New energy grid-connected power supply side power fluctuation stabilizing energy storage optimal configuration method

Abstract

The invention discloses a new energy grid-connected power supply side power fluctuation stabilizing energy storage optimal configuration method, relates to the technical field of electric power, and aims to realize accurate description of fluctuation characteristics by constructing a power supply power fluctuation quantitative index, comprehensively consider power peak-valley fall and directional change frequency density, and improve the stability of the power supply side power fluctuation stabilizing energy storage optimal configuration. The unstable degree of node power is effectively reflected; a fluctuation index space change curve is generated based on the power supply trend of a power grid, power supply side node interval sections needing to be emphatically stabilized are locked in combination with threshold analysis, the optimal energy storage configuration position of each interval section is determined by adopting an area averaging strategy, and the energy storage capacity is calculated according to the fluctuation index curve integral area, so that precise and differentiated configuration of energy storage resources is realized. And constructing a power supply side node group by taking the electrical distance as a clustering condition, forming a regionalized association architecture of the energy storage nodes and the power supply nodes, and executing synchronous feedback adjustment of charging or discharging by monitoring the total power of the group in real time and comparing the total power with a target interval.

Description

Technical Field

[0001] This invention belongs to the field of power technology, specifically, it relates to a method for optimizing the configuration of energy storage to mitigate power fluctuations on the power supply side of new energy grid connection. Background Technology

[0002] The problem of power fluctuations on the power supply side is becoming increasingly prominent, which not only affects power quality but also restricts the absorption capacity of new energy sources.

[0003] In existing technologies, the energy storage configuration methods commonly used to address the power fluctuation problem caused by the grid connection of new energy sources often rely on simple statistics of historical data or rough estimations based on empirical formulas, making it difficult to accurately capture the dynamic fluctuation characteristics of power supply nodes during real-time operation. Existing methods often focus on the amplitude of power fluctuations or the rate of change on a single time scale, neglecting the impact of fluctuation frequency and the density of fluctuation direction changes on energy storage configuration requirements. This results in the configured energy storage nodes being unable to effectively cope with complex and ever-changing power fluctuation patterns. Furthermore, when determining the installation location of energy storage devices, existing technologies often employ uniform distribution or load center-based methods, failing to consider the actual power supply direction of the power grid and the spatial distribution characteristics of the fluctuation quantification index, thus hindering the effective handling of energy storage nodes. It is difficult to maximize the effectiveness of energy storage in the fluctuation ranges that require the most suppression, resulting in waste of energy storage resources or poor suppression effect; in terms of energy storage capacity configuration, a fixed ratio or simple calculation based on the maximum peak-valley difference is often used, resulting in either excessive redundancy in capacity configuration, increasing investment costs, or insufficient capacity, which cannot effectively suppress fluctuations; after the energy storage nodes are put into operation, the existing technology lacks a mechanism for dynamic grouping and synchronous feedback adjustment of power supply side nodes. Each energy storage node often operates independently or adjusts only based on local information, and cannot achieve collaborative optimization based on distance as a clustering condition. As a result, when power imbalance occurs in a local power grid, the energy storage nodes cannot form effective power mutual assistance and synchronous response, which reduces the power supply stability and fluctuation suppression efficiency of the entire power grid.

[0004] To address the aforementioned issues, this invention proposes a method for optimizing energy storage configuration to mitigate power fluctuations on the power supply side of new energy grid-connected systems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for optimizing energy storage configuration to mitigate power fluctuations on the power supply side of new energy grid connection, solving the problem of existing technologies lacking precise quantification and dynamic grouping coordination for energy storage optimization configuration.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for optimizing energy storage configuration to mitigate power fluctuations on the power supply side of new energy grid connection, the method comprising: Step 1: Determine the power supply associated with each power supply node in the target power grid in real time during the monitoring period. Based on the numerical change characteristics of the power supply power of each power supply node, calculate the power supply power fluctuation quantification index associated with each power supply node during the monitoring period. Step 2: Use the power supply fluctuation quantification index in combination with the power grid supply trend to generate the power supply fluctuation quantification index change curve associated with the target power grid; Based on the trend analysis of the power supply fluctuation quantification index change curve, the target power supply side node interval corresponding to the target power grid within the monitoring period is identified. Determine the optimal energy storage node configuration location in each target power supply side node interval, generate the energy storage node capacity by combining the power supply power fluctuation quantification index change curve of the corresponding target power supply side node interval, and execute the energy storage node configuration. Step 3: Monitor the power supply of each power supply node after the energy storage node configuration is completed in real time. Use distance as the clustering condition to group the power supply node groups and perform feedback regulation on the energy storage nodes associated with the power supply node groups in a synchronous adjustment manner. As a further aspect of the present invention, the specific method for determining the power supply associated with each power supply node in the target power grid in real time during the monitoring period in step one is as follows: Identify the target power grid; The total number of power supply-side nodes in the target power grid is denoted as j; Lock any power supply side node, denoted as Qi, where i is the counting index, 1≤i≤j; Determine the total number of moments in the monitoring period T, denoted as m; The first moment after the target power grid is in normal operation is marked as the start time t1 of a monitoring period T. The power supply power of the power supply node Qi is monitored from the start time t1 until the end time tm of the monitoring period T. Record the power supply power of power supply node Qi at m times within the monitoring period T, and arrange them in chronological order as the power supply power sequence P1, P2, ..., Pm; Repeat the above steps to determine the power supply sequence associated with each of the j power supply-side nodes in the target power grid.

