A method and system for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling

Abstract

This application relates to the field of virtual power plant regulation capacity assessment technology, specifically to a method and system for assessing the regulation capacity of virtual power plants based on multi-parameter coupling. The method includes: acquiring operating status data of power generation units and energy storage units within the virtual power plant; calculating a fluctuation characteristic index based on the power generation units and constructing an initial feasible region for regulation capacity based on the energy storage units; constructing a source-storage coupling model, defining the mapping relationship between the fluctuation characteristic index and reserved capacity, and removing reserved capacity from the initial feasible region to generate a corrected confidence feasible region; calculating the maximum regulation power within the confidence feasible region; and constructing a three-dimensional assessment model using a convolution algorithm and outputting a comprehensive regulation capacity assessment curve. This application significantly improves the accuracy and robustness of virtual power plant regulation capacity assessment by removing reserved capacity used to balance internal fluctuations from the initial feasible region.

Description

Technical Field

[0001] This application relates to the field of virtual power plant regulation capacity assessment technology. More specifically, this application relates to a method and system for virtual power plant regulation capacity assessment based on multi-parameter coupling. Background Technology

[0002] With the rapid development of new power systems, virtual power plants, as important carriers aggregating distributed power sources, controllable loads, and energy storage, are playing an increasingly important role in the electricity market and ancillary services market. Assessing the regulation capacity of virtual power plants is a crucial step in formulating their market application strategies and executing dispatch plans; the accuracy of the assessment results directly affects the market returns of virtual power plants and the safe and stable operation of the power grid.

[0003] Existing methods for assessing the regulation capacity of virtual power plants primarily employ a linear summation approach based on resource capabilities. Specifically, this involves simply summing the theoretical maximum regulation power of various resources within the virtual power plant to obtain its overall regulation capacity. For example, the predicted output of photovoltaic power generation, the maximum charging and discharging power of energy storage batteries, and the adjustable power of air conditioning load are directly added together to represent the regulation capacity declared by the virtual power plant. This method is simple to calculate and easy to implement, and can meet basic assessment needs in scenarios with a single resource type or where resources are independent of each other.

[0004] However, the aforementioned linear superposition method neglects the coupling constraints between various resources within the virtual power plant. In actual operation, the output power of stochastic power sources such as photovoltaic and wind power exhibits significant fluctuations and uncertainties. These fluctuations require rapid responses from energy storage units to maintain the internal power balance of the virtual power plant. When stochastic power source fluctuations are severe, energy storage units need to operate frequently to smooth out internal fluctuations, resulting in a corresponding reduction in their available capacity for external regulation services. Furthermore, the response speeds of different types of resources vary significantly; power-type energy storage can respond within seconds, while controllable loads typically require minutes or even longer to complete regulation actions. Existing methods fail to effectively characterize these coupling relationships between multiple parameters, leading to assessment results that often exceed actual executable capabilities. This causes deviations in the execution of dispatch commands by the virtual power plant, affecting the safe and stable operation of the power grid. Therefore, there is an urgent need for a virtual power plant regulation capability assessment method that can accurately reflect the coupling relationships between multiple parameters. Summary of the Invention

[0005] The purpose of this application is to propose a method and system for evaluating the regulation capacity of virtual power plants based on multi-parameter coupling, so as to solve the problem of insufficient accuracy in the evaluation of the regulation capacity of virtual power plants in the prior art.

[0006] In a first aspect, this application provides a method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling, comprising: acquiring the operating status data of each aggregation unit within the virtual power plant, wherein the aggregation unit includes at least a power supply unit and an energy storage unit; calculating a fluctuation characteristic index based on the historical output power of the power supply unit, and constructing an initial regulation capacity feasible region based on the state of charge and health of the energy storage unit; constructing a source-storage coupling model, wherein the source-storage coupling model defines the mapping relationship between the fluctuation characteristic index and the reserved capacity in the energy storage unit for balancing internal fluctuations; inputting the fluctuation characteristic index into the source-storage coupling model, and removing the reserved capacity output by the source-storage coupling model from the initial regulation capacity feasible region to generate a corrected confidence feasible region; within the confidence feasible region, calculating the maximum upward adjustment power and the maximum downward adjustment power at different response time scales, and constructing a three-dimensional evaluation model including a confidence dimension using a convolution algorithm, and outputting a comprehensive regulation capacity evaluation curve of the virtual power plant.

[0007] This application establishes a source-storage coupling model, defining the mapping relationship between the fluctuation characteristic index and the reserved capacity of energy storage units, thus realizing dynamic capacity assessment under multi-parameter coupling. This method overcomes the limitations of traditional assessments that rely on simple linear superposition of resource capabilities. By removing the reserved capacity output by the source-storage coupling model from the initial feasible regulation capacity domain, the externally presented regulation capacity becomes more realistic and reliable, significantly improving the accuracy and feasibility of virtual power plants participating in electricity market regulation services.

