Wind and light storage configuration method considering transient voltage constraint and inertia constraint

Abstract

The invention discloses a wind-light storage configuration method considering transient voltage constraint and inertia constraint, and the method comprises the steps: obtaining a wind-light typical output scene and a direct-current power transmission plan power through employing a k-means clustering method based on wind-light output and receiving end load data, employing a bilevel programming model for wind-light-storage capacity configuration, and obtaining a wind-light storage capacity configuration model; the upper-layer model takes the minimization of the total investment cost as an optimization target, and constructs a transient overvoltage constraint, an inertia support constraint and a capacity constraint based on the short-circuit capacity; and the lower layer model takes the operation cost of the thermal power generating unit and the new energy power abandoning cost as targets, considers the power balance constraint, the operation constraint and the like of the wind-light-storage channel direct current power transmission delivery system, introduces the power transmission confidence probability constraint, and realizes the measurement of the delivery reliability of the wind-light storage channel. An improved grey wolf optimization algorithm is adopted, a Logistic-Tent mapping opposite learning mechanism is added, a mutation operator, a crossover operator and a selection operator are introduced in the differential mutation process of the algorithm, the population number and category richness in the algorithm are improved, and the solving speed is increased.

Description

Technical Field

[0001] This invention relates to the field of power resource allocation technology, specifically a wind-solar-storage allocation method that considers transient voltage constraints and inertia constraints. Background Technology

[0002] The large-scale development and utilization of new energy sources such as photovoltaics, wind, and electricity has significantly increased the proportion of new energy power generation, alleviating the global energy crisis. However, wind and solar power generation is significantly affected by weather conditions, exhibiting high randomness and intermittency, leading to significant uncertainty in renewable energy supply and weakening the power quality of the sending-end system to some extent. This uncertainty poses a severe challenge to the capacity allocation of wind-solar-storage systems, directly impacting the stable operation and economic benefits of the power grid. Currently, research on the capacity allocation of wind-solar-storage-synchronous modulators mainly focuses on the capacity allocation of new energy sources under load changes, neglecting the role of DC transmission systems, resulting in significant deviations in the established capacity configuration models. Existing capacity configuration constraints fail to fully consider the spatiotemporal correlation of new energy power generation and ignore the distribution characteristics of renewable energy supply, relying solely on power and other constraints to solve the model, leading to large fluctuations in results and difficulty in achieving accurate capacity allocation calculations. This results in resource waste, unstable power supply, ineffective balancing of energy storage systems, and high volatility of renewable energy.

[0003] The new energy transmission base consists of equipment such as wind power generation, photovoltaic power generation, thermal power units, and energy storage power stations, such as... Figure 1 As shown, the electrical energy generated by the system is transmitted over long distances via high-voltage direct current (HVDC) transmission lines. In the system configuration, the HVDC transmission channel capacity and the scale of thermal power units are fixed parameters, while the installed capacity of wind power, the scale of photovoltaic power plants, and the capacity of energy storage systems are design variables to be optimized. To ensure the independence and sustainability of system operation, the power output must meet the periodic power balance condition, that is, within a specific period (such as one week), the net power output of the system should remain zero. In large-scale HVDC transmission projects for renewable energy, the receiving-end grid generally does not have large-scale renewable energy units. Therefore, the planned power curve of the HVDC transmission is highly matched with the load curve. Summary of the Invention

[0004] The application aims to provide a wind-solar-storage configuration method considering transient voltage constraints and inertia constraints.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the application is as follows:

[0006] A wind-solar-storage configuration method considering transient voltage constraints and inertia constraints comprises the following steps:

[0007] Step one: based on wind-solar output and load data at the receiving end, the k-means clustering method is adopted to obtain the direct current transmission planned power corresponding to the wind-solar-storage typical load scenario;

[0008] Step two: an optimal capacity configuration double-layer model of wind-solar-storage is established, the upper-layer model takes the minimization of total investment cost of the wind-solar-storage system as the optimization objective function, and constructs the transient overvoltage constraint, inertia support constraint and capacity constraint based on short-circuit capacity; the lower-layer model takes the operation cost of thermal power units and the cost of abandoned electricity of new energy as the target, considers the power balance constraint and operation constraint of the wind-solar-storage system through the direct current transmission system, and introduces the power transmission confidence probability constraint to realize the reliability measurement of the wind-solar-storage system through the direct current transmission system; the operation constraint of the wind-solar-storage system through the direct current transmission system comprises new energy output constraint, thermal power operation constraint, storage operation constraint, system reserve capacity constraint, new energy output proportion constraint and power grid support constraint;

[0009] Step three: the Logistic-Tent chaotic mapping is introduced in the initial stage of the population of the grey wolf optimization algorithm, the improved grey wolf optimization algorithm is adopted to cyclically and iteratively solve the wind-solar-storage capacity configuration model established in step two, and the wind-solar-storage capacity configuration scheme is obtained.

[0010] Preferably, the upper-layer model takes the minimization of total investment cost of the wind-solar-storage system as the objective function.

[0011]

[0012] In the formula, F invis the total investment cost of the wind-solar-storage system; Nw1, Np1, Ng, Ne are the number of wind farms, photovoltaic power stations, thermal power units and energy storage power stations, respectively; Cw, Cp, Cg, Ce are the investment and construction costs of wind farms, photovoltaic power stations, thermal power units and energy storage power stations, respectively; is the typical daily operation cost of the wind-solar-storage system.

[0013] Preferably, the sending-end system of the wind-solar-storage DC power transmission system in step one must satisfy the inertia support constraint condition:

[0014]

[0015] wherein g is the number of thermal power plants; Nw1, Np1, Ng, Ne are the number of wind farms, photovoltaic power stations, thermal power units and energy storage power stations, respectively; Pw1, Pp1, Pg, Pe are the equivalent output power of the w1th wind farm, the p1th photovoltaic power station, the gth thermal power unit and the e th energy storage power station, respectively; Hw1, Hp1, Hg, He are the virtual synchronous inertia of the w1th wind farm, the p1th photovoltaic power station, the gth thermal power unit and the e th energy storage power station, respectively; Hg is the synchronous inertia of the gth thermal power unit; H is the inertia demand of the wind-solar-storage DC power transmission system. w_e,w1,i is the equivalent output power of the w1th wind farm; P p_e,p1,i is the equivalent output power of the p1th photovoltaic power station; P e_e,e,n is the equivalent output power of the e th energy storage power station; P g,t is the output power of the gth thermal power unit at time period t; H w,w1,t is the virtual synchronous inertia of the w1th wind farm; H p,p1,t is the virtual synchronous inertia of the p1th photovoltaic power station; H e,e,t is the virtual synchronous inertia of the e th energy storage power station; H g,t is the synchronous inertia of the gth thermal power unit; H demand,t is the inertia demand of the wind-solar-storage DC power transmission system;

[0016] The sending-end system of the wind-solar-storage DC power transmission system in step one must satisfy the transient voltage constraint based on short-circuit capacity as follows:

