New energy maximum grid-connected capacity margin evaluation method based on comprehensive benefit index

Abstract

The invention discloses a new energy maximum grid-connected capacity margin evaluation method based on a comprehensive benefit index, and relates to the technical field of power system planning and operation control. A simulation model is produced by integrating a panoramic time sequence; establishing a multi-dimensional index system covering technical safety, economic operation, ecological environment and social driven benefits, and emphatically quantifying the sand stabilization value and microclimate improvement factors of vegetation restoration under the photovoltaic panel; determining an index weight by adopting a combined weighting algorithm combining an improved analytic hierarchy process and an entropy weight method; an optimization model with the maximum weighted comprehensive benefit as the target and the system safety and stability as the constraint is constructed, an improved particle swarm algorithm is adopted for solving, and the maximum grid-connected capacity reference value is obtained through panoramic production simulation dynamic verification; calculating a capacity margin and generating a grading early warning and capacity expansion strategy; according to the method, the grid-connected capacity of the new energy base is scientifically evaluated and improved on the premise of guaranteeing the safety of a power grid, and the comprehensive benefits of a project are fully reflected.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and operation control technology, and specifically relates to a method for assessing the maximum grid-connectable capacity margin of new energy sources based on comprehensive benefit indicators. Background Technology

[0002] With the advancement of the "dual carbon" target, my country is vigorously developing large-scale wind and solar power bases, focusing on desert, Gobi, and arid regions. However, these regions typically have weak power grid architectures, low system inertia, and insufficient peak-shaving capacity, posing a significant challenge to the large-scale grid connection of new energy sources.

[0003] However, the development of new energy sources in the "sand-go barren" region is uniquely complex. It is not merely a simple energy construction project, but also carries the important mission of desertification control and ecological restoration. The power grid infrastructure in this region is typically weak, and the random fluctuations of new energy sources are intertwined with the long-term benefits of ecological restoration. This makes traditional grid-connected capacity assessment methods, which focus solely on the safety of a single technology, insufficient to comprehensively measure the true value of the project. Traditional maximum grid-connected capacity assessment methods primarily focus on technical constraints, such as thermal stability limits, voltage deviations, harmonic limitations, and system stability.

[0004] This reveals the following problems with existing technologies: 1. Neglecting economic efficiency: Blindly pursuing maximum grid connection capacity may lead to excessively high wind and solar curtailment rates, or excessively high energy storage and grid transformation costs for grid integration, resulting in overall project losses; 2. Lack of a multi-dimensional perspective: Failure to comprehensively consider the ecological governance benefits (such as photovoltaic desertification control) and social benefits in desert areas, underestimating the overall value of the project; 3. Limitations of static assessment: Traditional methods are mostly based on typical daily or static power flow calculations, which are difficult to adapt to the dynamic time-series changes under the integrated operation of "source, grid, load, and storage"; Therefore, a method for assessing the maximum grid-connectable capacity margin of new energy sources that can comprehensively balance technical safety, economic feasibility, ecological and environmental benefits, and social driving benefits is still needed. Summary of the Invention

[0005] (a) Technical problems to be solved To address the problems in related technologies, this invention provides a method for assessing the maximum grid-connectable capacity margin of new energy sources based on comprehensive benefit indicators, thereby overcoming the aforementioned technical problems existing in existing related technologies.

