Partitioned flexible interconnected power grid planning method

Abstract

The invention relates to a partitioned flexible interconnected power grid planning method, and belongs to the technical field of electric power communication. According to the technical scheme, the partition flexible interconnected power grid planning method based on space-time stability uncertainty driving is provided, and the overall performance of an optimization algorithm and the adaptive capacity to space-time stability and uncertainty are effectively improved. In the process of selecting the bird nest, an alternating step length selection mechanism considering the space-time stability uncertainty is designed, different search step lengths are selected under the conditions of different stability change trends and uncertainty states, the algorithm can be assisted to search in the directions of stability enhancement and uncertainty influence weakening, the convergence efficiency and the global optimal performance of the algorithm are improved, and the method is suitable for large-scale popularization and application. And adaptive adjustment of space-time stability and uncertainty is realized. In the process of abandoning the bird nest, a self-adaptive search probability model is constructed, convergence and global optimality can be balanced, search preferences are adjusted according to the actual uncertainty state, and the overall performance of the algorithm is improved.

Description

Technical Field

[0001] The invention relates to a partitioned flexible interconnected power grid planning method, belonging to the technical field of power communication. Background Art

[0002] With the development of AC / DC hybrid power grids and the connection of large-scale renewable energy to the grid, grid zoning has become an important direction for simplifying operation management and improving efficiency. At present, many regional power grids have successfully implemented 220kV grid zoning, which has enhanced the safety and stability of the power system by effectively controlling short-circuit currents. However, the number and capacity of zoning substations in the 500kV power grid are relatively small, and their power supply and safety assurance functions must be fully utilized, and the 220kV method cannot be simply borrowed. At the same time, 500kV power grids, especially in core load areas, often involve large-capacity DC transmission and multiple DC access points. In addition to solving short-circuit current and electromagnetic ring network problems, they also need to deal with complex challenges such as voltage stability of multiple DC systems and power flow control under AC / DC parallel operation. Traditional zoning flexible interconnected power grid planning methods still need to solve the following challenges:

[0003] First, existing technologies rely on static voltage stability indicators, which may not reflect stability under dynamic conditions; the indicator calculation method and selection may not be sufficient to fully reflect the system characteristics; the indicator system does not fully consider the impact of the time domain on the main grid planning of the power grid, which limits the assessment of the reliability of the power grid under various conditions in the future, thereby affecting the scientific nature and effectiveness of the planning; it ignores factors such as the power grid topology and control strategy; the accuracy of the area division may be insufficient, and the calculation complexity is high.

[0004] Second, the existing technology uses a fixed search step size and search probability, and does not consider the stability and uncertainty factors in the time and space domains, resulting in poor exploration performance and convergence speed of the algorithm. It is easy to fall into local optimality and is difficult to adapt to the planning problems of partitioned flexible interconnected power grids with highly uncertain factors.

[0005] Therefore, the present invention proposes a partitioned flexible interconnected power grid planning method, establishes a partitioned flexible interconnected power grid multi-objective optimization model taking into account the time-space characteristics, and proposes a partitioned flexible interconnected power grid planning method driven by time-space stability uncertainty, so as to optimize the time-space stability of the partitioned flexible interconnected power grid, improve the accuracy and adaptability of the planning, help identify and respond to potential risks, improve the resilience and reliability of the system, ensure that the power grid can still operate stably under extreme conditions, and provide strong support for the scientific planning and decision-making of the AC / DC interconnected main grid. Summary of the invention

[0006] In order to solve the above technical problems, the purpose of the present invention is to provide a partitioned flexible interconnected power grid planning method.

[0007] A partitioned flexible interconnected power grid planning method of the present invention comprises the following specific planning steps:

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

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

[0010] Based on the multi-objective optimization model of the partitioned interconnected flexible power grid taking into account the time-domain and space-domain characteristics, the fitness of each bird's nest is calculated using the partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainty, and the optimal bird's nest location is obtained according to the fitness ranking. 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 δ, the current best bird's nest position representing the best planning scheme is output to complete the partitioned flexible interconnected power grid planning.

[0011] Furthermore, the establishment of a multi-objective optimization model for a partitioned interconnected flexible power grid taking into account time-domain and space-domain characteristics specifically includes:

[0012] Taking into account the time-domain and space-domain characteristics of the power grid, two performance indicators covering the time-domain and space-domain characteristics are proposed: multi-DC feed-in balance and TSC-epsilon power supply capability.

[0013] Taking into account the stability, balance, safety and economy of power grid operation, an objective function covering the time and space characteristics of the power grid is proposed;

[0014] Considering the uncertainty of wind power output and load demand in time and space domains, a risk-averse multi-source joint uncertainty theory robust model is established.

