Design method and device of grid-connected point of optical storage system in power distribution network, computer equipment, readable storage medium and program product

Abstract

The application relates to a grid-connected point design method and device of a light storage system in a power distribution network, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: based on historical daily load data and photovoltaic output data of the power distribution network, determining all-day power quality indexes of each observation point after the light storage system is connected to each candidate grid-connected point; based on the power quality indexes, constructing a time sequence index matrix; based on the time sequence index matrix and the power quality index limit value, determining a single-dimensional power quality factor; based on the time sequence index matrix, constructing a three-dimensional data tensor; based on the three-dimensional data tensor, a preset load sensitivity weight and a load capacity, determining a comprehensive power quality value; based on the comprehensive power quality value, a power quality index weight and a power quality index limit value, determining a comprehensive power quality factor; and based on the single-dimensional power quality factor and the comprehensive power quality factor, determining an optimal grid-connected point. The application can consider the cumulative deterioration effect of the power quality indexes when the optimal grid-connected point is determined.

Description

Technical Field

[0001] This application relates to the field of grid connection point design technology for photovoltaic-storage systems in distribution networks, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for designing grid connection points of photovoltaic-storage systems in distribution networks. Background Technology

[0002] With the rapid development of renewable energy grid connection technologies in distribution networks, photovoltaic-storage combined grid connection technology has been widely applied. This technology can effectively smooth out fluctuations in photovoltaic output, enhance the grid's ability to absorb renewable energy, and has the advantages of peak shaving and valley filling, as well as supporting the stability of distribution network voltage. It has now become the mainstream solution for distributed photovoltaic grid connection.

[0003] Currently, the industry generally adopts traditional grid connection node selection methods for planning the grid connection of photovoltaic and energy storage systems. However, the above-mentioned traditional grid connection node selection methods do not take into account the cumulative degradation effect of power quality indicators throughout the day, and the evaluation results cannot reflect the true power quality level of the distribution network under long-term operation. Summary of the Invention

[0004] Based on this, it is necessary to provide a design method, device, computer equipment, computer-readable storage medium, and computer program product for the grid connection point of a photovoltaic-storage system in a distribution network that can take into account the cumulative degradation effect of power quality indicators throughout the day.

[0005] Firstly, this application provides a method for designing the grid connection point of a photovoltaic-storage system in a distribution network, including:

[0006] For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point are determined; based on the power quality indicators, a time-series indicator matrix is ​​constructed.

[0007] Based on the time series index matrix and power quality index limits, a one-dimensional power quality factor is determined.

[0008] Based on the time-series index matrix, a three-dimensional data tensor is constructed, including nodes, time points, and power quality indicators. Based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, the weights of power quality indicators are determined. Based on the power quality indicator weights and the three-dimensional data tensor, the comprehensive power quality indicator value is determined. Based on the comprehensive power quality value, power quality indicator weights, and power quality indicator limits, the comprehensive power quality factor corresponding to the grid connection point is determined.

[0009] The optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0010] In one embodiment, a one-dimensional power quality factor is determined based on a time-series index matrix and power quality index limits, including:

[0011] Based on the full-time values ​​of each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the time-series curve and the time axis for each power quality indicator. Based on the power quality indicator limits for each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the limit curve and the time axis for each power quality indicator. Based on the ratio of the area enclosed by the indicator to the area enclosed by the limit for each power quality indicator, the one-dimensional power quality factor for each power quality indicator is determined.

[0012] In one embodiment, the power quality index weights are determined based on a three-dimensional data tensor, preset load sensitivity weights, and load capacity, including:

[0013] Based on each power quality index at each time step in the three-dimensional data tensor, the ratio of the standard deviation to the mean is calculated to obtain the time-series sensitivity coefficient; based on the preset load sensitivity weight and the load capacity of each load point, the load weight is determined; based on the time-series sensitivity coefficient and the load weight, the power quality index weight corresponding to each power quality index at each time step is determined.

[0014] In one embodiment, the comprehensive power quality value is determined based on power quality index weights and a three-dimensional data tensor, including:

[0015] Based on the power quality index weights corresponding to each power quality index at each time point, the corresponding power quality indices in the three-dimensional data tensor are weighted and summed to obtain the comprehensive power quality value.

[0016] In one embodiment, the comprehensive power quality factor corresponding to the grid connection point is determined based on the comprehensive power quality value, power quality index weights, and power quality index limits, including:

[0017] The limits of various power quality indicators are weighted and calculated based on the power quality indicator weights to generate a dynamic comprehensive benchmark limit. The comprehensive actual area is obtained by integrating the time series curve and time axis of the comprehensive power quality value. The comprehensive benchmark area is obtained by integrating the time series curve and time axis of the dynamic comprehensive benchmark limit. The comprehensive power quality factor is determined based on the ratio of the comprehensive actual area to the comprehensive benchmark area.

[0018] In one embodiment, the optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point, including:

[0019] The single-dimensional power quality factor and the comprehensive power quality factor under each candidate grid connection point are summed and normalized in turn to obtain the grid connection evaluation coefficient of each candidate grid connection point. The grid connection evaluation coefficients are sorted from small to large, and the grid connection point corresponding to the grid connection evaluation coefficient with the highest sorted grid connection evaluation coefficient is taken as the optimal grid connection point of the photovoltaic-storage system.

[0020] Secondly, this application also provides a grid connection point design device for a photovoltaic-storage system in a distribution network, comprising:

[0021] The module is used to determine the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point, based on the historical daily load data and photovoltaic output data of the distribution network, for any candidate grid connection point in the distribution network; and to construct a time-series indicator matrix based on the power quality indicators.

