Alternating current and direct current power distribution network structure selection method, device, equipment and medium

Abstract

The invention discloses an AC / DC power distribution network structure selection method comprising the following steps: obtaining typical scene data and candidate grid structure information, the typical scene data comprising scene load, distributed energy node information and a typical daily power curve; establishing a flexibility adjustment capability demand index system, and forming a multi-dimensional demand vector; calculating a flexibility evaluation index of each grid structure in a typical scene, and forming a capability vector; carrying out maximum and minimum normalization processing on the demand vector and the capability vector; and calculating the cosine similarity of the normalized capability vector and the demand vector as a matching degree, and sorting and selecting an optimal grid structure based on the matching degree. According to the method, multiple dimensions such as economical efficiency, new energy consumption rate and electric energy quality can be comprehensively considered, a scientific and visual decision basis is provided for complex AC / DC power distribution network architecture type selection, so that the problem that multiple targets are difficult to consider in a traditional method is solved, and the planning accuracy and efficiency are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fuzzy evaluation, and more particularly, to a method, device, equipment and medium for AC / DC power distribution network structure selection. BACKGROUND

[0002] In recent years, AC / DC hybrid power distribution networks gradually become an important direction of power distribution system development because they have the advantages of both traditional AC power distribution networks and DC power distribution networks. AC / DC hybrid power distribution networks exhibit great advantages in power supply flexibility, energy scheduling efficiency, and adaptation to new energy access. However, different typical scenarios (such as high-proportion distributed power scenarios, large-scale electric vehicle charging scenarios, and emergency power restoration scenarios) have significantly different flexibility requirements for power distribution networks, which makes the architecture design and selection of AC / DC hybrid power distribution networks a complex problem.

[0003] In the prior art, the network architecture design of AC / DC hybrid power distribution networks mostly focuses on a single scenario or a single target, such as economic efficiency, power supply reliability, or power quality as the optimization target, lacking systematic analysis of multi-scenario and multi-target flexibility requirements. At the same time, there is also a lack of unified indicators and scientific quantitative methods for evaluating the flexibility of AC / DC hybrid power distribution networks in different typical scenarios. Due to these deficiencies, the existing power distribution network architecture design method cannot effectively match the actual requirements of typical scenarios, limiting the improvement of the flexibility and adaptability of AC / DC hybrid power distribution networks. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide an AC / DC hybrid power distribution network structure selection method, which can comprehensively consider economic efficiency, new energy consumption rate, power quality and other multiple dimensions, and provide scientific and intuitive decision-making basis for complex AC / DC hybrid power distribution network architecture selection, thereby solving the problem that traditional methods cannot consider multiple targets, and significantly improving the accuracy and efficiency of planning.

[0005] According to a first aspect of the present disclosure, an AC / DC hybrid power distribution network structure selection method is provided, comprising: obtaining typical scenario data and candidate network architecture structure information, wherein the typical scenario data includes scenario load, distributed energy node information, and typical daily power curve; establishing a flexibility adjustment capability demand index system to form a multi-dimensional demand vector; calculating the flexibility evaluation index of each network architecture structure in the typical scenario to form a capability vector; performing maximum minimum normalization processing on the demand vector and the capability vector; calculating the cosine similarity between the normalized capability vector and the demand vector as the matching degree, and selecting the optimal network architecture structure based on the matching degree.

[0006] The acquisition of typical scenario data and candidate network structure information includes: Based on power grid characteristics and load types, typical operating scenarios are defined; Collect basic data for typical scenarios, including load curves, distributed power generation output, and electricity price information; Based on the basic data, a set of candidate grid structures including various AC / DC hybrid structures was determined.

[0007] The establishment of a flexibility and adaptability requirement index system, forming a multi-dimensional requirement vector, specifically includes: The core requirements of typical scenarios are analyzed and quantified into key evaluation indicators, including new energy consumption rate, voltage deviation, line overload rate and economic cost. Set ideal target values ​​or expected ranges for each indicator, wherein the ideal target values ​​or expected ranges are set according to the specific objectives of the scenario; A multi-dimensional demand vector is constructed by combining the target values ​​of various indicators. This demand vector represents the ideal adjustment demand in a typical scenario.

