Distributed new energy access power distribution network bearing capacity evaluation method

Abstract

The invention relates to a distributed new energy access power distribution network bearing capacity assessment method. The method comprises the following steps: S1, constructing planning scenes under different distributed new energy access capacities; s2, based on the planning scene, establishing a distributed new energy bearing capacity optimization model taking maximization of distributed new energy access capacity as an optimization target and taking multiple performance index constraints as constraints; s3, on the basis of the established distributed new energy bearing capacity optimization model, calculating the maximum capacity of the distributed new energy which can be accessed to the power distribution network by quantifying each performance index, and determining the maximum bearing capacity of the power distribution network for the distributed new energy; and S4, displaying an evaluation result through a visual means. According to the method, the accuracy and reliability of the maximum bearing capacity evaluation of the distributed new energy can be improved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and particularly to a method for evaluating the carrying capacity of a distribution network with distributed new energy access. Background Art

[0002] Under the new power system, the power supply side has changed from being mainly dominated by deterministic and controllable coal power generation to being mainly dominated by uncertain and random distributed new energy generation. Compared with traditional power sources, distributed new energy has greater flexibility and low carbon characteristics. However, the volatility and intermittency of distributed new energy output pose new risk challenges to the safe and stable operation of the power system. Therefore, it is necessary to evaluate the maximum carrying capacity of distributed new energy under the new power system. The traditional evaluation method is based on the worst-case scenario at a single time section, resulting in a conservative and highly redundant evaluation result with insufficient practicality. The traditional method relies on empirical data and is difficult to accurately reflect the influence of complex indicators such as voltage fluctuations and reverse load rates in the new power system. Therefore, there is an urgent need for a refined and multi-dimensional constrained evaluation method. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for evaluating the carrying capacity of a distribution network with distributed new energy access, which can improve the accuracy and reliability of the evaluation of the maximum carrying capacity of distributed new energy.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is: a method for evaluating the carrying capacity of a distribution network with distributed new energy access, including the following steps:

[0005] S1. Construct planning scenarios with different distributed new energy access capacities;

[0006] S2. Based on the planning scenarios, establish a distributed new energy carrying capacity optimization model with maximizing the distributed new energy access capacity as the optimization goal and multiple performance index constraints as the constraints;

[0007] S3. Based on the established distributed new energy carrying capacity optimization model, calculate the maximum capacity of distributed new energy that can be accessed by the distribution network by quantifying each performance index, and determine the maximum carrying capacity of the distribution network for distributed new energy;

[0008] S4. Display the evaluation results through visualization means.

[0009] Further, in step S1, planning scenarios with different distributed new energy access capacities are constructed based on actual operation data, and the actual operation data includes the historical operation data of the distribution network, current load data, meteorological data, and distributed new energy access data.

[0010] Further, in step S2, the optimization goal is to maximize the distributed new energy access capacity, and its expression is:

[0011]

[0012] Among them, Cap i,z is the access capacity of the z-th type of distributed new energy at node i, represents the number of types of distributed new energy at node i, represents the number of all nodes in the system, and Cap i is the access capacity of distributed new energy at node i.

[0013] Furthermore, in step S2, the multiple performance index constraints include:

[0014] Distributed new energy access capacity limit;

[0015] Power balance constraint;

[0016] Line capacity constraint;

[0017] Voltage deviation constraint;

[0018] Voltage fluctuation constraint;

[0019] Reverse load rate constraint;

[0020] Short-circuit current constraint.

[0021] Furthermore, the distributed new energy access capacity limit is:

[0022]

[0023] In the formula, Cap max,i,z is the maximum accessible capacity of the z-th type of distributed new energy at node i;

[0024] The power balance constraint is:

[0025]

[0026] In the formula, P DG,i,k,t and Q DG,i,k,t are the active and reactive power output values of the distributed new energy at time t in the k-th planning scenario of node i; P i,k,t and Q i,k,t are the active and reactive power values of the load at time t in the k-th planning scenario of node i; U i,k,t is the voltage amplitude at time t in the k-th planning scenario of node i; G i,j and B i,j are the conductance and susceptance between node i and node j respectively; δ i,j,k,t is the phase angle difference between the voltages of node i and node j; is the set of all nodes in the system;

[0027] The line capacity constraint is as follows:

[0028]

[0029] In the formula, I b,k,t is the current flowing through branch b at time t of the k-th planning scenario; I max,b is the maximum current that branch b can carry; is the set of all branches in the system;

[0030] The voltage deviation constraint is as follows:

[0031]

[0032] In the formula, U N is the rated voltage; ε min , ε max are the upper and lower limits of the voltage deviation respectively;

[0033] The voltage fluctuation constraint is as follows:

[0034]

[0035] In the formula, ΔU max is the maximum allowable ratio of voltage fluctuation;

[0036] The reverse load rate constraint is as follows:

[0037]

[0038] In the formula, S max,b is the capacity limit of branch b;

[0039] The short-circuit current constraint is as follows:

[0040]

[0041] In the formula, I i,s is the short-circuit current of node i; I max,s is the maximum allowable value of the short-circuit current.

