Source-load collaborative optimization planning method, device and equipment based on consumption matching degree

Abstract

This invention discloses a source-load collaborative optimization planning method, apparatus, and equipment based on absorption matching degree. The method includes: acquiring historical power grid operation data; optimizing the source-load curve based on the historical power grid operation data to construct source-load access schemes; calculating the absorption matching degree corresponding to each source-load access scheme using an absorption matching degree calculation model; sorting the absorption matching degrees of each source-load access scheme from high to low, and determining the distribution network planning scheme by combining the installed capacity of distributed new energy and the load size; the absorption matching degree calculation model is expressed as: m = ρr + (1 - ρ)c, where m represents the absorption matching degree; r represents the similarity evaluation coefficient, reflecting the strength of the linear similarity between new energy and load; c represents the new energy absorption evaluation coefficient, reflecting the degree to which the new energy power generation is absorbed locally by the load; and ρ represents the weight value of the similarity evaluation coefficient. This method can improve the absorption matching degree between new energy and load in the distribution network and the level of local absorption of new energy.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and specifically to a source-load collaborative optimization planning method, device, and equipment based on absorption matching degree. Background Technology

[0002] Distribution network planning is an important component of power grid planning. It serves as a guiding document for the development of distribution networks and the basis for their construction and renovation. Conducting distribution network planning and design, and formulating scientific and reasonable planning schemes, can improve the power supply capacity, reliability, and quality of the distribution network, meet load growth, adapt to flexible access by power sources and users, achieve economical and efficient system operation, and effectively enhance the quality and efficiency of distribution network development.

[0003] Traditional power grid planning methods rely on planners' experience to conduct qualitative analysis and load forecasting. Based on this forecasted load data and power source infrastructure, they then assess local conditions, develop several feasible network expansion plans, and analyze and gradually filter these plans. Through comprehensive analysis and comparison, they estimate the investment and operating costs of various construction projects, weigh the pros and cons, and finally recommend a plan that offers reliable power supply, rapid infrastructure construction, low investment, low operating costs, convenient maintenance, and high economic benefits. The advantage of this traditional method is its intuitiveness and ability to be combined with the planners' valuable experience, allowing for tailored planning solutions based on local conditions.

[0004] With the introduction of the "dual carbon" target, the large-scale access of distributed new energy sources, represented by wind power and photovoltaics, and diversified loads to the distribution network has given the power distribution system a new form of diversified power supply, interactive power consumption, and power electronics. The planning and operation mode of the distribution network will also change from "source follows load" to "source and load interaction".

[0005] In the planning and operation of power distribution networks, distributed power sources are mainly connected to different voltage levels according to different installed capacity. When connecting loads, only the superposition of the maximum power load of different power users is considered as the load forecast result to determine the scale of power grid construction and user access system scheme. The matching relationship between the output of distributed power sources and the load they supply is not considered, which can easily lead to a mismatch between source and load supply and demand, widen the load peak-valley difference, reduce equipment utilization efficiency, and hinder the local consumption of new energy. Summary of the Invention

[0006] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, one objective of this invention is to propose a source-load coordinated optimization planning method and system that improves the matching degree of renewable energy and load absorption in distribution networks.

[0007] This invention proposes a source-load collaborative optimization planning method based on absorption matching degree, the method comprising:

[0008] Acquire historical power grid operation data, which includes new energy data, load data, and equipment configuration data;

[0009] Based on the historical power grid operation data, the source-load curve is optimized to construct a source-load access scheme;

[0010] The absorption matching degree calculation model is used to calculate the absorption matching degree corresponding to each source load access scheme;

[0011] The matching degree of each source-load access scheme is ranked from high to low, and the distribution network planning scheme is determined by combining the scale of distributed new energy installed capacity and load size.

[0012] The formula for calculating the absorption matching degree is expressed as follows:

[0013] m=ρr+(1-ρ)c

[0014] In the formula: m represents the matching degree of absorption; r represents the similarity evaluation coefficient, which reflects the strength of the linear similarity between new energy and load; c represents the new energy absorption evaluation coefficient, which reflects the degree to which the new energy power generation is absorbed by the load on-site; ρ represents the weight value of the similarity evaluation coefficient, 0 < ρ < 1.

[0015] Preferably, the step of optimizing the source-load curve based on the historical power grid operation data and constructing a source-load access scheme includes:

[0016] Based on the new energy data, the new energy characteristic curve is determined; based on the load data, the scale of flexible load resources and the characteristic curve of multiple loads are determined.