[0007] As a further aspect of the present invention, in step one, the specific method for calculating the power supply fluctuation quantification index associated with each power supply node during the monitoring period based on the numerical change characteristics of the power supply power of each power supply node is as follows: Extract the power supply sequence P1, P2, ..., Pm of any power supply node Qi; Calculate the difference between the maximum and minimum power supply, and denot it as the maximum peak-valley difference ΔP; Traverse the power supply sequence P1, P2, ..., Pm, and count the number of times the power supply changes directionally, denoted as sum; The power supply fluctuation quantification index PSF_i of the power supply side node Qi during the monitoring period is calculated using PSF_i=ΔP×(1+sum / m), where sum / m represents the frequency density of power supply fluctuation. The larger the power supply fluctuation quantification index PSF_i, the greater the power supply fluctuation, and vice versa. Similarly, determine the power fluctuation quantification index associated with each power supply node.

[0008] As a further aspect of the present invention, in step one, the directional change is defined as: the direction of increase or decrease of the power supply at the current moment relative to the previous moment changes, that is, from increasing to decreasing or from decreasing to increasing.

[0009] As a further aspect of the present invention, the specific method for generating the power supply fluctuation quantification index change curve associated with the target power grid in step two, using the power supply fluctuation quantification index in combination with the power grid power supply trend, is as follows: Arrange j power supply side nodes according to the power supply direction of the power grid, and denote them as Q1, Q2, ..., Qj; The power supply fluctuation quantization indexes corresponding to each power supply node are arranged in the order of Q1, Q2, ..., Qj, and are denoted as the power supply fluctuation quantization index sequence PSF_1, PSF_2, ..., PSF_j. Construct a two-dimensional coordinate system with the horizontal axis representing j power supply side nodes Q1, Q2, ..., Qj and their spacing, and the vertical axis representing the power supply fluctuation quantification index value; The power supply fluctuation quantization index sequence PSF_1, PSF_2, ..., PSF_j is plotted in a two-dimensional coordinate system in the form of data points, resulting in j data points. The curve is fitted to this data point and denoted as the power supply fluctuation quantization index change curve W_PSF.

[0010] As a further aspect of the present invention, the specific method for locking the target power grid corresponding to the target power supply side node interval within the monitoring period in step two is as follows: Extract the preset power supply fluctuation quantization index threshold PSF_yu; Mark PSF_yu on the vertical axis of the two-dimensional coordinate system, and construct a straight line passing through PSF_yu that is perpendicular to the vertical axis and parallel to the horizontal axis, denoted as L1; The closed region above the straight line L1 is determined by the power supply fluctuation quantification index change curve W_PSF, and is denoted as the target smoothing power supply side node interval segment. It is summarized in the positive direction of the horizontal axis as the target smoothing power supply side node interval segment sequence R1, R1, ..., Ro, where o represents the total number of target smoothing power supply side node interval segments.

[0011] As a further aspect of the present invention, the specific method for determining the optimal energy storage node configuration location in each target power supply side node interval segment in step two is as follows: Obtain any target power supply side node interval Ru from the target power supply side node interval sequence R1, R1, ..., Ro, where u is the counting index and 0 ≤ u ≤ o; In a two-dimensional coordinate system, the target power supply side node interval segment Ru is locked, and the power supply side nodes corresponding to the target power supply side node interval segment Ru are extracted and combined into a target power supply side node set. In a two-dimensional coordinate system, the area of ​​the target smoothing power supply side node interval segment Ru is evenly divided by a straight line L2 that is perpendicular to the horizontal axis and parallel to the vertical axis. The scale of the straight line L2 on the horizontal axis is locked. Based on this scale, the power supply side node that is closest to the target smoothing power supply side node set is determined, and the position of this power supply side node is marked as the optimal energy storage node configuration position. Similarly, determine the optimal energy storage node configuration location in each target power supply side node interval and arrange them according to the power supply direction of the target power grid, denoted as the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo.