[0017]

[0018] wherein k ST is the short-circuit capacity provided by thermal power; S T is the short-circuit capacity provided by thermal power at the rectifier side converter bus; p aw_total,i is the equivalent operation capacity of wind farms considering the confidence level of wind power generation; p ap_total,j is the equivalent operation capacity of photovoltaic power stations considering the confidence level of photovoltaic power generation; p ae_total,n is the equivalent operation capacity of energy storage considering the confidence level of energy storage; Z p_L,i is the equivalent impedance of wind farms to the rectifier side converter bus; Z p_L,j is the equivalent impedance of photovoltaic power stations to the rectifier side converter bus; Z p_L,n is the equivalent impedance of energy storage power stations to the rectifier side converter bus; kSw,i k is the ratio of the short-circuit capacity provided by the wind turbine at the grid connection point to its installed capacity. Sw,j k is the ratio of the short-circuit capacity provided by the photovoltaic power plant at the grid connection point to its installed capacity. Sw,n p is the ratio of the short-circuit capacity provided by the energy storage power station at the grid connection point to its installed capacity. T_total Nw represents the total installed capacity of thermal power generation; Np represents the number of wind farms; Ne represents the number of photovoltaic power plants; and Ne represents the number of energy storage power plants.

[0019] Preferably, after introducing transient voltage constraints and inertia support constraints into the upper-level model, the capacity constraints for wind farms, photovoltaic power stations, and energy storage power stations are as follows:

[0020]

[0021] In the formula: c w,min c w,max These represent the lower and upper limits of wind power installed capacity, respectively; c p,min c p,max These represent the lower and upper limits of photovoltaic installed capacity, respectively; c g,min c g,max These represent the lower and upper limits of thermal power installed capacity, respectively; c e,min c e,max These represent the lower and upper limits of energy storage installed capacity, respectively; p w p p p T p e These refer to the capacities of individual wind power, solar power, thermal power, and energy storage, respectively.

[0022] The capacity constraints of a DC transmission system are:

[0023]

[0024] In the formula: P w_e,i P represents the equivalent output power of the wind farm. p_e,i P represents the equivalent output power of a photovoltaic power station. e_e,n k is the equivalent output power of the energy storage power station. w_e k represents the average output level of the wind farm. p_e k represents the average output level of a photovoltaic power plant. e_e p represents the average output level of the energy storage power station. w_total,i p p_total,j p e_total,n This refers to the total installed capacity of wind farms, photovoltaic power stations, and energy storage power stations.

[0025]

[0026] In the formula: P dN This is the rated DC transmission power.

[0027] Preferably, the objective function of the lower-level model is:

[0028]

[0029] In the formula: C g,t Let g be the power generation cost of thermal power unit g in time period t; ag, bg, and cg are the power generation cost coefficients of the thermal power unit; P g,t Let g be the output of thermal power unit g during time period t; The start-up and shutdown cost of thermal power unit g during time period t; and These are the start-up cost and shutdown cost of thermal power unit g, respectively; and These represent the start-up and shutdown state variables of thermal power unit g during time period t; N T P represents the number of time periods in the grid support balance constraint dispatch cycle, with a value of 168; λ is the renewable energy curtailment penalty cost coefficient; t dw P represents the total power output of the wind farm during time period t. t dp To provide total dispatch output for photovoltaic power plants during time period t; Let be the predicted power of wind turbine i in time period t; Let represent the predicted power of photovoltaic power station i during time period t.

[0030] Preferably, the power balance constraint condition is:

[0031]

[0032] In the formula: P t s P represents the energy storage output power during time period t. t HVDC P represents the output power of the DC transmission system during time period t. t sup The supporting power provided by the AC power grid during time period t; P t dw P represents the total power output of the wind farm during time period t. t dp For the total dispatch output of the photovoltaic power station during time period t; P g,t The output of thermal power unit i during time period t;

[0033] The constraints on new energy output are:

[0034]

[0035] In the formula: Let be the predicted power of wind turbine i in time period t; Let be the predicted power of photovoltaic power station i in time period t;

[0036] The operating constraints for thermal power plants are:

[0037] u g,t P g,min ≤P g,t ≤u g,t P g,max

[0038] In the formula: P g,max P g,min These are the upper and lower limits of the active power output of thermal power unit g, respectively; u g,t This represents the operating status of thermal power unit g during time period t;

[0039]

[0040] In the formula: ΔT represents the downward and upward ramp rates of thermal power unit g, respectively; ΔT is the dispatch time interval.

[0041]

[0042] In the formula: and For thermal power unit g, the upward and downward reserve capacity during time period t;

[0043] The constraints for energy storage operation are:

[0044]

[0045] In the formula: E s,t This refers to the state of charge of the energy storage power station. and These represent the upper and lower limits of the energy storage power station's capacity, respectively; η c and η d For energy storage charging and discharging efficiency; P t s,c and P t s,d These represent the charging power and discharging power of the energy storage power station during time period t, respectively.

[0046]

[0047] In the formula: For the charging state variables of the energy storage power station; For the discharge state variables of the energy storage power station; This represents the maximum charging power of the energy storage power station. This represents the maximum discharge power of the energy storage power station.

[0048]

[0049] In the formula: Upward backup capacity provided for energy storage power stations; Downward backup capacity provided for energy storage power stations;

[0050] The system's reserve capacity constraints are as follows:

[0051]

[0052] In the formula: r t up and r t dn These represent the upward and downward reserve capacities of the system during time period t, respectively.

[0053] The constraint on the proportion of new energy power output is:

[0054]

[0055] In the formula: δ represents the required proportion of new energy sources;

[0056] The power grid support constraints are as follows:

[0057]

[0058] In the formula: P t HVDC P represents the output power of the DC transmission system during time period t. t sup ξ is the supporting power provided by the AC grid during time period t; ξ is the grid support coefficient (the ratio of the upper limit of grid support power to the planned DC transmission power); N T The number of time periods in the grid support balance constraint dispatch cycle is set to 168.

