A loess mountain landscape and field power distribution method of collecting circuit

Abstract

The present application relates to the field of new energy power system control, and specifically discloses a method for power coordination and distribution of loess mountain wind-solar co-located power transmission line, comprising: synchronously collecting multi-source data of wind-solar operation, meteorological topography, power grid and loess settlement water content; calculating topographic shading coefficient to correct wind-solar output prediction; based on settlement displacement and water content, dynamically correcting line impedance to inversely calculate real-time safety current-carrying threshold; taking line current-carrying, grid-connected point voltage and external power transmission as hard constraints, and taking minimum network loss and minimum abandoned power as targets to solve active and reactive power distribution instructions; issuing to each unit for collaborative execution, and iteratively optimizing control weight through closed-loop verification. The present application solves the problem that the existing method is not adapted to loess mountain topographic shading and collapsible loess settlement disturbance, which is easy to cause line overload and voltage out-of-limit, improves the utilization rate and voltage stability of mountain wind-solar line, reduces the network loss of the station, and adapts to loess gully wind-solar co-location and extra-high voltage external transmission scenarios.

Description

Technical Field

[0001] This invention relates to the field of new energy power system control technology, specifically a method for coordinated power distribution of wind and solar power collection lines in loess mountainous areas. Background Technology

[0002] With the large-scale development of new energy power generation in my country, integrated wind and solar power development has become the mainstream model for cost reduction and efficiency improvement in mountainous new energy projects. In the loess gully mountainous landscape, due to the undulating terrain, collapsible loess geology, and land acquisition limitations, sharing a 35kV power collection line between wind turbines and photovoltaic power can significantly reduce the amount of civil engineering excavation, lower land acquisition costs, and reduce vegetation damage. As integrated energy projects with supporting ultra-high voltage transmission channels continue to be implemented, higher requirements are placed on the safe operation and efficient utilization of the power collection line.

[0003] Currently, conventional power distribution methods for wind and solar co-generation lines are mostly designed for flat plains and rely primarily on fixed line nominal parameters. They achieve line safety management through simple output ratio allocation and overload current limiting protection. Some optimized schemes introduce ultra-short-term wind and solar power output prediction and single-unit reactive power regulation to improve operational economy. However, when these methods are directly applied to loess gully and mountainous terrain, they suffer from several technical shortcomings: First, the terrain adaptability is insufficient, resulting in large deviations in power output prediction. In the loess gully region, mountains are scattered, and wind turbines are affected by valley wind shear and turbulent disturbances, leading to power output fluctuations and frequencies far exceeding those in plain wind fields. Slope photovoltaic systems are affected by mountain shading and differences in slope orientation, resulting in uneven spatial and temporal distribution of irradiance. Existing general power output prediction models do not incorporate terrain shading correction coefficients based on mountain digital elevation data, leading to generally high prediction errors. This easily causes line overload tripping during peak daytime photovoltaic and nighttime wind power periods, while line capacity is wasted during low-load periods, hindering efficient utilization of line capacity.

[0004] Secondly, the geological disturbance of collapsible loess was not considered, and the safety boundary of the line was not solidified. Cable trenches in collapsible loess areas are prone to erosion by rainwater and slope settlement due to the weight of the soil. Accompanying this are dynamic changes in soil moisture content, cable burial depth shifts, and continuous changes in surrounding heat dissipation conditions, leading to a dynamic decrease in the actual safe current-carrying capacity of the cable. Existing methods generally use fixed nominal impedance and constant current-carrying threshold, which cannot dynamically adjust the safety boundary according to geological conditions: if the threshold is set too conservatively, it will significantly limit the transmission capacity of the line; if the threshold is set too high, it will fail to identify the hidden overload risk caused by settlement, which can easily lead to long-term overheating operation of the cable, accelerated insulation aging, and even burn-out failure.

[0005] Third, the decoupling of active and reactive power regulation leads to insufficient voltage stability. Existing power distribution schemes mostly focus only on current-limiting active power allocation, failing to utilize the reactive power margins of wind turbines and photovoltaic inverters to provide overall voltage support. Mountainous power collection lines are generally long and have high impedance, resulting in significant voltage deviation at the end of the lines. Large fluctuations in wind and solar power output can easily cause voltage exceedances at the grid connection point, affecting not only the power quality of the power plants but also the operational stability of the supporting ultra-high-voltage transmission channels.

