Self-adaptive charging and discharging optimization scheduling method and platform for micro-grid energy storage

Abstract

The invention relates to the technical field of micro-grid energy storage system optimization scheduling, and discloses a micro-grid energy storage oriented adaptive charging and discharging optimization scheduling method and platform, and the method comprises the steps: constructing a micro-grid state matrix and a load demand matrix; generating an annular geometric distribution structure and determining an equipment distribution radius; collecting renewable energy power generation fluctuation quantity in real time; the charging and discharging tasks of the energy storage equipment are subjected to preliminary self-adaptive distribution, and continuous adjustment is carried out in combination with a rolling optimization algorithm. In the prior art, a scheduling method which cannot effectively cope with load fluctuation and instability of renewable energy sources cannot effectively cope with, and especially under the condition of rapid load demand fluctuation or large-amplitude renewable energy source fluctuation, efficient and flexible energy storage equipment charging and discharging scheduling cannot be realized. According to the invention, through combination of the annular geometric data structure and the intelligent optimization algorithm, self-adaptive charging and discharging task distribution is realized, so that the problems of over-charging and over-discharging of the energy storage equipment are avoided, and the stability of the micro-grid system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid energy storage system optimization scheduling, and in particular relates to an adaptive charging and discharging optimization scheduling method and platform for microgrid energy storage. Background Art

[0002] At present, there are many deficiencies in the charging and discharging scheduling technology of microgrid energy storage systems. For example, the existing technologies mostly adopt fixed scheduling strategies or scheduling methods based on simple load forecasting. In the face of large fluctuations in load demand and unstable renewable energy generation, the charging and discharging strategies of energy storage devices are often unable to be adjusted in time, resulting in delayed system response, overcharging or over-discharging of energy storage devices, and even affecting the service life of the equipment and the operational stability of the microgrid. In addition, the existing technologies usually do not fully consider multi-dimensional factors such as the health status, charging and discharging efficiency, power status and maximum power of energy storage devices, resulting in a lack of flexibility in scheduling schemes and the inability to achieve the optimal allocation of energy storage devices under different health states and operating conditions. Especially in environments with high-frequency load fluctuations and drastic power fluctuations of renewable energy (such as solar energy and wind energy), traditional scheduling methods are difficult to balance system stability, energy efficiency and equipment life. Therefore, there is an urgent need for an adaptive charging and discharging optimization scheduling method that can dynamically adapt to load demand and renewable energy fluctuations. This method should be able to achieve efficient and flexible charging and discharging task allocation while considering the state of energy storage devices and the overall load demand of the microgrid, so as to improve the energy management efficiency, equipment operating life and overall system stability of the microgrid system. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose an adaptive charging and discharging optimization scheduling method for microgrid energy storage, aiming to solve the technical problem that the scheduling methods in the prior art cannot effectively cope with load fluctuations and instability of renewable energy, especially under the conditions of rapid fluctuations in load demand or large fluctuations in renewable energy, and cannot achieve efficient and flexible charging and discharging scheduling of energy storage equipment.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an adaptive charging and discharging optimization scheduling method for microgrid energy storage.

[0005] The adaptive charging and discharging optimization scheduling method for microgrid energy storage includes:

[0006] Step S10: Obtain the microgrid energy storage operation information from the background resource management process within a preset time period T, including the health status data, current charge-discharge efficiency data, current power percentage data, maximum rated power data, real-time load demand of the microgrid, change amount of the load demand, and renewable energy generation power of the microgrid, and construct a microgrid state matrix E and a microgrid load demand matrix G according to the microgrid energy storage operation information;

[0007] Step S20: First, store the microgrid energy storage operation information of n microgrid energy storage devices in JSON data format to obtain a microgrid energy storage device set, and perform an initial arrangement on the microgrid energy storage device set to obtain ring-shaped geometric structure data. Then, dynamically calculate the device fitness F of the i-th energy storage device according to the microgrid state matrix E and the microgrid load demand matrix G i and determine the distribution radius R of the i-th energy storage device in the ring-shaped geometric structure data i ;

