Intelligent management system for light-storage direct-flexible micro-grid

Abstract

The invention discloses an intelligent management system for an optical storage direct flexible micro-grid, and relates to the technical field of power grid management, a second target battery pack is screened out by obtaining a battery health anomaly coefficient of a first target battery pack, and the current electric quantity, temperature, last discharge time interval and discharge depth are obtained from the battery packs; the priority release index is calculated by combining the corresponding abnormal health coefficient of the battery, and the energy storage battery pack with the maximum priority release index is selected as the target of electric energy release management, so that in the process of balancing power supply and demand by dispatching the energy storage batteries to release electric energy in the peak period of power consumption, the energy storage battery pack with the maximum priority release index is not required; a certain group of energy storage batteries can be selected to release electric energy according to the actual battery pack state, the overall efficiency of the system is ensured, and the stability of power supply is guaranteed; and in the process of monitoring the state of the energy storage battery, potential safety hazards of the energy storage battery can be found in time, the stability and safety of the whole micro-grid system are ensured, and the operation risk and the maintenance cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid management, and in particular to an intelligent management system for a photovoltaic-storage-direct-flexible microgrid. Background Art

[0002] The photovoltaic storage direct flexible microgrid is a distributed energy system that integrates photovoltaic power generation, energy storage devices and direct current flexible transmission technology. The system converts solar energy into electrical energy through photovoltaic panels, uses energy storage batteries to store electricity when electricity demand is low, and releases it during peak hours to adjust the grid load. In addition, direct current flexible transmission technology makes the transmission of electricity more efficient and stable, avoiding the energy loss and frequency fluctuation problems of traditional AC transmission. This type of microgrid can realize the self-generation, self-use and optimized scheduling of electricity, which not only improves energy utilization efficiency, but also provides backup power for important loads during power outages. It is an intelligent energy solution that is both environmentally friendly and reliable, suitable for local scenarios such as factories and communities.

[0003] In the process of managing a photovoltaic-storage-direct-flexible microgrid, when electricity consumption is at its peak, energy storage batteries are dispatched to release electricity to balance electricity supply and demand. However, during the dispatch and use of energy storage batteries, the status of different batteries is different. Blindly selecting a group of energy storage batteries to release electricity may reduce the overall efficiency of the system and affect the stability of power supply. In addition, the existing monitoring of the status of energy storage batteries can often only monitor the basic information of the battery, such as power, voltage and temperature to judge the status of the energy storage battery, and cannot promptly discover the safety hazards of the energy storage battery. If it is still put into use in a photovoltaic-storage-direct-flexible microgrid system, it may pose a threat to the stability and safety of the entire microgrid system, and increase operating risks and maintenance costs. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and provide a photovoltaic-storage-direct-flexible microgrid intelligent management system.

[0005] The present invention proposes an intelligent management system for a photovoltaic-storage-direct-flexible microgrid, the system comprising:

[0006] Battery health module: record each energy storage battery group as a first target battery group, obtain the capacity, internal resistance, and voltage of the first target battery group to obtain the battery health abnormality coefficient;

[0007] Health status judgment module: judges the battery health status corresponding to the first target battery group according to the battery health abnormality coefficient and the preset battery health abnormality coefficient threshold, and marks the battery with a normal battery health status as the second target battery group;

[0008] Priority release calculation module: for each second target battery pack, obtain the current power, temperature, last discharge time interval and discharge depth of the second target battery pack, and combine the corresponding battery health abnormality coefficient to obtain a priority release index;

[0009] Release management module: The energy storage battery group with the largest priority release index among all the second target battery groups is taken as the final battery group to perform electric energy release management.

[0010] Optionally, obtaining the battery health abnormality coefficient according to the capacity attenuation coefficient, internal resistance, and voltage of the first target battery group includes:

[0011] Get the actual internal resistance of each battery in the first target battery group and mark it as Q k , k represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., k, l is a positive integer;

[0012] Calculate the initial internal resistance F of each battery k The absolute difference between the actual internal resistance and the absolute difference between the absolute difference and the preset maximum internal resistance increase value Ba is calculated to obtain the internal resistance increase degree. The calculation formula is:

[0013]

[0014] Wherein, pmn is the internal resistance increase of the first target battery pack;

[0015] The battery health abnormality coefficient is obtained according to the capacity attenuation, internal resistance increase and voltage of the first target battery group.

