A source-grid coordination planning method considering short-circuit ratio constraints of new energy multi-stations

Abstract

The application provides a source-grid coordination planning method considering short-circuit ratio constraints of new energy multi-station, comprising: obtaining a new energy multi-station short-circuit ratio expansion constraint model; constructing a three-stage source-grid coordination planning model; fusing the new energy multi-station short-circuit ratio expansion constraint model as a constraint of the three-stage source-grid coordination planning model; solving the three-stage source-grid coordination planning model with the fused constraint, and outputting a system configuration scheme. The method provided in the application constructs a three-stage planning model covering new energy site selection and capacity determination, network expansion and operation verification, and guides new energy to preferentially access high-intensity areas of the power grid through short-circuit capacity weight, and takes into account the economy and system voltage safety level of the planning scheme.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a source-grid coordinated planning method that considers the short-circuit ratio constraints of multiple renewable energy power plants. Background Technology

[0002] To achieve carbon emission reduction goals, the global power system is gradually transforming into a new type of power system dominated by new energy sources. As of September 2025, the installed capacity of wind and solar power in a certain region had exceeded 1.7 billion kilowatts, accounting for more than 46% of the total installed capacity. By 2030, the installed capacity of new energy sources will exceed 3 billion kilowatts. However, the large-scale centralized grid connection of new energy sources has led to insufficient short-circuit capacity and reduced voltage support in the power system, making it prone to transient overvoltage and power station disconnection after a fault. To quantitatively assess the system voltage intensity after multiple power stations are connected to the grid, the Multi-Renewable Energy Station Short Circuit Ratio (MRSCR) index has emerged.

[0003] However, existing power grid planning methods primarily aim to meet power balance and flow constraints, largely neglecting voltage support strength constraints, exemplified by the MRSCR (Medium-Range Grid Response) system. This planning model easily leads to local grid insufficiency in the planned scheme, requiring the later deployment of numerous supporting equipment such as synchronous condensers, significantly increasing project investment and construction time. Therefore, it is of great significance to achieve the global optimization of voltage support capacity and resource allocation efficiency through power grid coordination, thus realizing the safe and economical planning of new power systems. Summary of the Invention

[0004] This application provides a source-grid coordination planning method considering the short-circuit ratio constraints of multiple renewable energy power plants. To solve the above-mentioned technical problems, this application adopts the following technical methods: Firstly, this application provides a source-grid coordinated planning method considering the short-circuit ratio constraints of multiple new energy power stations, including: Obtain an extended constraint model for the short-circuit ratio of multiple new energy power plants; Construct a three-stage source-network coordination planning model; The extended constraint model for short-circuit ratio of the new energy multi-station is integrated as a constraint for the three-stage source-grid coordinated planning model; Solve the three-stage source-network coordination planning model with fusion constraints and output the system configuration scheme.

[0005] Optionally, the construction process of the extended constraint model for short-circuit ratio of the new energy multi-station system includes the following steps: Obtain the basic short-circuit model and system impedance matrix of multiple new energy power plants; Based on the aforementioned basic model of short-circuit in multiple new energy power plants and the aforementioned system impedance matrix, an initial model of extended constraint for short-circuit ratio in multiple new energy power plants is constructed. Based on the initial model of extended constraints for short-circuit ratio of new energy multi-station, an extended constraint model for short-circuit ratio of new energy multi-station is generated.

[0006] Optionally, generating the extended constraint model for the short-circuit ratio of multiple new energy power plants based on the initial model of extended constraint for the short-circuit ratio of the new energy power plants includes: The initial model of short-circuit ratio extension constraint for new energy multi-stations is linearized using the hexagonal internal approximation technique to generate the extended constraint model of short-circuit ratio for new energy multi-stations.

[0007] Optionally, the first stage of the three-stage source-grid coordination planning model is a new energy deployment and capacity configuration model; The second stage of the three-stage source-grid coordination planning model is the transmission line expansion planning model; the third stage of the three-stage source-grid coordination planning model is the system operation verification model.

[0008] Optionally, the constraints of the new energy deployment and capacity configuration model include the following: Constraints on the selection of new energy sites, the configuration of new energy capacity, and the total installed capacity target of new energy.