[0022] The first calculation module is used to determine the one-dimensional power quality factor based on the time series index matrix and power quality index limits;

[0023] The second calculation module is used to construct a three-dimensional data tensor based on the time-series index matrix, including nodes, time points, and power quality indicators; determine the power quality indicator weights based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity; determine the comprehensive power quality value based on the power quality indicator weights and the three-dimensional data tensor; and determine the comprehensive power quality factor corresponding to the grid connection point based on the comprehensive power quality value, power quality indicator weights, and power quality indicator limits.

[0024] The output module is used to determine the optimal grid connection point for the photovoltaic-storage system based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0025] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0026] For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point are determined; based on the power quality indicators, a time-series indicator matrix is ​​constructed.

[0027] Based on the time series index matrix and power quality index limits, a one-dimensional power quality factor is determined.

[0028] Based on the time-series index matrix, a three-dimensional data tensor is constructed, including nodes, time points, and power quality indicators. Based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, the weights of power quality indicators are determined. Based on the power quality indicator weights and the three-dimensional data tensor, the comprehensive power quality value is determined. Based on the comprehensive power quality value, the weights of power quality indicators, and the limits of power quality indicators, the comprehensive power quality factor corresponding to the grid connection point is determined.

[0029] The optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0030] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0031] For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point are determined; based on the power quality indicators, a time-series indicator matrix is ​​constructed.

[0032] Based on the time series index matrix and power quality index limits, a one-dimensional power quality factor is determined.

[0033] Based on the time-series index matrix, a three-dimensional data tensor is constructed, including nodes, time points, and power quality indicators. Based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, the weights of power quality indicators are determined. Based on the power quality indicator weights and the three-dimensional data tensor, the comprehensive power quality value is determined. Based on the comprehensive power quality value, the weights of power quality indicators, and the limits of power quality indicators, the comprehensive power quality factor corresponding to the grid connection point is determined.

[0034] The optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0036] For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point are determined; based on the power quality indicators, a time-series indicator matrix is ​​constructed.

[0037] Based on the time series index matrix and power quality index limits, a one-dimensional power quality factor is determined.

[0038] Based on the time-series index matrix, a three-dimensional data tensor is constructed, including nodes, time points, and power quality indicators. Based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, the weights of power quality indicators are determined. Based on the power quality indicator weights and the three-dimensional data tensor, the comprehensive power quality value is determined. Based on the comprehensive power quality value, the weights of power quality indicators, and the limits of power quality indicators, the comprehensive power quality factor corresponding to the grid connection point is determined.

[0039] The optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0040] The above-mentioned design method, device, computer equipment, computer-readable storage medium, and computer program product for grid connection points of photovoltaic and energy storage systems in the distribution network, for any candidate grid connection point in the distribution network, determines the all-day power quality index of each observation point after the photovoltaic and energy storage system is connected to the candidate grid connection point, based on the historical daily load data and photovoltaic output data of the distribution network; constructs a time-series index matrix based on the power quality index; determines a one-dimensional power quality factor based on the time-series index matrix and power quality index limits; constructs a three-dimensional data tensor including three dimensions: node, time, and power quality index, based on the time-series index matrix; determines the power quality index weights based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity; determines the comprehensive power quality value based on the power quality index weights and the three-dimensional data tensor; determines the comprehensive power quality factor corresponding to the grid connection point based on the comprehensive power quality value, power quality index weights, and power quality index limits; and determines the optimal grid connection point of the photovoltaic and energy storage system based on the one-dimensional power quality factor and comprehensive power quality factor corresponding to each grid connection point. This application conducts power quality assessment by combining all-day time-series operation data, quantifies the degree of long-term cumulative degradation of indicators, and integrates time-series sensitivity and load characteristics to construct a dynamic comprehensive weight, thereby achieving a synergistic evaluation of multiple power quality indicators. This can comprehensively reflect the actual operating status of the distribution network after the integration of photovoltaic and energy storage, and effectively improve the accuracy of grid connection point selection. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the grid connection point design method for a photovoltaic-storage system in a distribution network in one embodiment.

[0043] Figure 2This is a flowchart illustrating the process of determining a one-dimensional power quality factor in one embodiment.

[0044] Figure 3 This is a structural block diagram of the grid connection point design device for a photovoltaic-storage system in a distribution network in one embodiment;

[0045] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0048] In one embodiment, such as Figure 1 As shown, a method for designing the grid connection point of a photovoltaic-storage system in a distribution network is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0049] Step 102: For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, determine the all-day power quality index of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point; based on the power quality index, construct a time-series index matrix.

[0050] For complex distribution network scenarios involving various electronic loads such as LEDs, charging piles, and residential loads, a pre-defined set of candidate nodes for photovoltaic (PV) and energy storage (ESS) integration is first determined. Using typical daily load operation curves and PV output curves as data foundation, the PV-ESS system is sequentially virtually integrated into each candidate node within the set, while the remaining candidate nodes remain unconnected. Power flow calculations are performed sequentially, and the values ​​of various power quality indicators for each node across the entire network at different times throughout the day are fully recorded, providing raw data support for subsequent power quality assessment calculations.

