A partitioned flexible interconnected power grid planning method

Abstract

The present application relates to a kind of partition flexible interconnected power grid planning method, belong to electric power communication technical field.The present application technical scheme proposes the partition flexible interconnected power grid planning method based on space-time stability uncertainty drive, effectively improves the overall performance of optimization algorithm and the self-adaptive ability to space-time stability, uncertainty.In the process of selecting nest, an alternating step selection mechanism considering space-time stability uncertainty is designed, different search step is selected under different stability variation trend, uncertainty state, which can help algorithm search in the direction of stability enhancement, uncertainty influence weakening, improve algorithm convergence efficiency and global optimal performance, realize the adaptive adjustment to space-time stability, uncertainty.In the process of discarding nest, an adaptive search probability model is constructed, which can balance convergence and global optimality, adjust search preference according to actual uncertainty state, improve the overall performance of algorithm.

Description

Technical Field

[0001] This invention relates to a method for planning a zoned flexible interconnected power grid, belonging to the field of power communication technology. Background Technology

[0002] With the development of hybrid AC / DC power grids and the integration of large-scale renewable energy sources, grid zoning has become an important direction for simplifying operation management and improving efficiency. Currently, many regional power grids have successfully implemented 220kV grid zoning, enhancing the safety and stability of the power system through effective control of short-circuit currents. However, the number and capacity of zoning substations in 500kV power grids are relatively small, and their power supply and safety assurance functions must be fully utilized; the 220kV method cannot be simply adopted. Furthermore, 500kV power grids, especially in core load areas, often involve large-capacity DC transmission and multiple DC connection points. In addition to addressing short-circuit current and electromagnetic loop problems, they also need to cope with complex challenges such as voltage stability of multiple DC systems and power flow control under AC / DC parallel operation. Traditional zoning flexible interconnection power grid planning methods still need to address the following challenges:

[0003] First, existing technologies rely on static voltage stability indicators, which may not reflect stability under dynamic conditions; the calculation methods and selection of indicators may not be sufficient to fully reflect system characteristics; the indicator system does not fully consider the impact of the time domain on the planning of the main power grid, which limits the assessment of the reliability of the power grid under various future conditions, thus affecting the scientificity and effectiveness of the planning; factors such as the power grid topology and control strategies are ignored; the accuracy of the regional division may be insufficient, and the calculation complexity is high.

[0004] Second, existing technologies use fixed search step size and search probability, without considering temporal and spatial stability and uncertainty factors, resulting in poor exploration performance and convergence speed of the algorithm, making it prone to getting trapped in local optima and difficult to adapt to the planning problem of partitioned flexible interconnected power grids with high uncertainty factors.

[0005] Therefore, this invention proposes a regional flexible interconnected power grid planning method, establishes a multi-objective optimization model for regional flexible interconnected power grids that takes into account time-space characteristics, and proposes a regional flexible interconnected power grid planning method driven by spatiotemporal stability uncertainties. This method optimizes the spatiotemporal stability of regional flexible interconnected power grids, improves the accuracy and adaptability of planning, helps identify and respond to potential risks, enhances the resilience and reliability of the system, and ensures that the power grid can still operate stably under extreme conditions. This provides strong support for the scientific planning and decision-making of AC / DC interconnected main grid. Summary of the Invention

[0006] To address the aforementioned technical problems, the purpose of this invention is to provide a method for planning a zoned flexible interconnected power grid.

[0007] The present invention provides a method for planning a zoned flexible interconnected power grid, the specific planning steps of which are as follows:

[0008] Establish a multi-objective optimization model for a zoned interconnected flexible power grid that incorporates time-space characteristics;

[0009] Construct a partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainties;

[0010] A multi-objective optimization model for a partitioned flexible grid based on time-domain and spatial characteristics is used. The fitness of each "bird's nest" (a type of electrical structure) is calculated using a planning method driven by spatiotemporal stability uncertainties. The optimal "bird's nest" location is then determined based on the fitness ranking. Compare the fitness of the current best location for the Bird's Nest with the historical best location. If the difference between the two is less than 1, the fitness is determined by the following criteria: Then, the current optimal location of the Bird's Nest, representing the best planning scheme, will be output, thus completing the planning of the regional flexible interconnected power grid.

[0011] Furthermore, the establishment of a multi-objective optimization model for a partitioned interconnected flexible power grid that incorporates time-space characteristics specifically includes:

[0012] Taking into account the time-space characteristics of the power grid, two performance indicators covering both time-space and spatial characteristics are proposed: the balance of multiple DC feeds and the power supply capacity of TSC-epsilon.

