Energy storage optimization method and system for new energy power generation

Abstract

The invention relates to the technical field of power distribution and energy storage, in particular to an energy storage optimization method and system for new energy power generation. The method comprises the following steps: firstly, acquiring a node power supply index of each node device, and further performing optimization matching on the node devices and the energy storage devices in each micro-grid by combining the residual capacity of each energy storage device and considering the spatial distance; and further classifying and acquiring all the power supply microgrids and all the power consumption microgrids, and evaluating a networking coefficient between each power supply microgrid and each power consumption microgrid so as to acquire a final networking result. Based on the supply and consumption condition of each node device in the micro-grid, the capacity and spatial distribution of each energy storage device are combined, the regulation and control mode in the micro-grid is optimized, and the internal balance of the micro-grid is ensured; furthermore, the networking mode of the micro-grids is optimized according to the power supply capacity and the energy storage condition of each micro-grid, so that the electric power is distributed in order, and accurate regulation and control and efficient energy storage are carried out on the new energy power generation and energy storage system.

Description

Technical Field

[0001] The present invention relates to the field of power distribution and energy storage technology, and in particular to an energy storage optimization method and system for renewable energy power generation. Background Art

[0002] New energy power generation refers to the use of renewable resources such as wind energy or solar energy to generate electricity, reducing dependence on traditional energy sources such as oil and coal. The new energy power generation and energy storage system is a system that combines power generation and energy storage technology. Microgrids are an important part of it. They are small-scale power networks that include power stations, power loads, distribution facilities and energy storage equipment. They can operate independently or in a network. Microgrids store daily surplus electricity and connect to other microgrids during peak hours to provide power support and maintain grid balance and stability.

[0003] However, renewable energy power generation is greatly affected by the environment and has volatility and uncertainty, which will affect the operational stability of the microgrid in which it is located. In addition, the networking of microgrids in renewable energy power generation and energy storage is usually done nearby for charging or discharging, that is, the current microgrid provides power support to other microgrids or is provided with power support by other microgrids. This may cause overcharging or over-discharging of energy storage devices in some microgrids, which will be detrimental to maintaining the supply and consumption balance of microgrid networking in the renewable energy power generation and energy storage system, thereby affecting the efficient energy storage of the renewable energy power generation and energy storage system. Summary of the invention

[0004] In order to solve the technical problem that the existing technology has poor energy storage effect on the energy storage system of new energy power generation, the purpose of the present invention is to provide a method and system for optimizing energy storage for new energy power generation. The technical solutions adopted are as follows:

[0005] The present invention provides a method for optimizing energy storage for renewable energy power generation, the method comprising:

[0006] In each microgrid of the new energy power generation and energy storage system, obtain the power supply and consumption difference curve of each node device in a preset historical period at the time of waiting for networking, and the remaining capacity of each energy storage device at the time of waiting for networking;

[0007] According to the fluctuation information of each power supply and consumption difference curve, the node power supply index of the corresponding node device is obtained; in each microgrid, according to the node power supply index of each node device and the remaining capacity of each energy storage device, combined with the spatial distance between each node device and each energy storage device, the matching coefficient between each node device and each energy storage device is obtained; the node devices and energy storage devices in each microgrid are matched according to the matching coefficient, and the remaining capacity of each energy storage device is continuously updated during the matching process;

[0008] According to the node power supply index of all node devices in each microgrid and the remaining capacity of all energy storage devices after matching, the networking power supply coefficient of each microgrid is obtained; all microgrids are classified according to the networking power supply coefficient to obtain all power supply microgrids and all power consumption microgrids; according to the networking power supply coefficient of each power supply microgrid and the remaining capacity of all node devices in the power supply microgrid at the time of waiting for networking, combined with the spatial distance between each power supply microgrid and each power consumption microgrid, the networking coefficient between each power supply microgrid and each power consumption microgrid is obtained;

[0009] The networking result of the new energy power generation and energy storage system at the time of waiting for networking is obtained according to the networking coefficient.

[0010] Furthermore, the method for obtaining the node power supply index includes:

[0011] According to the concentrated characteristics of all the power supply and consumption difference data on each of the power supply and consumption difference curves, the power supply capacity parameters of the corresponding node device are obtained;

[0012] According to the fluctuation deviation of each power supply and consumption difference data on each power supply and consumption difference curve relative to the average level, the power supply stability parameter of the corresponding node device is obtained;

[0013] According to the power supply capacity parameter and the power supply stability parameter of each node device, a node power supply index of each node device is obtained; the power supply capacity parameter and the power supply stability parameter are both positively correlated with the node power supply index.

[0014] Furthermore, the method for obtaining the matching coefficient includes:

[0015] Classify all node devices in each microgrid according to the node power supply index to obtain all power supply node devices and all power consumption node devices; use the negative correlation mapping result of the spatial distance between each node device and each energy storage device as a transmission matching parameter;

[0016] According to the node power supply index of each power supply node device and the remaining capacity of each energy storage device at the time of waiting for networking, a first power matching parameter between each power supply node device and each energy storage device is obtained, and the node power supply index and the remaining capacity are both positively correlated with the first power matching parameter; the first power matching parameter and the transmission matching parameter are integrated to obtain a matching coefficient between each power supply node device and each energy storage device;

[0017] According to the node power supply index of each power-consuming node device and the remaining capacity of each energy storage device at the time of waiting for networking, the second power matching parameter between each power-consuming node device and each energy storage device is obtained, and the node power supply index and the remaining capacity are negatively correlated with the second power matching parameter; the second power matching parameter and the transmission matching parameter are integrated to obtain the matching coefficient between each power-consuming node device and each energy storage device.

[0018] Furthermore, the method for acquiring the power supply node device and the power consumption node device includes:

[0019] The node device whose node power supply index is greater than a preset threshold is regarded as a power supply node device, and the node device whose node power supply index is less than or equal to the preset threshold is regarded as a power consumption node device.

[0020] Furthermore, the method for matching the node device and the energy storage device in each microgrid according to the matching coefficient includes:

[0021] Sort all node devices in descending order according to the node power supply index, and use each node device in the sorted order as a to-be-matched device in turn; in the matching process of each to-be-matched device, use the energy storage device corresponding to the maximum matching coefficient with the to-be-matched device as the matching energy storage device of the to-be-matched device;

[0022] Among them, after each device to be matched matches the corresponding energy storage device, the remaining capacity of the corresponding matching energy storage device before the next matching is updated according to the power supply and consumption difference curve of each device to be matched, and based on the updated remaining capacity of each matching energy storage device, the matching coefficient between each remaining device to be matched that does not participate in the matching and each energy storage device is updated.

