A power system optimization method under high-proportion wind power consumption scenario

Abstract

The present invention discloses a power system optimization method for a high-proportion wind power consumption scenario, belonging to the field of optimal power flow calculation after wind power grid integration. The method comprises: constructing a probabilistic optimal power flow multi-objective optimization model with the objectives of minimizing wind curtailment, voltage margin, static voltage stability margin, power generation cost, and maximizing line margin; solving the model under preset constraints; selecting a decision variable from the resulting solution set as the optimization result, and configuring each device accordingly; the decision variables include: the active power output of each node other than the balancing node, the terminal voltage of each generator, the tap ratio of each transformer, and the reactive power output of each reactive compensation device; the constraints include active power balance constraints and reactive power balance constraints, and the sum of the probabilities of each wind power scenario being 1. The present invention can improve the operational safety of the power grid while reducing power generation costs and reducing the waste of wind power resources when a high proportion of wind power is integrated into the power grid.

Description

Technical Field

[0001] The present invention belongs to the field of optimal power flow calculation after wind power grid connection, and more specifically, relates to a power system optimization method under a high-proportion wind power consumption scenario. Background Art

[0002] As wind power costs gradually decrease, some countries have already begun to actively use it as a means of electricity supply. However, because wind power is affected by many factors, such as climate and ocean currents, its power generation capacity is highly uncertain, and the impact of this uncertainty on the power system cannot be ignored.

[0003] Currently, integrating a high proportion of wind power into the grid is impacted by the operation of traditional turbines, sometimes failing to meet grid operational constraints, posing a threat to the safe and stable operation of the grid. Furthermore, the grid struggles to effectively absorb high proportions of wind power, leading to wind curtailment and a waste of wind power resources. Furthermore, the uncertainty of wind power generation can cause fluctuations in power generation costs, impacting grid costs. Based on these considerations, traditional probabilistic optimal power flow methods often only consider power generation costs when integrating wind power into the grid, while using grid safety as a constraint. This fails to simultaneously address the safe operation of the grid, the costs, and the curtailment issues associated with integrating a high proportion of wind power. Summary of the Invention

[0004] In response to the defects of the existing technology and the need for improvement, the present invention provides a power system optimization method for a high-proportion wind power consumption scenario, the purpose of which is to improve the operational safety of the power grid while reducing the power generation cost and reducing the waste of wind power resources when a high proportion of wind power is incorporated into the power grid.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for optimizing a power system in a high-proportion wind power consumption scenario is provided, comprising:

[0006] A multi-objective optimization model for probabilistic optimal power flow is constructed with the goals of minimizing wind curtailment, voltage margin, static voltage stability margin, power generation cost, and maximizing line margin. The multi-objective probabilistic optimal power flow model is solved under preset constraints. A decision variable is selected from the resulting solution set as the optimization result, and the various devices in the power system are configured according to the optimization result to complete the optimization.

[0007] Among them, the decision variables include: the active power output of each node other than the balancing node, the terminal voltage of each generator, the tap ratio of each transformer, and the reactive power output of each reactive compensation device; the preset constraints include equality constraints and inequality constraints; equality constraints include: active power balance constraints and reactive power balance constraints, and the sum of the probabilities of each wind power scenario is 1; inequality constraints include: the active power and reactive power output of each device are between the corresponding maximum and minimum values.

[0008] Furthermore, the calculation method of the wind curtailment AW includes: calculating the wind curtailment after each wind power scenario is connected to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the wind curtailment AW; any wind power scenario δ k After connecting to the power system, the wind curtailment is the sum of the wind curtailment of each wind farm, where the wind curtailment of any j-th wind farm is C j,k Follow these steps to calculate:

[0009] (S1) Initialize the abandoned air volume C j,k C j,k =0;

[0010] (S2) wind power scenario δ k After accessing the power system, the power flow calculation is performed to verify the satisfaction of the equation preset and the inequality constraints respectively. If both are satisfied, the process proceeds to step (S4); otherwise, the process proceeds to step (S3);

[0011] (S3) According to P wt,k =P wt,k –C step Update wind power scenario δ k The actual wind power P under wt,k , and follow C j,k =C j,k +C step Updated abandoned air volume C j,k Afterwards, proceed to step (S2);

[0012] (S4) Output the current C j,k , as the wind power scenario δ k The amount of wind curtailment C of the next j-th wind farm j,k ;

[0013] Among them, P wt,k Represents the wind power scenario δ k The actual wind power under step is the preset abandoned air volume.