[0006] (II) Technical Solution To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention provides a method for assessing the maximum grid-connectable capacity margin of new energy sources based on comprehensive benefit indicators, comprising the following steps: S1. Extract the random fluctuation characteristics of wind and solar resources, the transmission characteristics of ultra-high voltage DC transmission channels, and the regulation response characteristics of supporting energy storage systems, and perform time-series coupling to generate a panoramic time-series production simulation model. S2. Establish a multi-dimensional comprehensive benefit indicator system that includes technical safety benefits, economic operation benefits, ecological and environmental benefits, and social driving benefits; S3. Using the improved analytic hierarchy process and the entropy weight method based on time series data, calculate the subjective and objective weights of each indicator in the multidimensional comprehensive benefit index system; and apply the principle of least discriminant information to fuse the subjective and objective weights to obtain the combined weights. S4. Using the combined weights as weighting coefficients, and the system short-circuit ratio constraint, curtailment rate constraint, UHV channel utilization rate constraint, and node voltage deviation constraint as constraints, and taking the maximization of the weighted multidimensional comprehensive benefit index as the second objective function, construct the maximum grid-connectable capacity optimization model. S5. An improved particle swarm optimization algorithm is used to iteratively optimize the maximum grid-connected capacity optimization model. In each iteration, the capacity configuration scheme represented by the particles is input into the panoramic time-series production simulation model for verification, and the fitness value is calculated until convergence. The maximum grid-connected capacity benchmark value that satisfies the constraints and has the best comprehensive benefits is output. S6. Calculate the difference between the maximum grid-connected capacity benchmark value and the current planned grid-connected capacity to obtain the capacity margin; and generate a graded early warning signal based on the capacity margin to expand or limit the capacity. Preferably, step S1 includes the following steps: S11. Collect historical meteorological data of the target area, fit the wind speed probability density using the Weibull distribution, and fit the light intensity probability density using the Beta distribution; based on the fitted wind speed probability density and light intensity probability density, generate the power output time series curves of wind power and photovoltaic power. S12. Set minimum technical output constraints, ramp rate constraints, and tie line power fluctuation limits to establish an ultra-high voltage DC transmission channel model. S13. Establish a model for the supporting energy storage system; the supporting energy storage system includes an electrochemical energy storage power station and a solar thermal power station; for the solar thermal power station, establish the energy balance equations for the heat collection, heat storage and power generation links; S14. Integrate the wind and solar power output time-series curves, the UHVDC transmission channel model, and the supporting energy storage system through power simulation software to construct a panoramic time-series production simulation model. Preferably, step S2 includes the following steps: S21. Define technical safety benefit indicators; the technical safety benefit indicators include voltage over-limit probability, frequency response margin, line load rate and system short-circuit ratio; wherein, the system short-circuit ratio is used to characterize the voltage support capability in a weak power grid environment; S22. Define economic operation efficiency indicators; the economic operation efficiency indicators include grid connection cost per unit kilowatt, cost per kilowatt-hour over the entire life cycle, penalty costs incurred due to wind and solar curtailment, and revenue from participating in the ancillary services market. S23. Define ecological and environmental benefit indicators; the ecological and environmental benefit indicators include carbon emission reduction per unit capacity, land vegetation restoration rate, and reduction in soil and water loss; wherein, the land vegetation restoration rate is quantified by calculating the reduction in soil moisture evaporation caused by the shading effect of photovoltaic panels and the increase in vegetation coverage. S24. Define social driving effect indicators; the social driving effect indicators include local employment creation rate, infrastructure sharing rate, and energy supply guarantee contribution. Preferably, step S23 includes the following steps: S231. Construct a microclimate model for the photovoltaic array, calculate the shading coefficient and wind speed attenuation coefficient of the photovoltaic modules on the ground surface; calculate the amount of soil moisture evaporation inhibition based on the shading coefficient, and calculate the amount of reduction in surface wind erosion based on the wind speed attenuation coefficient. S232. Based on the amount of soil moisture evaporation inhibition and combined with the local native vegetation growth water requirement data, calculate the annual increase in vegetation coverage under the photovoltaic panels. S233. Using the ecosystem service value assessment method, the reduction in surface wind erosion and the increase in vegetation coverage are converted into sand fixation value. S234. Calculate the temperature reduction of the module backsheet based on the cooling effect of vegetation transpiration, calculate the percentage increase in power generation efficiency using the temperature coefficient of photovoltaic modules, and convert the percentage increase in power generation efficiency into equivalent power generation gain; calculate the carbon emission reduction per unit capacity based on coal consumption of thermal power plants, and multiply it by the carbon trading price to obtain the carbon emission reduction benefit. Preferably, step S3 includes the following steps: S31. For the multidimensional comprehensive benefit index system established in S2, a judgment matrix is ​​constructed using an improved analytic hierarchy process. When constructing the judgment matrix, a three-scale method is introduced to replace the nine-scale method, and the subjective weights of each index are calculated. S32. Collect historical operation data and benefit evaluation data of new energy power plants already in operation within the target area, and construct an indicator data matrix; after standardizing the indicator data matrix, calculate the information entropy and difference coefficient of each indicator, and use the entropy weight method to calculate the objective weight of each indicator. S33. Based on the principle of minimum discriminative information, the first objective function is to minimize the sum of the discriminative information of the subjective weights obtained from the combined weights in S31 and the objective weights obtained in S32, and the first constraint condition is that the sum of the weight coefficients is 1. The combined weights are then obtained by solving the problem. Preferably, step S33 includes the following steps: S331. Construct the first objective function and the first constraint conditions; S332. Based on the first objective function, the partial derivative of the combined weights is obtained by using the Lagrange multiplication method and setting the derivative to 0, thus obtaining the analytical solution formula for the combined weights. S333. Substitute the subjective and objective weights obtained from S31 and S32 into the analytical solution formula to calculate the final combined weights. Preferably, step S4 includes the following steps: S41. Define the decision variables as the grid-connected capacity of new energy sources and the capacity of supporting energy storage configuration; S42. Using the combined weights as weighting coefficients, with the goal of maximizing the weighted multidimensional comprehensive benefit index, and constructing a second objective function based on the grid-connected capacity of new energy and the capacity of supporting energy storage configuration; S43. Define the second set of constraints for the second objective function; the second set of constraints includes system short-circuit ratio constraint, curtailment rate constraint, UHV channel utilization rate constraint, and node voltage deviation constraint. Preferably, step S5 includes the following steps: S51. Initialize the particle swarm; each particle represents a set of potential new energy grid-connected capacity and supporting energy storage configuration schemes. S52. Input the position vector of each particle into the panoramic time-series production simulation model to perform time-series simulation; S53. Based on the simulation results, obtain the technical, economic, ecological and social benefit index values ​​of each particle, and calculate the comprehensive benefit fitness value by combining the combined weights obtained in S3 and the second objective function in S4. S54. For a particle that violates any of the constraints in S43, apply a penalty function to its fitness value so that it is eliminated in subsequent iterations. S55. Update the velocity and position of the particles; during the update process, introduce an adaptive mutation operator. When the population fitness variance is less than a preset threshold, randomly perturb and mutate the position of the globally optimal particle to escape the local optimum. S56. Determine whether the maximum number of iterations or fitness convergence accuracy has been reached; if so, output the new energy grid-connected capacity corresponding to the globally optimal particle as the benchmark value of the maximum grid-connectable capacity; otherwise, return to S52 to continue iterating. Preferably, step S52 includes the following steps: S521. At any given moment, priority shall be given to utilizing photovoltaic and wind power output to meet local load demand; S522. The remaining new energy power shall be transmitted to other regions first through the UHVDC channel; if the transmitted power does not reach the minimum technical output of the channel, the power generated by the supporting energy storage discharge or solar thermal power plant shall be used to make up the difference. S523. If the output of new energy exceeds the sum of the maximum transmission capacity of the UHV channel and the local consumption capacity, then the supporting energy storage will be used for charging. S524. If the energy storage is fully charged, the excess power will be included in the amount of power wasted. S525. Calculate the cumulative data for the whole year and output the curtailment rate, channel utilization hours, energy storage cycle count and system operating cost; Preferably, step S6 includes the following steps: S61. Obtain the current planned grid-connected capacity; calculate the difference between the maximum grid-connected capacity benchmark value obtained in S5 and the current planned grid-connected capacity to obtain the capacity margin; S62. Set the capacity expansion threshold and capacity limitation threshold; calculate the ratio of capacity margin to the currently planned grid-connectable capacity; compare the ratio with the capacity expansion threshold and capacity limitation threshold to generate a graded early warning signal, and carry out capacity expansion or capacity limitation.

[0007] (III) Beneficial Effects The present invention has the following beneficial effects: The assessment in this invention is more comprehensive, especially in quantifying the ecological and environmental benefits and using them as assessment indicators. Traditional assessments mainly focus on technical and economic feasibility, but now additional indicators of ecological protection and social benefits have been added. In particular, the benefits of "photovoltaic desertification control," such as windbreak and sand fixation and environmental improvement, have been converted into tangible economic value. This improvement changes the limitations of traditional methods that judge solely from a technical or economic perspective. For example, some projects that may not be worthwhile based on electricity revenue alone become very worthwhile when considering their significant ecological value, thus more accurately reflecting the comprehensive carrying capacity of desert areas. This enables the scientific assessment of the dual strategic value of energy output and ecological restoration when developing new energy sources in ecologically fragile areas like deserts, avoiding conservative investment decisions due to underestimating the comprehensive benefits of projects.

[0008] The evaluation method of this invention is more refined, and the evaluation results are more reliable. This invention establishes a panoramic simulation model to dynamically combine the fluctuations of wind and solar power generation, the limitations of ultra-high voltage transmission, and the regulating role of energy storage for evaluation. At the same time, it adopts a combined weighting method based on the principle of minimum discriminant information to combine expert experience and actual data to determine the weight of each indicator. This integrates expert experience with objective data laws, ensuring that the final maximum grid-connected capacity is obtained. It strictly adheres to all the hard regulations for grid safety and truly reflects the actual absorption capacity and comprehensive benefits of the system. The accuracy of the evaluation results and their engineering guidance significance are significantly improved.

[0009] The evaluation method of this invention does not only calculate a total capacity figure, but can provide a complete solution based on the total capacity. By calculating the capacity margin and setting clear expansion / limitation thresholds, it automatically generates graded early warning signals, which can intuitively reflect the system's acceptance potential and risk status. Based on the capacity margin analysis, it can obtain the optimal photovoltaic and wind power ratio and the amount of energy storage required, which greatly improves the practicality of the evaluation method and the feasibility of the results.

[0010] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, the drawings can be obtained from these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the method for assessing the maximum grid-connectable capacity margin of new energy sources based on comprehensive benefit indicators, as per the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the invention, and not all embodiments. Based on the embodiments of the invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the invention.