[0023] Furthermore, the method for updating the remaining capacity of the corresponding matching energy storage device before the next matching includes:

[0024] After each matching device matches the corresponding energy storage device, the integral value of the power supply and consumption difference curve of the matching device is used as the amount of power to be stored in the matching device, and the difference between the remaining capacity of the corresponding matching energy storage device and the amount of power to be stored is used as the updated remaining capacity of the corresponding matching energy storage device.

[0025] Furthermore, the method for obtaining the network power supply coefficient includes:

[0026] The sum of the node power supply indexes of all node devices in each microgrid is used as the first network power supply coefficient;

[0027] The sum of the remaining capacities of all energy storage devices in each microgrid after matching is negatively correlated, and the negative correlation mapping result is used as the second group network power supply coefficient;

[0028] The first networking power supply coefficient and the second networking power supply coefficient are integrated to obtain the networking power supply coefficient of the corresponding microgrid.

[0029] Furthermore, the method for obtaining the networking coefficient includes:

[0030] Obtaining a networking power supply parameter of each of the power supply microgrids according to the deviation of the networking power supply coefficient of each of the power supply microgrids from an average level of the networking power supply coefficients of all the power supply microgrids;

[0031] Using the negative correlation mapping result of the spatial distance between each of the power supply microgrids and each of the power consumption microgrids as the networking transmission parameter between each of the power supply microgrids and each of the power consumption microgrids;

[0032] According to the remaining capacity of all energy storage devices in each power supply microgrid at the time of waiting for networking, the power supply tendency parameter of each power supply microgrid is obtained; the remaining capacity is negatively correlated with the power supply tendency parameter;

[0033] The networking power supply parameters, the networking transmission parameters and the power supply tendency parameters are integrated to obtain a networking coefficient between a corresponding power supply microgrid and a corresponding power consumption microgrid; the networking power supply parameters, the networking transmission parameters and the power supply tendency parameters are all positively correlated with the networking coefficient.

[0034] Furthermore, the method for obtaining the networking result includes:

[0035] According to the networking power supply coefficient, all the power supply microgrids are sorted in descending order, and each power supply microgrid is used as a target microgrid for networking in the sorted order, and the networking results of all microgrids at the time of networking are obtained;

[0036] Each time the target microgrid is networked, the power-consuming microgrid corresponding to the maximum networking coefficient between the target microgrid and the power-consuming microgrid is used as the power-consuming microgrid to be networked, and the power-consuming microgrid to be networked is networked with the target microgrid; wherein, the target microgrid and the power-consuming microgrid that have participated in the networking are not networked for the second time.

[0037] The present invention also proposes a system for optimizing energy storage for renewable energy power generation, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method for optimizing energy storage for renewable energy power generation when executing the computer program.

[0038] The present invention has the following beneficial effects:

[0039] The present invention first obtains the power supply and consumption difference curve of each node device in each microgrid, and the remaining capacity of each energy storage device at the time of waiting for networking, so as to provide a basis for subsequent analysis; then, according to the fluctuation information of each power supply and consumption difference curve, the power supply capacity of each node device, i.e., the node power supply index, is evaluated; further, in each microgrid, the matching coefficient between each node device and each energy storage device is obtained, and the matching coefficient reflects the connection matching priority degree of power transmission between the node device and the energy storage device; then, the node devices and energy storage devices in each microgrid are matched according to the matching coefficient, and the remaining capacity of each energy storage device is continuously updated during the matching process to avoid overcharging or over-discharging of the energy storage device; further, a networking power supply coefficient reflecting the power supply capacity of each microgrid is obtained; then, all power supply microgrids and all power consumption microgrids are obtained according to the classification of the networking power supply coefficient; further, the networking coefficient between each power supply microgrid and each power consumption microgrid is obtained, and the networking coefficient reflects the priority networking degree between the two microgrids; finally, the networking result of the new energy power generation and energy storage system at the time of waiting for networking is obtained according to the networking coefficient. The present invention optimizes the internal control mode of the microgrid based on the supply and consumption of each node device in the microgrid, combined with the capacity and spatial distribution of each energy storage device, to ensure the internal balance of the microgrid; further optimizes the microgrid networking mode based on the power supply capacity and energy storage situation of each microgrid, so that power is distributed in an orderly manner, thereby accurately controlling the new energy power generation and energy storage system and efficiently storing energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0041] Figure 1 A flow chart of a method for optimizing energy storage for renewable energy power generation provided by one embodiment of the present invention;

[0042] Figure 2 A flow chart of a method for obtaining a matching coefficient provided by an embodiment of the present invention;

[0043] Figure 3 A flow chart of a method for obtaining a networking coefficient provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the energy storage optimization method and system for renewable energy power generation proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0046] The specific scheme of the energy storage optimization method and system for renewable energy power generation provided by the present invention is described in detail below with reference to the accompanying drawings.

[0047] See also Figure 1 , which shows a flow chart of a method for optimizing energy storage for renewable energy power generation provided by an embodiment of the present invention, specifically comprising:

[0048] Step S1, in each microgrid of the new energy power generation and energy storage system, obtain the power supply and consumption difference curve of each node device in a preset historical period at the time of waiting for networking, and the remaining capacity of each energy storage device at the time of waiting for networking.

[0049] It should be noted that the analysis and processing object of the embodiment of the present invention is a new energy power generation and energy storage system, which includes several microgrids, each of which includes several energy storage devices and several node devices. The node devices at least include power generation equipment for new energy power generation and power-consuming load equipment. This is a prior art and will not be elaborated on here.

[0050] In order to network all microgrids in a renewable energy power generation and energy storage system, and then optimize the energy storage of the renewable energy power generation and energy storage system under the premise of maintaining system balance; an embodiment of the present invention first obtains the power supply and consumption difference curve of each node device in each microgrid of the renewable energy power generation and energy storage system within a preset historical period at the time of waiting for networking, and also obtains the remaining capacity of each energy storage device in each microgrid at the time of waiting for networking, so as to provide a reference basis for analyzing the power supply capacity for the subsequent matching of node devices and the networking of microgrids.