[0042] Furthermore, in step S4, the visualization means includes drawing a relationship curve between the access capacity and each performance index.

[0043] Furthermore, the evaluation method of the bearing capacity of the distribution network is applicable to different grid structures and scales, urban and rural distribution networks, and regional power grids.

[0044] The present invention also provides a distributed new - energy - connected distribution network carrying - capacity evaluation system, which includes a memory, a processor, and computer program instructions stored on the memory and executable by the processor. When the processor runs the computer program instructions, the above - mentioned method can be implemented.

[0045] The present invention also provides a computer - readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above - mentioned method is implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a distributed new - energy - connected distribution network carrying - capacity evaluation method. This method establishes a planning scenario based on actual data, builds an optimization model with maximizing the distributed new - energy access capacity as the optimization goal, and imposes multiple performance - index constraints, thereby establishing a more accurate optimization model. On this basis, the maximum capacity of distributed new - energy that can be connected to the distribution network is calculated, thus greatly improving the accuracy of the evaluation of the maximum carrying - capacity of distributed new - energy and having strong practicability, overcoming the problems of the traditional evaluation method being too conservative and having poor practicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flowchart of the implementation of the distributed new - energy - connected distribution network carrying - capacity evaluation method provided by an embodiment of the present invention;

[0048] Figure 2 is the distributed photovoltaic unit - capacity output planning scenario in an embodiment of the present invention;

[0049] Figure 3 is a schematic diagram of the comparison of the maximum load rates of the carrying - capacity evaluation results of this method and the traditional method under actual operation data. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following further describes the present invention with reference to the drawings and embodiments.

[0051] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0052] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "comprise" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0053] Such asFigure 1 As shown in the figure, this embodiment provides a method for evaluating the carrying capacity of a distribution network with distributed new energy access, including the following steps:

[0054] S1. Construct planning scenarios under different distributed new energy access capacities.

[0055] Specifically, construct planning scenarios under different distributed new energy access capacities based on actual operation data, where the actual operation data includes the historical operation data of the distribution network, current load data, meteorological data, and distributed new energy access data.

[0056] S2. Based on the planning scenarios, establish an optimization model for distributed new energy carrying capacity with maximizing the distributed new energy access capacity as the optimization goal and multiple performance index constraints as the constraints.

[0057] The expression of the optimization goal is:

[0058]

[0059] where Cap i,z is the access capacity of the z-th type of distributed new energy at node i, represents the number of types of distributed new energy at node i, represents the number of all nodes in the system, and Cap i is the access capacity of distributed new energy at node i.

[0060] The multiple performance index constraints include:

[0061] (1) Distributed new energy access capacity limit

[0062]

[0063] In the formula, Cap max,i,z is the maximum accessible capacity of the z-th type of distributed new energy at node i.

[0064] (2) Power balance constraint

[0065]

[0066] In the formula, P DG,i,k,t , Q DG,i,k,t are the active and reactive power output values of distributed new energy at time t in the k-th planning scenario at node i respectively; P i,k,t , Q i,k,t are the active and reactive power values of the load at time t in the k-th planning scenario at node i respectively; U i,k,t is the voltage amplitude at time t in the k-th planning scenario at node i; G i,j , B i,jThe conductance and susceptance between nodes i and j respectively; δ i,j,k,t is the phase angle difference between the voltages of nodes i and j; is the set of all nodes in the system.

[0067] (3) Line capacity constraint

[0068]

[0069] In the formula, I b,k,t is the current flowing through branch b at time t of the k-th planning scenario; I max,b is the maximum value of the current that branch b can carry; is the set of all branches in the system.

[0070] (4) Voltage deviation constraint

[0071]

[0072] In the formula, U N is the rated voltage; ε min and ε max are the upper and lower limits of the voltage deviation respectively.

