Optimal configuration method and system for power capacity of new energy base

Abstract

The invention discloses a power capacity optimization configuration method of a new energy base. The method comprises the steps of obtaining parameter data related to capacity optimization in a to-be-configured area; constructing a double-layer optimization configuration model based on the parameter data; wherein an upper-layer model of the double-layer optimal configuration model is used for completing optimization of a direct current curve, and a lower-layer model of the double-layer optimal configuration model is used for completing optimization of power capacity; obtaining an optimal power supply configuration scheme according to the double-layer optimization configuration model; the method can improve the safety and stability performance and the overall income of the large-scale new energy base.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for optimizing the configuration of power capacity of a new energy base. Background Art

[0002] Building a new power system is the fundamental way to achieve "carbon peak and carbon neutrality" and an important means to help my country's economic and social high-quality transformation. With the advancement of the construction of a new power system under the "dual carbon" goal, large-scale new energy bases are being developed on a large scale and in a centralized manner, and their development is accelerated through the direct current transmission model.

[0003] Current energy base construction plans are all based on a certain scale of new energy installed capacity. They fail to fully consider the economic exploitability of new energy technologies and wind power generation characteristics in the planned area, and lack optimization of new energy installed capacity. Summary of the invention

[0004] The technical problem to be solved by the embodiments of the present invention is: to provide a method for optimizing the configuration of power capacity of a new energy base, which adopts a double-layer optimization model and can improve the safety, stability and overall benefits of a large-scale new energy base.

[0005] In a first aspect, an embodiment of the present invention provides a method for optimizing power capacity configuration of a new energy base, comprising:

[0006] Obtain parameter data related to capacity optimization in the area to be configured;

[0007] Constructing a double-layer optimization configuration model based on the parameter data; wherein the upper layer model of the double-layer optimization configuration model is used to optimize the DC curve, and the lower layer model of the double-layer optimization configuration model is used to optimize the power supply capacity;

[0008] According to the two-layer optimization configuration model, the optimal power supply configuration scheme is obtained.

[0009] Furthermore, obtaining the optimal power supply configuration scheme according to the two-layer optimization configuration model includes:

[0010] According to the upper model of the double-layer optimization configuration model, an optimal DC curve is obtained;

[0011] The optimal DC curve is input into the lower model of the double-layer optimization configuration model to output the optimal power supply configuration solution.

[0012] Furthermore, obtaining the optimal DC curve according to the upper model of the double-layer optimization configuration model includes:

[0013] Based on the parameter data, obtaining output data within a preset time period; wherein the output data includes wind power output data and photovoltaic output data;

[0014] Converting the output data into a per-unit output value, and obtaining an output input curve based on the per-unit output value;

[0015] Perform production simulation calculations, and output an optimal DC curve based on the results of the production simulation calculations and the output input curve.

[0016] Furthermore, the performing of the production simulation calculation and outputting the optimal DC curve according to the result of the production simulation calculation and the output input curve include:

[0017] Obtaining the coal-fired power unit related parameters, energy storage related parameters and new energy related parameters in the parameter data; wherein the coal-fired power unit related parameters include the rated capacity of a single coal-fired power unit, the number of coal-fired power units, the peak regulation depth and the power generation power of the coal-fired power unit; the energy storage related parameters include the rated working time of energy storage, the power generation power of energy storage and the maximum storage power; and the new energy related parameters include the total power generation power of new energy;

[0018] Calculate the new energy abandonment rate and channel confidence according to the relevant parameters of the coal-fired power unit, the relevant parameters of the energy storage, the relevant parameters of the new energy, and the curve parameters of the output input curve;

[0019] The curve confidence rate of the output input curve is calculated, and the optimal DC curve is output according to the curve confidence rate, the renewable energy power abandonment rate and the channel confidence; wherein the curve confidence rate is used to indicate the prediction capability of the DC power transmission curve.

[0020] Further, the calculating the curve confidence rate of the output input curve includes:

[0021] Calculating the curve confidence of direct current power transmission according to the output input curve;

[0022] The curve confidence rate of the direct current power transmission is calculated according to the curve confidence and the power transmission time of the direct current power transmission.

[0023] Furthermore, the step of inputting the optimal DC curve into the lower model of the double-layer optimization configuration model to output the optimal power configuration solution includes:

[0024] Each power source and the optimal DC curve corresponding to each power source are input into the lower model of the double-layer optimization model, and the optimal power source configuration scheme is output in combination with the operating cost and transmission benefit in the parameter data.