[0059] This constraint serves as a boundary condition for the power transmission confidence probability constraint in the lower-level model. Based on the K-means clustering algorithm, several typical load scenarios are identified, and corresponding high-voltage direct current (HVDC) transmission schemes are designed for each scenario. The planned power constraint for HVDC transmission corresponding to the typical wind-solar-storage load scenarios is as follows:

[0060] This indicates that the daily DC transmission power adjustment frequency does not exceed two times;

[0061]

[0062] To ensure continuous operation during the day, the transmission power must be consistent at the beginning and end of the day, as shown below;

[0063]

[0064] Power switching constraints are specified: when ut = 0, the planned power transmission capacity for time period t remains constant;

[0065]

[0066] Power balance constraints enable dynamic matching between the planned power supply of the DC channel and the load at the receiving end;

[0067]

[0068] To maximize the utilization efficiency of the DC channel in the DC transmission system, the planned DC power is obtained;

[0069]

[0070] In the formula: T is the number of time periods in 1 day, which is 24 in this case; u t The variable is 0-1, where 1 indicates that the DC transmission power is adjusted during time period t, and 0 indicates that the DC transmission power is not adjusted during time period t. t represents the planned DC transmission power for time period t; M represents the coefficient of the large M method; I represents the number of load curves corresponding to the current scenario category; Let be the value of the i-th load curve in time period t; Δt is the unit time interval, which is 1 at this time;

[0071] The power supply confidence probability constraint is:

[0072]

[0073] (ρ (0) ) T z≤1-α

[0074] z n ∈{0,1}, n=1,2,...,N

[0075] In the formula: P t dp P t dw P g,t These represent the power transmitted from photovoltaic, wind power, and thermal power to the DC channel during time period t in scenario n. Let N be the reference probability distribution vector; This indicates that each scenario occurs with the same probability.

[0076] The constraints for curtailment of renewable energy are:

[0077]

[0078] In the formula: μ is the upper limit of the curtailment rate of new energy.

[0079] Preferably, the process of iteratively solving the wind-solar-storage capacity configuration model established in step two using the improved gray wolf optimization algorithm in step three is as follows:

[0080] S3.1 Input wind and solar power output and receiving-end grid load data, and use the k-means algorithm to generate typical scenarios for grid load data to obtain DC planned power. Establish an upper-level model that considers transient overvoltage constraints and inertia constraints, introduce power transmission confidence probability constraints, and establish a lower-level model.

[0081] S3.2. Initialization of the upper-level population based on Logistic-Tent and opposition learning mechanism: Initialize the capacity configuration scheme of new energy units in the wind-solar-storage DC transmission system, that is, the initial configuration of the capacity of wind turbines, photovoltaic power stations, energy storage power stations and thermal power units, and generate a gray wolf population that satisfies the upper-level constraints.

[0082] S3.3 Lower-level population initialization based on Logistic-Tent and opposition learning mechanism: After the upper-level initialization, the position of the initial gray wolf population that satisfies the upper-level constraints is passed to the lower level, and the lower level performs initial settings for the system operation scheduling strategy according to the configuration information.

[0083] S3.4 Calculation of Lower-Level Fitness Function Value: The lower-level fitness function value is calculated using the objective function of the lower-level model, i.e., the system coordinates and schedules to achieve equilibrium; in iteration number T... 1 Not reached the upper limit T 1 ≤T 1 When the maximum value is reached, the population positions are updated. The selection probability P is calculated using the selection operator formula. If P ≥ rand, the positions of α wolves, β wolves, and γ wolves are updated according to the formulas for mutation, crossover, and selection operators. Otherwise, the positions of α wolves, β wolves, and γ wolves are updated according to the initial positions of the gray wolf population generated by the traditional gray wolf algorithm. When the number of iterations T... 1 When the upper limit is reached, the operating cost of thermal power units calculated at the lower level and the cost of curtailment of new energy are passed to the upper level.

[0084] S3.5 Calculation of Upper-Level Fitness Function Value: Calculate the minimum investment cost of the upper-level system using the upper-level objective function formula, and update the population position before the upper limit of the number of iterations is reached; when the number of iterations T... 1 Reaching the upper limit T 1 If the value is maxed out, output the minimum investment cost and the wind-solar-storage unit capacity configuration scheme; otherwise, return to step S3.4 to continue execution.

[0085] Preferably, the improved gray wolf optimization algorithm in step three is as follows:

[0086] (1) In the initial stage of the population, the formula for creating the initial population by introducing the Logistic-Tent chaotic mapping is introduced:

[0087]

[0088] In the formula: h is the control parameter of the Logistic-Tent chaotic mapping, with a value of [0,4]; modl is the numerical modulo operation; x c x represents the current system state value. c+1 The next state value is obtained after one Logistic-Tent mapping based on the current state;

[0089] The generated results are used to initialize the population location X. k X k The formula is:

[0090] X k =lb+(ub-lb)x c

[0091] In the formula: X k ub is the initialization position for the population; lb is the upper bound of the population solution space; lb is the lower bound of the population solution space.

[0092] (2) Position update based on differential evolution

[0093] The traditional gray wolf algorithm generates the initial position of the gray wolf population, and the formulas for α wolves, β wolves, and γ wolves to hunt prey and update their own positions are as follows:

[0094]

[0095]

[0096] In the formula: D is the distance between the gray wolf and its prey; C and A are coefficient vectors; t is the current iteration number. Location of prey hunted by alpha, beta, and gamma wolves; X t X1 represents the gray wolf's own position; X2 and X3 represent the updated positions of the α wolf, β wolf, and γ wolf, respectively.

[0097] (3) Individual perturbation operator

[0098]

[0099] In the formula: D op For perturbation operators; Let Euclidean distance be the distance between the positions of gray wolf i and gray wolf j. It is a random quantity uniformly distributed within [-1 / 2, 1 / 2].

[0100] Preferably, the improved gray wolf optimization algorithm introduces mutation operators, crossover operators, and selection operators in the differential mutation process to increase the population size and class richness in the algorithm;

[0101] The mutation operator is:

[0102]

[0103] In the formula: v is the mutated solution; t is the iteration number; x is the solution in the original population; N is the population size; r1, r2, and r3 are random integers in [1, N]; w is the mutation factor that affects the difference between individual solutions;

[0104] The crossover operator is:

[0105]

[0106] In the formula: rand is a random value within [0,1]; C R This represents the individual crossover coefficient.

[0107] The operator is selected as:

[0108]

[0109] In the formula: f(z) is the fitness function of z.

[0110] The probability that the gray wolf chooses a differential evolution strategy to update its position is:

[0111]

[0112] If P ≥ rand, differential evolution is not used to update the position; otherwise, differential mutation is used to update the position.

[0113] Based on wind and solar power output and receiving-end load data, this invention uses the k-means clustering method to obtain typical wind and solar power output scenarios and planned DC transmission power. A two-level programming model is adopted for the optimal capacity configuration of wind-solar-storage. The upper-level model takes minimizing the total investment cost as the optimization objective and constructs transient overvoltage constraints, inertia support constraints, and capacity constraints based on short-circuit capacity. The lower-level model takes the operating cost of thermal power units and the cost of renewable energy curtailment as objectives, and considers the power balance constraints and operating constraints of the wind-solar-storage DC transmission system. At the same time, a power transmission confidence probability constraint is introduced to measure the reliability of wind-solar-storage transmission.

[0114] Based on this, this invention employs an improved Grey Wolf optimization algorithm to solve the wind-solar-storage capacity configuration model, which can improve planning accuracy, operational reliability, and resource utilization efficiency while ensuring system dynamic stability. The improved Grey Wolf optimization algorithm adds a Logistic-Tent mapping opposition learning mechanism to the traditional optimization algorithm, improving the problems of the traditional Grey Wolf optimization algorithm being prone to getting trapped in local optima, low convergence efficiency, and slow optimization speed. Simultaneously, mutation operators, crossover operators, and selection operators are introduced in the differential mutation process of the algorithm, increasing the population size and class richness, and accelerating the solution speed.