[0016] Optionally, obtaining the battery health abnormality coefficient according to the capacity attenuation coefficient, internal resistance, and voltage of the first target battery group includes:

[0017] Get the actual internal resistance of each battery in the first target battery group and mark it as Q k , k represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., l, l is a positive integer;

[0018] Calculate the initial internal resistance F of each battery k The absolute difference between the actual internal resistance and the absolute difference between the absolute difference and the preset maximum internal resistance increase value Ba is calculated to obtain the internal resistance increase degree. The calculation formula is:

[0019]

[0020] Wherein, pmn is the internal resistance increase of the first target battery pack;

[0021] The battery health abnormality coefficient is obtained according to the capacity attenuation, internal resistance increase and voltage of the first target battery group.

[0022] Optionally, obtaining the battery health abnormality coefficient according to the capacity attenuation, internal resistance increase, and voltage of the first target battery pack includes:

[0023] Obtain the actual voltage value of each battery in the first target battery group at different times within a preset time period, and mark the actual voltage value as I 实 x , x represents the sequence number of the actual voltage value of each battery at different times within the preset time period, x=1, 2, 3, 4, ..., c, c is a positive integer;

[0024] Calculate the actual voltage average of each battery within a preset time period. The calculation formula is:

[0025] The voltage instability coefficient of each battery is calculated, and the voltage fluctuation value of the first target battery group is obtained according to the voltage instability coefficient of each battery. The calculation formula is:

[0026]

[0027] Wherein, sx is the voltage fluctuation value of the first target battery group, k represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., l, l is a positive integer;

[0028] The battery health abnormality coefficient is obtained according to the capacity attenuation, internal resistance increase and voltage fluctuation value of the first target battery group.

[0029] Optionally, the battery health abnormality coefficient obtained according to the capacity attenuation, internal resistance increase and voltage fluctuation value of the first target battery group includes:

[0030] The capacity attenuation, internal resistance increase, and voltage fluctuation value of the first target battery group are normalized, and the battery health abnormality coefficient of the first target battery group is obtained according to the normalized capacity attenuation, internal resistance increase, and voltage fluctuation value. The calculation formula is:

[0031]

[0032] In the formula, E R , S H , Q W are the capacity attenuation, internal resistance increase and voltage fluctuation values ​​after normalization, a1, a2 and a3 are E R , S H , Q W The preset proportional coefficient, and a1, a2, and a3 are all greater than 0.

[0033] Optionally, judging the battery health state corresponding to the first target battery group according to the battery health abnormality coefficient and a preset battery health abnormality coefficient threshold includes:

[0034] Comparing the battery health abnormality coefficient of the first target battery group with a preset battery health abnormality coefficient threshold;

[0035] If the battery health abnormality coefficient is less than the preset battery health abnormality coefficient threshold, it indicates that the health state of the corresponding first target battery group is normal;

[0036] If the battery health abnormality coefficient is not less than the preset battery health abnormality coefficient threshold, it means that the health state of the corresponding first target battery group is abnormal, and an alarm is issued.

[0037] Optionally, obtaining the current power, temperature, last discharge time interval and discharge depth of the second target battery pack, and combining the corresponding battery health abnormality coefficient to obtain a priority release index includes:

[0038] Obtaining the current power of each battery in the second target battery group, and calculating the average current power of all batteries in the second target battery group as the remaining power of the second target battery group;

[0039] Acquire the current temperature of each battery in the second target battery group, and calculate the current average temperature of all batteries in the second target battery group as the current temperature load of the second target battery group;

[0040] Obtain the number of times each battery in the second target battery group is discharged, and obtain the discharge depth ag during each discharge m , ag m Indicates the depth of discharge of each battery at the mth time, m = 1, 2, 3, 4, ..., n, n is a positive integer, and the discharge depth coefficient of the second target battery group is calculated by the following formula:

[0041]

[0042] Wherein, ed is the discharge depth coefficient of the second target battery group, k represents the sequence number of the batteries in the second target battery group, k=1, 2, 3, 4, ..., l, l is a positive integer;

[0043] The priority release index is obtained according to the remaining power of the second target battery group, the current temperature load, the time interval of the last discharge, the discharge depth coefficient and the corresponding battery health abnormality coefficient.

