrid energy storage system are obtained. Based on the charging / discharging state of the hybrid energy storage system and the corresponding conditions of their respective SOC values, the time constant T of the low-pass filter at the current moment is adjusted. This allows the battery or supercapacitor with a larger SOC value to provide the first power, and the one with a smaller SOC value to provide the second power, with the first power being greater than the second power. Alternatively, the battery with a larger SOC value can absorb the third power, and the one with a smaller SOC value can absorb the fourth power, with the third power being less than the fourth power. This achieves real-time adjustment and distribution of the power of the hybrid energy storage system, avoiding overcharging and over-discharging of the battery and supercapacitor.

[0004] However, traditional power allocation strategies have significant limitations and cannot meet the requirements for long-term, high-efficiency operation of photovoltaic hybrid energy storage systems. Specifically, traditional strategies only focus on the real-time State of Charge (SOC) of energy storage units, neglecting the differences in State of Health (SOH) among individual energy storage units. SOH, as a key indicator characterizing the aging degree of an energy storage unit, directly reflects the degree of degradation of its core performance parameters, such as current maximum usable capacity and equivalent internal resistance, relative to its pristine state. In actual operation, even energy storage units from the same batch and of the same model can exhibit significant differences in aging rates due to factors such as the number of charge-discharge cycles, charge-discharge rates, ambient temperature fluctuations, and differences in self-discharge rates, resulting in distinct individual differences in SOH. Under traditional power allocation strategies, the system cannot detect this State of Health (SOH) difference and may continuously assign high-current charging and discharging tasks to energy storage units with severely degraded SOH. The aging energy storage unit's tolerance to charging and discharging currents significantly decreases. High-current operation further intensifies the internal chemical reactions, accelerating electrode material wear and electrolyte degradation, leading to rapid SOH deterioration and ultimately premature failure of the energy storage unit. The failure of a single energy storage unit not only reduces the energy storage capacity and power output of the entire hybrid energy storage system but may also trigger a "weakest link" effect. To compensate for the performance gap left by the failed unit, other units must bear a greater operating load, thus accelerating the aging process of the entire system and significantly shortening its overall service life. Simultaneously, frequent energy storage unit replacements significantly increase the maintenance and operating costs of the photovoltaic system, limiting the large-scale promotion and application of photovoltaic hybrid energy storage technology. Therefore, how to accurately perceive the differences in health status of each energy storage unit in a photovoltaic hybrid energy storage system and realize intelligent dynamic power allocation in order to slow down the aging of energy storage units, extend the overall service life of the system, and reduce operation and maintenance costs has become a technical problem that urgently needs to be solved in the current photovoltaic energy storage field. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the purpose of this application is to provide a power distribution method, apparatus, medium and equipment for a photovoltaic hybrid energy storage system.

[0006] A first aspect of this application provides a power distribution method for a photovoltaic hybrid energy storage system, comprising: The health status of the battery and supercapacitor is acquired in real time. The health status of the battery is the ratio of the current maximum discharge capacity to the initial rated capacity. The health status of the supercapacitor is determined based on the capacitance decay rate and / or the equivalent series resistance growth rate. Based on the health status, a preset nonlinear weighting function is used to determine the power allocation weighting coefficients of the battery and the supercapacitor, respectively. The power allocation weighting coefficients of the battery and the supercapacitor are corrected by using the life cost factor, respectively, to determine the corrected power allocation coefficients of the battery and the supercapacitor. Based on the real-time state of charge values ​​of the battery and the supercapacitor, the first power allocation of the battery and the supercapacitor is determined respectively. The second power allocation for the battery and the supercapacitor is determined based on the modified power allocation weighting coefficient and the first power allocation.

[0007] Optionally, the real-time acquisition of the health status of the battery and the supercapacitor includes: A preset power distribution controller or a preset battery management system is used to collect the health status of the battery and the supercapacitor in real time.

[0008] Optionally, the step of determining the power allocation weighting coefficients of the battery and the supercapacitor respectively based on the health status using a preset nonlinear weighting function includes: The health status of the battery is input into the preset nonlinear weighting function to determine the power allocation weighting coefficient of the battery. The health status of the supercapacitor is input into the preset nonlinear weighting function to determine the power allocation weighting coefficient of the supercapacitor.

[0009] Optionally, the preset nonlinear weighting function can be any one of an exponential function, a sigmoid function, or a piecewise function.

[0010] Optionally, determining the second power allocation for the battery and the supercapacitor based on the modified power allocation weighting coefficient and the first power allocation includes: The second power allocation of the battery is determined by multiplying the modified power allocation weighting coefficient of the battery with the first power allocation of the battery. The second power allocation of the supercapacitor is determined by multiplying the modified power allocation weighting coefficient of the supercapacitor with the first power allocation of the supercapacitor.

[0011] A second aspect of this application provides a photovoltaic hybrid energy storage system according to the power distribution method of the photovoltaic hybrid energy storage system provided in the first aspect of this application. The system includes: a battery, a supercapacitor, a DC / DC converter for the battery, a DC / DC converter for the supercapacitor, and a power distribution controller. The battery is connected to the DC / DC converter for the battery, the DC / DC converter for the battery is connected to the power distribution controller, the supercapacitor is connected to the DC / DC converter for the supercapacitor, and the DC / DC converter for the supercapacitor is connected to the power distribution controller. The power distribution controller uses the power distribution method of the photovoltaic hybrid energy storage system described in any one of the first aspects of this application to distribute power between the battery and the supercapacitor.

[0012] Optionally, the DC / DC converter of the battery is used for DC voltage level conversion, the DC / DC converter of the supercapacitor is used for DC voltage level conversion, and the power distribution controller is also used to collect the health status and real-time state of charge value of the battery and the supercapacitor in real time.

