Virtual power plant scale measuring and calculating method

Abstract

The invention discloses a virtual power plant scale measurement and calculation method. The method comprises the following steps: S1, collecting influence factor data of virtual power plant scale measurement and calculation; the influence factors comprise internal resource factors and external demand factors; s2, constructing a dynamic weight model based on the new energy proportion and the marketization level, and calculating the dynamic weight of each influence factor through the dynamic weight model; s3, according to the dynamic weight, obtaining an initial scale of the virtual power plant through a weighted measurement and calculation model; and S4, on the basis of the initial scale, performing closed-loop iteration in a net present value and closed-loop iteration outside an internal return rate in sequence to correct a current iteration scale until a preset iteration termination condition is met, and outputting an optimal scale. Through dynamic weight optimization and double closed-loop income feedback iteration, scale measurement and calculation is upgraded from static technical parameter driving to dynamic multi-factor coupling and economic income linkage driving, and optimal scale output accurately adapted to scene change is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual power plant planning and operation, and particularly relates to a virtual power plant scale calculation method. BACKGROUND

[0002] With the deepening of the national strategy of "carbon peak and carbon neutrality", virtual power plant (VPP) as an intelligent aggregation of distributed resources such as distributed power, energy storage system, controllable load (such as electric vehicles, flexible air conditioners), etc., has become a key technical means to improve the accommodation capacity of new energy of power grid, enhance the flexibility of power grid regulation, and ensure the safe and stable operation of power system.

[0003] In the planning and construction stage of virtual power plant, its scale calculation (i.e., determining the optimal aggregation capacity configuration) is the primary and core link, and the accuracy of the calculation result is directly related to the investment economy, operation reliability of virtual power plant and response ability to power grid regulation demand. At present, the industry mainly relies on multi-factor comprehensive analysis or experience model based on fixed weight to calculate the scale of virtual power plant. Specifically, the existing technology usually sets fixed weight coefficients for key factors affecting the scale, such as output characteristics of distributed power, configuration parameters of energy storage system, capacity of controllable load and regulation demand of power grid, and then calculates the recommended scale through linear weighting or regression analysis.

[0004] However, such traditional methods have significant limitations and have been difficult to adapt to the dynamic and complex scenarios of high proportion of new energy access and deepening of power market reform in new power system, specifically: Firstly, the weight setting is static, and the scene adaptability is poor. The fixed weight system used in the existing method cannot effectively respond to the dynamic changes of key scene parameters. For example, when the new energy penetration rate (r) in a certain area increases from a low level (such as 20%) to a high level (such as 60%), the influence weight of distributed power output fluctuation on scale decision should be increased accordingly; similarly, in areas with different degree of electricity marketization (γ) (such as from pilot market to mature spot market), the guiding weight of electricity price signal and auxiliary service income on scale optimization should also be dynamically adjusted. Static weight leads to a serious deviation of calculation result from actual situation, and easily causes problems of excess or insufficient scale configuration when the scene changes.

[0005] Second, the coupling of technical economy is insufficient, and the benefit feedback mechanism is lacking. The traditional scale calculation is only based on the static calculation of technical parameters (such as new energy output and load capacity), and the economic core indicators such as investment return (such as degree electricity income and auxiliary service income) are not deeply integrated into the iterative calculation process as a constraint condition, ignoring the reverse guiding effect of investment return on scale optimization, which is easy to cause investment decision-making mistakes, and the problems of "waste of investment caused by oversize" or "revenue growth restricted by small size".

[0006] Third, the precision of the calculation model is limited, and it is difficult to support accurate decision-making. Due to the lack of consideration of the dynamic coupling relationship between multiple factors and the real-time feedback of economic benefits, the calculation error of the traditional static model usually exceeds 15% when facing complex scenes such as "high new energy proportion and high marketization level", which cannot provide reliable data support for accurate planning and efficient investment of virtual power plants.

