High-robustness power grid design method based on parameter adjustable heterogeneous modeling

Abstract

The invention relates to the field of power grid design, in particular to a high-robustness power grid design method based on parameter adjustable heterogeneous modeling. The scheme comprises the following steps: constructing a network topology based on a small-world network; constructing a heterogeneous collaborative model; optimizing the permeability of the asynchronous machine, and reconstructing and calculating an admittance matrix; whether the permeability of the asynchronous machine needs to be adjusted is judged, if yes, the step of optimizing the permeability of the asynchronous machine is executed, and if not, node betweenness centrality analysis is conducted, and then the layout strategy of the asynchronous machine is adjusted; judging whether the asynchronous machine layout strategy needs to be adjusted or not, and if yes, returning to the synchronous machine layout strategy adjustment step; if not, adjusting the coupling strength of the synchronous machine, and adjusting the inertial parameter and the damping coefficient of the asynchronous machine; judging whether the coupling strength needs to be adjusted or not, if yes, returning to the step of adjusting the coupling strength of the synchronous machine, and if not, outputting the permeability of the current asynchronous machine, the node sequence number of the asynchronous machine, the coupling strength of the synchronous machine, the inertial parameter of the asynchronous machine and the damping coefficient. The method is suitable for power grid design.

Description

Technical Field

[0001] The present invention relates to the field of power grid design, and particularly to a high-robustness power grid design method based on parameter-adjustable heterogeneous modeling. Background Art

[0002] The global energy shortage and environmental problems have accelerated the transformation of the global energy system towards low-carbon and clean energy. As a core area, the power system is undergoing profound changes. In this process, the power system is gradually shifting from the traditional synchronous machine-dominated mode to a mode dominated by asynchronous machines (such as wind power, photovoltaic power, etc.). The high proportion of new energy grid connection poses a severe challenge to the stability of the power grid. Research shows that under the background of high proportion of asynchronous machine access, the power grid faces problems such as reduced inertia and frequency instability. Traditional power grid control and stability analysis methods can no longer effectively cope with this situation. In particular, existing research usually focuses on the analysis of synchronous machine nodes, and uses simplified first-order dynamic models to explore the overall behavior of the power grid, but ignores the important impact of asynchronous machines on the dynamic characteristics of the power grid, and cannot accurately describe the desynchronization mechanism of the power grid after high proportion of renewable energy access. Therefore, it is particularly important to construct a new power grid design method that can accurately describe the interaction between different types of generating units in a heterogeneous power grid and has high robustness.

[0003] A heterogeneous power grid refers to a power grid composed of various types of power equipment and generating units with different technical systems, including traditional synchronous generators, asynchronous generators such as wind power and photovoltaic power, and energy storage systems, etc. Due to the large differences in the dynamic responses and stability characteristics of these devices in the power grid, the design and optimization of heterogeneous power grids face unprecedented challenges. Traditional power grid analysis methods generally have the problem of insufficient parameter adjustability and cannot flexibly adjust the access ratio of different types of generating units, the positions of generating nodes, and the coupling strength between them. Specifically, a parameter-adjustable design method should include the adjustment ability of multiple factors such as the types, installed capacities, grid connection positions, and mutual coupling relationships of various generating units in the system, so as to achieve the dynamic optimization of the power grid and the improvement of robustness. By flexibly regulating these parameters, it is possible to ensure that the power grid can still maintain stable operation in the face of load fluctuations, system failures, etc. under the condition of high proportion of renewable energy access, and achieve higher system robustness and adaptability.

[0004] Therefore, the parameter-adjustable heterogeneous modeling method can be used as the core tool for power grid design and optimization. By comprehensively considering the dynamic responses of different types of power equipment and flexibly adjusting the key parameters of the system, the dynamic optimization of the power grid can be achieved, ensuring that the power system can still operate efficiently, stably and reliably under the condition of high proportion of renewable energy access. Summary of the Invention

[0005] The object of the present invention is to overcome the shortcomings of the prior art, provide a high-robustness power grid design method based on parameter-adjustable heterogeneous modeling, realize the closed-loop optimization of multi-dimensional control parameters, and solve the technical bottlenecks in aspects such as heterogeneous power source coupling modeling and distributed control strategies of traditional methods.