[0013] A third aspect of this application provides a power distribution system for a photovoltaic hybrid energy storage system, comprising: The real-time health status acquisition module is used to acquire the health status of the battery and the supercapacitor in real time. The health status of the battery is the ratio of the current maximum discharge capacity to the initial rated capacity. The health status of the supercapacitor is determined based on the capacitance decay rate and / or the equivalent series resistance growth rate. The power allocation weight determination module is used to determine the power allocation weight coefficients of the battery and the supercapacitor respectively based on the health status and using a preset nonlinear weight function. The life cost correction module is used to correct the power allocation weight coefficients of the battery and the supercapacitor using the life cost factor, and to determine the corrected power allocation coefficients of the battery and the supercapacitor. The first power distribution module is used to determine the first power distribution of the battery and the supercapacitor respectively based on the real-time state of charge values ​​of the battery and the supercapacitor. The second power allocation module is used to determine the second allocation power of the battery and the second allocation power of the supercapacitor based on the modified power allocation weight coefficient and the first allocation power, respectively.

[0014] A third aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the methods provided in the first aspect of this application.

[0015] A fourth aspect of this application provides an electronic device comprising: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of any of the methods provided in the first aspect of this application.

[0016] The power allocation method for the photovoltaic hybrid energy storage system disclosed in this application introduces the health status of the battery and the supercapacitor as core control variables. It dynamically determines the power allocation weight coefficients of the battery and supercapacitor using a preset nonlinear weight function, and corrects the power allocation weight coefficients of the battery and supercapacitor using a life cost factor. Combined with the real-time state of charge values ​​of the battery and the supercapacitor, the power allocation of the battery and supercapacitor is dynamically adjusted, thereby delaying the aging process of the photovoltaic hybrid energy storage system, extending the service life of the photovoltaic hybrid energy storage system, and reducing the maintenance and replacement costs of the devices.

[0017] Other technical effects resulting from the additional features will be further illustrated in the corresponding embodiments. Attached Figure Description

[0018] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic flowchart illustrating a power distribution method for a photovoltaic hybrid energy storage system according to an exemplary embodiment.

[0019] Figure 2 This is a schematic diagram of the power distribution system of a photovoltaic hybrid energy storage system according to an exemplary embodiment.

[0020] Figure 3 This is a schematic diagram of the power management architecture of a photovoltaic hybrid energy storage system according to an exemplary embodiment.

[0021] In the diagram: 1. Energy Management System (EMS); 2. Photovoltaic Hybrid Energy Storage System; 21. Battery; 22. Supercapacitor; 3. Human-Machine Interface (HMI); 4. Cloud Platform. Detailed Implementation

[0022] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0023] In the description of the embodiments in this application, "multiple" means two or more, unless otherwise explicitly specified. In this application, unless otherwise explicitly specified and limited, the terms "installed," "connected," "linked," "fixed," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0024] The terms "comprising" and "having," and any variations thereof, in the embodiments of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or devices.

[0025] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0026] Existing power allocation strategies for photovoltaic hybrid energy storage systems use the state of charge (SOC) of the energy storage units as the control basis. They monitor the SOC values ​​of batteries and supercapacitors in real time and allocate power accordingly to avoid overcharging or over-discharging. However, they neglect the health status of batteries and supercapacitors, continuously allocating high-current charging and discharging tasks to energy storage units whose health status has been severely degraded. This leads to further deterioration of the health status of the energy storage units and affects their service life.

[0027] Figure 1 This is a schematic flowchart illustrating a power distribution method for a photovoltaic hybrid energy storage system according to an exemplary embodiment.

[0028] Reference Figure 1 As shown in one embodiment of this application, a power distribution method for a photovoltaic hybrid energy storage system is provided, including steps S11 to S15.

[0029] S11 can acquire the health status of the battery and supercapacitor in real time.

[0030] Specifically, the State of Health (SOH) of a battery is SOH_Batt, and the State of Health (SOH) of a supercapacitor is SOH_SC.

[0031] The health status of a battery is the ratio of its current maximum discharge capacity to its initial rated capacity. A ratio within a preset range indicates a healthy state. For example, a ratio between 80% and 100% indicates a healthy state.

[0032] For example, if the health status of a battery after full charge and discharge calibration is between 80% and 100%, it indicates that the battery is in a healthy state.

[0033] S12, based on the health status, uses a preset nonlinear weighting function to determine the power allocation weighting coefficients for the battery and supercapacitor respectively.

[0034] S13, the power allocation weight coefficients of the battery and the supercapacitor are corrected by using the life cost factor, and the corrected power allocation coefficients of the battery and the supercapacitor are determined.

[0035] S14. Determine the first power allocation for the battery and the supercapacitor based on their real-time state of charge values.

[0036] Specifically, the first allocated power represents the power determined based on the state of charge.

[0037] S15, based on the modified power allocation weighting coefficient and the first allocation power, determine the second allocation power for the battery and the supercapacitor respectively.

[0038] The second power allocation represents the power determined based on both the state of charge and the state of health.

[0039] The embodiments described above in this application introduce the health status of the battery and the supercapacitor as core control variables. A preset nonlinear weighting function is used to dynamically determine the power allocation weighting coefficients of the battery and the supercapacitor. A life cost factor is used to correct the power allocation weighting coefficients of the battery and the supercapacitor. Combined with the real-time state of charge values ​​of the battery and the supercapacitor, the power allocation of the battery and the supercapacitor is dynamically adjusted, which slows down the aging process of the photovoltaic hybrid energy storage system, extends the service life of the photovoltaic hybrid energy storage system, and reduces the maintenance and replacement costs of the devices.

[0040] To collect the health status of the battery and supercapacitor, in some specific embodiments of this application, for S11, the real-time acquisition of the health status of the battery and supercapacitor can be achieved by: A preset power distribution controller or a preset battery management system is used to collect the health status of the battery and the supercapacitor in real time.