[0115] This invention considers transient overvoltage constraints and inertia support constraints based on short-circuit capacity in the upper-level model, enabling the wind-solar-storage capacity allocation to take into account system stability while maintaining the economic efficiency of the capacity allocation model. The lower-level model introduces a power transmission confidence probability index, improving the accuracy of the capacity allocation results.

[0116] The method described in this invention significantly improves the planning accuracy, operational reliability, and resource utilization efficiency of new energy transmission systems, providing a reference for the coordinated planning of large-scale new energy bases such as the Gobi Desert and ultra-high voltage direct current transmission lines. Attached Figure Description

[0117] Figure 1 This is a schematic diagram of a new energy transmission base model;

[0118] Figure 2 This is a topology diagram of a wind-solar-storage DC power transmission system.

[0119] Figure 3 It is a model framework for a wind-solar-storage DC transmission system;

[0120] Figure 4 This is a flowchart of the capacity allocation process for the new energy transmission system. Detailed Implementation

[0121] The present invention will now be described in further detail with reference to the accompanying drawings.

[0122] Please describe the method in the invention description section by incorporating specific implementation parameters and relevant data.

[0123] A wind-solar-storage configuration method considering transient voltage constraints and inertia constraints includes the following steps:

[0124] Step 1: Based on wind and solar power output and receiving-end load data, the k-means clustering method is used to obtain the planned DC transmission power corresponding to typical wind-solar-storage load scenarios;

[0125] Step 2: Establish a two-layer model for optimal capacity configuration of wind-solar-storage. The upper-layer model takes minimizing the total investment cost of the wind-solar-storage system capacity as the optimization objective function, and constructs transient overvoltage constraints, inertia support constraints, and capacity constraints based on short-circuit capacity. The lower-layer model takes the operating cost of thermal power units and the cost of renewable energy curtailment as objectives, considers the power balance constraints and operational constraints of the wind-solar-storage system transmitted via DC transmission, and introduces power transmission confidence probability constraints to measure the reliability of wind-solar-storage transmission. The operational constraints of the wind-solar-storage system transmitted via DC transmission include renewable energy output constraints, thermal power operation constraints, energy storage operation constraints, system reserve capacity constraints, renewable energy output ratio constraints, and grid support constraints.

[0126] Step 3: In the initial stage of the population optimization algorithm, the Logistic-Tent chaotic mapping is introduced. The improved Gray Wolf Optimization Algorithm is used to iteratively solve the wind-solar-storage capacity configuration model established in Step 2 to obtain the wind-solar-storage capacity configuration scheme.

[0127] The objective function for minimizing the total investment cost of the wind-solar-storage system in the upper-level model is:

[0128]

[0129] In the formula: F inv The total investment cost of the wind-solar-storage system is represented by Nw1, Np1, Ng, and Ne, which represent the number of wind farms, photovoltaic power plants, thermal power units, and energy storage power plants, respectively. The investment and construction costs of the wind farms, photovoltaic power plants, thermal power units, and energy storage power plants are represented by Cw, Cp, Cg, and Ce, respectively. This represents the typical daily operating cost of a wind-solar-storage system.

[0130] In step one, the sending-end system of the wind-solar-storage DC transmission system must meet the inertia support constraint condition as follows:

[0131]

[0132] In the formula: g represents the number of thermal power plants; Nw1, Np1, Ng, and Ne represent the number of wind farms, photovoltaic power plants, thermal power units, and energy storage power plants, respectively; P w_e,w1,i P represents the equivalent output power of the w1-th wind farm. p_e,p1,i P represents the equivalent output power of the p1th photovoltaic power station; e_e,e,n P represents the equivalent output power of the e-th energy storage power station. g,t H represents the output power of thermal power unit g during time period t. w,w1,t Let H be the virtual synchronous inertia of the w1-th wind farm. p,p1,t Let H be the virtual synchronous inertia of the p1th photovoltaic power station. e,e,t Let H be the virtual synchronous inertia of the e-th energy storage power station.g,t H is the synchronous inertia of the g-th thermal power unit; demand,t This is to meet the inertia requirements of the wind-solar-storage DC power transmission system.

[0133] In step one, the sending-end system of the wind-solar-storage DC transmission system must meet the transient voltage constraint based on short-circuit capacity as follows:

[0134] In the formula: k ST Short-circuit capacity provided for thermal power plants; S T The short-circuit capacity provided by thermal power at the converter bus on the rectifier side; p aw_total,i This represents the equivalent operating capacity of a wind farm that takes into account the confidence level of wind power generation; p ap_total,j This refers to the equivalent operating capacity of a photovoltaic power plant that takes into account the confidence level of photovoltaic power generation; p ae_total,n The equivalent operating capacity of energy storage considering the energy storage confidence level; Z p_L,i Z is the equivalent impedance from the wind farm to the rectifier-side converter bus; p_L,j Z is the equivalent impedance from the photovoltaic power station to the rectifier-side converter bus; p_L,n k is the equivalent impedance from the energy storage power station to the rectifier-side converter bus; Sw,i k is the ratio of the short-circuit capacity provided by the wind turbine at the grid connection point to its installed capacity. Sw,j k is the ratio of the short-circuit capacity provided by the photovoltaic power plant at the grid connection point to its installed capacity. Sw,n p is the ratio of the short-circuit capacity provided by the energy storage power station at the grid connection point to its installed capacity. T_total Nw represents the total installed capacity of thermal power generation; Np represents the number of wind farms; Ne represents the number of photovoltaic power plants; and Ne represents the number of energy storage power plants.

[0135] After introducing transient voltage constraints and inertia support constraints into the upper-level model, the capacity constraints of wind-solar-storage are as follows:

[0136]

[0137] In the formula: c w,min c w,max These represent the lower and upper limits of wind power installed capacity, respectively; c p,min c p,max These represent the lower and upper limits of photovoltaic installed capacity, respectively; c g,min c g,max These represent the lower and upper limits of thermal power installed capacity, respectively; c e,min c e,max These represent the lower and upper limits of energy storage installed capacity, respectively; p w p p p T p eThese refer to the capacities of individual wind power, solar power, thermal power, and energy storage, respectively.

[0138] The generation capacity constraints of DC transmission systems are as follows:

[0139]

[0140] In the formula: P w_e,i P represents the equivalent output power of the wind farm. p_e,i P represents the equivalent output power of a photovoltaic power station. e_e,n k is the equivalent output power of the energy storage power station. w_e k represents the average output level of the wind farm. p_e k represents the average output level of a photovoltaic power plant. e_e p represents the average output level of the energy storage power station. w_total,i p p_total,j p e_total,n This refers to the total installed capacity of wind farms, photovoltaic power stations, and energy storage power stations.