[0015] Furthermore, the specific method of taking into account the time-space characteristics of the power grid and proposing two performance indicators covering the time and space characteristics, namely, the multi-DC feed-in balance and the TSC-epsilon power supply capacity is as follows:

[0016] The multi-DC feed balance is defined as the mean square error of the multi-feed short-circuit ratio (MISCR) of the DC landing point. The multi-DC feed balance indexes based on spatial and temporal characteristics are established to cover the time domain and spatial domain characteristics, which are expressed as:

[0017]

[0018] In the formula, q = 1,…,ξ under a certain partition scheme, corresponding to the power network with ξ DC points and c partitions containing DC points; k = 1,…,b L , corresponding to the Lth partition with b L DC points; γ=1,…,T, indicating that each DC point is sampled T times; λ s,λ t are the weighted coefficients of the multi-DC feed balance in time and space dimensions respectively, and the weighted coefficients satisfy λ s +λ t =1, and the weight is determined according to the actual time and space operation status of the power grid; M ISCR 、M ISCRt Respectively represent the multi-DC feed balance degree in space and time characteristics; Indicates the multi-DC feed balance degree of the kth DC point in the Lth partition; It represents the multi-DC feed balance degree obtained by performing the γth sampling calculation on the DC drop point.

[0019] Based on the maximum power supply capacity TSC index, a TSC-epsilon power supply capacity index covering time domain and space domain characteristics is proposed. This index indicates that when the wind power output interval is [(1-ò)P wmax ,P wmax ], P wmax The maximum output of wind power, represents the maximum deviation of wind power output, and the overall maximum available load of the power grid under this partitioning method is expressed as:

[0020]

[0021] Where P w (t), is the actual output of wind power, P Li is the active load of node i, N b is the total number of nodes; It represents the sum of active loads of all nodes in the power grid, that is, it represents the maximum power supply capacity of the power grid when wind power output fluctuates.

[0022] Furthermore, the specific method of comprehensively considering the stability, balance, safety and economy of power grid operation and proposing an objective function covering the time domain and space domain characteristics of the power grid is as follows:

[0023] Therefore, 0-1 integer variables are used for encoding; the key channel of the power grid is defined as l c , when its value is 0, it means that the corresponding candidate line is selected and disconnected, otherwise the channel remains connected; the key channel set of the power grid is L c ;

[0024] Using the objective function Measures the power transfer support strength of each partitioning scheme, K trans is the ratio of the AC power of the power flow transfer channel to the section power limit after a fault occurs;

[0025] Using the objective function Measure the balance of multiple DC feeds in the time and space domains for each zoning scheme;

[0026] Using the objective function Measuring the average strength of the power grid in each partition scheme, S ac is the mean square error of short-circuit capacity;

[0027] Using the objective function Measures the maximum power supply capacity of the power grid in the time domain and space domain;

[0028] Introducing weight coefficients λ1, λ2, λ3, λ4, the weighted sum of the above four objective functions is used as the optimization target, expressed as

[0029] F=min(-λ1f1+λ2f2+λ3f3-λ4f4) (3)

[0030] Where λ1, λ2, λ3, and λ4 represent the weighted coefficients of the four corresponding objective functions.

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

[0032] The multi-source joint uncertainty theory robust model of risk aversion is expressed as:

[0033]

[0034] In the formula, are the predicted values ​​of wind power output and load demand respectively; P w (t), P L (t) is the actual value corresponding to wind power output and load demand; α ω , α L are the uncertainty radius of wind power output and load demand, respectively, which are dimensionless values; the comprehensive uncertainty radius of the system is obtained by weighted sum form, expressed as Ψ = λ w α w +λ L α L ,λ ω , L are the weight coefficients of wind power output and load demand uncertainty radius, F b is the benchmark value of the optimization target, that is, α = 0, and the predicted values ​​of wind power output and load are substituted into the deterministic model to solve the objective function value; (1+κ)F b is the expected value of the risk aversion optimization target, κ is the robustness level factor, and the larger its value is, the greater the risk aversion is and the more robust the scheme is.