[0012] As a further aspect of the present invention, the specific method for configuring the energy storage node in step two is as follows: Obtain the optimal energy storage node configuration location Zu from the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo; The integral area of ​​the target smoothing power supply side node interval segment Ru corresponding to the optimal energy storage node configuration location Zu in the power supply power fluctuation quantification index change curve W_PSF is extracted and denoted as Ar_u; The energy storage node capacity Cu of the target power supply side node interval Ru is calculated using Cu=H×Area_u, where H is a preset capacity conversion coefficient; Configure an energy storage node Gu with a capacity of Cu at the optimal energy storage node configuration location Zu; Repeat the above steps until all optimal energy storage node configuration locations are completed, and denote them in order as the energy storage node sequence G1, G2, ..., Go.

[0013] As a further aspect of the present invention, in step three, distance is used as a clustering condition to group the power supply side nodes. The specific method for constructing the power supply side node groups is as follows: Extract the energy storage node sequence G1, G2, ..., Go and the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo; Obtain any power supply node Qi, calculate its electrical distance to each energy storage node, and assign the power supply node Qi to the energy storage node with the closest electrical distance, forming a power supply node group centered on each energy storage node, denoted as the power supply node group sequence S1, S2, ..., So. If there exists any power supply node with equal and minimum electrical distances to multiple energy storage nodes, then assign it to an upstream energy storage node based on the power supply direction of the power grid.

[0014] As a further aspect of the present invention, the specific method for performing feedback adjustment on the energy storage nodes grouped together with the power supply side nodes in step three using synchronous adjustment is as follows: Real-time monitoring of the power supply power of each power supply node within any power supply node group Su, and calculation of the total power supply power PSu; Compare the total power supply PSu with the preset target power supply range [Pmin, Pmax]; When PSu > Pmax, the energy storage node Gu is adjusted to perform charging at PSu - Pmax. When PSu < Pmax, the energy storage node Gu is adjusted to discharge at PSu - Pmax. Conversely, the energy storage node Gu is instructed not to perform any operation; Similarly, feedback adjustments are performed simultaneously on each energy storage node.

[0015] The beneficial effects of this invention are: This invention quantifies the power fluctuation characteristics of each power supply node and constructs a fluctuation index change curve based on the power supply direction of the power grid. This enables dynamic locking of the fluctuation range and optimal configuration of energy storage nodes, improving the stability and economy of new energy grid connection. It not only accurately identifies the key range that needs to be smoothed through trend analysis, avoiding blind resource allocation; but also ensures the scientific nature of energy storage configuration by generating the optimal capacity based on the fluctuation curve. Furthermore, it enables synchronous adjustment of energy storage nodes by grouping power supply-side nodes through distance clustering, facilitating rapid response to real-time changes in the local power grid, suppressing the spread of power fluctuations, and enhancing the adaptability of the entire power grid. This invention monitors the power supply sequence of each power supply node in real time and constructs a power supply fluctuation quantification index based on the power value change characteristics to assess the degree of power fluctuation of each node. It not only considers the extreme value difference of power, but also introduces the frequency and density of fluctuation direction changes, which can more sensitively reflect the frequent fluctuation characteristics of power and avoid misjudgment that may be caused by relying solely on amplitude changes. This helps power grid operation and maintenance personnel to accurately identify nodes with strong fluctuations and provide early warning of potential instability factors. This invention visualizes the abstract power grid fluctuation state by constructing a quantitative index change curve of power supply fluctuation, enabling operation and maintenance personnel to intuitively identify the sections with severe fluctuations. It innovatively introduces quantitative analysis based on integral area and preset threshold, which can automatically identify the target power supply node sections that require key intervention to smooth out the fluctuations. It can also accurately calculate the optimal configuration point in each fluctuation interval through the area equalization method, solving the pain points of traditional energy storage deployment. Secondly, it uses a capacity conversion coefficient to directly convert the fluctuation integral area into specific energy storage capacity, realizing the precise on-demand deployment of energy storage resources, avoiding waste caused by capacity redundancy or insufficiency, and smoothing out power fluctuations with the most efficient cost. This invention achieves precise matching between power supply nodes and energy storage nodes by introducing electrical distance as a clustering condition, breaking away from the traditional extensive scheduling mode and instead constructing a microgrid centered on energy storage units. It fully considers the physical topology and power flow of the power grid, following the upstream priority allocation principle when electrical distances are equal, effectively avoiding increased line losses caused by circulating currents or power backflow, and improving the targeting and economy of energy regulation. Secondly, it introduces a dynamic feedback regulation mechanism based on the total power of groups. By monitoring the deviation between the total power supply of each group and the target range in real time, it performs synchronous charging and discharging operations on associated energy storage nodes, avoiding the risk of voltage exceeding limits caused by over-adjustment of a single node. Through group autonomy, it achieves real-time local power balance, thereby reducing the computational pressure on the main control center. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart illustrating the method described in this invention; Figure 2 This is a flowchart illustrating the method described in Embodiment 3 of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, this application provides a method for optimizing the configuration of energy storage to mitigate power fluctuations on the power supply side of new energy grid connection; As an embodiment 1 of this application, it specifically includes: Step 1: Determine the power supply associated with each power supply node in the target power grid in real time during the monitoring period. Based on the numerical change characteristics of the power supply power of each power supply node, calculate the power supply power fluctuation quantification index associated with each power supply node during the monitoring period. Step 2: Use the power supply fluctuation quantification index in combination with the power grid supply trend to generate the power supply fluctuation quantification index change curve associated with the target power grid; Based on the trend analysis of the power supply fluctuation quantification index change curve, the target power supply side node interval corresponding to the target power grid within the monitoring period is identified. Determine the optimal energy storage node configuration location in each target power supply side node interval, generate the energy storage node capacity by combining the power supply power fluctuation quantification index change curve of the corresponding target power supply side node interval, and execute the energy storage node configuration. Step 3: Monitor the power supply of each power supply node after the energy storage node is configured in real time. Use distance as the clustering condition to group the power supply nodes and construct power supply node groups. Perform feedback adjustment on the energy storage nodes associated with the power supply node groups in a synchronous adjustment manner.