[0051] Furthermore, candidate grid connection points in the distribution network refer to bus nodes or distribution substation nodes that are pre-selected based on the distribution network structure, line current carrying capacity, equipment capacity, and new energy access planning, and that meet the access conditions for photovoltaic and energy storage systems. These are the alternative locations for subsequent selection to determine the final grid connection location. Historical daily load data and photovoltaic output data are selected from measured data of typical operating days of the distribution network, and the data sampling interval is set according to the power quality monitoring standards of the distribution network. The power quality indicators for the whole day include four conventional indicators: voltage deviation, total harmonic distortion rate, voltage flicker, and three-phase voltage imbalance. The time-series indicator matrix records the values ​​of various power quality indicators at different times for each observation point in chronological order, with the observation point and time as the dimensions.

[0052] Optionally, the time series index matrix is:

[0053]

[0054] in, It is any power quality index. It is a set of nodes. It is the number of candidate nodes. This represents the total number of typical intraday measurement moments. This is for when optical storage is connected to different candidate nodes, the node is in Indicators at various times The calculated value of this matrix directly reflects the distribution and changes in power quality at each node throughout the day, without the need for additional curve reconstruction or interpolation.

[0055] Step 104: Determine the single-dimensional power quality factor based on the time series index matrix and power quality index limits.

[0056] Among them, the power quality index limit is the index limit stipulated by the current national power quality standards; the single-dimensional power quality factor is calculated for a single power quality index, and this factor is used to quantify the overall deterioration degree of a single index within a single day.

[0057] Step 106: Based on the time-series index matrix, construct a three-dimensional data tensor including nodes, time points, and power quality indicators; based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, determine the power quality indicator weights; based on the power quality indicator weights and the three-dimensional data tensor, determine the comprehensive power quality value; based on the comprehensive power quality value, power quality indicator weights, and power quality indicator limits, determine the comprehensive power quality factor corresponding to the grid connection point.

[0058] In this context, a node refers to various electrical connection points in the distribution network, including busbars, distribution substations, and line connection endpoints. It is also the basic unit for power quality data collection, indicator monitoring, and computational analysis, covering all electrical points used for operational status observation across the entire network. Candidate grid connection points are part of the network's nodes. A three-dimensional data tensor integrates three types of data: network-wide observation nodes, daily operating times, and various power quality indicators, carrying multi-dimensional operational information of the distribution network after photovoltaic and energy storage integration. Power quality indicator weights are a combination of subjective and objective weights, derived from the time-series sensitivity coefficient calculated based on the three-dimensional data tensor, combined with preset load sensitivity weights and subjective weights corrected for load capacity. The comprehensive power quality value is the result of weighted summation of various indicators at the same node and at the same time, according to their corresponding power quality indicator weights. The comprehensive power quality factor is used to comprehensively evaluate the overall power quality level of the distribution network after photovoltaic and energy storage integration.

[0059] Optionally, the three-dimensional data tensor is:

[0060]

[0061] in, The quantity of power quality indicators (such as voltage deviation, harmonics, flicker, unbalance, etc.); The number of operating hours throughout the day. This represents the total number of nodes in the distribution network.

[0062] Step 108: Determine the optimal grid connection point for the photovoltaic-storage system based on the single-dimensional power quality factor and comprehensive power quality factor corresponding to each grid connection point.

[0063] Among them, the grid connection evaluation coefficient corresponding to each candidate grid connection point is determined based on the single-dimensional power quality factor and the comprehensive power quality factor of all observation points under the same candidate grid connection point; the smaller the evaluation coefficient value, the lower the degree of impact on the power quality of the distribution network after the point is connected to the photovoltaic and energy storage system.

[0064] The above-mentioned grid connection point design method for photovoltaic-storage systems in the distribution network involves the following steps: For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point are determined; based on the power quality indicators, a time-series indicator matrix is ​​constructed; based on the time-series indicator matrix and power quality indicator limits, a one-dimensional power quality factor is determined; based on the time-series indicator matrix, a three-dimensional data tensor including nodes, time, and power quality indicators is constructed; based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, power quality indicator weights are determined; based on the power quality indicator weights and the three-dimensional data tensor, a comprehensive power quality value is determined; based on the comprehensive power quality value, power quality indicator weights, and power quality indicator limits, a comprehensive power quality factor corresponding to the grid connection point is determined; and based on the one-dimensional power quality factor and comprehensive power quality factor corresponding to each grid connection point, the optimal grid connection point for the photovoltaic-storage system is determined. This application conducts power quality assessment by combining all-day time-series operation data, quantifies the degree of long-term cumulative degradation of indicators, and integrates time-series sensitivity and load characteristics to construct a dynamic comprehensive weight, thereby achieving a synergistic evaluation of multiple power quality indicators. This can comprehensively reflect the actual operating status of the distribution network after the integration of photovoltaic and energy storage, and effectively improve the accuracy of grid connection point selection.

[0065] In one exemplary embodiment, such as Figure 2 As shown, based on the time-series index matrix and power quality index limits, the single-dimensional power quality factor is determined, including:

[0066] Based on the full-time values ​​of each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the time-series curve and the time axis for each power quality indicator. Based on the power quality indicator limits for each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the limit curve and the time axis for each power quality indicator. Based on the ratio of the area enclosed by the indicator to the area enclosed by the limit for each power quality indicator, the one-dimensional power quality factor for each power quality indicator is determined.