[0006] Fourth, the optimization objectives are singular and difficult to adapt to the requirements of power transmission dispatch. Most existing control methods only take the prevention of line overload as the sole constraint objective, without incorporating the power dispatch instructions at the UHV transmission gateway and the optimization of network losses across the entire substation area into the optimization framework. This makes it impossible to simultaneously meet the multiple requirements of line safety, power transmission compliance, and economical operation, and is incompatible with the substation positioning for cross-regional UHV power transmission in the Loess Mountains.

[0007] Therefore, there is an urgent need for a power coordination and allocation method for wind and solar co-current power collection lines that can adapt to the terrain and geological characteristics of loess gullies and mountains, and take into account line safety, voltage stability and economic operation, so as to solve the technical problems of high line overload risk, low capacity utilization and insufficient voltage support capacity in mountainous scenarios. Summary of the Invention

[0008] To address the aforementioned problems in the prior art, this invention provides a method for coordinated power distribution of power collection lines for both mountainous and scenic landscapes. This method solves the problems of existing methods not being adapted to the terrain shading and subsidence disturbances of loess hills, which can easily lead to line overload and voltage exceeding limits. This method improves the utilization rate and voltage stability of mountainous and scenic power collection lines.

[0009] To achieve the above objectives, this invention proposes a method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas, comprising the following steps: S1. Real-time acquisition and preprocessing of multi-source data: Simultaneously acquire operating data of wind turbines and photovoltaics, meteorological and topographic data, power collection lines and grid connection points, and geological data of loess settlement displacement and soil moisture content along cable trenches; S2. Wind and solar power output prediction correction based on terrain shading coefficient: The wind shear shading coefficient of each wind turbine and the slope shading coefficient of each photovoltaic string are calculated according to the digital elevation model of the station. The original ultra-short-term power output prediction value is corrected to obtain the power output prediction sequence adapted to the loess gully terrain. S3. Dynamic update of line parameters based on loess settlement: Based on real-time settlement displacement and soil moisture content, the impedance per unit length of the collector line is dynamically corrected to obtain the corrected impedance per unit length of the collector line, and the real-time dynamic safe current carrying threshold is calculated in reverse. S4. Use the objective function of multi-objective optimization to solve the multi-constraint multi-objective power allocation optimization: take the line current not exceeding the dynamic safe current carrying threshold, the grid connection point voltage being qualified, and the external power supply meeting the dispatch instructions as hard constraints, and take the minimum line loss and the minimum wind and solar curtailment rate as optimization objectives to solve for the active and reactive power allocation instructions of each unit. S5. Power command issuance and coordinated execution for each device: Active power and reactive power commands are broken down and issued to each wind turbine and photovoltaic inverter, and the wind turbines synchronously correct the maximum power point tracking power limit. S6. Closed-loop verification and adaptive iteration of operating conditions: Collect execution results to form error feedback, identify the current operating conditions and switch optimization weights, and iteratively correct the shading coefficient and impedance model parameters.

[0010] Preferably, in step S2, the calculation formulas for the wind shear shading coefficient of the wind turbine and the slope shading coefficient of each photovoltaic string are as follows: ; ; In the formula, The preset wind shear loss coefficient, The elevation difference between the wind turbine location and the surrounding mountains that provide cover. The height of the wind turbine hub. The slope angle of the photovoltaic module. The effective irradiance incident angle deviation is caused by the mountain's shielding.

[0011] Preferably, in S3, the corrected impedance per unit length of the collector line The calculation formula is: ; In the formula, The nominal impedance per unit length of the line. This represents the loess settlement and displacement in the cable trench. The soil moisture content, , This is the preset impedance attenuation coefficient.

[0012] Preferably, in S3, the dynamic safe current carrying threshold Based on the corrected impedance The maximum allowable operating temperature of the cable is obtained by inverse calculation using the cable heat balance equation, and the expression is: ; In the formula, The maximum permissible temperature of the cable conductor. The ambient temperature of the surrounding soil. The thermal resistance per unit length of cable, This is the corrected AC resistance per unit length of conductor.

[0013] Preferably, in step S4, the objective function for multi-objective optimization is: ; In the formula, F is the comprehensive optimization objective value. The total active power loss of the collector line. This represents the total curtailment of wind and solar power. , Let be the weighting coefficient, satisfying + =1.

