Sagomean new energy base source network direct current storage planning and parameter optimization method

Abstract

The invention relates to the technical field of new energy power supply, and particularly discloses a Saggoga new energy base source network direct current storage planning and parameter optimization method, which comprises the following steps: analyzing wind and light output characteristics by adopting Gaussian kernel density estimation according to annual wind and light historical data of desert and gobi desert areas; generating a wind and light output scene according to the obtained probability distribution function, and performing k-medoids clustering to obtain a wind and light typical day scene; establishing a power supply planning and operation simulation model of the new energy base of Sagomean, simulating and generating a power supply capacity planning scheme and a daily simulation running diagram, and performing clustering analysis on core parameters of different schemes by k-means clustering; and formulating technical wind and light absorption and carbon emission indexes, analyzing whether the indexes under different planning operation schemes meet requirements or not, and repeatedly iterating the step 3 to obtain an optimal planning scheme and a parameter result. The method has the advantages that the targets of low-carbon environmental protection, wind and light absorption and the like are considered in parameter optimization, and the new energy utilization rate is improved and the pollution to the environment is reduced on the premise of ensuring that the investment cost of a new energy base is low.

Description

Technical Field

[0001] The present invention relates to the field of new energy power supply technology, and in particular to a source-grid-storage-direct current planning and parameter optimization method for a Shagohuang new energy base. Background Art

[0002] Carry out the application of source-grid-storage-direct current planning technology in large-scale new energy bases in typical desert and Gobi areas with the aim of improving comprehensive benefits, give full play to the complementary characteristics of various power sources, the regulation characteristics of flexible resources, and the synergistic characteristics of power sources and DC transmission, and improve the system's ability to operate economically, environmentally, safely and reliably.

[0003] The planning of the source, grid, storage and direct current of the Shagohuang new energy base needs to be based on the source, grid, storage and direct current planning technical solution, combined with the actual operation data of the source, grid, storage and direct current projects that have been put into operation, to fit and correct the core parameters of the production simulation model and the optimization planning model, so as to form a time series operation simulation model and source, grid, storage and direct current optimization planning technology suitable for the actual operation of the power grid in the Shagohuang area. The existing planning schemes for new energy bases mainly include the optimization configuration of a single station for wind, solar, thermal power sources and energy storage, as well as the optimization and application of source, grid, storage and direct current planning of large new energy bases. As for parameter optimization, the main technical parameters of new energy sources such as wind, solar, thermal, and solar heat are considered to optimize and improve the power guarantee capability.

[0004] In summary, most of the current planning and research on DC transmission power systems in new energy bases is based on the optimization goal of minimizing the total economic cost under the constraints of grid security and load demand. It does not deeply consider the multi-DC coupling and multi-DC operation stability of the system for the future large-scale and multi-DC system development in the Shagohuang area. How to establish a precise model of source-grid-storage-direct-current coordinated planning that meets the actual situation in the Shagohuang area with the goal of optimal comprehensive benefits, and propose technical guiding principles and typical guiding parameters for source-grid-storage-direct-current coordinated planning that takes into account flexible resource allocation, DC operation mode, power market and carbon market mechanism is still a technical difficulty. Therefore, the planning and research on new energy bases should take into account the needs in different scenarios, consider more comprehensive and unique factors, and propose guiding parameters and parameter optimization methods for the planning of Shagohuang new energy bases in a targeted manner. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a source-grid-storage-direct planning and parameter optimization method for a Shagohuang new energy base.

[0006] The purpose of the present invention is achieved through the following technical solutions: A method for source-grid-storage-direct planning and parameter optimization of a Shagohuang new energy base, comprising the following steps:

[0007] Step S1: Based on the historical data of wind and solar power in a desert Gobi desert area throughout the year, Gaussian kernel density estimation is used to analyze the wind and solar power output characteristics;

[0008] Step S2: Based on the probability distribution function obtained by Gaussian kernel density estimation, inverse transformation random sampling is performed to generate wind and solar output scenarios, and k-medoids clustering is performed to obtain typical wind and solar day scenarios;

[0009] Step S3: Establish a power supply planning and operation simulation model for the Shagohuang New Energy Base. Based on the typical day scene of wind and solar power in step S2, simulate and generate a power supply capacity planning scheme and a daily simulation operation diagram. K-means clustering is used to perform cluster analysis on the core parameters of different schemes.