[0067] For example, the monitoring values ​​of a specific power quality indicator throughout the day are extracted from the time-series indicator matrix, and these values ​​are plotted chronologically to form a curve. Then, using a piecewise trapezoidal integral, the total area enclosed by this curve and the horizontal axis (time axis) is calculated. This area represents the actual cumulative operating level of the indicator throughout the day. Next, the corresponding national standard limit for this power quality indicator is retrieved, and a constant limit curve is plotted based on this limit. Again, using a piecewise trapezoidal integral, the area enclosed by the limit curve and the time axis is calculated as a benchmark. The actual operating area obtained earlier is divided by the limit reference area to calculate the single-dimensional power quality factor corresponding to this power quality indicator. Following the above process, the calculation is repeated for each power quality indicator in the time-series indicator matrix to obtain the single-dimensional power quality factor for each indicator. The formula for calculating the single-dimensional power quality factor is as follows:

[0068]

[0069] in, Candidate nodes for photovoltaic grid connection Time node In indicators Power quality level factor on Candidate nodes for photovoltaic grid connection Time node index At any moment The calculated value, For a unit of time, As an indicator The national standard limits are as follows. The calculation results are represented in matrix form, as shown in the following expression:

[0070]

[0071] In this embodiment, the single-dimensional power quality factor is quantified by the ratio of the actual time-series enclosed area to the national standard limit enclosed area. This transforms the abstract power quality fluctuation state into an intuitive and comparable quantitative value, accurately reflecting the degree of exceedance and operational degradation of individual power quality indicators. This enables refined and time-series evaluation of each power quality indicator, providing accurate and reliable basic data support for subsequent multi-indicator comprehensive evaluation and optimal grid connection point selection, effectively improving the comprehensiveness and accuracy of the photovoltaic-storage grid connection point selection evaluation.

[0072] In one exemplary embodiment, the power quality index weights are determined based on a three-dimensional data tensor, preset load sensitivity weights, and load capacity, including:

[0073] Based on each power quality index at each time step in the three-dimensional data tensor, the ratio of the standard deviation to the mean is calculated to obtain the time-series sensitivity coefficient; based on the preset load sensitivity weight and the load capacity of each load point, the load weight is determined; based on the time-series sensitivity coefficient and the load weight, the power quality index weight corresponding to each power quality index at each time step is determined.

[0074] Among them, the time-series sensitivity coefficient can objectively reflect the degree of discrete fluctuation of the power quality index in the entire network nodes. The greater the index fluctuation and the stronger the node difference, the higher the corresponding objective sensitivity coefficient.

[0075] Optionally, since different load types and load capacities both affect the power quality level of the distribution network system, a sensitivity-capacity hybrid weight (power quality index weight) is introduced to characterize the differentiated tolerance of different load types (commercial lighting LED, charging pile loads, etc.) to power quality indicators. The load sensitivity weight of each indicator is preset. For example, taking voltage deviation and total harmonic distortion (THD) as examples, the rectifier stage of a charging pile is quite sensitive to both voltage deviation and harmonic distortion. Therefore, it is possible to set... Commercial LED lighting drivers are less sensitive to voltage deviations but more sensitive to harmonics; settings can be adjusted accordingly. Where voltage is the voltage deviation and THD (Total Harmonic Distortion) is the total harmonic distortion rate.

[0076] For example, all data from all observation nodes in the three-dimensional data tensor at the same time and under the same power quality index are obtained. By calculating the ratio of the standard deviation to the mean of this set of data, the time-series sensitivity coefficient corresponding to the current time and the current index is obtained, as shown in the following formula:

[0077]

[0078] in, , For photovoltaic energy storage grid connection points At time t, the standard deviation and mean of index m at each node are given. Based on engineering experience, load sensitivity weights corresponding to each load type are pre-set and matched and corrected according to the actual load capacity of each load point in the distribution network. This results in a comprehensive load weight that closely reflects the actual load operation characteristics, achieving a correction of the index weights from the perspective of the importance of the power grid load. The formula is as follows:

[0079]

[0080] in, For load connection point; For nodes On the load capacity; As an indicator Under load The subjective sensitivity weight is calculated. The calculated time-series objective sensitivity coefficient is then fused with the load subjective weight, and these are mutually coupled and corrected to obtain the dynamic comprehensive power quality weight for each power quality index at each time point. The formula is as follows:

[0081]

[0082] in, For photovoltaic energy storage grid connection points At that time, the indicator The overall weight of power quality at time t.

[0083] In this embodiment, the comprehensive power quality weight is obtained by integrating time-series characteristics and load characteristics. This approach takes into account both the changing patterns of the data itself and the operating characteristics of different loads, effectively improving the scientificity and rationality of the weight assignment.

[0084] In one exemplary embodiment, a comprehensive power quality value is determined based on power quality index weights and a three-dimensional data tensor, including:

[0085] Based on the power quality index weights corresponding to each power quality index at each time point, the corresponding power quality indices within the three-dimensional data tensor are weighted and summed to obtain the comprehensive power quality value.

[0086] For example, for any observation node and any operating time within the distribution network, the specific values ​​of various power quality indicators at that node and time are extracted from the three-dimensional data tensor. Simultaneously, the power quality comprehensive weights corresponding to each indicator at that time are retrieved. The value of each indicator is multiplied by its corresponding weight, and all the multiplication results are summed sequentially to calculate the comprehensive power quality value of the node at the current time. By traversing all observation nodes and all times throughout the day using the above calculation method, a complete time-series comprehensive power quality value sequence can be obtained, intuitively reflecting the overall power quality status of each node at different times. The formula for calculating the comprehensive power quality value is as follows:

[0087]

[0088] in, For photovoltaic energy storage grid connection points At time n, node n The overall power quality value.

[0089] In this embodiment, by performing a weighted summation operation on multiple power quality indicators, various individual indicators are integrated into a unified comprehensive quantitative result, which can comprehensively characterize the overall power quality level of the distribution network at a single node and a single time point, breaking the limitations of traditional single-indicator evaluation. At the same time, the calculation is carried out by combining time-sharing dynamic weights, which fully adapts to the time-series changes of power quality indicators and load characteristics, making the calculation results more consistent with the actual operating conditions of the power grid.