[0014] Preferably, in step S4, the corresponding hard constraint condition expression is: ; In the formula, This represents the real-time current of the collector line. The voltage at the grid connection point. ,respectively These are the upper and lower limits of the allowable voltage. This represents the actual power output at the export gateway. The power transmission command is given to the scheduler.

[0015] Preferably, in S5, the allocation of active power follows the following rules: priority is given to ensuring full photovoltaic power consumption, and the remaining line capacity is allocated to the active power output of wind turbines; on the wind turbine side, the increase or decrease instructions are allocated proportionally according to the proportion of the adjustable active power margin of each unit.

[0016] Preferably, in S5, the reactive power distribution follows the following rules: the reactive power compensation is distributed according to the voltage gradient of the collector line, the units at the end of the line take priority in undertaking the task of increasing reactive power, and the proportion of reactive power output increases step by step from the beginning to the end of the line to achieve uniform voltage support throughout the line.

[0017] Preferably, in step S6, the operating conditions are divided into three categories: daytime photovoltaic-dominated operating conditions, nighttime wind power-dominated operating conditions, and high wind and turbulence operating conditions; the weight coefficients of the objective function are automatically adjusted under different operating conditions. , During daytime operating conditions, the power curtailment weight is reduced, while during windy and turbulent operating conditions, the voltage stability constraint priority is increased.

[0018] Preferably, in step S6, a rolling time-domain model predictive control algorithm is used for closed-loop verification and adaptive iteration of operating conditions. The prediction time domain is set to 5 minutes, and the control cycle is set to 100 ms. Every 5 minutes, the shielding coefficient and impedance attenuation coefficient are corrected in reverse according to the execution error. The weight coefficient is iteratively optimized by statistically analyzing the operating condition duration daily.

[0019] Therefore, this invention proposes a method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas, with the following beneficial effects: (1) The introduction of terrain shading coefficient to correct the wind and solar power output prediction is adapted to the characteristics of loess gully mountain shading and wind shear, which effectively reduces the prediction deviation of mountain power output, avoids the problem of wind and solar peak shifting and overload, and significantly improves the capacity utilization rate of the collection line. (2) Based on the dynamic correction of line impedance and safe current carrying threshold by loess settlement displacement and moisture content, the limitation of fixed current carrying limit is broken. While avoiding the hidden overload risk caused by settlement and ensuring the safe operation of the cable, the transmission capacity of the line is fully released. (3) Construct a multi-constraint, multi-objective active and reactive power collaborative optimization framework to simultaneously meet the requirements of line safety, voltage stability and UHV transmission dispatch, effectively reduce the network loss of the station, and improve the operation economy and grid adaptability of the mountain wind and solar power station.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall process of the present invention, which describes a method for coordinated power distribution of power collection lines in the same field for landscape and solar power in loess hills. Figure 2 This is a multi-source data hierarchical acquisition architecture diagram of a power coordination and allocation method for power collection lines in the same field of loess mountain scenery according to the present invention; Figure 3 This is a multi-constraint, multi-objective optimization solution logic diagram for a power coordination and allocation method for simultaneous power collection lines in loess mountainous areas, as proposed in this invention. Detailed Implementation

[0022] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below. The described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0023] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0024] like Figures 1-3 As shown, the present invention provides a method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas, comprising the following steps: Includes the following steps: S1. Real-time acquisition and preprocessing of multi-source data: Simultaneously acquire operating data of wind turbines and photovoltaics, meteorological and topographic data, power collection lines and grid connection points, and geological data of loess settlement displacement and soil moisture content along cable trenches; S2. Wind and solar power output prediction correction based on terrain shading coefficient: The wind shear shading coefficient of each wind turbine and the slope shading coefficient of each photovoltaic string are calculated according to the digital elevation model of the station. The original ultra-short-term power output prediction value is corrected to obtain the power output prediction sequence adapted to the loess gully terrain. The calculation formulas for the wind shear shading coefficient of the wind turbine and the slope shading coefficient of each photovoltaic string are as follows: ; ; In the formula, The preset wind shear loss coefficient, The elevation difference between the wind turbine location and the surrounding mountains that provide cover. The height of the wind turbine hub. The slope angle of the photovoltaic module. The effective irradiance incident angle deviation is caused by the mountain's shielding.