[0014] Furthermore, the calculation method of the line margin LM includes: calculating the line margin of each wind power scenario after connecting to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the line margin LM; any wind power scenario δ k After connecting to the power system, the line margin is calculated as follows:

[0015] (T1) In the wind power scenario δ k After connecting to the power system, follow the S i,k =|S i |-S base,i Calculate the line margin of the i-th branch; Si represents the apparent power of the i-th branch, S base,i represents the rated apparent power of long-distance transmission on the i-th branch;

[0016] (T2) The maximum line margin of all branches is taken as the wind power scenario δ k Line margin after connection to the power system.

[0017] Furthermore, the voltage margin VM is calculated by calculating the voltage margin of each wind power scenario after it is connected to the power system, multiplying the voltage margin by the probability of the wind power scenario, and summing the results as the voltage margin VM; any wind power scenario δ k After connecting to the power system, the voltage margin is the sum of the voltage margins of each node, where the voltage margin of any i-th node is V j,k is the voltage value of the node V j to its rated voltage.

[0018] Furthermore, the calculation method of the static voltage stability margin SVSM includes: calculating the static voltage stability margin after each wind power scenario is connected to the power system, multiplying it with the probability of the wind power scenario, and summing the results as the static voltage stability margin SVSM; any wind power scenario δ k After connecting to the power system, the static voltage stability margin L k The calculation expression is:

[0019]

[0020] Among them, N L and N G Represent the number of load nodes and generator nodes respectively, V k,j and V k,i They represent the voltages of load node j and generator node i respectively; F k,ji according to Calculated, V k,0j Indicates the equilibrium voltage.

[0021] Furthermore, the calculation method of the power generation cost GC includes: calculating the power generation cost of each wind power scenario after connecting to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the power generation cost GC; any wind power scenario δ k After connecting to the power system, the power generation cost is

[0022] Among them, P i represents the output of the i-th unit, α i , β i ,λ i Both represent the fuel efficiency of the i-th unit.

[0023] Furthermore, the actual wind power and its corresponding occurrence probability in each wind power scenario are obtained through fine stratified sampling.

[0024] Furthermore, the solution algorithm for the multi-objective probabilistic optimal power flow model is the NSGA-II algorithm.

[0025] Furthermore, the power system optimization method for a high proportion of wind power consumption scenario provided by the present invention also includes: selecting a reference point, and using the reference point and the solution set obtained by solving the multi-objective probabilistic optimal power flow model to calculate the super volume index; if the super volume index shows that the multi-objective probabilistic optimal power flow model does not converge, then re-solving it.

[0026] According to another aspect of the present invention, a computer-readable storage medium is provided, comprising a stored computer program; when the computer program is executed by a processor, the device where the computer-readable storage medium is located is controlled to execute the power system optimization method for a high-proportion wind power absorption scenario provided by the present invention.

[0027] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects: The present invention has found that in the scenario where a high proportion of wind power is connected to the power grid, there is a close coupling relationship between the power generation cost, the amount of wind curtailment and the safe operation of the power grid. The probabilistic optimal power flow model established by the traditional method only considers the power generation cost, and takes the safe operation of the power grid and the amount of wind curtailment as constraints, ignoring the coupling relationship between different factors. The final optimization result cannot make the overall performance of the power system reach the optimal level; based on this discovery, the multi-objective probabilistic optimal power flow model established by the present invention takes the wind curtailment AW, line margin LM, voltage margin VM and static voltage stability margin SVSM, together with the power generation cost GC, as targets for joint optimization, which can fully consider the inherent connection between the targets, while reducing the power generation cost and reducing the waste of wind power resources, improving the operation safety of the power grid, and providing an important reference for the formulation of wind power grid connection strategy; it provides a reliable basis for the planning and operation mode of power systems with a high proportion of wind power access. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flow chart of a power system optimization method for a high-proportion wind power consumption scenario provided by an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of the topological structure of an IEEE-30 node power system connected to a wind farm provided in an embodiment of the present invention;

[0030] Figure 3The actual wind power sampled in different wind power scenarios by the refined stratified sampling (RSS) provided in the embodiment of the present invention;

[0031] Figure 4 The probability of occurrence of the wind power scenario sampled by RSS provided in the embodiment of the present invention;