[0035] Furthermore, the method for planning a zoned flexible interconnected power grid driven by spatiotemporal stability uncertainty is constructed; a multi-objective optimization model for a zoned flexible interconnected power grid based on time-space characteristics is constructed, and the fitness of each bird's nest is calculated using the method for planning a zoned flexible interconnected power grid driven by spatiotemporal stability uncertainty, and the best bird's nest position is obtained according to the fitness ranking. 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 δ, then output the current best bird's nest position representing the best planning scheme. The specific steps to complete the partitioned flexible interconnected power grid planning are:

[0036] (1) Initialize the number of iterations τ = 1, set the population size to N, and the initial set of bird nest locations to X = {x1, x2, x3, …, x n ,…,x N}, the iterative convergence error is δ;

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

[0038]

[0039] In the formula, α is the step length control value, α max , α min are the maximum and minimum step length control quantities respectively; Ψ is the source charge uncertainty radius, Ψ max is the maximum value of the uncertainty radius; τ max is the maximum number of iterations; σ s is the spatiotemporal stability index, σ s The larger the value, the greater the improvement in spatiotemporal stability; s It is expressed as:

[0040]

[0041] In the formula, v trans 、v bal 、v S 、v TSC The power transfer support strength is an indicator of spatiotemporal stability;

[0042] After updating the nest position, calculate the nest fitness, and choose to keep the current solution or maintain the historical optimal solution according to the fitness;

[0043] (3) After the updated position is solved once, the bird's nest is discarded by random selection; specifically, the search probability P is set α , generate a random number r∈[0,1], if r<P α , then the nest is abandoned and a new nest is selected, otherwise it is retained;

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

[0045]

[0046] (4) Based on the results of abandoned bird nests, calculate the fitness of each bird nest again, and sort them according to the fitness to obtain the best bird nest location

[0047] (5) After each round of iteration, the fitness of the current best bird’s nest position is compared with the historical best position. If the difference between the two is less than δ, the iteration ends and the current best bird’s nest position representing the best planning scheme is output; otherwise, τ = τ + 1, and the iteration continues until the partitioned flexible interconnected power grid planning is completed.

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

[0049] Modeling module: used to establish a multi-objective optimization model of partitioned interconnected flexible power grid taking into account time-domain and space-domain characteristics;

[0050] Planning module: used to construct a partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainty;

[0051] Computation and solution module: It is used for the multi-objective optimization model of the partitioned interconnected flexible power grid based on the time-domain-spatial domain characteristics. The fitness of each bird's nest is calculated by using the partitioned flexible interconnected power grid planning method driven by time-space stability uncertainty, and the best bird's nest location is obtained according to the fitness ranking. 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 δ, the current best bird's nest position representing the best planning scheme is output to complete the partitioned flexible interconnected power grid planning.

[0052] Furthermore, in the modeling module, establishing a multi-objective optimization model of a partitioned interconnected flexible power grid taking into account time-domain and space-domain characteristics specifically includes:

[0053] Taking into account the time-domain and space-domain characteristics of the power grid, two performance indicators covering the time-domain and space-domain characteristics are proposed: multi-DC feed-in balance and TSC-epsilon power supply capability.

[0054] Taking into account the stability, balance, safety and economy of power grid operation, an objective function covering the time and space characteristics of the power grid is proposed;

[0055] Considering the uncertainty of wind power output and load demand in time and space domains, a risk-averse multi-source joint uncertainty theory robust model is established.

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

[0057] (1) Initialize the number of iterations τ = 1, set the population size to N, and the initial set of bird nest locations to X = {x1, x2, x3, …, x n ,…,x N}, the iterative convergence error is δ;

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

[0059]

[0060] In the formula, α is the step length control value, α max , α min are the maximum and minimum step length control quantities respectively; Ψ is the source charge uncertainty radius, Ψ max is the maximum value of the uncertainty radius; τ max is the maximum number of iterations; σ s is the spatiotemporal stability index, σ s The larger the value, the greater the improvement in spatiotemporal stability; s It is expressed as:

[0061]

[0062] In the formula, v trans 、v bal 、v S 、v TSC The power transfer support strength is an indicator of spatiotemporal stability;

[0063] After updating the nest position, calculate the nest fitness, and choose to keep the current solution or maintain the historical optimal solution according to the fitness;

[0064] (3) After the updated position is solved once, the bird's nest is discarded by random selection; specifically, the search probability P is set α , generate a random number r∈[0,1], if r<P α , then the nest is abandoned and a new nest is selected, otherwise it is retained;

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

[0066]

[0067] (4) Based on the results of abandoned bird nests, calculate the fitness of each bird nest again, and sort them according to the fitness to obtain the best bird nest location

[0068] (5) After each round of iteration, the fitness of the current best bird’s nest position is compared with the historical best position. If the difference between the two is less than δ, the iteration ends and the current best bird’s nest position representing the best planning scheme is output; otherwise, τ = τ + 1, and the iteration continues until the partitioned flexible interconnected power grid planning is completed.