[0008] Step S30: Real-time collect the renewable energy generation power fluctuation amount ΔP renew and dynamically adjust the distribution radius of the i-th energy storage device in the ring-shaped geometric structure data according to the renewable energy generation power fluctuation amount ΔP to obtain an optimized distribution radius R; renew Dynamic adjustment of the distribution radius of the i-th energy storage device in the ring-shaped geometric structure data to obtain an optimized distribution radius R;

[0009] Step S40: Based on the optimized distribution radius R, perform a preliminary adaptive allocation of the charge-discharge tasks of each energy storage device;

[0010] Step S50: Combine the rolling optimization algorithm to continuously perform an adaptive allocation of the charge-discharge tasks of each energy storage device.

[0011] Preferably, in Step S10, the microgrid state matrix E is expressed as:

[0012]

[0013] where SOH i is the health status data of the i-th energy storage device, Eff i is the current charge-discharge efficiency data of the i-th energy storage device, SOC i is the current power percentage data of the i-th energy storage device, P max, is the maximum rated power data of the i-th energy storage device, and N is the total number of microgrid energy storage devices;

[0014] The microgrid load demand matrix G is expressed as:

[0015]

[0016] where is the real-time load demand of the microgrid for the i-th energy storage device, is the change in the load demand of the i-th energy storage device, is the renewable energy power generation of the microgrid for the i-th energy storage device.

[0017] Preferably, in step S20, the device fitness F of the i-th energy storage device is dynamically calculated according to the microgrid state matrix E and the microgrid load demand matrix G i The step of using the formula:

[0018]

[0019] where w 1 , w 2 , w 3 , w 4 are the weight factors of each factor, ||G|| is the norm of matrix G, ||E|| is the norm of matrix E, and τ is the global adjustment factor of device fitness.

[0020] Preferably, in step S20, the calculation formula of the distribution radius R i is:

[0021]

[0022] where R max is the preset outer ring radius threshold, used to represent the position of the device with the lowest task priority; R min is the preset inner ring radius threshold, used to represent the position of the device with the highest task priority; max(F) is the maximum value of all device fitnesses, used for normalization.

[0023] Preferably, in step S30, the calculation formula for optimizing the distribution radius R is:

[0024]

[0025] where k is the adjustment coefficient of the influence of fluctuations on device distribution.

[0026] Preferably, in step S40, the step of initially and adaptively allocating the charging and discharging tasks for each energy storage device based on the optimized distribution radius R specifically includes:

[0027] Step S401: When the renewable energy power generation of the i-th energy storage device exceeds the real-time load demand trigger the charging mode task instruction and calculate the charging power P charge,i ;

[0028] Step S402: When the renewable energy power generation Not exceeding the real-time load demand Trigger the discharge mode task instruction and calculate the discharge power P discharge,i 。

[0029] Preferably, the charging power P charge,i The calculation formula is The discharge power P discharge,i The calculation formula is Where, η charge Is the preset charging efficiency coefficient, η discharge Is the preset discharge efficiency coefficient.

[0030] The present invention also provides an adaptive charge and discharge optimization scheduling platform for microgrid energy storage, including:

[0031] A data acquisition and state modeling module, which is used to collect the operation information of the microgrid energy storage within a preset time period T, including the health status data, the current charge and discharge efficiency data, the current power percentage data, the maximum rated power data, the real-time load demand of the microgrid, the change amount of the load demand, and the renewable energy power generation power of the microgrid, and construct a microgrid state matrix E and a microgrid load demand matrix G according to the operation information of the microgrid energy storage;

[0032] A circular geometric structure generation module, which is used to first store the operation information of the microgrid energy storage of n microgrid energy storage devices in JSON data format to obtain a microgrid energy storage device set, and perform an initial arrangement on the microgrid energy storage device set to obtain circular geometric structure data, and then dynamically calculate the device fitness F of the i-th energy storage device according to the microgrid state matrix E and the microgrid load demand matrix G i And determine the distribution radius R of the i-th energy storage device in the circular geometric structure data i ;