[0073] (5) Voltage fluctuation constraint

[0074]

[0075] In the formula, ΔU max is the maximum allowable ratio of voltage fluctuation.

[0076] (6) Reverse load rate constraint

[0077]

[0078] In the formula, S max,b is the capacity limit of branch b.

[0079] (7) Short-circuit current constraint

[0080]

[0081] In the formula, I i,s is the short-circuit current of node i; I max,s is the maximum allowable value of the short-circuit current.

[0082] S3. Based on the established distributed new energy carrying capacity optimization model, by quantifying each performance index, calculate the maximum capacity of distributed new energy that can be accessed by the distribution network, and determine the maximum carrying capacity of the distribution network for distributed new energy.

[0083] S4. Display the evaluation results through visualization means.

[0084] The visualization means includes drawing a relationship curve between access capacity and various performance indicators.

[0085] This evaluation method is applicable to different grid structures and scales, urban and rural distribution networks and regional power grids. This evaluation method makes full use of the actual operation data of the distribution network, and comprehensively considers the diversity of new energy access capacity and access capacity limitations, line load rate, voltage offset, voltage fluctuation, reverse load rate, short-circuit current and other performance indicators. This method takes safety as an important aspect of the evaluation. For example, through the evaluation of indicators such as line load rate and short-circuit current, it focuses on whether the access of distributed new energy will cause line overload and affect the safety of the power grid in the event of a fault. This method specifically evaluates voltage offset and voltage fluctuation, and considers the impact of distributed new energy access on voltage quality. When evaluating each performance indicator, judgment is made based on the set performance indicator threshold, such as whether the line load rate exceeds the rated value and whether the voltage offset is within the allowable range.

[0086] The specific implementation process of this evaluation method is as follows:

[0087] 1. Data preparation and scenario setting: Collect relevant data of the distribution network, including existing historical operation data, access information of distributed renewable energy, load data, etc. These data will provide basic information for subsequent evaluation.

[0088] 2. Calculation and evaluation of performance indicators: For each node, the planned access capacity and the calculated maximum access capacity of the node are compared. This requires considering the capacity requirements of the grid after the access of distributed renewable energy based on relevant electrical calculation rules and grid topology. Determine whether the access capacity restriction conditions are met, that is, check whether the access capacity is within the allowable range to prevent node overload.

[0089] 3. Results collation and comprehensive evaluation: The evaluation results of the above performance indicators are collated and summarized, and the changing trends of each indicator under different distributed renewable energy access capacities are analyzed. Based on the evaluation results of the above performance indicators, a comprehensive evaluation is conducted on the distributed renewable energy carrying capacity of the distribution network. According to the various indicators under different access capacities, the maximum distributed renewable energy access capacity of the distribution network in the current state is determined. The evaluation results are used to provide decision support for the planning and operation of the distribution network, such as determining whether the current distributed renewable energy access plan is reasonable, how to adjust the access capacity to meet the performance indicator requirements, and how to transform the distribution network or adjust the operation strategy to improve the distributed renewable energy carrying capacity.

[0090] Figure 2 This is the distributed photovoltaic unit capacity output planning scenario in this embodiment, which shows the result of generating the output planning scenario based on historical operation data and meteorological data.

[0091] Figure 3 It is a schematic diagram comparing the maximum load rates of the bearing capacity evaluation results of this method and traditional methods under actual operation data, which demonstrates the advantages of this method over traditional methods.

[0092] The distributed new energy access power distribution network bearing capacity evaluation method provided by the present invention has the following technical advantages:

[0093] 1. Integrity of multi-performance index constraints: Covers various performance indexes such as access capacity limit, line load rate, voltage deviation, voltage fluctuation, reverse load rate, short-circuit current, etc. as constraint conditions, comprehensively considering various influencing factors of distributed new energy access on the safe and stable operation of the power distribution network. This integrity ensures that when calculating the maximum capacity of distributed new energy that can be accessed by the system, it can be evaluated and restricted from multiple key perspectives, avoiding evaluation deviations caused by omitting important indexes, making the evaluation results more reliable and practical, and being able to effectively guide new energy access decisions in actual power grid planning and operation.

[0094] 2. Rigor and practicality of the evaluation process: By simulating the whole-network operation status under different distributed new energy access capacities, comprehensively evaluating the safety stability and reliability of the power distribution network, and closely combining with the actual power grid operation requirements, it provides an evaluation process with practical operation guiding significance for power grid planning and operation personnel, reflecting rigorous engineering thinking and strong practicality.