[0008] The formula for calculating the new energy absorption rate is as follows:

[0009] In the formula, For a moment The actual amount of renewable power received. For available renewable power, Evaluation period discrete time index; Time step.

[0010] The formula for calculating the line load rate is:

[0011] In the formula, For line overload rate, For the line exist The trend Let L be the upper limit of its thermal stability, and L be the number of lines.

[0012] The formula for the matching degree is:

[0013] In the formula, For element-wise multiplication, The closer the value is to 1, the higher the consistency between the structure and the ideal requirements of the scenario across multiple indicator spaces. The largest is superior; For capability vectors, For the demand vector, for .

[0014] According to the second aspect of the present disclosure, a DC / AC power distribution network structure selection device is also provided, comprising: A data acquisition module acquires typical scenario data and candidate network structure information, wherein the typical scenario data comprises scenario load, distributed energy node information and typical daily power curve; An index system construction module establishes a flexible regulation capacity demand index system to form a multi-dimensional demand vector; A calculation module calculates the flexible evaluation index of each network structure under the typical scenario to form a capacity vector; A data processing module performs maximum and minimum normalization processing on the demand vector and the capacity vector; An output module calculates the cosine similarity of the normalized capacity vector and the demand vector as the matching degree, and selects the optimal network structure based on the matching degree.

[0015] According to the third aspect of the present disclosure, an electronic device is also provided, comprising a processor, and a memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; the processor retrieves a computer program from the non-volatile memory through the network interface when running, and runs the computer program through the memory, to execute the above-mentioned DC / AC power distribution network structure selection method.

[0016] According to the fourth aspect of the present disclosure, a storage medium is also provided, which stores computer program instructions, and the instructions make the processor execute the above-mentioned DC / AC power distribution network structure selection method when executed by the processor.

[0017] The present application establishes a flexible demand target system of typical scenarios systematically, clearly defines the key demands of various scenarios, optimizes the network structure to meet the flexible demand of different scenarios by using the DC / AC power distribution network planning model, and quantitatively evaluates the adaptability of the network structure in the typical scenario, so as to ensure the best matching of flexible demand and regulation capacity. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A DC / AC power distribution network structure selection method flowchart of the present application; Figure 2 A DC / AC power distribution network structure scheme schematic diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0019] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of the present application.

[0020] The specific steps of the present application include: Step 1: Obtain the typical scenario data to be evaluated and the candidate AC-DC power distribution network framework structure information, wherein the typical scenario data includes scenario load, distributed energy node information and typical daily power curve.

[0021] Step 2: Establish the flexibility adjustment capability demand index system of the typical scenario to form a multi-dimensional demand vector.

[0022] Step 3: For each candidate network framework structure, use the power grid flow operation model to calculate the flexibility adjustment capability evaluation index under the typical scenario to form a multi-dimensional capability vector.

[0023] Step 4: Perform maximum minimum normalization processing on the demand vector and all capability vectors respectively to eliminate the dimensional influence between different indexes.

[0024] Step 5: For each network framework structure, use cosine similarity algorithm to calculate the matching degree between the normalized capability vector and the normalized demand vector.

[0025] Step 6: Sort the matching degrees calculated for all candidate network framework structures, and select the network framework structure with the highest matching degree as the optimal type.

[0026] The step 1 includes the following steps: Step 1.1: According to the power grid characteristics, load types and development planning of different regions, define one or more typical operation scenarios, for example: a city edge area scenario with high proportion of renewable energy penetration or an industrial park scenario with extremely high requirement for power supply reliability.

[0027] Step 1.2: Collect the basic data of the defined typical scenario, including the load curve of the typical day, the predicted output curve of the distributed power supply, the electricity price information, etc.

[0028] Step 1.3: Determine the candidate AC-DC power distribution network framework structure set to be evaluated, which can include but is not limited to: SOP interconnected power distribution network, AC-DC interconnected power distribution network, AC-DC-AC interconnected power distribution network, and two AC-DC interconnected power distribution network, etc.

[0029] The step 2 includes the following steps: Step 2.1: For each typical scenario, systematically analyze its core requirements and translate them into quantifiable key evaluation indicators.