[0014] To resolve the above issues, please refer to [link / reference]. Figure 1 This invention discloses a method for assessing the maximum grid-connectable capacity margin of new energy sources based on comprehensive benefit indicators, including the following steps: This embodiment provides a method for assessing the maximum grid-connectable capacity margin of new energy sources based on comprehensive benefit indicators, applied to a large-scale wind and solar power base in a desert area in Northwest my country (hereinafter referred to as the "target base"). The target base is located on the edge of the desert, with a fragile ecological environment and a relatively weak power grid architecture. It mainly transmits power to the eastern load center through a ±800kV ultra-high voltage DC channel. This embodiment aims to solve the problems of traditional assessment methods that only consider electrical constraints, leading to an underestimation of the ecological value of photovoltaic desertification control in desert areas, and the failure to fully utilize the synergistic capabilities of source, grid, load and storage, resulting in a conservative assessment of grid-connected capacity.

[0015] S1. Extract the random fluctuation characteristics of wind and solar resources, the transmission characteristics of ultra-high voltage DC transmission channels, and the regulation response characteristics of supporting energy storage systems, and perform time-series coupling to generate a panoramic time-series production simulation model. S2. Establish a multi-dimensional comprehensive benefit indicator system that includes technical safety benefits, economic operation benefits, ecological and environmental benefits, and social driving benefits; S3. Using the improved analytic hierarchy process and the entropy weight method based on time series data, calculate the subjective and objective weights of each indicator in the multidimensional comprehensive benefit index system; and apply the principle of least discriminant information to fuse the subjective and objective weights to obtain the combined weights. S4. Using the combined weights as weighting coefficients, and the system short-circuit ratio constraint, curtailment rate constraint, UHV channel utilization rate constraint, and node voltage deviation constraint as constraints, and taking the maximization of the weighted multidimensional comprehensive benefit index as the second objective function, construct the maximum grid-connectable capacity optimization model. S5. An improved particle swarm optimization algorithm is used to iteratively optimize the maximum grid-connected capacity optimization model. In each iteration, the capacity configuration scheme represented by the particles is input into the panoramic time-series production simulation model for verification, and the fitness value is calculated until convergence. The maximum grid-connected capacity benchmark value that satisfies the constraints and has the best comprehensive benefits is output. S6. Calculate the difference between the maximum grid-connected capacity benchmark value and the current planned grid-connected capacity to obtain the capacity margin; and generate a graded early warning signal based on the capacity margin to expand or limit the capacity. The above embodiments propose an innovative comprehensive benefit assessment method to address the challenges of assessing new energy grid connection in desert and barren areas. By constructing a high-precision panoramic time-series simulation model, quantifying comprehensive benefit indicators such as ecological sand fixation, employing a subjective and objective weighting system to determine weights, and utilizing an improved intelligent algorithm for global optimization, the maximum grid-connectable capacity and its margin are calculated under the premise of ensuring grid security. This method overcomes the limitations of traditional single-technology assessments, scientifically reveals the underestimated ecological value and system synergy potential, and significantly improves the developable capacity of the base while strictly adhering to safety red lines. It also provides a quantitative scheme for planning decisions, including specific allocation and site selection recommendations, strongly supporting the scientific layout and construction of large-scale national clean energy bases.

[0016] S1. Extract the random fluctuation characteristics of wind and solar resources, the transmission characteristics of ultra-high voltage DC transmission channels, and the regulation response characteristics of supporting energy storage systems, and perform time-series coupling to generate a panoramic time-series production simulation model. The above embodiment S1 includes the following steps: S11. Collect historical meteorological data of the target area, fit the wind speed probability density using the Weibull distribution, and fit the light intensity probability density using the Beta distribution; based on the fitted wind speed probability density and light intensity probability density, generate the power output time series curves of wind power and photovoltaic power. In specific implementation, the above embodiment S11 specifically involves: accessing 30 years of historical meteorological data from the target base's wind measurement tower and radiation observation station; for wind speed data, extracting hourly wind speed samples at different heights (100m, 120m), and fitting the wind speed probability density using the Weibull distribution function; the formula for the wind speed probability density is: ;in, f ( v ) represents the wind speed probability density; k Represents the shape factor. c Indicates the scale parameter. v The wind speed is represented by a shape coefficient of 2.1 and a scale parameter of 7.5 m / s obtained by fitting historical customs sample data in this embodiment. Based on the fitting parameters, an 8760-hour wind speed sequence for a typical year (2019) is generated and converted into a wind turbine output sequence. Similarly, for illumination data, the irradiance (illuminance probability density) is fitted using a Beta distribution. The formula for the irradiance probability density is: ;in, B Indicates normalization, g Indicates light intensity. α , β This represents the shape coefficient, which is fitted to this embodiment. α 0.8 β The value is 0.5; when generating the photovoltaic output sequence, considering the large diurnal temperature range in the "Shagolan" region, the influence coefficient of temperature on photovoltaic module efficiency was specifically corrected. t The temperature coefficient of the photovoltaic module in this embodiment t -0.35% / ℃; S12. Set minimum technical output constraints, ramp rate constraints, and tie line power fluctuation limits to establish an ultra-high voltage DC transmission channel model. In specific implementation, the above embodiment S12 specifically involves: establishing a ±800kV ultra-high voltage DC transmission channel model; and setting the rated transmission power. P Hr =8000MW, operational constraints set: minimum technical output P Hm =0.1× P Hr =800MW (to ensure normal thyristor commutation); ramp rate limits the variable power ΔP H ≤600MW / min; the power fluctuation limit of the tie line is that the number of power adjustments within the day does not exceed 10 times, thus obtaining the UHVDC transmission channel model; S13. Establish a supporting energy storage system model; the supporting energy storage system includes an electrochemical energy storage power station and a solar thermal power station; for the solar thermal power station, establish the energy balance equation for the heat collection, heat storage and power generation links, and regard the solar thermal power station as a flexible regulating power source with spinning standby capability. In specific implementation, the above embodiment S13 is as follows: the solar thermal power plant is modeled as a flexible power source with rotational inertia, with an installed capacity of 200MW and a heat storage duration of 12 hours, and the energy balance equations for heat collection, heat storage, and power generation are established: ; E T ( t )express t Energy in the thermal storage tank at any time or loss For heat loss rate, or g For power generation efficiency, P sth ( t ) indicates in t The heat power collected and stored in the thermal storage tank by the solar thermal collector at all times. P gh ( t Δ represents the heat power extracted from the thermal storage tank and fed into the power generation system. t The time step is indicated; the solar thermal power plant not only provides electrical energy but also provides synchronous inertia support, improving the system's short-circuit ratio; similarly, the electrochemical energy storage is set as a lithium iron phosphate battery with an installed capacity of 2000 / 4000MW, and the modeling considers charge-discharge efficiency and cycle life degradation; the charge-discharge efficiency in this embodiment is 88%; S14. Integrate the wind and solar power output time-series curves, the UHVDC transmission channel model, and the supporting energy storage system using power simulation software (such as PSD-BPA or CloudPSS) to construct a panoramic time-series production simulation model. In specific implementation, the above embodiment S14 is as follows: In this embodiment, the simulation is performed in PSD-BPA. The wind and solar power output time series curves serve as the source data input for new energy power generation and are the driving force of the entire simulation. The UHVDC transmission channel model serves as the core power transmission and constraint link, receiving and transmitting new energy power. Its minimum technical output, ramp rate, and other rigid constraints directly affect the real-time consumption and dispatch of power. The supporting energy storage system model acts as a key flexible adjustment resource and buffer, dynamically executing charging (consuming surplus power) or discharging (making up for power deficit) operations according to the fluctuations in wind and solar power output and the constraints of the channel. The three are controlled by the software's preset time step Δ t(e.g., 1 hour) Iterative and tightly coupled: At each simulation moment, the program allocates wind and solar power output according to priority (e.g., local load, external transmission channel, energy storage) and verifies channel constraints and energy storage status in real time, thereby constructing a panoramic time-series production simulation model that can simulate the coordinated operation and energy balance of source-grid-storage for 8760 hours throughout the year. The above embodiments integrate a high-precision meteorological fitting model, an ultra-high voltage model considering physical constraints, and a hybrid energy storage model that includes solar thermal inertia support to construct a panoramic time-series production simulation platform that can truly reflect the operational characteristics of the "Shagohuang" base. This effectively solves the problem that traditional static models cannot capture the contradiction between the volatility of new energy sources and the rigidity of ultra-high voltage regulation, and provides a high-fidelity simulation environment for the capacity assessment in the subsequent S5 step.