[0007] The statements herein only provide background technology related to the present application, and do not necessarily constitute prior art. SUMMARY

[0008] The purpose of the present application is to provide a virtual power plant scale calculation method capable of integrating dynamic weight adjustment and real-time benefit feedback, to overcome the problems of poor scene adaptability, low calculation precision and insufficient economic feasibility caused by the static weight and the lack of benefit feedback in the traditional virtual power plant scale calculation method, and to provide technical support for scientific planning and stable investment of virtual power plants.

[0009] In order to achieve the above purpose, the present application provides a virtual power plant scale calculation method, comprising the following steps: S1, collecting the influence factor data of the virtual power plant scale; the influence factors include internal resource factors and external demand factors; S2, based on the new energy proportion and the marketization level, a dynamic weight model is constructed, and the dynamic weight of each influence factor is calculated through the dynamic weight model; S3, according to the dynamic weight, the initial scale of the virtual power plant is obtained through the weighted calculation model; S4, based on the initial scale, the net present value internal closed loop iteration and the internal rate of return external closed loop iteration are carried out in turn to correct the current iteration scale until the iteration termination condition is met, and the optimal scale is output.

[0010] Optionally, in step S2, the dynamic weight model is: ; Wherein, ω i is the dynamic weight of the i th influence factor; ω i0 is the basic weight of the i th influence factor; k iis the new energy proportion sensitive coefficient of the ith influencing factor; m i is the marketization level sensitive coefficient of the ith influencing factor; ω j0 is the basic weight of the jth influencing factor; k j is the new energy proportion sensitive coefficient of the jth influencing factor; m j is the marketization level sensitive coefficient of the jth influencing factor; r is the new energy proportion; and γ is the marketization level.

[0011] Optionally, in step S3, the weighted measurement model is: ; wherein, S0 is an initial scale; S i0 is a benchmark scale corresponding to the ith influencing factor.

[0012] Optionally, k i > 0 of the internal resource factor, k i of the external demand factor is -0.1-0.3; m i > 0 of the external demand factor, and m i of the internal resource factor is -0.05-0.2.

[0013] Optionally, the internal resource factor includes at least one of a distributed power supply characteristic, an energy storage system parameter, a controllable load capacity, and an equipment investment cost; and the external demand factor includes at least one of a power grid regulation demand and a market transaction price.

[0014] Optionally, in step S4, the net present value inner loop iteration includes: S4.1, calculating the net present value by the following calculation formula: ; wherein, NPV is the net present value; I t is total income of the tth year; C t is operation and maintenance cost of the tth year; q is a benchmark income rate; C inv，0 is initial total investment; and S4.2, comparing the net present value calculated by the calculation formula of step S4.1 with a target net present value; when the calculated net present value is less than the target net present value, adjusting a current iteration scale according to a scale correction amount; the scale correction amount is: ; wherein, ΔS1 is the scale correction amount; S0 is the initial scale calculated by step S3; NPV target is the target net present value; S4.3, repeating steps S4.1-S4.2 until the calculated net present value ≥ the target net present value, and outputting an adjusted scale.

[0015] Optionally, the total revenue includes power sale revenue and ancillary service revenue.

[0016] Optionally, 6%≤q≤8%.

[0017] Optionally, in step S4, the internal rate of return outer closed loop iteration includes: S4.4, after the calculated net present value ≥ target net present value, the adjustment scale output by the net present value inner closed loop iteration is substituted into the following calculation formula to calculate the internal rate of return: ; Wherein, IRR is the internal rate of return. S4.5, compare the internal rate of return calculated by the calculation formula of step S4.4 with the target internal rate of return; when the internal rate of return is less than the target internal rate of return, the dynamic weight is corrected, and the net present value inner closed loop iteration is performed again. S4.6, cycle step S4.4 to step S4.5 until the calculated internal rate of return ≥ target internal rate of return, and the net present value ≥ target net present value.

[0018] Optionally, the iteration termination condition is that the absolute value of the difference between the internal rate of return and the target internal rate of return ≤ 0.5%, and the absolute value of the difference between the net present value and the target net present value ≤ 5%.