[0013] Taking into account the stability, balance, security and economy of power grid operation, an objective function covering the time domain and spatial domain characteristics of the power grid is proposed.

[0014] Considering the uncertainties of wind power output and load demand in the time and spatial domains, a robust model based on multi-source joint uncertainty theory for risk avoidance is established.

[0015] Furthermore, considering the time-space characteristics of the power grid, the specific method for proposing two performance indicators covering both time-space and spatial characteristics—multi-DC feed-in balance and TSC-epsilon power supply capacity—is as follows:

[0016] The equalization degree of multiple DC infeeds is defined as the root mean square error of the short-circuit ratio (MISCR) of multiple infeeds at DC landing points. Indices for the equalization degree of multiple DC infeeds are established in both spatial and temporal characteristics to cover both time and spatial features, expressed as follows:

[0017] (1)

[0018] In the formula, under a certain partitioning scheme The corresponding power network has DC landing point, A partition containing DC landing points; , corresponding to the Each partition has One DC landing point; This indicates that each DC landing point was processed. Secondary sampling; , These are the weighting coefficients for the equalization of multiple DC feeds in the time and spatial dimensions, respectively. The weighting coefficients satisfy... The constraints and weight selection are determined based on the actual time-domain and spatial-domain operating conditions of the power grid. , These represent the balance of multiple DC feeds in terms of spatial and temporal characteristics, respectively. Indicates the first Within the first partition The balance of multiple DC feeds at each DC landing point; This indicates the first step in determining the DC landing point. The equalization of multiple DC feeds is calculated using sub-sampling.

[0019] Based on the maximum power supply capacity (TSC) index, a TSC-epsilon power supply capacity index covering both time and spatial characteristics is proposed. This index represents the power supply capacity when the wind power output range is... hour, To maximize wind power output, The maximum deviation in wind power output, representing the maximum available load of the entire power grid under this zoning method, is expressed as:

[0020] (2)

[0021] In the formula, To contribute actual power to wind power For nodes Active load, The total number of nodes; It represents the sum of active loads at all nodes in the power grid, which characterizes the maximum power supply capacity of the power grid under fluctuating wind power output.

[0022] Furthermore, considering the stability, balance, security, and economy of power grid operation, the specific method for proposing an objective function that covers the time and spatial characteristics of the power grid is as follows:

[0023] Therefore, 0-1 integer variables are used for encoding; the key power grid channel is defined as... When its value is 0, it indicates that the corresponding candidate line has been selected and disconnected; otherwise, the channel remains connected. The key power grid channels are set as follows: ;

[0024] Using the objective function The power transfer support strength of each partition scheme is measured. This refers to the ratio of AC power to the cross-sectional power limit of the power transfer channel after a fault occurs.

[0025] Using the objective function The balance of multiple DC feeds in the time and spatial domains of each partition scheme is evaluated.

[0026] Using the objective function The average power grid strength of each regional scheme is measured. This represents the mean square error of the short-circuit capacity.

[0027] Using the objective function It measures the maximum power supply capacity of the power grid in the time and spatial domains;

[0028] Introducing weighting coefficients The weighted sum of the four objective functions mentioned above is taken as the optimization objective, which is expressed as:

[0029] (3)

[0030] In the formula, These represent the weighting coefficients of the four corresponding objective functions.

[0031] Furthermore, the specific method for establishing a robust multi-source joint uncertainty theory model for risk avoidance, considering the uncertainties of wind power output and load demand in the time and spatial domains, is as follows:

[0032] A robust model for risk aversion based on multi-source joint uncertainty theory is expressed as follows:

[0033] (4)

[0034] In the formula, , These are the predicted values ​​for wind power output and load demand, respectively. , These are the actual values ​​corresponding to wind power output and load demand. , Let be the uncertain radii of wind power output and load demand, respectively, and both be dimensionless values. The comprehensive uncertain radius of the system is obtained through a weighted summation, expressed as: , , These are the weighting coefficients for the uncertain radius of wind power output and load demand, respectively. To optimize the baseline value of the objective, i.e., take The objective function value is obtained by substituting the predicted values ​​of wind power output and load into the deterministic model for solution. To mitigate risk, optimize the expected value of the target. This is the robustness factor; the larger the value, the greater the degree of risk aversion and the stronger the robustness of the solution.