[0090] In an exemplary embodiment, the comprehensive power quality factor corresponding to the grid connection point is determined based on the comprehensive power quality value, power quality index weights, and power quality index limits, including:

[0091] The limits of various power quality indicators are weighted and calculated based on the power quality indicator weights to generate a dynamic comprehensive benchmark limit. The comprehensive actual area is obtained by integrating the time series curve and time axis of the comprehensive power quality value. The comprehensive benchmark area is obtained by integrating the time series curve and time axis of the dynamic comprehensive benchmark limit. The comprehensive power quality factor is determined based on the ratio of the comprehensive actual area to the comprehensive benchmark area.

[0092] The higher the value of the comprehensive power quality factor, the more severe the cumulative power quality degradation experienced by that node throughout the day.

[0093] For example, using the obtained power quality index weights, a weighted calculation is performed on the national standard limits for power quality to obtain a comprehensive benchmark limit that dynamically changes over time. The comprehensive power quality values ​​for all times throughout the day are extracted, and these values ​​are plotted as a continuous time-series curve in chronological order. The area enclosed by this curve and the time axis is calculated using a piecewise trapezoidal integral method. This area is the comprehensive actual area, representing the cumulative level of overall power quality of the node throughout the day after photovoltaic and energy storage integration. A corresponding benchmark curve is plotted based on the comprehensive benchmark limit, and the comprehensive benchmark area enclosed by the benchmark curve and the time axis is also calculated using a piecewise trapezoidal integral. The comprehensive actual area is divided by the comprehensive benchmark area to calculate the comprehensive power quality factor corresponding to the current candidate grid-connected point. By completing the calculation for all candidate grid-connected points according to this process, the comprehensive power quality factor for all points can be obtained. The calculation formula is as follows:

[0094]

[0095] in, Candidate nodes for photovoltaic grid connection Time node Power quality level factors under multiple indicators; Candidate nodes for photovoltaic and energy storage access Time node Multiple indicators at any time The calculated value; The unit of time is denoted as . The calculation results are represented in matrix form as follows:

[0096]

[0097] in, For nodes The calculation results of the power quality level factor with multiple indicators are in matrix form; It is a comprehensive matrix of power quality level factors under multiple indicators.

[0098] In this embodiment, by combining weights to weight the national standard limits, a dynamic comprehensive benchmark limit adapted to different time periods and operating conditions is constructed. By quantifying the cumulative effect of the overall power quality throughout the day using a time-series integration method, the power quality changes of the nodes can be fully reflected throughout the entire operating cycle, rather than relying solely on instantaneous data.

[0099] In an exemplary embodiment, the optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point, including:

[0100] The single-dimensional power quality factor and the comprehensive power quality factor under each candidate grid connection point are summed and normalized in turn to obtain the grid connection evaluation coefficient of each candidate grid connection point. The grid connection evaluation coefficients are sorted from small to large, and the grid connection point corresponding to the grid connection evaluation coefficient with the highest sorted grid connection evaluation coefficient is taken as the optimal grid connection point of the photovoltaic-storage system.

[0101] For example, for each candidate grid connection point, all single-dimensional power quality factors and comprehensive power quality factors for that point are first summarized and accumulated. To eliminate the influence of differences in the dimensions of different indicators, the accumulated results are normalized and converted into values ​​under a unified standard, finally obtaining the grid connection evaluation coefficient corresponding to the candidate grid connection point; the calculation formula is as follows:

[0102]

[0103] in, For photovoltaic and energy storage grid connection points In terms of indicators The grid connection evaluation coefficients are calculated for all candidate grid connection points. After all grid connection evaluation coefficients are calculated, they are sorted in ascending order of value. Since a smaller coefficient value indicates a lower degree of power quality degradation after the point is connected to the photovoltaic-storage system, the node corresponding to the highest-ranked coefficient is selected as the final optimal grid connection point for the photovoltaic-storage system.

[0104] In this embodiment, by summing and normalizing the two types of factors, namely single-dimensional and comprehensive factors, the evaluation results of individual indicators and overall power quality are integrated. At the same time, the interference caused by different data dimensions and numerical ranges is eliminated, so that there is a basis for horizontal comparison between candidate nodes.

[0105] In an exemplary embodiment, a method for designing the grid connection point of a photovoltaic-storage system in a distribution network includes: for any candidate grid connection point in the distribution network, determining the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point based on the historical daily load data and photovoltaic output data of the distribution network; constructing a time-series indicator matrix based on the power quality indicators; determining the area enclosed by the time-series curve and the time axis of each power quality indicator based on the all-time value of each power quality indicator in the time-series indicator matrix; determining the area enclosed by the power quality indicator limit curve and the time axis of each power quality indicator based on the power quality indicator limit value of each power quality indicator in the time-series indicator matrix; and determining the one-dimensional power quality factor of each power quality indicator based on the ratio of the area enclosed by the indicator to the area enclosed by the limit value. Based on a time-series index matrix, a three-dimensional data tensor is constructed, comprising nodes, time points, and power quality indices. For each power quality index at each time point in the three-dimensional data tensor, the ratio of standard deviation to mean is calculated to obtain a time-series sensitivity coefficient. Load weights are determined based on preset load sensitivity weights and the load capacity of each load point. Based on the time-series sensitivity coefficients and load weights, the power quality index weights corresponding to each power quality index at each time point are determined. Based on the power quality index weights corresponding to each power quality index at each time point, the corresponding power quality indices within the three-dimensional data tensor are weighted and summed to obtain a comprehensive power quality value. Weighted calculations are performed on the limits of each power quality index based on the power quality index weights to generate a dynamic comprehensive benchmark limit. The comprehensive actual area is obtained by integrating the time-series curve and time axis of the comprehensive power quality value. The comprehensive benchmark area is obtained by integrating the time-series curve and time axis of the dynamic comprehensive benchmark limit. The comprehensive power quality factor is determined based on the ratio of the comprehensive actual area to the comprehensive benchmark area. The single-dimensional power quality factor and the comprehensive power quality factor under each candidate grid connection point are summed and normalized in turn to obtain the grid connection evaluation coefficient of each candidate grid connection point. The grid connection evaluation coefficients are sorted from small to large, and the grid connection point corresponding to the grid connection evaluation coefficient with the highest sorted grid connection evaluation coefficient is taken as the optimal grid connection point of the photovoltaic-storage system.