[0019] Compared with the prior art, the technical scheme of the present application at least has the following beneficial effects: The present application combines dynamic weight adjustment with double closed loop revenue feedback iteration, which works together to realize that the virtual power plant scale calculation method can adapt to different scenarios of new energy proportion (10%~80%) and marketization level (0.2~1.0), balance technical feasibility and economy, and improve the calculation accuracy. First, by the dynamic weight model constructed in step S2, the present application introduces new energy proportion and marketization level as key variables into weight calculation, so that the weight of each influencing factor is no longer a static preset value, but a variable that can be dynamically adjusted with external scene parameters, overcoming the inherent defect of poor adaptability of traditional virtual power plant scale calculation method when the scene changes, and greatly improving the accuracy of the calculation result. Further, the present application constructs a double closed loop revenue feedback iteration mechanism through step S4, which couples scale calculation with investment return indicators (net present value and internal rate of return). Through the inner closed loop, the scale is preliminarily corrected by the net present value, and then through the outer closed loop, the weight distribution is optimized by the internal rate of return, forming a closed loop oriented to the final economic revenue, so that the scale scheme has both technical feasibility and investment economy. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1A flow chart of a virtual power plant scale measurement method provided by the present application.

[0021] Figure 2 A virtual power plant scale measurement model architecture diagram provided by the present application.

[0022] Figure 3 A flow chart of a double closed loop benefit feedback iteration provided by the present application. DETAILED DESCRIPTION

[0023] The virtual power plant scale measurement method provided by the present application is further described in detail below in combination with the drawings and specific embodiments. The advantages and features of the present application will be clearer according to the following description. It should be noted that the drawings are greatly simplified and all use non-precise proportions, only to facilitate, clearly assist in the purpose of describing the embodiments of the present application. In order to make the purpose, features and advantages of the present application more obvious and easy to understand, please refer to the drawings. It should be noted that the structure, proportion, size, etc. shown in the drawings attached to the present specification are only used to cooperate with the content disclosed in the specification, so that those skilled in the art can understand and read, and are not used to limit the conditions of the present application. Embodiment, therefore, any modification of structure, change of proportion relationship or adjustment of size, without affecting the effect and purpose that can be produced by the present application, should still fall within the scope of the technical content disclosed by the present application.

[0024] As described in the background, the existing virtual power plant scale measurement method has the problems of poor scene adaptability, low measurement accuracy and insufficient economic feasibility.

[0025] To solve the above-mentioned defects, the present application provides a virtual power plant scale measurement method based on improved multi-factor dynamic weighting and benefit feedback. On the one hand, the proportion of new energy and marketization level are introduced as core variables to dynamically adjust the weight of each influencing factor, so that the scale measurement method can adapt to different regions and different development stages, and solve the problem of poor adaptability of static model. On the other hand, two core economic indicators: net present value (NPV) and internal rate of return (IRR) are taken as constraint conditions and embedded in the iterative optimization process of scale measurement, and a double closed loop feedback process of "scale measurement-benefit calculation-deviation correction" is constructed. The scale measurement is upgraded from static technical parameter driving to dynamic multi-factor coupling and economic benefit joint driving, and the optimal scale output that accurately adapts to scene changes is realized.

[0026] The technical solutions of the present application are further described in detail below in combination with the drawings and specific embodiments.

[0027] As Figure 1As shown, the present application provides a virtual power plant scale calculation method based on improved multi-factor dynamic weighting and benefit feedback, comprising the following steps: S1, collecting influence factor data of virtual power plant scale calculation; the influence factors include internal resource factors and external demand factors.

[0028] In this embodiment, first, a core influence factor system for virtual power plant scale calculation is constructed, and the influence factors are divided into two categories: internal resource factors and external demand factors. Internal resource factors (F1~F4): F1: Distributed power characteristics (new energy ratio r, output fluctuation rate σ DG ); F2: Energy storage system parameters (energy storage capacity E ESS , charge and discharge efficiency η ESS ); F3: Controllable load capacity (electric vehicle ownership N EV , air conditioning load elasticity coefficient α AC ); F4: Equipment investment cost (unit installed capacity cost C inv , operation and maintenance cost C op ).

[0029] External demand factors (F5~F6): F5: Grid regulation demand (peak shaving capacity demand P peak , frequency response speed v freq ); F6: Market transaction price (electricity spot price p spot , auxiliary service benefit coefficient β as ).