[0010] Step S4: Formulate technical wind and solar power consumption and carbon emission indicators, analyze whether the indicators under different planning and operation schemes meet the requirements, modify the technical parameters, and repeatedly iterate the power planning and operation simulation model to obtain the optimal planning scheme and parameter results. Specifically, the Gaussian kernel density estimation analysis of wind and solar power output characteristics in step S1 is:

[0011]

[0012] Where: t=1,2....,24, which is 24 time periods; x and y are the wind turbine and photovoltaic output in time period t, respectively. and are the wind turbine and photovoltaic outputs at time t on day d; n is the number of historical days; h is the bandwidth; K() is the Gaussian kernel function, that is:

[0013]

[0014] Specifically, in step S2, the inverse transformation method is used to perform random sampling to generate a large number of sampling scenes, and its core definition is as follows:

[0015] It is known that X is a continuous random variable, and its probability distribution function CDF is Fx, then:

[0016]

[0017] Inverse generate a random variate X using a uniform distribution based on the CDF.

[0018] Specifically, in step S2, the K-mediods algorithm based on the partitioning clustering method is used to partition the sample set to achieve clustering, and the center point closest to the cluster center is selected as the representative point of the cluster. The specific steps are as follows:

[0019] Step S201: randomly select a group of cluster samples as the center point set;

[0020] Step S202: Each center point corresponds to a cluster;

[0021] Step S203: Calculate the distance between each sample point and each center point, and put the sample point into the cluster with the shortest distance to the center point. Assume that X i and X j are two N-dimensional sample points, which are considered as general points in the clustering process, and the Euclidean distance between them is calculated by the following formula:

[0022]

[0023] Step S204: Perform a test. In each cluster, select the point with the smallest absolute error from each sample point in the cluster as the new center point. The sum of squares of clustering errors is calculated by the following formula:

[0024]

[0025] Where: K represents the number of clusters that the decision maker plans to achieve; X km represents the mth sample point in the kth cluster; M is the number of sample points in the kth cluster; C k is the current class center of the kth cluster;

[0026] Step S205: According to the test result of step S204, if the new cluster center point is consistent with the current cluster center point, the algorithm stops; otherwise, the algorithm continues to loop from step S202;

[0027] The sampling results are clustered using a clustering method to generate k typical day scenes, specifically:

[0028] First, the 365 days of the year are uniformly divided into three categories: summer, winter and transitional seasons according to the regional seasonal characteristics, with the vertical time scale being days and the horizontal time scale being the 24 time points of a day.

[0029] Secondly, the 8760-hour output data is normalized, and the Gaussian kernel density estimation is performed using equations (1.1) and (1.2) for the normalized data in three seasons to obtain the probability density estimation and cumulative probability distribution of the estimated quantity.

[0030] Finally, the inverse transformation method is used to randomly sample the obtained cumulative probability distribution to generate a large number of sampling samples, forming M groups of sampling scenarios. Based on the clustering method, the K-mediods algorithm is used to cluster the M groups of sampling scenarios to generate typical scenarios.

[0031] Specifically, the power supply planning and operation simulation model in step S3 is:

[0032] min(C p +C w +C g +C ess,p +Cess,E ) (2.1)

[0033] min(C OM +C co2 +C en +C fuel ) (2.2)

[0034]

[0035] (1-w p ) p,max (t)≤P p (t)≤(1+w p ) p,max (t) (2.4)

[0036] (1-w w ) w,max (t)≤P w (t)≤(1+w w ) w,max (t) (2.5)

[0037] 0≤P g (t)≤S g (2.6)

[0038] -λΔt≤P g (t)-P g (t-1)≤λΔt (2.7)

[0039]

[0040]

[0041]

[0042]

[0043] 0≤P ch (t)≤P ess,unit (2.12)

[0044] 0≤P dis (t)≤P ess,unit (2.13)

[0045] 0≤P ch (t)≤Mθ t (2.14)

[0046] Among them, formula (2.1) and (2.2) are objective functions, with the goal of minimizing the investment cost of wind, solar, thermal and energy storage. In the formula, C p Represents the photovoltaic configuration capacity, C wRepresents wind power configuration capacity; C g Represents the thermal power configuration capacity; C ess,p Indicates the energy storage configuration power; C ess,E Represents the energy storage configuration capacity; C OM represents the operation and maintenance cost, C co2 represents the carbon emission cost; C en represents the environmental penalty cost; C fuel represents fuel cost;