[0025] In a second aspect, the present invention provides a power capacity optimization configuration system for a new energy base, comprising:

[0026] A data acquisition module, used to acquire parameter data related to capacity optimization in the area to be configured;

[0027] A model building module, used to build a double-layer optimization configuration model based on the parameter data; wherein the upper model of the double-layer optimization configuration model is used to optimize the DC curve, and the lower model of the double-layer optimization configuration model is used to optimize the power supply capacity;

[0028] The solution output module is used to obtain the optimal power supply configuration solution according to the double-layer optimization configuration model.

[0029] Furthermore, the solution output module is also used for:

[0030] According to the upper model of the double-layer optimization configuration model, an optimal DC curve is obtained;

[0031] The optimal DC curve is input into the lower model of the double-layer optimization configuration model to output the optimal power supply configuration solution.

[0032] Furthermore, the solution output module is also used for:

[0033] Based on the parameter data, obtaining output data within a preset time period; wherein the output data includes wind power output data and photovoltaic output data;

[0034] Converting the output data into a per-unit output value, and obtaining an output input curve based on the per-unit output value;

[0035] Perform production simulation calculations, and output an optimal DC curve based on the results of the production simulation calculations and the output input curve.

[0036] Furthermore, the solution output module is also used for:

[0037] Obtaining the coal-fired power unit related parameters, energy storage related parameters and new energy related parameters in the parameter data; wherein the coal-fired power unit related parameters include the rated capacity of a single coal-fired power unit, the number of coal-fired power units, the peak regulation depth and the power generation power of the coal-fired power unit; the energy storage related parameters include the rated working time of energy storage, the power generation power of energy storage and the maximum storage power; and the new energy related parameters include the total power generation power of new energy;

[0038] Calculate the new energy abandonment rate and channel confidence according to the relevant parameters of the coal-fired power unit, the relevant parameters of the energy storage, the relevant parameters of the new energy, and the curve parameters of the output input curve;

[0039] The curve confidence rate of the output input curve is calculated, and the optimal DC curve is output according to the curve confidence rate, the renewable energy power abandonment rate and the channel confidence; wherein the curve confidence rate is used to indicate the prediction capability of the DC power transmission curve.

[0040] Compared with the prior art, the method for optimizing the power capacity configuration of a new energy base provided by an embodiment of the present invention has the following beneficial effects: by acquiring parameter data related to capacity optimization in the area to be configured; building a two-layer optimization configuration model based on the parameter data; wherein the upper model of the two-layer optimization configuration model is used to optimize the DC curve, and the lower model of the two-layer optimization configuration model is used to optimize the power capacity; according to the two-layer optimization configuration model, an optimal power configuration scheme is obtained; and the safety, stability performance and overall benefits of large-scale new energy bases can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical features of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 It is a flow chart of an embodiment of a method for optimizing power capacity configuration of a new energy base provided by the present invention;

[0043] Figure 2 It is a structural schematic diagram of an embodiment of a power capacity optimization configuration system for a new energy base provided by the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In a first aspect, an embodiment of the present invention provides a method for optimizing the configuration of power capacity of a new energy base, see Figure 1 , which is a flow chart of an embodiment of a method for optimizing power capacity configuration of a new energy base provided by the present invention.

[0046] like Figure 1 As shown, the method comprises the following steps:

[0047] S1: Obtain parameter data related to capacity optimization in the area to be configured;

[0048] S2: constructing a double-layer optimization configuration model based on the parameter data; wherein the upper layer model of the double-layer optimization configuration model is used to optimize the DC curve, and the lower layer model of the double-layer optimization configuration model is used to optimize the power supply capacity;

[0049] S3: Obtaining an optimal power supply configuration solution according to the two-layer optimization configuration model.

[0050] In the specific implementation, we first obtain parameter data related to capacity optimization configuration within the configuration area, including parameters related to coal-fired power units, energy storage, new energy, and DC curves. We then build an optimization configuration model based on the obtained parameter data, and obtain the optimal power configuration plan based on the optimization configuration model.

[0051] In an optional implementation, obtaining an optimal power supply configuration scheme according to the two-layer optimization configuration model includes:

[0052] According to the upper model of the double-layer optimization configuration model, an optimal DC curve is obtained;

[0053] The optimal DC curve is input into the lower model of the double-layer optimization configuration model to output the optimal power supply configuration solution.