[0032] Figure 5 The hypervolume index of the NSGA-II algorithm provided in the embodiment of the present invention during the solution process;

[0033] Figure 6 A Pareto surface represented in the form of a parallel coordinate graph obtained after solving the NSGA-II algorithm provided in an embodiment of the present invention;

[0034] Figure 7 The Pareto surface between the wind curtailment volume, line margin, and voltage margin provided in the embodiment of the present invention;

[0035] Figure 8 The Pareto surface between the wind curtailment volume, line margin, and static voltage stability margin provided in the embodiment of the present invention;

[0036] Figure 9 The Pareto surface between the amount of wind curtailment, line margin, and power generation cost provided in the embodiment of the present invention;

[0037] Figure 10 The Pareto surface between the wind curtailment rate, voltage margin, and static voltage stability margin provided in the embodiment of the present invention;

[0038] Figure 11 The Pareto surface between the amount of wind curtailment, voltage margin, and power generation cost provided in the embodiment of the present invention;

[0039] Figure 12 The Pareto surface between the amount of wind curtailment, the static voltage stability margin, and the power generation cost provided in the embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0041] In the present invention, the terms "first", "second", etc. (if any) in the present invention and the drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0042] In order to reduce power generation costs, minimize the waste of wind power resources, and improve the operational safety of the power grid, the present invention provides a power system optimization method for a high-proportion wind power consumption scenario. The overall idea is to fully consider the intrinsic relationship between power generation costs, wind curtailment, and power grid safe operation goals, establish a multi-objective probabilistic optimal power flow model, and optimize multiple objectives simultaneously to optimize the overall performance of the system.

[0043] The following are examples.

[0044] Example 1:

[0045] A power system optimization method under high-proportion wind power consumption scenario, such as Figure 1 Shown, including:

[0046] A multi-objective optimization model for probabilistic optimal power flow is constructed with the goals of minimizing wind curtailment, voltage margin, static voltage stability margin, power generation cost, and maximizing line margin. The multi-objective probabilistic optimal power flow model is solved under preset constraints. A decision variable is selected from the resulting solution set as the optimization result, and the various devices in the power system are configured according to the optimization result to complete the optimization.

[0047] Among them, the decision variables include: the active power output of each node other than the balancing node, the terminal voltage of each generator, the tap ratio of each transformer, and the reactive power output of each reactive compensation device; the preset constraints include equality constraints and inequality constraints; equality constraints include: active power balance constraints and reactive power balance constraints, and the sum of the probabilities of each wind power scenario is 1; inequality constraints include: the active power and reactive power output of each device are between the corresponding maximum and minimum values.

[0048] In this embodiment, AW, -LM, VM, SVSM, and GC represent the wind curtailment rate, voltage margin, voltage margin, static voltage stability margin, and power generation cost, respectively. The objective function of the probabilistic optimal power flow multi-objective optimization model established in this embodiment can be expressed as:

[0049] min[AW,-LM,VM,SVSM,GC]

[0050] In order to obtain different wind power scenarios and the occurrence probabilities of each wind power scenario, as an optional implementation method, in this embodiment, a refined stratified sampling (RSS) method is used to sample the wind power scenarios. Through sampling, the actual wind power and its corresponding occurrence probabilities under different wind power scenarios can be obtained. Specifically, the target wind speed of each wind farm is sampled and the probability of each target wind speed is obtained. Then, the wind power output of each scenario is calculated based on the wind power characteristics as the actual wind power under the corresponding scenario. The wind power characteristics are represented as follows:

[0051] Use the predicted speed v fore The actual wind speed v is represented by the prediction error Δv. In a shorter time scale, v can be expressed as v fore and Δv:

[0052] v=v fore +Δv

[0053] Among them, Δv obeys a normal distribution with mean 0 and variance σ:

[0054] Wind turbine output can be expressed by the wind power characteristics of the wind turbine:

[0055]

[0056]

[0057] Among them, P wt represents the active power actually output by the wind turbine, v represents the target wind speed obtained by sampling; v ci 、v ra 、v co Respectively represent the cut-in wind speed, rated wind speed and cut-out wind speed; P ra Indicates the rated power of the wind turbine.