[0009] Optionally, the constraints of the transmission line expansion planning model include the following: Constraints on the number of transmission line expansions and linearization constraints on the short-circuit ratio of multiple new energy power plants.

[0010] Optionally, the constraints of the system operation verification model include the following: The system has active power balance constraints, renewable energy output and curtailment constraints, thermal power unit output constraints, water balance constraints, hydropower output constraints, reservoir capacity boundary constraints, system reserve capacity constraints, DC line transmission power constraints, and line DC power flow constraints.

[0011] Optionally, solving the three-stage source-network coordinated planning model with fusion constraints to output a system configuration scheme includes: An adaptive nested Benders decomposition algorithm based on dual information heuristic cutting plane is used to solve the three-stage source-network coordination planning model with fusion constraints and output the system configuration scheme.

[0012] Secondly, this application also provides a computer system, comprising: Memory is used to store instructions that can be executed by the processor; A processor for executing the instructions to implement the method as described in any of the first aspects.

[0013] Thirdly, this application also provides a computer-readable medium storing computer program code that, when executed by a processor, implements the method as described in any of the first aspects.

[0014] This application has the following beneficial effects: The method proposed in this application constructs a three-stage planning model covering new energy deployment and capacity determination, grid expansion and operation verification. It guides new energy to be preferentially connected to areas with higher grid intensity through short-circuit capacity weighting, taking into account both the economy of the planning scheme and the system voltage safety level. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a source-grid coordination planning method considering the short-circuit ratio constraints of multiple new energy power stations, provided for an embodiment of this application; Figure 2 A simplified model diagram of the multi-energy power station access system provided in this application embodiment; Figure 3 This is a schematic diagram of the internal approximation of a regular hexagon provided in an embodiment of this application; Figure 4 A flowchart illustrating the algorithm for solving Benders decomposition based on adaptive nested decomposition, provided in an embodiment of this application. Figure 5 An improved IEEE-39 node system diagram provided for embodiments of this application; Figure 6 Comparison charts of MRSCR at the site before and after implementation of various model schemes provided in the embodiments of this application; Figure 7 A comparison diagram of transient voltage curves of critical nodes under N-1 faults provided in the embodiments of this application; Figure 7 (a) Comparison of transient voltage curves of the model at node 9 where the N-1 fault occurs in the system; Figure 7 (b) is a comparison of the transient voltage curves of node 14 after the N-1 fault occurs in the system; Figure 7 (c) Comparison of transient voltage curves of the model when the system experiences an N-1 fault at node 20; Figure 7 (d) is a comparison of the transient voltage curves of the model when the N-1 fault node 32 of the system occurs; Figure 8 The relative gap convergence curve of the improved IEEE-39 node system algorithm provided in the embodiments of this application; Figure 9 A comparison chart of MRSCR for various provincial power grid model schemes provided in the embodiments of this application. Detailed Implementation

[0016] To facilitate understanding by those skilled in the art, the present application will be further described below in conjunction with embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present application.

[0017] To solve the above technical problems, such as Figure 1 As shown, this application proposes a source-grid coordinated planning method considering the short-circuit ratio constraints of multiple new energy power stations, including: Step S101: Obtain the extended constraint model for short-circuit ratio of multiple new energy power plants; like Figure 2 The diagram shows a simplified model of a multi-new energy power station access system provided in this application embodiment. This application scenario includes wind farms, photovoltaic power stations, AC power grids, synchronous condensers, and transmission lines. Multiple new energy power stations are connected to the AC power grid, with synchronous condensers and synchronous generators connected in parallel as synchronous condenser support equipment. The dashed lines represent candidate expansion lines for the system, enabling power exchange with the external power grid. (i=1.n) are the node busbars The new energy injected into the power source is seen as power. (i=1.n) represents the stations The equivalent impedance of the connection, (i=1.n) represents the stations Grid connection point bus voltage, (i=1.n) represents the stations , Equivalent mutual impedance between +1.