[0035] Furthermore, the method for planning a partitioned flexible interconnected power grid driven by spatiotemporal stability uncertainties is constructed; a multi-objective optimization model for a partitioned flexible interconnected power grid based on time-domain and spatial characteristics is used; the fitness of each bird's nest is calculated using the method for planning a partitioned flexible interconnected power grid driven by spatiotemporal stability uncertainties; and the optimal bird's nest location is obtained by ranking the fitness. Compare the fitness of the current best location for the Bird's Nest with the historical best location. If the difference between the two is less than 1, the fitness is determined by the following criteria: Then, the output represents the current optimal location of the Bird's Nest, representing the best planning scheme. The specific steps to complete the planning of the regional flexible interconnected power grid are as follows:

[0036] (1) Initialize the number of iterations Let the population size be... The initial set of locations for the Bird's Nest is The iterative convergence error is ;

[0037] (2) Select bird nests and design an alternating step size selection mechanism with spatiotemporal stability uncertainty to update the position of each bird nest. The formula is as follows:

[0038] (5)

[0039] In the formula, For step size control, , These are the maximum and minimum step size control values, respectively; Let the source load uncertainty radius be . This represents the maximum value of the uncertainty radius; This represents the maximum number of iterations. As a spatiotemporal stability index, The larger the value, the greater the improvement in spacetime stability; Represented as:

[0040] (6)

[0041] In the formula, , , , The power transfer support strength is a spatiotemporal stability indicator.

[0042] After updating the nest location, calculate the nest fitness and choose to retain the current solution or maintain the historical best solution based on the fitness.

[0043] (3) After solving and updating the position in one go, the bird's nest is randomly selected to be discarded; specifically, the search probability is set. Generate random numbers ,like If the nest is found to be unsuitable, then discard the nest and choose a new one; otherwise, keep it.

[0044] The proposed adaptive search probability model is expressed as follows:

[0045] (7)

[0046] (4) Based on the results of abandoning the nests, recalculate the fitness of each nest and obtain the best nest location by ranking them according to fitness. ;

[0047] (5) After each iteration, compare the fitness of the current best Bird's Nest location with the historical best location. If the difference between the two is less than 1, the fitness is determined by the comparison of the fitness of the current best Bird's Nest location with the historical best location. If the iteration ends, the current optimal location of the Bird's Nest, representing the best planning solution, is output; otherwise, the iteration ends. Continue iterating until the regional flexible interconnected power grid plan is completed.

[0048] A partitioned flexible interconnected power grid planning device, comprising:

[0049] Modeling module: Used to establish a multi-objective optimization model for a partitioned interconnected flexible power grid that considers time-domain and spatial characteristics;

[0050] Planning module: Used to construct a regional flexible interconnected power grid planning method driven by spatiotemporal stability uncertainties;

[0051] The computational solution module is used for a multi-objective optimization model of a partitioned flexible power grid based on time-domain and spatial characteristics. It calculates the fitness of each "bird's nest" (a type of electrical structure) using the planning method for partitioned flexible power grids driven by spatiotemporal stability uncertainties, and obtains the optimal "bird's nest" location based on the fitness ranking. Compare the fitness of the current best location for the Bird's Nest with the historical best location. If the difference between the two is less than 1, the fitness is determined by the following criteria: Then, the current optimal location of the Bird's Nest, representing the best planning scheme, will be output, thus completing the planning of the regional flexible interconnected power grid.

[0052] Furthermore, the modeling module specifically includes establishing a multi-objective optimization model for a partitioned interconnected flexible power grid that considers time-domain and spatial characteristics, specifically comprising:

[0053] Taking into account the time-space characteristics of the power grid, two performance indicators covering both time-space and spatial characteristics are proposed: the balance of multiple DC feeds and the power supply capacity of TSC-epsilon.

[0054] Taking into account the stability, balance, security and economy of power grid operation, an objective function covering the time domain and spatial domain characteristics of the power grid is proposed.

[0055] Considering the uncertainties of wind power output and load demand in the time and spatial domains, a robust model based on multi-source joint uncertainty theory for risk avoidance is established.

[0056] Furthermore, the specific solution method of the calculation and solution module is as follows:

[0057] (1) Initialize the number of iterations Let the population size be... The initial set of locations for the Bird's Nest is The iterative convergence error is ;

[0058] (2) Select bird nests and design an alternating step size selection mechanism with spatiotemporal stability uncertainty to update the position of each bird nest. The formula is as follows:

[0059] (5)

[0060] In the formula, For step size control, , These are the maximum and minimum step size control values, respectively; Let the source load uncertainty radius be . This represents the maximum value of the uncertainty radius; This represents the maximum number of iterations. As a spatiotemporal stability index, The larger the value, the greater the improvement in spacetime stability; Represented as:

[0061] (6)

[0062] In the formula, , , , The power transfer support strength is a spatiotemporal stability indicator.

[0063] After updating the nest location, calculate the nest fitness and choose to retain the current solution or maintain the historical best solution based on the fitness.