[0141]

[0142] In the formula: P dN This is the rated DC transmission power.

[0143] The objective function of the lower-level model is:

[0144]

[0145] In the formula: C g,t Let g be the power generation cost of thermal power unit g in time period t; ag, bg, and cg are the power generation cost coefficients of the thermal power unit; P g,t Let g be the output of thermal power unit g during time period t; The start-up and shutdown cost of thermal power unit g during time period t; and These are the start-up cost and shutdown cost of thermal power unit g, respectively; and These represent the start-up and shutdown state variables of thermal power unit g during time period t; N T P represents the number of time periods in the grid support balance constraint dispatch cycle, with a value of 168; λ is the renewable energy curtailment penalty cost coefficient; t dw P represents the total power output of the wind farm during time period t. t dp To provide total dispatch output for photovoltaic power plants during time period t; Let be the predicted power of wind turbine i in time period t; Let represent the predicted power of photovoltaic power station i during time period t.

[0146] The power balance constraint is:

[0147]

[0148] In the formula: P t s P represents the energy storage output power during time period t. t HVDC P represents the output power of the DC transmission system during time period t. t sup The supporting power provided by the AC power grid during time period t; P t dw P represents the total power output of the wind farm during time period t. t dp For the total dispatch output of the photovoltaic power station during time period t; P g,t The output of thermal power unit i during time period t;

[0149] The constraints on new energy output are:

[0150]

[0151] In the formula: Let be the predicted power of wind turbine i in time period t; Let be the predicted power of photovoltaic power station i in time period t;

[0152] The operating constraints for thermal power plants are:

[0153] u g,t P g,min ≤P g,t ≤u g,t P g,max

[0154] In the formula: P g,max P g,min These are the upper and lower limits of the active power output of thermal power unit g, respectively; u g,t This represents the operating status of thermal power unit g during time period t;

[0155]

[0156] In the formula: α and αT represent the downward and upward ramp rates of thermal power unit g, respectively; αT is the dispatch time interval.

[0157]

[0158] In the formula: and For thermal power unit g, the upward and downward reserve capacity during time period t;

[0159] The constraints for energy storage operation are:

[0160]

[0161]

[0162] In the formula: E s,t This refers to the state of charge of the energy storage power station. and These represent the upper and lower limits of the energy storage power station's capacity, respectively; η c and η d For energy storage charging and discharging efficiency; P t s,c and P t s,d These represent the charging power and discharging power of the energy storage power station during time period t, respectively.

[0163]

[0164] In the formula: For the charging state variables of the energy storage power station; For the discharge state variables of the energy storage power station; This represents the maximum charging power of the energy storage power station. This represents the maximum discharge power of the energy storage power station.

[0165]

[0166] In the formula: Upward backup capacity provided for energy storage power stations; Downward backup capacity provided for energy storage power stations;

[0167] The system's reserve capacity constraints are as follows:

[0168]

[0169] In the formula: r t up and r t dn These represent the upward and downward reserve capacities of the system during time period t, respectively.

[0170] The constraint on the proportion of new energy power output is:

[0171]

[0172] In the formula: δ represents the required proportion of new energy sources;

[0173] The power grid support constraints are as follows:

[0174]

[0175] In the formula: P t HVDCP represents the output power of the DC transmission system during time period t. t sup ξ is the supporting power provided by the AC grid during time period t; ξ is the grid support coefficient (the ratio of the upper limit of grid support power to the planned DC transmission power); N T The number of time periods in the grid support balance constraint dispatch cycle is set to 168.

[0176] This constraint condition serves as a boundary condition for limiting the power transmission confidence probability constraint in the lower-level model.

[0177] Based on the K-means clustering algorithm, several typical load scenarios were identified, and corresponding high-voltage direct current (HVDC) transmission schemes were designed for each scenario. The planned power constraints for HVDC transmission corresponding to the typical wind-solar-storage load scenarios are as follows:

[0178] This indicates that the daily DC transmission power adjustment frequency does not exceed two times;

[0179]

[0180] To ensure continuous operation during the day, the transmission power must be consistent at the beginning and end of the day, as shown below;

[0181]

[0182] Power switching constraints are specified: when ut = 0, the planned power transmission capacity for time period t remains constant;

[0183]

[0184] Power balance constraints enable dynamic matching between the planned power supply of the DC channel and the load at the receiving end;

[0185]

[0186] To maximize the utilization efficiency of the DC channel in the DC transmission system, the planned DC power is obtained;

[0187]

[0188] In the formula: T is the number of time periods in 1 day, which is 24 in this case; u t The variable is 0-1, where 1 indicates that the DC transmission power is adjusted during time period t, and 0 indicates that the DC transmission power is not adjusted during time period t. t represents the planned DC transmission power for time period t; M represents the coefficient of the large M method; I represents the number of load curves corresponding to the current scenario category; Let be the value of the i-th load curve in time period t; Δt is the time interval per unit time period, which is 1 at this time.

[0189] The power supply confidence probability constraint is:

[0190]

[0191] (ρ (0) ) T z≤1-α

[0192] z n ∈{0,1}, n=1,2,…,N

[0193] In the formula: P t dp P t dw P g,t These represent the power transmitted from photovoltaic, wind power, and thermal power to the DC channel during time period t in scenario n. Let N be the reference probability distribution vector; This indicates that each scenario occurs with the same probability.

[0194] The constraints for curtailment of renewable energy are:

[0195]

[0196] In the formula: μ is the upper limit of the curtailment rate of new energy.

[0197] The improved Grey Wolf optimization algorithm in step three iteratively solves the wind-solar-storage capacity configuration model established in step two as follows:

[0198] S3.1 Input wind and solar power output and receiving-end grid load data, and use the k-means algorithm to generate typical scenarios for grid load data to obtain DC planned power. Establish an upper-level model that considers transient overvoltage constraints and inertia constraints, introduce power transmission confidence probability constraints, and establish a lower-level model.

[0199] S3.2. Initialization of the upper-level population based on Logistic-Tent and opposition learning mechanism: Initialize the capacity configuration scheme of new energy units in the wind-solar-storage DC transmission system, that is, the initial configuration of the capacity of wind turbines, photovoltaic power stations, energy storage power stations and thermal power units, and generate a gray wolf population that satisfies the upper-level constraints.

[0200] S3.3 Lower-level population initialization based on Logistic-Tent and opposition learning mechanism: After the upper-level initialization, the position of the initial gray wolf population that satisfies the upper-level constraints is passed to the lower level, and the lower level performs initial settings for the system operation scheduling strategy according to the configuration information.