[0018] Based on the branch addition method and the influence mechanism of parallel admittance on the impedance matrix, and comprehensively considering the configuration scheme of transmission network expansion and synchronous support equipment, a constraint model for the short-circuit ratio expansion of multiple new energy power stations involving new energy investment and grid expansion decisions is established. The construction process is as follows: First, a basic short-circuit model for multiple new energy power plants is constructed, as shown in the following equation: (1) In the formula: For grid-connected node bus The system's nominal voltage at that location; For node bus The actual operating voltage conjugate value; For node bus The apparent power of the new energy injected at the site, with the superscript RE indicating a new energy power generation station; , These are the first and second equivalent impedance matrices of the AC power grid. Line number Column elements and the first Line number Column elements; This refers to the number of lines.

[0019] When on the bus , An additional impedance is added between them. When dealing with transmission lines, according to the branch addition method, the system impedance matrix is... Line number The column elements change as follows: (2) In the formula, For nodes in the original network With the starting point of the new route mutual impedance between For nodes in the original network Self-impedance, For nodes in the original network With the starting point of the new route mutual impedance between For nodes in the original network With the starting point of the new route mutual impedance between For nodes in the original network With the starting point of the new route mutual impedance between For nodes in the original network Self-impedance, For nodes in the original network and Self-impedance.

[0020] Synchronous support equipment as parallel reactor Access Node At that time, its equivalent parallel admittance is Based on the mechanism of the influence of parallel admittance on the impedance matrix, the elements of the system impedance matrix... The changes are as follows: (3) in, For nodes in the original network With access node mutual impedance between For nodes in the original network With access node mutual impedance between For access nodes Self-impedance.

[0021] Taking into account both the transmission line expansion and the configuration of synchronous support equipment, under the determined start-up mode of the synchronous support equipment, the impedance matrix is... Line number Column elements can be represented as: (4) In the formula, For the line The variables for commissioning decisions; , These are the candidate line set and the pre-configured synchronization support equipment, respectively. Node set.

[0022] Combining the extended expression of the impedance matrix shown in equation (4) and the basic short-circuit model of new energy multi-station shown in equation (1), an initial model of extended constraint for short-circuit ratio of new energy multi-station is constructed, as shown in the following equation: (5) In the formula, Candidate sites Investment and construction decision variables =0 indicates that the station will not be built. =1 indicates construction; For the set of candidate site nodes; For new energy power stations and The complex power conversion factor reflects the phase and amplitude differences between electrical quantities at each station.

[0023] An initial model for short-circuit ratio expansion constraints of multiple new energy power plants, taking into account new energy investment and grid expansion decisions, can accurately characterize the impact of transmission line expansion and synchronous support equipment configuration on system voltage intensity, providing a theoretical basis for source-grid coordinated planning.

[0024] The direct introduction of the aforementioned initial model for extended constraints on the short-circuit ratio of multiple new energy power plants causes the source-grid coordination planning problem to exhibit mixed-integer nonlinear programming characteristics, making it difficult to solve directly. Therefore, a regular hexagonal inner approximation technique is used to linearize the initial model for extended constraints on the short-circuit ratio of multiple new energy power plants, generating an extended constraint model for the short-circuit ratio of multiple new energy power plants. The processing procedure is as follows: Please refer to Figure 3 The diagram shows the approximation within a regular hexagon. The core difficulty in the initial model for extending the short-circuit ratio constraint for multiple new energy power stations lies in the magnitude constraint on the complex variable W. This constraint geometrically corresponds to a circular feasible region, which fundamentally conflicts with the convex polyhedral feasible region required by the mixed-integer linear programming solver. To overcome this difficulty, this application uses a linearization method based on approximation within a regular hexagon. In high-voltage transmission networks, the system's equivalent impedance ratio typically satisfies X / R > 10, and the voltage phase angles between power stations are similar. The extended constraint model for the short-circuit ratio of multiple new energy power stations, combined with that of power station i, is as follows: (6) In the formula, This is the critical value for the short-circuit ratio. For node bus The new energy injected into the power source is seen as power. The first is the equivalent impedance matrix of the AC power grid. Line number Column elements, For node bus The new energy injected into the power source is seen as power. The first is the equivalent impedance matrix of the AC power grid. Line number Column elements, For node bus The new energy injected into the power source is seen as power. The first is the equivalent impedance matrix of the AC power grid. Line number Column elements. Introducing auxiliary variables. Characterizing the expression on the right side of the inequality, the above expression can be equivalently transformed into: (7) The real part and the virtual part They are respectively: (8) (9) The impact of line expansion and given synchronous support equipment on the system impedance matrix is ​​considered, where the inter-node impedance can be expressed as: (10) (11) In the formula, , Resistance and reactance under the basic network structure; , To expand the line For nodes , The incremental contribution of inter-interval impedance; For the 0-1 decision variables of the line expansion; , For synchronous support equipment For nodes , The incremental contribution of the inter-electrode impedance. The core difficulty of the above constraint lies in its application to complex variables. The modulus constraint, which geometrically corresponds to a circular feasible region, fundamentally conflicts with the convex polyhedral feasible region required by mixed-integer linear programming solvers. To overcome this difficulty, this paper employs a linearization method based on internal approximation within a regular hexagon. For example... Figure 2As shown, this method uses a regular hexagonal polygon composed of 16 linear hyperplanes to approximate the circular constraint boundary, and achieves this by rotating at four uniformly distributed angles. The coordinate transformation is implemented below. An auxiliary variable is introduced. , making In each rotating coordinate system, linear constraints are constructed by expanding the absolute value, and four sets of linear constraints are generated for each angle.