[0054] Specifically, the upper model of the two-layer optimization configuration model optimizes the DC curve according to the base transmission curve under the determined power capacity to obtain a set of feasible power supply and transmission curve planning schemes. The lower model of the two-layer optimization configuration model receives the output of the upper model, and based on the optimized DC curve, outputs the optimal power configuration capacity for multiple different power configuration capacities combined with economic analysis.

[0055] In an optional implementation, obtaining the optimal DC curve according to the upper model of the double-layer optimization configuration model includes:

[0056] Based on the parameter data, obtaining output data within a preset time period; wherein the output data includes wind power output data and photovoltaic output data;

[0057] Converting the output data into a per-unit output value, and obtaining an output input curve based on the per-unit output value;

[0058] Perform production simulation calculations, and output an optimal DC curve based on the results of the production simulation calculations and the output input curve.

[0059] Specifically, the upper model of the two-layer optimization configuration model is used to optimize the DC curve. First, the output data within a preset time period is obtained according to the acquired parameter data. The output data includes wind power output data and photovoltaic output data. For example, the actual output of wind power for 8760 hours per year in the study area in the past 10 years is obtained through investigation and converted into the wind power output per unit value P. η·t·w And photovoltaic output per unit value P η·t·s The output per unit value is the ratio of the actual output to the installed capacity. The wind power output coefficient is calculated based on the wind power output per unit value and the photovoltaic output per unit value. and photovoltaic output coefficient The output input curve is obtained based on the wind power output coefficient and the photovoltaic output coefficient, and a production simulation calculation is performed. The results of the production simulation calculation are analyzed, and the optimal DC curve is output according to the analysis results and the output input curve.

[0060] In an optional implementation, the performing of the production simulation calculation and outputting the optimal DC curve according to the result of the production simulation calculation and the output input curve includes:

[0061] Obtaining the coal-fired power unit related parameters, energy storage related parameters and new energy related parameters in the parameter data; wherein the coal-fired power unit related parameters include the rated capacity of a single coal-fired power unit, the number of coal-fired power units, the peak regulation depth and the power generation power of the coal-fired power unit; the energy storage related parameters include the rated working time of energy storage, the power generation power of energy storage and the maximum storage power; and the new energy related parameters include the total power generation power of new energy;

[0062] Calculate the new energy abandonment rate and channel confidence according to the relevant parameters of the coal-fired power unit, the relevant parameters of the energy storage, the relevant parameters of the new energy, and the curve parameters of the output input curve;

[0063] The curve confidence rate of the output input curve is calculated, and the optimal DC curve is output according to the curve confidence rate, the renewable energy power abandonment rate and the channel confidence; wherein the curve confidence rate is used to indicate the prediction capability of the DC power transmission curve.

[0064] Specifically, the parameter data includes unit-related parameters, which include the rated capacity G of a single coal-fired power unit. c0 , the number of coal-fired power units n, the peak load depth λ and the power generation power P of the coal-fired power units at time t c·t , energy storage related parameters include energy storage rated working time T es , the energy storage power generation power P at time t es·t and maximum storage capacity E eg , new energy related parameters include the total power S of new energy generation n·t, where the total power calculation formula for renewable energy generation is as follows:

[0065]

[0066] Among them, G w represents the scale of wind power installed capacity, represents the wind power output coefficient at time t, G s represents the scale of photovoltaic installed capacity, Represents the photovoltaic output coefficient at time t.

[0067] The curve parameters include the rated power G of the DC curve dc , the power transmission value P at time t dc·t , Annual utilization hours T dc The peak and valley power transmission power of DC in spring, summer, autumn and winter at noon and evening, including the peak and valley power transmission power of DC in spring at noon, summer, autumn and winter. dc·sp·mg , DC spring evening peak power transmission power P dc·sp·ng , DC spring low-peak power transmission power P dc·sp·lo , DC power transmission power P during summer noon peak dc·su·mg , DC summer evening peak power transmission power P dc·su·ng , DC summer low-peak power transmission power P dc·su·lo , DC autumn noon peak power transmission power P dc·au·mg , DC autumn evening peak power transmission power P dc·au·ng , DC autumn low-peak power transmission power P dc·au·lo , DC winter noon peak power transmission power P dc·wi·mg , DC winter evening peak power transmission power P dc·wi·ng and DC winter low-peak transmission power P dc·wi·lo .