[0025] S3. Dynamic update of line parameters based on loess settlement: Based on real-time settlement displacement and soil moisture content, the impedance per unit length of the collector line is dynamically corrected to obtain the corrected impedance per unit length of the collector line, and the real-time dynamic safe current carrying threshold is calculated in reverse. Corrected impedance per unit length of collector line The calculation formula is: ; In the formula, The nominal impedance per unit length of the line. This represents the loess settlement and displacement in the cable trench. The soil moisture content, , This is the preset impedance attenuation coefficient.

[0026] Dynamic safety current carrying threshold Based on the corrected impedance The maximum allowable operating temperature of the cable is obtained by inverse calculation using the cable heat balance equation, and the expression is: ; In the formula, The maximum permissible temperature of the cable conductor. The ambient temperature of the surrounding soil. The thermal resistance per unit length of cable, This is the corrected AC resistance per unit length of conductor.

[0027] S4. Use the objective function of multi-objective optimization to solve the multi-constraint multi-objective power allocation optimization: take the line current not exceeding the dynamic safe current carrying threshold, the grid connection point voltage being qualified, and the external power supply meeting the dispatch instructions as hard constraints, and take the minimum line loss and the minimum wind and solar curtailment rate as optimization objectives to solve for the active and reactive power allocation instructions of each unit. Objective function for multi-objective optimization: ; In the formula, F is the comprehensive optimization objective value. The total active power loss of the collector line. This represents the total curtailment of wind and solar power. , Let be the weighting coefficient, satisfying + =1.

[0028] The corresponding hard constraint expression is: ; In the formula, This represents the real-time current of the collector line. The voltage at the grid connection point. ,respectively These are the upper and lower limits of the allowable voltage. This represents the actual power output at the export gateway. The power transmission command is given to the scheduler.

[0029] S5. Power command issuance and coordinated execution for each device: Active power and reactive power commands are broken down and issued to each wind turbine and photovoltaic inverter, and the wind turbines synchronously correct the maximum power point tracking power limit. The allocation of active power follows these rules: priority is given to ensuring full consumption of photovoltaic power, and the remaining line capacity is allocated to the active power output of wind turbines; on the wind turbine side, the increase or decrease instructions are allocated proportionally according to the proportion of the adjustable active power margin of each unit.

[0030] The reactive power distribution follows these rules: reactive power compensation is distributed according to the voltage gradient of the collector line, with the units at the end of the line taking priority in reactive power generation. The proportion of reactive power output increases gradually from the beginning to the end of the line to achieve uniform voltage support across the entire line.

[0031] S6. Closed-loop verification and adaptive iteration of operating conditions: Collect execution results to form error feedback, identify the current operating conditions and switch optimization weights, and iteratively correct the shading coefficient and impedance model parameters.

[0032] Operating conditions are divided into three categories: daytime photovoltaic-dominated operating condition, nighttime wind power-dominated operating condition, and high wind and turbulence operating condition; the weighting coefficients of the objective function are automatically adjusted under different operating conditions. , During daytime operating conditions, the power curtailment weight is reduced, while during windy and turbulent operating conditions, the voltage stability constraint priority is increased.

[0033] A rolling time-domain model predictive control algorithm is used for closed-loop verification and adaptive iteration of operating conditions. The prediction time domain is set to 5 minutes and the control cycle is set to 100 ms. Every 5 minutes, the shielding coefficient and impedance attenuation coefficient are corrected in reverse based on the execution error. The weight coefficient is iteratively optimized by statistically analyzing the operating condition duration daily.

[0034] This invention takes a new energy power station in a loess gully mountainous area that shares a 35kV collector line with both solar and wind power as an example. This embodiment utilizes real-time data collected from the station's existing operation monitoring system, meteorological and topographic monitoring devices, and cable trench geological settlement monitoring units. Combined with the station's collector line topology and grid dispatch constraints, it verifies the implementation process and operational effectiveness of a power coordination allocation method for power collection lines in a loess mountainous area with both solar and wind power. The specific implementation process is as follows: The first step is multi-source data acquisition and preprocessing. Simultaneously, operational data from wind turbines and slope photovoltaic units within the site, meteorological and topographic monitoring data from the site, power grid operation data from collection lines and grid connection points, and geological data on loess settlement displacement and soil moisture content along the cable trenches are collected. All collected data are filtered to remove abnormal fluctuations before being uniformly sent to the site's central controller for subsequent calculations.