[0017] Based on the equipment configuration data, the smooth output of energy storage is determined, and the new energy characteristic curve is optimized to obtain the optimized new energy characteristic curve.

[0018] Considering the demand-side response of the flexible resource load scale, the multi-element load characteristic curve is optimized to obtain the optimized multi-element load characteristic curve;

[0019] Based on the optimized new energy characteristic curve and the optimized multi-element load characteristic curve, and combined with the regional spatial load prediction results, the source-load access scheme is constructed.

[0020] Preferably, the similarity evaluation coefficient r is calculated using the following formula:

[0021]

[0022] In the formula: x iThis represents the value of sampling point i on the new energy output curve; y i represents the value of sampling point i of the load curve; N represents the total number of sampling points; r represents the correlation between the new energy output curve x and the load demand curve y, with a value range of [-1, 1].

[0023] Preferably, the formula for calculating the new energy consumption evaluation coefficient c is:

[0024]

[0025] In the formula: x i This represents the value of sampling point i on the new energy output curve; y i This represents the value of sampling point i on the load curve; when x i =y i When, min(x) i ,y i )=x i or y i ;c represents the degree to which the renewable energy generation is absorbed locally by the load, and its value ranges from [0,1]; when x i >y i At that time, the output of new energy sources cannot be fully absorbed by the load; when x i ≤y i At that time, the power output of new energy sources was completely absorbed by the load.

[0026] Preferably, the weight value ρ of the similarity evaluation coefficient is 0.5.

[0027] Preferably, after ranking the absorption matching degree of each source-load access scheme from high to low, and determining the distribution network planning scheme in combination with the scale of distributed new energy installed capacity and load size, the method further includes:

[0028] Based on the medium-voltage line current carrying capacity and equipment parameter configuration, a four-level verification method is used to simulate and verify the power distribution network planning scheme, and the verification results are obtained.

[0029] Based on the verification results, the power distribution network planning scheme is adjusted and optimized.

[0030] The most economical planning scheme among the adjusted and optimized distribution network planning schemes is selected as the optimal distribution network planning scheme.

[0031] Preferably, the four-level verification method includes transformer-level verification, feeder-level verification, medium-voltage busbar-level verification, and main transformer-level verification.

[0032] Furthermore, this invention also proposes a source-load collaborative optimization planning device based on absorption matching degree, the device comprising:

[0033] The data acquisition module is used to acquire historical power grid operation data, which includes new energy data, load data, and equipment configuration data.

[0034] The source-load access scheme construction module is used to optimize the source-load curve based on the historical data of the power grid operation and construct a source-load access scheme.

[0035] The load matching degree calculation module is used to calculate the load matching degree corresponding to each source load access scheme using the load matching degree calculation model.

[0036] The planning scheme determination module is used to sort the absorption matching degree of each source load access scheme from high to low, and determine the distribution network planning scheme in combination with the scale of distributed new energy installed capacity and load size.

[0037] The formula for calculating the absorption matching degree is expressed as follows:

[0038] m=ρr+(1-ρ)c

[0039] In the formula: m represents the matching degree of absorption; r represents the similarity evaluation coefficient, which reflects the strength of the linear similarity between new energy and load; c represents the new energy absorption evaluation coefficient, which reflects the degree to which the new energy power generation is absorbed by the load on-site; ρ represents the weight value of the similarity evaluation coefficient, 0 < ρ < 1.

[0040] Furthermore, the present invention also proposes an apparatus comprising a memory and a processor; wherein the processor runs a program corresponding to the executable program code stored in the memory to implement the method described above.

[0041] Furthermore, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0042] According to the source-load coordinated optimization planning method, apparatus and equipment based on absorption matching degree of the present invention, by considering the strength of the linear similarity between new energy and load and the degree to which the power generation of new energy is absorbed locally by the load, the correlation between new energy and load can be quantified, compared, referenced and applied, thereby determining the distribution network planning scheme based on absorption matching degree, which can improve the source-load coordination of the distribution network, realize the optimized layout of new energy and the optimized access of multiple loads, and promote the local absorption of distributed new energy.