[0041] For example, a preset power distribution controller is used to directly collect the health status of the battery and supercapacitor.

[0042] For example, a preset battery management system is used to directly collect the health status of the battery and supercapacitor.

[0043] The health status of batteries and supercapacitors is counted as a percentage, with the health status value ranging from 0% to 100%. 100% indicates a brand new condition, and 0% indicates a total loss condition.

[0044] In the embodiments described above, a preset power distribution controller or a preset battery management system is used to collect and monitor the health status of the battery and the supercapacitor in real time, and the health status of the battery and the supercapacitor is introduced into the power control as the core control variables.

[0045] In some specific embodiments of this application, while collecting the health status of the battery and the supercapacitor, a preset power distribution controller can also be used to collect the real-time state of charge (SOC) values ​​of the battery and the supercapacitor, so as to perform power distribution on the battery and the supercapacitor based on the real-time SOC values.

[0046] In some specific embodiments of this application, the health status of the battery and the supercapacitor can also be obtained in the following ways.

[0047] The maximum discharge capacity of a battery can be obtained using standard measurement methods: Following the manufacturer's recommended charging method, charge at constant current and constant voltage until the rated full voltage is reached. When the current drops below 0.02C (where C represents the charging / discharging rate), let it stand for 1-2 hours until the voltage stabilizes. Then, in an environment of 25℃±2℃, discharge at a constant current at the standard discharge rate (usually 0.2C) until the discharge cutoff voltage specified by the manufacturer is reached, such as 2.75V for a single cell and 10.5V for a 12V lead-acid battery.

[0048] Read the "discharge capacity" directly from the discharge meter / battery tester, in mAh / Ah. This value represents the maximum discharge capacity at present. ,in, This represents the constant current discharge current (A). Indicates the total discharge duration (h), obtain : =

[0049] in, This indicates the initial rated power.

[0050] For battery systems with a battery management system (BMS) or power distribution controller, the coulomb method can also be used for rapid estimation: The battery management system (BMS) or power distribution controller accumulates the charge / discharge capacity by integrating the current in real time; when the battery completes a full charge-discharge cycle, the total discharge amount recorded by the coulomb counter is the maximum discharge capacity. .

[0051] The health status of a supercapacitor is determined based on the capacitance decay rate and / or the equivalent series resistance growth rate.

[0052] Based on the initial rated value of the supercapacitor, calculate the capacitance attenuation rate. and / or equivalent series resistance growth rate Then, the health status of the supercapacitor is calculated by weighting it using single or dual indicators. .

[0053] Calculate the capacitance attenuation rate and equivalent series resistance growth rate The method is as follows: Initial / Nominal Capacity of Supercapacitor Current measured capacity of supercapacitors ; Initial / factory equivalent series resistance of supercapacitor The current measured performance of supercapacitors and their series resistance. .

[0054] Specifically, the initial / nominal capacity of the supercapacitor You can obtain this information by checking the markings on the supercapacitor itself, the component datasheet, or the circuit BOM.

[0055] Initial / factory equivalent series resistance of supercapacitor You can read the data directly by consulting the supercapacitor's datasheet.

[0056] Current measured capacity of supercapacitors The series resistance of the supercapacitor and its current measured performance. It can be measured manually using a handheld or desktop LCR digital bridge, and the reading can be obtained through the LCR digital bridge.

[0057] Capacitor capacitance decay rate:

[0058] The greater the capacitance decay rate, the more severe the capacitance loss and the more serious the aging.

[0059] Equivalent series resistance growth rate:

[0060] The greater the increase in equivalent series resistance, the more severe the degradation of internal resistance.

[0061] The capacity decay failure threshold is: The equivalent series resistance failure threshold is: .

[0062] 1. A method for calculating the health status of supercapacitors using only capacitance decay:

[0063] in, =20%, which is the capacity limit decay value.

[0064] For example, , ;

[0065]

[0066] 2. The method for calculating the health status of a supercapacitor using only the equivalent series resistance growth rate is as follows:

[0067] in, =100%, which is the limit growth rate of the equivalent series resistance.

[0068] For example, , ;

[0069]

[0070] 3. Using capacitance attenuation rate and equivalent series resistance growth rate Weighted fusion:

[0071] in, The weighting coefficient represents the capacitance attenuation rate. The weighting coefficient represents the growth rate of the equivalent series resistance. This indicates the health status corresponding to the capacitance decay rate. This indicates the health status corresponding to the growth rate of the equivalent series resistance.

[0072] Weighting coefficients: =0.5, =0.5 (general purpose), temperature range 10℃~55℃; Under high-temperature operating conditions: =0.4, =0.6, temperature 55℃~85℃; Under low-temperature ripple conditions: =0.6, =0.4, temperature less than 10℃.

[0073] For example, under normal conditions, the health status of a supercapacitor is as follows:

[0074] In some specific embodiments of this application, the preset nonlinear weighting function is any one of an exponential function, an S (Sigmoid) function, and a piecewise function.

[0075] The preset nonlinear weighting function can also be set according to specific needs and is not limited to the types mentioned above.

[0076] The preset nonlinear weighting function has a clear structure, and its parameters, such as threshold and curvature coefficient, can be flexibly adjusted according to different energy storage element types, such as lithium iron phosphate batteries and ternary lithium batteries, as well as application scenarios, making it highly versatile.

[0077] To achieve dynamic acquisition of power allocation weight coefficients for batteries and supercapacitors, in some specific embodiments of this application, for S12, a preset nonlinear weight function is used to determine the power allocation weight coefficients for batteries and supercapacitors respectively based on the health status, which can be S121 to S122.

[0078] S121, input the health status of the battery into a preset nonlinear weighting function to determine the power distribution weighting coefficient of the battery.

[0079] Specifically, the power allocation weighting coefficient for the battery is W_Batt.