[0044] Optionally, the priority release index is obtained according to the remaining power of the second target battery group, the current temperature load, the time interval of the last discharge, the discharge depth coefficient and the corresponding battery health abnormality coefficient, including:

[0045] The remaining power, current temperature load, last discharge time interval, discharge depth coefficient, and battery health abnormality coefficient of the second target battery group are normalized, and the priority release index of the second target battery group is obtained according to the normalized remaining power, current temperature load, last discharge time interval, discharge depth coefficient, and battery health abnormality coefficient. The calculation formula is:

[0046]

[0047] Wherein, Qaz is the priority release index of the second target battery group, dcv, dfm, dzs, fgp, and tb are the remaining power after normalization, the current temperature load, the time interval of the last discharge, the discharge depth coefficient, and the battery health abnormality coefficient, respectively; b1, b2, b3, b4, and b5 are the preset proportional coefficients of dcv, dfm, dzs, fgp, and tb, respectively, and b1, b2, b3, b4, and b5 are all greater than 0.

[0048] Beneficial effects of the present invention:

[0049] The present invention proposes an intelligent management system for a photovoltaic-storage-direct-flexible microgrid. When the power consumption is at peak, the energy storage batteries are dispatched to release electric energy to balance the power supply and demand. A certain group of energy storage batteries can be selected to release electric energy according to the actual battery group status, thereby ensuring the overall efficiency of the system and the stability of the power supply. In addition, in the process of monitoring the status of the energy storage batteries, the potential safety hazards of the energy storage batteries can be discovered in time, thereby ensuring the stability and safety of the entire microgrid system and reducing operating risks and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] Figure 1 This is a framework diagram of a solar-storage-direct-flexible microgrid intelligent management system. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0054] The embodiment of the present invention provides a photovoltaic-storage-direct-flexible microgrid intelligent management system. Figure 1 , Figure 1 A framework diagram of a photovoltaic-storage-direct-flexible microgrid intelligent management system provided by an embodiment of the present invention. The system includes:

[0055] Battery health module: record each energy storage battery group as a first target battery group, obtain the capacity, internal resistance, and voltage of the first target battery group to obtain the battery health abnormality coefficient;

[0056] Health status judgment module: judges the battery health status corresponding to the first target battery group according to the battery health abnormality coefficient and the preset battery health abnormality coefficient threshold, and marks the battery with a normal battery health status as the second target battery group;

[0057] Priority release calculation module: for each second target battery pack, obtain the current power, temperature, last discharge time interval and discharge depth of the second target battery pack, and combine the corresponding battery health abnormality coefficient to obtain a priority release index;

[0058] Release management module: The energy storage battery group with the largest priority release index among all the second target battery groups is taken as the final battery group to perform electric energy release management.

[0059] Based on a photovoltaic-storage-direct-flexible microgrid intelligent management system provided in an embodiment of the present invention, through the above-mentioned method, when at peak power consumption, by scheduling the energy storage batteries to release electric energy to balance power supply and demand, a certain group of energy storage batteries can be selected to release electric energy according to the actual battery group status, thereby ensuring the overall efficiency of the system and guaranteeing the stability of power supply; and in the process of monitoring the status of the energy storage batteries, the potential safety hazards of the energy storage batteries can be discovered in time, thereby ensuring the stability and safety of the entire microgrid system and reducing operating risks and maintenance costs.

[0060] In one embodiment, obtaining the capacity, internal resistance, and voltage of the first target battery group to obtain the battery health abnormality coefficient includes:

[0061] Get the actual capacity of each battery in the first target battery group and mark it as W k , l represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., k, l is a positive integer;

[0062] Calculate the rated capacity P of each battery k The difference between the rated capacity and the actual capacity is calculated, and the difference is divided by the rated capacity to obtain the capacity decrease rate of each battery, and the capacity attenuation of the first target battery pack is obtained according to the capacity decrease rate of each battery. The calculation formula is:

[0063]

[0064] Wherein, pmn is the capacity attenuation of the first target battery pack;

[0065] The battery health abnormality coefficient is obtained according to the capacity decay, internal resistance and voltage of the first target battery group.