[0058] RSS, or refined stratified sampling, has fewer sampling scenarios and lower algorithm complexity than other sampling methods. In scenarios with a high proportion of wind power consumption, the sampled samples are more reliable. The sampling process is as follows:

[0059] First, initialize the sample set size N = 1 and assign all samples to the layer Ω j , j represents the number of layers, j=(1,…,N j ), N j Indicates the maximum number of strata allowed; the weights of all samples in this stratum are defined as:

[0060] w j =P(Ω j ) / M j

[0061] in, P(Ω j ) represents Ω j The probability of occurrence, M j For layered Ω j The number of samples in ;

[0062] Then, for all layers, if there is only one layer Ω k satisfy Then set it as the original layer. If there are S layers that satisfy w k =max(w j ), k = 1, 2, ... S, then among these layers, a layer is randomly selected as the original layer with a probability of 1 / S;

[0063] Then, the original layer Ω k To perform a halved split, you need to first calculate the non-layered length:

[0064]

[0065] in, and Ω k The probability upper and lower limits corresponding to the i-th sampling component, n is Ω k The number of samples in

[0066] Furthermore, for the maximum non-stratified length Λ k =max(λ i,k ), if there is only one sampling component i corresponding to Λ k , then the original layer is divided according to i, if there are C components that satisfy λ i,k =Λ k ,i=1,2,...,C, then a component is randomly selected for splitting with a probability of 1 / C;

[0067] In addition, random sampling is performed in the newly generated stratum to obtain the sample set X=(x l ),l=1,2,...,n, and merge it with the existing sample set to obtain a new sample set, and recalculate the sample weight corresponding to each sample in the sample set, that is, the occurrence probability corresponding to each sample.

[0068] It should be noted that RSS is only a preferred sampling method of the present invention. In other embodiments of the present invention, other sampling methods may also be used.

[0069] After obtaining the actual wind power of each wind power scenario and the corresponding occurrence probability of the wind power scenario, in this embodiment, the calculation method of the wind abandonment amount AW includes: calculating the wind abandonment amount after each wind power scenario is connected to the power system, multiplying it by the probability of the wind power scenario, and then summing the results, and taking the result as the wind abandonment amount AW; any wind power scenario δ k After connecting to the power system, the wind curtailment is the sum of the wind curtailment of each wind farm, where the wind curtailment of any j-th wind farm is C j,k Follow these steps to calculate:

[0070] (S1) Initialize the abandoned air volume C j,k C j,k =0;

[0071] (S2) wind power scenario δ k After connecting to the power system, a power flow calculation is performed to verify whether the equation presets and inequality constraints are satisfied. If both are satisfied, it means that the power flow has converged, the wind power is fully absorbed, and there is no need to abandon the wind power, and the process goes to step (S4). Otherwise, it means that the wind power is not fully absorbed and needs to be abandoned, and the process goes to step (S3).

[0072] (S3) According to P wt,k =P wt,k –C step Update wind power scenario δ k The actual wind power P under wt,k , and follow C j,k =C j,k +C step Updated abandoned air volume C j,k Afterwards, proceed to step (S2);

[0073] (S4) Output the current C j,k , as the wind power scenario δ k The amount of wind curtailment C of the next j-th wind farm j,k ;

[0074] Among them, P wt,k Represents the wind power scenario δ k The actual wind power under step is the preset abandoned air volume; based on the above calculation method, in this embodiment, the abandoned air volume AW can be expressed as:

[0075]

[0076] Among them, N k Indicates the total number of wind power scenarios, N farm represents the total number of wind farms, Represents the wind power scenario δ kThe probability of occurrence of wind power curtailment in this embodiment is that a gradual wind curtailment strategy is adopted when calculating the wind curtailment amount under each wind power scenario, that is, when wind power is not fully absorbed, the wind curtailment amount C is calculated according to the preset wind curtailment amount. step Gradually abandon wind power. After each abandonment, the wind power absorption situation will be re-verified until the power flow converges and the wind power is fully absorbed. Through the gradual abandonment strategy, this embodiment can minimize the amount of abandoned wind and avoid wasting wind power resources while ensuring the convergence of system power flow and the complete absorption of wind power.