[0006] The present invention adopts the following technical solutions to achieve the above object. The present invention provides a high-robustness power grid design method based on parameter-adjustable heterogeneous modeling, including:

[0007] S1. Construct a network topology based on the small-world network;

[0008] S2. Construct a heterogeneous cooperation model based on the principle of energy conservation;

[0009] S3. Perform the optimization of the asynchronous machine penetration rate and the reconstruction calculation of the admittance matrix;

[0010] S4. Calculate the maximum frequency deviation and the convergence time under the current asynchronous machine penetration rate, and judge whether the current maximum frequency deviation is less than the set optimal frequency deviation threshold and whether the convergence time is less than the set optimal convergence threshold. If so, go to step S5; otherwise, return to step S3;

[0011] S5. Perform the node betweenness centrality analysis, and adjust the asynchronous machine layout strategy according to the node betweenness centrality analysis;

[0012] S6. Calculate the maximum frequency deviation and the convergence time under the current layout strategy, and judge whether the current maximum frequency deviation is less than the set optimal frequency deviation threshold and whether the convergence time is less than the set optimal convergence threshold. If so, go to step S7; otherwise, return to step S5;

[0013] S7. Increase the synchronous machine coupling strength in increments of a set first step size, and adjust the asynchronous machine inertia parameter and damping coefficient in a set second step size;

[0014] S8. Calculate the maximum frequency deviation and the convergence time under the current synchronous machine coupling strength, asynchronous machine inertia parameter and damping coefficient, and judge whether the current maximum frequency deviation is less than the set optimal frequency deviation threshold and whether the convergence time is less than the set optimal convergence threshold. If so, go to step S9; otherwise, return to step S7;

[0015] S9. Output the current asynchronous machine penetration rate, asynchronous machine node number, synchronous machine coupling strength, asynchronous machine inertia parameter and damping coefficient.

[0016] Furthermore, step S1 specifically includes:

[0017] Generate a node architecture with a set number of nodes based on a small-world network. Divide the nodes into synchronous machine nodes, asynchronous machine nodes, and load nodes, which are represented by different shapes respectively. Connect the nodes to each other with straight lines, and set the number of generator nodes and the number of load nodes.

[0018] Furthermore, step S2 specifically includes:

[0019] Construct a heterogeneous cooperation model based on the principle of energy conservation. The heterogeneous cooperation model consists of the second-order dynamic equation of the synchronous machine and the virtual synchronous control equation of the asynchronous machine;

[0020] Second-order dynamic equation of the synchronous machine:

[0021]

[0022] Virtual synchronous control equation of the asynchronous machine:

[0023]

[0024] Among them, θ i is the output phase angle of the i-th generator node, M i is the damping coefficient, D i is the inertia coefficient, a ij is the equivalent coupling coefficient, P i is the equivalent input power, E i is the equivalent voltage, Y ij is the nodal admittance matrix;

[0025] Solve using the fourth-order Runge-Kutta method, and output the frequency deviation data of each node and the time required to recover stability after being disturbed.

[0026] Furthermore, step S3 specifically includes:

[0027] Optimize the asynchronous machine penetration rate ρ, N e is the number of asynchronous machine nodes in the network topology, N net is the total number of nodes in the network topology, then:

[0028]

[0029] Gradually increase the penetration rate ρ with a set step size to respond to the power system gradually changing from a synchronous machine-dominated mode to an asynchronous machine-dominated mode.

[0030] Furthermore, step S5 specifically includes:

[0031] σ(s,t) is the number of all shortest paths from node s to node t, and σ(s,v,t) is the number of paths passing through node v among the shortest paths from node s to node t. Then, for node v, the betweenness centrality is:

[0032]

[0033] The layout strategy includes: arranging the asynchronous machines at the location where the sum of all asynchronous machine nodes C(v) is minimized. If there are multiple layout schemes, adjust the layout strategy in ascending order of node numbers.