[0043] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a source-load collaborative optimization planning method based on absorption matching degree in one embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram illustrating the correlation between the power output curve and the load curve of a new energy source in one embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of the new energy absorption coefficient in one embodiment of the present invention;

[0047] Figure 4 This is an overall flowchart of source-load collaborative optimization planning based on absorption matching degree in one embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the source-load collaborative optimization planning device based on absorption matching degree in one embodiment of the present invention. Detailed Implementation

[0049] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0050] The following is a reference appendix. Figure 1 This invention describes a source-load collaborative optimization planning method based on absorption matching degree, the method comprising the following steps:

[0051] S1. Obtain historical power grid operation data, which includes new energy data, load data, and equipment configuration data;

[0052] It should be noted that by sorting through historical new energy data, load datasets, and equipment configuration data, characteristic curves of new energy and diversified loads can be obtained. Based on the plan, the installed capacity of new energy, diversified loads (including flexible loads), and energy storage configuration can be predicted. Combined with the current grid structure and capacity configuration, the distributed power absorption capacity and load carrying capacity of the current power grid can be evaluated.

[0053] S2. Based on the historical data of the power grid operation, optimize the source-load curve and construct a source-load access scheme;

[0054] It should be noted that, based on the scale of energy storage configuration for new energy power generation and considering the role of energy storage in stabilizing output, the output characteristic curve of new energy is optimized and adjusted; based on the scale of flexible loads such as electric vehicles and air conditioning cooling, considering the peak-shaving and valley-filling effects of flexible load demand-side response, the characteristic curve of diversified loads is optimized and adjusted. Combining regional spatial load forecasting results, and based on the spatial layout of internal new energy and diversified loads, a new energy and load access scheme is constructed.

[0055] S3. Calculate the absorption matching degree corresponding to each source load access scheme using the absorption matching degree calculation model;

[0056] S4. Sort the absorption matching degree of each source load access scheme in descending order, and determine the distribution network planning scheme in combination with the scale of distributed new energy installed capacity and load size.

[0057] The formula for calculating the absorption matching degree is expressed as follows:

[0058] m=ρr+(1-ρ)c

[0059] In the formula: m represents the matching degree of absorption; r represents the similarity evaluation coefficient, which reflects the strength of the linear similarity between new energy and load; c represents the new energy absorption evaluation coefficient, which reflects the degree to which the new energy power generation is absorbed by the load on-site; ρ represents the weight value of the similarity evaluation coefficient, 0 < ρ < 1.

[0060] It should be noted that the correlation between renewable energy output and load is mainly reflected in two aspects: firstly, the similarity between the trend of renewable energy output change (fluctuation) and the trend of load change. The higher the similarity between the two trends, the higher the renewable energy output during peak load periods. Secondly, the renewable energy absorption level (absorption rate). The higher the absorption rate, the higher the reliability of renewable energy output. Corresponding to the above two aspects, this embodiment constructs a renewable energy absorption matching degree model between renewable energy and load from the perspectives of correlation evaluation and absorption evaluation. This improves the absorption matching degree between renewable energy and load in the distribution network, effectively enhances the local absorption level of renewable energy, reduces power flow transmission, ensures the reliability and power quality of power supply, and reduces the cost of grid construction and operation.

[0061] This embodiment considers the strength of the linear similarity between new energy sources and loads, as well as the degree to which new energy generation is absorbed locally by the loads. This enables the correlation between new energy sources and loads to be quantifiable, comparable, referable, and applicable, thereby determining a distribution network planning scheme based on absorption matching degree. This can improve the source-load coordination of the distribution network, achieve optimized layout of new energy sources and optimized access of diverse loads, and promote the local absorption of distributed new energy sources.

[0062] As a further preferred technical solution, in step S2, based on the historical data of the power grid operation, the source-load curve is optimized to construct a source-load access scheme, including the following steps:

[0063] S21. Determine the new energy characteristic curve based on the new energy data, and determine the flexible load resource scale and multi-load characteristic curve based on the load data;

[0064] S22. Based on the equipment configuration data, determine the smooth output of energy storage, optimize the new energy characteristic curve, and obtain the optimized new energy characteristic curve;

[0065] S22. Considering the demand-side response of the flexible resource load scale, optimize the multi-element load characteristic curve to obtain the optimized multi-element load characteristic curve;

[0066] S23. Based on the optimized new energy characteristic curve and the optimized multi-element load characteristic curve, and combined with the regional spatial load prediction results, construct the source-load access scheme.