[0064] (3) After solving and updating the position in one go, the bird's nest is randomly selected to be discarded; specifically, the search probability is set. Generate random numbers ,like If the nest is found to be unsuitable, then discard the nest and choose a new one; otherwise, keep it.

[0065] The proposed adaptive search probability model is expressed as follows:

[0066] (7)

[0067] (4) Based on the results of abandoning the nests, recalculate the fitness of each nest and obtain the best nest location by ranking them according to fitness. ;

[0068] (5) After each iteration, compare the fitness of the current best Bird's Nest location with the historical best location. If the difference between the two is less than 1, the fitness is determined by the comparison of the fitness of the current best Bird's Nest location with the historical best location. If the iteration ends, the current optimal location of the Bird's Nest, representing the best planning solution, is output; otherwise, the iteration ends. Continue iterating until the regional flexible interconnected power grid plan is completed.

[0069] By means of the above-described solution, the present invention has at least the following advantages:

[0070] (1) The technical solution of this invention proposes a multi-objective optimization model for a flexible grid interconnected by zone, which takes into account the time-space characteristics. This system comprehensively considers the balance of the short-circuit ratio of DC landing points in space and time characteristics and the uncertainty of wind power output in time and space. Based on traditional optimization indicators, it considers the impact of time-domain characteristic changes on the reliability of the main grid planning, establishes a multi-source joint uncertainty robust model for risk avoidance, improves the accuracy and adaptability of planning, helps to identify and deal with potential risks, improves the resilience and reliability of the system, and ensures that the zone planning strategy can still operate stably under extreme conditions. It provides strong support for the scientific planning and decision-making of the AC / DC interconnected main grid.

[0071] (2) The technical solution of this invention proposes a partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainty, which effectively improves the overall performance of the optimization algorithm and its adaptive ability to spatiotemporal stability and uncertainty. In the process of selecting the Bird's Nest, an alternating step size selection mechanism considering spatiotemporal stability uncertainty is designed. Different search step sizes are selected under different stability change trends and uncertainty states, which can help the algorithm search in the direction of enhanced stability and weakened uncertainty, improve the algorithm's convergence efficiency and global optimal performance, and realize adaptive adjustment to spatiotemporal stability and uncertainty. In the process of discarding the Bird's Nest, an adaptive search probability model is constructed, which can balance convergence and global optimality, adjust search preferences according to the actual uncertainty state, and improve the overall performance of the algorithm.

[0072] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0073] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show a certain embodiment of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a flowchart of the steps of a partitioned flexible interconnected power grid planning method according to the present invention; Detailed Implementation

[0075] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0076] like Figure 1 As shown, this invention proposes a partitioned flexible interconnected power grid planning method, which includes two parts: a multi-objective optimization model for partitioned flexible interconnected power grids that takes into account time-space characteristics and a partitioned flexible interconnected power grid planning method driven by time-space stability uncertainties.

[0077] 1. Multi-objective optimization model for regionally interconnected flexible power grids that considers time-space characteristics

[0078] This invention proposes a multi-objective optimization model for a partitioned interconnected flexible power grid that considers time-domain and spatial characteristics. The specific steps are as follows:

[0079] S1.1: This invention defines the multi-DC feed-in balance as the root mean square error of the multi-infeed short-circuit ratio (MISCR). Considering the uncertainties of wind power output and load fluctuations, multi-DC feed-in balance indices are established in both spatial and temporal characteristics to cover both time and spatial features, expressed as follows:

[0080] (1)

[0081] In the formula, under a certain partitioning scheme The corresponding power network has DC landing point, A partition containing DC landing points; , corresponding to the Each partition has One DC landing point. This indicates that each DC landing point was processed. Secondary sampling. , These are the weighting coefficients for the equalization of multiple DC feeds in the time and spatial dimensions, respectively. The weighting coefficients satisfy... The constraints and weight selection need to be determined based on the actual time and spatial operating conditions of the power grid. This indicator reflects the spatial and temporal balance of DC landing points among different zones under a certain zoning scheme, avoiding the "barrel effect" where the MISCR of individual zones is low, leading to a decrease in the AC power grid's DC support capacity.

[0082] This invention, based on the Total Supply Capability (TSC) index, further considers the uncertainties in the time and spatial domains of offshore wind power output, and proposes a TSC-epsilon power supply capability index that covers both time and spatial characteristics. This index represents the power output range when the wind power output is within a certain range. hour, To maximize wind power output, This indicates the maximum deviation in wind power output, representing the maximum available load of the entire power grid under this zoning method.