[0033] A distribution radius determination module, which is used to collect the renewable energy power generation power fluctuation amount ΔP in real time renew , according to the renewable energy power generation power fluctuation amount ΔP renew Dynamically adjust the distribution radius of the i-th energy storage device in the circular geometric structure data to obtain the optimized distribution radius R;

[0034] A preliminary adaptive allocation module, which is used to perform a preliminary adaptive allocation of the charge and discharge tasks of each energy storage device based on the optimized distribution radius R;

[0035] A continuous adaptive allocation module, which is used to perform a continuous adaptive allocation of the charge and discharge tasks of each energy storage device in combination with the rolling optimization algorithm.

[0036] The present invention also provides a computer program product, including an adaptive charge and discharge optimization scheduling program for microgrid energy storage. When the adaptive charge and discharge optimization scheduling program for microgrid energy storage is executed by a processor, the above-mentioned adaptive charge and discharge optimization scheduling method for microgrid energy storage is realized.

[0037] The beneficial effects of the present invention are as follows: Compared with the scheduling methods in the prior art that cannot effectively cope with load fluctuations and the instability of renewable energy, especially under the conditions of rapid load demand fluctuations or large fluctuations in renewable energy, the technical problem of being unable to achieve efficient and flexible charge and discharge scheduling of energy storage devices. By combining a circular geometric data structure with an intelligent optimization algorithm, the present application realizes adaptive charge and discharge task allocation, thus avoiding the problems of overcharging and over-discharging of energy storage devices and improving the stability of the microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a schematic flowchart of the first embodiment of an adaptive charge and discharge optimization scheduling method for microgrid energy storage according to the present invention.

[0040] Figure 2 It is a schematic diagram of storing the microgrid energy storage device set in the JSON data format in the first embodiment.

[0041] Figure 3 It is a schematic diagram of storing fitness and distribution radius data in the first embodiment.

[0042] Figure 4 It is a schematic diagram of the comparison of the distribution radius of the device before and after optimization and the charging and discharging allocation power ratio in the first embodiment.

[0043] Figure 5 It is a schematic diagram of the device of an adaptive charge and discharge optimization scheduling method for microgrid energy storage according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] Example 1: As Figure 1 shown, it is a schematic flowchart of the first embodiment of the adaptive charge and discharge optimization scheduling method for microgrid energy storage of the present invention, and the first embodiment of the adaptive charge and discharge optimization scheduling method for microgrid energy storage of the present invention is proposed.

[0046] In the first embodiment, the adaptive charge and discharge optimization scheduling method for microgrid energy storage includes:

[0047] Step S10: Obtain the microgrid energy storage operation information from the background resource management process within a preset time period T, including the health status data, the current charge and discharge efficiency data, the current power percentage data, the maximum rated power data, the real-time load demand of the microgrid, the change amount of the load demand, and the renewable energy power generation of the microgrid, and construct a microgrid state matrix E and a microgrid load demand matrix G according to the microgrid energy storage operation information;

[0048] It should be noted that in step S10, the microgrid state matrix E is expressed as:

[0049]

[0050] where SOH i is the health status data of the i-th energy storage device, Eff i is the current charge and discharge efficiency data of the i-th energy storage device, SOC i is the current power percentage data of the i-th energy storage device, P max, is the maximum rated power data of the i-th energy storage device, and N is the total number of microgrid energy storage devices;

[0051] The microgrid load demand matrix G is expressed as:

[0052]

[0053] where is the real-time load demand of the microgrid of the i-th energy storage device, is the change amount of the load demand of the i-th energy storage device, is the renewable energy power generation of the microgrid of the i-th energy storage device.