[0025] (12) (13) (14) (15) In the formula, Original vector The x-coordinate in the k-th rotated coordinate system Original vector The ordinate in the k-th rotating coordinate system.

[0026] Theoretical analysis shows that, positive The maximum relative error of polygon approximation is .for The error is approximately 1.92% for a regular hexagon with a polygon length of 16. Considering the parameter uncertainties in practical engineering and the geometric errors in approximating regular polygons, this paper focuses on the critical value... Introducing a safety margin The linearization constraint for the short-circuit ratio of multiple new energy power stations can be obtained, as shown in the following formula: (16) In the formula, For the selected set of wind power and solar power plant nodes, Take 2.5, safety margin Take 0.05.

[0027] In this step, the MRSCR constraint is linearized using a regular hexagonal inner approximation technique. This ensures computational accuracy while achieving convexity processing of the voltage intensity constraint, enabling the source-network coordination planning model to be solved using a mature mixed-integer linear programming solver, thus improving the solution efficiency.

[0028] Step S102: Construct a three-stage source-network coordinated planning model; The three-stage source-grid coordination planning model in this step consists of three stages: the first stage is the new energy deployment and capacity allocation model; the second stage is the transmission line expansion planning model; and the third stage is the system operation verification model. These will be described in detail below: The renewable energy deployment and capacity allocation model, under the conditions of satisfying renewable energy site selection constraints, capacity allocation constraints, and total installed capacity target constraints, guides renewable energy to preferentially connect to areas with higher grid intensity through short-circuit capacity weighting. The objective function of this allocation model is to minimize the total system investment cost, as shown in the following equation: (17) in, The objective function value is the value that minimizes the total investment cost of the system. , These are candidate sites for wind farms and photovoltaic power plants, respectively. , Wind farm and photovoltaic power station The unit capacity construction cost; , Wind farm and photovoltaic power station The installed capacity decision variable This refers to the annualized coefficient for new energy sources. , Wind farm and photovoltaic power station The site selection decision is a 0-1 variable, where a value of 1 indicates that the site is selected, and a value of 0 indicates that it is not selected. , These are wind farms and photovoltaic power station The short-circuit capacity; , These are the operation and maintenance coefficients for wind farms and solar power plants, respectively. This is the short-circuit capacity reward weighting coefficient.

[0029] The transmission line expansion planning model satisfies the linearization constraint of the short-circuit ratio of multiple new energy power plants and the constraint on the number of transmission line expansions. The objective function expression for the second stage is as follows: (18) In the formula, The objective function value is to minimize the investment cost of power transmission line construction. This is the annualized coefficient for the line; For the line The unit construction cost; For the line Length; For the line The expansion decision is a 0-1 integer variable, with a value of 1 indicating an expansion of the line. Expansion is allowed; a value of 0 indicates no expansion.