[0106] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0107] In one exemplary embodiment, such as Figure 3 As shown, a grid connection point design device for a photovoltaic-storage system in a power distribution network is provided, comprising: a construction module 301, a first calculation module 302, a second calculation module 303, and an output module 304, wherein:

[0108] The module is used to determine the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point, based on the historical daily load data and photovoltaic output data of the distribution network, for any candidate grid connection point in the distribution network; and to construct a time-series indicator matrix based on the power quality indicators.

[0109] The first calculation module is used to determine the one-dimensional power quality factor based on the time series index matrix and power quality index limits;

[0110] The second calculation module is used to construct a three-dimensional data tensor based on the time-series index matrix, including nodes, time points, and power quality indicators; determine the power quality indicator weights based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity; determine the comprehensive power quality value based on the power quality indicator weights and the three-dimensional data tensor; and determine the comprehensive power quality factor corresponding to the grid connection point based on the comprehensive power quality value, power quality indicator weights, and power quality indicator limits.

[0111] The output module is used to determine the optimal grid connection point for the photovoltaic-storage system based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0112] In one embodiment, the first computing module is further configured to:

[0113] Based on the full-time values ​​of each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the time-series curve and the time axis for each power quality indicator. Based on the power quality indicator limits for each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the limit curve and the time axis for each power quality indicator. Based on the ratio of the area enclosed by the indicator to the area enclosed by the limit for each power quality indicator, the one-dimensional power quality factor for each power quality indicator is determined.

[0114] In one embodiment, the second computing module is further configured to:

[0115] Based on each power quality index at each time step in the three-dimensional data tensor, the ratio of the standard deviation to the mean is calculated to obtain the time-series sensitivity coefficient; based on the preset load sensitivity weight and the load capacity of each load point, the load weight is determined; based on the time-series sensitivity coefficient and the load weight, the power quality index weight corresponding to each power quality index at each time step is determined.

[0116] In one embodiment, the second computing module is further configured to:

[0117] Based on the power quality index weights corresponding to each power quality index at each time point, the corresponding power quality indices in the three-dimensional data tensor are weighted and summed to obtain the comprehensive power quality value.

[0118] In one embodiment, the second computing module is further configured to:

[0119] The limits of various power quality indicators are weighted and calculated based on the power quality indicator weights to generate a dynamic comprehensive benchmark limit. The comprehensive actual area is obtained by integrating the time series curve and time axis of the comprehensive power quality value. The comprehensive benchmark area is obtained by integrating the time series curve and time axis of the dynamic comprehensive benchmark limit. The comprehensive power quality factor is determined based on the ratio of the comprehensive actual area to the comprehensive benchmark area.

[0120] In one embodiment, the output module is further configured to:

[0121] The single-dimensional power quality factor and the comprehensive power quality factor under each candidate grid connection point are summed and normalized in turn to obtain the grid connection evaluation coefficient of each candidate grid connection point. The grid connection evaluation coefficients are sorted from small to large, and the grid connection point corresponding to the grid connection evaluation coefficient with the highest sorted grid connection evaluation coefficient is taken as the optimal grid connection point of the photovoltaic-storage system.

[0122] The various modules in the grid connection point design device of the aforementioned photovoltaic-storage system in the power distribution network can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0123] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores historical daily load data and photovoltaic output data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a grid connection point design method for a photovoltaic-storage system in a power distribution network.

[0124] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0125] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0126] For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point are determined; based on the power quality indicators, a time-series indicator matrix is ​​constructed.

[0127] Based on the time series index matrix and power quality index limits, a one-dimensional power quality factor is determined.

[0128] Based on the time-series index matrix, a three-dimensional data tensor is constructed, including nodes, time points, and power quality indicators. Based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, the weights of power quality indicators are determined. Based on the power quality indicator weights and the three-dimensional data tensor, the comprehensive power quality value is determined. Based on the comprehensive power quality value, the weights of power quality indicators, and the limits of power quality indicators, the comprehensive power quality factor corresponding to the grid connection point is determined.

[0129] The optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0130] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0131] Based on the full-time values ​​of each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the time-series curve and the time axis for each power quality indicator. Based on the power quality indicator limits for each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the limit curve and the time axis for each power quality indicator. Based on the ratio of the area enclosed by the indicator to the area enclosed by the limit for each power quality indicator, the one-dimensional power quality factor for each power quality indicator is determined.