[0054] It is understandable that the health status data reflects the service life and health level of the energy storage device, and its value range is usually between 0 and 1. The closer the value is to 1, the healthier the device is; the current charge-discharge efficiency data represents the current charge-discharge conversion efficiency of the energy storage device. The higher the value, the smaller the energy conversion loss; the current battery percentage data shows the percentage of the current battery power of the device relative to its maximum capacity, and the value range is usually from 0% to 100%; the maximum rated power data represents the maximum power that the device can withstand during charging or discharging, and the unit is kilowatt (kW); the real-time load demand of the microgrid represents the total power demand of the current microgrid; the change amount of the load demand reflects the change trend of the load demand and is used to measure the load fluctuation; the renewable energy power generation of the microgrid represents the real-time power generation of renewable energy such as solar energy and wind energy in the system.

[0055] Step S20: First, store the microgrid energy storage operation information of n microgrid energy storage devices in JSON data format to obtain a microgrid energy storage device set, and perform an initial arrangement on the microgrid energy storage device set to obtain ring-shaped geometric structure data. Then, dynamically calculate the device fitness F of the i-th energy storage device according to the microgrid state matrix E and the microgrid load demand matrix G i , and determine the distribution radius R of the i-th energy storage device in the ring-shaped geometric structure data i ;

[0056] It should be noted that in step S20, the device fitness F of the i-th energy storage device is dynamically calculated according to the microgrid state matrix E and the microgrid load demand matrix G i using the formula:

[0057]

[0058] where w 1 , w 2 , w 3 , w 4 are the weight factors of each factor, ||G|| is the norm of matrix G, ||E|| is the norm of matrix E, and τ is the global adjustment factor of device fitness.

[0059] In step S20, the calculation formula for the distribution radius R i is:

[0060]

[0061] where R max is the preset outer ring radius threshold, which is used to represent the position of the device with the lowest task priority; R min is the preset inner ring radius threshold, which is used to represent the position of the device with the highest task priority; max(F) is the maximum value of the device fitness of all devices and is used for normalization.

[0062] It should be understood that the equipment fitness reflects the comprehensive performance of the energy storage equipment under the current system state. The higher the fitness of the equipment, the smaller the distribution radius R i The smaller it is, the more priority it has to participate in the charge and discharge scheduling. By combining the equipment status and the system load demand matrix and introducing the global adjustment factor of equipment fitness, the adaptive adjustment of equipment scheduling can be realized, dynamically responding to the load fluctuations of the system and the changes of renewable energy.

[0063] For example, as Figure 2 shown, the equipment operation information is stored in the JSON data format, which is convenient for transmission and processing within the system. According to the JSON data, the system will initialize the equipment set.

[0064] Fitness calculation formula: Assume w 1 = w 2 = w a = w 4 = 0.25, τ = 1, calculate the fitness F of each device i : Device 1: F 1 = (0.225 + 0.2125 + 0.15 + 0.15) = 0.7375; Device 2: F 2 = (0.2 + 0.225 + 0.1 + 0.05) = 0.575; Device 3: F 3 = (0.175 + 0.2 + 0.05 + 0.0833) = 0.5083.

[0065] Distribution radius calculation formula: Assume R max = 10m, R min = 2 m, max(F) = 0.7375, calculate the distribution radius R of each device i : Device 1: Device 2: Device 3:

[0066] As Figure 3 shown, the calculated fitness and distribution radius are also stored in the JSON format.

[0067] Step S30: Real-time collect the renewable energy power generation fluctuation amount ΔP renew , according to the renewable energy power generation fluctuation amount ΔP renewDynamically adjust the distribution radius of the i-th energy storage device in the ring-shaped geometric structure data to obtain the optimized distribution radius R;

[0068] It should be noted that in step S30, the calculation formula for the optimized distribution radius R is:

[0069]

[0070] where k is the adjustment coefficient for the impact of fluctuations on device distribution.

[0071] It should be understood that based on the original fitness, the optimized distribution radius R will be dynamically adjusted according to the fluctuation amount of renewable energy. The greater the fluctuation, the more obvious the adjustment of the distribution radius, enabling the system to more flexibly respond to the instability of load and power generation. By introducing the fluctuation adjustment factor k, the system can achieve flexible control under different environmental conditions.