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

[0070] (1) The technical solution of the present invention proposes a multi-objective optimization model for a zoned interconnected flexible power grid that takes into account both time-domain and space-domain characteristics. The system comprehensively considers the balance of the short-circuit ratio of multiple infeeds at the DC point in terms of spatial and temporal characteristics and the uncertainty of wind power output in the time and space domains. On the basis of traditional optimization indicators, it considers the impact of changes in time-domain characteristics on the reliability of main grid planning, and establishes a risk-averse multi-source joint uncertainty robust model to improve the accuracy and adaptability of planning, help identify and respond to potential risks, improve the resilience and reliability of the system, ensure that the zone planning strategy can still operate stably under extreme conditions, and provide strong support for the scientific planning and decision-making of AC / DC interconnected main grids.

[0071] (2) The technical solution of the present 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 selection mechanism that considers 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 effects, improve the algorithm's convergence efficiency and global optimal performance, and achieve adaptive adjustment of spatiotemporal stability and uncertainty. In the process of abandoning the bird's nest, an adaptive search probability model is constructed to balance convergence and global optimality, adjust the search preference according to the actual uncertainty state, and improve the overall performance of the algorithm.

[0072] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show a certain embodiment of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0074] Figure 1It is a flowchart of the steps of a partitioned flexible interconnected power grid planning method of the present invention; DETAILED DESCRIPTION

[0075] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0076] like Figure 1 As shown, the present invention proposes a partitioned flexible interconnected power grid planning method, which includes two parts: a partitioned flexible interconnected power grid multi-objective optimization model taking into account time-domain and space-domain characteristics and a partitioned flexible interconnected power grid planning method driven by time-space stability uncertainty.

[0077] 1. Multi-objective optimization model of partitioned interconnected flexible power grid considering time-space characteristics

[0078] The present invention proposes a multi-objective optimization model for a partitioned interconnected flexible power grid taking into account time-domain and space-domain characteristics, and the specific steps are as follows:

[0079] S1.1: The present invention defines the multi-DC feed balance as the mean square error of the multi-infeed short circuit ratio (MISCR) of the DC landing point. Considering the uncertainty of wind power output and load fluctuation, the multi-DC feed balance indexes on spatial and temporal characteristics are established respectively to cover the time domain and space domain characteristics, which are expressed as:

[0080]

[0081] In the formula, q = 1,…,ξ under a certain partition scheme, corresponding to the power network with ξ DC points and c partitions containing DC points; k = 1,…,b L , corresponding to the Lth partition with b L DC points. γ=1,…,T, which means that each DC point is sampled T times. s ,λ t are the weighted coefficients of the multi-DC feed balance in time and space dimensions respectively, and the weighted coefficients satisfy λ s +λ t = 1, and the weight selection needs to be determined according to the actual time and space operation status of the power grid. This indicator reflects the spatial and temporal balance of DC drop points between partitions under a certain partitioning scheme, avoiding the "barrel effect" of a low MISCR in an individual partition, which leads to a decrease in the AC power grid's support capacity for DC.

[0082] Based on the Total Supply Capability (TSC) indicator, the present invention further considers the uncertainty of offshore wind power output in the time domain and space domain, and proposes the TSC-epsilon power supply capability indicator covering the time domain and space domain characteristics. This indicator indicates that when the wind power output interval is When P wmax The maximum output of wind power, It represents the maximum deviation of wind power output and the maximum available load of the power grid under this partitioning method.

[0083]

[0084] Where P w (t), is the actual output of wind power, P Li is the active load of node i, N b is the total number of nodes. In the actual time and air domain operation of the power grid, due to seasonal wind speed differences and changes in meteorological conditions, the output of offshore wind power in winter and summer is different. The load demand will also bring uncertainty as user behavior changes. The continuous fluctuation of offshore wind power output will cause changes in system flow. Assuming that all partitions are flexibly interconnected through interconnection devices, all partitions are a whole. It represents the sum of active loads of all nodes in the power grid, that is, it represents the maximum power supply capacity of the power grid when wind power output fluctuates.

[0085] S1.2: The present invention comprehensively considers the stability, balance, safety and economy of the power grid operation, and proposes the following four objective functions covering the time domain and the space domain. The on-off of the key channels of the power grid is used as the optimization variable, and the change of the power grid partitioning scheme is achieved by changing the on-off of the key channels of the power grid. Because there are only two states (connected and disconnected) of the lines to be removed in the power grid partition, they are encoded with 0-1 integer variables. Define the key channel of the power grid as l c When its value is 0, it means that the corresponding candidate line is selected and disconnected, otherwise the channel remains connected. The key channel set of the power grid is L c .

[0086] Using the objective function Measures the power transfer support strength of each partitioning scheme, K trans It is the ratio of the AC power of the power transfer channel to the section power limit after a fault occurs. Under the premise that the system meets the safety constraints, the greater the power transfer support strength, that is, the closer it is to 1, the higher the utilization rate of the power transfer channel. This objective function takes into account the balance between safety and economy.