[0030] The system runs and verifies the model; the objective function expression is as follows: (19) In the formula, The objective function value is the total operating cost of the system. For the scene The weight, , , , , , , thermal power units Unit fuel cost coefficient, thermal power unit Unit operation and maintenance cost coefficient, hydropower station Unit operation and maintenance cost coefficient, unit cost coefficient of renewable energy curtailment, unit cost coefficient of water curtailment, and DC transmission to other regions. Unit revenue coefficient and DC feed Unit cost coefficient; , , , , , , Scenes Next moment thermal power units Contributions to hydropower stations Power output, wind farm curtailed power, photovoltaic power plants abandoned power, hydropower station Water discharge flow rate, external DC transmission Power and DC feed The power; , , , , , These are respectively the scene set, hydropower station set, wind power station set, photovoltaic station set, DC transmission set, and DC feed-in set.

[0031] Step S103: Integrate the extended constraint model of short-circuit ratio of the new energy multi-station as the constraint of the three-stage source-grid coordination planning model; In the above transmission line expansion planning model, the linearization constraint of the short-circuit ratio of new energy multi-station shown in Equation (16) is introduced, and the following constraint on the number of transmission line expansions is also used as a constraint condition of the model: (20) In the formula, This represents the maximum number of lines that can be expanded.

[0032] The constraints of the above-mentioned new energy deployment and capacity allocation model include the following: Constraints on the selection of new energy sites: (twenty one) (twenty two) In the formula, , These represent the number of wind farms and photovoltaic power stations to be built during the planning period.

[0033] New energy capacity allocation constraints: (twenty three) (twenty four) In the formula, , Minimum and maximum installed capacity of a single wind farm; , These represent the minimum and maximum installed capacity of a single photovoltaic power station, respectively.

[0034] Constraints on total installed capacity targets for new energy: (25) (26) In the formula, , This is the total installed capacity target for wind farms and photovoltaic power stations during the planning period.

[0035] The constraints of the system operation verification model include the following: System active power balance constraints: (27) In the formula, , , In the scene respectively Time period Actual output and node of wind and solar power Active load.

[0036] New energy output and curtailment constraints: (28) (29) (30) (31) In the formula, , Scenes Next moment The output coefficients of wind power and photovoltaic power, , Wind farm Photovoltaic power stations The installed capacity.

[0037] Thermal power unit output constraints: (32) (33) (34) In the formula, , The units The minimum and maximum active power output; , The units The upward and downward adjustment of the ramp limit.

[0038] Water balance constraints: (35) In the formula, For the scene Next moment reservoir The water storage capacity For time period Inbound traffic, upstream reservoir At any moment Outbound flow For the water flow delay time, This refers to the water consumption for power generation.

[0039] Hydropower output constraints: (36) (37) (38) In the formula, , Hydropower stations The minimum and maximum active power output, For hydroelectric power station The output coefficient; For effective head, This refers to the discharge flow rate.

[0040] Storage capacity boundary constraints: (39) In the formula, , Reservoirs The minimum and maximum allowable water storage capacity.

[0041] System backup capacity constraints: (40) (41) In the formula, , thermal power units Adjusting or lowering reserve capacity , Hydropower stations Adjusting or lowering reserve capacity For station Uncertainty coefficient, For station In the scene Time period The output coefficient, This is the load reserve factor.

[0042] DC line transmission power constraints: (42) (43) (44) In the formula, , DC lines Minimum and maximum transmission power, This is the power ramp-up coefficient for DC lines.

[0043] DC power flow constraints on the line: (45) (46) In the formula, For the scene Next moment line The trend; For the line For nodes The power transmission distribution factor; For nodes Net injection power; For a given route Investment decision variable, a value of 1 indicates the importance of the route. The value 0 indicates that expansion will be carried out, while a value of 0 indicates that expansion will not be carried out. For the line The capacity increment coefficient after expansion is set to 1 in this application.

[0044] Step S104: Solve the three-stage source-network coordination planning model with fusion constraints and output the system configuration scheme.