[0030] Step 2.2: Set an ideal target value or desired range for each key evaluation indicator based on the specific goals of the scenario. For example, in a high proportion of renewable energy scenario, set a higher target value for "new energy consumption rate".

[0031] Step 2.3: Combine the target values of all indicators to build a multi-dimensional "demand vector" representing the ideal demand of the scenario.

[0032] The step 3 includes the following steps: Step 3.1: For each candidate grid structure, use power grid analysis software to establish its corresponding power flow simulation model.

[0033] Step 3.2: Take the basic data of typical scenarios (load, power output, etc.) as input, and perform power flow calculation in the model to simulate the actual operation state of the grid structure in the typical scenario.

[0034] Step 3.3: Extract the actual calculated values of the same key evaluation indicators from the simulation results as in step 2.1, and combine these values into the "capability vector" of the grid structure.

[0035] The step 4 includes the following steps: Step 4.1: Identify the dimensional and numerical range differences of each indicator in the demand vector and the capability vector, and confirm the necessity of normalization.

[0036] Step 4.2: Use the Min-Max Normalization method to process the demand vector B and each capability vector A respectively, linearly map all indicator values to the unified interval [0, 1], to eliminate the dimensional differences that interfere with subsequent calculations.

[0037] The step 5 includes the following steps: Step 5.1: Based on the normalized capability vector and demand vector, use cosine similarity algorithm, etc., to quantitatively calculate the directional consistency of each candidate grid structure's capability vector and the scenario demand vector in multi-dimensional space, i.e. matching degree.

[0038] Step 5.2: Take the calculated cosine value (range between 0 and 1) as the final matching degree score of the grid structure and the scenario demand. The closer the score is to 1, the more consistent the comprehensive capability performance of the grid structure is with the comprehensive demand of the scenario.

[0039] The step 6 includes the following steps: Step 6.1: Sum up the matching degree scores of all candidate grid structures calculated under the specific scenario, and arrange them in descending order.

[0040] Step 6.2: According to the ranking result, select the grid structure with the highest score to determine it as the optimal AC / DC power distribution grid structure best adapted to the target scenario.

[0041] A specific embodiment is provided to illustrate the AC / DC power distribution grid structure obtained by the method of the present application.

[0042] Step 1: Input the typical scenario data of the industrial park.

[0043] Industrial park load: Provide a typical day 24-point load curve ; Distributed power supply: Photovoltaic / wind power node information and predicted output curve ; Economic parameters: Time-of-use electricity price, electricity purchase and sale settlement rules, equipment annualization / depreciation parameters Grid and equipment: Node voltage limit, line thermal stability upper limit , etc. Candidate grid structures: SOP interconnection type, AC-DC interconnection type (AC-DC), AC-DC-AC interconnection type, "two AC and one DC" interconnection type.

[0044] This example demonstrates the evaluation results of four schemes S1, S2, S3, and S.

[0045] Step 2: Establish the flexibility adjustment capability demand index system for the industrial park scenario to form a multi-dimensional demand vector.

[0046] This paper takes four types of core indicators as an example (economic cost, new energy consumption, voltage deviation, and line overload), which are :

[0047] The physical quantities and symbols of each original indicator are as follows: Economic cost is:

[0048] Among them, is the electricity purchase / online electricity fee, is the network loss cost, is the line / equipment investment or amortization cost.

[0049] New energy consumption rate The formula is:

[0050] where, is the time instant actual admitted renewable power, is the available renewable power, : evaluation period discrete-time index; : time step.

[0051] voltage deviation The formula is:

[0052] where, is the node at time instant voltage amplitude, : number of nodes, is the per-unit base voltage.

[0053] line overload rate The formula is:

[0054] where is the line at power flow, is the number of lines, is the line thermal stability upper limit.

[0055] Industrial park scenario target setting (example indicators): Cost: the lower the better (target "with voltage drop"); Absorption: ; Voltage deviation: ; Line overload: For demonstration simplicity, this example maps "ideal demand" to (indicating that the closer the above four items are to the ideal, the better).