[0066] It should be noted that the actual capacity and rated capacity of each battery are collected in real time through integrated sensors and monitoring systems. Specifically, the actual capacity can be obtained by monitoring the charging and discharging process of the battery through the battery management system (BMS). The rated capacity is usually provided by the manufacturer and clearly stated in the battery specification. Other acquisition methods are also possible, which are not limited or elaborated on.

[0067] It should be noted that the greater the capacity attenuation of the first target battery group, the worse the battery health of the first target battery group, because the capacity attenuation directly reflects the degree of decline in the actual energy storage capacity of the battery. When the gap between the actual capacity and the rated capacity of the battery increases, it means that the battery cannot store and release the expected electrical energy, thus affecting its power supply capacity during peak power consumption; in addition, the increase in capacity attenuation is usually closely related to battery aging, the increase in the number of cycles, and the deterioration of use conditions such as temperature and charge and discharge rates; this not only affects the efficiency and reliability of the battery, but may also cause battery overheating, failure or safety accidents, thus posing a serious threat to the operational safety and economy of the entire photovoltaic storage direct flexible microgrid. Therefore, it is particularly important to monitor and optimize the health status of the battery pack in a timely manner to ensure the stability and efficiency of the system.

[0068] In one embodiment, obtaining the battery health abnormality coefficient according to the capacity attenuation coefficient, internal resistance, and voltage of the first target battery group includes:

[0069] Get the actual internal resistance of each battery in the first target battery group and mark it as Q k , k represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., k, l is a positive integer;

[0070] Calculate the initial internal resistance F of each battery k The absolute difference between the actual internal resistance and the absolute difference between the absolute difference and the preset maximum internal resistance increase value Ba is calculated to obtain the internal resistance increase degree. The calculation formula is:

[0071]

[0072] Wherein, pmn is the internal resistance increase of the first target battery pack;

[0073] The battery health abnormality coefficient is obtained according to the capacity attenuation, internal resistance increase and voltage of the first target battery group.

[0074] It should be noted that the initial internal resistance and actual internal resistance of each battery are monitored in real time through the battery management system (BMS); the initial internal resistance is usually measured and recorded when the battery is first put into use, while the actual internal resistance is calculated by monitoring the difference between the open circuit voltage and the load voltage of the battery under specific load conditions and the current flowing through the battery. In addition, the preset maximum increase value of the internal resistance is set based on the battery performance specifications and the safety standards provided by the manufacturer; it can also be obtained in other ways, which are not limited or elaborated on.

[0075] It should be noted that the greater the increase in the internal resistance of the first target battery group, the worse the battery health of the first target battery group, because the increase in internal resistance directly affects the energy transmission efficiency of the battery. When the internal resistance increases, the battery will face higher energy loss during the charging and discharging process, which not only causes the heat generation of the battery to increase when it is working, but may also cause overheating, reduce discharge capacity, and even cause battery performance degradation and failure. In addition, higher internal resistance will increase the charging time of the battery, reduce its reaction speed, and affect the overall responsiveness of the battery, especially under high load demand. In the long run, the increase in the internal resistance increase may mean that the chemical properties and structural integrity of the battery are compromised, which will threaten the stability and reliability of the entire photovoltaic storage direct flexible microgrid.

[0076] In one embodiment, obtaining the battery health abnormality coefficient according to the capacity attenuation, internal resistance increase, and voltage of the first target battery group includes:

[0077] Obtain the actual voltage value of each battery in the first target battery group at different times within a preset time period, and mark the actual voltage value as I 实 x , x represents the sequence number of the actual voltage value of each battery at different times within the preset time period, x=1, 2, 3, 4, ..., c, c is a positive integer;

[0078] Calculate the actual voltage average of each battery within a preset time period. The calculation formula is:

[0079] The voltage instability coefficient of each battery is calculated, and the voltage fluctuation value of the first target battery group is obtained according to the voltage instability coefficient of each battery. The calculation formula is:

[0080]

[0081] Wherein, sx is the voltage fluctuation value of the first target battery group, k represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., l, l is a positive integer;

[0082] The battery health abnormality coefficient is obtained according to the capacity attenuation, internal resistance increase and voltage fluctuation value of the first target battery group.