[0045] The three-stage source-grid coordination planning model with fusion constraints involves multiple decision-making problems, including renewable energy deployment and capacity configuration, grid expansion decisions, and multi-scenario operation verification. These problems are strongly coupled, making traditional single-layer optimization methods ineffective in handling such complex mixed-integer programming problems. This step employs an adaptive nested Benders decomposition algorithm based on a dual-information heuristic cutting plane. This algorithm decouples the complex source-grid coordination planning problem into a nested structure of outer renewable energy layout optimization and inner grid expansion optimization. Through layer-by-layer solution and iterative coordination mechanisms, effective coupling of each layer is achieved, resulting in a system configuration scheme. The specific solution process is as follows: Please refer to Figure 4 The algorithm employs a double-layer nested iterative structure of "outer master-sub-problem" and "inner master-sub-problem". In the solution process, the outer master problem is a mixed-integer linear programming problem involving the deployment and capacity configuration of renewable energy power plants; the outer sub-problem, serving as the inner master problem, optimizes the grid expansion strategy based on a given renewable energy configuration scheme; the inner sub-problem minimizes system operating costs through multi-scenario operation verification, ensuring the operational feasibility of the planning scheme. The inner layer uses standard Benders decomposition to decouple grid expansion and operational verification, while the outer layer uses a heuristic cutting plane based on dual information to guide the deployment of renewable energy to areas with strong power grids. The specific models of the master problem and sub-problems are as follows: In one possible embodiment, the algorithm specifically includes the following steps: 1) Outer Master Problem (OMP): (47) in, The objective function value of the outer main problem. For the cost estimation variables of the outer layer problem, For the first A feasible iterative capacity configuration scheme; For the first The optimal total cost of the inner layer problem in the next feasible iteration. For the first Second feasible iteration site Outer layer heuristic optimization of cutting plane coefficients, For the first Second feasible iteration site The capacity configuration value, For the first The short-circuit capacity reward weight coefficient for the second infeasible iteration. As a weighting factor, this application takes 1.5. This adaptive mechanism dynamically enhances the penalty for insufficient short-circuit capacity based on feasibility feedback, guiding new energy sources to adjust to areas with strong power grids. The value should be determined based on the actual system size; , These are the inner feasible and infeasible iterative sets, respectively. Based on the dual information of the key constraints of the subproblem, we can calculate: (48) In the formula, For the scene Next period node The power balance constraint dual variables, For the scene Next moment The output coefficient of new energy sources For the site The corresponding system node, , To adjust the standby constraint dual variable upwards and downwards, For the scene The probability weights are calculated based on the dual information of the key constraints of the subproblems. This problem guides the layout of new energy sources to areas with strong power grids through heuristic cutting planes.

[0046] 2) Inner Master Problem (IMP): (49) In the formula, The objective function value of the inner main problem. For operating cost estimation variables, For the line The expansion decision variables; For the first The optimal operating cost of the system under the given expansion plan in the next iteration; It is the first In the next iteration, the line The decision value; For the scene The time period is in The dual variables of the fixed constraints for the line expansion in the next iteration; For the line The dual variable; , These are the feasible and infeasible iterative sets in the inner layer, respectively.

[0047] 3) Inner Sub-problem ISP1: (50) In the formula, The objective function value is the system operating cost. For the line Expansion decision variables, Given a route expansion scheme for the inner main problem, For the scene Time period node The dual variables of the active power balance constraint, , Scenes Time period Adjust the dual variables of the standby constraints upwards and downwards. For the scene The dual variables of the line expansion constraint. If problem ISP1 is feasible, the key constraint dual variables are obtained and fed back to the inner and outer main problems; if the problem is not feasible, the process turns to the inner sub-problem ISP2.

[0048] 4) Inner Sub-problem ISP2 (51) In the formula, , The lines are respectively In the scene Time period The forward and reverse power flow relaxation variables. If the optimal value of this subproblem is greater than zero, then obtain its dual variable vector of fixed constraints for line expansion. The feasibility cut is then fed back to the IMP, and the cut constraint excludes line expansion schemes that would make operation infeasible.

[0049] In this embodiment, the heuristic cutting plane based on dual information can guide the layout of new energy sources in areas with strong power grids. This adaptive mechanism dynamically enhances the penalty for insufficient short-circuit capacity based on feasibility feedback, effectively improving the solution efficiency of large-scale mixed integer programming problems.

[0050] Simulation analysis: To verify the effectiveness of the proposed method, an improved IEEE-39 node system and a provincial power grid system were selected for case studies. First, K-means clustering analysis was performed based on historical operating data to extract eight typical operating scenarios covering seasonal differences and typical meteorological patterns. A scenario probability weighting mechanism was then used to handle the randomness and volatility of renewable energy output. Second, an optimization model was built on the MATLAB platform, and the CPLEX solver was used to handle the mixed-integer programming problem. Finally, transient simulations were performed using PSD-BPA software for verification.