[0056] Step 3: For each candidate grid structure, use the power grid power flow operation model to calculate its flexibility adjustment capability evaluation index under the typical scenario, forming a multi-dimensional capability vector. Table 1 is the flexibility adjustment capability of different grid structures under the typical scenario.

[0057] Table 1 Flexibility adjustment capability of different grid structures under the typical scenario

[0058] Step 4: Perform maximum and minimum normalization processing on the demand vector and all capability vectors respectively to eliminate the dimensional influence between different indicators.

[0059] To eliminate the dimension and unify the "the bigger the better" direction, the Min-Max rule is adopted; and the boundary of this example project is given: Cost category

[0060] Benefit category ; Cost category ; Cost category .

[0061] Normalization formula: Benefit category: ; Cost category

[0062] According to this, the ability vector (four dimensions are 0-1) and its components (taking three decimal places) of each scheme are obtained: Table 2 is the flexible adjustment ability vector of different grid structures normalized by Min-Max under a typical scenario

[0063] From Table 2, it can be seen that S4 is close to 1 in the four normalized components, showing all-dimensional dominance; S1 performs outstandingly in terms of consumption rate and line overload, and is also relatively good in cost and voltage quality, but its comprehensive performance is inferior to S4; S3 is in the middle in the four indicators; S2 is low in all four components, and its overall ability is relatively insufficient. This shows that under the same scenario and boundary setting, the comprehensive adjustment ability of S4 is more consistent with the ideal demand of the scenario, S1 is second, and S2 needs to be improved greatly in performance.

[0064] Step 5: For each grid structure, calculate the matching degree between its normalized ability vector and normalized demand vector by using the cosine similarity calculation formula.

[0065] Weighted cosine similarity is adopted (in this example, the weights are equal, and AHP / entropy weight can also be replaced):

[0066] Under the , we get (taking three decimal places): Table 3 Weighted Cosine Similarity of Normalized Ability Vector and Demand Vector

[0067] As can be seen from Table 3, S4 has the highest matching degree (0.999), and is almost completely consistent with the target scene demand; S1 (0.956) is the second, and can also better meet the scene demand; S3 (0.915) is usable but has room for improvement; S2 (0.795) is obviously insufficient, and needs to be further optimized in key indicators as an alternative solution.

[0068] Step 6: Sort the matching degrees calculated for all candidate grid structures, and select the grid structure with the highest matching degree as the optimal type. The matching degrees in descending order are: S4 > S1 > S3 > S2. Therefore, under the above weight / boundary assumptions, S4 is the optimal type in the typical scene of the industrial park.

[0069] The embodiment of the present disclosure also provides an alternating current-direct current power distribution network structure selection device. The alternating current-direct current power distribution network structure selection device provided by the embodiment of the present disclosure comprises a data acquisition module, a demand index system construction module, a calculation module, a data processing module and an output module. The data acquisition module acquires typical scene data and candidate grid structure information, and the typical scene data comprises scene load, distributed energy node information and typical daily power curve. The demand index system construction module establishes a flexible regulation capacity demand index system to form a multi-dimensional demand vector. The calculation module calculates flexible evaluation indexes of each grid structure under the typical scene to form a capacity vector. The data processing module performs maximum minimum normalization processing on the demand vector and the capacity vector. The output module calculates the cosine similarity between the normalized capacity vector and the demand vector as a matching degree, and selects an optimal grid structure based on the matching degree.

[0070] The embodiment of the present disclosure provides a structural diagram of a hardware architecture of a computing device. The computing device comprises an input device, an input interface, a central processor, a memory, an output interface, and an output device. The input interface, the central processor, the memory, and the output interface are connected to each other through a bus. The input device and the output device are connected to the bus through the input interface and the output interface, respectively, and further connected to other components of the computing device.

[0071] The computing device disclosed in the embodiment can also be implemented as an alternating current-direct current power distribution network structure selection method. The computing device can comprise a processor and a memory storing computer executable instructions. The processor can implement the alternating current-direct current power distribution network structure selection method provided by the embodiment of the present disclosure when executing the computer executable instructions.

[0072] The embodiment of the present disclosure also provides a computer readable storage medium, which stores computer program instructions. The computer program instructions are executed by a processor to implement the photovoltaic power generation power prediction method provided by the embodiment of the present disclosure.