[0067] It should be noted that this embodiment considers the role of energy storage in smoothing the output of new energy sources and the demand-side response of flexible loads, and proposes a method for optimizing and adjusting the characteristic curves of new energy sources and loads.

[0068] As a further preferred technical solution, the formula for calculating the similarity evaluation coefficient r is:

[0069]

[0070] In the formula: x i This represents the value of sampling point i on the new energy output curve; y i represents the value of sampling point i of the load curve; N represents the total number of sampling points; r represents the correlation between the new energy output curve x and the load demand curve y, with a value range of [-1, 1].

[0071] In detail, similarity evaluation is used to reflect the similarity between the output of new energy sources and the changing trends of load. The similarity evaluation coefficient reflects the strength of the linear similarity between new energy sources and load. The larger the coefficient, the stronger the similarity, and vice versa.

[0072] The similarity evaluation coefficients were calculated at the actual discrete time scale. The correspondence between the similarity evaluation coefficients and the source-load correlation strength is shown in Table 1.

[0073] Table 1 Correspondence between similarity evaluation coefficient and source-load correlation strength

[0074]

[0075] (1) When r xy When the value is >0, it indicates that the output of new energy sources is positively correlated with the load, meaning that if one value changes, the other value will also change in the same direction.

[0076] (2) When r<0, it indicates that the output of new energy is negatively correlated with the load, that is, when one value changes, the other value will move in the opposite direction;

[0077] (3) When r = 0, it indicates that the output of new energy sources is not related to the load;

[0078] (4) When r = 1 or -1, it means that the output of new energy sources is completely positively correlated with the load and completely negatively correlated with the load.

[0079] For the actual output and load curves of new energy sources (taking photovoltaics as an example), the correlation between the curves is reflected by a similarity coefficient, such as... Figure 2 As shown.

[0080] As a further preferred technical solution, the formula for calculating the new energy consumption evaluation coefficient c is as follows:

[0081]

[0082] In the formula: x i This represents the value of sampling point i on the new energy output curve; y i This represents the value of sampling point i on the load curve; when x i =y i When, min(x) i ,y i )=x i or y i ;c represents the degree to which the renewable energy generation is absorbed locally by the load, and its value ranges from [0,1]; when x i >y i At that time, the output of new energy sources cannot be fully absorbed by the load; when x i ≤y i At that time, the power output of new energy sources was completely absorbed by the load.

[0083] In detail, the absorption evaluation reflects the degree to which renewable energy generation is absorbed locally by the load. For the renewable energy output curve X and the load curve Y, within the time interval Δt, when x i >y i At this time, the output of new energy sources cannot be fully absorbed by the load. During this period, the amount of new energy absorbed depends on the load size y. i Decision; when x i ≤y i At this time, the output of new energy is completely absorbed by the load. The amount of new energy absorbed during this period is calculated by multiplying the output of new energy by the output of new energy. i Decide.

[0084] For the actual output and load curves of new energy sources (taking photovoltaics as an example), the degree of absorption of new energy output is reflected by the absorption evaluation coefficient, such as... Figure 3 As shown.

[0085] As a further preferred technical solution, a weighted summation method is used to calculate the absorption matching degree for similarity evaluation and absorption evaluation. Considering the actual situation, since both similarity evaluation and absorption evaluation indicators play an equal role in influencing the magnitude of the source-load absorption matching degree, their weights are both set to 0.5. Therefore, the formula for calculating the absorption matching degree m is:

[0086] m = 0.5r + 0.5c

[0087] The larger the value of m, the higher the degree of matching between the output of new energy sources and the load absorption.

[0088] This embodiment utilizes a matching degree calculation model to calculate the matching degree between renewable energy and diverse loads, ranking them from highest to lowest. Combining the installed capacity of distributed renewable energy and load size, it constructs an optimized source-load matching relationship and provides an optimized distribution network planning scheme. Considering different load characteristics, different distributed renewable energy output characteristics, and the impact of energy storage configuration and flexible load scale, it improves the matching degree between renewable energy and loads in the distribution network, effectively enhancing the local consumption level of renewable energy, reducing power flow transmission, ensuring power supply reliability and power quality, and simultaneously reducing the cost of grid construction and operation.

[0089] As a further preferred technical solution, such as Figure 4 As shown, after step S4: sorting the absorption matching degree of each source-load access scheme from high to low, and determining the distribution network planning scheme in combination with the scale of distributed new energy installed capacity and load size, the method further includes the following steps:

[0090] Based on the medium-voltage line current carrying capacity and equipment parameter configuration, a four-level verification method is used to simulate and verify the power distribution network planning scheme, and the verification results are obtained.