[0132] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0133] Based on each power quality index at each time step in the three-dimensional data tensor, the ratio of the standard deviation to the mean is calculated to obtain the time-series sensitivity coefficient; based on the preset load sensitivity weight and the load capacity of each load point, the load weight is determined; based on the time-series sensitivity coefficient and the load weight, the power quality index weight corresponding to each power quality index at each time step is determined.

[0134] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0135] Based on the power quality index weights corresponding to each power quality index at each time point, the corresponding power quality indices in the three-dimensional data tensor are weighted and summed to obtain the comprehensive power quality value.

[0136] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0137] The limits of various power quality indicators are weighted and calculated based on the power quality indicator weights to generate a dynamic comprehensive benchmark limit. The comprehensive actual area is obtained by integrating the time series curve and time axis of the comprehensive power quality value. The comprehensive benchmark area is obtained by integrating the time series curve and time axis of the dynamic comprehensive benchmark limit. The comprehensive power quality factor is determined based on the ratio of the comprehensive actual area to the comprehensive benchmark area.

[0138] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0139] The single-dimensional power quality factor and the comprehensive power quality factor under each candidate grid connection point are summed and normalized in turn to obtain the grid connection evaluation coefficient of each candidate grid connection point. The grid connection evaluation coefficients are sorted from small to large, and the grid connection point corresponding to the grid connection evaluation coefficient with the highest sorted grid connection evaluation coefficient is taken as the optimal grid connection point of the photovoltaic-storage system.

[0140] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0141] For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point are determined; based on the power quality indicators, a time-series indicator matrix is ​​constructed.

[0142] Based on the time series index matrix and power quality index limits, a one-dimensional power quality factor is determined.

[0143] Based on the time-series index matrix, a three-dimensional data tensor is constructed, including nodes, time points, and power quality indicators. Based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, the weights of power quality indicators are determined. Based on the power quality indicator weights and the three-dimensional data tensor, the comprehensive power quality value is determined. Based on the comprehensive power quality value, the weights of power quality indicators, and the limits of power quality indicators, the comprehensive power quality factor corresponding to the grid connection point is determined.

[0144] The optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0145] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0146] Based on the full-time values ​​of each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the time-series curve and the time axis for each power quality indicator. Based on the power quality indicator limits for each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the limit curve and the time axis for each power quality indicator. Based on the ratio of the area enclosed by the indicator to the area enclosed by the limit for each power quality indicator, the one-dimensional power quality factor for each power quality indicator is determined.

[0147] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0148] Based on each power quality index at each time step in the three-dimensional data tensor, the ratio of the standard deviation to the mean is calculated to obtain the time-series sensitivity coefficient; based on the preset load sensitivity weight and the load capacity of each load point, the load weight is determined; based on the time-series sensitivity coefficient and the load weight, the power quality index weight corresponding to each power quality index at each time step is determined.

[0149] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0150] Based on the power quality index weights corresponding to each power quality index at each time point, the corresponding power quality indices in the three-dimensional data tensor are weighted and summed to obtain the comprehensive power quality value.

[0151] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0152] The limits of various power quality indicators are weighted and calculated based on the power quality indicator weights to generate a dynamic comprehensive benchmark limit. The comprehensive actual area is obtained by integrating the time series curve and time axis of the comprehensive power quality value. The comprehensive benchmark area is obtained by integrating the time series curve and time axis of the dynamic comprehensive benchmark limit. The comprehensive power quality factor is determined based on the ratio of the comprehensive actual area to the comprehensive benchmark area.

[0153] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0154] The single-dimensional power quality factor and the comprehensive power quality factor under each candidate grid connection point are summed and normalized in turn to obtain the grid connection evaluation coefficient of each candidate grid connection point. The grid connection evaluation coefficients are sorted from small to large, and the grid connection point corresponding to the grid connection evaluation coefficient with the highest sorted grid connection evaluation coefficient is taken as the optimal grid connection point of the photovoltaic-storage system.

[0155] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0156] For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality indicators of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point are determined; based on the power quality indicators, a time-series indicator matrix is ​​constructed.

[0157] Based on the time series index matrix and power quality index limits, a one-dimensional power quality factor is determined.

[0158] Based on the time-series index matrix, a three-dimensional data tensor is constructed, including nodes, time points, and power quality indicators. Based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, the weights of power quality indicators are determined. Based on the power quality indicator weights and the three-dimensional data tensor, the comprehensive power quality value is determined. Based on the comprehensive power quality value, the weights of power quality indicators, and the limits of power quality indicators, the comprehensive power quality factor corresponding to the grid connection point is determined.

[0159] The optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

[0160] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0161] Based on the full-time values ​​of each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the time-series curve and the time axis for each power quality indicator. Based on the power quality indicator limits for each power quality indicator within the time-series indicator matrix, piecewise trapezoidal integration is used to determine the area enclosed by the limit curve and the time axis for each power quality indicator. Based on the ratio of the area enclosed by the indicator to the area enclosed by the limit for each power quality indicator, the one-dimensional power quality factor for each power quality indicator is determined.

[0162] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0163] Based on each power quality index at each time step in the three-dimensional data tensor, the ratio of the standard deviation to the mean is calculated to obtain the time-series sensitivity coefficient; based on the preset load sensitivity weight and the load capacity of each load point, the load weight is determined; based on the time-series sensitivity coefficient and the load weight, the power quality index weight corresponding to each power quality index at each time step is determined.

[0164] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0165] Based on the power quality index weights corresponding to each power quality index at each time point, the corresponding power quality indices in the three-dimensional data tensor are weighted and summed to obtain the comprehensive power quality value.