[0087] Using the objective function The balance of multiple DC feeds in each zoning scheme in the time domain and space domain is measured, and the MISCR mean square error concept is used to describe the balance of DC landing points in each zoning scheme. The smaller the index is, the more balanced the DC support capacity of each zoning AC power grid is.

[0088] Using the objective function Measuring the average strength of the power grid in each partition scheme, S ac It is the mean square error of the short-circuit capacity, which describes the balance of the anti-disturbance capability of each partition scheme. On the premise that the system short-circuit current meets the switch interrupting capacity constraint, the smaller the index is, the more balanced the 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 domain and space domain. The larger the index is, the greater the maximum stable load that the power grid can supply to meet the constraints when the offshore wind power output continues to fluctuate.

[0090] Since the goal of achieving the optimal power transfer support strength, multi-DC feed-in balance, average grid strength and maximum power supply capacity is contradictory, and considering that different systems have different requirements for the above optimization goals covering the time domain and space domain, weight coefficients λ1, λ2, λ3, λ4 are introduced, and the weighted sum of the above four objective functions is used as the optimization goal, which is expressed as

[0091] F=min(-λ1f1+λ2f2+λ3f3-λ4f4) (3)

[0092] Where λ1, λ2, λ3, and λ4 represent the weighted coefficients of the four corresponding objective functions.

[0093] S1.3: In actual systems, wind power output and load demand are uncertain and volatile in the time and space domains. Due to seasonal differences in wind speed and changes in meteorological conditions, offshore wind power output is different in winter and summer, and load demand will also bring uncertainty as user behavior changes.

[0094] The present invention proposes a multi-source joint uncertainty theory robust model for risk avoidance. The risk avoidance strategy is to seek the maximum uncertainty radius of the uncertainty while ensuring that the optimization target is within an acceptable range. The larger the value of the uncertainty radius, the less sensitive the scheme is to the fluctuation of the uncertainty, the better the robustness of the model, and the stronger the system's ability to avoid risks. A double-layer uncertainty robust optimization model is established. The inner layer is optimized to find the optimal fitness value and optimize the power grid zoning scheme when the wind power output and load demand fluctuate within the uncertainty set and the optimization target cannot exceed the expected value; the outer layer is optimized to continuously adjust the uncertainty radius and find the maximum uncertainty radius to ensure that the optimal solution of the inner layer remains robust under a wider range of uncertainty conditions.

[0095] Since the fluctuation of wind power output will affect the stability of the energy structure, an excessively high proportion of wind power may make it difficult for the power grid to maintain a stable power supply. The fluctuation of wind power output will also lead to a decrease in power quality, resulting in voltage fluctuations, frequency deviations and other problems, affecting the normal operation of electrical equipment. Therefore, the worst case of the objective function in the time and space domains that reflects the economy, safety, stability and balance of the power grid proposed in the present invention will be obtained at the uncertainty boundary, that is, when the wind power output fluctuation is the largest. It is further concluded that when the wind power output is the largest and the load demand is the largest, the higher the optimization target value of the system. Therefore, in order to improve the solution efficiency, it is simplified into a single-layer optimization model, which can be expressed as:

[0096]

[0097] In the formula, are the predicted values ​​of wind power output and load demand respectively; P w (t), P L (t) is the actual value corresponding to wind power output and load demand; α ω , α L are the uncertainty radius of wind power output and load demand, respectively, which are dimensionless values. The comprehensive uncertainty radius of the system is obtained by weighted sum form, expressed as Ψ = λ w α w +λ L α L ,λ ω , L are the weight coefficients of wind power output and load demand uncertainty radius, which can be determined according to actual conditions to reflect the different requirements of decision makers for system wind power and load uncertainty. b is the benchmark value of the optimization target, that is, α = 0, and the predicted values ​​of wind power output and load are substituted into the deterministic model to solve the objective function value; (1+κ)F b is the expected value of the risk aversion optimization target, κ is the robustness level factor, and the larger its value is, the greater the risk aversion is and the more robust the scheme is.

[0098] The optimization model also needs to meet a series of constraints. When the node is connected to the device, it is necessary to meet the capacity constraints of the partition interconnected device and the dynamic reactive output range constraints of the partition interconnected device. At the same time, the power transmission between the power grid and the device needs to be considered, and the flow constraints need to be met. It should also ensure that the partition scheme has basic safety and stability constraints, such as short-circuit current does not exceed the standard, bus voltage does not exceed the limit, branch power does not exceed the upper limit, etc., and ensure that each partition has at least one UHV substation hub to meet the requirements of multiple DC feed-in short-circuit ratios and flow transfers that do not exceed the limit.