[0020] Example 2 This embodiment, based on Embodiment 1, further discloses a method for calculating the power supply fluctuation quantification index associated with each power supply node during the monitoring period, specifically including the following: First, it is necessary to acquire the power grid to be processed and mark it as the target power grid. It should be noted that the target power grid mentioned here includes several power supply side nodes. The power supply side nodes can be understood as the starting point for supplying power to users, such as substations. Users obtain electrical energy from the target power grid through the power supply side nodes. Count the total number of power supply-side nodes in the target power grid and denote it as j; Lock any one power supply node Qi from the determined j power supply nodes, perform example processing on power supply node Qi, and perform the same processing on the other power supply nodes at the same time. It should be noted that i is the counting index, 1≤i≤j. Obtain the monitoring period T preset by the operator, where the monitoring period T needs to be able to display the periodic changes of the target power grid, such as one day, one week, or one month; Next, the total number of moments within the monitoring period T is determined and denoted as m. The time is not equivalent to 1 second or 1 minute, but is a sampling frequency set by the operator.

[0021] Real-time monitoring of the target power grid; once the target power grid is in normal operation, extract the first moment corresponding to the normal operation state and mark this moment as the start moment of a monitoring cycle T, denoted as t1; Then, starting from the start time t1, monitor the power supply of node Qi on the power supply side until the end of a monitoring cycle T, and record the end time of monitoring cycle T as tm, that is, a total of m times from the start time t1 to the end time tm. Next, the power supply power corresponding to the power supply node Qi at m times within the monitoring period T is recorded, and the m power supply power values ​​are arranged in chronological order and denoted as the power supply power sequence P1, P2, ..., Pm.

[0022] Because of the inherent volatility of the target power grid, the power supply sequence P1, P2, ..., Pm changes in real time. By serializing the discrete power supply, we can use it as the data basis for subsequent quantitative analysis.

[0023] Next, further analysis is performed based on the power supply sequence P1, P2, ..., Pm of any power supply node Qi. First, the maximum and minimum power supply values ​​in the power supply power sequence P1, P2, ..., Pm are extracted. The difference between the maximum and minimum power supply values ​​is calculated by subtracting the minimum power supply value from the maximum power supply value. This difference is recorded as the maximum peak-to-valley difference ΔP associated with the power supply node Qi (only the numerical value is taken, without units or dimensions). The maximum peak-to-valley difference ΔP is used to reflect the magnitude of the power supply power fluctuation of the power supply node Qi. Then, iterate through the power supply sequence P1, P2, ..., Pm, count the number of times the power supply changes directionally, and record the number as sum. It should be noted that a directional change is defined as a change in the direction of the power supply at the current moment relative to the previous moment, that is, from increasing to decreasing or from decreasing to increasing. During this process, if the power supply keeps increasing or decreasing and exceeds the safety range preset by the operator, it is considered an abnormal situation (the target power grid is considered to be in an abnormal operating state). At this time, the operator needs to be notified to carry out maintenance and stop monitoring and subsequent operations. It should also be noted that the definition of directional change in power supply also needs to take into account the specific change value per unit time. For example, if a single change is very small and within the tolerance range preset by the operator based on the characteristics of the target power grid, it is not considered a directional change. Only when the change value per unit time exceeds the tolerance range is it considered a fluctuation, and sum is incremented by one.