[0030] S2, based on the new energy ratio and the market level, a dynamic weight model is constructed, and the dynamic weight of each influence factor is calculated through the dynamic weight model.

[0031] Among them, the market level (γ) is determined by the proportion of spot transaction electricity, 0≤γ≤1.

[0032] The dynamic weight model is: ; In the formula, ω i is the dynamic weight of the i-th influence factor; ω i0 is the basic weight of the i-th influence factor, ω i0 is determined by the Analytic Hierarchy Process (AHP); k i is the new energy ratio sensitivity coefficient of the i-th influence factor; m iω is the marketization level sensitive coefficient of the ith influencing factor; ω j0 k is the basic weight of the jth influencing factor; k j m is the new energy proportion sensitive coefficient of the jth influencing factor; m j ω is the marketization level sensitive coefficient of the ith influencing factor; ω

[0033] In the dynamic weight model, when calculating the internal resource factors (F1~F4), k i > 0; when calculating the external demand factors (F5~F6), k i The value of k depends on the correlation between the influencing factor and the new energy proportion (r), -0.1≤k i ≤0.3.

[0034] In the dynamic weight model, when calculating the internal resource factors (F1~F4), m i The value of m depends on the correlation between the influencing factor and the marketization level (γ), -0.05≤m i ≤0.2; when calculating the external demand factors (F5~F6), m i > 0.

[0035] Through the calculation of the dynamic weight model, for example, when the new energy proportion (r) increases from 20% to 50%, the dynamic weight of F1 (distributed power characteristics) will correspondingly increase from 0.20 to 0.35; when the marketization level (γ) increases from 0.3 to 0.8, the dynamic weight of F6 (marketization transaction price) will correspondingly increase from 0.15 to 0.28, realizing the dynamic matching of weight and scene.

[0036] S3, according to the dynamic weight, a weighted estimation model is used to obtain the initial scale of the virtual power plant.

[0037] The weighted estimation model (multiple linear regression model) is: ; In the formula, S0 is the initial scale; S i0 is the reference scale corresponding to the ith influencing factor, and S i0 is determined by industry standards or historical data.

[0038] S4, based on the initial scale, the net present value inner closed loop iteration and the internal rate of return outer closed loop iteration are sequentially performed to correct the current iteration scale until the iteration termination condition is met, and the optimal scale is output.

[0039] Wherein, the net present value (NPV) refers to the difference between the present value of the income and the present value of the investment in the whole life cycle (usually 20 years) of the virtual power plant; the internal rate of return (IRR) is the discount rate when the net present value is equal to zero, reflecting the return on investment, and is used to measure the return ability of the investment scheme.

[0040] In this embodiment, the "scale calculation-income calculation-deviation correction" double-loop income feedback iteration process is constructed through the net present value inner loop iteration (NPV feedback, or NPV inner loop iteration) and the internal rate of return outer loop iteration (IRR feedback). Specifically, the net present value inner loop iteration (NPV feedback) includes: S4.1, based on the current iteration scale, the net present value (NPV) is calculated by the following calculation formula: ; In the formula, I t is the total income in the tth year, which includes the electricity sales income and the ancillary service income; C t is the operation and maintenance cost in the tth year; q is the benchmark income rate, 6%≤q≤8%; C inv，0 is the initial total investment.

[0041] S4.2, compare the net present value calculated by the calculation formula of step S4.1 with the target net present value; when the calculated net present value is less than the target net present value, adjust the current iteration scale according to the scale correction amount; the scale correction amount ΔS1 is: ; In the formula, ΔS1 is the scale correction amount; S0 is the initial scale calculated in step S3; NPV target is the target net present value, NPV target is set by the investment requirement.

[0042] In step 4.2, the adjusted scale S=S0+ΔS1.

[0043] S4.3, loop steps S4.1 to S4.2 until the calculated net present value≥target net present value (NPV≥NPV target ), and output the adjusted scale.

[0044] After step S4.3, the net present value inner loop iteration outputs the adjusted scale S1.