[0034] The beneficial effects of the present invention are as follows:

[0035] By constructing a dynamic collaborative model with parameter reconfigurability characteristics, the present invention simultaneously introduces synchronous generators and asynchronous generators into a heterogeneous power grid system, determines and optimizes the initial layout of asynchronous machines based on node betweenness centrality, and performs regional autonomous parameter regulation and multi-scenario adaptation modeling on the power grid system, so as to achieve the coordinated improvement of local stability and global robustness to cope with challenges such as reduced inertia and frequency instability faced by high-proportion new energy systems. Brief Description of the Drawings

[0036] Figure 1 is a flowchart of a high-robustness power grid design method based on parameter-adjustable heterogeneous modeling provided by an embodiment of the present invention;

[0037] Figure 2 is a specific flowchart of the power grid design method provided by an embodiment of the present invention;

[0038] Figure 3 is an example diagram of the implementation effect of the designed 30-node power grid system provided by an embodiment of the present invention. Detailed Embodiment

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0040] As Figure 1 shown, the present invention provides a high-robustness power grid design method based on parameter-adjustable heterogeneous modeling, including the following stages: the power grid dynamic collaborative modeling stage, including constructing a complex network topology, establishing a heterogeneous dynamic equation, and defining a three-dimensional parameter space; the multi-dimensional parameter optimization stage, including introducing a admittance matrix reconstruction algorithm, node betweenness centrality analysis, and dynamic parameter injection; the dynamic stability evaluation stage, including solving using the fourth-order Runge-Kutta method and analyzing the double-index stability criterion; the closed-loop feedback optimization stage, including real-time data recording and adaptive iterative optimization.

[0041] As Figure 2 shown, the specific steps of the high-robustness power grid design method based on parameter-adjustable heterogeneous modeling of the present invention include:

[0042] S1: Generate a node architecture with a set number of nodes based on a small-world network. Divide the nodes into synchronous machine nodes, asynchronous machine nodes, and load nodes, and represent them with different shapes, such as using squares, triangles, and circles. Connect the nodes to each other with straight lines, and set the number of generator nodes and the number of load nodes.

[0043] S2: Establish a heterogeneous dynamic equation set and solve it using the fourth-order Runge-Kutta method; for power grid dynamic collaborative modeling, the second-order dynamic equation (1) of the synchronous machine and the virtual synchronous control equation (2) of the asynchronous machine constructed based on the principle of energy conservation form a heterogeneous collaborative model:

[0044]

[0045] where, θ i is the output phase angle of the i-th generator node, M i is the damping coefficient, D i is the inertia coefficient, a ij is the equivalent coupling coefficient, P i is the equivalent input power, E i is the equivalent voltage, Y ij is the node admittance matrix;

[0046] Solve it using the fourth-order Runge-Kutta method, and output the frequency deviation data of each node and the time required to recover stability after being disturbed, and further perform visualization processing.

[0047] S3: Optimize the asynchronous machine penetration rate ρ, N e is the number of asynchronous machine nodes in the network system, N net is the total number of nodes in the network system, and define:

[0048]

[0049] Gradually increase the penetration rate ρ in steps of 10% to respond to the trend that the power system is gradually shifting from the traditional synchronous machine-dominated mode to the asynchronous machine (such as wind power, photovoltaic, etc.)-dominated trend.

[0050] S4: Conduct admittance matrix reconstruction calculation. Increasing the number of asynchronous machine accesses in step S3 will cause the admittance matrix to expand. Introduce admittance matrix reconstruction calculation, use the characteristic that the bus current injection is 0 to delete redundant nodes, and merge its influence into the remaining network to simplify and achieve full interconnection between all generator nodes.

[0051] S5: Calculate the double-index stability criterion in the current situation. The double-index stability criterion of the present invention includes two parts: transient constraint and dynamic constraint, where the transient constraint is the maximum frequency deviation Δθ MAX . Specifically, after the system network is disturbed, the frequency deviation Δθ of all nodes iThe maximum value among them defines the maximum frequency deviation Δθ MAX <4.0 is a better design solution. Among them, the dynamic constraint is the convergence time T s , specifically, after the unified network is interfered, the time T required for the frequency deviation of all nodes to recover to 0 i The maximum value among them defines the convergence time T s <50s is a better design solution. Therefore, the double-index stability criterion of the present invention is: when both conditions of the maximum frequency deviation being less than 4.0 and the convergence time being less than 50s are satisfied, it is regarded as a better solution. Breaking through the limitations of a single criterion.