[0051] Specifically, the method for obtaining the power supply and consumption difference curve is: respectively obtain the power generation curve and power consumption curve of each node device in the preset historical period at the time of networking; subtract the power generation curve and power consumption curve of each node device to obtain the power supply and consumption difference curve of each node device. The preset historical period is 30 minutes, which can also be defined by the implementer.

[0052] Among them, the power generation curve and the power consumption curve can be collected and monitored based on the smart meter at each node device, with a collection frequency of once per minute; they can also be obtained based on the intelligent management platform integrated with the new energy power generation and energy storage system. At the same time, the intelligent management platform can also be used to obtain the capacity of each energy storage device at the time of networking, such as the remaining capacity, where the remaining capacity is the standard capacity of the energy storage device minus the stored electricity. These are all existing technologies and will not be elaborated here.

[0053] It should be noted that when subtracting the two curves, attention should be paid to timing alignment, that is, the power generation and power consumption at the same collection time should be subtracted to ensure the accuracy and effectiveness of the power supply and consumption difference curve; since the node equipment may be of various types such as pure power generation, pure power consumption or mixed types, the power supply and consumption difference data in the power supply and consumption difference curve may be all positive, negative or 0.

[0054] Step S2, according to the fluctuation information of each power supply and consumption difference curve, obtain the node power supply index of the corresponding node device; in each microgrid, according to the node power supply index of each node device and the remaining capacity of each energy storage device, combined with the spatial distance between each node device and each energy storage device, obtain the matching coefficient between each node device and each energy storage device; match the node devices and energy storage devices in each microgrid according to the matching coefficient, and continuously update the remaining capacity of each energy storage device during the matching process.

[0055] Taking into account that at each node device, renewable energy power generation is greatly affected by the environment, such as when there is sufficient light or strong wind, the power generation of the power generation equipment is also large; at the same time, the power consumption at each node device is affected by human production and life, such as the existence of peak electricity consumption; and because the fluctuation information of the power supply and consumption difference curve at each node device reflects the changes in its surplus power generation within the preset historical period, if the data point in the power supply and consumption difference curve is greater than or equal to 0, it means that the node device can meet its own power demand at the corresponding moment; otherwise, it needs power support from other node devices.

[0056] Therefore, the embodiment of the present invention will evaluate the node power supply index of the corresponding node device based on the fluctuation information of each power supply and consumption difference curve; the node power supply index reflects whether each node device can meet its own power supply needs, and also reflects its ability to provide power support for other node devices.

[0057] Preferably, in one embodiment of the present invention, considering that the concentrated characteristics of all data in the power supply and consumption difference curve can roughly reflect the surplus power generation of the corresponding node device, the larger the surplus power generation, the stronger its ability to provide power support for other node devices in the microgrid; and considering that the more drastic the fluctuation of the power supply and consumption difference curve corresponding to each node device, the more unstable it is when providing power support for other node devices in the microgrid, and the weaker the corresponding power supply support capability;

[0058] Based on this, the methods for obtaining the node power supply index include:

[0059] According to the concentrated characteristics of all power supply and consumption difference data on each power supply and consumption difference curve, the power supply capacity parameters of the corresponding node equipment are obtained; according to the fluctuation deviation of each power supply and consumption difference data on each power supply and consumption difference curve relative to the average level, the power supply stability parameters of the corresponding node equipment are obtained; according to the power supply capacity parameters and power supply stability parameters of each node equipment, the node power supply index of each node equipment is obtained; the power supply capacity parameters and power supply stability parameters are positively correlated with the node power supply index.

[0060] As an example, taking any microgrid as an example, the calculation formula of the node power supply index is:

[0061] Where b is the serial number of the node device; γ b is the node power supply index of the b-th node device; norm() is the standard normalization function; i is the sequence number of the power supply and consumption difference data in the power supply and consumption difference curve of the b-th node device; n is the total number of power supply and consumption difference data in the power supply and consumption difference curve of the b-th node device; is the mean value of the power supply and consumption difference data in the power supply and consumption difference curve of the b-th node device, and is also the power supply capacity parameter of the b-th node device; is the power supply stability parameter of the b-th node device; || is the absolute value symbol; a b,i is the ith power supply and consumption difference data in the power supply and consumption difference curve of the bth node device; c is a preset non-zero positive parameter, which is taken as 0.01 in this example to prevent the denominator from being zero.

[0062] In this example, the mean value is used to evaluate the concentrated features and obtain the power supply capacity parameters. Then, the absolute values ​​of the differences between each power supply and consumption difference data and the mean in the power supply and consumption difference curve are accumulated to evaluate the degree of fluctuation deviation. Then, the accumulated sum is added with a preset non-zero positive parameter and a reciprocal operation is performed to adjust the logic so that the greater the degree of fluctuation deviation, the smaller the corresponding power supply stability parameter. Then, the power supply capacity parameter and the power supply stability parameter are multiplied and combined, and then linearly normalized to obtain the node power supply index of each node device.

[0063] In other examples, the mode or median can be used instead of the mean to evaluate the power supply capacity parameters; the variance or standard deviation of the absolute value of the difference between each power supply and consumption difference data and the mean in the power supply and consumption difference curve can be calculated instead of the cumulative sum to evaluate the degree of fluctuation deviation; then the variance or standard deviation is mapped using other negative correlation methods, such as using it as the x in the exponential function exp(-x) with the natural constant e as the base to obtain the power supply stability parameter; then the power supply capacity parameter and the power supply stability parameter are merged using other basic mathematical operations or related mapping methods such as addition or weighted summation, and the merged result is normalized using other normalization methods to obtain the node power supply index of the corresponding node device; all of which are existing technologies and will not be elaborated here.

[0064] When the surplus power generation of the node equipment in the microgrid is transmitted and stored, or when power support is provided to the node equipment in the microgrid, the storage condition of the energy storage equipment must be considered. The smaller the remaining capacity of the energy storage equipment, the greater the possibility that it will provide power support to the node equipment in the microgrid. On the contrary, the greater the possibility that the surplus power generation of the node equipment will be transmitted and stored in it. At the same time, in the transmission process, the transmission loss caused by the transmission distance and the like must also be considered.