[0091] Based on the verification results, the power distribution network planning scheme is adjusted and optimized.

[0092] The most economical planning scheme among the adjusted and optimized distribution network planning schemes is selected as the optimal distribution network planning scheme.

[0093] It should be noted that this embodiment utilizes a time-series simulation platform to perform digital simulation and operational analysis of the optimized planning scheme. A four-level verification method (distribution transformer level, feeder level, medium-voltage bus level, and main transformer level) is applied to conduct safety verification with the goals of fully integrating distributed renewable energy locally, optimizing distribution network resource allocation, and achieving optimal economic efficiency. Based on the verification results, the connection location, scale, interconnection, and equipment models of renewable energy and loads are optimized, and the planning scheme is adjusted. Under the premise of meeting safety verification requirements, the most economically efficient planning scheme is selected to achieve the best matching of distributed renewable energy and loads at different voltage levels and in different regions.

[0094] As a further preferred technical solution, the four-level verification method includes transformer-level verification, feeder-level verification, medium-voltage busbar-level verification, and main transformer-level verification.

[0095] In detail, the transformer-level verification involves verifying whether the power backfeed exceeds the transformer limit, that is, comprehensively considering the backfeed limit that meets the transformer's capacity requirements after the power supply is connected.

[0096] Feeder-level and medium-voltage busbar-level verification: Verify whether the power backfeed exceeds the line limit, that is, comprehensively consider the backfeed limit to meet the line voltage requirements after the power supply is connected (the maximum voltage deviation and the maximum voltage fluctuation do not exceed the limit, and the maximum voltage deviation caused solely by the load does not exceed the limit).

[0097] Transformer-level verification: Verify whether the power backfeed exceeds the transformer limit, that is, comprehensively consider the backfeed limit that meets the transformer capacity requirements after the power supply is connected.

[0098] This embodiment utilizes a time-series simulation platform to perform digital simulation and operational analysis of the planning scheme, and proposes a planning optimization method that coordinates the supply and demand sides of the distribution network to achieve the best matching of distributed new energy sources and loads in different voltage levels and different regions.

[0099] In addition, such as Figure 5 As shown, an embodiment of the present invention also proposes a source-load collaborative optimization planning device based on absorption matching degree, the device comprising:

[0100] Data acquisition module 1 is used to acquire historical power grid operation data, which includes new energy data, load data and equipment configuration data;

[0101] Source-load access scheme construction module 2 is used to optimize the source-load curve based on the historical data of the power grid operation and construct a source-load access scheme;

[0102] The absorption matching degree calculation module 3 is used to calculate the absorption matching degree corresponding to each source load access scheme using the absorption matching degree calculation model;

[0103] The planning scheme determination module 4 is used to sort the absorption matching degree of each source load access scheme from high to low, and determine the distribution network planning scheme in combination with the scale of distributed new energy installed capacity and load size.

[0104] The formula for calculating the absorption matching degree is expressed as follows:

[0105] m=ρr+(1-ρ)c

[0106] In the formula: m represents the matching degree of absorption; r represents the similarity evaluation coefficient, which reflects the strength of the linear similarity between new energy and load; c represents the new energy absorption evaluation coefficient, which reflects the degree to which the new energy power generation is absorbed by the load on-site; ρ represents the weight value of the similarity evaluation coefficient, 0 < ρ < 1.

[0107] This embodiment considers the strength of the linear similarity between new energy sources and loads, as well as the degree to which new energy generation is absorbed locally by the loads. This enables the correlation between new energy sources and loads to be quantifiable, comparable, referable, and applicable, thereby determining a distribution network planning scheme based on absorption matching degree. This can improve the source-load coordination of the distribution network, achieve optimized layout of new energy sources and optimized access of diverse loads, and promote the local absorption of distributed new energy sources.

[0108] As a further preferred technical solution, the source-load access scheme construction module 2 includes:

[0109] The data analysis unit is used to determine the new energy characteristic curve based on the new energy data, and to determine the flexible load resource scale and multi-load characteristic curve based on the load data.

[0110] The first curve optimization unit is used to determine the smooth output of energy storage based on the equipment configuration data, optimize the new energy characteristic curve, and obtain the optimized new energy characteristic curve.