[0068] Calculate the difference θ between the DC curve transmission power at time t and the total power of renewable energy generation and energy storage generation power at time t. The specific calculation formula is as follows:

[0069] θ=(P H·0 , P H·2 , ... P H·23 );

[0070] P H·t =G dc·t ·P dc·t -S n·t -P es·t ;

[0071] According to the above difference and the number of coal-fired power units in operation, calculate the working capacity P of the coal-fired power unit at time t c·t , the specific calculation formula is as follows:

[0072]

[0073] Among them, n i Indicates the number of coal-fired power units in operation.

[0074] According to the above calculated parameters, the new energy abandonment rate and the channel confidence are calculated, and the curve confidence rate of the power input curve is calculated. According to the curve confidence rate, the new energy abandonment rate and the channel confidence rate, the optimal DC curve is output.

[0075] In an optional implementation manner, the calculating the curve confidence rate of the output input curve includes:

[0076] Calculating the curve confidence of direct current power transmission according to the output input curve;

[0077] The curve confidence rate of the direct current power transmission is calculated according to the curve confidence and the power transmission time of the direct current power transmission.

[0078] Specifically, first calculate the sum of all power outputs of the DC power transmission project. The formula is as follows:

[0079]

[0080] Among them, P ps·t Represents the sum of all power outputs at time t, G w represents the scale of wind power installed capacity, represents the wind power output coefficient at time t, G s represents the scale of photovoltaic installed capacity, Represents the photovoltaic output coefficient at time t, P es·t represents the energy storage power generation at time t, P c·t Represents the power generation capacity of the coal-fired power unit at time t.

[0081] For a given DC input curve P dc , calculate the DC power transmission confidence at time t, the specific formula is as follows:

[0082]

[0083] Among them, I t Indicates the confidence level of DC power transmission, P dc·t It represents the output of the DC curve at time t. When the DC curve output is less than or equal to the sum of all power outputs, the curve confidence is 1. When the DC curve output is greater than the sum of all power outputs, the curve confidence is 0.

[0084] Statistics are collected for the annual transmission time of DC power transmission and the peak transmission time at noon and at night in spring, summer, autumn and winter. The annual transmission time is 8760 hours. Spring is defined as 0:00 on March 1st to 23:00 on May 31st, summer is defined as 0:00 on June 1st to 23:00 on August 31st, autumn is defined as 0:00 on September 1st to 23:00 on November 30th, and winter is defined as 0:00 on January 1st to 23:00 on February 28th and 0:00 on December 1st to 23:00 on December 31st. For example, T sp·mg represents the power transmission duration during the spring noon peak, T sp·ng represents the peak power transmission duration in the spring evening, T su·mg represents the peak power transmission time in summer at noon, T su·ng represents the peak power transmission time in the summer evening, T au·mg represents the peak power transmission time in autumn at noon, T au·ng represents the peak power transmission time in the autumn evening, T wi·mg represents the peak power transmission time in winter at noon, T wi·ng Indicates the duration of peak power supply in winter evenings.

[0085] According to the curve confidence calculated above and the DC power transmission duration, the curve confidence rate of DC power transmission is calculated, including the annual power transmission confidence rate β and the peak period power transmission confidence rate β peakhours , the specific formula is as follows:

[0086]

[0087] T peakhours =T sp·mg +T sp@ng +T su·mg +T su·ng +T au·mg +T au·ng +T wi·mg +T wi·ng ;

[0088] Among them, T peak h ours Indicates the duration of power supply during the peak hours in the afternoon and evening throughout the year.

[0089] In an optional implementation, the step of inputting the optimal DC curve into the lower model of the double-layer optimization configuration model to output the optimal power configuration solution includes:

[0090] Each power source and the optimal DC curve corresponding to each power source are input into the lower model of the double-layer optimization model, and the optimal power source configuration scheme is output in combination with the operating cost and transmission benefit in the parameter data.

[0091] Specifically, the lower model of the two-layer optimization configuration model receives the optimal DC curve output by the upper model.

[0092] The economically exploitable upper limit of wind power technology Sw, photovoltaic technology Ss and coal-fired power technology Sc in the sending area, as well as operating cost data and transmission revenue data, are obtained to conduct economic analysis and output the optimal power configuration capacity.