[0017] S2. Establish a multi-dimensional comprehensive benefit indicator system that includes technical safety benefits, economic operation benefits, ecological and environmental benefits, and social driving benefits; The above embodiment S2 includes the following steps: S21. Define technical safety benefit indicators I Tech The technical safety benefit indicators include voltage over-limit probability, frequency response margin, line load rate, and system short-circuit ratio; among which, the system short-circuit ratio is used to characterize the voltage support capability in a weak power grid environment. In specific implementation, the above embodiment S21 specifically involves: selecting the grid connection point and calculating the system short-circuit ratio. S CR = S AC / P RE ,in, S AC Indicates the short-circuit capacity of the AC system. P RE This represents the total active power capacity of new energy (renewable energy) power generation connected to the grid; in this embodiment, if... S CR If the voltage is less than 1.5, it is considered an extremely weak power grid, which is an unsafe state; the voltage over-limit probability is the percentage of time in the annual simulation where the grid connection point voltage deviates from the rated value by ±5%; the frequency response margin is quantified by the difference between the lowest frequency point and the safety threshold under the maximum power deficit of the system; the line load rate is the average load rate of the key section lines, with the target to be maintained in the range of 60%-80%; S22. Define economic operation efficiency indicators I Eco The economic efficiency indicators include the unit grid connection cost per kilowatt, the cost per kilowatt-hour over the entire life cycle, the penalty costs incurred due to wind and solar curtailment, and the revenue from participating in the ancillary services market. In specific implementation, the above embodiment S22 specifically involves: calculating the levelized cost of electricity (L); ;in, I 0 This represents the initial investment (including the cost of installing photovoltaic desertification control equipment). O This refers to the operating and maintenance costs, which are the annual expenses incurred after the project is put into operation, including operation, maintenance, repair, insurance, and management. n Indicates the project duration (in years; in this example, it is 25). t This indicates the time index, which is the item's... t Year; r This represents the discount rate, which is 6% in this example; E t Indicates the first t Net on-grid power generation in the year; The penalty price for curtailing wind and solar power is set at 0.1 yuan / kWh; ancillary service revenue refers to the revenue obtained by energy storage from participating in peak shaving and frequency regulation. S23. Define ecological and environmental benefit indicators I Env The ecological and environmental benefit indicators include carbon emission reduction per unit capacity, land vegetation restoration rate, and reduction in soil and water loss; among which, the land vegetation restoration rate is quantified by calculating the reduction in soil moisture evaporation caused by the shading effect of photovoltaic panels and the increase in vegetation coverage. The above embodiment S23 includes the following steps: S231. Construct a microclimate model for the photovoltaic array, calculate the shading coefficient and wind speed attenuation coefficient of the photovoltaic modules on the ground surface; calculate the amount of soil moisture evaporation inhibition based on the shading coefficient, and calculate the amount of reduction in surface wind erosion based on the wind speed attenuation coefficient. In specific implementation, the above embodiment S231 specifically involves: using CFD software to establish a three-dimensional numerical wind tunnel that includes typical photovoltaic array arrangement (tilt angle, spacing), terrain, and typical wind speed and direction, to obtain a photovoltaic array microclimate model; The flow field distribution in front of, behind and below the photovoltaic panel array near the ground surface was simulated and calculated. The shading and disturbance effects of photovoltaic modules on surface wind speed were quantitatively analyzed, and the theoretical wind speed attenuation coefficient and theoretical soil evaporation inhibition amount were obtained. Miniature weather stations and soil moisture sensors were deployed within existing photovoltaic power plants to conduct long-term comparative observations (under the panels and in bare sand areas) to obtain measured attenuation data of wind speed and evaporation. By calibrating the theoretical wind speed attenuation coefficient and the theoretical soil evaporation inhibition amount, the model output data was made consistent with the measured data, establishing universal quantitative coefficients that can be directly used for the target area. In this embodiment, the reduction in surface wind erosion is 0.6, and the inhibition amount of soil evaporation is 30%. This embodiment of the invention provides empirical support for the beneficial effects of each step by introducing specific data and design parameters. When constructing the microclimate model of the photovoltaic array, the wind speed attenuation coefficient was determined to be 0.6 and the inhibition amount of soil evaporation was determined to be 30% through CFD simulation and on-site measurement verification, providing reliable quantitative input for the subsequent accurate monetization assessment of ecological value. S232. Based on the amount of soil moisture evaporation inhibition and combined with the local native vegetation growth water requirement data, calculate the annual increase in vegetation coverage under the photovoltaic panels. In specific implementation, the above embodiment S232 specifically involves: constructing a dynamic balance equation for soil moisture under the photovoltaic panel; the input terms of the equation are precipitation and runoff collection at the edge of the panel, and the output terms are vegetation transpiration and soil evaporation (which has been determined to be 70% of the bare sand area in soil evaporation based on the 30% soil evaporation inhibition in the S231 model). Based on the physiological water requirement characteristics and growth curves of local native vegetation (such as Artemisia arenaria and Artemisia argyi), the water threshold required for unit biomass growth of local native vegetation was determined. By converting the evaporation inhibition obtained from S231 into an increase in available soil water, vegetation biomass growth was calculated. The interannual increase in vegetation cover was obtained through the relationship between vegetation cover and biomass. After verification using observational data from local ecological restoration projects, the predicted annual increase in cover Δ was finally determined under the conditions of this embodiment. C veg It is 5%; S233. Using the ecosystem service value assessment method, the reduction in surface wind erosion and the increase in vegetation coverage are converted into sand fixation value. In specific implementation, the above embodiment S233 specifically involves: converting the increase in vegetation coverage into biological sand-fixing value. V sa1 The first transformation formula is: ;in, A pv Indicates the area of ​​the photovoltaic power station (25,000 mu per GW in this embodiment), Δ C veg This represents the projected annual increase in coverage. P sa This indicates the unit price of sand fixation ecological services per unit area (based on local ecological compensation standards, set at 800 yuan / mu / year in this embodiment). Convert the reduction in surface wind erosion into physical sand fixation value.V sa2 The second transformation formula is: ; where Δ C This represents the annual reduction in wind erosion (obtained by combining the reduction in surface wind erosion with the surface area of ​​the photovoltaic power station; in this example, it is 1.2 tons / acre). r This indicates the bulk density of the land (based on the local sand bulk density of 1.5 tons / cubic meter). C so The value of the soil per unit weight or the cost of its treatment is expressed as a reference to the cost of earthwork treatment, set at 80 yuan / ton. S234. Calculate the temperature reduction of the module backsheet based on the cooling effect of vegetation transpiration, calculate the percentage increase in power generation efficiency using the photovoltaic module temperature coefficient, and convert the percentage increase in power generation efficiency into equivalent power generation gain; calculate the carbon emission reduction per unit capacity based on the coal consumption of thermal power plants of 300g / kWh, and multiply it by the carbon trading price (50 yuan / ton) to obtain the carbon emission reduction benefit; the photovoltaic module temperature coefficient decreases as the temperature increases, depending on the specific photovoltaic module used in the implementation. In this embodiment, the photovoltaic module temperature coefficient is -0.35% / ℃. S24. Define social driving effect indicators I Soc The aforementioned social impact indicators include local employment creation rate, infrastructure sharing rate, and contribution to energy supply security. In specific implementation, the above embodiment S24 specifically refers to: the infrastructure sharing rate being the proportion of existing substations and transmission lines utilized by the new expansion project. In this embodiment, the infrastructure sharing rate is a quantitative ratio, that is, the percentage of key facility resources such as the capacity of existing substations and transmission line corridors utilized by the new expansion project to the total demand of the project. In this embodiment, the second phase project fully utilizes the first phase transmission line corridor, and this ratio is quantified as 1.0 (i.e., 100%); the local employment creation rate is the number of local operation and maintenance and desertification control jobs created per MW of installed capacity. The employment creation rate is determined by statistical units. The number of local long-term operation and maintenance, module cleaning, and under-panel sand control and maintenance jobs created by the installed capacity (e.g., per MW) is quantified. In this embodiment, the data from the first phase project is referenced and set at 0.15 people / MW. The contribution to energy supply is obtained by evaluating the proportion of the project's adjustable output during peak load periods to the region's maximum load. This is quantified by statistically analyzing the project's stable dispatchable output (including supporting energy storage discharge capacity) during typical peak load periods of the regional power grid (e.g., within 10% of the highest annual load period) and calculating the proportion of dispatchable output to the region's maximum load during the same period. The above embodiments construct a four-dimensional indicator system that incorporates ecological sand-fixing value. In particular, they innovatively quantify the ecological service value of "power generation above the platform and sand fixation below," transforming it into calculable economic indicators. They also uncover "hidden" capacity by introducing ecological and social benefit indicators. This allows grid-connected capacity that was previously infeasible based solely on "economic" calculations to be compensated for by "ecological" and "social" considerations, accurately reflecting the true carrying capacity of the "desert wasteland" base and increasing the assessed capacity by approximately 15%. This improvement makes expansion plans that were previously infeasible based solely on economic or technical calculations feasible after incorporating ecological benefits, truly reflecting the strategic value of the "desert wasteland" base and enhancing the comprehensiveness and accuracy of the assessment results.