[0045] The internal rate of return outer loop iteration (IRR feedback, or IRR outer loop iteration) includes: S4.4, after the calculated net present value≥target net present value, the adjusted scale S1 output by the net present value inner loop iteration is substituted into the following calculation formula to calculate the internal rate of return (IRR): .

[0046] S4.5, compare the internal rate of return calculated by the calculation formula of step S4.4 with the target internal rate of return; when the internal rate of return is less than the target internal rate of return (the target internal rate of return is usually 8%~10%), correct the dynamic weight (for example, increase the F6 weight to increase the yield estimation accuracy), and re-perform the net present value inner loop iteration.

[0047] S4.6, loop steps S4.4 to S4.5 until the calculated internal rate of return is ≥ the target internal rate of return (IRR≥ IRR target ), and the net present value is ≥ the target net present value (NPV≥ NPV target ).

[0048] In this embodiment, in step S4, the iteration termination condition is that the absolute value of the difference between the internal rate of return and the target internal rate of return is ≤0.5% (|IRR- IRR target |≤0.5%), and the absolute value of the difference between the net present value and the target net present value is ≤5% (|NPV-NPV target |≤5%).

[0049] Figure 2 An architecture diagram of a virtual power plant scale estimation model based on improved multi-factor dynamic weighting and yield feedback is shown. The total process of virtual power plant scale estimation includes: (1) Data acquisition and preprocessing: Collect distributed power output data, energy storage data, load data, grid regulation demand, market transaction price, and other basic data, and remove outliers through filtering and interpolation.

[0050] (2) Dynamic weight calculation: According to the new energy proportion r and the market level γ of the current scene, the dynamic weight ω i of each influencing factor is calculated through the dynamic weight model (dynamic weight formula). In this embodiment, the dynamic weights ω1~ω 6。 of influencing factors F1~F6 are obtained.

[0051] (3) Initial scale estimation: Based on the weighted factor index, the initial scale S0 is calculated using a weighted estimation model (multivariate linear regression model).

[0052] (4) Double-loop yield feedback iteration: Perform NPV inner loop and IRR outer loop iteration, correct the scale to meet the termination condition, and output the optimal scale S opt .

[0053] Embodiment 1 Taking a virtual power plant in an industrial park as an example, this embodiment uses a virtual power plant scale calculation method based on improved multi-factor dynamic weighting and revenue feedback provided by the present invention to calculate the scale. The specific parameters are as follows: Basic scenario: New energy share r=30% (photovoltaic 20% + wind power 10%), marketization level γ=0.5 (spot trading electricity share 50%), corresponding to the "medium new energy + medium marketization" scenario; Target return: NPV target =5 million yuan, IRR target =9%.

[0054] 1. Data Acquisition and Preprocessing Collect basic data on a 1-year timescale: F1 Distributed Power Generation Characteristics: Photovoltaic installation candidate capacity 10MW ~ 50MW, output fluctuation rate σ DG =25%; F2 energy storage system parameters: charge / discharge efficiency η ESS =90%; F3 controllable load capacity: 200 electric vehicles in operation, air conditioning load elasticity coefficient α AC =0.6; F4 equipment investment cost: unit cost 1500 yuan / kWh; F5 Power Grid Regulation Demand: Peak Shaving Capacity Demand P peak =8MW; F6 market transaction price: average spot price 0.5 yuan / kWh, ancillary service revenue coefficient β as =0.2.

[0055] 2. Dynamic weight calculation Determine the basic weights ω using AHP. i0 The dynamic weight ω is calculated using r=30% and γ=0.5. i As shown in Table 1 below.

[0056] Table 1. Parameter settings of the dynamic weight model and the calculation results of the dynamic weights of each influencing factor.

[0057] 3. Scale Calculation and Iteration Initial size calculation: S0 = 28MW (based on dynamic weights and multiple linear regression model); NPV inner loop: Calculate in ten thousand yuan (< 5 million yuan), adjust the scale to S1 = 32MW, at this time NPV = 5.3 million yuan (≥ 5 million yuan). IRR outer closed loop: when S1=32 MW, IRR=8.2% (<9%), correct the weight of F6 to 0.16, recalculate the scale S2=34 MW, at this time IRR=9.1% (≥9%), NPV=5.6 million yuan (≥500,000 yuan), iteration is terminated, and the optimal scale S is output opt =34 MW.