[0093] In summary, the present invention fully considers the wind and solar power output characteristics and the economic exploitable amount of new energy technology in the study area, and can truly reflect the upper limit of the supporting scale of various types of power sources at the sending end base. The proposed two-layer optimization configuration model includes both the optimization of new energy configuration capacity and the optimization of DC transmission curve, which can achieve the optimal investment in the base itself and can be widely used in the DC transmission planning scheme of large energy bases with a high proportion of new energy.

[0094] In a second aspect, an embodiment of the present invention provides a power capacity optimization configuration system for a new energy base, see Figure 2 , which is a structural schematic diagram of an embodiment of a power capacity optimization configuration system for a new energy base provided by the present invention.

[0095] like Figure 2 As shown, the system comprises:

[0096] A data acquisition module 21 is used to acquire parameter data related to capacity optimization in the area to be configured;

[0097] A model building module 22, used to build a double-layer optimization configuration model based on the parameter data; wherein the upper model of the double-layer optimization configuration model is used to optimize the DC curve, and the lower model of the double-layer optimization configuration model is used to optimize the power supply capacity;

[0098] The solution output module 23 is used to obtain the optimal power supply configuration solution according to the double-layer optimization configuration model.

[0099] In an optional implementation, the solution output module 23 is further used to:

[0100] According to the upper model of the double-layer optimization configuration model, an optimal DC curve is obtained;

[0101] The optimal DC curve is input into the lower model of the double-layer optimization configuration model to output the optimal power supply configuration solution.

[0102] In an optional implementation, the solution output module 23 is further used to:

[0103] Based on the parameter data, obtaining output data within a preset time period; wherein the output data includes wind power output data and photovoltaic output data;

[0104] Converting the output data into a per-unit output value, and obtaining an output input curve based on the per-unit output value;

[0105] Perform a production simulation calculation, and output an optimal DC curve based on the result of the production simulation calculation and the output input curve.

[0106] In an optional implementation, the solution output module 23 is further used for:

[0107] Obtaining the coal-fired power unit related parameters, energy storage related parameters and new energy related parameters in the parameter data; wherein the coal-fired power unit related parameters include the rated capacity of a single coal-fired power unit, the number of coal-fired power units, the peak regulation depth and the power generation power of the coal-fired power unit; the energy storage related parameters include the rated working time of energy storage, the power generation power of energy storage and the maximum storage power; and the new energy related parameters include the total power generation power of new energy;

[0108] Calculate the new energy abandonment rate and channel confidence according to the relevant parameters of the coal-fired power unit, the relevant parameters of the energy storage, the relevant parameters of the new energy, and the curve parameters of the output input curve;

[0109] The curve confidence rate of the output input curve is calculated, and the optimal DC curve is output according to the curve confidence rate, the renewable energy power abandonment rate and the channel confidence; wherein the curve confidence rate is used to indicate the prediction capability of the DC power transmission curve.

[0110] In an optional implementation, the solution output module 23 is further used to:

[0111] Calculating the curve confidence of direct current power transmission according to the output input curve;

[0112] The curve confidence rate of the direct current power transmission is calculated according to the curve confidence and the power transmission time of the direct current power transmission.

[0113] In an optional implementation, the solution output module 23 is further used for:

[0114] Each power source and the optimal DC curve corresponding to each power source are input into the lower model of the double-layer optimization model, and the optimal power source configuration scheme is output in combination with the operating cost and transmission benefit in the parameter data.

[0115] The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. It should be pointed out that for those skilled in the art, several equivalent obvious variations and / or equivalent substitutions can be made without departing from the technical principles of the present invention. These obvious variations and / or equivalent substitutions should also be regarded as the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of power capacity of a new energy base, characterized in that: include: Obtain parameter data related to capacity optimization in the area to be configured; Constructing a double-layer optimization configuration model based on the parameter data; wherein the upper layer model of the double-layer optimization configuration model is used to optimize the DC curve, and the lower layer model of the double-layer optimization configuration model is used to optimize the power supply capacity; According to the two-layer optimization configuration model, the optimal power supply configuration scheme is obtained.

2. The power capacity optimization configuration method of the new energy base according to claim 1, characterized in that: The step of obtaining an optimal power supply configuration scheme according to the two-layer optimization configuration model includes: According to the upper model of the double-layer optimization configuration model, an optimal DC curve is obtained; The optimal DC curve is input into the lower model of the double-layer optimization configuration model to output the optimal power supply configuration solution.

