An adaptive charging and discharging optimization scheduling method and platform for microgrid energy storage

Abstract

The present invention relates to the technical field of microgrid energy storage system optimization and scheduling, and discloses an adaptive charging and discharging optimization scheduling method and platform for microgrid energy storage, wherein the method includes: constructing a microgrid state matrix and a load demand matrix; generating a ring-shaped geometric distribution structure and determining the equipment distribution radius; collecting the fluctuation amount of renewable energy power generation in real time; and making preliminary adaptive allocation of the charging and discharging tasks of the energy storage equipment, and continuously adjusting them in combination with a rolling optimization algorithm. Compared with the scheduling methods in the prior art that cannot effectively cope with load fluctuations and renewable energy instability, especially under conditions of rapid fluctuations in load demand or large fluctuations in renewable energy, the technical problem of being unable to achieve efficient and flexible energy storage equipment charging and discharging scheduling, this application realizes adaptive charging and discharging task allocation by combining a ring-shaped geometric data structure with an intelligent optimization algorithm, thereby avoiding the problem of overcharging and over-discharging of energy storage equipment and improving the stability of the microgrid system.

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] Currently, charging and discharging scheduling technologies for microgrid energy storage systems suffer from numerous shortcomings. For example, existing technologies often employ fixed scheduling strategies or scheduling methods based on simple load forecasting. These methods often fail to adjust the charging and discharging strategies of energy storage devices in a timely manner when faced with large fluctuations in load demand and unstable renewable energy generation. This can lead to delayed system response, overcharging or over-discharging of energy storage devices, and even impact the device's service life and the operational stability of the microgrid. Furthermore, existing technologies often fail to fully consider multidimensional factors such as the health status, charge and discharge efficiency, state of charge, and maximum power of energy storage devices. This results in a lack of flexibility in scheduling schemes and an inability to optimally allocate energy storage devices under varying health states and operating conditions. In particular, in environments with high-frequency load fluctuations and significant power fluctuations in renewable energy sources (such as solar and wind power), traditional scheduling methods struggle to balance system stability, energy efficiency, and device lifespan. Therefore, an adaptive charging and discharging optimization scheduling method that can dynamically adapt to load demand and renewable energy fluctuations is urgently needed. This method should be able to efficiently and flexibly allocate charging and discharging tasks while taking into account the status of energy storage devices and the overall load demand of the microgrid, thereby improving the energy management efficiency, device lifespan, and overall system stability of the microgrid system. Summary of the Invention

[0003] In response to 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 existing technology cannot effectively cope with load fluctuations and renewable energy instability, especially under 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: Acquire microgrid energy storage operation information from the background resource management process within a preset time period T, including health status data, current charge and discharge efficiency data, current power percentage data, maximum rated power data, real-time load demand of the microgrid, load demand change, and microgrid renewable energy generation power, and construct a microgrid state matrix E and a microgrid load demand matrix G based on the microgrid energy storage operation information;

[0007] 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 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 ;

[0008] Step S30: Real-time collection of renewable energy power generation fluctuation ΔP renew , according to the fluctuation of renewable energy power generation power 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;

[0009] Step S40: performing preliminary adaptive allocation of charging and discharging tasks for each energy storage device based on the optimized distribution radius R;

[0010] Step S50: continuously and adaptively allocate the charging and discharging tasks of each energy storage device in combination with the rolling optimization algorithm.

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

[0012]

[0013] Among them, 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;

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

[0015]

[0016] 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.

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

[0018]

[0019] Among them, w1, w2, w3, and 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.

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

[0021]

[0022] 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 fitness of all devices, which is 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 for the impact of fluctuations on equipment distribution.

[0026] Preferably, 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:

[0027] 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 ;

[0028] 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 .

[0029] Preferably, the 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.

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

[0031] The data acquisition and state modeling module is used to collect microgrid energy storage operation information within a preset time period T, including health status data, current charge and discharge 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 based on the microgrid energy storage operation information;

[0032] The ring geometry structure 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 ;

[0033] Distribution radius determination module, used to collect real-time fluctuations in renewable energy power generation ΔP renew , according to the fluctuation of renewable energy power generation power 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;

[0034] A preliminary adaptive allocation module is used to perform preliminary adaptive allocation of charging and discharging tasks for each energy storage device based on the optimized distribution radius R;

[0035] 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.

[0036] The present invention also provides a computer program product, including an adaptive charge and discharge optimization scheduling program for microgrid energy storage, which implements the adaptive charge and discharge optimization scheduling method for microgrid energy storage when executed by a processor.