[0047] Formulas (2.3)-(2.14) are constraints. Formula (2.3) is the power balance constraint, which indicates the total output P of wind, solar, thermal and storage at each moment. i Greater than or equal to the load P at each moment i,load ; Formulas (2.4)-(2.5) are wind and solar output constraints, where w p and w w is the wind uncertainty, w p and w w is the wind and solar uncertainty, P p,max is the upper limit of wind power output, P w,max is the upper limit of photovoltaic output, P p For wind power output, P w is the photovoltaic output; Formulas (2.6)-(2.7) are the thermal power output and climbing constraints, where λ is the upper limit of the climbing capacity, P g For thermal power generation, S g is the thermal power configuration capacity; Formula (2.8) is the power curtailment constraint, μ represents the upper limit of the power curtailment rate, which is set to 10%, P p,max is the upper limit of wind power output, P w,max is the upper limit of photovoltaic output; Formula (2.9) is the constraint on the proportion of new energy, indicating that the wind and solar output is greater than or equal to the thermal power output at each moment; Formulas (2.10)-(2.14) are energy storage operation constraints, where E is the energy storage capacity at each moment; P ch , P dis is the charge and discharge amount of energy storage at each moment, η ch , η dis SOC is the energy storage charging and discharging efficiency; min , SOC max Indicates the energy storage charge state; E ess,unit Represents the energy storage configuration capacity; P ess,unit Represents the energy storage charging and discharging power; θ t Energy storage 0-1 charge and discharge state variable; M is a real number.

[0048] Specifically, the power planning and operation simulation model established in step S3 is a mixed integer linear programming model. Then, based on different scenarios, the mature solver Cplex is used to solve it, and the power capacity planning scheme and daily operation simulation results under multiple new energy base scenarios are generated. The k-means algorithm is used to perform cluster analysis on the core parameters of the planning results. After the simulation is completed, various indicators including the total carbon emissions are statistically analyzed.

[0049] Specifically, the specific steps of step S4 are:

[0050] Step S41: First, determine the wind and solar output characteristics and scenarios of the Shagohuang area in the planning year, and formulate various planning and operation standards and energy development goals;

[0051] Step S42: Perform power capacity and operation simulation according to different scenarios, use the average power generation hours of various power sources as the initial parameters of the power planning and operation simulation model, and perform cluster analysis on the parameters of the optimization scheme under each scenario;

[0052] Step S43: After the first power optimization scheme simulation, analyze whether CO2 emissions, wind and solar power consumption, and supply shortage rate all meet the target requirements. If not, in the second optimization simulation, adjust the parameters according to the power generation hours and reliability constraints of various units obtained in the previous simulation;

[0053] Step S44: Through repeated iterative calculations of the power supply planning and operation simulation model, planning and operation are integrated so that the power supply planning scheme meets all the operation requirements of the system, minimizes the overall cost, and obtains the final planning scheme and parameter results.

[0054] The present invention has the following advantages:

[0055] 1. The present invention aims at the distribution of deserts and Gobi deserts in the region, takes into account the safety, flexibility, economy and environmental protection factors of power system operation, and generates source-grid-storage-direct-current planning schemes under multiple new energy base scenarios based on source-grid-storage-direct-current optimization planning technology. The core parameters of the planning results are clustered and analyzed to extract the technical principles of collaborative planning of new energy bases in deserts and Gobi deserts under various typical scenarios, and proposes typical parameters that meet the configuration scheme of DC transmission power systems, providing a guidance method for planning and site selection of new energy bases and access to power systems;

[0056] 2. The parameter optimization method of the present invention takes into account the goals of low-carbon environmental protection and wind and solar power consumption. Under the premise of ensuring that the investment cost of the new energy base is relatively low, it improves the utilization rate of new energy and reduces pollution to the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the process of the present invention;

[0058] Figure 2 It is a parameter optimization flow chart of the present invention;

[0059] Figure 3 This is a power operation diagram for a typical day scenario; DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0061] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0062] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0063] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description. Figures 1 to 3 As shown, a source-grid-storage-direct planning and parameter optimization method for Shagohuang new energy base is characterized in that it includes the following steps:

[0064] Step S1: According to the historical wind and solar data of a desert Gobi desert area throughout the year, Gaussian kernel density estimation is used to analyze the wind and solar output characteristics; the wind and solar output is simulated based on basic physical data such as historical wind speed, solar radiation intensity, temperature, etc. Generally speaking, the estimation of wind and solar output is to fit and match historical data by debugging the parameters in the known probability distribution such as Bernoulli distribution or binomial distribution, Poisson distribution, geometric distribution, etc. to describe the characteristics of wind and solar output; however, wind and solar output is directly affected by multiple factors such as weather and regional differences, and often presents an irregular multi-peak distribution, and the probability distribution is difficult to determine. Therefore, a non-parametric estimation method is used to directly fit and extract distribution characteristics without knowing the probability distribution. The histogram method and the kernel density estimation method are two commonly used non-parametric estimation methods, but the accuracy of the histogram method is relatively low. Compared with the histogram method, the estimation results obtained by the kernel density estimation method are continuous and high in accuracy, as follows:

[0065]

[0066] Where: t=1,2....,24, which is 24 time periods; x and y are the wind turbine and photovoltaic output in time period t, respectively. and are the wind turbine and photovoltaic outputs at time t on day d; h is the bandwidth; K() is the Gaussian kernel function, that is:

[0067]

[0068] Step S2: Based on the probability distribution function obtained by Gaussian kernel density estimation, inverse transformation random sampling is used to generate wind and solar output scenarios, and k-medoids clustering is used to obtain typical wind and solar day scenarios; based on the 8760-hour basic data of photovoltaic output and wind turbine output, the probability distribution characteristics can be described based on the kernel density estimation method, and the cumulative distribution function CDF is obtained. The inverse transformation method is further used for random sampling to generate a large number of sampling scenarios. The core definition is as follows:

[0069] It is known that X is a continuous random variable, and its probability distribution function CDF is Fx, then:

[0070]

[0071] Generate random variable X using uniform distribution based on CDF;

[0072] Clustering is performed on the relatively large random sampling results to obtain and form a typical analysis scenario. The K-mediods algorithm, a clustering method based on partitioning, is selected to divide the sample set to achieve clustering. The object closest to the cluster center (called the center point) is selected as the representative point of the cluster. The objective function can still use the square error criterion. The specific steps are as follows:

[0073] Step S201: randomly select a group of cluster samples as the center point set, that is, the initial cluster center point;

[0074] Step S202: Each center point corresponds to a cluster, which is divided into initial clustering results;

[0075] Step S203: Calculate the distance between each sample point and each center point, and put the sample point into the cluster with the shortest distance to the center point. Assume that X i and X j are two N-dimensional sample points, that is, the sampling points obtained by the inverse transformation method. They are considered to be general points in the clustering process, and the Euclidean distance between them is calculated by the following formula:

[0076]

[0077] Step S204: Perform a test. In each cluster, select the point with the smallest absolute error from each sample point in the cluster as the new center point. The sum of squares of clustering errors is calculated by the following formula:

[0078]

[0079] Where: K represents the number of clusters that the decision maker plans to achieve; X km represents the mth sample point in the kth cluster; M is the number of sample points in the kth cluster; C k is the current class center of the kth cluster;

[0080] Step S205: According to the test result of step S204, if the new cluster center point is consistent with the current cluster center point, the algorithm stops; otherwise, the algorithm continues to loop from step S202;

[0081] For a larger sampling scale, the above clustering method is used to cluster the sampling results to generate k typical day scenes, specifically:

[0082] First, the 365 days of the year are uniformly divided into three categories: summer, winter and transitional seasons according to the regional seasonal characteristics, with the vertical time scale being days and the horizontal time scale being the 24 time points of a day;

[0083] Secondly, the 8760-hour output data is normalized, and the Gaussian kernel density estimation is performed using equations (1.1) and (1.2) for the normalized data in three seasons to obtain the probability density estimation and cumulative probability distribution of the estimated quantity.

[0084] Finally, the inverse transformation method is used to randomly sample the obtained cumulative probability distribution to generate a large number of sampling samples, forming M groups of sampling scenarios. Based on the clustering method, the K-mediods algorithm is used to cluster the M groups of sampling scenarios to generate typical scenarios as the analysis basis for subsequent capacity optimization configuration.