[0095] 3. Intuitiveness of result display: The evaluation results are displayed through visualization means, which is convenient for decision-making analysis, helps power grid planning and operation personnel better understand the evaluation results, and formulate reasonable access plans and operation strategies.

[0096] This embodiment also provides a distributed new energy access power distribution network bearing capacity evaluation system, including a memory, a processor, and computer program instructions stored on the memory and capable of being run by the processor. When the processor runs the computer program instructions, the above-mentioned method can be implemented.

[0097] This embodiment also provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by the processor, the above-mentioned method is implemented.

[0098] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0100] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0102] As described above, it is only the preferred embodiments of the present invention, and it is not a limitation of the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating the carrying capacity of a distribution network with distributed new energy access, characterized in that It includes the following steps: S1. Construct planning scenarios under different distributed new energy access capacities; S2. Based on the planning scenarios, establish an optimization model for the bearing capacity of distributed new energy with the maximization of the distributed new energy access capacity as the optimization goal and multiple performance index constraints as the constraints; S3. Based on the established optimization model for the bearing capacity of distributed new energy, calculate the maximum capacity of distributed new energy that can be accessed by the distribution network by quantifying each performance index, and determine the maximum bearing capacity of the distribution network for distributed new energy; S4. Display the evaluation results through visualization means.

2. The method for evaluating the carrying capacity of a distribution network with distributed new energy access according to claim 1, characterized in that, In step S1, planning scenarios under different distributed new energy access capacities are constructed based on actual operation data, and the actual operation data includes the historical operation data of the distribution network, current load data, meteorological data, and distributed new energy access data.

3. The method for evaluating the carrying capacity of a distribution network with distributed new energy access according to claim 1, characterized in that, In step S2, the optimization goal is to maximize the distributed new energy access capacity, and its expression is: Among them, Cap i,z is the access capacity of the z-th type of distributed new energy at node i, represents the number of types of distributed new energy available at node i, represents the number of all nodes in the system, Cap i is the access capacity of distributed new energy at node i.

4. The method for evaluating the carrying capacity of a distribution network with distributed new energy access according to claim 3, wherein In step S2, the multiple performance index constraints include: Distributed new energy access capacity limit; Power balance constraint; Line capacity constraint; Voltage deviation constraint; Voltage fluctuation constraint; Reverse load rate constraint; Short-circuit current constraint.

5. The method for evaluating the carrying capacity of a distribution network with distributed new energy access according to claim 4, characterized in that, The distributed new energy access capacity limit is: where Cap max,i,z is the maximum accessible capacity of the z-th type of distributed new energy at node i; The power balance constraint is: Wherein, P DG,i,k,t , Q DG,i,k,t are respectively the active and reactive power output values of distributed new energy at time t in the k-th planning scenario of node i; P i,k,t , Q i,k,t are respectively the active and reactive power values of the load at time t in the k-th planning scenario of node i; U i,k,t is the voltage amplitude at time t in the k-th planning scenario of node i; G i,j , B i,j are respectively the conductance and susceptance between node i and node j; δ i,j,k,t is the phase angle difference between the voltages of node i and node j; is the set of all nodes in the system; The line capacity constraint is: where I b,k,t is the current flowing through branch b at time t of the k-th planning scenario; I max,b is the maximum current that branch b can carry; is the set of all branches in the system; The voltage deviation constraint is: Where, U N is the rated voltage; ε min , ε max are the upper and lower limits of voltage deviation respectively; The voltage fluctuation constraint is: where ΔU max is the maximum allowable ratio of voltage fluctuation; The reverse load rate constraint is: where S max,b is the capacity limit of branch b; The short-circuit current constraint is: Where, I i,s is the short-circuit current of node i; I max,s is the maximum allowable value of the short-circuit current.

6. The method for evaluating the carrying capacity of a distribution network with distributed new energy access according to claim 1, wherein In step S4, the visualization means includes plotting the relationship curves between the access capacity and each performance index.

7. The method for evaluating the carrying capacity of a distribution network with distributed new energy access according to claim 1, characterized in that, The method for evaluating the bearing capacity of the distribution network is applicable to different grid structures and scales, urban and rural distribution networks, and regional power grids.

8. A distribution network carrying capacity evaluation system for distributed new energy access, characterized in that, It includes a memory, a processor, and computer program instructions stored on the memory and capable of being run by the processor. When the processor runs the computer program instructions, the method described in any one of claims 1-7 can be implemented.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method described in any one of claims 1-7 is implemented.

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