[0072] For example, for device 1: R 1 = 2m, for device 2: R 2 = 3.77m, for device 3: R 3 = 4.48m, the fluctuation amount of renewable energy generation power ΔP renew = 4kW, The maximum rated power P of the device max, : For device 1: 5kW, for device 2: 10kW, for device 3: 15kW, the fluctuation adjustment coefficient k = 1; calculate the optimized distribution radius R,

[0074] Before adjustment, device 1 was distributed in the innermost layer (2 meters) with the highest scheduling priority, and devices 2 and 3 were distributed in the outer layer with lower scheduling priorities. After adjustment (due to increased fluctuations): The distribution radii of all devices increase, indicating the system's response to renewable energy fluctuations. Although the priority of device 1 is still relatively high, the gap between devices has narrowed, indicating that the system requires more devices to jointly respond to load changes to ensure system stability.

[0075] Step S40: Based on the optimized distribution radius R, perform a preliminary adaptive allocation of the charging and discharging tasks for each energy storage device;

[0076] It should be noted that in step S40, the steps for performing a preliminary adaptive allocation of the charging and discharging tasks for each energy storage device based on the optimized distribution radius R specifically include:

[0077] Step S401: When the renewable energy generation power of the i-th energy storage device exceeds the real-time load demand trigger the charging mode task instruction and calculate the charging power P charge,i ;

[0078] Step S402: When the renewable energy generation power of the i-th energy storage device does not exceed the real-time load demand trigger the discharge mode task instruction and calculate the discharge power P discharge,i .

[0079] The charging power P charge,i is calculated by the formula The discharge power P discharge,i is calculated by the formula where η charge is the preset charging efficiency coefficient, and η discharge is the preset discharge efficiency coefficient.

[0080] It should be understood that the optimized distribution radius R is an important parameter for preliminary adaptive allocation. The closer the device is to the load center, the higher its scheduling priority. The system will preferentially schedule these devices to participate in charging and discharging, and the charging and discharging power is proportional to the reciprocal of the distribution radius. This design ensures the priority of devices with high fitness (small distribution radius) in system scheduling, and at the same time avoids devices far from the load center from frequently participating in scheduling, reducing energy transmission losses.

[0081] For example, as Figure 4 shown, there are obvious differences in the initial distribution radius and the optimized distribution radius (hatched filling) on devices 1 to 5. The distribution radius of device 1 increases from 2m to 10.5m, with the largest change amplitude, indicating that the priority of this device in system scheduling is reduced and its participation frequency is decreased. The optimized distribution radii of devices 3, 4, and 5 change less, indicating that the system scheduling algorithm considers the states of these devices to be better and suitable for frequently participating in scheduling under the current load and fluctuations.

[0082] Through the adjustment of the distribution radius, the system can effectively distinguish the priorities of devices, reduce the participation frequency of devices with poor states or poor load adaptability, and improve the overall efficiency of the system. The optimized distribution radius takes into account the real-time load and renewable energy fluctuations, enabling the system to maintain the best scheduling strategy under different working conditions and improving the stability and reliability of the system.

[0083] Step S50: Continuously and adaptively allocate the charging and discharging tasks of each energy storage device in combination with the rolling optimization algorithm.

[0084] It should be noted that the rolling optimization algorithm introduced in step S50 is a dynamic adjustment method based on time series. By regularly updating the system state and prediction information, it can optimize the charging and discharging tasks of energy storage devices in real time. This method can continuously adjust the scheduling strategy of energy storage devices under the changing load demand and renewable energy fluctuations, thereby improving the flexibility and reliability of the system.

[0085] In addition, an adaptive charge-discharge optimization scheduling platform for microgrid energy storage provided by the present invention adopts an adaptive charge-discharge optimization scheduling method in the above-mentioned embodiment, and can solve the technical problem of an adaptive charge-discharge optimization scheduling for microgrid energy storage. Compared with the prior art, the beneficial effects of the adaptive charge-discharge optimization scheduling platform for microgrid energy storage provided by the present invention are the same as those of the adaptive charge-discharge optimization scheduling method for microgrid energy storage provided by the above-mentioned embodiment, and other technical features in the adaptive charge-discharge optimization scheduling platform for microgrid energy storage are the same as the features disclosed in the method of the above-mentioned embodiment, and will not be elaborated herein.