[0085] Step S3: Establish a power supply planning and operation simulation model for the Shagohuang new energy base. Based on the typical wind and solar day scenario in step S2, simulate and generate a power supply capacity planning scheme and a daily simulation operation diagram. K-means clustering is used to perform clustering analysis on the core parameters of different schemes. Considering the economic efficiency and taking the minimum investment cost as the goal, an optimal configuration model for the wind, solar, thermal and storage capacity of the new energy base is established:

[0086] min(C p +C w +C g +C ess,p +C ess,E ) (2.1)

[0087] min(C OM +C co2 +C en +C fuel ) (2.2)

[0088]

[0089] (1-w p ) p,max (t)≤P p (t)≤(1+w p ) p,max (t) (2.4)

[0090] (1-w w ) w,max (t)≤P w (t)≤(1+w w ) w,max (t) (2.5)

[0091] 0≤P g (t)≤S g (2.6)

[0092] -λΔt≤P g (t)-P g (t-1)≤λΔt (2.7)

[0093]

[0094]

[0095]

[0096] 0≤P ch (t)≤P ess,unit (2.12)

[0097] 0≤P dis (t)≤P ess,unit (2.13)

[0098] 0≤P ch (t)≤Mθ t (2.14)

[0099] Among them, formula (2.1) and (2.2) are objective functions, with the goal of minimizing the investment cost of wind, solar, thermal and energy storage. In the formula, C p Represents the photovoltaic configuration capacity, C w Represents wind power configuration capacity; C g Represents the thermal power configuration capacity; C ess,p Represents the energy storage configuration power; C ess,E Represents the energy storage configuration capacity; C OM represents the operation and maintenance cost, C co2 represents the carbon emission cost; C en represents the environmental penalty cost; C fuel represents fuel cost;

[0100] Formulas (2.3)-(2.14) are constraints. Formula (2.3) is the power balance constraint, which indicates the total output P of wind, solar, thermal and storage at each moment. i Greater than or equal to the load P at each moment i,load ; Formulas (2.4)-(2.5) are wind and solar output constraints, where w p and w w is the wind uncertainty, w p and w w is the wind and solar uncertainty, P p,max is the upper limit of wind power output, P w,max is the upper limit of photovoltaic output, P p For wind power output, P w is the photovoltaic output; Formulas (2.6)-(2.7) are the thermal power output and climbing constraints, where λ is the upper limit of the climbing capacity, P g For thermal power generation, S g is the thermal power configuration capacity; Formula (2.8) is the power curtailment constraint, μ represents the upper limit of the power curtailment rate, which is set to 10%, P p,max is the upper limit of wind power output, P w,max is the upper limit of photovoltaic output; Formula (2.9) is the constraint on the proportion of new energy, indicating that the wind and solar output is greater than or equal to the thermal power output at each moment; Formulas (2.10)-(2.14) are energy storage operation constraints, where E is the energy storage capacity at each moment; P ch , Pdis is the charge and discharge amount of energy storage at each moment, η ch η dis SOC is the energy storage charging and discharging efficiency; min , SOC max Indicates the energy storage charge state; E ess,unit Represents the energy storage configuration capacity; P ess,unit Represents the energy storage charging and discharging power; θ t Energy storage 0-1 charge and discharge state variable; M is a real number.

[0101] The power planning and operation simulation model established is a mixed integer linear programming model. Then, based on different scenarios, the mature solver Cplex is used to solve it, and the power capacity planning scheme and daily operation simulation results under multiple new energy base scenarios are generated. The k-means algorithm is used to perform cluster analysis on the core parameters of the planning results. After the simulation is completed, various indicators including total carbon emissions are statistically analyzed.

[0102] Step S4: Formulate technical wind and solar power consumption and carbon emission indicators, analyze whether the indicators under different planning and operation schemes meet the requirements, correct the technical parameters, repeatedly iterate the power planning and operation simulation model to obtain the optimal planning scheme and parameter results, bring the corrected technical parameters into the power planning and operation simulation model, obtain new results clustering to obtain new parameter indicators, and repeatedly iterate this step.

[0103] Based on the wind and solar power output characteristics in the Shagohuang area, according to the power planning process, power optimization, production simulation, correction parameters, power re-optimization and simulation cycle calculation are performed. The specific steps of step S4 are:

[0104] Step S41: First, determine the wind and solar output characteristics and scenarios of the Shagohuang area in the planning year, and formulate various planning and operation standards and energy development goals;

[0105] Step S42: Perform power capacity and operation simulation according to different scenarios, use the average power generation hours of various power sources as the initial parameters of the power planning and operation simulation model, and perform cluster analysis on the parameters of the optimization scheme under each scenario;

[0106] Step S43: After the first power optimization scheme simulation, analyze the CO 2 Whether the emissions, wind and solar power consumption, and shortage rate all meet the target requirements. If not, in the second optimization simulation, the parameters are adjusted according to the power generation hours and reliability constraints of various units obtained in the previous simulation;

[0107] Step S44: Through repeated iterative calculations of the power supply planning and operation simulation model, planning and operation are integrated so that the power supply planning scheme meets all the operation requirements of the system, minimizes the overall cost, and obtains the final planning scheme and parameter results.