[0083] It should be noted that the preset time period is set by professionals based on actual conditions and will not be limited or elaborated on in detail;

[0084] It should be noted that the actual voltage value of each battery is monitored in real time within a preset time period by utilizing a battery management system (BMS). These data are usually collected through sensors, which can accurately measure the instantaneous voltage of the battery and store it as time series data. Other acquisition methods are also possible, which are not limited or elaborated on in detail.

[0085] It should be noted that the greater the voltage fluctuation value of the first target battery group, the worse the battery health status of the first target battery group, because the increase in the voltage fluctuation value means that the voltage variation of the battery during the charging and discharging process is larger, which reflects the instability of the battery performance. The deterioration of battery health usually leads to problems such as increased internal resistance, uneven chemical reactions, and battery aging, which makes it impossible for the battery to maintain a stable voltage output when the load changes. This instability not only affects the discharge efficiency of the battery, but also may cause the battery to be overvoltage or undervoltage under high load conditions, thereby increasing the risk of system failure. In addition, the increase in voltage fluctuations may also affect the power supply quality of the entire microgrid, causing unreliable power supply, thereby affecting the normal operation of the equipment and systems connected to it.

[0086] In one embodiment, the battery health abnormality coefficient obtained according to the capacity attenuation, internal resistance increase and voltage fluctuation value of the first target battery group includes:

[0087] The capacity attenuation, internal resistance increase, and voltage fluctuation value of the first target battery group are normalized, and the battery health abnormality coefficient of the first target battery group is obtained according to the normalized capacity attenuation, internal resistance increase, and voltage fluctuation value. The calculation formula is:

[0088]

[0089] In the formula, E R , S H , Q W are the capacity attenuation, internal resistance increase and voltage fluctuation values ​​after normalization, a1, a2 and a3 are E R , S H , Q W The preset proportional coefficient, and a1, a2, and a3 are all greater than 0.

[0090] It should be noted that a1, a2, and a3 are set by professionals according to actual conditions. Generally, the sum of a1, a2, and a3 is 1. For example, a1, a2, and a3 can be 0.4, 0.3, and 0.3, respectively, or other numbers, which are not specifically limited. In addition, commonly used normalization processing methods include Min-Max normalization, Z-Score normalization, etc. The specific method is selected by professionals according to actual conditions, which is not specifically limited or elaborated.

[0091] In one embodiment, judging the battery health state corresponding to the first target battery group according to the battery health abnormality coefficient and the preset battery health abnormality coefficient threshold includes:

[0092] Comparing the battery health abnormality coefficient of the first target battery group with a preset battery health abnormality coefficient threshold;

[0093] If the battery health abnormality coefficient is less than the preset battery health abnormality coefficient threshold, it indicates that the health state of the corresponding first target battery group is normal;

[0094] If the battery health abnormality coefficient is not less than the preset battery health abnormality coefficient threshold, it means that the health state of the corresponding first target battery group is abnormal, and an alarm is issued.

[0095] It should be noted that the preset battery health abnormal coefficient threshold is set by professionals according to actual conditions, and no specific limitation or elaboration is made;

[0096] It should be noted that when the health status of the first target battery group is normal, it means that the health status of the first target battery group is good. When the energy storage battery group is dispatched to release electric energy to balance the power supply and demand during peak power consumption, the corresponding first target battery group can be selected to release electric energy; when the health status of the first target battery group is abnormal, it means that the health status of the first target battery group is very bad. When the energy storage battery group is dispatched to release electric energy to balance the power supply and demand during peak power consumption, not only the corresponding first target battery group is not selected to release electric energy, but the corresponding first target battery group is no longer suitable for the entire photovoltaic storage direct flexible microgrid. If the corresponding first target battery group is still placed in the photovoltaic storage direct flexible microgrid for operation, it may lead to a series of negative consequences; first, the battery group in the abnormal state cannot provide stable power under high load conditions, and may have problems such as voltage fluctuations, over-discharge or over-charging, which will not only affect the overall power supply quality of the microgrid, but also may damage the equipment connected to it, increasing the risk of equipment failure. Secondly, if the battery pack in poor health continues to be left in the system, it may cause more serious safety hazards, such as battery heating, leakage or even fire, which will pose a threat to operators and the surrounding environment; in addition, the presence of abnormal batteries may also lead to a decrease in the overall efficiency of the system, increase energy loss, and thus increase operating costs; therefore, when the battery pack has an abnormal health status, an alarm needs to be issued to promptly remind the staff, and it is very necessary to remove it from the photovoltaic storage direct flexible microgrid in time and repair or replace it to ensure the safety, stability and economy of the system, thereby ensuring the efficient operation of the photovoltaic storage direct flexible microgrid.