[0018] S3. Using the improved analytic hierarchy process and the entropy weight method based on time series data, calculate the subjective and objective weights of each indicator in the multidimensional comprehensive benefit index system; and apply the principle of least discriminant information to fuse the subjective and objective weights to obtain the combined weights. The above embodiment S3 includes the following steps: S31. An expert group composed of power system planning experts, environmental protection and ecological experts, and economic evaluation experts was selected. The improved analytic hierarchy process was used to construct a judgment matrix for the multi-dimensional comprehensive benefit index system established in S2. When constructing the judgment matrix, the three-scale method was introduced to replace the traditional nine-scale method to reduce the cognitive burden on experts and calculate the subjective weight of each index. In specific implementation, the above embodiment S31 is as follows: Five experts are invited (two from power grid planning, two from ecological and environmental protection, and one from an economist); a "0-1-2" three-scale method is adopted (0, 1, and 2 represent unimportant, equally important, and important, respectively), which is the "improved analytic hierarchy process." By replacing the nine-scale method in the traditional analytic hierarchy process with a three-scale method, the simplification of the scale reduces the ambiguity and cognitive burden of expert judgment, resulting in an initial judgment matrix; the judgment matrix is ​​solved using the root method or eigenvector method to obtain the subjective weights of each indicator (in order: technical safety benefits, economic operation benefits, ecological and environmental benefits, and social driving benefits). W sub =[0.30,0.20,0.35,0.15]; S32. Collect historical operation data and benefit evaluation data of new energy power plants already in operation within the target area, and construct an indicator data matrix; after standardizing the indicator data matrix, calculate the information entropy and difference coefficient of each indicator, and use the entropy weight method to calculate the objective weight of each indicator. In specific implementation, the above embodiment S32 is as follows: This embodiment collects historical data from three similar bases already in operation in the vicinity; constructs a data matrix, calculates the information entropy after normalization, and obtains the objective weights. W sux=[0.45,0.25,0.15,0.15]; The significant difference between the objective weight and the subjective weight in terms of technical safety benefits and ecological environment benefits is due to the extreme fluctuations in wind and solar power output in the "Shagohuang" region, resulting in a small data entropy value (large amount of information) for technical indicators. Therefore, the objective weight tends to favor technical indicators. S33. Based on the principle of minimum discriminative information, the first objective function is to minimize the sum of the discriminative information of the subjective weights obtained from combining the weights in S31 and the objective weights obtained in S32. The first constraint condition is that the sum of the weight coefficients is 1. The optimal combined weights are then obtained. The above embodiment S33 includes the following steps: S331. Construct the first objective function and the first constraint conditions; In specific implementation, the above embodiment S331 is as follows: ;wherein, min J This represents minimizing the sum of the discriminative information from subjective and objective weights; m This indicates the total number of indicators; in this example, it is 4. j Indicates the first j One indicator; w cj , w sj , w oj They represent the first j The combined weights, subjective weights, and objective weights of each indicator; the constraints are as follows: , w cj ≥0m; S332. Based on the first objective function, the partial derivative of the combined weights is obtained by using the Lagrange multiplication method and setting the derivative to 0, thus obtaining the analytical solution formula for the combined weights. In specific implementation, the above embodiment S332 is as follows: the formula for the analytical solution of the combined weights is: ;in, k Indicates the first k One indicator, m This represents the total number of indicators. w sk , w ok They represent the first k The subjective and objective weights of each indicator; S333. Substitute the subjective and objective weights obtained from S31 and S32 into the analytical solution formula to calculate the final combined weights. In specific implementation, the above embodiment S333 specifically involves: taking the combined weight of technical security benefits as an example; substituting into the calculation: Similarly, the calculated combined weights of economic operating benefits, ecological environment benefits, and social driving benefits are 0.231, 0.236, and 0.155, respectively. The weights in this embodiment scientifically integrate expert judgment (subjective) and historical data patterns (objective), significantly increasing the weight of ecological benefits from an objective 0.15 to 0.236, ensuring that the optimization target accurately reflects the ecological strategic positioning of the "Shagohuang" base. The above embodiments adopt a combined weighting method based on the principle of minimum discriminative information, which scientifically integrates expert experience and objective data laws. The modified weights retain the core position of technical security (0.378) and significantly increase the weight of ecological benefits (from an objective 0.15 to 0.236), reflecting the method's accurate adaptation to the ecological function positioning of the "Shagohuang" base, and providing scientific weighting coefficients for the construction of the objective function in the subsequent S4 step.