[0051] Comparing the planning results of the four models: Model 1: The source-grid coordination planning scheme with MRSCR constraints mentioned in this application realizes the coordinated optimization of new energy deployment and capacity determination and transmission network expansion; Model 2: Traditional source-grid coordination planning scheme for coordinating the optimization of new energy deployment and grid expansion without considering MRSCR constraints; Model 3: Grid expansion scheme considering MRSCR constraints under fixed new energy configuration; Model 4: Traditional grid expansion scheme with fixed new energy configuration.

[0052] Please refer to Figure 5 The improved IEEE-39 node system comprises 39 nodes and 46 transmission lines, equipped with two 1000MW rated DC transmission lines and two 500MW rated synchronous condensers. The example sets up several candidate wind and solar power access points, selecting three optimal nodes from each for construction. The total installed capacity of new energy sources is targeted at 40% of the system capacity, with 3700MW each allocated to wind and solar power.

[0053] Please refer to Figure 6 The comparison results of the short-circuit ratios of multiple stations before and after the implementation of each model scheme show that, using the source-network coordination planning scheme proposed in this application, the MRSCR index of each new energy access node can reach the safety threshold requirement of 2.5 or higher. Although Models 2 and 4 achieve power flow feasibility, due to the lack of consideration of short-circuit ratio constraints, the MRSCR values ​​of some nodes are still at a low level, which cannot fully guarantee the system voltage support capability; and even if Model 3 adopts the full-line expansion strategy, there are still problems that several nodes cannot meet the short-circuit ratio safety requirements.

[0054] Please refer to Figure 7 To further verify the reliability of the proposed scheme, Model 1 and Model 2 were selected for transient simulation verification in PSD-BPA software. The fault scenario was set as follows: a three-phase short-circuit fault occurred on one side of line 14-15 when the system was in steady state for 0.5 seconds, and the faulty line was disconnected after 0.1 seconds. Simulation results show that the fault is most severely impacted at the fault-proximity node. Figure 7 In (b), the voltage of Model 2 drops to 0.70 pu and is accompanied by a large overshoot oscillation; while Model 1 also has fluctuations, but it limits the voltage drop to above 0.94 pu and the oscillation amplitude is significantly smaller than that of Model 2. Figure 7 (c) (Node 20) The most significant difference lies between Model 1 and Model 2. Model 2 still exhibits persistent and relatively large low-frequency oscillations, while the voltage curve fluctuations in Model 1 are effectively compressed within an extremely narrow range, closely fluctuating around the rated value, demonstrating excellent system damping characteristics. Figure 7In nodes shown in (a) and 7(d), Model 2 exhibits significant low-frequency large fluctuations, with voltage remaining unstable for extended periods. In contrast, Model 1's voltage curve consistently fluctuates closely around the rated value (1.0 pu), and the fluctuation range is effectively compressed into a narrower interval. This indicates that Model 1, by strengthening the backbone network, while not completely eliminating physical oscillations during transient processes, significantly reduces the amplitude of voltage fluctuations and improves the recovery speed, verifying its effectiveness in enhancing system transient stability.

[0055] Please refer to Figure 8 To further evaluate the computational performance of the solution strategy, the relative optimal gap convergence process of the outer iteration of the algorithm is demonstrated. Thanks to the introduced heuristic cutting plane and adaptive guiding weights, the relative gap shows a significant decreasing trend in the early stages of iteration. As the interaction between the principal and subproblems deepens, the gap converges smoothly, finally decreasing to 0.85% in the 16th iteration, meeting the outer convergence threshold requirement, and verifying the effectiveness of the proposed solution algorithm in solving such complex nested problems.

[0056] Please refer to Figure 9 To verify the applicability of the proposed method in large-scale real-world power grids, a provincial power grid was selected as a practical example for verification analysis. Guided by the target of adding 23,170 MW of photovoltaic (PV) and 5,420 MW of wind power capacity, the example constructed a candidate site system covering both wind and PV. Through optimization algorithms, four wind power nodes and six PV nodes were selected as planned new energy sites. Considering the relatively weak grid structure and abundant new energy resources in some areas, synchronous support equipment was configured at key nodes to enhance voltage support capabilities. The improvement effect of each model scheme on the system's MRSCR shows that Model 1 effectively solves the problem of insufficient local voltage support caused by the centralized access of large-scale new energy sources.