[0080] S122, input the health status of the supercapacitor into a preset nonlinear weighting function to determine the power allocation weighting coefficient of the supercapacitor.

[0081] Specifically, the power allocation weighting coefficient for supercapacitors is W_SC.

[0082] For example, the preset nonlinear weighting function adopts an exponential weighting function, as follows:

[0083] in, W_i Indicates the first i Power allocation weighting coefficients for each energy storage unit Indicates the first i Health status values ​​of each energy storage unit This represents the minimum health status value required to participate in power allocation. This represents the curvature adjustment coefficient.

[0084] Specifically, the curvature adjustment coefficient is used to control the degree to which the power allocation weighting coefficient decreases with the health status value.

[0085] For example, the minimum health status value allowed to participate in power sharing It can be set to 20%.

[0086] The minimum health status value is determined by comprehensively considering the rated voltage of the supercapacitor, the minimum operating voltage of the subsequent power electronic converter, the system dynamic response requirements, and the life management strategy. , is a configurable parameter.

[0087]

[0088] in, This represents the minimum health status value required to participate in power allocation. This indicates the minimum operating voltage of the subsequent DC / DC converter. This indicates the rated voltage of the supercapacitor.

[0089] The minimum health state value allowed to participate in power sharing in this application is used to ensure that the terminal voltage of the supercapacitor is sufficient to drive the subsequent power electronic converter during the discharge process, while reserving a certain energy buffer to cope with dynamic power demands.

[0090] In typical applications, the rated voltage of a supercapacitor is... =2.7 V, and the minimum operating voltage of the subsequent power electronic converter is: When V = 0.54 V, the corresponding minimum healthy state value for participation in power sharing is =20%. =0.2.

[0091] Those skilled in the art can reasonably configure the minimum health status value allowed to participate in power distribution within the range of 10% to 30%, depending on the specific application scenario and specific power electronic interface parameters. .

[0092] When the health status value (SOH) of a certain energy storage unit is higher than the preset first health threshold, for example, the preset first health threshold is set to 80%, the power allocation weight coefficient of the energy storage unit approaches the maximum value of 1, indicating that the energy storage unit is in good condition and is encouraged to undertake more power tasks.

[0093] When the health status value (SOH) of a certain energy storage unit is lower than the preset second health threshold but greater than... SOH_minFor example, if the preset second health threshold is set to 60%, the power allocation weight coefficient of the energy storage unit will decrease significantly and non-linearly as the SOH decreases, indicating that the power output capability of the energy storage unit is greatly limited. In this case, the impactful, high-power load will be transferred to the energy storage unit with a higher SOH, that is, the energy storage unit with an SOH higher than the preset second health threshold.

[0094] The power allocation weighting coefficient exhibits a smooth transition characteristic between the preset second health threshold and the preset first health threshold, which can avoid drastic fluctuations in power allocation.

[0095] In the above embodiments of this application, a preset nonlinear weighting function is used to calculate the power allocation weighting coefficient. The nonlinear response characteristics are more in line with the aging law of the device. When the battery health status is below 80%, its internal resistance increases at an accelerated rate, and the capacity decay rate increases significantly. Using an exponential function can give a faster weight decay rate in the low SOH region, thereby timely reducing the power burden of the deteriorated energy storage unit and delaying its further aging. If a step or piecewise linear function is used, it may cause frequent switching of energy storage units near the threshold, causing power fluctuations. The exponential function is continuously differentiable throughout the entire range, which is beneficial to the stable operation of the control system. Using a nonlinear exponential weighting function only requires adjusting the curvature coefficient to flexibly adjust the steepness of the curve, adapting to different application scenarios.

[0096] In some specific embodiments of this application, a low-pass filtering algorithm is used to decompose the total power demand into low-frequency components and high-frequency components. The battery is used to provide the power demand corresponding to the low-frequency components, and the supercapacitor is used to provide the power demand corresponding to the high-frequency components, thereby realizing differentiated power supply between the battery and the supercapacitor.

[0097] Specifically, the total power requirement is A first-order low-pass filter can be used to reduce the total power demand. Decomposed into low-frequency components and high frequency components :

[0098]

[0099] in, Indicates the system control cycle. Indicates the filter time constant. Indicates low-frequency components, Represents high-frequency components. It represents a discrete quantity.

[0100] Filtering time constant The typical value is 10-100s. The filter time constant is used to set the power band division boundary and low-frequency components. High-frequency components allocated to the battery Discrete quantities allocated to supercapacitors Used to control the periodic index, k=0,1,2....

[0101] Batteries have high energy density but slow power response and limited cycle life, typically less than 6,000 cycles; supercapacitors have high power density and extremely long cycle life, exceeding 500,000 cycles, making them suitable for absorbing and releasing instantaneous power.

[0102] By using a first-order low-pass filter to decompose the total power, the battery only bears the low-frequency power, while the supercapacitor is responsible for the high-frequency power. This fundamentally matches the physical characteristics of the two types of components, avoids imposing the second-level fluctuations caused by photovoltaic cloud shading on the battery, and significantly reduces its charge-discharge cycle count.

[0103] Read the battery health status SOH_Batt and the supercapacitor health status SOH_SC collected by the preset battery management system (BMS); substitute the battery health status SOH_Batt and the supercapacitor health status SOH_SC into the above exponential weighting function: Set k=0.03, SOH_min=20%; If SOH_Batt = 90% (healthy), then W_Batt ≈ 1 - exp(-0.03) (90-20)) ≈ 0.88; If SOH_SC = 50% (moderate aging), then W_SC ≈ 1 - exp(-0.03) (50-20)) ≈ 0.59; Therefore, the power allocation weighting coefficient for a battery with a health state SOH_Batt of 90% is determined to be 0.88, and the power allocation weighting coefficient for a supercapacitor with a health state SOH_SC of 50% is determined to be 0.59.