[0111] The second curve optimization unit is used to optimize the multi-element load characteristic curve by taking into account the demand-side response of the flexible resource load scale, and to obtain the optimized multi-element load characteristic curve.

[0112] The source-load scheme determination unit is used to construct the source-load access scheme based on the optimized new energy characteristic curve and the optimized multi-element load characteristic curve, combined with the regional spatial load prediction results.

[0113] As a further preferred technical solution, the formula for calculating the similarity evaluation coefficient r is:

[0114]

[0115] In the formula: x i This represents the value of sampling point i on the new energy output curve; y i represents the value of sampling point i of the load curve; N represents the total number of sampling points; r represents the correlation between the new energy output curve x and the load demand curve y, with a value range of [-1, 1].

[0116] As a further preferred technical solution, the formula for calculating the new energy consumption evaluation coefficient c is as follows:

[0117]

[0118] In the formula: x i This represents the value of sampling point i on the new energy output curve; y i This represents the value of sampling point i on the load curve; when x i =y i When, min(x) i ,y i )=x i or y i ;c represents the degree to which the renewable energy generation is absorbed locally by the load, and its value ranges from [0,1]; when x i >y i At that time, the output of new energy sources cannot be fully absorbed by the load; when x i ≤y i At that time, the power output of new energy sources was completely absorbed by the load.

[0119] As a further preferred technical solution, a weighted summation method is used to calculate the absorption matching degree for similarity evaluation and absorption evaluation. Considering the actual situation, since both similarity evaluation and absorption evaluation indicators play an equal role in influencing the magnitude of the source-load absorption matching degree, their weights are both set to 0.5. Therefore, the formula for calculating the absorption matching degree m is:

[0120] m = 0.5r + 0.5c

[0121] The larger the value of m, the higher the degree of matching between the output of new energy sources and the load absorption.

[0122] As a further preferred technical solution, the device further includes:

[0123] The simulation verification module is used to perform simulation verification of the power distribution network planning scheme based on the medium-voltage line current carrying capacity and equipment parameter configuration, and to obtain the verification results.

[0124] The scheme optimization module is used to adjust and optimize the power distribution network planning scheme based on the verification results;

[0125] The optimal solution determination module is used to select the most economical planning scheme from the adjusted and optimized distribution network planning schemes as the optimal planning scheme for the distribution network.

[0126] As a further preferred technical solution, the four-level verification method includes transformer-level verification, feeder-level verification, medium-voltage busbar-level verification, and main transformer-level verification.

[0127] In detail, the transformer-level verification involves verifying whether the power backfeed exceeds the transformer limit, that is, comprehensively considering the backfeed limit that meets the transformer's capacity requirements after the power supply is connected.

[0128] This embodiment utilizes a time-series simulation platform to perform digital simulation and operational analysis of the planning scheme, and proposes a planning optimization method that coordinates the supply and demand sides of the distribution network to achieve the best matching of distributed new energy sources and loads in different voltage levels and different regions.

[0129] Furthermore, one embodiment of the present invention also proposes a device, the device including a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the source-load collaborative optimization planning method based on absorption matching degree described above.

[0130] Furthermore, one embodiment of the present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the source-load collaborative optimization planning method based on absorption matching degree as described above.

[0131] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0132] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0133] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A source-load collaborative optimization planning method based on absorption matching degree, characterized in that, The method includes: Acquire historical power grid operation data, which includes new energy data, load data, and equipment configuration data; Based on the historical power grid operation data, the source-load curve is optimized to construct a source-load access scheme; The absorption matching degree calculation model is used to calculate the absorption matching degree corresponding to each source load access scheme; The matching degree of each source-load access scheme is ranked from high to low, and the distribution network planning scheme is determined by combining the scale of distributed new energy installed capacity and load size. The formula for calculating the absorption matching degree is expressed as follows: In the formula: m Indicates the degree of matching in terms of absorption; r The similarity evaluation coefficient is used to reflect the strength of the linear similarity between new energy sources and loads. c This represents the evaluation coefficient for renewable energy consumption, used to reflect the degree to which renewable energy generation is consumed locally by the load. This represents the weight value of the similarity evaluation coefficient. The similarity evaluation coefficient r The calculation formula is: In the formula: x i Indicates the sampling points of the new energy output curve i The value; y i Indicates the sampling points of the load curve i The value; N Indicates the total number of sampling points; Represents the power output curve of new energy sources x and load demand curve y The degree of correlation between them, with values ​​ranging from [-1, 1]; The evaluation coefficient for new energy consumption c The calculation formula is: In the formula: x i Indicates the sampling points of the new energy output curve i The value; y i Indicates the sampling points of the load curve i The value; when x i =y i When, min( x i ,y i )= x i or y i ; c This indicates the degree to which renewable energy generation is absorbed locally by the load, and its value ranges from [0,1]. x i > y i At that time, the output of new energy sources cannot be fully absorbed by the load; when x i ≤ y i At that time, the power output of new energy sources was completely absorbed by the load.