[0024] The maximum peak-to-valley difference ΔP and the number of peaks sum are extracted. The power supply fluctuation quantification index PSF_i of the power supply node Qi during the monitoring period is calculated by using PSF_i=ΔP×(1+sum / m). Here, sum / m represents the frequency density of power supply fluctuation, and 1+sum / m represents an amplification factor. The larger the value of sum / m, the greater the difference in the power supply fluctuation quantification index. The larger the value of the power supply fluctuation quantification index PSF_i, the greater the power supply fluctuation, and vice versa. It needs to be explained that in PSF_i=ΔP×(1+sum / m), ΔP can only reflect the magnitude of the power supply fluctuation, but cannot reflect the speed of the fluctuation. Therefore, the number of fluctuations, sum, is needed for comprehensive processing. In the actual operation of the power grid, frequent small fluctuations are often more difficult to manage than a one-time rise, and the impact on equipment life is also different. Therefore, the number of fluctuations, sum, is constructed as an amplification factor to regulate the maximum peak-valley difference ΔP.

[0025] By repeating the above steps, the power fluctuation quantification index associated with each power supply node can be determined.

[0026] Example 3 This embodiment, based on Embodiment 2, further discloses a method for optimizing the configuration of power grid energy storage nodes by combining the fluctuation characteristics of the power supply-side nodes in the target power grid, such as... Figure 2 As shown, it specifically includes the following: First, determine the power supply direction in the target power grid. Then, arrange the j power supply nodes counted above according to the determined power supply direction as follows: Q1, Q2, ..., Qj. Next, the power supply fluctuation quantization indexes of the j power supply side nodes are synchronously sorted according to the sorting order of Q1, Q2, ..., Qj, to obtain the power supply fluctuation quantization index sequence PSF_1, PSF_2, ..., PSF_j associated with the j power supply side nodes.

[0027] Next, a two-dimensional coordinate system is constructed, with j power supply side nodes Q1, Q2, ..., Qj and their spacing as the horizontal axis and the value of the power supply fluctuation quantification index as the vertical axis. The horizontal axis of the two-dimensional coordinate system is scaled according to a ratio preset by the operator to represent the actual spacing between power supply side nodes, and the position of each power supply side node is marked on the scale of the horizontal axis.

[0028] Next, the power fluctuation quantization index sequence PSF_1, PSF_2, ..., PSF_j is extracted, and the position of each power supply node is aligned to plot the power fluctuation quantization index of each power supply node in the form of data points in a two-dimensional coordinate system. Thus, in the constructed two-dimensional coordinate system, j data points are obtained by generating the power fluctuation quantization index sequence PSF_1, PSF_2, ..., PSF_j. Then, a curve fitting operation is performed on the j data points to obtain a complete curve, which is denoted as the power fluctuation quantization index change curve W_PSF, which characterizes the power fluctuation of the j power supply side nodes in the target power grid.

[0029] Then, obtain the power supply fluctuation quantification index threshold PSF_yu preset by the operator. The power supply fluctuation quantification index threshold PSF_yu represents the upper limit of power supply fluctuation that the target power grid can tolerate. On the vertical axis of the constructed two-dimensional coordinate system, determine the scale corresponding to the power fluctuation quantization index threshold PSF_yu. Then, construct a straight line that passes through the scale where the power fluctuation quantization index threshold PSF_yu is located, is perpendicular to the vertical axis, and is parallel to the horizontal axis. This line is denoted as L1. At this point, the power supply fluctuation quantification index change curve W_PSF may intersect the straight line L1 at several points. Among them, all closed areas of the power supply fluctuation quantification index change curve W_PSF above the straight line L1 are marked as target smoothing power supply side node intervals (representing the part exceeding the upper limit of power supply fluctuation that the target power grid can tolerate). All target smoothing power supply side node intervals are summarized in the positive direction of the horizontal axis and denoted as the target smoothing power supply side node interval sequence R1, R1, ..., Ro, where o represents the total number of target smoothing power supply side node intervals.