[0065] Therefore, in each microgrid, the embodiment of the present invention obtains the matching coefficient between each node device and each energy storage device according to the node power supply index of each node device and the remaining capacity of each energy storage device, combined with the spatial distance between each node device and each energy storage device. The matching coefficient reflects the connection matching priority between the node device and the energy storage device for power transmission. The larger the matching coefficient, the more likely the node device in the microgrid should be connected to the energy storage device for transmission.

[0066] Preferably, in one embodiment of the present invention, the method for obtaining the matching coefficient includes:

[0067] See also Figure 2 , which shows a flow chart of a method for obtaining a matching coefficient provided by an embodiment of the present invention, specifically comprising:

[0068] Step S201, classify all node devices in each microgrid according to the node power supply index, obtain all power supply node devices and all power consumption node devices; and use the negative correlation mapping result of the spatial distance between each node device and each energy storage device as a transmission matching parameter.

[0069] Taking into account that the node devices may need power supply support or need to transmit energy storage, an embodiment of the present invention will first classify the node devices to obtain all power supply node devices and all power consumption node devices in each microgrid; the power supply node devices transmit surplus electricity to the energy storage devices, and the power consumption node devices require the energy storage devices to provide them with power support.

[0070] In a preferred embodiment of the present invention, a node device whose node power supply index is greater than a preset threshold is specifically regarded as a power supply node device, and a node device whose node power supply index is less than or equal to the preset threshold is regarded as a power consumption node device. The preset threshold is set to 0.5, and the implementer can also customize it. In another embodiment, the implementer can also regard the node device whose power supply capacity parameter is greater than 0 as a power supply node device, and the node device whose power supply capacity parameter is less than or equal to 0 as a power consumption node device when obtaining the node power supply index.

[0071] In one embodiment of the present invention, the spatial distance between each node device and each energy storage device is specifically reciprocally calculated to normalize the negative correlation mapping and adjust the logic, so that the closer the spatial distance is, the larger the transmission matching parameter is; the transmission matching parameter reflects the connection matching priority between the node device and the energy storage device for power transmission at the transmission distance level. In other embodiments, the implementer may also adopt other negative correlation mapping methods, such as taking the spatial distance as the x in the exponential function exp(-x) with the natural constant e as the base, which will not be repeated here.

[0072] Step S202: According to the node power supply index of each power supply node device and the remaining capacity of each energy storage device at the time of waiting for networking, obtain the first power matching parameter between each power supply node device and each energy storage device, and the node power supply index and the remaining capacity are both positively correlated with the first power matching parameter; integrate the first power matching parameter and the transmission matching parameter to obtain the matching coefficient between each power supply node device and each energy storage device.

[0073] Considering that the surplus power generation of node devices in the microgrid should usually be stored in the nearest energy storage device with larger remaining capacity in priority, so as to avoid multiple adjustments of power transmission and storage paths between the node devices and the energy storage devices; therefore, we first analyze and obtain the first power matching parameters between each power supply node device and each energy storage device; and then combine the transmission matching parameters to obtain the matching coefficient.

[0074] As an example, taking any microgrid as an example, the calculation formula for the matching coefficient between each power supply node device and each energy storage device is:

[0075] u b1,k =norm(γ b1 ×R k ×D b1,k ), where b1 is the serial number of the power supply node device; k is the serial number of the energy storage device; γ b1 is the node power supply index of the b1th power supply node device; u b1,k is the matching coefficient between the b1th power supply node device and the kth energy storage device; norm() is the standard normalization function; R kis the remaining capacity of the kth energy storage device; γ b1 ×R k is the first power matching parameter between the b1th power supply node device and the kth energy storage device; D b1,k is the transmission matching parameter between the b1th power supply node device and the kth energy storage device.

[0076] In this example, the node power supply index of each power supply node device and the remaining capacity of each energy storage device are multiplied and combined. When the node power supply index of the power supply node device is larger and the remaining capacity of the energy storage device is larger, it means that the corresponding power matching priority is greater and the first power matching parameter is larger; then the first power matching parameter and the transmission matching parameter are multiplied and combined and linearly normalized to obtain the matching coefficient between each power supply node device and each energy storage device; in other examples, the implementer may also use other basic mathematical operations such as addition or weighted summation or related mapping methods to combine the first power matching parameter and the transmission matching parameter, which will not be repeated here.

[0077] Step S203, according to the node power supply index of each power-consuming node device and the remaining capacity of each energy storage device at the time of waiting for networking, obtain the second power matching parameter between each power-consuming node device and each energy storage device, the node power supply index and the remaining capacity are negatively correlated with the second power matching parameter; integrate the second power matching parameter and the transmission matching parameter to obtain the matching coefficient between each power-consuming node device and each energy storage device.

[0078] Considering that the node device may be a pure power-consuming device, its surplus power generation may be negative, that is, it needs power support from the energy storage device. It should be matched and connected with the nearby energy storage device with less remaining capacity and more stored power to receive the power support from the energy storage device. Therefore, the second power matching parameter between each power-consuming node device and each energy storage device is first analyzed and obtained; and then the matching coefficient is obtained in combination with the transmission matching parameter.

[0079] As an example, taking any microgrid as an example, the calculation formula for the matching coefficient between each power consumption node device and each energy storage device is:

[0080] u b2,k =norm(exp(-γ b2 ×R k )×D b2,k ), where b2 is the serial number of the power-consuming node device; k is the serial number of the energy storage device; γ b2 is the node power supply index of the b2th power-consuming node device; u b2,k is the matching coefficient between the b2th power consumption node device and the kth energy storage device; norm() is the standard normalization function; exp() is the exponential function with the natural constant e as the base; Rk is the remaining capacity of the kth energy storage device; exp(-γ b2 ×R k ) is the second power matching parameter between the b2th power consumption node device and the kth energy storage device; D b2,k is the transmission matching parameter between the b2th power consumption node device and the kth energy storage device.

[0081] In this example, the node power supply index of each power-consuming node device and the remaining capacity of each energy storage device are multiplied and combined, and negative correlation mapping is performed as the x in the exponential function exp(-x) with the natural constant e as the base. When the node power supply index of the power-consuming node device is smaller and the remaining capacity of the energy storage device is smaller, it means that the power-consuming node device needs the supply support of the energy storage device and the supply capacity of the energy storage device is stronger, the corresponding power matching priority is greater, and the second power matching parameter is larger; then the second power matching parameter is multiplied and combined with the transmission matching parameter and linearly normalized to obtain the matching coefficient between each power supply node device and each energy storage device; in other examples, the implementer may also use other basic mathematical operations or related mapping means such as addition or weighted summation to combine the second power matching parameter and the transmission matching parameter, or use negative correlation mapping means such as inverse operation to obtain the second power matching parameter, which will not be repeated here.