[0108] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical content without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any changes, modifications, equivalent changes and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention belong to the protection scope of the present technical solution.

Claims

1. A method for source-grid-storage-direct planning and parameter optimization of a Shagohuang new energy base, characterized in that: The following steps are involved: Step S1: Based on the historical data of wind and solar power in a desert Gobi desert area throughout the year, Gaussian kernel density estimation is used to analyze the wind and solar power output characteristics; Step S2: Based on the probability distribution function obtained by Gaussian kernel density estimation, inverse transformation random sampling is performed to generate wind and solar output scenarios, and k-medoids clustering is performed to obtain typical wind and solar day scenarios; Step S3: Establish a power supply planning and operation simulation model for the Shagohuang New Energy Base. Based on the typical day scene of wind and solar power in step S2, simulate and generate a power supply capacity planning scheme and a daily simulation operation diagram. K-means clustering is used to perform cluster analysis on the core parameters of different schemes. Step S4: Formulate technical wind and solar power consumption and carbon emission indicators, analyze whether the indicators under different planning and operation schemes meet the requirements, modify the technical parameters, and repeatedly iterate the power supply planning and operation simulation model to obtain the optimal planning scheme and parameter results.

2. According to claim 1, a method for source-grid-storage-direct planning and parameter optimization of Shagohuang new energy base is characterized by: In step S1, Gaussian kernel density estimation is used to analyze the wind and solar power output characteristics: Where: t=1,2....,24, which is 24 time periods; x and y are the wind turbine and photovoltaic output in time period t, respectively. and are the wind turbine and photovoltaic outputs at time t on day d; n is the number of historical days; h is the bandwidth; K() is the Gaussian kernel function, that is:

3. The method for source-grid-storage-direct current planning and parameter optimization of the Shagohuang new energy base according to claim 1 is characterized by: In step S2, the inverse transformation method is used to perform random sampling to generate a large number of sampling scenes, and its core definition is as follows: It is known that X is a continuous random variable, and its probability distribution function CDF is Fx, then: Inverse generate a random variate X using a uniform distribution based on the CDF.

4. The method for source-grid-storage-direct current planning and parameter optimization of the Shagohuang new energy base according to claim 3 is characterized by: In step S2, the K-mediods algorithm is used to divide the sample set to achieve clustering, and the center point closest to the cluster center is selected as the representative point of the cluster. The specific steps are as follows: Step S201: randomly select a group of cluster samples as the center point set; Step S202: Each center point corresponds to a cluster; Step S203: Calculate the distance between each sample point and each center point, and put the sample point into the cluster with the shortest distance to the center point. Assume that X i and X j are two N-dimensional sample points, which are considered as general points in the clustering process, and the Euclidean distance between them is calculated by the following formula: Step S204: Perform a test. In each cluster, select the point with the smallest absolute error from each sample point in the cluster as the new center point. The sum of squares of clustering errors is calculated by the following formula: Where: K represents the number of clusters that the decision maker plans to achieve; X km represents the mth sample point in the kth cluster; M is the number of sample points in the kth cluster; C k is the current class center of the kth cluster; Step S205: According to the test result of step S204, if the new cluster center point is consistent with the current cluster center point, the algorithm stops; otherwise, the algorithm continues to loop from step S202; The sampling results are clustered using a clustering method to generate k typical day scenes, specifically: First, the 365 days of the year are uniformly divided into three categories: summer, winter and transitional seasons according to the regional seasonal characteristics, with the vertical time scale being days and the horizontal time scale being the 24 time points of a day. Secondly, the 8760-hour output data is normalized, and the Gaussian kernel density estimation is performed using equations (1.1) and (1.2) for the normalized data in three seasons to obtain the probability density estimation and cumulative probability distribution of the estimated quantity. Finally, the inverse transformation method is used to randomly sample the obtained cumulative probability distribution to generate a large number of sampling samples, forming M groups of sampling scenarios. Based on the clustering method, the K-mediods algorithm is used to cluster the M groups of sampling scenarios to generate typical scenarios.