[0201] S3.4 Calculation of Lower-Level Fitness Function Value: The lower-level fitness function value is calculated using the objective function of the lower-level model, i.e., the system coordinates and schedules to achieve equilibrium; in iteration number T... 1 Not reached the upper limit T1 ≤T 1 When the maximum value is reached, the population positions are updated. The selection probability P is calculated using the selection operator formula. If P ≥ rand, the positions of α wolves, β wolves, and γ wolves are updated according to the formulas for mutation, crossover, and selection operators. Otherwise, the positions of α wolves, β wolves, and γ wolves are updated according to the initial positions of the gray wolf population generated by the traditional gray wolf algorithm. When the number of iterations T... 1 When the upper limit is reached, the operating cost of thermal power units calculated at the lower level and the cost of curtailment of new energy are passed to the upper level.

[0202] S3.5 Calculation of Upper-Level Fitness Function Value: Calculate the minimum investment cost of the upper-level system using the upper-level objective function formula, and update the population position before the upper limit of the number of iterations is reached; when the number of iterations T... 1 Reaching the upper limit T 1 If the value is maxed out, output the minimum investment cost and the wind-solar-storage unit capacity configuration scheme; otherwise, return to step S3.4 to continue execution.

[0203] The improved gray wolf optimization algorithm in step three is as follows:

[0204] (1) Initial stage of population

[0205] Introducing the formula for creating the initial population using the Logistic-Tent chaotic map:

[0206]

[0207] In the formula: h is the control parameter of the Logistic-Tent chaotic mapping, with a value of [0,4]; modl is the numerical modulo operation; x c x represents the current system state value. c+1 The next state value is obtained after one Logistic-Tent mapping based on the current state;

[0208] The generated results are used to initialize the population location X. k X k The formula is:

[0209] X k =lb+(ub-lb)x c

[0210] In the formula: X k ub is the initialization position for the population; lb is the upper bound of the population solution space; lb is the lower bound of the population solution space.

[0211] (2) Position update based on differential evolution

[0212] The traditional gray wolf algorithm generates the initial position of the gray wolf population, and the formulas for α wolves, β wolves, and γ wolves to hunt prey and update their own positions are as follows:

[0213]

[0214] In the formula: D is the distance between the gray wolf and its prey; C and A are coefficient vectors; t is the current iteration number. Location of prey hunted by alpha, beta, and gamma wolves; X t X1 represents the gray wolf's own position; X2 and X3 represent the updated positions of the α wolf, β wolf, and γ wolf, respectively.

[0215] (3) Individual perturbation operator

[0216]

[0217] In the formula: D op For perturbation operators; Let Euclidean distance be the distance between the positions of gray wolf i and gray wolf j. It is a random quantity uniformly distributed within [-1 / 2, 1 / 2].

[0218] The improved gray wolf optimization algorithm introduces mutation, crossover, and selection operators in the differential mutation process to increase the population size and class richness in the algorithm.

[0219] The mutation operator is:

[0220]

[0221] In the formula: v is the mutated solution; t is the iteration number; x is the solution in the original population; N is the population size; r1, r2, and r3 are random integers in [1, N]; w is the mutation factor that affects the difference between individual solutions;

[0222] The crossover operator is:

[0223]

[0224] In the formula: rand is a random value within [0,1]; C R This represents the individual crossover coefficient.

[0225] The operator is selected as:

[0226]

[0227] In the formula: f(z) is the fitness function of z.

[0228] The probability that the gray wolf chooses a differential evolution strategy to update its position is:

[0229]

[0230] If P ≥ rand, differential evolution is not used to update the position; otherwise, differential mutation is used to update the position.

[0231] Example Demonstration

[0232] To verify the effectiveness of the proposed proportioning method, two planning schemes were established for comparative analysis.

[0233] Option 1: Wind-solar-storage DC transmission system planning scheme without considering transient overvoltage constraints and inertia constraints. Option 2: Wind-solar-storage DC transmission system planning scheme considering transient overvoltage constraints and inertia constraints.

[0234]

[0235] Scheme 2 increases the total installed capacity of new energy sources compared to Scheme 1. Specifically, Scheme 2 increases the installed capacity of wind farms and photovoltaic power generation by 572 MW and 120 MW respectively, while also increasing the total investment cost. Furthermore, Scheme 2 exhibits lower transient overvoltage, with an amplitude of 1.30 pu, 8.25% lower than Scheme 1. Therefore, Scheme 2 effectively reduces the transient overvoltage level after DC faults, ensuring system operational safety. By sacrificing some economic efficiency, Scheme 2 reduces the risk of renewable energy disconnection from the grid while ensuring system safety.

[0236] This invention discloses a wind-solar-storage configuration method considering transient overvoltage constraints and inertia constraints, aiming to solve the problem of unreasonable capacity allocation caused by insufficient voltage support and inertia-frequency support in the operation of high-proportion wind power, photovoltaic, and energy storage systems in large-scale new energy bases. First, based on wind and solar power output and receiving-end load data, k-means clustering is used to obtain typical wind and solar power output scenarios and planned DC transmission power. Second, a two-level programming model is adopted for the optimal capacity configuration of wind-solar-storage. The upper-level model takes minimizing the total investment cost as the optimization objective and constructs transient overvoltage constraints, inertia support constraints, and capacity constraints based on short-circuit capacity. The lower-level model takes the operating cost of thermal power units and the cost of new energy curtailment as objectives, considering power balance constraints and operational constraints of the wind-solar-storage DC transmission system, and introduces transmission confidence probability constraints to measure the transmission reliability of wind, solar, and storage. Finally, an improved gray wolf optimization algorithm is used to solve the wind-solar-storage capacity configuration model. This method significantly improves the planning accuracy, operational reliability, and resource utilization efficiency of new energy transmission systems, providing a reference for the coordinated planning of large-scale new energy bases such as the Gobi Desert and ultra-high voltage direct current transmission lines.

[0237] The above are merely preferred embodiments of the present invention. It should be noted that, for those skilled in the art, other equivalent modifications and improvements can be made based on the technical teachings provided by the present invention, and these should also be considered within the scope of protection of the present invention.

Claims

1. A wind-solar-storage configuration method considering transient voltage constraints and inertia constraints, characterized in that: Includes the following steps: Step 1: Based on wind and solar power output and receiving-end load data, the k-means clustering method is used to obtain the planned DC transmission power corresponding to typical wind-solar-storage load scenarios; Step 2: Establish a two-layer model for optimal capacity configuration of wind-solar-storage. The upper-layer model takes minimizing the total investment cost of the wind-solar-storage system capacity as the optimization objective function, and constructs transient overvoltage constraints, inertia support constraints, and capacity constraints based on short-circuit capacity. The lower-layer model takes the operating cost of thermal power units and the cost of renewable energy curtailment as objectives, considers the power balance constraints and operational constraints of the wind-solar-storage system transmitted via DC transmission, and introduces power transmission confidence probability constraints to measure the reliability of wind-solar-storage transmission. The operational constraints of the wind-solar-storage system transmitted via DC transmission include renewable energy output constraints, thermal power operation constraints, energy storage operation constraints, system reserve capacity constraints, renewable energy output ratio constraints, and grid support constraints. Step 3: In the initial stage of the population optimization algorithm, the Logistic-Tent chaotic mapping is introduced. The improved Gray Wolf Optimization Algorithm is used to iteratively solve the wind-solar-storage capacity configuration model established in Step 2 to obtain the wind-solar-storage capacity configuration scheme.