[0035] The second step is to calculate the terrain shading coefficient and correct the power output prediction. The digital elevation model of the wind turbine is used to calculate the valley wind shear shading coefficient for each wind turbine and the slope mountain shading coefficient for each photovoltaic string. The two types of shading coefficients are substituted into the original ultra-short-term wind and solar power output prediction model, and the original prediction values ​​are corrected for losses to obtain a wind and solar power output prediction sequence adapted to the loess gully terrain.

[0036] The third step is to dynamically update the line parameters. Based on the real-time collected settlement displacement and soil moisture content data, the preset settlement-impedance mapping model is invoked to dynamically correct the impedance per unit length of the collector line. Based on the corrected line impedance and cable thermal balance characteristics, the real-time dynamic safe current-carrying threshold is calculated and used as the core safety constraint boundary for subsequent power distribution.

[0037] The fourth step is to optimize the power allocation under multiple constraints and objectives. Hard constraints include ensuring that the real-time current of the collector lines does not exceed the dynamic safe current-carrying threshold, the grid connection voltage is within the acceptable range, and the power transmitted to the grid meets the grid dispatch instructions. A multi-objective optimization function is constructed that balances minimizing active power loss in the collector lines and minimizing wind and solar power curtailment. A rolling time-domain optimization algorithm is used for iterative solution to obtain the active and reactive power allocation instructions for each wind turbine and photovoltaic unit.

[0038] The fifth step is the issuance and coordinated execution of power commands. The obtained active and reactive power commands are broken down and issued to each wind turbine converter and photovoltaic inverter. Active power allocation prioritizes photovoltaic power consumption, and the remaining line capacity is allocated to wind turbine active power. The power limit of maximum power point tracking is adjusted synchronously on the wind turbine side. Reactive power allocation is arranged along the line voltage gradient, and the units at the end of the line take priority in undertaking the task of increasing reactive power generation, so as to achieve uniform voltage support throughout the line.

[0039] Step 6: Closed-loop verification and adaptive iteration based on operating conditions. Real-time data collection of actual operating results for line current, node voltage, and transmitted power is used to compare with the optimized target values ​​and generate error feedback. Three operating conditions are automatically identified: daytime photovoltaic-dominated, nighttime wind-dominated, and high-wind turbulence, with corresponding adjustments to the weighting coefficients of the multi-objective function. Simultaneously, the shading coefficient and impedance attenuation model parameters are corrected in reverse at fixed intervals to continuously improve the method's adaptability to site operating conditions.

[0040] In summary, this embodiment, through the above-mentioned closed-loop control of the entire process, can effectively adapt to the terrain and geological characteristics of the loess gully mountains, improve the line utilization efficiency while ensuring the safe operation of the collection line, improve the voltage quality and operating economy of the station, and meet the engineering application requirements of wind and solar power transmission with ultra-high voltage.

[0041] Therefore, this invention provides a method for coordinated power allocation of power collection lines in loess mountainous areas with both solar and wind power, addressing the shortcomings of existing technologies that are difficult to adapt to loess gully mountainous scenarios. It overcomes technical weaknesses such as large output prediction deviations caused by terrain shading, dynamic attenuation of line current-carrying capacity due to loess subsidence, and insufficient voltage stability in active and reactive power decoupling control. This method effectively improves the capacity utilization of mountainous solar and wind power lines, avoids hidden overload risks, reduces substation network losses, ensures stable voltage at grid connection points, and meets the operational requirements of ultra-high voltage transmission channels by correcting output predictions based on terrain shading, dynamically updating the line's safe current-carrying threshold, and collaboratively optimizing active and reactive power allocation through multi-constraint and multi-objective mechanisms.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for coordinated power distribution of power collection lines for both landscape and solar power in loess mountain areas, characterized in that, Includes the following steps: S1. Real-time acquisition and preprocessing of multi-source data: Simultaneously acquire operating data of wind turbines and photovoltaics, meteorological and topographic data, power collection lines and grid connection points, and geological data of loess settlement displacement and soil moisture content along cable trenches; S2. Wind and solar power output prediction correction based on terrain shading coefficient: The wind shear shading coefficient of each wind turbine and the slope shading coefficient of each photovoltaic string are calculated according to the digital elevation model of the station. The original ultra-short-term power output prediction value is corrected to obtain the power output prediction sequence adapted to the loess gully terrain. S3. Dynamic update of line parameters based on loess settlement: Based on real-time settlement displacement and soil moisture content, the impedance per unit length of the collector line is dynamically corrected to obtain the corrected impedance per unit length of the collector line, and the real-time dynamic safe current carrying threshold is calculated in reverse. S4. Use the objective function of multi-objective optimization to solve the multi-constraint multi-objective power allocation optimization: take the line current not exceeding the dynamic safe current carrying threshold, the grid connection point voltage being qualified, and the external power supply meeting the dispatch instructions as hard constraints, and take the minimum line loss and the minimum wind and solar curtailment rate as optimization objectives to solve for the active and reactive power allocation instructions of each unit. S5. Power command issuance and coordinated execution for each device: Active power and reactive power commands are broken down and issued to each wind turbine and photovoltaic inverter, and the wind turbines synchronously correct the maximum power point tracking power limit. S6. Closed-loop verification and adaptive iteration of operating conditions: Collect execution results to form error feedback, identify the current operating conditions and switch optimization weights, and iteratively correct the shading coefficient and impedance model parameters.