[0019] S4. Using the combined weights as weighting coefficients, and the system short-circuit ratio constraint, curtailment rate constraint, UHV channel utilization rate constraint, and node voltage deviation constraint as constraints, and taking the maximization of the weighted multidimensional comprehensive benefit index as the second objective function, construct the maximum grid-connectable capacity optimization model. The above embodiment S4 includes the following steps: S41. Define the decision variable as the grid-connected capacity of new energy sources. P RE and supporting energy storage capacity P ESS ; In specific implementation, the above embodiment S41 specifically refers to: This embodiment will increase the grid-connected capacity of new energy sources. P RE Divided into total photovoltaic installed capacity P PV Total installed capacity of wind power P Wind Supporting energy storage capacity P ESS For energy storage installations; the total planned photovoltaic installation capacity in this embodiment is... P PV The total installed capacity of wind power is 8000MW. P Wind It is 4000MW; S42. Using the combined weights as weighting coefficients, the goal is to maximize the weighted multidimensional comprehensive benefit index, based on the grid-connected capacity of new energy sources. P RE and supporting energy storage capacity P ESS Construct the second objective function max F ( P RE , PESS ); In specific implementation, the above embodiment S42 is as follows: The formula for the second objective function is: ;in, P RE Indicates the grid-connected capacity of new energy sources; P PV This indicates the total installed capacity of photovoltaic power. P Wind This indicates the total installed capacity of wind power. P ESS Indicates the capacity of the supporting energy storage configuration. I Tech Indicators representing technical safety benefits I Eco Indicators representing economic performance efficiency I Env Indicators representing ecological and environmental benefits I Soc This represents the social driving effect indicator; all indicators in the formula have been standardized to the range [0, 1] before use. S43. Define the second set of constraints for the second objective function; the second set of constraints includes system short-circuit ratio constraint, curtailment rate constraint, UHV channel utilization rate constraint, and node voltage deviation constraint. In specific implementation, the above embodiment S43 specifically includes: system short-circuit ratio constraint: short-circuit ratio SCR = S ac / P RE ≥1.5, S ac Represents the short-circuit capacity of the AC system; curtailment rate constraint: curtailment rate R cu = E cu / E total ≤5%, E cu This indicates the actual amount of electricity wasted in a given year. E total This represents the theoretical annual abandoned power volume; the utilization rate constraint for UHV transmission channels is ≥4500 hours; the node voltage deviation constraint is ≤5%. The above embodiments are specifically designed for the weak power grid environment of the "Shagolan" region, taking the short-circuit ratio and the stability of UHV transmission as core hard constraints, ensuring that capacity expansion does not come at the expense of safety.

[0020] S5. An improved particle swarm optimization algorithm is used to iteratively optimize the maximum grid-connected capacity optimization model. In each iteration, the capacity configuration scheme represented by the particles is input into the panoramic time-series production simulation model for verification, and the fitness value is calculated until convergence. The maximum grid-connected capacity benchmark value that satisfies the constraints and has the best comprehensive benefits is output. The above embodiment S5 includes the following steps: S51. Initialize the particle swarm; each particle represents a group of potential renewable energy grid-connected capacities. P RE and supporting energy storage capacity P ESS Configuration scheme; In specific implementation, the above embodiment S51 specifically involves: constructing a particle set, and setting the size of the particle set to... r The particle set is then represented as: In this embodiment, the particle swarm size is set. r= 50, maximum number of iterations: 100; z i Represents the first in the set of particles The nth particle, i.e., the nth Potential grid-connected capacity of new energy sources P RE and supporting energy storage capacity P ESS Configuration schemes, such as [1000MW, 500MW, 200MW], represent an additional 1GW of photovoltaic power, 0.5GW of wind power, and 0.2GW of energy storage. S52. Input the position vector of each particle into the panoramic time-series production simulation model to perform time-series simulation for 8760 hours throughout the year. The above embodiment S52 includes the following steps: S521, at every moment t Prioritize the use of photovoltaic and wind power to meet local load demand; S522. The remaining new energy power shall be transmitted to other regions first through the UHVDC channel; if the transmitted power does not reach the minimum technical output of the channel, the power generated by the supporting energy storage discharge or solar thermal power plant shall be used to make up the difference. S523. If the output of new energy exceeds the sum of the maximum transmission capacity of the UHV channel and the local consumption capacity, then the supporting energy storage will be used for charging. S524. If the energy storage is fully charged, the excess power will be included in the amount of power wasted. S525. Calculate the cumulative data for the whole year and output the curtailment rate, channel utilization hours, energy storage cycle count and system operating cost; In specific implementation, the above embodiment S52 is specifically: time tThe wind and solar power output prioritizes meeting local loads, with surplus power transmitted to ultra-high voltage transmission lines. If the wind and solar power output is less than 800MW, solar thermal power and battery discharge are used to supplement it to 800MW. If the wind and solar power output is greater than 8000MW, excess power is stored in energy storage, and once the storage is full, the power is discarded. The short-circuit capacity at the grid connection point is monitored in real time, and if the solar thermal power unit is online, it provides short-circuit capacity support. This embodiment clarifies the collaborative scheduling logic using a solar thermal power plant (200MW / 12-hour thermal storage) as a synchronous inertia source, enabling the algorithm to dynamically evaluate the real impact of different schemes on the system short-circuit ratio (SCR) during the optimization process, and realizing dynamic closed-loop verification of safety constraints. S53. Based on the simulation results, obtain the technical, economic, ecological and social benefit index values ​​of each particle, and calculate the comprehensive benefit fitness value by combining the combined weights obtained in S3 and the second objective function in S4. S54. For a particle that violates any of the constraints in S43, apply a penalty function to its fitness value so that it is eliminated in subsequent iterations. S55. Update the velocity and position of the particles; during the update process, introduce an adaptive mutation operator. When the population fitness variance is less than a preset threshold, randomly perturb and mutate the position of the globally optimal particle to escape the local optimum. In specific implementation, the above embodiment S55 is as follows: In the 20th iteration, it is found that the swarm is trapped in a local optimum (preferring not to increase wind power, but only to increase photovoltaic power); at this time, adaptive mutation is triggered to perturb the optimal particle position and force the wind power ratio to increase in order to test the wind-solar complementarity; in the improved particle swarm algorithm of this embodiment, an adaptive mutation mechanism is introduced. When the algorithm is trapped in a local optimum of "only increasing photovoltaic power", it is actively perturbed to force the exploration of wind-solar complementary schemes, which effectively improves the global search capability in the solution space; S56. Determine whether the maximum number of iterations or fitness convergence accuracy has been reached; if so, output the particle corresponding to the global optimum. P RE Use this as the benchmark value for the maximum grid-connectable capacity; otherwise, return to S52 to continue iteration. In specific implementation, the above embodiment S55 is as follows: After 85 iterations, the algorithm converges, and the total capacity corresponding to the globally optimal particle is: 9500MW of photovoltaic power, 4800MW of wind power, and 2500MW of supporting energy storage. At this time, the curtailment rate is 4.8% (close to the 5% boundary); the SCR is 1.6 (meeting the safety constraints, thanks to the support of the solar thermal power plant); the ecological benefits are as follows: since the newly added 1.5GW of photovoltaic power is mainly deployed in the mobile sand dune area, the sand fixation value is significant, resulting in the highest comprehensive benefit score. The above embodiments achieve global optimization under complex nonlinear constraints through the closed-loop interaction between the improved particle swarm optimization algorithm and the panoramic time-series production simulation model. In particular, the introduction of an adaptive mutation mechanism effectively avoids the algorithm from getting stuck in local optima of "only increasing photovoltaic power without increasing wind power". Finally, the optimal configuration scheme that includes wind, solar and energy storage synergy is solved, ensuring the scientific and practical nature of the evaluation results.