[0086] The present invention provides an adaptive charge-discharge optimization scheduling device for microgrid energy storage. Please refer to Figure 5, An adaptive charge-discharge optimization scheduling device for microgrid energy storage includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute an adaptive charge-discharge optimization scheduling method for microgrid energy storage in Embodiment 1 above. An adaptive charge-discharge optimization scheduling device in an embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. An adaptive charge-discharge optimization scheduling device for microgrid energy storage is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention. An adaptive charge-discharge optimization scheduling device for microgrid energy storage may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of an adaptive charge-discharge optimization scheduling device for microgrid energy storage are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following platforms may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow an adaptive charge-discharge optimization scheduling device for microgrid energy storage to communicate with other devices wirelessly or wireline to exchange data. Although an adaptive charge-discharge optimization scheduling device with various platforms is shown in the figure, it should be understood that it is not required to implement or have all the shown platforms. Instead, more or fewer platforms may be implemented or had.

[0087] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of an adaptive charge-discharge optimization scheduling method for microgrid energy storage as described above. The computer program product provided by the present invention can solve the technical problem of adaptive charge-discharge optimization scheduling for microgrid energy storage. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the adaptive charge-discharge optimization scheduling method for microgrid energy storage provided in the above embodiments, and will not be elaborated herein.

[0088] In particular, according to the embodiments disclosed by the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment disclosed by the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, it executes the above functions defined in the methods of the embodiments disclosed by the present invention.

[0089] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0090] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. An adaptive charging and discharging optimization scheduling method for microgrid energy storage, characterized in that: Methods include: Step S10: obtaining microgrid energy storage operation information from the background resource management process within a preset time period T, including health status data, current charging and discharging efficiency data, current power percentage data, maximum rated power data, real-time load demand of the microgrid, change in load demand, and microgrid renewable energy generation power, and constructing a microgrid state matrix E and a microgrid load demand matrix G according to the microgrid energy storage operation information; Step S20: First, the microgrid energy storage operation information of n microgrid energy storage devices is stored in JSON data format to obtain a microgrid energy storage device set, and the microgrid energy storage device set is initialized and arranged to obtain a ring geometric structure data, and then the device fitness F of the i-th energy storage device is dynamically calculated according to the microgrid state matrix E and the microgrid load demand matrix G. i , and determine the distribution radius R of the i-th energy storage device in the ring geometry data i ; Step S30: Real-time collection of renewable energy power generation power fluctuation ΔP renew , according to the fluctuation of renewable energy power generation ΔP renew Dynamically adjust the distribution radius of the i-th energy storage device in the ring geometry data to obtain the optimized distribution radius R; Step S40: performing preliminary adaptive allocation of charging and discharging tasks for each energy storage device based on the optimized distribution radius R; Step S50: Continuously and adaptively allocate the charging and discharging tasks of each energy storage device in combination with the rolling optimization algorithm.

2. The adaptive charging and discharging optimization scheduling method for microgrid energy storage according to claim 1, characterized in that: In step S10, the microgrid state matrix E is expressed as: Among them, SOH i is the health status data of the i-th energy storage device, Eff i is the current charging and discharging efficiency data of the i-th energy storage device, SOC i is the current power percentage data of the i-th energy storage device, P max,i is the maximum rated power data of the i-th energy storage device, and N is the total number of microgrid energy storage devices; The microgrid load demand matrix G is expressed as: in, is the real-time load demand of the microgrid of the i-th energy storage device, is the change in load demand of the i-th energy storage device, is the microgrid renewable energy generation power of the i-th energy storage device.

3. The adaptive charging and discharging optimization scheduling method for microgrid energy storage according to claim 2, characterized in that: In step S20, the device fitness F of the i-th energy storage device is dynamically calculated according to the microgrid state matrix E and the microgrid load demand matrix G. i Steps, using the formula: Among them, w1, w2, w3, w4 are the weight factors of each factor, ||G|| is the modulus of the matrix G, ||E|| is the modulus of the matrix E, and τ is the global adjustment factor of the device fitness.