[0082] After obtaining the matching coefficients between each node device and each energy storage device, the node devices and energy storage devices in each microgrid can be matched according to the matching coefficients; at the same time, in order to avoid the possibility of overcharging or over-discharging of the energy storage device due to multiple node devices with strong power supply capabilities or strong power consumption capabilities corresponding to one energy storage device, the embodiment of the present invention will continuously update the remaining capacity of each energy storage device during the matching process, thereby avoiding such situations during the iterative matching process.

[0083] Preferably, in one embodiment of the present invention, considering that the device with a larger node power supply index should be preferentially matched with an energy storage device with a larger remaining capacity; and considering that the power supply and consumption difference curve of the node device can indirectly reflect the surplus power generation of each node device, after each node device is matched with the energy storage device, the remaining capacity of each energy storage device can be further updated based on the surplus power generation to avoid overcharging or over-discharging of a single energy storage device; after the remaining capacity of each energy storage device is updated, the matching parameters between the remaining unmatched node devices and the energy storage device need to be updated for the next step of matching, so as to optimize the energy storage mode in each microgrid; based on this, the method for matching the node devices and energy storage devices in each microgrid according to the matching coefficient includes:

[0084] Sort all node devices in descending order according to the node power supply index, and use each node device in the sorted order as a to-be-matched device in turn; in the matching process of each to-be-matched device, use the energy storage device corresponding to the maximum matching coefficient with the to-be-matched device as the matching energy storage device of the to-be-matched device;

[0085] Among them, after each to-be-matched device matches the corresponding energy storage device, the remaining capacity of the corresponding matching energy storage device before the next matching is updated according to the power supply and consumption difference curve of each to-be-matched device, and based on the updated remaining capacity of each matching energy storage device, the matching coefficient between each remaining to-be-matched device that does not participate in the matching and each energy storage device is updated.

[0086] In a preferred embodiment of the present invention, the method for updating the remaining capacity of the corresponding matching energy storage device before the next matching includes:

[0087] After each matching device matches the corresponding energy storage device, the integral value of the power supply and consumption difference curve of the matching device is used as the amount of power to be stored in the matching device, and the difference between the remaining capacity of the corresponding matching energy storage device and the amount of power to be stored is used as the updated remaining capacity of the corresponding matching energy storage device.

[0088] It should be noted that the integral value of the curve within the specified interval is an existing technology well known to those skilled in the art and will not be elaborated here; the integral value reflects the amount of electricity to be stored, that is, the surplus power generation, of the device to be matched. When the amount of electricity to be stored is a positive value, it means that the device to be matched transmits electric energy to the corresponding matching energy storage device, and the updated remaining capacity becomes smaller and smaller; when the amount of electricity to be stored is a negative value, it means that the device to be matched needs the corresponding matching energy storage device to provide power support, and the updated remaining capacity becomes larger and larger.

[0089] As an example, take any microgrid as an example to briefly describe the matching process between the node devices and the energy storage devices within it:

[0090] First, all node devices are sorted in descending order according to the node power supply index to determine the matching order of each node device. The larger the node power supply index, the higher the matching order. Each node device is matched as a to-be-matched device in turn.

[0091] The energy storage device corresponding to the maximum matching coefficient with the first device to be matched is used as the matching energy storage device for the first device to be matched; the integral value of the power supply and consumption difference curve of the first device to be matched is used as the amount of electricity to be stored in the first device to be matched, and the difference between the remaining capacity of the corresponding matching energy storage device and the amount of electricity to be stored is used as the updated remaining capacity;

[0092] Based on the updated remaining capacity of the matching energy storage device of the first device to be matched and the remaining capacity of all other energy storage devices, according to the method for obtaining the matching coefficient between each power-consuming node device and each energy storage device described in steps S201-S203, the matching coefficient between each device to be matched that is not involved in the matching and each energy storage device is re-obtained and updated, and the energy storage device corresponding to the maximum matching coefficient with the second device to be matched that is not involved in the matching is used as the matching energy storage device of the second device to be matched; then the updated remaining capacity of the matching energy storage device corresponding to the second device to be matched is obtained;

[0093] Based on the updated remaining capacity of the matching energy storage devices corresponding to the first device to be matched and the second device to be matched, and the remaining capacity of all other energy storage devices, the matching coefficient between each device to be matched that does not participate in the matching and each energy storage device is re-obtained and updated, and then the matching energy storage device of the third device to be matched that does not participate in the matching is obtained; the matching energy storage devices of all devices to be matched are traversed in turn.

[0094] It should be noted that, in each matching process, the remaining capacity of the energy storage device after updating may be 0 or negative, that is, it exceeds its standard capacity. In this case, after the device to be matched matches the corresponding energy storage device, when the remaining capacity is 0, the power transmission from the device to be matched to the corresponding matching energy storage device is interrupted, and the corresponding matching energy storage device is removed from all energy storage devices and no longer participates in subsequent matching; at the same time, according to the matching coefficient between the device to be matched and the remaining energy storage devices, the spare matching energy storage devices are screened, such as selecting the energy storage device with the second largest matching coefficient as the first spare matching energy storage device in turn, and when the remaining capacity of the first spare matching energy storage device is 0, the second spare matching energy storage device continues to be screened; the remaining untransmitted to-be-stored power of the device to be matched is sequentially transmitted to the spare matching energy storage device, and the remaining capacity of the spare matching energy storage device is updated.

[0095] At this point, internal energy storage optimization has been performed for each microgrid.

[0096] Step S3, according to the node power supply index of all node devices in each microgrid and the remaining capacity of all energy storage devices after matching, obtain the networking power supply coefficient of each microgrid; classify all microgrids according to the networking power supply coefficient to obtain all power supply microgrids and all power consumption microgrids; according to the networking power supply coefficient of each power supply microgrid and the remaining capacity of all node devices in the power supply microgrid at the time of waiting for networking, combined with the spatial distance between each power supply microgrid and each power consumption microgrid, obtain the networking coefficient between each power supply microgrid and each power consumption microgrid.