[0030] Then, extract any target power supply side node interval Ru from the target power supply side node interval sequence R1,R1,...,Ro, where u is the counting index, 0≤u≤o. In subsequent operations, the target power supply side node interval Ru is processed as an example, and the remaining target power supply side node intervals are processed in the same and synchronous manner as the target power supply side node interval Ru.

[0031] Next, in the two-dimensional coordinate system, lock the target smoothing power supply side node interval segment Ru, and correspondingly set it to the horizontal axis of the two-dimensional coordinate system. Extract all power supply side nodes corresponding to the target smoothing power supply side node interval segment Ru from the horizontal axis, and combine them to form the target smoothing power supply side node set.

[0032] Next, the target smoothing power supply side node interval Ru is locked. The target smoothing power supply side node interval Ru is a closed area. By using a straight line L2 that is perpendicular to the horizontal axis and parallel to the vertical axis, the area of ​​the target smoothing power supply side node interval Ru is divided equally (divided into two equal parts on the left and right). Then, the scale of the straight line L2 on the horizontal axis is locked. At this time, the scale of the straight line L2 on the horizontal axis is the equilibrium point of the target smoothing power supply side node interval Ru. Then, based on the scale of the straight line L2 on the horizontal axis, the power supply side node that is closest to the straight line L2 in the target smoothing power supply side node set is determined, and the position of this power supply side node is marked as the optimal energy storage node configuration position. It should be noted that the optimal location for energy storage nodes is not determined by the position of line L2 on the horizontal axis. This approach is for economic reasons. While using the position of line L2 on the horizontal axis would yield the best performance, it would require rebuilding the relevant grid connection from the target grid. Using the nearest power supply node as the location for energy storage nodes can effectively avoid this problem and achieve a better balance between economy and performance.

[0033] Repeat the above steps to determine the optimal energy storage node configuration locations in all target power supply side node segments, and arrange all the determined optimal energy storage node configuration locations according to the power supply direction of the target power grid, denoted as the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo.

[0034] Once the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo is determined, the energy storage node configuration can begin, as follows: First, obtain any optimal energy storage node configuration location Zu in the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo and perform example processing; Obtain the target power supply side node interval segment Ru associated with the optimal energy storage node configuration location Zu, and determine the integral area of ​​the target power supply side node interval segment Ru in the power supply fluctuation quantification index change curve W_PSF (implemented based on a two-dimensional coordinate system), and denote the value of the integral area as Ar_u; Next, the energy storage node capacity Cu of the target power supply side node interval Ru is calculated using Cu=H×Area_u, where H is a preset capacity conversion coefficient that needs to be set by the operator in combination with the power of the energy storage system, the discharge time and the fluctuation of the power supply of the target power grid. Finally, at the optimal energy storage node configuration location Zu, an energy storage node Gu with a capacity of Cu is configured. Following the above method, the configuration of energy storage nodes at all optimal energy storage node configuration locations is synchronized to complete the configuration of energy storage nodes at all optimal energy storage node configuration locations. All configured energy storage nodes are recorded as energy storage node sequences G1, G2, ..., Go according to the power supply direction of the target power grid.

[0035] Example 4 This embodiment, based on embodiment 3, further discloses a method for performing feedback regulation on energy storage nodes grouped and associated with power supply side nodes, specifically including the following: First, it is necessary to perform grouping operations on the power supply side nodes and build interconnected power supply side node groups; Extract the energy storage node sequence G1, G2, ..., Go determined in Example 3, and the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo associated with the energy storage node sequence G1, G2, ..., Go; Similarly, any one of the j power supply nodes, Qi, is extracted for example processing. The electrical distance between power supply node Qi and o energy storage nodes in the energy storage node sequence G1, G2, ..., Go is calculated. The electrical distance refers to the impedance value, not the physical spatial distance, and reflects the power transmission loss and the tightness of voltage coupling between nodes. Next, the power supply node Qi is assigned to the energy storage node with the closest electrical distance to the o energy storage nodes. This process is repeated until a power supply node group centered on each energy storage node is formed, denoted as the power supply node group sequence S1, S2, ..., So. If there are any power supply node with equal and minimum electrical distances to multiple energy storage nodes, it is assigned to the upstream energy storage node based on the power supply direction of the power grid, ensuring the logical consistency of the power supply flow.