[0104] The power allocation weight coefficient corresponding to the health status of the energy storage unit is calculated using a preset nonlinear weight function. This coefficient serves as the health weight of the battery and supercapacitor within their respective power frequency bands. It is then used to further integrate the cycle cost difference and the real-time available power limit with the first allocation power of the battery and the second allocation power of the supercapacitor to form the final executable weight.

[0105] The embodiments described above in this application, by employing a preset nonlinear weighting function, dynamically determine the power allocation weighting coefficient of the energy storage unit based on its health status. Using the state of health (SOH) as the control input, they achieve a leap from "passive monitoring of aging" to "actively extending the lifespan of the energy storage unit." This proactively protects aging energy storage units, slows down their performance degradation rate, and thus extends the overall lifespan of the photovoltaic hybrid energy storage system. It also avoids sudden failures caused by low SOH energy storage units continuously bearing excessive stress, thereby improving the reliability and stability of the photovoltaic hybrid energy storage system.

[0106] In some specific embodiments of this application, a life cost factor η can be introduced to correct the power allocation weight coefficient corresponding to the state of health (SOH). The life cost factor is used to measure the relationship between equipment life and cost. The life cost factor η is represented as η∈(0.1,0.3).

[0107] Wherein, the life cost factor η:

[0108] Where i = battery (Batt): =

[0109] If i = supercapacitor (SC): =1 in, This represents the unit cycle equivalent cost of the battery (yuan / cycle). This represents the unit cycle equivalent cost of a supercapacitor (RMB / cycle).

[0110] However, in order to avoid excessively suppressing the power provided by energy storage units, the range of the lifetime cost factor η is usually limited to (0.1, 0.3).

[0111] The life cost factor η is calculated as follows: Determine the unit cycle equivalent cost of the battery : 1. Determine key parameters: initial procurement cost (RMB), which includes installation and transportation; design life cycle count. (Times), derived from the manufacturer's datasheet, such as 1000 times / 3000 times; maintenance costs during the service life. (RMB), such as regular inspections and leveling maintenance; residual value / recovery value: (Yuan) The value that can be recovered after scrapping.

[0112] 2. Calculate the equivalent cost of a single battery cycle:

[0113] For example, a lithium iron phosphate battery: Procurement costs =10,000 yuan; Manufacturer's nominal cycle life =3000 cycles, maintenance cost over the lifespan =1000 yuan, residual value =500 yuan.

[0114] =3.5 yuan / time Determine the unit cycle equivalent cost of a supercapacitor : 1. Determine key parameters: initial procurement cost (Yuan), which includes modules and equalization circuits; design life cycle count. (Times), capacitor disassembly cycles are typically 100,000 to 1,000,000; lifespan maintenance costs. (Yuan), usually much lower than the battery value, can be approximated as 0; residual value / recycling value: (Yuan).

[0115] 2. Calculate the equivalent cost of a single cycle for a supercapacitor:

[0116] For example, the procurement cost of a certain supercapacitor module... =8000 yuan, nominal cycle life =500,000 yuan, maintenance cost =0 yuan, residual value =200 yuan.

[0117] =0.0156 yuan / time Based on the above methods, the life cost factor of the battery... for:

[0118] To avoid excessively suppressing the power output of energy storage units, the lifetime cost factor is typically limited to [a certain value]. ,thus:

[0119] but, It is limited to 0.01.

[0120] Lifetime cost factor of supercapacitors for: =1 In some possible implementations, the life cost factor η can also be calculated empirically, such as the life cost factor of a battery. Take a fixed value between 0.1 and 0.3, such as 0.2; Lifetime cost factor for supercapacitors. The value is fixed at 1.

[0121] The calculation of the life cost factor mentioned above in this application is the nominal value under standard operating conditions.

[0122] Specifically, for S13, the power allocation weighting coefficients of the battery and supercapacitor are corrected using the life cost factor, and the corrected power allocation coefficients of the battery and supercapacitor are determined as follows: If we assume that the unit cycle cost of a storage battery is 5–10 times that of a supercapacitor, and η is taken as 0.25, we can adjust the power allocation weighting coefficients for storage batteries and supercapacitors as follows: W′=W×η Specifically, W′ represents the modified power allocation weighting coefficient, W represents the unmodified power allocation weighting coefficient, and η represents the lifetime cost factor.

[0123] By using the life cost factor to correct the power allocation weight coefficient, even if the battery is in high health, the actual power ratio it provides will be suppressed due to high cost, thus reducing the total life cycle maintenance cost from the perspective of control strategy.

[0124] For example, W_Batt=0.88, η=0.25, and the corrected power distribution weighting coefficient of the battery is W_Batt′=0.22; W_SC=0.59 (assuming the supercapacitor cost factor is 1), the corrected power allocation weighting coefficient for the supercapacitor is W_SC′=0.59; Originally: 0.88 : 0.59 ≈ 3 : 2, with the battery bearing 60% and the supercapacitor bearing 40%; Currently: 0.22 : 0.59 ≈ 1 : 2.7, with the battery accounting for 27% and the supercapacitor accounting for 73%; Even if the health condition of the supercapacitor (50%) is much worse than that of the battery (90%), the system still prioritizes the supercapacitor to handle more power.

[0125] The embodiments described above in this application introduce a life-cycle cost factor η to limit the power allocation of batteries and supercapacitors based on cost issues, thereby reducing the total life-cycle operation and maintenance costs from the perspective of control strategies.

[0126] To obtain the power allocation result of the energy storage unit based on the real-time state of charge value, i.e., step S14 above, the first allocated power of the battery and the supercapacitor is determined according to the real-time state of charge value of the battery and the supercapacitor, respectively. This can be achieved by using existing methods for allocating power based on the real-time state of charge value.