[0097] Considering that each microgrid can operate independently or in a network, when the power supply capacity of a microgrid is insufficient to support its own consumption, it needs to be networked with other microgrids. Therefore, it is necessary to evaluate the power supply capacity of each microgrid first. Considering the node power supply index of all node devices in each microgrid, and the remaining capacity of all energy storage devices after matching the node devices and energy storage devices in the microgrid, the power supply capacity of the microgrid can be reflected indirectly.

[0098] Therefore, the embodiment of the present invention will obtain the networking power supply coefficient of each microgrid based on the node power supply index of all node devices in each microgrid and the remaining capacity of all energy storage devices after matching. The networking power supply coefficient reflects the ability of each microgrid to provide power supply support for other microgrids in the new energy power generation and energy storage system.

[0099] Preferably, in one embodiment of the present invention, considering that the larger the node power supply index of all node devices in the microgrid is, the stronger the power supply capacity of the microgrid is; and considering that the smaller the remaining capacity of all energy storage devices in each microgrid after matching and acceptance is, the larger the storage capacity is, the stronger the ability to provide power support is; therefore, the method for obtaining the network power supply coefficient includes:

[0100] The sum of the node power supply indexes of all node devices in each microgrid is used as the first group of network power supply coefficients;

[0101] The sum of the remaining capacities of all energy storage devices in each microgrid after matching is negatively correlated, and the negative correlation mapping result is used as the second group network power supply coefficient;

[0102] The first network power supply coefficient and the second network power supply coefficient are integrated to obtain the network power supply coefficient of the corresponding microgrid.

[0103] As an example, the larger the sum of the node power supply indexes, the larger the first network power supply coefficient, which means that the power supply capacity of the microgrid is stronger; the sum of the remaining capacities of all energy storage devices after the matching is completed is added with a preset non-zero positive parameter such as 0.1, and then a reciprocal operation is performed to perform a negative correlation mapping adjustment logic. The larger the reciprocal value, the stronger the power supply capacity and the larger the second network power supply coefficient; then the first network power supply coefficient is multiplied and combined with the second network power supply coefficient, and the product is linearly normalized to obtain the network power supply coefficient of the corresponding microgrid.

[0104] In other examples, implementers may also use other negative correlation mapping methods, such as using the sum of the remaining capacities of all energy storage devices after the matching is completed as the x in the exponential function exp(-x) with the natural constant e as the base for negative correlation mapping to obtain the second group of network power supply coefficients; other basic mathematical operations such as addition or weighted summation or related mapping methods may also be used to fuse the first group of network power supply coefficients and the second group of network power supply coefficients, and other normalization methods may also be used, which will not be repeated here.

[0105] Taking into account that a microgrid may need power supply support or need to transmit energy storage, an embodiment of the present invention will first classify all microgrids according to the network power supply coefficient to obtain all power supply microgrids and all power consumption microgrids; wherein the power supply microgrid provides power support or transmits power to other microgrids, and the power consumption microgrid provides power support for other microgrids in need.

[0106] Taking into account that when microgrids are networked, there are certain geographical restrictions and grid design restrictions. At the same time, in order to minimize power loss, when a microgrid receives power support from other microgrids, it should choose a microgrid with a closer spatial distance and stronger networking power supply capacity to transmit power to it; and considering that the remaining capacity of all node devices in each microgrid at the time of waiting for networking can reflect its power supply or power consumption behavior tendency in the previous networking process. When it tends to provide power support to other microgrids in the previous networking process, it should minimize power output in the current networking process to ensure the emergency response capability of the microgrid itself, thereby improving the balance of the grid.

[0107] Therefore, the embodiment of the present invention will obtain the networking coefficient between each power supply microgrid and each power consumption microgrid based on the networking power supply coefficient of each power supply microgrid and the remaining capacity of all node devices in the power supply microgrid at the time of waiting for networking, combined with the spatial distance between each power supply microgrid and each power consumption microgrid; the networking coefficient reflects the priority networking degree between the two microgrids, so as to determine the networking result later.

[0108] Preferably, in one embodiment of the present invention, the method for obtaining the networking coefficient includes:

[0109] See also Figure 3 , which shows a flow chart of a method for obtaining a networking coefficient provided by an embodiment of the present invention, specifically comprising:

[0110] Step S301, obtaining the networking power supply parameters of each power supply microgrid according to the deviation of the networking power supply coefficient of each power supply microgrid relative to the average level of the networking power supply coefficients of all power supply microgrids.

[0111] Considering that the networking power supply coefficient reflects the supply support capability of each power supply microgrid, the larger its relative average level is, the larger its networking power supply parameter is; the networking power supply parameter further reflects the relative supply support capability of each microgrid by comparing with other power supply microgrids.

[0112] As an example, the calculation method of the networking power supply parameters is: take the networking power supply coefficient of each power supply microgrid as the numerator, add the mean of the networking power supply coefficients of all power supply microgrids plus a preset minimum positive parameter such as 0.001 as the denominator, and take the ratio of the numerator to the denominator as the networking power supply parameter of each power supply microgrid; when the ratio is greater than 1 and the larger it is, it means that the supply support capacity of the power supply microgrid is greater than the average level, its networking power supply parameters are relatively large, and it should provide power supply support for other microgrids.

[0113] In another example, the implementer may also linearly normalize the difference between the networking power supply coefficient of each power supply microgrid and the mean of the networking power supply coefficients of all power supply microgrids, and use the normalized result as the networking power supply parameter of the corresponding power supply microgrid; if the difference is negative and the smaller it is, the smaller the normalization result is, and the larger the networking power supply parameter is; if the difference is positive and the larger it is, the larger the normalization result is, and the larger the networking power supply parameter is.

[0114] Step S302: using the negative correlation mapping result of the spatial distance between each power supply microgrid and each power consumption microgrid as the networking transmission parameter between each power supply microgrid and each power consumption microgrid.

[0115] As an example, the spatial distance between each power supply microgrid and each power consumption microgrid is specifically reciprocally calculated to normalize the negative correlation mapping and adjust the logic, so that the closer the spatial distance, the larger the networking transmission parameter; the networking transmission parameter reflects the networking priority of the power supply microgrid and the power consumption microgrid for power transmission at the transmission distance level. In other embodiments, the implementer may also adopt other negative correlation mapping methods, such as taking the spatial distance as the x in the exponential function exp(-x) with the natural constant e as the base, which will not be repeated here.