[0037] The beneficial effect of the present invention is that compared with the scheduling methods in the prior art that 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, the technical problem of being unable to achieve efficient and flexible charging and discharging scheduling of energy storage equipment, this application combines a ring-shaped geometric data structure with an intelligent optimization algorithm to achieve adaptive charging and discharging task allocation, thereby avoiding the problem of overcharging and over-discharging of energy storage equipment and improving the stability of the microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 This is a flow chart of a first embodiment of an adaptive charging and discharging optimization scheduling method for microgrid energy storage according to the present invention.

[0040] Figure 2 This is a schematic diagram of a microgrid energy storage device set obtained by storing the JSON data format in Example 1.

[0041] Figure 3 This is a schematic diagram of the storage of fitness and distribution radius data in Example 1.

[0042] Figure 4 Schematic diagram of the equipment distribution radius before and after optimization and the charge and discharge distribution power comparison in Example 1.

[0043] Figure 5 Schematic diagram of the equipment of an adaptive charging and discharging optimization scheduling method for microgrid energy storage according to the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0045] Example 1: Figure 1, which is a flow chart of the first embodiment of the adaptive charging and discharging optimization scheduling method for microgrid energy storage of the present invention, and proposes the first embodiment of the adaptive charging and discharging optimization scheduling method for microgrid energy storage of the present invention.

[0046] In a first embodiment, the adaptive charging and discharging optimization scheduling method for microgrid energy storage includes:

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

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

[0055] 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 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 ;

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

[0057]

[0058] Among them, w1, w2, w3, and 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.

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

[0060]

[0061] 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 fitness of all devices, which is used for normalization.

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

[0063] For example, if Figure 2 As shown, the device operation information is stored in JSON data format, which is convenient for transmission and processing within the system. Based on the JSON data, the system will initialize the device collection.

[0064] Fitness calculation formula: Assume w1=w2=w a =w4=0.25,τ=1, calculate the fitness F of each device i :device 1: F1 = (0.225 + 0.2125 + 0.15 + 0.15) = 0.7375; Device 2: F2 = (0.2 + 0.225 + 0.1 + 0.05) = 0.575; Device 3: F3=(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] like Figure 3 As shown, the calculated fitness and distribution radius are also stored in JSON format.

[0067] Step S30: Real-time collection of renewable energy power generation fluctuation ΔP renew , according to the fluctuation of renewable energy power generation power 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;

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

[0069]

[0070] Where k is the adjustment coefficient for the impact of fluctuations on equipment distribution.

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

[0072] For example, device 1: R1 = 2m, device 2: R2 = 3.77m, device 3: R3 = 4.48m, the power fluctuation of renewable energy generation ΔP renew =4kW, Maximum rated power of the equipment P max, :Device 1: 5kW, Device 2: 10kW, Device 3: 15kW, Fluctuation adjustment coefficient k = 1; Calculate the optimized distribution radius R,

[0073] Before the adjustment, device 1 was distributed in the innermost layer (2 meters) and had the highest scheduling priority. Devices 2 and 3 were distributed in the outer layer and had lower scheduling priorities. After the adjustment (due to increased fluctuations), the distribution radius of all devices increased, indicating the system's response to renewable energy fluctuations. Although the priority of device 1 was still higher, the gap between devices narrowed, indicating that the system needed more devices to jointly respond to load changes to ensure system stability.

[0074] Step S40: performing preliminary adaptive allocation of charging and discharging tasks for each energy storage device based on the optimized distribution radius R;

[0075] It should be noted 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:

[0076] 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 ;

[0077] 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 .

[0078] Charging power Pcharge,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.

[0079] It should be understood that the optimized distribution radius R is a key parameter for preliminary adaptive allocation. The closer the device is to the load center, the higher its scheduling priority, and the system will prioritize these devices for charging and discharging. The charging and discharging power is proportional to the inverse of the distribution radius. This design ensures that devices with high adaptability (small distribution radius) are prioritized in system scheduling, while preventing devices far from the load center from being frequently scheduled, reducing energy transmission losses.

[0080] For example, if Figure 4 As shown, the initial and optimized distribution radii (filled with backslashes) show significant differences for devices 1 through 5. Device 1's distribution radius increases the most, from 2m to 10.5m, indicating a lower priority for this device in system scheduling, reducing its frequency of participation. The optimized distribution radii for devices 3, 4, and 5 show less change, indicating that the system scheduling algorithm considers these devices to be in better condition and suitable for frequent scheduling under current load and fluctuation conditions.