[0036] Then, the power supply of each power supply node in any power supply node group Su is monitored in real time, and the summation is calculated. The summation result is recorded as the total power supply PSu. The total power supply PSu is compared with the preset target power supply range [Pmin, Pmax]. The target power supply range [Pmin, Pmax] refers to the ideal operating range set by the power supply side node group Su under normal load conditions, combined with the economic operating range of the equipment and the target power grid dispatch instructions. If PSu > Pmax, then the energy storage node Gu is regulated to perform charging at PSu - Pmax. This indicates that there may be multiple new energy sources connected to the grid in the area where the power supply side node group Su is located, resulting in too much power generation or too little overall load, leading to a power surplus. If PSu < Pmax, then the energy storage node Gu will discharge at PSu - Pmax, indicating that there are few new energy grid connections in the area where the power supply side node group Su is located, or the overall load is too heavy, resulting in a power supply shortage. Conversely, the energy storage node Gu is instructed not to perform any operation; Repeat the above steps to synchronously perform feedback regulation on each energy storage node, forming a grid control strategy that combines regional autonomy with local balance.

[0037] All data in the formulas described above have been calculated with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0038] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

[0039] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A method for optimizing energy storage configuration to mitigate power fluctuations on the power supply side of new energy grid-connected systems, characterized in that, The method includes: Step 1: Determine the power supply associated with each power supply node in the target power grid in real time during the monitoring period. Based on the numerical change characteristics of the power supply power of each power supply node, calculate the power supply power fluctuation quantification index associated with each power supply node during the monitoring period. Step 2: Use the power supply fluctuation quantification index in combination with the power grid supply trend to generate the power supply fluctuation quantification index change curve associated with the target power grid; Based on the trend analysis of the power supply fluctuation quantification index change curve, the target power supply side node interval corresponding to the target power grid within the monitoring period is identified. Determine the optimal energy storage node configuration location in each target power supply side node interval, generate the energy storage node capacity by combining the power supply power fluctuation quantification index change curve of the corresponding target power supply side node interval, and execute the energy storage node configuration. Step 3: Monitor the power supply of each power supply node after the energy storage node is configured in real time. Use distance as the clustering condition to group the power supply nodes and construct power supply node groups. Perform feedback adjustment on the energy storage nodes associated with the power supply node groups in a synchronous adjustment manner.

2. The method according to claim 1, characterized in that, In step one, the specific method for determining the power supply associated with each power supply node in the target power grid in real time during the monitoring period is as follows: Identify the target power grid; The total number of power supply-side nodes in the target power grid is denoted as j; Lock any power supply side node, denoted as Qi, where i is the counting index, 1≤i≤j; Determine the total number of moments in the monitoring period T, denoted as m; The first moment after the target power grid is in normal operation is marked as the start time t1 of a monitoring period T. The power supply power of the power supply node Qi is monitored from the start time t1 until the end time tm of the monitoring period T. Record the power supply power of power supply node Qi at m times within the monitoring period T, and arrange them in chronological order as the power supply power sequence P1, P2, ..., Pm; Repeat the above steps to determine the power supply sequence associated with each of the j power supply-side nodes in the target power grid.

3. The method according to claim 2, characterized in that, In step one, the specific method for calculating the power supply fluctuation quantification index associated with each power supply node during the monitoring period, based on the numerical change characteristics of the power supply power of each power supply node, is as follows: Extract the power supply sequence P1, P2, ..., Pm of any power supply node Qi; Calculate the difference between the maximum and minimum power supply, and denot it as the maximum peak-valley difference ΔP; Traverse the power supply sequence P1, P2, ..., Pm, and count the number of times the power supply changes directionally, denoted as sum; The power supply fluctuation quantification index PSF_i of the power supply side node Qi during the monitoring period is calculated using PSF_i=ΔP×(1+sum / m), where sum / m represents the frequency density of power supply fluctuation. The larger the power supply fluctuation quantification index PSF_i, the greater the power supply fluctuation, and vice versa. Similarly, determine the power fluctuation quantification index associated with each power supply node.

4. The method according to claim 3, characterized in that, In step one, directional change is defined as: the direction of increase or decrease of power supply at the current moment relative to the previous moment changes, that is, from increase to decrease or from decrease to increase.