[0099] 2. A method for planning a zoned flexible interconnected power grid driven by spatiotemporal stability uncertainty

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

[0101] S2.1: Initialize the number of iterations τ = 1, set the population size to N, and the initial set of bird nest locations to X = {x1, x2, x3, ..., x n ,...,x N}, the iterative convergence error is δ.

[0102] S2.2: Select the bird nests and design an alternating step selection mechanism that takes into account the uncertainty of spatiotemporal stability to update the position of each bird nest. The formula is as follows

[0103]

[0104] In the formula, α is the step length control value, α max , α min are the maximum and minimum step length control quantities respectively. Ψ is the source charge uncertainty radius, Ψ max is the maximum value of the uncertainty radius. τ max is the maximum number of iterations. s is the spatiotemporal stability index, which is used to measure the changing trend of spatiotemporal stability of the partitioned flexible interconnected power grid, σ s The larger the value, the greater the improvement in spatiotemporal stability. s Expressed as

[0105]

[0106] In the formula, v trans 、v bal 、v S 、v TSC is the power transfer support strength of the spatiotemporal stability index. By introducing an alternating step selection mechanism that considers the uncertainty of spatiotemporal stability, when the stability becomes worse and the uncertainty radius decreases, the step size can be increased to adjust the search direction and improve the search efficiency; when the stability becomes better and the uncertainty becomes worse, the step size can be reduced to improve the search accuracy and pursue the global optimal solution. This improves the algorithm performance and realizes adaptive adjustment to the spatiotemporal uncertainty environment.

[0107] After updating the nest position, calculate the nest fitness, and choose to keep the current solution or maintain the historical optimal solution based on the fitness.

[0108] S2.3: After the updated position is solved once, the bird's nest is discarded by random selection. Specifically, the search probability P is setα , generate a random number r∈[0,1], if r<P α , then the bird's nest is abandoned and a new one is selected, otherwise it is retained.

[0109] P α The value has a direct impact on the performance of the cuckoo algorithm. When the value is small, the search speed is faster but tends to the local optimum. When the value is large, it tends to the global optimum, but the convergence speed is slow. To this end, the present invention proposes an adaptive search probability model. On the one hand, considering the effect of the number of iterations on P α Dynamic adjustment is performed, and a larger value is taken at the beginning of the iteration to encourage the algorithm to conduct global exploration and prevent the algorithm from falling into a local optimal solution; as the number of iterations increases, P α Gradually decrease, so that the algorithm focuses on local optimization and accelerates the convergence speed. On the other hand, considering the uncertainty radius, α Adaptive adjustment is performed. When the uncertainty radius is large, the solution space of the algorithm is large, so let P α A larger value encourages global exploration; when P α When is small, the algorithm has a small solution space, so let P take a small value to encourage local optimization. The adaptive search probability model is expressed as

[0110]

[0111] S2.4 Based on the result of abandoning the bird nest, calculate the fitness of each bird nest again, and get the best bird nest location according to the fitness ranking

[0112] S2.5 After each round of 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 δ, the iteration ends and the current best bird's nest position representing the best planning solution is output. Otherwise, τ=τ+1 and continue the iteration.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be regarded as 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: Establish a multi-objective optimization model for zoned interconnected flexible power grid taking into account time-space characteristics; Construct a zoned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainty; Based on the multi-objective optimization model of the partitioned interconnected flexible power grid taking into account the time-domain and space-domain characteristics, the fitness of each bird's nest is calculated using the partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainty, and the optimal bird's nest location is obtained according to the fitness ranking. 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 δ, the current best bird's nest position representing the best planning scheme is output to complete the partitioned flexible interconnected power grid planning.

2. A method for planning a zoned flexible interconnected power grid according to claim 1, characterized in that: The establishment of a multi-objective optimization model for a zoned interconnected flexible power grid taking into account time-domain and space-domain characteristics specifically includes: Taking into account the time-domain and space-domain characteristics of the power grid, two performance indicators covering the time-domain and space-domain characteristics are proposed: multi-DC feed-in balance and TSC-epsilon power supply capability. Taking into account the stability, balance, safety and economy of power grid operation, an objective function covering the time and space characteristics of the power grid is proposed; Considering the uncertainty of wind power output and load demand in time and space domains, a risk-averse multi-source joint uncertainty theory robust model is established.