[0021] S6. Calculate the difference between the maximum grid-connected capacity benchmark value and the current planned grid-connected capacity to obtain the capacity margin; and generate a graded early warning signal based on the capacity margin to expand or limit the capacity. The above embodiment S6 includes the following steps: S61. Obtain the current planned grid-connected capacity; calculate the difference between the maximum grid-connected capacity benchmark value obtained in S5 and the current planned grid-connected capacity to obtain the capacity margin; S62. Set expansion threshold and capacity limitation threshold; calculate the ratio of capacity margin to the currently planned grid-connectable capacity; compare the ratio with the expansion threshold and capacity limitation threshold, generate a graded early warning signal, and perform capacity expansion or capacity limitation. In specific implementation, the above embodiment S6 specifically refers to: the maximum grid-connectable capacity benchmark value. P Base =9500 + 4800 = 14300MW; Current planned grid-connected capacity P Cu =8000+4000=12000MW; calculated capacity margin M=14300−12000=2300MW; this embodiment sets the expansion threshold to 10%, expansion occurs if it is greater than 10%, and sets the capacity limitation threshold to -10%, capacity limitation occurs if it is less than -10%; it remains unchanged at -10% and 10%; and because the margin is greater than 10%, a green warning level is generated; based on this, the following expansion strategy is recommended: it is recommended that the second phase of the project add 2300 MW of new energy installed capacity, with a suggested ratio of 1500 MW of new photovoltaic power and 800 MW of wind power, and 500 MW of electrochemical energy storage are provided simultaneously; it is particularly recommended that the new photovoltaic power be preferentially located in the area with strong wind and sand activity on the northwest side of the base, and the ecological benefits be maximized by using the "power generation on the plate, sand fixation under the plate" model; this embodiment strongly demonstrates the significant effect of the present invention in scientifically improving the new energy acceptance capacity and comprehensive value under the premise of ensuring safety; If the above embodiments use traditional methods (considering only electrical constraints and curtailment rates), without calculating ecological benefits and the inertia support of solar thermal energy, the calculated maximum grid-connected capacity is only 12,800 MW, with a margin of only 800 MW. The method in this embodiment uncovers an additional 1,500 MW of potential capacity. This capacity may be on the edge of the break-even point on an economic scale, but after taking into account the huge ecological value of windbreak and sand fixation, its comprehensive rate of return is significantly positive, which is in line with the national strategic intention of building "desert and wasteland" bases. The above embodiments clearly demonstrate how the present invention, through panoramic modeling, multi-dimensional indicator system (especially the quantification of ecological indicators), and combined weighted optimization, scientifically assesses and improves the grid-connected capacity margin of new energy bases while ensuring safety.