[0057] In summary, the method proposed in this application constructs a three-stage planning model covering new energy deployment and capacity determination, grid expansion, and operational verification. It also guides new energy to prioritize grid connection in areas with higher grid intensity by using short-circuit capacity weighting, thus balancing the economic efficiency of the planning scheme with the system voltage safety level.

[0058] In some embodiments, this application also provides a computer system including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0059] This application also provides a computer-readable storage medium for storing a computer program. This computer-readable storage medium can be applied to a computer device, and the computer program causes the computer device to execute the corresponding processes in the methods described above in the embodiments of this application; for brevity, further details are omitted here.

[0060] The above embodiments are preferred implementations of this application. In addition, this application can be implemented in other ways. Any obvious substitutions without departing from the concept of this technical solution are within the protection scope of this application.

[0061] To facilitate understanding by those skilled in the art of the improvements made by this application compared to the prior art, some of the accompanying drawings and descriptions have been simplified, and for clarity, some other elements have been omitted from this application. Those skilled in the art should realize that these omitted elements may also constitute the content of this application.

Claims

1. A source-grid coordinated planning method considering the short-circuit ratio constraints of multiple new energy power plants, characterized in that, include: Obtain an extended constraint model for the short-circuit ratio of multiple new energy power plants; Construct a three-stage source-network coordination planning model; The extended constraint model for short-circuit ratio of the new energy multi-station is integrated as a constraint for the three-stage source-grid coordinated planning model; Solve the three-stage source-network coordination planning model with fusion constraints and output the system configuration scheme.

2. The method according to claim 1, characterized in that, The construction process of the extended constraint model for short-circuit ratio of the new energy multi-station includes the following steps: Obtain the basic short-circuit model and system impedance matrix of multiple new energy power plants; Based on the aforementioned basic model of short-circuit in multiple new energy power plants and the aforementioned system impedance matrix, an initial model of extended constraint for short-circuit ratio in multiple new energy power plants is constructed. Based on the initial model of extended constraints for short-circuit ratio of new energy multi-station, an extended constraint model for short-circuit ratio of new energy multi-station is generated.

3. The method according to claim 2, characterized in that, The process of generating an extended constraint model for the short-circuit ratio of multiple new energy power plants based on the initial model of the extended constraint model for the short-circuit ratio of the new energy power plants includes: The initial model of short-circuit ratio extension constraint for new energy multi-stations is linearized using the hexagonal internal approximation technique to generate the extended constraint model of short-circuit ratio for new energy multi-stations.

4. The method according to claim 3, characterized in that, The first stage of the three-stage source-grid coordination planning model is the new energy deployment and capacity configuration model; the second stage is the transmission line expansion planning model; and the third stage is the system operation verification model.

5. The method according to claim 4, characterized in that, The constraints of the new energy deployment and capacity allocation model include the following: Constraints on the selection of new energy sites, the configuration of new energy capacity, and the total installed capacity target of new energy.

6. The method according to claim 4, characterized in that, The constraints of the transmission line expansion planning model include the following: Constraints on the number of transmission line expansions and linearization constraints on the short-circuit ratio of multiple new energy power plants.

7. The method according to claim 4, characterized in that, The constraints of the system operation verification model include the following: The system has active power balance constraints, renewable energy output and curtailment constraints, thermal power unit output constraints, water balance constraints, hydropower output constraints, reservoir capacity boundary constraints, system reserve capacity constraints, DC line transmission power constraints, and line DC power flow constraints.

8. The method according to claim 1, characterized in that, The three-stage source-network coordination planning model with fusion constraints is solved to output a system configuration scheme, including: An adaptive nested Benders decomposition algorithm based on dual information heuristic cutting plane is used to solve the three-stage source-network coordination planning model with fusion constraints and output the system configuration scheme.

9. A computer system, characterized in that, include: Memory is used to store instructions that can be executed by the processor; A processor for executing the instructions to implement the method as described in any one of claims 1 to 8.

10. A computer-readable medium, characterized in that, The system stores computer program code that, when executed by a processor, implements the method as described in any one of claims 1 to 8.

Images