[0083] (2)

[0084] In the formula, To contribute actual power to wind power For nodes Active load, Given the total number of nodes, during the actual time and space operation of the power grid, due to seasonal differences in wind speed and changes in meteorological conditions, the output of offshore wind power differs between winter and summer. Load demand also becomes uncertain due to changes in user behavior. Continuous fluctuations in offshore wind power output can alter the system power flow. Assuming all zones are flexibly interconnected via interconnection devices, then all zones are considered as a single entity. It represents the sum of active loads at all nodes in the power grid, which characterizes the maximum power supply capacity of the power grid under fluctuating wind power output.

[0085] S1.2: This invention comprehensively considers the stability, balance, security, and economy of power grid operation, and proposes the following four objective functions covering the time and spatial domains. The on / off state of key power grid channels is used as the optimization variable, and the power grid partitioning scheme is changed by altering the on / off state of these key channels. Because the lines to be removed in a power grid partitioning process have only two states (connected and disconnected), 0-1 integer variables are used for encoding. The key power grid channel is defined as... A value of 0 indicates that the corresponding candidate line has been selected and disconnected; otherwise, the channel remains connected. The key power grid channels are set as follows: .

[0086] Using the objective function The power transfer support strength of each partition scheme is measured. The ratio of AC power to the sectional power limit of the power transfer channel after a failure is defined as follows: Under the premise that the system meets safety constraints, the greater the power transfer support strength (i.e., the closer it is to 1), the higher the utilization rate of the power transfer channel. This objective function balances safety and economy.

[0087] Using the objective function The balance of multiple DC feeds in the time and spatial domains of each regional scheme is measured. The MISCR (mean square error) concept is used to describe the balance of DC feed points in each regional scheme. The smaller the index, the more balanced the AC grid support capacity for DC in each regional scheme is.

[0088] Using the objective function The average power grid strength of each regional scheme is measured. The mean square error of the short-circuit capacity describes the balance of the disturbance rejection capability of each partition scheme. Under the premise that the system short-circuit current meets the switch breaking capacity constraint, the smaller this index is, the more balanced the power grid strength of each partition is.

[0089] Using the objective function It measures the maximum power supply capacity of the power grid in the time and spatial domains. The larger this indicator is, the greater the maximum stable load that the power grid can provide to meet the constraints under the condition of continuous fluctuations in offshore wind power output.

[0090] Since achieving optimal results for all four indicators—power transfer support strength, multi-DC feed-in balance, grid average strength, and maximum power supply capacity—is contradictory, and considering the different optimization requirements of various systems for the aforementioned coverage time and spatial domains, weighting coefficients are introduced. The weighted sum of the four objective functions mentioned above is taken as the optimization objective, which is expressed as:

[0091] (3)

[0092] In the formula, These represent the weighting coefficients of the four corresponding objective functions.

[0093] S1.3: In actual systems, wind power output and load demand are uncertain and fluctuating in both the time and spatial domains. Due to seasonal differences in wind speed and changes in meteorological conditions, offshore wind power output differs between winter and summer, and load demand also becomes uncertain as user behavior changes.

[0094] This invention proposes a robust multi-source joint uncertainty theory model for risk avoidance. The risk avoidance strategy seeks the maximum uncertainty radius of the uncertain quantities while ensuring that the optimization objective is within an acceptable range. The larger the uncertainty radius, the less sensitive the solution is to fluctuations in the uncertain quantities, the better the robustness of the model, and the stronger the system's ability to avoid risks. A two-layer uncertainty robust optimization model is established. The inner layer optimization finds the optimal fitness value and optimizes the grid zoning scheme when wind power output and load demand fluctuate within the uncertainty set, provided that the optimization objective cannot exceed the expected value. The outer layer optimization continuously adjusts the uncertainty radius to find the largest uncertainty radius, ensuring that the optimal solution of the inner layer remains robust under a wider range of uncertain conditions.

[0095] Because fluctuations in wind power output can affect the stability of the energy structure, an excessively high proportion of wind power may make it difficult for the grid to maintain a stable power supply. Fluctuations in wind power output can also lead to a decline in power quality, causing voltage fluctuations, frequency deviations, and other problems, affecting the normal operation of electrical equipment. Therefore, the worst-case scenario of the objective function in the time and spatial domains proposed in this invention, reflecting the economy, security, stability, and balance of the power grid, will be obtained at the uncertainty boundary, i.e., when wind power output fluctuations are at their maximum. Furthermore, it is concluded that the system's optimization objective value is higher when both wind power output and load demand are at their maximum. Therefore, to improve solution efficiency, it is simplified to a single-layer optimization model, which can be expressed as:

[0096] (4)