[0073] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product.

[0074] Those skilled in the art can realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0075] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0076] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0077] Finally: the above is merely preferred embodiments of the present application, and is not intended to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for selecting an AC / DC distribution network structure, characterized by, The method comprises the following steps: obtaining typical scenario data and candidate grid structure information, wherein the typical scenario data comprises scenario load, distributed energy node information and typical daily power curve; establishing a flexibility regulation capability demand index system to form a multi-dimensional demand vector; calculating the flexibility evaluation index of each grid structure under the typical scenario to form a capability vector; performing maximum-minimum normalization processing on the demand vector and the capability vector; calculating the cosine similarity between the normalized capability vector and the demand vector as the matching degree, and selecting the optimal grid structure based on the matching degree ranking.

2. The method of claim 1, wherein the AC / DC power distribution network structure is selected based on at least one of a power supply type, a power consumption type, and a power supply capacity. The step of obtaining the typical scenario data and the candidate grid structure information comprises the following steps: defining a typical operation scenario based on the grid characteristics and load types; collecting the basic data of the typical scenario, including load curve, distributed power output and electricity price information; determining a candidate grid structure set containing various AC-DC hybrid structures according to the basic data.

3. The method of claim 2, wherein the AC / DC power distribution network structure is selected based on a power supply structure of the power distribution network. The step of establishing the flexibility regulation capability demand index system to form a multi-dimensional demand vector comprises the following steps: analyzing the core demand of the typical scenario and quantifying it into key evaluation indexes, wherein the key evaluation indexes include new energy consumption rate, voltage deviation, line overload rate and economic cost; setting ideal target values or expected ranges for each index, wherein the ideal target values or expected ranges are set according to the scenario specific target; combining the target values of each index to construct a multi-dimensional demand vector, wherein the demand vector represents the ideal regulation demand of the typical scenario.

4. The method of claim 2, wherein the AC / DC power distribution network structure is selected based on a power supply structure of the power distribution network. The calculation formula of the new energy consumption rate is: In the formula, is the time the actually admitted renewable power, is the available renewable power, : evaluation period discrete-time index; : time step.

5. The method of claim 4, wherein the step of selecting the AC / DC power distribution network structure is performed based on at least one of a power supply type, a power consumption type, and a power supply capacity of the power supply system. The calculation formula of the line overload rate is: wherein L is the number of lines In the tide, L is the number of lines.

6. The method of claim 1, wherein the AC / DC power distribution network structure is selected based on at least one of a power supply type, a power consumption type, and a power supply capacity. The formula of the matching degree is: In the formula, is an element-by-element multiplication, The closer to 1, the higher the structure is consistent with the ideal demand direction of the scene in the multi-index space, and the maximum is taken The maximum is the best; is the capability vector, is the demand vector, is .

7. An AC / DC distribution network structure selection device, characterized in that, The method comprises the following steps: a data acquisition module is configured to obtain typical scenario data and candidate grid structure information, wherein the typical scenario data comprises scenario load, distributed energy node information and typical daily power curve; an index system construction module is configured to establish a flexibility regulation capability demand index system to form a multi-dimensional demand vector; a calculation module is configured to calculate the flexibility evaluation index of each grid structure under the typical scenario to form a capability vector; a data processing module is configured to perform maximum-minimum normalization processing on the demand vector and the capability vector; an output module is configured to calculate the cosine similarity between the normalized capability vector and the demand vector as the matching degree, and select the optimal grid structure based on the matching degree ranking.

8. The AC / DC power distribution network structure selection apparatus according to claim 7, characterized by: The data acquisition module further comprises the following steps: defining a typical operation scenario based on the grid characteristics and load types; collecting the basic data of the typical scenario, including load curve, distributed power output and electricity price information; determining a candidate grid structure set containing various AC-DC hybrid structures according to the basic data.

9. An electronic device, comprising: The method comprises a processor, a memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; the processor retrieves a computer program from the non-volatile memory through the network interface when running, and runs the computer program through the memory to execute the method of any one of claims 1-6.

10. A storage medium characterized by: The computer storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the method of any one of claims 1-6.

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