2. The wind-solar-storage configuration method according to claim 1, characterized in that: The objective function for minimizing the total investment cost of the wind-solar-storage system in the upper-level model is: In the formula: F inv The total investment cost of the wind-solar-storage system is represented by Nw1, Np1, Ng, and Ne, which represent the number of wind farms, photovoltaic power plants, thermal power units, and energy storage power plants, respectively. The investment and construction costs of the wind farms, photovoltaic power plants, thermal power units, and energy storage power plants are represented by Cw, Cp, Cg, and Ce, respectively. This represents the typical daily operating cost of a wind-solar-storage system.

3. The wind-solar-storage configuration method according to claim 1, characterized in that: In step one, the sending-end system of the wind-solar-storage DC transmission system must meet the inertia support constraint condition as follows: In the formula: g represents the number of thermal power plants; Nw1, Np1, Ng, and Ne represent the number of wind farms, photovoltaic power plants, thermal power units, and energy storage power plants, respectively; P w_e,w1,i P represents the equivalent output power of the w1-th wind farm. p_e,p1,i P represents the equivalent output power of the p1th photovoltaic power station; e_e,e,n P represents the equivalent output power of the e-th energy storage power station. g,t H represents the output power of thermal power unit g during time period t. w,w1,t Let H be the virtual synchronous inertia of the w1-th wind farm. p,p1,t Let H be the virtual synchronous inertia of the p1th photovoltaic power station. e,e,t Let H be the virtual synchronous inertia of the e-th energy storage power station. g,t H is the synchronous inertia of the g-th thermal power unit; demand,t The inertia requirement for the wind-solar-storage DC power transmission system; In step one, the sending-end system of the wind-solar-storage DC transmission system must meet the transient voltage constraint based on short-circuit capacity as follows: In the formula: k ST Short-circuit capacity provided for thermal power plants; S T The short-circuit capacity provided by thermal power at the converter bus on the rectifier side; p aw_total,i This represents the equivalent operating capacity of a wind farm that takes into account the confidence level of wind power generation; p ap_total,j This refers to the equivalent operating capacity of a photovoltaic power plant that takes into account the confidence level of photovoltaic power generation; p ae_total,n The equivalent operating capacity of energy storage considering the energy storage confidence level; Z p_L,i Z is the equivalent impedance from the wind farm to the rectifier-side converter bus; p_L,j Z represents the equivalent impedance from the photovoltaic power station to the rectifier-side converter bus; p_L,n k is the equivalent impedance from the energy storage power station to the rectifier-side converter bus. Sw,i k is the ratio of the short-circuit capacity provided by the wind turbine at the grid connection point to its installed capacity. Sw,j k is the ratio of the short-circuit capacity provided by the photovoltaic power plant at the grid connection point to its installed capacity. Sw,n p is the ratio of the short-circuit capacity provided by the energy storage power station at the grid connection point to its installed capacity. T_total N represents the total installed capacity of thermal power generation; Nw represents the number of wind farms; Np represents the number of photovoltaic power plants; and Ne represents the number of energy storage power plants.

4. The wind-solar-storage configuration method according to claim 1, characterized in that: After introducing transient voltage constraints and inertia support constraints into the upper-level model, the capacity constraints for wind farms, photovoltaic power plants, and energy storage power plants are as follows: In the formula: c w,min c w,max These represent the lower and upper limits of wind power installed capacity, respectively; c p,min c p,max These represent the lower and upper limits of photovoltaic installed capacity, respectively; c g,min c g,max These represent the lower and upper limits of thermal power installed capacity, respectively; c e,min c e,max These represent the lower and upper limits of energy storage installed capacity, respectively; p w p p p T p e These refer to the capacities of individual wind power, solar power, thermal power, and energy storage, respectively. The capacity constraints of a DC transmission system are: In the formula: P w_e,i P represents the equivalent output power of the wind farm. p_e,i P represents the equivalent output power of a photovoltaic power station. e_e,n k is the equivalent output power of the energy storage power station. w_e k represents the average output level of the wind farm. p_e k represents the average output level of a photovoltaic power plant. e_e p represents the average output level of the energy storage power station. w_total,i p p_total,j p e_total,n This refers to the total installed capacity of wind farms, photovoltaic power stations, and energy storage power stations. In the formula: P dN This is the rated DC transmission power.

5. The wind-solar-storage configuration method according to claim 1, characterized in that: The objective function of the lower-level model is: In the formula: C g,t Let g be the power generation cost of thermal power unit g in time period t; ag, bg, and cg are the power generation cost coefficients of the thermal power unit; P g,t Let g be the output of thermal power unit g during time period t; The start-up and shutdown cost of thermal power unit g during time period t; and These are the start-up cost and shutdown cost of thermal power unit g, respectively; and These represent the start-up and shutdown state variables of thermal power unit g during time period t; N T λ represents the number of time periods in the grid support balance constraint dispatch cycle, with a value of 168; λ is the renewable energy curtailment penalty cost coefficient. P t dw P is the total dispatch output of the wind farm during time period t; t dp To provide total dispatch output for photovoltaic power plants during time period t; Let be the predicted power of wind turbine i in time period t; Let represent the predicted power of photovoltaic power station i during time period t.