3. A method for planning a zoned flexible interconnected power grid according to claim 2, characterized in that: The specific method of taking into account the time-domain and space-domain characteristics of the power grid and proposing two performance indicators covering the time-domain and space-domain characteristics, namely, the multi-DC feed-in balance and the TSC-epsilon power supply capability, is as follows: The multi-DC feed balance is defined as the mean square error of the multi-feed short-circuit ratio (MISCR) of the DC landing point. The multi-DC feed balance indexes based on spatial and temporal characteristics are established to cover the time domain and spatial domain characteristics, which are expressed as: In the formula, q = 1,…,ξ under a certain partition scheme, corresponding to the power network with ξ DC points and c partitions containing DC points; k = 1,…,b L , corresponding to the Lth partition with b L DC points; γ=1,…,T, indicating that each DC point is sampled T times; λ s ,λ t are the weighted coefficients of the multi-DC feed balance in time and space dimensions respectively, and the weighted coefficients satisfy λ s +λ t =1, and the weight is determined according to the actual time and space operation status of the power grid; M ISCR 、M ISCRt Respectively represent the multi-DC feed balance degree in space and time characteristics; M ISCRLk M represents the multi-DC feed balance degree of the kth DC point in the Lth partition; ISCRtγ It represents the multi-DC feed balance degree obtained by calculating the γth sampling of the DC drop point; Based on the maximum power supply capacity TSC index, a TSC-epsilon power supply capacity index covering time domain and space domain characteristics is proposed. This index indicates that when the wind power output interval is [(1-ò)P wmax ,P wmax ], P wmax is the maximum output of wind power, ò is the maximum deviation of wind power output, and the overall maximum available load of the power grid under this partitioning method is expressed as: Where P w (t), is the actual output of wind power, P Li is the active load of node i, N b is the total number of nodes; f TSC-ò It represents the sum of active loads of all nodes in the power grid, that is, it represents the maximum power supply capacity of the power grid when wind power output fluctuates.

4. A method for planning a zoned flexible interconnected power grid according to claim 2, characterized in that: The specific method of comprehensively considering the stability, balance, safety and economy of power grid operation and proposing an objective function covering the time domain and space domain characteristics of the power grid is as follows: Therefore, 0-1 integer variables are used for encoding; the key channel of the power grid is defined as l c , when its value is 0, it means that the corresponding candidate line is selected and disconnected, otherwise the channel remains connected; the key channel set of the power grid is L c ; Using the objective function Measures the power transfer support strength of each partitioning scheme, K trans is the ratio of the AC power of the power flow transfer channel to the section power limit after a fault occurs; Using the objective function Measure the balance of multiple DC feeds in the time and space domains for each zoning scheme; Using the objective function Measuring the average strength of the power grid in each partition scheme, S ac is the mean square error of short-circuit capacity; Using the objective function Measures the maximum power supply capacity of the power grid in the time domain and space domain; Introducing weight coefficients λ1, λ2, λ3, λ4, the weighted sum of the above four objective functions is used as the optimization target, expressed as F=min(-λ1f1+λ2f2+λ3f3-λ4f4) (3) Where λ1, λ2, λ3, and λ4 represent the weighted coefficients of the four corresponding objective functions.

5. A method for planning a zoned flexible interconnected power grid according to claim 2, characterized in that: The specific method of establishing a risk-averse multi-source joint uncertainty theory robust model considering the uncertainty of wind power output and load demand in the time domain and space domain is as follows: The multi-source joint uncertainty theory robust model of risk aversion is expressed as: In the formula, are the predicted values ​​of wind power output and load demand respectively; P w (t), P L (t) is the actual value corresponding to wind power output and load demand; α ω , α L are the uncertainty radius of wind power output and load demand, respectively, which are dimensionless values; the comprehensive uncertainty radius of the system is obtained by weighted sum form, expressed as Ψ = λ w α w +λ L α L ,λ ω , L are the weight coefficients of wind power output and load demand uncertainty radius, F b is the benchmark value of the optimization target, that is, α = 0, and the predicted values ​​of wind power output and load are substituted into the deterministic model to solve the objective function value; (1+κ)F b is the expected value of the risk aversion optimization target, κ is the robustness level factor, and the larger its value is, the greater the risk aversion is and the more robust the scheme is.