[0022] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] The preferred embodiments of the invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for evaluating the maximum grid-connection capacity margin of new energy based on a comprehensive benefit index, characterized in that, The method comprises the following steps: S1, extracting the random fluctuation characteristics of wind and light resources, the power transmission characteristics of the extra-high voltage direct current external transmission channel, and the regulation response characteristics of the supporting energy storage system, and performing time sequence coupling to generate a panoramic time sequence production simulation model; S2, establishing a multi-dimensional comprehensive benefit index system including technical safety benefits, economic operation benefits, ecological environment benefits, and social driving benefits; S3, respectively using an improved analytic hierarchy process and an entropy weight method based on time series data to calculate the subjective weight and the objective weight of each index in the multi-dimensional comprehensive benefit index system; and applying the minimum discrimination information principle to fuse the subjective weight and the objective weight to obtain a combined weight; S4, taking the combined weight as a weighted coefficient, taking system short-circuit ratio constraints, curtailment rate constraints, extra-high voltage channel utilization rate constraints, and node voltage deviation constraints as constraint conditions, and taking the maximized weighted multi-dimensional comprehensive benefit index as a second objective function to construct a maximum grid-connected capacity optimization model; S5, using an improved particle swarm optimization algorithm to iteratively optimize the maximum grid-connected capacity optimization model, inputting a capacity configuration scheme represented by a particle into the panoramic time sequence production simulation model for verification in each iteration process, and calculating a fitness value until convergence, and outputting a maximum grid-connected capacity benchmark value; S6, calculating the difference between the maximum grid-connected capacity benchmark value and the currently planned grid-connected capacity to obtain a capacity margin; and generating a hierarchical early warning signal for expansion or capacity limiting according to the capacity margin. 2.The method for evaluating the maximum grid-connected capacity margin of new energy based on the comprehensive benefit index according to claim 1, characterized in that, The S1 comprises the following steps: S11, collecting historical meteorological data of a target region, fitting wind speed probability density using a Weibull distribution, and fitting light intensity probability density using a Beta distribution; based on the fitted wind speed probability density and light intensity probability density, generating wind power and photovoltaic output time sequence curves; S12, setting minimum technical output constraints, ramp rate constraints, and tie-line power fluctuation limits, and establishing an extra-high voltage direct current external transmission channel model; S13, establishing a supporting energy storage system model; the supporting energy storage system comprises an electrochemical energy storage power station and a solar-thermal power station; for the solar-thermal power station, energy balance equations of the heat collection, heat storage, and power generation links are established; S14, integrating the wind and light output time sequence curves, the extra-high voltage direct current external transmission channel model, and the supporting energy storage system through power simulation software to construct a panoramic time sequence production simulation model. 3.The method of claim 1, wherein the method is characterized by, The S2 comprises the following steps: S21, defining a technical safety benefit index; the technical safety benefit index comprises voltage out-of-limit probability, frequency response margin, line load rate, and system short-circuit ratio; wherein, the system short-circuit ratio is used to represent the voltage support capability in a weak power grid environment; S22, defining an economic operation benefit index; the economic operation benefit index comprises unit kilowatt grid-connected cost, full life cycle kilowatt-hour cost, penalty cost due to curtailed wind and light, and income from participating in the auxiliary service market; S23, defining an ecological environment benefit index; the ecological environment benefit index comprises unit capacity carbon emission reduction amount, land vegetation restoration rate, and water and soil loss reduction amount; wherein, the land vegetation restoration rate is quantified by calculating the amount of soil moisture evaporation reduction and the increment of vegetation coverage caused by the shading effect of photovoltaic panels. S24, define the social driving benefit index; the social driving benefit index includes local employment driving rate, infrastructure sharing rate and energy supply contribution degree. 4.The method for evaluating the maximum grid-connected capacity margin of new energy based on the comprehensive benefit index according to claim 3, characterized in that, The S23 includes the following steps: S231, construct a photovoltaic array microclimate model, calculate the shading coefficient of the photovoltaic module to the ground and the wind speed attenuation coefficient; calculate the soil water evaporation inhibition amount based on the shading coefficient, and calculate the ground wind erosion reduction amount based on the wind speed attenuation coefficient; S232, according to the soil water evaporation inhibition amount, combined with the local original vegetation growth water requirement data, calculate the annual increment of vegetation coverage under the photovoltaic panel; S233, using the ecosystem service value evaluation method, the ground wind erosion reduction amount and the vegetation coverage increment are converted into sand fixation value; S234, according to the vegetation transpiration cooling effect, the temperature reduction value of the module backboard is calculated, the temperature coefficient of the photovoltaic module is used to calculate the percentage of power generation efficiency improvement, and the percentage of power generation efficiency improvement is converted into equivalent power generation gain; according to the coal consumption of thermal power, the carbon emission reduction amount per unit capacity is calculated, and multiplied by the carbon trading price to obtain the carbon emission reduction benefit. 5.The method for evaluating the maximum grid-connected capacity margin of new energy based on the comprehensive benefit index according to claim 1, characterized in that, The S3 includes the following steps: S31, the multi-dimensional comprehensive benefit index system established in S2 is constructed by using the improved analytic hierarchy process to construct the judgment matrix; when constructing the judgment matrix, the three scale method is introduced to replace the nine scale method, and the subjective weight of each index is calculated; S32, collect the historical operation data and benefit evaluation data of the new energy power station in the target area, and construct the index data matrix; after standardizing the index data matrix, the information entropy and difference coefficient of each index are calculated, and the objective weight of each index is calculated by using the entropy weight method; S33, based on the minimum discrimination information principle, taking the sum of the discrimination information between the combined weight and the subjective weight and the objective weight as the first objective function, and taking the sum of the weight coefficients as 1 as the first constraint condition, the combined weight is solved. 6.The method for evaluating the maximum grid-connected capacity margin of new energy based on the comprehensive benefit index according to claim 5, characterized in that, The S33 includes the following steps: S331, construct the first objective function and the first constraint condition; S332, based on the first objective function, the partial derivative of the combined weight is obtained by Lagrange multiplication and the derivative is 0, and the analytical solution formula of the combined weight is obtained; S333, the subjective weight and the objective weight obtained by S31 and S32 are substituted into the analytical solution formula, and the final combined weight is calculated.

7. The method for evaluating the maximum grid-connected capacity margin of new energy based on the comprehensive benefit index according to claim 1, characterized in that, The S4 includes the following steps: S41, define the decision variable as the new energy grid-connected capacity and the supporting energy storage configuration capacity; S42, take the combined weight as the weighting coefficient, maximize the weighted multi-dimensional comprehensive benefit index as the target, and construct the second objective function based on the new energy grid-connected capacity and the supporting energy storage configuration capacity as the decision variable; S43, set the second constraint condition set of the second objective function; the second constraint condition set includes system short-circuit ratio constraint, power rejection rate constraint, ultra-high voltage channel utilization rate constraint and node voltage deviation constraint. 8.The method for evaluating the maximum grid-connected capacity margin of new energy based on the comprehensive benefit index according to claim 1, characterized in that, The S5 includes the following steps: S51, initialize the particle swarm; each particle represents a set of potential new energy grid-connected capacity and supporting energy storage configuration capacity configuration scheme; S52, input the position vector of each particle into the panoramic time sequence production simulation model to perform time sequence simulation; S53, according to the simulation results, obtain the technical, economic, ecological and social benefit index values of each particle, and combine the combination weight obtained in S3 and the second objective function in S4 to calculate the comprehensive benefit fitness value; S54, for the particle violating any constraint condition in S43, a penalty function is applied to the fitness value of the particle, so that it is eliminated in subsequent iterations; S55, update the speed and position of the particle; in the updating process, an adaptive mutation operator is introduced, when the population fitness variance is less than a preset threshold, the position of the global optimal particle is randomly disturbed and mutated to jump out of the local optimum; S56, judge whether the maximum iteration number or the fitness convergence precision is reached; if yes, output the new energy grid-connected capacity corresponding to the global optimal particle as the maximum grid-connected capacity benchmark value; otherwise, return to S52 for iteration. 9.The method of claim 8, wherein the method is characterized by, The S52 comprises the following steps: S521, at each time, preferentially utilize photovoltaic and wind power output to meet local load demand; S522, the remaining new energy power is preferentially exported through the UHV DC channel; if the exported power does not reach the minimum technical output of the channel, the matching energy storage is discharged or the photothermal power station is called to generate power to make up; S523, if the new energy output exceeds the sum of the maximum transmission capacity of the UHV channel and the local consumption capacity, the matching energy storage is called to charge; S524, if the state of charge of the energy storage is full, the excess power is counted into the abandoned power; S525, count the annual cumulative data, and output the abandoned power rate, channel utilization hours, energy storage cycle number and system operation cost. 10.The method for evaluating the maximum grid-connected capacity margin of new energy based on the comprehensive benefit index according to claim 1, characterized in that, The S6 comprises the following steps: S61, obtain the currently planned grid-connected capacity; calculate the difference between the maximum grid-connected capacity benchmark value obtained in S5 and the currently planned grid-connected capacity to obtain the capacity margin; S62, set the expansion threshold and the capacity limit threshold; calculate the ratio of the capacity margin to the currently planned grid-connected capacity; compare the ratio with the expansion threshold and the capacity limit threshold to generate a hierarchical early warning signal, and perform expansion or capacity limitation.

Images