[0097] In one embodiment, obtaining the current power, temperature, last discharge time interval and discharge depth of the second target battery pack, and combining the corresponding battery health abnormality coefficient to obtain the priority release index includes:

[0098] Obtaining the current power of each battery in the second target battery group, and calculating the average current power of all batteries in the second target battery group as the remaining power of the second target battery group;

[0099] Acquire the current temperature of each battery in the second target battery group, and calculate the current average temperature of all batteries in the second target battery group as the current temperature load of the second target battery group;

[0100] Obtain the number of times each battery in the second target battery group is discharged, and obtain the discharge depth ag during each discharge m , ag m Indicates the depth of discharge of each battery at the mth time, m = 1, 2, 3, 4, ..., n, n is a positive integer, and the discharge depth coefficient of the second target battery group is calculated by the following formula:

[0101]

[0102] Wherein, ed is the discharge depth coefficient of the second target battery group, d represents the sequence number of the batteries in the second target battery group, d=1, 2, 3, 4, ..., l, l is a positive integer;

[0103] The priority release index is obtained according to the remaining power of the second target battery group, the current temperature load, the time interval of the last discharge, the discharge depth coefficient and the corresponding battery health abnormality coefficient.

[0104] It should be noted that the power sensor data of each battery can be directly read through the battery management system (BMS) or the battery monitoring system. These sensors monitor the status of the battery in real time and feed back the current power information to the management system. For the current temperature of each battery, a temperature sensor is usually used to measure the operating temperature of the battery. The BMS will aggregate the data of each sensor to calculate the temperature average of the battery pack. When recording the number of discharges and the depth of discharge, the system will track the charge and discharge history of each battery and record the depth data of each discharge. In addition, the time interval of the last discharge of the second target battery pack can be obtained through the timestamp record in the battery management system (BMS). The BMS will record the start and end time of the operation during each charge and discharge operation. These timestamps can be used to calculate the time interval of the last discharge of the battery pack.

[0105] When the remaining power of the second target battery group is larger, the current temperature load is smaller, the discharge depth coefficient is larger, the time interval of the last discharge is longer, and the corresponding battery health abnormality coefficient is smaller, when the energy storage battery is dispatched to release electric energy to balance the power supply and demand, the corresponding second target battery group has a higher priority for discharge and a higher priority release index. The reason is that when the remaining power of the second target battery group is larger, it means that the battery group can provide more electric energy, thereby effectively supporting the power supply during the peak period of power consumption and reducing the dependence on other power sources. The smaller the current temperature load, the lower the battery is running at a lower temperature, which usually leads to better performance and higher energy conversion efficiency, which helps to extend the battery life and improve the stability of discharge. The smaller the discharge depth coefficient, the battery has not experienced deep discharge, its health state is relatively good, and it has better discharge capacity and efficiency. In addition, the longer the time interval of the last discharge, the battery has enough rest time to recover, so that it can better release electric energy during the next discharge. Finally, the smaller the health abnormality coefficient, the better the health of the battery, the fewer faults and hidden dangers, and the better safety and reliability when releasing electric energy. Therefore, all these factors work together so that when the second target battery group has the above characteristics, its priority release index can be significantly improved, so that when scheduling energy storage batteries, this battery group can be selected first to ensure stable and efficient power supply and optimize the operating efficiency and safety of the overall microgrid.