5. The method for source-grid-storage-direct current planning and parameter optimization of the Shagohuang new energy base according to claim 4 is characterized by: The power supply planning and operation simulation model in step S3 is: min(C p +C w +C g +C ess,p +C ess,E )(2.1) min(C OM +C co2 +C en +C fuel )(2.2) (1-w p )P p,max (t)≤P p (t)≤(1+w p )P p,max (t)(2.4) (1-w w )P w,max (t)≤P w (t)≤(1+w w )P w,max (t)(2.5) 0≤P g (t)≤S g (2.6) -λΔt≤P g (t)-P g (t-1)≤λΔt(2.7) 0≤P ch (t)≤P ess,unit (2.12) 0≤P dis (t)≤P ess,unit (2.13) 0≤P ch (t)≤Mθ t (2.14) Among them, formula (2.1) and (2.2) are objective functions, with the goal of minimizing the investment cost of wind, solar, thermal and energy storage. In the formula, C p Represents the photovoltaic configuration capacity, C w Represents wind power configuration capacity; C g Represents the thermal power configuration capacity; C ess,p Indicates the energy storage configuration power; C ess,E Represents the energy storage configuration capacity; C OM represents the operation and maintenance cost, C co2 represents the carbon emission cost; C en represents the environmental penalty cost; C fuel represents fuel cost; Formulas (2.3)-(2.14) are constraints. Formula (2.3) is the power balance constraint, which indicates the total output P of wind, solar, thermal and storage at each moment. i Greater than or equal to the load P at each moment i,load ; Formulas (2.4)-(2.5) are wind and solar output constraints, where w p and w w is the wind uncertainty, w p and w w is the wind and solar uncertainty, P p,max is the upper limit of wind power output, P w,max is the upper limit of photovoltaic output, P p For wind power output, P w is the photovoltaic output; Formulas (2.6)-(2.7) are the thermal power output and climbing constraints, where λ is the upper limit of the climbing capacity, P g For thermal power generation, S g is the thermal power configuration capacity; Formula (2.8) is the power curtailment constraint, μ represents the upper limit of the power curtailment rate, which is set to 10%, P p,max is the upper limit of wind power output, P w,max is the upper limit of photovoltaic output; Formula (2.9) is the constraint on the proportion of new energy, indicating that the wind and solar output is greater than or equal to the thermal power output at each moment; Formulas (2.10)-(2.14) are energy storage operation constraints, where E is the energy storage capacity at each moment; P ch , P dis is the charge and discharge amount of energy storage at each moment, η ch η dis SOC is the energy storage charging and discharging efficiency; min , SOC max Indicates the energy storage charge state; E ess,unit Represents the energy storage configuration capacity; P ess,unit Represents the energy storage charging and discharging power; θ t Energy storage 0-1 charge and discharge state variable; M is a real number.

6. The method for source-grid-storage-direct current planning and parameter optimization of the Shagohuang new energy base according to claim 5 is characterized by: The power planning and operation simulation model established in step S3 is a mixed integer linear programming model. Then, based on different scenarios, the mature solver Cplex is used to solve it, and the power capacity planning schemes and daily operation simulation results under multiple new energy base scenarios are generated. The k-means algorithm is used to perform cluster analysis on the core parameters of the planning results. After the simulation is completed, various indicators including the total carbon emissions are statistically analyzed.

7. The method for source-grid-storage-direct current planning and parameter optimization of the Shagohuang new energy base according to claim 6 is characterized by: The specific steps of step S4 are: Step S41: First, determine the wind and solar output characteristics and scenarios of the Shagohuang area in the planning year, and formulate various planning and operation standards and energy development goals; Step S42: Perform power capacity and operation simulation according to different scenarios, use the average power generation hours of various power sources as the initial parameters of the power planning and operation simulation model, and perform cluster analysis on the parameters of the optimization scheme under each scenario; Step S43: After the first power optimization scheme simulation, analyze whether CO2 emissions, wind and solar power consumption, and supply shortage rate all meet the target requirements. If not, in the second optimization simulation, adjust the parameters according to the power generation hours and reliability constraints of various units obtained in the previous simulation; Step S44: Through repeated iterative calculations of the power supply planning and operation simulation model, planning and operation are integrated so that the power supply planning scheme meets all the operation requirements of the system, minimizes the overall cost, and obtains the final planning scheme and parameter results.

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