[0081] By adjusting the distribution radius, the system effectively prioritizes devices, reducing the frequency of participation for devices in poor condition or with poor load adaptability, thereby improving overall system efficiency. The optimized distribution radius accounts for real-time load and renewable energy fluctuations, enabling the system to maintain optimal scheduling strategies under varying operating conditions, improving system stability and reliability.

[0082] Step S50: continuously and adaptively allocate the charging and discharging tasks of each energy storage device in combination with the rolling optimization algorithm.

[0083] It should be noted that the rolling optimization algorithm introduced in step S50 is a time-series-based dynamic adjustment method that optimizes the charging and discharging tasks of energy storage devices in real time by regularly updating system status and forecast information. This method can continuously adjust the energy storage device scheduling strategy as load demand and renewable energy sources fluctuate, thereby improving system flexibility and reliability.

[0084] In addition, the present invention provides an adaptive charge-discharge optimization scheduling platform for microgrid energy storage, which adopts an adaptive charge-discharge optimization scheduling method for microgrid energy storage in the above embodiment, and 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 adaptive charge-discharge optimization scheduling platform for microgrid energy storage provided by the present invention are the same as the beneficial effects of the adaptive charge-discharge optimization scheduling method for microgrid energy storage provided by the above embodiment, and the other technical features of the adaptive charge-discharge optimization scheduling platform for microgrid energy storage are the same as the features disclosed in the above embodiment method, and are not repeated here.

[0085] The present invention provides an adaptive charge and discharge optimization scheduling device for microgrid energy storage, please refer to Figure 5An adaptive charging and discharging 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 perform the adaptive charging and discharging optimization scheduling method for microgrid energy storage described in the first embodiment. The adaptive charging and discharging optimization scheduling device for microgrid energy storage in the 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 Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The adaptive charging and discharging optimization scheduling device for microgrid energy storage is merely an example and should not limit the functionality and scope of use of the embodiment of the present invention. An adaptive charge-discharge optimization scheduling device for microgrid energy storage may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (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 in RAM 1004. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following platforms can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 can allow an adaptive charge-discharge optimization scheduling device for microgrid energy storage to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an adaptive charge-discharge optimization scheduling device for microgrid energy storage with various platforms, it should be understood that it is not required to implement or have all of the platforms shown. More or fewer platforms may be implemented or have alternatively.

[0086] The present invention also provides a computer program product, including a computer program. When executed by a processor, the computer program implements the steps of the above-described method for adaptive charging and discharging optimization scheduling for microgrid energy storage. The computer program product provided by the present invention can solve the technical problem of adaptive charging and discharging 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 method for adaptive charging and discharging optimization scheduling for microgrid energy storage provided in the above-mentioned embodiment, and are not further described here.

[0087] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are performed.

[0088] It should be understood that the various parts disclosed in the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0089] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An adaptive charging and discharging optimization scheduling method for microgrid energy storage, characterized in that: Methods include: Step S10: Obtain the microgrid energy storage operation information from the background resource management process within the preset time period T, including health status data, current charge and discharge efficiency data, current power percentage data, maximum rated power data, real-time load demand of the microgrid, load demand change and microgrid renewable energy generation power, and construct a microgrid state matrix based on the microgrid energy storage operation information and the microgrid load demand matrix G; 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 ring geometric structure data, and then according to the microgrid state matrix and the microgrid load demand matrix G to dynamically calculate the Equipment adaptability of energy storage equipment , and determine the The distribution radius of energy storage devices in the ring geometry data ; According to the microgrid state matrix and the microgrid load demand matrix G to dynamically calculate the Equipment adaptability of energy storage equipment Steps, using the formula: in, is the weight factor of each factor, is the modulus of the matrix G, is the modulus of the matrix E, is the global adjustment factor of device fitness; For the The health status data of each energy storage device, For the Current charge and discharge efficiency data of each energy storage device, For the The current power percentage data of each energy storage device, For the Maximum rated power data of each energy storage device; Distribution radius The calculation formula is: ,in, It is the preset outer ring radius threshold, which is used to represent the device location with the lowest task priority; The preset inner ring radius threshold is used to represent the device location with the highest task priority; max is the maximum value of all equipment fitness, used for normalization; Step S30: Real-time collection of renewable energy power generation fluctuations , according to the fluctuation of renewable energy power generation Dynamically adjust the The distribution radius of each energy storage device in the ring geometry data is used to obtain the optimized distribution radius R. The calculation formula for the optimized distribution radius R is: ,in, is the adjustment coefficient for the impact of fluctuations on equipment distribution; For the The real-time load demand of the microgrid with energy storage devices, For the The change in load demand of each energy storage device, For the The renewable energy generation capacity of the microgrid with energy storage equipment; Step S40: Performing preliminary adaptive allocation of the charge and discharge tasks of each energy storage device based on the optimized distribution radius R; wherein, the step of performing preliminary adaptive allocation of the charge and discharge tasks of each energy storage device based on the optimized distribution radius R specifically includes: Step S401: When the first Renewable energy generation capacity per energy storage device Exceeding real-time load demand , trigger the charging mode task instruction and calculate the charging power ; Step S402: When the Renewable energy generation capacity per energy storage device Do not exceed real-time load demand , trigger the discharge mode task instruction and calculate the discharge power ; 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 Expressed as: in, For the The health status data of each energy storage device, For the Current charge and discharge efficiency data of each energy storage device, For the The current power percentage data of each energy storage device, For the The maximum rated power data of each energy storage device, N is the total number of energy storage devices in the microgrid; The microgrid load demand matrix G is expressed as: in, For the The real-time load demand of the microgrid with energy storage devices, For the The change in load demand of each energy storage device, For the The renewable energy generation power of the microgrid with a storage device.