[0127] Determine if the real-time state of charge (SOC) of the supercapacitor is greater than 80%. If it is, prioritize capacitor discharge. If the SOC of the supercapacitor is less than 20%, stop discharging and switch to battery discharge.

[0128] The preset power distribution controller of this application can combine the acquired power distribution weight coefficients of the battery and the supercapacitor with the power distribution command based on the real-time state of charge value to generate the final power execution command, which controls the power ratio of the DC / DC converter output of the battery and the supercapacitor respectively.

[0129] In order to obtain the final power allocation result of the energy storage unit and generate the final power execution command, in some specific embodiments of this application, for S15, the second allocation power of the battery and the supercapacitor are determined according to the modified power allocation weight coefficient and the first allocation power, including: S151 to S152.

[0130] S151, the modified power allocation weighting coefficient of the battery is multiplied by the first power allocation of the battery to determine the second power allocation of the battery.

[0131] S152, multiply the modified power allocation weighting coefficient of the supercapacitor by the first allocated power of the supercapacitor to determine the second allocated power of the supercapacitor.

[0132] For example, when the battery's health status SOH_Batt = 90% and the supercapacitor's health status SOH_SC = 50%, their corresponding power allocation weight coefficients are 0.88 and 0.59, respectively; after correction by the lifetime cost factor η, the power allocation weight coefficients are 0.22 and 0.59, respectively; by multiplying the corrected power allocation weight coefficients by the first allocated power, the first allocated power is further integrated with the cycle cost difference and the real-time available power limit to achieve weight refinement and form the final executable allocation weight.

[0133] After cost adjustments and power limiting, the actual power distribution ratio of the battery can be reduced to 0.22. Due to its high-frequency properties, the supercapacitor still bears all the power demand of the high-frequency components.

[0134] The power allocation weighting coefficients of batteries and supercapacitors are corrected by using the life cost factor η. The corrected power allocation weighting coefficient of the battery is multiplied by the first allocated power of the battery to obtain the second allocated power of the battery. The corrected power allocation weighting coefficient of the supercapacitor is multiplied by the first allocated power of the supercapacitor to obtain the second allocated power of the supercapacitor. This is a feasible solution that comprehensively considers the characteristics, economy and physical constraints of the components and has clear engineering significance.

[0135] The embodiments described above in this application combine the power allocation weighting coefficient of health status control with cost correction and power limiting, and then with the power allocation result based on real-time state of charge (SOC). This enables energy storage units with better health status to undertake more power than traditional power allocation results based solely on real-time state of charge (SOC), especially for the base load. The transient impact power task allocated to severely aged batteries or supercapacitors is correspondingly reduced, protecting the batteries or supercapacitors and extending their service life.

[0136] This application provides a power allocation method for a photovoltaic hybrid energy storage system. By introducing health status as a core control variable, it can significantly reduce the downtime and replacement costs of the photovoltaic hybrid energy storage system caused by the premature failure of individual energy storage units, thereby improving the economic benefits throughout the entire life cycle.

[0137] The preferred features in the above embodiments can be used individually in any embodiment, or in any combination thereof, provided they do not conflict with each other. Furthermore, parts not described in detail in the embodiments can be implemented using existing technologies.

[0138] The following examples and comparative examples will be used to further illustrate this application in order to better understand the above-mentioned technical solutions. It should be understood that the following are only some examples and are not intended to limit this application.

[0139] The power distribution comparison experiment involved three groups of 50Ah lithium iron phosphate modules of the same model (initial SOH=100%±0.5%); the load condition was UDDS dynamic discharge + 1C fast charge cycle (temperature 25±2℃); the termination condition was that the SOH of any single cell was ≤80% or the polarization voltage exceeded the limit; the recorded indicators were the number of cycles at the end of life, the SOH range, the internal resistance growth rate, the peak temperature rise, and the inflection point of capacity decay.

[0140] Comparison Group 1: Power is allocated based solely on real-time state of charge values; Comparison Group 2: Power is allocated using a preset linear weighting function, such as W_i = SOH_i; Comparison Group 3: Power is allocated using a preset nonlinear weighting function, such as W_i = SOH_i^α (α=2.5).

[0141] The experimental results are as follows:

[0142] Table 1 Referring to Table 1, the use of a nonlinear SOH weighting function for power allocation, compared to the traditional SOC benchmark and linear SOH strategy, significantly extends the system cycle life by 32.7% and greatly reduces the module SOH range to 3.1%, while effectively suppressing internal resistance growth and peak temperature rise. This result directly confirms that the nonlinear weighting function can accurately match the nonlinear aging characteristics of accelerated battery degradation in the later stages. By breaking the vicious cycle of the "weakest link effect" through differentiated stress compensation before the inflection point, it achieves synergistic optimization of system life and health consistency, resulting in significant technological advancements that linear models could not anticipate.

[0143] In one embodiment of this application, a photovoltaic hybrid energy storage system is provided, comprising: a battery, a supercapacitor, a DC / DC converter for the battery, a DC / DC converter for the supercapacitor, and a power distribution controller.

[0144] The battery is connected to the battery's DC / DC converter, the battery's DC / DC converter is connected to the power distribution controller, the supercapacitor is connected to the supercapacitor's DC / DC converter, and the supercapacitor's DC / DC converter is connected to the power distribution controller.

[0145] Specifically, the positive terminal of the battery is connected to the positive input terminal of the battery's DC / DC converter, and the negative terminal of the battery is connected to the negative input terminal of the battery's DC / DC converter; the positive terminal of the supercapacitor is connected to the positive input terminal of the supercapacitor's DC / DC converter, and the negative terminal of the supercapacitor is connected to the negative input terminal of the supercapacitor's DC / DC converter.

[0146] The output terminals of the DC / DC converter of the battery and the DC / DC converter of the supercapacitor are connected in parallel through wires to form a common DC bus. The common DC bus is used to connect loads, such as motor controllers, inverters, etc., or to connect to the AC power grid or AC loads through the inverter.