[0052] S6: Determine whether it is necessary to adjust the penetration rate ρ of the asynchronous machine. The judgment basis is the double-index stability criterion in the current situation in S5. If so, return to step S3. If not, enter step S7.

[0053] S7: For node betweenness centrality analysis, σ(s,t) is the number of all shortest paths from node s to node t, and σ(s,v,t) is the number of paths passing through node v among the shortest paths from node s to node t. For node v, the betweenness centrality is defined as:

[0054]

[0055] If the betweenness centrality of a node is very high, it means that its existence is crucial for the flow of information or resources. In the design of the present invention, try to place the asynchronous machine away from the nodes with high betweenness centrality.

[0056] S8: Adjust the layout strategy of the asynchronous machine. Arrange the asynchronous machine at the place where the sum of all asynchronous machine nodes C(v) is the smallest. Specifically, if there are multiple layout schemes, adjust the layout strategy in ascending order of node numbers.

[0057] S9: Calculate the double-index stability criterion in the current situation. According to the double-index stability criterion in the current situation, determine whether it is necessary to adjust the layout strategy of the asynchronous machine. If so, return to step S7. If not, enter step S10.

[0058] S10: Adjust the coupling strength K of the synchronous machine. For the synchronous machine, the larger K is, the better the system stability, but the higher the cost input required for the system. Specifically, start with 5 as the initial value and increase the coupling strength K of the synchronous machine in steps of 0.5.

[0059] S11: Adjust the inertia parameter M and damping coefficient D of the asynchronous machine. For the asynchronous machine, the smaller the ratio of the inertia parameter M to the damping coefficient D, the better the system stability, but the higher the cost input required for the system. Specifically, start with M / D = 1 as the initial value and decrease the inertia parameter M and damping coefficient D of the asynchronous machine in steps of 0.1.

[0060] S12: Calculate the double-index stability criterion under the current situation. According to the double-index stability criterion under the current situation, determine whether it is necessary to adjust the system coupling strength. If so, return to step S10; if not, proceed to step S13.

[0061] S13: Output the current asynchronous machine penetration rate ρ.

[0062] S14: Output the current layout position of the asynchronous machine. Specifically, the output content is the asynchronous machine node number.

[0063] S15: Output the current coupling system strength parameters. Specifically, the output content is the synchronous machine coupling strength K, the asynchronous machine inertia parameter M, and the damping coefficient D.

[0064] S16: Draw the optimized 30-node architecture diagram, where squares represent synchronous machine nodes, triangles represent asynchronous machine nodes, and circles represent load nodes.

[0065] As Figure 3 shown, a 30-node system implementation example based on the system design method of the present invention is described. Figure 3 (1) For the initial network topology setting, specifically, the asynchronous machine penetration rate ρ = 80%, the asynchronous machines are placed at nodes 1, 2, 5, 8, and 13, the synchronous machine coupling strength K = 5, and the ratio of the asynchronous machine inertia parameter M to the damping coefficient D is 1. For the initial system, the visual information of its double-index stability criterion is as Figure 3 (A) shown.

[0066] Iterate steps S3 - S6 with other parameters unchanged, and optimize the asynchronous machine penetration rate to ρ = 50%. The visual information of its double-index stability criterion is as Figure 3 (B) shown. Specifically, the maximum frequency deviation Δθ MAX is reduced by 2, and the convergence time T s is reduced by 10 s.

[0067] Iterate steps S7 - S10 with other parameters unchanged, and optimize the placement nodes of the asynchronous machines to 1, 5, and 13. The visual information of its double-index stability criterion is as Figure 3 (C) shown. Specifically, the maximum frequency deviation Δθ MAX is reduced by 8, and the convergence time T s is reduced by 20 s.

[0068] Iterate steps S11 - S13 with other parameters unchanged, and optimize the synchronous machine coupling strength K = 8, and the ratio of the asynchronous machine inertia parameter M to the damping coefficient D is 0.3. The visual information of its double-index stability criterion is as Figure 3 (D) shown. Specifically, the maximum frequency deviation Δθ MAX is reduced by 1 (<4), and the convergence time Ts Reduced by 10 s (<50 s), and the optimal design solution is output.