[0077] In this example, the calculation method of the line margin LM includes: calculating the line margin of each wind power scenario after connecting to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the line margin LM; any wind power scenario δ k After connecting to the power system, the line margin is calculated as follows:

[0078] (T1) In the wind power scenario δ k After connecting to the power system, follow the S i,k =|S i |-S base,i Calculate the line margin of the i-th branch;

[0079] S i represents the apparent power of the i-th branch;

[0080] S base,i It represents the rated apparent power of long-distance transmission on the i-th branch, and its calculation formula is: represents the rated power of the i-th branch, S base It is a power benchmark for long-distance transmission of power system lines;

[0081] (T2) The maximum line margin of all branches is taken as the wind power scenario δ k Line margin after connection to the power system;

[0082] Based on the above calculation method, in this embodiment, the line margin LM can be expressed as:

[0083]

[0084] Among them, N b Indicates the total number of branches.

[0085] In this embodiment, the voltage margin VM is calculated by calculating the voltage margin of each wind power scenario after it is connected to the power system, multiplying the voltage margin by the probability of the wind power scenario, and summing the results as the voltage margin VM; any wind power scenario δ k After connecting to the power system, the voltage margin is the sum of the voltage margins of each node, where the voltage margin of any i-th node is Vj,k is the voltage value of the node V j to its rated voltage, V j,k =|V j -1|;

[0086] Based on the above calculation method, in this embodiment, the voltage margin VM can be expressed as:

[0087]

[0088] Among them, N PR Indicates the total number of nodes.

[0089] In this embodiment, the static voltage stability margin SVSM is used to measure the distance between the actual operating state of the power system and its stability limit. Its calculation method includes: calculating the static voltage stability margin after each wind power scenario is connected to the power system, multiplying it by the probability of the wind power scenario, and then summing the results to obtain the static voltage stability margin SVSM; any wind power scenario δ k After connecting to the power system, the static voltage stability margin L k The calculation expression is:

[0090]

[0091] Among them, N L and N G Represent the number of load nodes and generator nodes respectively, V k,j and V k,i They represent the voltages of load node j and generator node i respectively; F k,ji according to Calculated, V k,0j represents the equilibrium voltage;

[0092] Based on the above calculation method, in this embodiment, the static voltage stability margin SVSM can be expressed as:

[0093]

[0094] The minimization of line margin LM, voltage margin VM and static voltage stability margin SVSM in the model ensures the security of transmission lines, node voltages and the overall state of the power grid.

[0095] In this embodiment, the calculation method of the power generation cost GC includes: calculating the power generation cost of each wind power scenario after connecting to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the power generation cost GC; any wind power scenario δ k After connecting to the power system, the power generation cost is

[0096] Among them, Ns Indicates the total number of units; P i represents the output of the i-th unit, α i , β i ,λ i Both represent the fuel efficiency of the i-th unit;

[0097] Based on the above calculation method, this embodiment regards the power generation cost GC as the sum of the costs of each unit and uses a quadratic cost function to represent the cost of the power system, which can be specifically expressed as:

[0098]

[0099] In this embodiment, the equality constraints in the constraints of the probabilistic optimal power flow multi-objective optimization model are as follows:

[0100]

[0101]

[0102]

[0103] The above three expressions correspond to the active power balance constraint and the reactive power balance constraint, and the sum of the probability of each wind power scenario is 1; where P Gi With Q Gi is the active and reactive power of access node i; P Di With Q Di is the active and reactive power requirements of access node i; V i With V j represents the voltage amplitude between nodes i and j, N i is the total number of nodes adjacent to node i; G ij and B ij is the transmission conductance and susceptance between nodes i and j; θ ij It represents the voltage phase angle between i and j

[0104] In this embodiment, the state variables of the multi-objective optimization model of the probability optimal power flow are represented by x T It is used to calculate the constraints, specifically expressed as:

[0105] x T =[P G1 ,V L1 ,...,V LND ,Q G1 ,...,Q GNG ,S1,...,S NE ]

[0106] Among them, P G1 Represents the active power of the balancing node generator, VL1 ~V LNG Represents the voltage of the 1st to NDth load busbars respectively, ND represents the total number of loads; Q G1 ~Q GNG Respectively represent the reactive power output by the 1st to NGth generators, NG represents the total number of generators; S1~S NE Represents the power loss of the 1st to NEth branches respectively, and NE represents the total number of branches.