3. The adaptive charging and discharging optimization scheduling method for microgrid energy storage according to claim 1, characterized in that: Charging power The calculation formula is , discharge power The calculation formula is ,in, is the preset charging efficiency coefficient, is the preset discharge efficiency coefficient.

4. An adaptive charging and discharging optimization scheduling platform for microgrid energy storage, characterized by: The adaptive charging and discharging optimization scheduling platform for microgrid energy storage includes: The data acquisition and state modeling module is used to collect 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 a microgrid state matrix based on the microgrid energy storage operation information. and the microgrid load demand matrix G; The ring geometry structure 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 the microgrid energy storage device set to obtain the ring geometry structure data, and then according to the microgrid state matrix and the microgrid load demand matrix G to dynamically calculate the Equipment adaptability of energy storage equipment , and determine the The distribution radius of energy storage devices in the ring geometry data ; According to the microgrid state matrix and the microgrid load demand matrix G to dynamically calculate the Equipment adaptability of energy storage equipment Steps, using the formula: in, is the weight factor of each factor, is the modulus of the matrix G, is the modulus of the matrix E, is the global adjustment factor of device fitness; For the The health status data of each energy storage device, For the Current charge and discharge efficiency data of each energy storage device, For the The current power percentage data of each energy storage device, For the Maximum rated power data of each energy storage device; Distribution radius The calculation formula is: ,in, It is the preset outer ring radius threshold, which is used to represent the device location with the lowest task priority; The preset inner ring radius threshold is used to represent the device location with the highest task priority; max is the maximum value of all equipment fitness, used for normalization; Distribution radius determination module, used to collect real-time fluctuations in renewable energy power generation , according to the fluctuation of renewable energy power generation Dynamically adjust the The distribution radius of each energy storage device in the ring geometry data is used to obtain the optimized distribution radius R. The calculation formula for the optimized distribution radius R is: ,in, is the adjustment coefficient for the impact of fluctuations on equipment distribution; For the The real-time load demand of the microgrid with energy storage devices, For the The change in load demand of each energy storage device, For the The renewable energy generation capacity of the microgrid with energy storage equipment; The preliminary adaptive allocation module is used to perform preliminary adaptive allocation of the charging and discharging tasks of each energy storage device based on the optimized distribution radius R; wherein the steps of performing preliminary adaptive allocation of the charging and discharging tasks of each energy storage device based on the optimized distribution radius R specifically include: step S401: when the first Renewable energy generation capacity per energy storage device Exceeding real-time load demand , trigger the charging mode task instruction and calculate the charging power ; Step S402: When the Renewable energy generation capacity per energy storage device Do not exceed real-time load demand , trigger the discharge mode task instruction and calculate the discharge power ; 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.

5. 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 includes: 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 according to any one of claims 1 to 3 is implemented.

6. A computer program product, characterized in that The computer program product includes an adaptive charging and discharging optimization scheduling program for microgrid energy storage. When the adaptive charging and discharging optimization scheduling program for microgrid energy storage is executed by a processor, it implements the adaptive charging and discharging optimization scheduling method for microgrid energy storage according to any one of claims 1 to 3.

Images