[0106] In one embodiment, the priority release index is obtained according to the remaining power of the second target battery group, the current temperature load, the time interval of the last discharge, the discharge depth coefficient and the corresponding battery health abnormality coefficient, including:

[0107] The remaining power, current temperature load, last discharge time interval, discharge depth coefficient, and battery health abnormality coefficient of the second target battery group are normalized, and the priority release index of the second target battery group is obtained according to the normalized remaining power, current temperature load, last discharge time interval, discharge depth coefficient, and battery health abnormality coefficient. The calculation formula is:

[0108]

[0109] Wherein, Qaz is the priority release index of the second target battery group, dcv, dfm, dzs, fgp, and tb are the remaining power after normalization, the current temperature load, the time interval of the last discharge, the discharge depth coefficient, and the battery health abnormality coefficient, respectively; b1, b2, b3, b4, and b5 are the preset proportional coefficients of dcv, dfm, dzs, fgp, and tb, respectively, and b1, b2, b3, b4, and b5 are all greater than 0.

[0110] It should be noted that b1, b2, b3, b4, and b5 are set by professionals according to actual conditions. Generally, the sum of b1, b2, b3, b4, and b5 is 1. For example, b1, b2, b3, b4, and b5 can be 0.2, 0.2, 0.2, 0.2, and 0, respectively, or can be other numbers without specific limitation. In addition, commonly used normalization processing methods include Min-Max normalization, Z-Score standardization, etc. The specific method is selected by professionals according to actual conditions, and is not specifically limited or elaborated.

[0111] In one implementation method, the energy storage battery group with the largest priority release index among all the second target battery groups is used as the final battery group for power release management. When the power consumption is at peak, the energy storage batteries are scheduled to release power to balance the power supply and demand. According to the actual battery group status, a certain group of energy storage batteries can be selected to release power, thereby ensuring the overall efficiency of the system and guaranteeing the stability of the power supply.

[0112] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent management system for a photovoltaic and energy-storage direct-flexible microgrid, characterized in that: The system comprises: Battery health module: record each energy storage battery group as a first target battery group, obtain the capacity, internal resistance, and voltage of the first target battery group to obtain the battery health abnormality coefficient; Health status judgment module: judges the battery health status corresponding to the first target battery group according to the battery health abnormality coefficient and the preset battery health abnormality coefficient threshold, and marks the battery with a normal battery health status as the second target battery group; Priority release calculation module: for each second target battery pack, obtain the current power, temperature, last discharge time interval and discharge depth of the second target battery pack, and combine the corresponding battery health abnormality coefficient to obtain a priority release index; Release management module: The energy storage battery group with the largest priority release index among all the second target battery groups is taken as the final battery group to perform electric energy release management.

2. According to claim 1, a photovoltaic energy storage direct flexible microgrid intelligent management system is characterized in that: The battery health abnormality coefficient obtained according to the capacity attenuation coefficient, internal resistance and voltage of the first target battery pack includes: Get the actual internal resistance of each battery in the first target battery group and mark it as Q k , k represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., l, l is a positive integer; Calculate the initial internal resistance F of each battery k The absolute difference between the actual internal resistance and the absolute difference between the absolute difference and the preset maximum internal resistance increase value Ba is calculated to obtain the internal resistance increase degree. The calculation formula is: Wherein, pmn is the internal resistance increase of the first target battery pack; The battery health abnormality coefficient is obtained according to the capacity attenuation, internal resistance increase and voltage of the first target battery group.

3. According to claim 2, a photovoltaic energy storage direct flexible microgrid intelligent management system is characterized in that: The battery health abnormality coefficient obtained according to the capacity attenuation coefficient, internal resistance and voltage of the first target battery pack includes: Get the actual internal resistance of each battery in the first target battery group and mark it as Q k , k represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., l, l is a positive integer; Calculate the initial internal resistance F of each battery k The absolute difference between the actual internal resistance and the absolute difference between the absolute difference and the preset maximum internal resistance increase value Ba is calculated to obtain the internal resistance increase degree. The calculation formula is: Wherein, pmn is the internal resistance increase of the first target battery pack; The battery health abnormality coefficient is obtained according to the capacity attenuation, internal resistance increase and voltage of the first target battery group.