2. The source-load collaborative optimization planning method based on absorption matching degree as described in claim 1, characterized in that, The optimization of the source-load curve based on the historical power grid operation data and the construction of the source-load access scheme include: Based on the new energy data, the new energy characteristic curve is determined; based on the load data, the scale of flexible load resources and the characteristic curve of multiple loads are determined. Based on the equipment configuration data, the smooth output of energy storage is determined, and the new energy characteristic curve is optimized to obtain the optimized new energy characteristic curve. Considering the demand-side response of the flexible resource load scale, the multi-element load characteristic curve is optimized to obtain the optimized multi-element load characteristic curve; Based on the optimized new energy characteristic curve and the optimized multi-element load characteristic curve, and combined with the regional spatial load prediction results, the source-load access scheme is constructed.

3. The source-load collaborative optimization planning method based on absorption matching degree as described in claim 1, characterized in that, The weight values ​​of the similarity evaluation coefficient The value is 0.

5.

4. The source-load collaborative optimization planning method based on absorption matching degree as described in claim 1, characterized in that, After ranking the absorption matching degree of each source-load access scheme from high to low, and determining the distribution network planning scheme in combination with the scale of distributed new energy installed capacity and load size, the method further includes: Based on the medium-voltage line current carrying capacity and equipment parameter configuration, a four-level verification method is used to simulate and verify the power distribution network planning scheme, and the verification results are obtained. Based on the verification results, the power distribution network planning scheme is adjusted and optimized. The most economical planning scheme among the adjusted and optimized distribution network planning schemes is selected as the optimal distribution network planning scheme.

5. The source-load collaborative optimization planning method based on absorption matching degree as described in claim 4, characterized in that, The four-level verification method includes transformer-level verification, feeder-level verification, medium-voltage busbar-level verification, and main transformer-level verification.

6. A source-load collaborative optimization planning device based on absorption matching degree, characterized in that, The device includes: The data acquisition module is used to acquire historical power grid operation data, which includes new energy data, load data, and equipment configuration data. The source-load access scheme construction module is used to optimize the source-load curve based on the historical data of the power grid operation and construct a source-load access scheme. The load matching degree calculation module is used to calculate the load matching degree corresponding to each source load access scheme using the load matching degree calculation model. The planning scheme determination module is used to sort the absorption matching degree of each source load access scheme from high to low, and determine the distribution network planning scheme in combination with the scale of distributed new energy installed capacity and load size. The formula for calculating the absorption matching degree is expressed as follows: In the formula: m Indicates the degree of matching in terms of absorption; r The similarity evaluation coefficient is used to reflect the strength of the linear similarity between new energy sources and loads. c This represents the evaluation coefficient for renewable energy consumption, used to reflect the degree to which renewable energy generation is consumed locally by the load. This represents the weight value of the similarity evaluation coefficient. ; The similarity evaluation coefficient r The calculation formula is: In the formula: x i Indicates the sampling points of the new energy output curve i The value; y i Indicates the sampling points of the load curve i The value; N Indicates the total number of sampling points; Represents the power output curve of new energy sources x and load demand curve y The degree of correlation between them, with values ​​ranging from [-1, 1]; The evaluation coefficient for new energy consumption c The calculation formula is: In the formula: x i Indicates the sampling points of the new energy output curve i The value; y i Indicates the sampling points of the load curve i The value; when x i =y i When, min( x i ,y i )= x i or y i ; c This indicates the degree to which renewable energy generation is absorbed locally by the load, and its value ranges from [0,1]. x i > y i At that time, the output of new energy sources cannot be fully absorbed by the load; when x i ≤ y i At that time, the power output of new energy sources was completely absorbed by the load.

7. A device, characterized in that, The device includes a memory and a processor; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, so as to implement the method as described in any one of claims 1-5.

8. 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 method as described in any one of claims 1-5.

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