3. The power capacity optimization configuration method of the new energy base according to claim 2, characterized in that: The step of obtaining an optimal DC curve according to the upper model of the double-layer optimization configuration model comprises: Based on the parameter data, obtaining output data within a preset time period; wherein the output data includes wind power output data and photovoltaic output data; Converting the output data into a per-unit output value, and obtaining an output input curve based on the per-unit output value; Perform production simulation calculations, and output an optimal DC curve based on the results of the production simulation calculations and the output input curve.

4. The power capacity optimization configuration method of the new energy base according to claim 3, characterized in that: The performing of the production simulation calculation and outputting the optimal DC curve according to the result of the production simulation calculation and the output input curve include: Obtaining the coal-fired power unit related parameters, energy storage related parameters and new energy related parameters in the parameter data; wherein the coal-fired power unit related parameters include the rated capacity of a single coal-fired power unit, the number of coal-fired power units, the peak regulation depth and the power generation power of the coal-fired power unit; the energy storage related parameters include the rated working time of energy storage, the power generation power of energy storage and the maximum storage power; and the new energy related parameters include the total power generation power of new energy; Calculate the new energy abandonment rate and channel confidence according to the relevant parameters of the coal-fired power unit, the relevant parameters of the energy storage, the relevant parameters of the new energy, and the curve parameters of the output input curve; The curve confidence rate of the output input curve is calculated, and the optimal DC curve is output according to the curve confidence rate, the renewable energy power abandonment rate and the channel confidence; wherein the curve confidence rate is used to indicate the prediction capability of the DC power transmission curve.

5. The power capacity optimization configuration method of the new energy base according to claim 4, characterized in that: The calculating the curve confidence rate of the output input curve includes: Calculating the curve confidence of direct current power transmission according to the output input curve; The curve confidence rate of the direct current power transmission is calculated according to the curve confidence and the power transmission time of the direct current power transmission.

6. The power capacity optimization configuration method of the new energy base according to claim 2 is characterized in that: The step of inputting the optimal DC curve into the lower model of the double-layer optimization configuration model and outputting the optimal power supply configuration solution comprises: Each power source and the optimal DC curve corresponding to each power source are input into the lower model of the double-layer optimization model, and the optimal power source configuration scheme is output in combination with the operating cost and transmission benefit in the parameter data.

7. A power capacity optimization configuration system for a new energy base, characterized in that: include: A data acquisition module, used to acquire parameter data related to capacity optimization in the area to be configured; A model building module, used to build a double-layer optimization configuration model based on the parameter data; wherein the upper model of the double-layer optimization configuration model is used to optimize the DC curve, and the lower model of the double-layer optimization configuration model is used to optimize the power supply capacity; The solution output module is used to obtain the optimal power supply configuration solution according to the double-layer optimization configuration model.

8. The power capacity optimization configuration system for a new energy base according to claim 7, characterized in that: The solution output module is also used for: According to the upper model of the double-layer optimization configuration model, an optimal DC curve is obtained; The optimal DC curve is input into the lower model of the double-layer optimization configuration model to output the optimal power supply configuration solution.

9. The power capacity optimization configuration system for a new energy base according to claim 8, characterized in that: The solution output module is also used for: Based on the parameter data, obtaining output data within a preset time period; wherein the output data includes wind power output data and photovoltaic output data; Converting the output data into a per-unit output value, and obtaining an output input curve based on the per-unit output value; Perform production simulation calculations, and output an optimal DC curve based on the results of the production simulation calculations and the output input curve.

10. The power capacity optimization configuration system of the new energy base according to claim 9, characterized in that: The solution output module is also used for: Obtaining the coal-fired power unit related parameters, energy storage related parameters and new energy related parameters in the parameter data; wherein the coal-fired power unit related parameters include the rated capacity of a single coal-fired power unit, the number of coal-fired power units, the peak regulation depth and the power generation power of the coal-fired power unit; the energy storage related parameters include the rated working time of energy storage, the power generation power of energy storage and the maximum storage power; and the new energy related parameters include the total power generation power of new energy; Calculate the new energy abandonment rate and channel confidence according to the relevant parameters of the coal-fired power unit, the relevant parameters of the energy storage, the relevant parameters of the new energy, and the curve parameters of the output input curve; The curve confidence rate of the output input curve is calculated, and the optimal DC curve is output according to the curve confidence rate, the renewable energy power abandonment rate and the channel confidence; wherein the curve confidence rate is used to indicate the prediction capability of the DC power transmission curve.

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