6. The wind-solar-storage configuration method according to claim 1, characterized in that: The power balance constraint condition is: In the formula: P t s P represents the energy storage output power during time period t. t HVDC P represents the output power of the DC transmission system during time period t. t sup The supporting power provided by the AC power grid during time period t; P t dw P is the total dispatch output of the wind farm during time period t; t dp For the total dispatch output of the photovoltaic power station during time period t; P g,t The output of thermal power unit i during time period t; The constraints on the output of new energy sources are: In the formula: Let be the predicted power of wind turbine i in time period t; Let be the predicted power of photovoltaic power station i in time period t; The operating constraints for thermal power plants are: u g,t Q g,min ≤P g,t ≤u g,t Q g,max In the formula: P g,max P g,min These are the upper and lower limits of the active power output of thermal power unit g, respectively; u g,t This represents the operating status of thermal power unit g during time period t; In the formula: ΔT represents the downward and upward ramp rates of thermal power unit g, respectively; ΔT is the dispatch time interval. In the formula: and For thermal power unit g, the upward and downward reserve capacity during time period t; The constraints for energy storage operation are: In the formula: E s,t This refers to the state of charge of the energy storage power station. and These are the upper and lower limits of the power capacity of the energy storage power station, respectively. η c and η d For energy storage charging and discharging efficiency; P t s,c and P t s,d These represent the charging power and discharging power of the energy storage power station during time period t, respectively. In the formula: For the charging state variables of the energy storage power station; For the discharge state variables of the energy storage power station; This represents the maximum charging power of the energy storage power station. This represents the maximum discharge power of the energy storage power station. In the formula: Upward backup capacity provided for energy storage power stations; Downward backup capacity provided for energy storage power stations; The system's reserve capacity constraints are as follows: In the formula: r t up and r t dn These represent the upward and downward reserve capacities of the system during time period t, respectively. The constraint on the proportion of new energy power output is: In the formula: δ represents the required proportion of new energy sources; The power grid support constraints are: In the formula: P t HVDC P represents the output power of the DC transmission system during time period t. t sup The supporting power provided to the AC power grid during time period t; ξ is the grid support coefficient (the ratio of the upper limit of grid support power to the planned DC transmission power); N T The number of time periods in the grid support balance constraint dispatch cycle is set to 168. Based on the K-means clustering algorithm, several typical load scenarios were identified, and corresponding high-voltage direct current (HVDC) transmission schemes were designed for each scenario. The planned power constraints for HVDC transmission corresponding to the typical wind-solar-storage load scenarios are as follows: This indicates that the daily DC transmission power adjustment frequency does not exceed two times; To ensure continuous operation during the day, the transmission power must be consistent at the beginning and end of the day, as shown below; Power switching constraints are specified: when ut = 0, the planned power transmission capacity for time period t remains constant; Power balance constraints enable dynamic matching between the planned power supply of the DC channel and the load at the receiving end; To maximize the utilization efficiency of the DC channel in the DC transmission system, the planned DC power is obtained; In the formula: T is the number of time periods in 1 day, which is 24 in this case; u t The variable is 0-1, where 1 indicates that the DC transmission power is adjusted during time period t, and 0 indicates that the DC transmission power is not adjusted during time period t. t represents the planned DC transmission power for time period t; M is the coefficient of the large M method; I represents the number of load curves corresponding to the current scene category; Let be the value of the i-th load curve in time period t; Δt is the unit time interval, which is 1 at this time; The power supply confidence probability constraint is: (r (0) ) T z≤1-α z n ∈{0,1},n=1,2,…,N In the formula: P t dp P t dw P g,t These represent the power transmitted from photovoltaic, wind power, and thermal power to the DC channel during time period t in scenario n. Let N be the reference probability distribution vector; This indicates that each scenario occurs with the same probability. The constraints for curtailment of renewable energy are: In the formula: μ is the upper limit of the curtailment rate of new energy.

7. The wind-solar-storage configuration method according to claim 1, characterized in that: The improved Grey Wolf optimization algorithm in step three iteratively solves the wind-solar-storage capacity configuration model established in step two as follows: S3.1 Input wind and solar power output and receiving-end grid load data, and use the k-means algorithm to generate typical scenarios for grid load data to obtain DC planned power. Establish an upper-level model that considers transient overvoltage constraints and inertia constraints, introduce power transmission confidence probability constraints, and establish a lower-level model. S3.

2. Initialization of the upper-level population based on Logistic-Tent and opposition learning mechanism: Initialize the capacity configuration scheme of new energy units in the wind-solar-storage DC transmission system, that is, the initial configuration of the capacity of wind turbines, photovoltaic power stations, energy storage power stations and thermal power units, and generate a gray wolf population that satisfies the upper-level constraints. S3.3 Lower-level population initialization based on Logistic-Tent and opposition learning mechanism: After the upper-level initialization, the position of the initial gray wolf population that satisfies the upper-level constraints is passed to the lower level, and the lower level performs initial settings for the system operation scheduling strategy according to the configuration information. S3.4 Calculation of Lower-Level Fitness Function Value: The lower-level fitness function value is calculated using the objective function of the lower-level model, i.e., the system coordinates and schedules to achieve equilibrium; in iteration number T... 1 Not reached the upper limit T 1 ≤T 1 When the maximum value is reached, the population positions are updated. The selection probability P is calculated using the selection operator formula. If P ≥ rand, the positions of α wolves, β wolves, and γ wolves are updated according to the formulas for mutation, crossover, and selection operators. Otherwise, the positions of α wolves, β wolves, and γ wolves are updated according to the initial positions of the gray wolf population generated by the traditional gray wolf algorithm. When the number of iterations T... 1 When the upper limit is reached, the operating cost of thermal power units calculated at the lower level and the cost of curtailment of new energy are passed to the upper level. S3.5 Calculation of Upper-Level Fitness Function Value: Calculate the minimum investment cost of the upper-level system using the upper-level objective function formula, and update the population position before the upper limit of the number of iterations is reached; when the number of iterations T... 1 Reaching the upper limit T 1 If the value is maxed out, output the minimum investment cost and the wind-solar-storage unit capacity configuration scheme; otherwise, return to step S3.4 to continue execution.

8. The wind-solar-storage configuration method according to claim 1, characterized in that: The improved gray wolf optimization algorithm in step three is as follows: (1) In the initial stage of the population, the formula for creating the initial population by introducing the Logistic-Tent chaotic mapping is introduced: In the formula: h is the control parameter of the Logistic-Tent chaotic mapping, with a value of [0,4]; modl is the numerical modulo operation; x c x represents the current system state value. c+1 The next state value is obtained after one Logistic-Tent mapping based on the current state; The generated results are used to initialize the population location X. k X k The formula is: X k =lb+(ub-lb)x c In the formula: X k ub is the initialization position for the population; lb is the upper bound of the population solution space; lb is the lower bound of the population solution space. (2) Position update based on differential evolution The traditional gray wolf algorithm generates the initial position of the gray wolf population, and the formulas for α wolves, β wolves, and γ wolves to hunt prey and update their own positions are as follows: In the formula: D is the distance between the gray wolf and its prey; C and A are coefficient vectors; t is the current iteration number. Location of prey hunted by alpha, beta, and gamma wolves; X t X1 represents the gray wolf's own position; X2 and X3 represent the updated positions of the α wolf, β wolf, and γ wolf, respectively. (3) Individual perturbation operator In the formula: D op For perturbation operators; Let Euclidean distance be the distance between the positions of gray wolf i and gray wolf j. It is a random quantity uniformly distributed within [-1 / 2, 1 / 2].

9. The wind-solar-storage configuration method according to claim 8, characterized in that: The improved gray wolf optimization algorithm introduces mutation operators, crossover operators, and selection operators in the differential mutation process to increase the population size and class richness in the algorithm. The mutation operator is: In the formula: v is the mutated solution; t is the iteration number; x is the solution in the original population; N is the population size; r1, r2, and r3 are random integers in [1, N]; w is the mutation factor that affects the difference between individual solutions; The crossover operator is: In the formula: rand is a random value within [0,1]; C R This represents the individual crossover coefficient. The operator is selected as: In the formula: f(z) is the fitness function of z. The probability that the gray wolf chooses a differential evolution strategy to update its position is: If P ≥ rand, differential evolution is not used to update the position; otherwise, differential mutation is used to update the position.

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