6. A method for planning a zoned flexible interconnected power grid according to claim 1, characterized in that: The method for planning a zoned flexible interconnected power grid driven by spatiotemporal stability uncertainty is constructed; a multi-objective optimization model for a zoned flexible interconnected power grid based on time-space characteristics is constructed, the fitness of each bird's nest is calculated using the method for planning a zoned flexible interconnected power grid driven by spatiotemporal stability uncertainty, and the best bird's nest position is obtained according to the fitness ranking. 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 δ, then output the current best bird's nest position representing the best planning scheme. The specific steps to complete the partitioned flexible interconnected power grid planning are: (1) Initialize the number of iterations τ = 1, set the population size to N, and the initial set of bird nest locations to X = {x1, x2, x3, …, x n ,…,x N }, the iterative convergence error is δ; (2) Select the bird nests and design an alternating step selection mechanism with spatiotemporal stability uncertainty to update the position of each bird nest. The formula is as follows: In the formula, α is the step length control value, α max , α min are the maximum and minimum step length control quantities respectively; Ψ is the source charge uncertainty radius, Ψ max is the maximum value of the uncertainty radius; τ max is the maximum number of iterations; σ s is the spatiotemporal stability index, σ s The larger the value, the greater the improvement in spatiotemporal stability; s It is expressed as: In the formula, v trans 、v bal 、v S 、v TSC The power transfer support strength is an indicator of spatiotemporal stability; After updating the nest position, calculate the nest fitness and choose to keep the current solution or maintain the historical optimal solution according to the fitness; (3) After the updated position is solved once, the bird's nest is discarded by random selection; specifically, the search probability P is set α , generate a random number r∈[0,1], if r<P α , then the nest is abandoned and a new nest is selected, otherwise it is retained; The proposed adaptive search probability model is expressed as: (4) Based on the results of abandoned bird nests, calculate the fitness of each bird nest again, and sort them according to the fitness to obtain the best bird nest location (5) After each round of iteration, the fitness of the current best bird’s nest position is compared with the historical best position. If the difference between the two is less than δ, the iteration ends and the current best bird’s nest position representing the best planning scheme is output; otherwise, τ = τ + 1, and the iteration continues until the partitioned flexible interconnected power grid planning is completed.

7. A zoned flexible interconnected power grid planning device, characterized in that: include: Modeling module: used to establish a multi-objective optimization model of partitioned interconnected flexible power grid taking into account time-domain and space-domain characteristics; Planning module: used to construct a partitioned flexible interconnected power grid planning method driven by spatiotemporal stability uncertainty; Computation and solution module: It is used for the multi-objective optimization model of the partitioned interconnected flexible power grid based on the time-domain-spatial domain characteristics. The fitness of each bird's nest is calculated by using the partitioned flexible interconnected power grid planning method driven by time-space stability uncertainty, and the best bird's nest location is obtained according to the fitness ranking. 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 δ, the current best bird's nest position representing the best planning scheme is output to complete the partitioned flexible interconnected power grid planning.

8. The device for planning a zoned flexible interconnected power grid according to claim 7, characterized in that: In the modeling module, establishing a multi-objective optimization model of a partitioned interconnected flexible power grid taking into account time-domain and space-domain characteristics specifically includes: Taking into account the time-domain and space-domain characteristics of the power grid, two performance indicators covering the time-domain and space-domain characteristics are proposed: multi-DC feed-in balance and TSC-epsilon power supply capability. Taking into account the stability, balance, safety and economy of power grid operation, an objective function covering the time and space characteristics of the power grid is proposed; Considering the uncertainty of wind power output and load demand in time and space domains, a risk-averse multi-source joint uncertainty theory robust model is established.

9. The device for planning a zoned flexible interconnected power grid according to claim 7, characterized in that: The specific solution method of the calculation solution module is: (1) Initialize the number of iterations τ = 1, set the population size to N, and the initial set of bird nest locations to X = {x1, x2, x3, …, x n ,…,x N }, the iterative convergence error is δ; (2) Select the bird nests and design an alternating step selection mechanism with spatiotemporal stability uncertainty to update the position of each bird nest. The formula is as follows: In the formula, α is the step length control value, α max , α min are the maximum and minimum step length control quantities respectively; Ψ is the source charge uncertainty radius, Ψ max is the maximum value of the uncertainty radius; τ max is the maximum number of iterations; σ s is the spatiotemporal stability index, σ s The larger the value, the greater the improvement in spatiotemporal stability; s It is expressed as: In the formula, v trans 、v bal 、v S 、v TSC The power transfer support strength is an indicator of spatiotemporal stability; After updating the nest position, calculate the nest fitness and choose to keep the current solution or maintain the historical optimal solution according to the fitness; (3) After the updated position is solved once, the bird's nest is discarded by random selection; specifically, the search probability P is set α , generate a random number r∈[0,1], if r<P α , then the nest is abandoned and a new nest is selected, otherwise it is retained; The proposed adaptive search probability model is expressed as: (4) Based on the results of abandoned bird nests, calculate the fitness of each bird nest again, and sort them according to the fitness to obtain the best bird nest location (5) After each round of iteration, the fitness of the current best bird’s nest position is compared with the historical best position. If the difference between the two is less than δ, the iteration ends and the current best bird’s nest position representing the best planning scheme is output; otherwise, τ = τ + 1, and the iteration continues until the partitioned flexible interconnected power grid planning is completed.

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