[0116] Step S303, obtaining the power supply tendency parameter of each power supply microgrid according to the remaining capacity of all energy storage devices in each power supply microgrid at the time of waiting for networking; the remaining capacity is negatively correlated with the power supply tendency parameter.

[0117] As an example, first, the sum of the remaining capacities of all energy storage devices in each power supply microgrid at the time of networking is taken as the remaining capacity of each power supply microgrid; the remaining capacity of each power supply microgrid is added with a preset minimum positive parameter such as 0.1, and then a reciprocal operation is performed, and the reciprocal value is taken as the power supply tendency parameter; the larger the remaining capacity, the more inclined it is to provide power support for other microgrids in the last networking process, and the power output should be reduced as much as possible in this networking process, and the smaller its power supply tendency parameter is; in other examples, the implementer may also adopt other negative correlation mapping methods, which are not repeated here.

[0118] Step S304, integrating the networking power supply parameters, networking transmission parameters and power supply tendency parameters to obtain the networking coefficient between the corresponding power supply microgrid and the corresponding power consumption microgrid; the networking power supply parameters, networking transmission parameters and power supply tendency parameters are all positively correlated with the networking coefficient.

[0119] As an example, the formula for calculating the networking coefficient is: z,m =norm(Y z ×Q z ×D z,m ); where z is the serial number of the power supply microgrid; m is the serial number of the power consumption microgrid; norm() is the standard normalization function; Y z is the network power supply parameter of the zth power supply microgrid; Q z is the power supply tendency parameter of the zth power supply microgrid; D z,m is the networking coefficient between the zth power supply microgrid and the mth power consumption microgrid.

[0120] In this example, the three are combined by multiplication, and the product is linearly normalized to obtain the corresponding networking coefficient; in other examples, the implementer may also use other basic mathematical operations such as addition or weighted summation or related mapping methods to combine the three, or use other normalization methods, which will not be repeated here.

[0121] Step S4, obtaining the networking result of the new energy power generation and energy storage system at the time of waiting for networking according to the networking coefficient.

[0122] After obtaining the networking coefficient between each power supply microgrid and each power consumption microgrid, the networking result of the new energy power generation and energy storage system at the time of networking can be obtained according to the networking coefficient.

[0123] Preferably, in one embodiment of the present invention, the method for obtaining the networking result includes:

[0124] All power supply microgrids are sorted in descending order according to the networking power supply coefficient, and each power supply microgrid is used as a target microgrid for networking in the sorted order, and the networking results of all microgrids at the time of networking are obtained;

[0125] Each time the target microgrid is networked, the power-consuming microgrid corresponding to the maximum networking coefficient between the target microgrid and the power-consuming microgrid is used as the power-consuming microgrid to be networked, and the power-consuming microgrid to be networked is networked with the target microgrid; wherein, the target microgrid and the power-consuming microgrid that have participated in the networking are not networked for the second time.

[0126] As an example, to facilitate understanding of the networking process, assume that the power supply microgrid set is {A, B, C, D}, and the power consumption microgrid set is {a, b, c, d}, where the characters represent microgrids; first, all power supply microgrids are sorted in descending order according to the networking power supply coefficient, and the networking order of each power supply microgrid is determined. The larger the networking power supply coefficient, the higher the networking order, the stronger its power supply support capability, and the higher the priority for networking; assuming that the descending sorting sequence of the power supply microgrid is {A, B, C, D}, each power supply microgrid is taken as the target microgrid in the sorting order; the first target microgrid The power-consuming microgrid b corresponding to the time when the matching coefficient between the two is the largest is used as the power-consuming microgrid to be networked of the first target microgrid A for networking, and the networking result Ab is obtained; then the power-consuming microgrid b is removed from the set of power-consuming microgrids, and the power-consuming microgrid a corresponding to the time when the matching coefficient between the two is the largest is used as the power-consuming microgrid to be networked of the second target microgrid B for networking, and the networking result Ba is obtained; then the power-consuming microgrid a is removed from the set of power-consuming microgrids, and subsequent networking matching is performed until all target microgrids are matched and networked to obtain the final networking result.

[0127] It should be noted that there is a situation where the number of power supply microgrids and power consumption microgrids are not equal. For example, if there are 5 power supply microgrids and 4 power consumption microgrids, there will be an extra power supply microgrid that cannot be networked and matched, and the microgrid that cannot be networked and matched will be operated alone; the networking and operation of microgrids is an existing technology well known to technical personnel in this field and will not be repeated here.

[0128] In summary, the present invention first obtains the node power supply index of the corresponding node device according to the fluctuation information of each power supply and consumption difference curve; further, in each microgrid, the matching coefficient between each node device and each energy storage device is obtained in combination with the remaining capacity of each energy storage device and the consideration of spatial distance, and then the matching is optimized in the microgrid; further, the networking power supply coefficient of each microgrid is obtained, so as to classify and obtain all power supply microgrids and all power consumption microgrids; then, the networking coefficient between each power supply microgrid and each power consumption microgrid is analyzed and obtained to obtain the final networking result. The present invention is based on the supply and consumption of each node device in the microgrid, combined with the capacity and spatial distribution of each energy storage device, to optimize the internal control mode of the microgrid to ensure the internal balance of the microgrid; further, the microgrid networking mode is optimized for the power supply capacity and energy storage of each microgrid, so that the power is distributed in an orderly manner, thereby accurately regulating and efficiently storing energy for the new energy power generation and energy storage system.

[0129] The present invention also proposes a system for optimizing energy storage for renewable energy power generation. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for optimizing energy storage for renewable energy power generation as described by radicals S1-S4 are implemented.

[0130] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0131] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for optimizing energy storage for renewable energy power generation, characterized in that: The method comprises: In each microgrid of the new energy power generation and energy storage system, obtain the power supply and consumption difference curve of each node device in a preset historical period at the time of waiting for networking, and the remaining capacity of each energy storage device at the time of waiting for networking; According to the fluctuation information of each power supply and consumption difference curve, the node power supply index of the corresponding node device is obtained; in each microgrid, according to the node power supply index of each node device and the remaining capacity of each energy storage device, combined with the spatial distance between each node device and each energy storage device, the matching coefficient between each node device and each energy storage device is obtained; the node devices and energy storage devices in each microgrid are matched according to the matching coefficient, and the remaining capacity of each energy storage device is continuously updated during the matching process; According to the node power supply index of all node devices in each microgrid and the remaining capacity of all energy storage devices after matching, the networking power supply coefficient of each microgrid is obtained; all microgrids are classified according to the networking power supply coefficient to obtain all power supply microgrids and all power consumption microgrids; according to the networking power supply coefficient of each power supply microgrid and the remaining capacity of all node devices in the power supply microgrid at the time of waiting for networking, combined with the spatial distance between each power supply microgrid and each power consumption microgrid, the networking coefficient between each power supply microgrid and each power consumption microgrid is obtained; The networking result of the new energy power generation and energy storage system at the time of waiting for networking is obtained according to the networking coefficient.