[0166] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0167] The limits of various power quality indicators are weighted and calculated based on the power quality indicator weights to generate a dynamic comprehensive benchmark limit. The comprehensive actual area is obtained by integrating the time series curve and time axis of the comprehensive power quality value. The comprehensive benchmark area is obtained by integrating the time series curve and time axis of the dynamic comprehensive benchmark limit. The comprehensive power quality factor is determined based on the ratio of the comprehensive actual area to the comprehensive benchmark area.

[0168] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0169] The single-dimensional power quality factor and the comprehensive power quality factor under each candidate grid connection point are summed and normalized in turn to obtain the grid connection evaluation coefficient of each candidate grid connection point. The grid connection evaluation coefficients are sorted from small to large, and the grid connection point corresponding to the grid connection evaluation coefficient with the highest sorted grid connection evaluation coefficient is taken as the optimal grid connection point of the photovoltaic-storage system.

[0170] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0171] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0172] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for designing the grid connection point of a photovoltaic-storage system in a distribution network, characterized in that, The method includes: For any candidate grid connection point in the distribution network, based on the historical daily load data and photovoltaic output data of the distribution network, the all-day power quality index of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point is determined; based on the power quality index, a time-series index matrix is ​​constructed. Based on the aforementioned time-series index matrix and power quality index limits, a one-dimensional power quality factor is determined. Based on the time-series index matrix, a three-dimensional data tensor is constructed, comprising nodes, time points, and power quality indicators. Based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity, the weights of each power quality indicator are determined. Based on the power quality indicator weights and the three-dimensional data tensor, a comprehensive power quality indicator value is determined. Based on the comprehensive power quality indicator value, the power quality indicator weights, and power quality indicator limits, the comprehensive power quality factor corresponding to the grid connection point is determined. The optimal grid connection point for the photovoltaic-storage system is determined based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

2. The method according to claim 1, characterized in that, The determination of the single-dimensional power quality factor based on the time-series index matrix and power quality index limits includes: Based on the full-time values ​​of each power quality indicator in the time series index matrix, piecewise trapezoidal integration is used to determine the area enclosed by the time series curve and the time axis for each power quality indicator. Based on the power quality index limit of each power quality index in the time series index matrix, piecewise trapezoidal integration is used to determine the area enclosed by the limit curve and the time axis for each power quality index. Based on the ratio of the area enclosed by the indicator to the area enclosed by the limit for each power quality indicator, the one-dimensional power quality factor for each power quality indicator is determined.

3. The method according to claim 1, characterized in that, The determination of the weights for each power quality index based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity includes: Based on each power quality index at each time step in the three-dimensional data tensor, the ratio of the standard deviation to the mean is calculated to obtain the time-series sensitivity coefficient. The load weight is determined based on the preset load sensitivity weight and the load capacity of each load point; Based on the time-series sensitivity coefficient and load weight, the power quality index weight corresponding to each power quality index at each time point is determined.

4. The method according to claim 1, characterized in that, The determination of the comprehensive power quality value based on the power quality weights and the three-dimensional data tensor includes: Based on the power quality weights corresponding to each power quality index at each time point, the corresponding power quality indices within the three-dimensional data tensor are weighted and summed to obtain the comprehensive power quality index value.

5. The method according to claim 1, characterized in that, The determination of the comprehensive power quality factor corresponding to the grid connection point based on the comprehensive power quality index value, the power quality index weight, and the power quality index limit includes: Based on the power quality index weights, the limits of each power quality index are weighted and calculated to generate a dynamic comprehensive benchmark limit. The comprehensive actual area is obtained by integrating the time-series curve and time axis based on the comprehensive power quality index value. The integrated benchmark area is obtained by integrating the time-series curve and time axis based on the dynamic integrated benchmark limit; The comprehensive power quality factor is determined based on the ratio of the comprehensive actual area to the comprehensive benchmark area.

6. The method according to claim 1, characterized in that, The process of determining the optimal grid connection point for the photovoltaic-storage system based on the single-dimensional power quality factor and comprehensive power quality factor corresponding to each grid connection point includes: The single-dimensional power quality factor and the comprehensive power quality factor under each candidate grid connection point are summed and normalized in turn to obtain the grid connection evaluation coefficient of each candidate grid connection point. The grid connection evaluation coefficients are sorted from smallest to largest, and the grid connection points corresponding to the highest grid connection evaluation coefficients are taken as the optimal grid connection points for the photovoltaic-storage system.

7. A grid connection point design device for a photovoltaic-storage system in a power distribution network, characterized in that, The device includes: The module is used to determine the all-day power quality index of each observation point after the photovoltaic-storage system is connected to the candidate grid connection point, based on the historical daily load data and photovoltaic output data of the distribution network, for any candidate grid connection point in the distribution network; and to construct a time-series index matrix based on the power quality index. The first calculation module is used to determine the one-dimensional power quality factor based on the time series index matrix and the power quality index limit. The second calculation module is used to construct a three-dimensional data tensor including nodes, time points, and power quality indicators based on the time-series indicator matrix; determine the power quality indicator weights based on the three-dimensional data tensor, preset load sensitivity weights, and load capacity; determine the comprehensive power quality value based on the power quality indicator weights and the three-dimensional data tensor; and determine the comprehensive power quality factor corresponding to the grid connection point based on the comprehensive power quality value, the power quality indicator weights, and power quality indicator limits. The output module is used to determine the optimal grid connection point for the photovoltaic-storage system based on the single-dimensional power quality factor and the comprehensive power quality factor corresponding to each grid connection point.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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