[0147] The DC / DC converter of the battery is used for DC voltage level conversion, the DC / DC converter of the supercapacitor is used for DC voltage level conversion, and the power distribution controller is used to collect the health status and real-time state of charge value of the battery and supercapacitor in real time and realize the power distribution of the battery and supercapacitor using the power distribution method of the photovoltaic hybrid energy storage system provided in this application.

[0148] In some specific embodiments of this application, the power divider can acquire real-time state-of-charge signals of the battery and supercapacitor through sensors.

[0149] Specifically, a current sensor is connected in series and a voltage sensor is connected in parallel at the output of the battery to collect the current and voltage of the battery in real time; a current sensor is also connected in series and a voltage sensor is connected in parallel at the output of the supercapacitor to collect the current and voltage of the supercapacitor in real time.

[0150] The signals from the current sensor and voltage sensor are connected to the signal input terminal of the preset power divider through signal lines. The preset power divider calculates the real-time power and real-time state of charge of the battery based on the real-time current and real-time voltage of the battery. The preset power divider also calculates the real-time power and real-time state of charge of the battery based on the real-time current and real-time voltage of the supercapacitor.

[0151] The preset power distribution controller is also used to receive total power demand commands.

[0152] A DC / DC converter for a battery can convert the output DC voltage of the battery from one DC voltage level to another; a DC / DC converter for a supercapacitor can convert the output DC voltage of the supercapacitor from one DC voltage level to another.

[0153] In this application, the output DC voltage of the battery and the output DC voltage of the supercapacitor are converted to the same DC voltage level by the DC / DC converter of the battery and the DC / DC converter of the supercapacitor.

[0154] Figure 2 This is a schematic diagram of the power distribution system of a photovoltaic hybrid energy storage system according to an exemplary embodiment.

[0155] Reference Figure 2 As shown in one embodiment of this application, a power distribution system for a photovoltaic hybrid energy storage system includes: a real-time health status acquisition module 110, a power distribution weight determination module 120, a lifetime cost correction module 130, a first power distribution module 140, and a second power distribution module 150.

[0156] The real-time health status acquisition module 110 is used to acquire the health status of the battery and the supercapacitor in real time. The health status of the battery is the ratio of the current maximum discharge capacity to the initial rated capacity. The health status of the supercapacitor is determined based on the capacitance decay rate and / or the equivalent series resistance growth rate. The power allocation weight determination module 120 is used to determine the power allocation weight coefficients of the battery and the supercapacitor respectively based on the health status and using a preset nonlinear weight function. The life cost correction module 130 is used to correct the power allocation weight coefficients of the battery and the supercapacitor respectively using the life cost factor, and to determine the corrected power allocation coefficients of the battery and the supercapacitor. The first power distribution module 140 is used to determine the first power distribution of the battery and the supercapacitor respectively based on the real-time state of charge values ​​of the battery and the supercapacitor. The second power allocation module 150 is used to determine the second allocation power of the battery and the second allocation power of the supercapacitor based on the modified power allocation weight coefficient and the first allocation power, respectively.

[0157] The embodiments described above in this application introduce the health status of the battery and the supercapacitor as core control variables. A preset nonlinear weighting function is used to dynamically determine the power allocation weighting coefficients of the battery and the supercapacitor. A life cost factor is used to correct the power allocation weighting coefficients of the battery and the supercapacitor. Combined with the real-time state of charge values ​​of the battery and the supercapacitor, the power allocation of the battery and the supercapacitor is dynamically adjusted, which slows down the aging process of the photovoltaic hybrid energy storage system, extends the service life of the photovoltaic hybrid energy storage system, and reduces the maintenance and replacement costs of the devices.

[0158] Regarding the embodiments of the above system, the specific ways in which each module performs operations have been described in detail in the embodiments of the method, and will not be elaborated here.

[0159] Figure 3 This is a schematic diagram of the power management architecture of a photovoltaic hybrid energy storage system according to an exemplary embodiment.

[0160] Reference Figure 3 As shown in one embodiment of this application, a power distribution system for a photovoltaic hybrid energy storage system is provided, including: an energy management system (EMS) 1, a photovoltaic hybrid energy storage system 2, a human-machine interface (HMI) 3, and a cloud platform 4.

[0161] Specifically, the photovoltaic hybrid energy storage system 2 includes a battery 21 and a supercapacitor 22, which are used for charging and discharging.

[0162] The Energy Management System (EMS1) is used to collect the State of Health (SOH) and Real-time State of Charge (SOC) values ​​of the battery 21 and the supercapacitor 22, receive power demand commands, determine the charging and discharging strategies of the battery 21 and the supercapacitor 22, execute the power allocation method of the photovoltaic hybrid energy storage system provided in this application, coordinate the power allocation between the battery 1 and the supercapacitor 2, and send power allocation commands to the photovoltaic hybrid energy storage system 2.

[0163] The HMI3 interactive window serves as the visualization window for the Energy Management System (EMS) 1. It is used to visually monitor the operating status of the power distribution system of the entire photovoltaic hybrid energy storage system, such as the health status (SOH) of battery 1 and supercapacitor 2, real-time charge (SOC) values, voltage, current, temperature, and other status parameters. It also supports operators to manually set parameters and switch control modes in emergency situations.

[0164] Cloud Platform 4 supports 4G network or WLAN communication technology to interconnect with Energy Management System EMS 1 and Human-Machine Interface (HMI). Cloud Platform 4 can acquire real-time field system data, perform centralized management of multiple systems across regions, and realize remote monitoring. Based on big data analysis, Cloud Platform 4 can also provide better control strategies for Energy Management System EMS 1 and support remote diagnosis and upgrades, reducing on-site maintenance costs.