[0107] In this embodiment, the decision variables of the probability optimal power flow multi-objective optimization model are u T Indicates that:

[0108] u T =[P G2 ,...,P GNG ,V G1 ,...,V GNG ,T1,...,T NT ,Q C1 ,...,Q CNC ]

[0109] Among them, P G2 ~P GNG Indicates the active power output of the 2nd to NGth nodes, that is, the nodes other than the balance node; T1~T NT Represents the tap ratios of the 1st to NTth transformers respectively, and NT represents the total number of transformers: Q C1 ~Q CNC They represent the outputs of the 1st to NCth reactive compensation devices respectively, and NC represents the total number of reactive compensation devices.

[0110] In order to solve the above decision variable u T Optionally, this embodiment specifically uses the NSGA-II (Nondominated Sorting Genetic Algorithm-II) algorithm to solve the multi-objective probabilistic optimal power flow model, and the solution set obtained by the solution constitutes a Pareto surface; a solution is selected from the solution set to obtain a decision variable;

[0111] To further ensure the reliability of the optimization results, after obtaining the solution set, this embodiment further includes: selecting a reference point, and using the reference point and the solution set obtained by solving the multi-objective probabilistic optimal power flow model to calculate the hypervolume index; if the hypervolume index shows that the multi-objective probabilistic optimal power flow model does not converge, resolving it.

[0112] Example 2:

[0113] A computer-readable storage medium includes a stored computer program; when the computer program is executed by a processor, the device containing the computer-readable storage medium is controlled to execute the power system optimization method for the high-proportion wind power consumption scenario provided in the above-mentioned embodiment 1.

[0114] The technical solution of the present invention is further explained below in conjunction with an actual application scenario.

[0115] Figure 2 The topology of the IEEE-30 node system is shown in Table 1. The system data is shown in Table 1. A wind farm is connected to each of nodes 2, 7, 10, 16, and 24. The configuration of each wind farm is shown in Table 2. The wind turbines in the wind farm are all doubly fed induction generators with a rated wind speed of v rate , cut-in wind speed v in With cut-out wind speed v out Set to 12.5m / s, 4m / s and 20m / s respectively.

[0116] Table 1 IEEE-30 bus system data

[0117]

[0118]

[0119] Table 2 Wind farm configuration

[0120]

[0121] The method provided in the above embodiment 1 is used to optimize the IEEE-30 node system connected to the wind farm. The specific process is as follows:

[0122] Based on the parameters in Tables 1 and 2 above, first, the predicted wind speed and prediction error of each wind farm are obtained according to the historical data of the wind farm, as shown in Table 3.

[0123] Table 3 Wind farm predicted wind speed and prediction error

[0124]

[0125] Based on the prediction results in Table 3, fine stratified sampling is used to sample the wind power scenarios, and the wind power scenario N is set. k = 20, the actual wind power and scenario occurrence probability obtained by sampling are as follows: Figure 3 and Figure 4 shown.

[0126] The average wind power of the 20 wind power scenarios is 117.72 MW. The total load of the IEEE-30 node system used in this example is 283.4 MW, and the wind power access ratio is 41.54%, which meets the requirement of high wind power access ratio.

[0127] In this application scenario, the decision variable u T It includes 24 variables, and the values ​​of each position and their upper and lower limits are shown in Table 4.

[0128] Table 4 Values ​​and upper and lower limits of control variables

[0129]

[0130] Calculate the amount of wind power abandoned after each wind power scenario is connected to the grid i ,i∈[1,2,...,20], as shown in Table 5.

[0131] Table 5 Wind curtailment corresponding to each wind power scenario

[0132]

[0133]

[0134] Calculate the line margin LM after each wind power scenario is connected to the grid i ,i∈[1,2,...,20], as shown in Table 6:

[0135] Table 6 Line margin corresponding to each wind power scenario

[0136]

[0137] The voltage margin after each wind power scenario is connected to the grid is calculated, as shown in Table 7:

[0138] Table 7 Voltage margin corresponding to each wind power scenario

[0139]

[0140] The static voltage stability margin of each wind power scenario after being connected to the grid is calculated, as shown in Table 8:

[0141] Table 8 Static voltage stability margin corresponding to each wind power scenario

[0142]

[0143] Calculate the power generation cost C of each wind power scenario after it is connected to the grid i ,i∈[1,2,...,20], as shown in Table 9:

[0144] Table 9 Power generation costs corresponding to various wind power scenarios

[0145]