[0058] Comparative Example 1 The difference from Example 1 is that the scale is calculated by using the traditional static weight method (fixed weight of each influencing factor, no income feedback).

[0059] Example 2 The difference from Example 1 is that the basic scene of this example is that the new energy ratio r=10% and the marketization level γ=0.2, corresponding to the "low new energy + low marketization" scene.

[0060] Comparative Example 2 The difference from Example 2 is that the scale is calculated by using the traditional static weight method (fixed weight of each influencing factor, no income feedback).

[0061] Example 3 The difference from Example 1 is that the basic scene of this example is that the new energy ratio r=60% and the marketization level γ=0.9, corresponding to the "high new energy + high marketization" scene.

[0062] Comparative Example 3 The difference from Example 3 is that the scale is calculated by using the traditional static weight method (fixed weight of each influencing factor, no income feedback).

[0063] Comparative Experiment and Results The method of this example is compared with the method of the comparative example (traditional static weight method), and the experiment covers three typical scenes. The calculation error is measured by "(calculated value-actual optimal value) / actual optimal value", and the results are shown in Table 2: Table 2 Comparison of calculation accuracy of the method of the present application and the method of the comparative example under different scenes

[0064] From Table 2, it can be concluded that, first, the method of the application controls the measurement error in different scenarios to be-2.3%~0%, which is significantly better than the traditional static weight method (-18.2%~-27.1%), and the measurement accuracy is improved by more than 87%; second, in the 'high new energy + high marketization' scenario, the traditional static weight method leads to a serious shortage of scale measurement (bias 27.1%) due to the static weight, while the method of the application has a bias of only 1.7% through dynamic weight and revenue feedback, further proving that the method of the application can accurately match the actual demand; third, the scale scheme measured by the method of the application meets the revenue target of NPV≥500,000 yuan and IRR≥9%, while the traditional static weight method does not meet the revenue requirement in 2 scenarios, verifying the economic feasibility of the method.

[0065] In summary, the application provides a virtual power plant scale calculation method based on improved multi-factor dynamic weighting and revenue feedback, which upgrades the scale calculation from static technical parameter driving to dynamic multi-factor coupling and economic revenue linkage driving through dynamic weight optimization and double closed-loop revenue feedback iteration, and realizes the optimal scale output that accurately adapts to scenario changes.

[0066] It should be noted that in this document, the terms "comprise", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or equipment. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of another identical element in the process, method, article or equipment that includes the element.

[0067] In the description of the application, it should be understood that the orientation or positional relationship indicated by the terms "center", "height", "thickness", "upper", "lower", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. In the description of the application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0068] In the description of the application, unless otherwise clearly specified and limited, the terms "mounting", "connection", "linking", "fixing" should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integral; can be mechanical connection, can also be electrical connection; can be direct connection, can also be indirect connection through intermediate medium, can be internal communication of two elements or interaction relationship of two elements. For those skilled in the art, the specific meaning of the above terms in the application can be understood according to the specific circumstances.

[0069] In the present application, unless otherwise clearly specified and limited, "on" or "under" of the first feature to the second feature can include that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, "on", "above" and "above" of the first feature to the second feature include that the first feature is directly above and obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. "Under", "below" and "below" of the first feature to the second feature include that the first feature is directly below and obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0070] Although the content of the present application has been described in detail through the above preferred embodiments, it should be recognized that the above description should not be considered as a limitation of the present application. After reading the above content, various modifications and alternatives of the present application will be obvious to those skilled in the art. Therefore, the protection scope of the present application should be defined by the appended claims.