4. According to claim 3, a photovoltaic energy storage direct flexible microgrid intelligent management system is characterized in that: The battery health abnormality coefficient obtained according to the capacity attenuation, internal resistance increase and voltage of the first target battery pack includes: Obtain the actual voltage value of each battery in the first target battery group at different times within a preset time period, and mark the actual voltage value as I 实 x , x represents the sequence number of the actual voltage value of each battery at different times within the preset time period, x=1, 2, 3, 4, ..., c, c is a positive integer; Calculate the actual voltage average of each battery within a preset time period. The calculation formula is: The voltage instability coefficient of each battery is calculated, and the voltage fluctuation value of the first target battery group is obtained according to the voltage instability coefficient of each battery. The calculation formula is: Wherein, sx is the voltage fluctuation value of the first target battery group, k represents the sequence number of the batteries in the first target battery group, k=1, 2, 3, 4, ..., l, l is a positive integer; The battery health abnormality coefficient is obtained according to the capacity attenuation, internal resistance increase and voltage fluctuation value of the first target battery group.

5. According to claim 4, a photovoltaic energy storage direct flexible microgrid intelligent management system is characterized in that: The battery health abnormality coefficients obtained based on the capacity attenuation, internal resistance increase and voltage fluctuation of the first target battery pack include: The capacity attenuation, internal resistance increase, and voltage fluctuation value of the first target battery group are normalized, and the battery health abnormality coefficient of the first target battery group is obtained according to the normalized capacity attenuation, internal resistance increase, and voltage fluctuation value. The calculation formula is: In the formula, E R , S H , Q W They are the capacity attenuation, internal resistance increase and voltage fluctuation value after normalization, a1, a2 and a3 are E R , S H , Q W The preset proportional coefficient, and a1, a2, and a3 are all greater than 0.

6. According to claim 1, a photovoltaic storage direct flexible microgrid intelligent management system is characterized in that: Determining the battery health status of the first target battery group according to the battery health abnormality coefficient and the preset battery health abnormality coefficient threshold includes: Comparing the battery health abnormality coefficient of the first target battery group with a preset battery health abnormality coefficient threshold; If the battery health abnormality coefficient is less than the preset battery health abnormality coefficient threshold, it indicates that the health state of the corresponding first target battery group is normal; If the battery health abnormality coefficient is not less than the preset battery health abnormality coefficient threshold, it means that the health state of the corresponding first target battery group is abnormal, and an alarm is issued.

7. According to claim 1, a photovoltaic energy storage direct flexible microgrid intelligent management system is characterized in that: The current power, temperature, last discharge time interval and discharge depth of the second target battery pack are obtained, and the priority release index is obtained by combining the corresponding battery health abnormality coefficient, including: Obtaining the current power of each battery in the second target battery group, and calculating the average current power of all batteries in the second target battery group as the remaining power of the second target battery group; Acquire the current temperature of each battery in the second target battery group, and calculate the current average temperature of all batteries in the second target battery group as the current temperature load of the second target battery group; Obtain the number of times each battery in the second target battery group is discharged, and obtain the discharge depth ag during each discharge m , ag m Indicates the depth of discharge of each battery at the mth time, m = 1, 2, 3, 4, ..., n, n is a positive integer, and the discharge depth coefficient of the second target battery group is calculated by the following formula: Wherein, ed is the discharge depth coefficient of the second target battery group, k represents the sequence number of the batteries in the second target battery group, k=1, 2, 3, 4, ..., l, l is a positive integer; The priority release index is obtained according to the remaining power of the second target battery group, the current temperature load, the time interval of the last discharge, the discharge depth coefficient and the corresponding battery health abnormality coefficient.

8. The intelligent management system of a photovoltaic-storage-direct-flexible microgrid according to claim 1 is characterized in that: The priority release index is obtained based on the remaining power of the second target battery group, the current temperature load, the time interval of the last discharge, the discharge depth coefficient and the corresponding battery health abnormality coefficient, including: The remaining power, current temperature load, last discharge time interval, discharge depth coefficient, and battery health abnormality coefficient of the second target battery group are normalized, and the priority release index of the second target battery group is obtained according to the normalized remaining power, current temperature load, last discharge time interval, discharge depth coefficient, and battery health abnormality coefficient. The calculation formula is: Wherein, Qaz is the priority release index of the second target battery group, dcv, dfm, dzs, fgp, and tb are the remaining power after normalization, the current temperature load, the time interval of the last discharge, the discharge depth coefficient, and the battery health abnormality coefficient, respectively; b1, b2, b3, b4, and b5 are the preset proportional coefficients of dcv, dfm, dzs, fgp, and tb, respectively, and b1, b2, b3, b4, and b5 are all greater than 0.

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