2. The method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas according to claim 1, characterized in that, In S2, the calculation formulas for the wind shear shading coefficient of the wind turbine and the slope shading coefficient of each photovoltaic string are as follows: ； ； In the formula, The preset wind shear loss coefficient, The elevation difference between the wind turbine location and the surrounding mountains that provide shade. The height of the wind turbine hub. The slope angle of the photovoltaic module. The effective irradiance incident angle deviation is caused by the mountain's shielding.

3. The method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas according to claim 2, characterized in that, In S3, the corrected impedance per unit length of the collector line The calculation formula is: ； In the formula, The nominal impedance per unit length of the line. This represents the loess settlement and displacement in the cable trench. The soil moisture content, , This is the preset impedance attenuation coefficient.

4. The method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas according to claim 3, characterized in that, In S3, the dynamic safe current carrying threshold Based on the corrected impedance The maximum allowable operating temperature of the cable is obtained by inverse calculation using the cable heat balance equation, and the expression is: ； In the formula, The maximum permissible temperature of the cable conductor. The ambient temperature of the surrounding soil. The thermal resistance per unit length of cable, This is the corrected AC resistance per unit length of conductor.

5. The method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas according to claim 4, characterized in that, In S4, the objective function for multi-objective optimization is: ； In the formula, F is the comprehensive optimization objective value. The total active power loss of the collector line. This represents the total curtailment of wind and solar power. , Let be the weighting coefficient, satisfying + =1.

6. The method for coordinated power allocation of a power collection line for both wind and solar power in loess hilly areas according to claim 5, characterized in that, In S4, the corresponding hard constraint expression is: ； In the formula, This represents the real-time current of the collector line. The voltage at the grid connection point. ,respectively These are the upper and lower limits of the allowable voltage. This represents the actual power output at the export gateway. The power transmission command is given to the scheduler.

7. A method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas according to claim 6, characterized in that, In S5, the allocation of active power follows these rules: priority is given to ensuring full photovoltaic power consumption, and the remaining line capacity is allocated to the active power output of wind turbines; on the wind turbine side, the increase or decrease instructions are allocated proportionally according to the proportion of the adjustable active power margin of each unit.

8. A method for coordinated power distribution of a co-current power collection line for wind and solar power in loess hilly areas according to claim 7, characterized in that, In S5, the reactive power distribution follows the following rules: reactive power compensation is distributed according to the voltage gradient of the collector line, the units at the end of the line take priority in undertaking the task of increasing reactive power, and the proportion of reactive power output increases step by step from the beginning to the end of the line to achieve uniform voltage support throughout the line.

9. A method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas according to claim 8, characterized in that, In S6, the operating conditions are divided into three categories: daytime photovoltaic-dominated operating condition, nighttime wind power-dominated operating condition, and high wind and turbulence operating condition; the weight coefficients of the objective function are automatically adjusted under different operating conditions. , During daytime operating conditions, the power curtailment weight is reduced, while during windy and turbulent operating conditions, the voltage stability constraint priority is increased.

10. A method for coordinated power allocation of power collection lines for both wind and solar power in loess mountainous areas according to claim 9, characterized in that, In S6, a rolling time-domain model predictive control algorithm is used for closed-loop verification and adaptive iteration of operating conditions. The prediction time domain is set to 5 minutes and the control cycle is set to 100 ms. Every 5 minutes, the shielding coefficient and impedance attenuation coefficient are corrected in reverse according to the execution error. The weight coefficient is iteratively optimized by statistically analyzing the operating condition duration every day.

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