[0146] Based on the wind curtailment, line margin, voltage margin, static voltage stability margin and power generation cost in the above scenarios, the corresponding probabilistic optimal power flow multi-objective optimization model is established and solved, and the corresponding objective function can be obtained in the state variable x. T Under the decision variable u T The result of the action:

[0147] AW(x T ,u T )=2.1661

[0148] LM(x T ,u T )=-0.3961

[0149] VM(x T ,u T )=0.2372

[0150] SVSM(x T ,u T )=0.1273

[0151] C(x T ,u T )=465.9434

[0152] The NSGA-II algorithm is used to solve the probability optimal power flow multi-objective optimization model, obtain the decision variables, and calculate the super-objective volume index and Pareto surface to represent the effect of the model, as shown in the following example. Figure 5 and Figure 6 As shown in Figure 2. The number of individuals in the NSGA-II algorithm for solving this model is set to 30, the maximum number of iterations is set to 200 generations, and the reference point W required to solve the hypervolume index is set to [5, 3, 3, 100, 600]. The hypervolume index obtained by calculation is shown in Figure 2. Figure 5 As shown in the figure, it can be seen that with the iteration of NSGA-II, the degree of change of the hypervolume index value gradually decreases, which means that the solution of the model can converge.

[0153] The obtained solution is further normalized according to the following formula:

[0154]

[0155] The normalized solution is used to express the Pareto surface of the model in the form of a parallel coordinate graph, such as Figure 6As shown, the Pareto surface expresses the trade-off between different functions. Specifically, when the system's wind curtailment is low, the line margin decreases, while the voltage margin and static voltage stability margin indicators increase. This can be interpreted as the fact that accommodating a high proportion of wind power affects the stability of the transmission lines, node security, and overall system operational stability. Furthermore, when wind curtailment decreases, the system's generation cost decreases, indicating that the integration of wind power mitigates the utilization of traditional energy sources and reduces economic consumption. When wind curtailment increases, meaning the system cannot absorb the incoming wind power, the load on the transmission lines is alleviated, as evidenced by an increase in the line margin indicator. Simultaneously, the voltage margin and static voltage stability margin indicators decrease, indicating improved node security and system stability. Clearly, the system's generation cost increases with increasing wind curtailment, meaning that wind curtailment reduces the advantages of a high wind power integration ratio. When a high proportion of wind power is connected to the grid, establishing a model based on the above relationship will help reveal the impact of the access of a high proportion of wind power on the security and cost of the power system, and provide an important reference for the formulation of wind power absorption strategies.

[0156] More specifically, Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 and Figure 12 A three-dimensional scatter plot illustrates the Pareto surface between wind curtailment and the other four objectives. When a high proportion of wind power is connected to the power system, the amount of wind curtailment is equivalent to the system's ability to absorb wind power. Lower wind curtailment indicates greater wind power absorption. The figure shows that when the system absorbs a large amount of wind power, the line margin decreases, while the voltage margin and static voltage stability margin increase. This indicates that absorbing wind power threatens the safety of the power system. Furthermore, lower wind curtailment also translates to lower power generation costs, as wind power reduces the pressure on generators, thereby lowering system costs. On the other hand, while increased wind curtailment wastes wind power resources, it also reduces the likelihood of system security crises, as evidenced by an increase in line margin and a decrease in voltage margin and static voltage stability margin. However, this also increases the pressure on generators, resulting in higher system power generation costs.

[0157] In general, the present invention reveals the impact of high-proportion wind power access on power system security and cost, which can provide an important reference for the formulation of wind power grid connection strategies and provide a reliable basis for the planning and operation of power systems with high-proportion wind power access.