Claims

1. A virtual power plant scale calculation method, characterized in that, The method comprises the following steps: S1, collecting influence factor data of the virtual power plant scale; the influence factors include internal resource factors and external demand factors; S2, constructing a dynamic weight model based on the new energy proportion and the marketization level, and calculating the dynamic weight of each influence factor through the dynamic weight model; S3, obtaining the initial scale of the virtual power plant through a weighted calculation model according to the dynamic weight; S4, based on the initial scale, sequentially performing internal closed-loop iteration of net present value and external closed-loop iteration of internal rate of return to correct the current iteration scale until the iteration termination condition is met, and outputting the optimal scale.

2. The virtual power plant scale calculation method of claim 1, wherein, In step S2, the dynamic weight model is: ； ωi(k) = ωi(k - 1) + αi(k - 1)(ωi(k - 1) - ωi(k - 2)) i ωi(k) = ωi(k - 1) + αi(k - 1)(ωi(k - 1) - ωi(k - 2)) i0 ωi(k) = ωi(k - 1) + αi(k - 1)(ωi(k - 1) - ωi(k - 2)) i ωi(k) = ωi(k - 1) + αi(k - 1)(ωi(k - 1) - ωi(k - 2)) i ωi(k) = ωi(k - 1) + αi(k - 1)(ωi(k - 1) - ωi(k - 2)) j0 ωi(k) = ωi(k - 1) + αi(k - 1)(ωi(k - 1) - ωi(k - 2)) j ωi(k) = ωi(k - 1) + αi(k - 1)(ωi(k - 1) - ωi(k - 2)) j r = r + βr(r - r) γ = γ + βγ(γ - γ) 3. The virtual power plant scale calculation method of claim 2, wherein, In step S3, the weighted calculation model is: ； wherein S0 is the initial size; S i0 is the reference size corresponding to the ith influencing factor.

4. The virtual power plant scale calculation method of claim 2, wherein, k of the internal resource factor i > 0, k of the external demand factor i -0.1~0.3; m of the external demand factor i > 0, m of the internal resource factor i -0.05~0.

2.

5. The virtual power plant scale calculation method of claim 1, wherein, The internal resource factors include at least one of distributed power characteristics, energy storage system parameters, controllable load capacity and equipment investment cost; and the external demand factors include at least one of power grid regulation demand and market transaction price.

6. The virtual power plant scale calculation method of claim 3, wherein, In step S4, the internal closed-loop iteration of net present value includes: S4.1, calculating the net present value through the following calculation formula: ； Wherein, NPV is the net present value; I t is the total income of the tth year; C t is the operation and maintenance cost of the tth year; q is the benchmark yield; C inv，0 is the initial total investment S4.2, comparing the net present value calculated by the calculation formula in step S4.1 with the target net present value; when the calculated net present value is less than the target net present value, adjusting the current iteration scale according to the scale correction amount; the scale correction amount is: ； Wherein, ΔS1 is the scale correction; S0 is the initial scale calculated by step S3; NPV target is the target net present value; S4.3, repeating steps S4.1 to S4.2 until the calculated net present value is greater than or equal to the target net present value, and outputting the adjusted scale.

7. The virtual power plant scale calculation method of claim 6, wherein, The total income includes power sales income and auxiliary service income. 8.The virtual power plant scale calculation method according to claim 6, characterized in that, 6%≤q≤8%。 9.The virtual power plant scale calculation method according to claim 6, characterized in that, In step S4, the external closed-loop iteration of internal rate of return includes: S4.4, after the calculated net present value is greater than or equal to the target net present value, substituting the adjusted scale output by the internal closed-loop iteration of net present value into the following calculation formula to calculate the internal rate of return: ； Wherein, IRR is the internal rate of return; S4.5, comparing the internal rate of return calculated by the calculation formula in step S4.4 with the target internal rate of return; when the internal rate of return is less than the target internal rate of return, correcting the dynamic weight and re-performing the internal closed-loop iteration of net present value; S4.6, repeating steps S4.4 to S4.5 until the calculated internal rate of return is greater than or equal to the target internal rate of return, and the net present value is greater than or equal to the target net present value. 10.The virtual power plant scale calculation method according to claim 1, characterized in that, The iteration termination condition is that the absolute value of the difference between the internal rate of return and the target internal rate of return is less than or equal to 0.5%, and the absolute value of the difference between the net present value and the target net present value is less than or equal to 5%.

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