[0097] In the formula, , These are the predicted values ​​for wind power output and load demand, respectively. , These are the actual values ​​corresponding to wind power output and load demand. , Let be the uncertain radii of wind power output and load demand, respectively, and both be dimensionless values. The combined uncertain radius of the system is obtained through a weighted summation, expressed as: , , These are the weighting coefficients for the uncertainty radius of wind power output and load demand, respectively. They can be determined based on actual conditions to reflect the different levels of uncertainty in system wind power and load demand perceived by decision-makers. To optimize the baseline value of the objective, i.e., take The objective function value is obtained by substituting the predicted values ​​of wind power output and load into the deterministic model for solution. To mitigate risk, optimize the expected value of the target. This is the robustness factor; the larger the value, the greater the degree of risk aversion and the stronger the robustness of the solution.

[0098] The optimization model also needs to meet a series of constraints. When nodes are connected to devices, the capacity constraints of the interconnected devices and the dynamic reactive power output range constraints of the interconnected devices need to be met. At the same time, considering the power transmission between the grid and the devices, the power flow constraints need to be met. It should also ensure that the zoning scheme has basic safety and stability constraints, such as short-circuit current not exceeding the limit, bus voltage not exceeding the limit, and branch power not exceeding the upper limit. In addition, it should be ensured that each zone has at least one UHV substation hub point to meet the requirements of multiple DC feed-in short-circuit ratio and power flow transfer not exceeding the limit.

[0099] 2. A Regional Flexible Interconnected Power Grid Planning Method Driven by Spatiotemporal Stability Uncertainty

[0100] This invention proposes a partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainty. It improves the traditional cuckoo search algorithm by designing an alternating step size selection mechanism and an adaptive search probability model to enhance the performance of the multi-objective optimization algorithm and adapt to spatiotemporal stability and uncertainty fluctuations. The specific steps are as follows:

[0101] S2.1: Initialize the number of iterations Let the population size be... The initial set of locations for the Bird's Nest is The iterative convergence error is .

[0102] S2.2: Selecting bird nests, designing an alternating step size selection mechanism that considers spatiotemporal stability uncertainties to update the position of each bird nest, the formula is expressed as follows:

[0103] (5)

[0104] In the formula, For step size control, , These are the maximum and minimum step size control values, respectively. Let the source load uncertainty radius be . This represents the maximum value of the uncertainty radius. This represents the maximum number of iterations. It is a spatiotemporal stability index used to measure the changing trend of spatiotemporal stability of regional flexible interconnected power grids. The larger the value, the greater the improvement in spacetime stability. Represented as

[0105] (6)

[0106] In the formula, , , , The power transfer support strength is used as a spatiotemporal stability index. By introducing an alternating step size selection mechanism that considers spatiotemporal stability uncertainties, the step size can be increased to adjust the search direction and improve search efficiency when stability deteriorates and the uncertainty radius decreases; conversely, the step size can be decreased when stability improves and uncertainty deteriorates to improve search accuracy and pursue the global optimum. This improves algorithm performance and enables adaptive adjustment to spatiotemporal uncertainty environments.

[0107] After updating the nest location, calculate the nest fitness and choose to retain the current solution or maintain the historical best solution based on the fitness.

[0108] S2.3: After updating the position in one solution, discard the bird's nest by randomly selecting one. Specifically, set the search probability. Generate random numbers ,like If the nest is found to be unsuitable, it should be discarded and a new nest should be chosen; otherwise, it should be kept.

[0109] Numerical values ​​directly impact the performance of the Cuckoo Algorithm. Smaller values ​​result in faster search speeds but tend towards local optima, while larger values ​​tend towards global optima but converge slowly. Therefore, this invention proposes an adaptive search probability model. On one hand, it considers the impact of the number of iterations on... Dynamic adjustments are made, with larger values ​​taken in the early stages of iteration to encourage global exploration and prevent the algorithm from getting trapped in local optima; as the number of iterations increases... Gradually decreasing the radius allows the algorithm to focus on local optimization, accelerating convergence. On the other hand, considering the uncertainty radius... Adaptive adjustments are made because the solution space of the algorithm is large when the uncertain radius is large, therefore... Larger values ​​encourage global exploration; when When the value of P is small, the solution space of the algorithm is small, so we set the value of P to be small to encourage local optimization. The adaptive search probability model is expressed as:

[0110] (7)

[0111] S2.4 Based on the results of abandoning nests, recalculate the fitness of each nest and sort them according to fitness to obtain the optimal nest location. .