[0158] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing a power system in a high-proportion wind power consumption scenario, characterized in that: include: Constructing a multi-objective probabilistic optimal power flow model with the objectives of minimizing wind curtailment, voltage margin, static voltage stability margin, power generation cost, and maximizing line margin; solving the multi-objective probabilistic optimal power flow model under preset constraints; selecting a decision variable from the resulting solution set as the optimization result; and configuring each device in the power system according to the optimization result to complete the optimization; The decision variables include: the active power output of each node other than the balancing node, the terminal voltage of each generator, the tap ratio of each transformer, and the reactive power output of each reactive compensation device; the preset constraints include equality constraints and inequality constraints; the equality constraints include: active power balance constraints and reactive power balance constraints, and the sum of the probabilities of each wind power scenario being 1; the inequality constraints include: the active power and reactive power output of each device are between the corresponding maximum and minimum values; The calculation method of the abandoned wind volume AW includes: calculating the abandoned wind volume after each wind power scenario is connected to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the abandoned wind volume AW; any wind power scenario δ k After connecting to the power system, the wind curtailment is the sum of the wind curtailment of each wind farm, where the wind curtailment of any j-th wind farm is C j,k Follow these steps to calculate: (S1) Initializing the abandoned air volume C j,k C j,k =0; (S2) The wind power scenario δ k After accessing the power system, a power flow calculation is performed to verify whether the equation preset and the inequality constraint are satisfied. If both are satisfied, the process proceeds to step (S4); otherwise, the process proceeds to step (S3); (S3) According to P wt,k =P wt,k –C step Update wind power scenario δ k The actual wind power P under wt,k , and follow C j,k =C j,k +C step Updated abandoned air volume C j,k Afterwards, proceed to step (S2); (S4) Output the current C j,k , as the wind power scenario δ k The amount of wind curtailment C of the next j-th wind farm j,k ; Among them, P wt,k Represents the wind power scenario δ k The actual wind power under step is the preset abandoned air volume.

2. The power system optimization method under the high-proportion wind power consumption scenario according to claim 1 is characterized in that: The calculation method of the line margin LM includes: calculating the line margin of each wind power scenario after connecting to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the line margin LM; any wind power scenario δ k After connecting to the power system, the line margin is calculated as follows: (T1) In the wind power scenario δ k After connecting to the power system, follow the S i,k =|S i |-S base,i Calculate the line margin of the i-th branch; S i represents the apparent power of the i-th branch, S base,i represents the rated apparent power of long-distance transmission on the i-th branch; (T2) The maximum line margin of all branches is taken as the wind power scenario δ k Line margin after connection to the power system.

3. The power system optimization method under the high-proportion wind power consumption scenario according to claim 1 is characterized in that: The calculation method of the voltage margin VM includes: calculating the voltage margin of each wind power scenario after connecting to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the voltage margin VM; any wind power scenario δ k After connecting to the power system, the voltage margin is the sum of the voltage margins of each node, where the voltage margin of any i-th node is V j,k is the voltage value of the node V j to its rated voltage.

4. The power system optimization method under a high-proportion wind power consumption scenario according to claim 1, characterized in that: The calculation method of the static voltage stability margin SVSM includes: calculating the static voltage stability margin after each wind power scenario is connected to the power system, multiplying it with the probability of the wind power scenario, and summing the results, and taking the result as the static voltage stability margin SVSM; any wind power scenario δ k After connecting to the power system, the static voltage stability margin L k The calculation expression is: Among them, N L and N G Represent the number of load nodes and generator nodes respectively, V k,j and V k,i They represent the voltages of load node j and generator node i respectively; F k,ji according to Calculated, V k,0j Indicates the equilibrium voltage.

5. The power system optimization method under the high-proportion wind power consumption scenario according to claim 1, characterized in that: The calculation method of the power generation cost GC includes: calculating the power generation cost of each wind power scenario after connecting to the power system, multiplying it by the probability of the wind power scenario, and summing the results as the power generation cost GC; any wind power scenario δ k After connecting to the power system, the power generation cost is Among them, P i represents the output of the i-th unit, α i , β i ,λ i Both represent the fuel efficiency of the i-th unit.

6. The power system optimization method in a high-proportion wind power consumption scenario according to any one of claims 1 to 5, characterized in that: The actual wind power and its corresponding occurrence probability under each wind power scenario are obtained through fine stratified sampling.

7. The power system optimization method in a high-proportion wind power consumption scenario according to any one of claims 1 to 5, characterized in that: The solution algorithm of the multi-objective probabilistic optimal power flow model is the NSGA-II algorithm.

8. The power system optimization method in a high-proportion wind power consumption scenario according to claim 7, characterized in that: Also includes: A reference point is selected, and a hypervolume index is calculated using the reference point and a solution set obtained by solving the multi-objective probabilistic optimal power flow model. If the hypervolume index shows that the multi-objective probabilistic optimal power flow model does not converge, the model is re-solved.

9. A computer-readable storage medium, characterized in that Including a stored computer program; when the computer program is executed by a processor, it controls the device where the computer-readable storage medium is located to execute the power system optimization method in a high-proportion wind power consumption scenario according to any one of claims 1 to 8.

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