4. The adaptive charging and discharging optimization scheduling method for microgrid energy storage according to claim 3 is characterized in that: In step S20, the distribution radius R i The calculation formula is: Among them, R max is the preset outer ring radius threshold, which is used to represent the device location with the lowest task priority; R min is the preset inner ring radius threshold, which is used to represent the device position with the highest task priority; max(F) is the maximum value of the fitness of all devices, which is used for normalization.

5. The adaptive charging and discharging optimization scheduling method for microgrid energy storage according to claim 4, characterized in that: In step S30, the calculation formula for optimizing the distribution radius R is: Where k is the adjustment coefficient of the impact of fluctuations on equipment distribution.

6. The adaptive charging and discharging optimization scheduling method for microgrid energy storage according to claim 5, characterized in that: In step S40, the step of performing preliminary adaptive allocation of the charging and discharging tasks of each energy storage device based on the optimized distribution radius R specifically includes: Step S401: When the renewable energy generation power of the i-th energy storage device Exceeding real-time load demand Trigger the charging mode task instruction and calculate the charging power P charge,i ; Step S402: When the renewable energy generation power of the i-th energy storage device Do not exceed real-time load demand Trigger the discharge mode task instruction and calculate the discharge power P discharge,i .

7. The adaptive charging and discharging optimization scheduling method for microgrid energy storage according to claim 6, characterized in that: Charging power P charge,i The calculation formula is Discharge power P discharge,i The calculation formula is Among them, η charge is the preset charging efficiency coefficient, η discharge is the preset discharge efficiency coefficient.

8. An adaptive charging and discharging optimization scheduling platform for microgrid energy storage, characterized in that: The adaptive charging and discharging optimization scheduling platform for microgrid energy storage includes: The data acquisition and state modeling module is used to collect the microgrid energy storage operation information within a preset time period T, including health status data, current charging and discharging efficiency data, current power percentage data, maximum rated power data, real-time load demand of the microgrid, change in load demand and microgrid renewable energy generation power, and construct the microgrid state matrix E and microgrid load demand matrix G according to the microgrid energy storage operation information; The ring geometry generation module is used to first store the microgrid energy storage operation information of n microgrid energy storage devices in JSON data format to obtain a microgrid energy storage device set, and initialize and arrange the microgrid energy storage device set to obtain the ring geometry structure data, and then dynamically calculate the device fitness F of the i-th energy storage device according to the microgrid state matrix E and the microgrid load demand matrix G. i , and determine the distribution radius R of the i-th energy storage device in the ring geometry data i ; The distribution radius determination module is used to collect the power fluctuation ΔP of renewable energy generation in real time. renew , according to the fluctuation of renewable energy power generation ΔP renew Dynamically adjust the distribution radius of the i-th energy storage device in the ring geometry data to obtain the optimized distribution radius R; A preliminary adaptive allocation module, used for performing preliminary adaptive allocation of charging and discharging tasks of each energy storage device based on an optimized distribution radius R; The continuous adaptive allocation module is used to continuously and adaptively allocate the charging and discharging tasks of each energy storage device in combination with the rolling optimization algorithm.

9. An adaptive charging and discharging optimization scheduling device for microgrid energy storage, characterized in that: The adaptive charging and discharging optimization scheduling device for microgrid energy storage comprises: a memory, a processor, and an adaptive charging and discharging optimization scheduling program for microgrid energy storage stored in the memory and executable on the processor. When the adaptive charging and discharging optimization scheduling program for microgrid energy storage is executed by the processor, the adaptive charging and discharging optimization scheduling method for microgrid energy storage described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes an adaptive charging and discharging optimization scheduling program for microgrid energy storage, and when the adaptive charging and discharging optimization scheduling program for microgrid energy storage is executed by a processor, the adaptive charging and discharging optimization scheduling method for microgrid energy storage described in any one of claims 1 to 7 is implemented.

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