[0112] S2.5 After each iteration, compare the fitness of the current best Bird's Nest position with the historical best position. If the difference between the two is less than [missing value], [missing value]. If the iteration ends, the current optimal location of the Bird's Nest, representing the best planning solution, is output. Otherwise, Continue iterating.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for planning a zoned flexible interconnected power grid, characterized in that... The specific planning steps are as follows: Establish a multi-objective optimization model for a zoned interconnected flexible power grid that incorporates time-space characteristics; Construct a partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainties; A multi-objective optimization model for a partitioned flexible grid based on time-domain and spatial characteristics is used. The fitness of each "bird's nest" (a type of electrical structure) is calculated using a planning method driven by spatiotemporal stability uncertainties. The optimal "bird's nest" location is then determined based on the fitness ranking. Compare the fitness of the current best location for the Bird's Nest with that of the historical best location. If the difference between the two is less than [missing value], [missing value]. Then, the current optimal location of the Bird's Nest, representing the best planning scheme, will be output, thus completing the planning of the zonal flexible interconnected power grid; The establishment of the multi-objective optimization model for the regional interconnected flexible power grid, which considers time-domain and spatial characteristics, specifically includes: Taking into account the time-space characteristics of the power grid, two performance indicators covering both time-space and spatial characteristics are proposed: the balance of multiple DC feeds and the power supply capacity of TSC-epsilon. Taking into account the stability, balance, security and economy of power grid operation, an objective function covering the time domain and spatial domain characteristics of the power grid is proposed. Considering the uncertainties of wind power output and load demand in the time and spatial domains, a robust model based on multi-source joint uncertainty theory for risk avoidance is established. The method constructs a partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainties; based on a multi-objective optimization model of the partitioned flexible interconnected power grid that considers time-domain and spatial characteristics, the fitness of each bird's nest is calculated using the partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainties, and the optimal bird's nest location is obtained by ranking the fitness. Compare the fitness of the current best location for the Bird's Nest with that of the historical best location. If the difference between the two is less than [missing value], [missing value]. Then, the output represents the current optimal location of the Bird's Nest, representing the best planning scheme. The specific steps to complete the planning of the regional flexible interconnected power grid are as follows: (1) Initialize the number of iterations Let the population size be... The initial set of locations for the Bird's Nest is The iterative convergence error is ; (2) Select bird nests and design an alternating step size selection mechanism with spatiotemporal stability uncertainty to update the position of each bird nest. The formula is as follows: （5） In the formula, For step size control, , These are the maximum and minimum step size control values, respectively; Let the source load uncertainty radius be . This represents the maximum value of the uncertainty radius; This represents the maximum number of iterations. As a spatiotemporal stability index, The larger the value, the greater the improvement in spacetime stability; Represented as: （6） In the formula, , , , The power transfer support strength is a spatiotemporal stability indicator. This refers to the ratio of AC power to the cross-sectional power limit of the power transfer channel after a fault occurs. After updating the nest location, calculate the nest fitness and choose to retain the current solution or maintain the historical best solution based on the fitness. (3) After solving and updating the position in one go, the bird's nest is randomly selected to be discarded; specifically, the search probability is set. Generate random numbers ,like If the nest is found to be unsuitable, then discard the nest and choose a new one; otherwise, keep it. The proposed adaptive search probability model is expressed as follows: （7） (4) Based on the results of abandoning the nests, recalculate the fitness of each nest and obtain the best nest location by ranking them according to fitness. ; (5) After each iteration, compare the fitness of the current best Bird's Nest location with the historical best location. If the difference between the two is less than 1, the fitness is determined by the comparison of the fitness of the current best Bird's Nest location with the historical best location. If the iteration ends, the current optimal location of the Bird's Nest, representing the best planning solution, is output; otherwise, the iteration ends. Continue iterating until the regional flexible interconnected power grid plan is completed.

2. The method for planning a zoned flexible interconnected power grid according to claim 1, characterized in that: The specific method for proposing two performance indicators covering both time and spatial characteristics—multi-DC feed-in balance and TSC-epsilon power supply capacity—that take into account the time-domain and spatial-domain characteristics of the power grid is as follows: The equalization degree of multiple DC infeeds is defined as the root mean square error of the short-circuit ratio (MISCR) of multiple infeeds at DC landing points. Indices for the equalization degree of multiple DC infeeds are established in both spatial and temporal characteristics to cover both time and spatial features, expressed as follows: （1） In the formula, under a certain partitioning scheme The corresponding power network has DC landing point, A partition containing DC landing points; , corresponding to the Each partition has One DC landing point; This indicates that each DC landing point was... Secondary sampling; , These are the weighting coefficients for the equalization of multiple DC feeds in the time and space dimensions, respectively. The weighting coefficients satisfy... The constraints and weight selection are determined based on the actual time-domain and spatial-domain operating conditions of the power grid. , These represent the balance of multiple DC feeds in terms of spatial and temporal characteristics, respectively. Indicates the first Within the first partition The balance of multiple DC feeds at each DC landing point; This indicates the first step in determining the DC landing point. The multi-DC feed balance obtained from the second sampling calculation; Based on the maximum power supply capacity (TSC) index, a TSC-epsilon power supply capacity index covering both time and spatial characteristics is proposed. This index represents the power supply capacity when the wind power output range is... hour, To maximize wind power output, The maximum deviation in wind power output, representing the maximum available load of the entire power grid under this zoning method, is expressed as: （2） In the formula, To contribute actual power to wind power For nodes Active load, The total number of nodes; It represents the sum of active loads at all nodes in the power grid, which characterizes the maximum power supply capacity of the power grid under fluctuating wind power output.