[0165] The embodiments described above in this application employ a power distribution system for a photovoltaic hybrid energy storage system and a power distribution method for a photovoltaic hybrid energy storage system. The efficient operation mode of the photovoltaic hybrid energy storage system, characterized by "local intelligent control + remote cloud management," improves the management efficiency of power distribution, effectively slows down the aging process of the photovoltaic energy storage system, and reduces the maintenance and replacement costs of the devices.

[0166] Based on the same technical concept, in some specific embodiments of this application, a terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and a method that the processor can use to execute when executing the program.

[0167] Based on the same technical concept, in some specific embodiments of this application, a computer-readable storage medium is provided on which a computer program is stored, which can be used to execute a method when the program is executed by a processor.

[0168] Optionally, the memory is used to store programs; the memory may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; the memory may also include non-volatile memory, such as flash memory. The memory is used to store computer programs (such as application programs and functional modules that implement the above methods), computer instructions, etc., and the aforementioned computer programs and computer instructions can be partitioned and stored in one or more memories. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by the processor.

[0169] The aforementioned computer programs, computer instructions, etc., can be stored in partitions within one or more memory locations. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by a processor.

[0170] A processor is used to execute a computer program stored in memory to implement the various steps of the methods involved in the above embodiments. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0171] The processor and memory can be separate structures or integrated structures. When the processor and memory are separate structures, they can be coupled together via a bus.

[0172] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0174] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0175] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0176] The foregoing has described some specific embodiments of this application. It should be understood that this application is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the substantive content of this application. The above-described preferred features can be used in any combination without conflict.

Claims

1. A power distribution method for a photovoltaic hybrid energy storage system, characterized in that, include: The health status of the battery and supercapacitor is acquired in real time. The health status of the battery is the ratio of the current maximum discharge capacity to the initial rated capacity. The health status of the supercapacitor is determined based on the capacitance decay rate and / or the equivalent series resistance growth rate. Based on the health status, a preset nonlinear weighting function is used to determine the power allocation weighting coefficients of the battery and the supercapacitor, respectively. The power allocation weighting coefficients of the battery and the supercapacitor are corrected by using the life cost factor, respectively, to determine the corrected power allocation coefficients of the battery and the supercapacitor. Based on the real-time state of charge values ​​of the battery and the supercapacitor, the first power allocation of the battery and the supercapacitor is determined respectively. The second power allocation for the battery and the supercapacitor is determined based on the modified power allocation weighting coefficient and the first power allocation.

2. The power distribution method for a photovoltaic hybrid energy storage system according to claim 1, characterized in that, The real-time acquisition of the health status of the battery and supercapacitor includes: A preset power distribution controller or a preset battery management system is used to collect the health status of the battery and the supercapacitor in real time.

3. The power distribution method for a photovoltaic hybrid energy storage system according to claim 1, characterized in that, The step of determining the power allocation weighting coefficients for the battery and the supercapacitor based on the health status using a preset nonlinear weighting function includes: The health status of the battery is input into the preset nonlinear weighting function to determine the power allocation weighting coefficient of the battery. The health status of the supercapacitor is input into the preset nonlinear weighting function to determine the power allocation weighting coefficient of the supercapacitor.

4. The power distribution method for a photovoltaic hybrid energy storage system according to claim 1, characterized in that, The preset nonlinear weighting function can be any one of an exponential function, a sigmoid function, or a piecewise function.

5. The power distribution method for a photovoltaic hybrid energy storage system according to claim 1, characterized in that, The step of determining the second power allocation for the battery and the supercapacitor based on the modified power allocation weighting coefficient and the first power allocation includes: The second power allocation of the battery is determined by multiplying the modified power allocation weighting coefficient of the battery with the first power allocation of the battery. The second power allocation of the supercapacitor is determined by multiplying the modified power allocation weighting coefficient of the supercapacitor with the first power allocation of the supercapacitor.

6. A photovoltaic hybrid energy storage system, characterized in that, include: The system comprises a battery, a supercapacitor, a DC / DC converter for the battery, a DC / DC converter for the supercapacitor, and a power distribution controller. The battery is connected to the DC / DC converter for the battery, the DC / DC converter for the battery is connected to the power distribution controller, the supercapacitor is connected to the DC / DC converter for the supercapacitor, and the DC / DC converter for the supercapacitor is connected to the power distribution controller. The power distribution controller uses the power distribution method of the photovoltaic hybrid energy storage system according to any one of claims 1 to 5 to achieve power distribution between the battery and the supercapacitor.

7. The photovoltaic hybrid energy storage system according to claim 6, characterized in that, The DC / DC converter of the battery is used for DC voltage level conversion, the DC / DC converter of the supercapacitor is used for DC voltage level conversion, and the power distribution controller is also used to collect the health status and real-time state of charge value of the battery and the supercapacitor in real time.

8. A power distribution system for a photovoltaic hybrid energy storage system, characterized in that, include: The real-time health status acquisition module is used to acquire the health status of the battery and the supercapacitor in real time. The health status of the battery is the ratio of the current maximum discharge capacity to the initial rated capacity. The health status of the supercapacitor is determined based on the capacitance decay rate and / or the equivalent series resistance growth rate. The power allocation weight determination module is used to determine the power allocation weight coefficients of the battery and the supercapacitor respectively based on the health status and using a preset nonlinear weight function. The life cost correction module is used to correct the power allocation weight coefficients of the battery and the supercapacitor using the life cost factor, and to determine the corrected power allocation coefficients of the battery and the supercapacitor. The first power distribution module is used to determine the first power distribution of the battery and the supercapacitor respectively based on the real-time state of charge values ​​of the battery and the supercapacitor. The second power allocation module is used to determine the second allocation power of the battery and the second allocation power of the supercapacitor based on the modified power allocation weight coefficient and the first allocation power, respectively.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-5.

10. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-5.

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