5. The method according to claim 3, characterized in that, In step two, the specific method for generating the power supply fluctuation quantification index change curve associated with the target power grid by combining the power supply fluctuation quantification index with the power grid power supply trend is as follows: Arrange j power supply side nodes according to the power supply direction of the power grid, and denote them as Q1, Q2, ..., Qj; The power supply fluctuation quantization indexes corresponding to each power supply node are arranged in the order of Q1, Q2, ..., Qj, and are denoted as the power supply fluctuation quantization index sequence PSF_1, PSF_2, ..., PSF_j. Construct a two-dimensional coordinate system with the horizontal axis representing j power supply side nodes Q1, Q2, ..., Qj and their spacing, and the vertical axis representing the power supply fluctuation quantification index value; The power supply fluctuation quantization index sequence PSF_1, PSF_2, ..., PSF_j is plotted in a two-dimensional coordinate system in the form of data points, resulting in j data points. The curve is fitted to this data point and denoted as the power supply fluctuation quantization index change curve W_PSF.

6. The method according to claim 5, characterized in that, In step two, the specific method for locking the target power grid within the target power supply side node interval during the monitoring period is as follows: Extract the preset power supply fluctuation quantization index threshold PSF_yu; Mark PSF_yu on the vertical axis of the two-dimensional coordinate system, and construct a straight line passing through PSF_yu that is perpendicular to the vertical axis and parallel to the horizontal axis, denoted as L1; The closed region above the straight line L1 is determined by the power supply fluctuation quantification index change curve W_PSF, and is denoted as the target smoothing power supply side node interval segment. It is summarized in the positive direction of the horizontal axis as the target smoothing power supply side node interval segment sequence R1, R1, ..., Ro, where o represents the total number of target smoothing power supply side node interval segments.

7. The method according to claim 6, characterized in that, In step two, the specific method for determining the optimal energy storage node configuration location in each target power supply side node interval is as follows: Obtain any target power supply side node interval Ru from the target power supply side node interval sequence R1, R1, ..., Ro, where u is the counting index and 0 ≤ u ≤ o; In a two-dimensional coordinate system, the target power supply side node interval segment Ru is locked, and the power supply side nodes corresponding to the target power supply side node interval segment Ru are extracted and combined into a target power supply side node set. In a two-dimensional coordinate system, the area of ​​the target smoothing power supply side node interval segment Ru is evenly divided by a straight line L2 that is perpendicular to the horizontal axis and parallel to the vertical axis. The scale of the straight line L2 on the horizontal axis is locked. Based on this scale, the power supply side node that is closest to the target smoothing power supply side node set is determined, and the position of this power supply side node is marked as the optimal energy storage node configuration position. Similarly, determine the optimal energy storage node configuration location in each target power supply side node interval and arrange them according to the power supply direction of the target power grid, denoted as the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo.

8. The method according to claim 7, characterized in that, In step two, the specific method for configuring the energy storage node is as follows: Obtain the optimal energy storage node configuration location Zu from the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo; The integral area of ​​the target smoothing power supply side node interval segment Ru corresponding to the optimal energy storage node configuration location Zu in the power supply power fluctuation quantification index change curve W_PSF is extracted and denoted as Ar_u; The energy storage node capacity Cu of the target power supply side node interval Ru is calculated using Cu=H×Area_u, where H is a preset capacity conversion coefficient; Configure an energy storage node Gu with a capacity of Cu at the optimal energy storage node configuration location Zu; Repeat the above steps until all optimal energy storage node configuration locations are completed, and denote them in order as the energy storage node sequence G1, G2, ..., Go.

9. The method according to claim 8, characterized in that, In step three, distance is used as a clustering condition to group the power supply side nodes. The specific method for constructing the power supply side node groups is as follows: Extract the energy storage node sequence G1, G2, ..., Go and the optimal energy storage node configuration location sequence Z1, Z2, ..., Zo; Obtain any power supply node Qi, calculate its electrical distance to each energy storage node, and assign the power supply node Qi to the energy storage node with the closest electrical distance, forming a power supply node group centered on each energy storage node, denoted as the power supply node group sequence S1, S2, ..., So. If there exists any power supply node with equal and minimum electrical distances to multiple energy storage nodes, then assign it to an upstream energy storage node based on the power supply direction of the power grid.

10. The method according to claim 9, characterized in that, In step three, the specific method for performing feedback regulation on the energy storage nodes grouped together with the power supply side nodes in a synchronous regulation manner is as follows: Real-time monitoring of the power supply power of each power supply node within any power supply node group Su, and calculation of the total power supply power PSu; Compare the total power supply PSu with the preset target power supply range [Pmin, Pmax]; When PSu > Pmax, the energy storage node Gu is adjusted to perform charging at PSu - Pmax. When PSu < Pmax, the energy storage node Gu is adjusted to discharge at PSu - Pmax. Conversely, the energy storage node Gu is instructed not to perform any operation; Similarly, feedback adjustments are performed simultaneously on each energy storage node.

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