3. The method for planning a zoned flexible interconnected power grid according to claim 1, characterized in that: The method for proposing an objective function that comprehensively considers the stability, balance, security, and economy of power grid operation, covering both the time and spatial characteristics of the power grid, is as follows: Therefore, 0-1 integer variables are used for encoding; the key power grid channel is defined as... When its value is 0, it indicates that the corresponding candidate line has been selected and disconnected; otherwise, the channel remains connected. The key power grid channels are set as follows: ; Using the objective function The power transfer support strength of each partition scheme is measured. This refers to the ratio of AC power to the cross-sectional power limit of the power transfer channel after a fault occurs. Using the objective function The balance of multiple DC feeds in the time and spatial domains of each partition scheme is evaluated. Using the objective function The average power grid strength of each regional scheme is measured. This represents the mean square error of the short-circuit capacity. Using the objective function It measures the maximum power supply capacity of the power grid in the time and spatial domains; Introducing weighting coefficients The weighted sum of the four objective functions mentioned above is taken as the optimization objective, which is expressed as: （3） In the formula, These represent the weighting coefficients of the four corresponding objective functions.

4. The method for planning a zoned flexible interconnected power grid according to claim 1, characterized in that: The specific method for establishing a robust multi-source joint uncertainty theory model for risk avoidance, considering the uncertainties of wind power output and load demand in the time and spatial domains, is as follows: A robust model for risk aversion based on multi-source joint uncertainty theory is expressed as follows: （4） In the formula, , These are the predicted values ​​for wind power output and load demand, respectively. , These are the actual values ​​corresponding to wind power output and load demand. , Let be the uncertain radii of wind power output and load demand, respectively, and both be dimensionless values. The comprehensive uncertain radius of the system is obtained through a weighted summation, expressed as: , , These are the weighting coefficients for the uncertain radii of wind power output and load demand, respectively. To optimize the baseline value of the objective, i.e., take The objective function value is obtained by substituting the predicted values ​​of wind power output and load into the deterministic model for solution. To mitigate risk, optimize the expected value of the target. This is the robustness factor; the larger the value, the greater the degree of risk aversion and the stronger the robustness of the solution.

5. A zoned flexible interconnected power grid planning device, characterized in that: The method for implementing the partitioned flexible interconnected power grid planning method of claim 1 includes: Modeling module: Used to establish a multi-objective optimization model for a partitioned interconnected flexible power grid that considers time-domain and spatial characteristics; Planning module: Used to construct a regional flexible interconnected power grid planning method driven by spatiotemporal stability uncertainties; The computational solution module is used for a multi-objective optimization model of a partitioned flexible power grid based on time-domain and spatial characteristics. It calculates the fitness of each "bird's nest" (a type of electrical structure) using the planning method for partitioned flexible power grids driven by spatiotemporal stability uncertainties, and obtains the optimal "bird's nest" location based on the fitness ranking. Compare the fitness of the current best location for the Bird's Nest with that of the historical best location. If the difference between the two is less than [missing value], [missing value]. Then, the current optimal location of the Bird's Nest, representing the best planning scheme, will be output, thus completing the planning of the regional flexible interconnected power grid.

6. The partitioned flexible interconnected power grid planning device according to claim 5, characterized in that: The modeling module specifically includes establishing a multi-objective optimization model for a partitioned interconnected flexible power grid that takes into account time-space characteristics: Taking into account the time-space characteristics of the power grid, two performance indicators covering both time-space and spatial characteristics are proposed: the balance of multiple DC feeds and the power supply capacity of TSC-epsilon. Taking into account the stability, balance, security and economy of power grid operation, an objective function covering the time domain and spatial domain characteristics of the power grid is proposed. Considering the uncertainties of wind power output and load demand in the time and spatial domains, a robust model based on multi-source joint uncertainty theory for risk avoidance is established.

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