2. According to the energy storage optimization method for renewable energy power generation described in claim 1, it is characterized in that: The method for obtaining the node power supply index includes: According to the concentrated characteristics of all the power supply and consumption difference data on each of the power supply and consumption difference curves, the power supply capacity parameters of the corresponding node device are obtained; According to the fluctuation deviation of each power supply and consumption difference data on each power supply and consumption difference curve relative to the average level, the power supply stability parameter of the corresponding node device is obtained; According to the power supply capacity parameter and the power supply stability parameter of each node device, a node power supply index of each node device is obtained; the power supply capacity parameter and the power supply stability parameter are both positively correlated with the node power supply index.

3. According to the energy storage optimization method for renewable energy power generation described in claim 1, it is characterized in that: The method for obtaining the matching coefficient includes: Classify all node devices in each microgrid according to the node power supply index to obtain all power supply node devices and all power consumption node devices; use the negative correlation mapping result of the spatial distance between each node device and each energy storage device as a transmission matching parameter; According to the node power supply index of each power supply node device and the remaining capacity of each energy storage device at the time of waiting for networking, a first power matching parameter between each power supply node device and each energy storage device is obtained, and the node power supply index and the remaining capacity are both positively correlated with the first power matching parameter; the first power matching parameter and the transmission matching parameter are integrated to obtain a matching coefficient between each power supply node device and each energy storage device; According to the node power supply index of each power-consuming node device and the remaining capacity of each energy storage device at the time of waiting for networking, the second power matching parameter between each power-consuming node device and each energy storage device is obtained, and the node power supply index and the remaining capacity are negatively correlated with the second power matching parameter; the second power matching parameter and the transmission matching parameter are integrated to obtain the matching coefficient between each power-consuming node device and each energy storage device.

4. According to the energy storage optimization method for renewable energy power generation described in claim 3, it is characterized in that: The method for acquiring the power supply node device and the power consumption node device comprises: The node device whose node power supply index is greater than a preset threshold is regarded as a power supply node device, and the node device whose node power supply index is less than or equal to the preset threshold is regarded as a power consumption node device.

5. According to the energy storage optimization method for renewable energy power generation described in claim 1, it is characterized in that: The method for matching the node device and the energy storage device in each microgrid according to the matching coefficient includes: Sort all node devices in descending order according to the node power supply index, and use each node device in the sorted order as a to-be-matched device in turn; in the matching process of each to-be-matched device, use the energy storage device corresponding to the maximum matching coefficient with the to-be-matched device as the matching energy storage device of the to-be-matched device; Among them, after each device to be matched matches the corresponding energy storage device, the remaining capacity of the corresponding matching energy storage device before the next matching is updated according to the power supply and consumption difference curve of each device to be matched, and based on the updated remaining capacity of each matching energy storage device, the matching coefficient between each remaining device to be matched that does not participate in the matching and each energy storage device is updated.

6. According to the energy storage optimization method for renewable energy power generation described in claim 5, it is characterized in that: The method for updating the remaining capacity of the matching energy storage device before the next matching includes: After each matching device matches the corresponding energy storage device, the integral value of the power supply and consumption difference curve of the matching device is used as the amount of power to be stored in the matching device, and the difference between the remaining capacity of the corresponding matching energy storage device and the amount of power to be stored is used as the updated remaining capacity of the corresponding matching energy storage device.

7. According to the energy storage optimization method for renewable energy power generation described in claim 1, it is characterized in that: The method for obtaining the network power supply coefficient includes: The sum of the node power supply indexes of all node devices in each microgrid is used as the first network power supply coefficient; The sum of the remaining capacities of all energy storage devices in each microgrid after matching is negatively correlated, and the negative correlation mapping result is used as the second group network power supply coefficient; The first networking power supply coefficient and the second networking power supply coefficient are integrated to obtain the networking power supply coefficient of the corresponding microgrid.

8. The energy storage optimization method for renewable energy power generation according to claim 1, characterized in that: The method for obtaining the networking coefficient includes: Obtaining a networking power supply parameter of each of the power supply microgrids according to the deviation of the networking power supply coefficient of each of the power supply microgrids from an average level of the networking power supply coefficients of all the power supply microgrids; Using the negative correlation mapping result of the spatial distance between each of the power supply microgrids and each of the power consumption microgrids as the networking transmission parameter between each of the power supply microgrids and each of the power consumption microgrids; According to the remaining capacity of all energy storage devices in each power supply microgrid at the time of waiting for networking, the power supply tendency parameter of each power supply microgrid is obtained; the remaining capacity is negatively correlated with the power supply tendency parameter; The networking power supply parameters, the networking transmission parameters and the power supply tendency parameters are integrated to obtain a networking coefficient between a corresponding power supply microgrid and a corresponding power consumption microgrid; the networking power supply parameters, the networking transmission parameters and the power supply tendency parameters are all positively correlated with the networking coefficient.

9. The energy storage optimization method for renewable energy power generation according to claim 1, characterized in that: The method for obtaining the networking result includes: According to the networking power supply coefficient, all the power supply microgrids are sorted in descending order, and each power supply microgrid is used as a target microgrid for networking in the sorted order, and the networking results of all microgrids at the time of networking are obtained; Each time the target microgrid is networked, the power-consuming microgrid corresponding to the maximum networking coefficient between the target microgrid and the power-consuming microgrid is used as the power-consuming microgrid to be networked, and the power-consuming microgrid to be networked is networked with the target microgrid; wherein, the target microgrid and the power-consuming microgrid that have participated in the networking are not networked for the second time.

10. A storage optimization system for renewable energy power generation, characterized in that: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the energy storage optimization method for renewable energy power generation as described in any one of claims 1 to 9 are implemented.

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