[0069] In summary, the present invention simultaneously introduces a synchronous generator and an asynchronous generator into a heterogeneous power grid system through a second-order dynamic equation set, determines and optimizes the initial layout of the asynchronous machine based on the node betweenness centrality, and performs regional autonomous parameter regulation and multi-scenario adaptation modeling on the power grid system to achieve the collaborative improvement of local stability and global robustness, so as to cope with challenges such as reduced inertia and frequency instability faced by a high-proportion new energy system.

[0070] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the concept described herein through the above teachings or the techniques or knowledge in related fields. Any changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A high-robustness power grid design method based on parameter-adjustable heterogeneous modeling, characterized in that Including: S1. Construct a network topology based on the small-world network; S2. Construct a heterogeneous cooperation model based on the principle of energy conservation; S3. Conduct the optimization of the asynchronous machine penetration rate and the reconstruction calculation of the admittance matrix; S4. Calculate the maximum frequency deviation and the convergence time under the current asynchronous machine penetration rate, and determine whether the current maximum frequency deviation is less than the set optimal frequency deviation threshold and whether the convergence time is less than the set optimal convergence threshold. If so, go to step S5; otherwise, return to step S3; S5. Conduct the analysis of node betweenness centrality and adjust the layout strategy of asynchronous machines according to the node betweenness centrality analysis; S6. Calculate the maximum frequency deviation and the convergence time under the current layout strategy, and determine whether the current maximum frequency deviation is less than the set optimal frequency deviation threshold and whether the convergence time is less than the set optimal convergence threshold. If so, go to step S7; otherwise, return to step S5; S7. Increase the synchronous machine coupling strength in increments of a set first step size, and adjust the asynchronous machine inertia parameter and damping coefficient in a set second step size; S8. Calculate the maximum frequency deviation and the convergence time under the current synchronous machine coupling strength, asynchronous machine inertia parameter and damping coefficient, and determine whether the current maximum frequency deviation is less than the set optimal frequency deviation threshold and whether the convergence time is less than the set optimal convergence threshold. If so, go to step S9; otherwise, return to step S7; S9. Output the current asynchronous machine penetration rate, asynchronous machine node number, synchronous machine coupling strength, asynchronous machine inertia parameter and damping coefficient.

2. The high-robustness power grid design method based on parameter-adjustable heterogeneous modeling according to claim 1, characterized in that Step S1 specifically includes: Generate a node architecture with a set number based on the small-world network, divide the nodes into synchronous machine nodes, asynchronous machine nodes and load nodes, represent them with different shapes respectively, interconnect the nodes with straight lines, and set the number of generator nodes and the number of load nodes.

3. The high-robustness power grid design method based on parameter-adjustable heterogeneous modeling according to claim 1, wherein Step S2 specifically includes: Construct a heterogeneous cooperation model based on the principle of energy conservation. The heterogeneous cooperation model consists of the synchronous machine second-order dynamic equation and the asynchronous machine virtual synchronous control equation; Synchronous machine second-order dynamic equation: Asynchronous machine virtual synchronous control equation: where, θ i is the output phase angle of the i-th generator node, M i is the damping coefficient, D i is the inertia coefficient, a ij is the equivalent coupling coefficient, P i is the equivalent input power, E i is the equivalent voltage, Y ij is the nodal admittance matrix.

4. The high-robustness power grid design method based on parameter-adjustable heterogeneous modeling according to claim 1, characterized in that Step S3 specifically includes: Optimize the asynchronous machine penetration rate ρ, N e is the number of asynchronous machine nodes in the network topology, N net is the total number of all nodes in the network topology, then: Gradually increase the penetration rate ρ in a set step size to respond to the power system gradually changing from a synchronous machine-dominated mode to an asynchronous machine-dominated mode.

5. The high-robustness power grid design method based on parametrically adjustable heterogeneous modeling according to claim 1, characterized in that, Step S5 specifically includes: If σ(s,t) is the number of all shortest paths from node s to node t, and σ(s,v,t) is the number of paths passing through node v among the shortest paths from node s to node t, then for node v, the betweenness centrality is: The layout strategy includes: arranging the asynchronous machines at the place where the sum of all asynchronous machine nodes C(v) is the smallest. If there are multiple layout schemes, adjust the layout strategy in ascending order of node numbers.

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