[0008] Optionally, the calculation of the fluctuation characteristic index includes: obtaining the historical output power sequence of the power supply unit within a preset time window; calculating the ratio of the standard deviation to the mean of the historical output power sequence to obtain the power variation coefficient; calculating the root mean square value of the power change at adjacent times based on the historical output power sequence to obtain the power volatility; normalizing the power volatility using the rated power of the power supply unit to obtain the normalized power volatility; and weighted summing the power variation coefficient and the normalized power volatility to obtain the fluctuation characteristic index.

[0009] This application considers both the power coefficient of variation and the normalized power volatility when calculating the fluctuation characteristic index. The power coefficient of variation reflects the overall dispersion of power output, while the normalized power volatility normalizes the root mean square value of power changes between adjacent time points using the rated power of the power unit, reflecting the severity of power changes. By combining the two indicators through a weighted summation, the fluctuation characteristics of the power unit can be more comprehensively characterized, providing a reliable quantitative basis for subsequent reserved capacity calculations.

[0010] Optionally, the construction of the initial regulation capability feasible region includes: obtaining the rated power, rated capacity, current state of charge, and health parameters of the energy storage unit; calculating the available charging energy and available discharging energy based on the current state of charge; performing attenuation correction on the rated power based on the health parameters to obtain the actual available power; and constructing the initial regulation capability feasible region with the actual available power as the power boundary and the available charging energy and available discharging energy as the energy boundary.

[0011] This application considers both the power boundary and energy boundary of the energy storage unit when constructing the initial feasible domain of regulation capability. By introducing a health parameter to correct the rated power for attenuation, the actual usable power is obtained, which can reflect the actual performance status of the energy storage unit after long-term operation. This avoids the problem of overestimating the capability due to ignoring equipment aging, and makes the evaluation results closer to the actual operating conditions.

[0012] Optionally, the process of constructing the source-storage coupling model includes: establishing a positive correlation function between the volatility characteristic index and the reservation coefficient; defining the reserved capacity as the product of the rated capacity and the reservation coefficient; and constructing the source-storage coupling model, wherein the input of the source-storage coupling model is the volatility characteristic index and the output is the reserved capacity.

[0013] Optionally, the function relationship of the reservation coefficient satisfies the following: when the fluctuation characteristic index is lower than the preset fluctuation threshold, the reservation coefficient is zero or maintains the minimum basic value; when the fluctuation characteristic index is higher than the fluctuation threshold, the reservation coefficient increases non-linearly with the increase of the fluctuation characteristic index until it reaches the preset reservation upper limit.

[0014] This application establishes a nonlinear mapping relationship between the volatility characteristic index and the reserved capacity by setting a reservation coefficient function. When the volatility characteristic index is higher than a preset volatility threshold, the reservation coefficient increases nonlinearly with the increase of the volatility characteristic index, thereby automatically increasing the reserved capacity used for internal balancing. This adaptive capacity reservation mechanism can dynamically adjust the externally presented regulatory capacity according to the actual volatility situation, achieving an effective match between assessment results and operational risks.

[0015] Optionally, the different response time scales include second-level time scales, minute-level time scales, and hour-level time scales; the second-level time scale corresponds to the regulation capability of power-type energy storage units; the minute-level time scale corresponds to the regulation capability of energy-type energy storage units; and the hour-level time scale corresponds to the regulation capability of energy-type energy storage units and controllable loads.

[0016] Optionally, the calculation of the maximum up-adjustment power and the maximum down-adjustment power under different response time scales includes: for each time scale, screening aggregate units whose response time meets the requirements of that time scale; obtaining the available power of each aggregate unit within the corrected confidence feasible region after screening; and summing the available power of each aggregate unit algebraically to obtain the theoretical maximum up-adjustment power and the theoretical maximum down-adjustment power under that time scale.

[0017] Optionally, the construction process of the three-dimensional evaluation model includes: constructing the power output probability density function of each aggregation unit within the confidence feasible region; for multiple aggregation units at the same time scale, using a convolution algorithm to operate on their probability density functions to obtain the probability distribution curve of the overall regulation power of the virtual power plant at the current time scale; the probability distribution curve describes the probability that the virtual power plant can achieve different regulation power values ​​at a specific time scale.

[0018] This application constructs a three-dimensional evaluation model including a confidence dimension by performing convolution operations on the power output probability density functions of multiple aggregation units at the same time scale. Compared with simple power superposition methods, convolution operations can correctly handle the uncertainties of the responses of various resources and their mutual influences. The generated evaluation results include confidence information, providing a risk quantification basis for virtual power plants to formulate application strategies.

[0019] Optionally, the comprehensive regulation capability evaluation curve of the output virtual power plant includes: stitching together the regulation power probability distribution curves corresponding to different time scales according to the time dimension to generate a three-dimensional regulation capability evaluation surface with the time scale as the X-axis, the regulation power as the Y-axis, and the confidence probability as the Z-axis; intercepting contour lines corresponding to the preset confidence threshold on the three-dimensional regulation capability evaluation surface, mapping the contour lines to a two-dimensional plane of time scale and regulation power to generate the final comprehensive regulation capability evaluation curve.

[0020] In the second aspect, a virtual power plant regulation capacity assessment system based on multi-parameter coupling includes: processor; The memory stores computer instructions for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling, which, when executed by the processor, cause the system to perform the aforementioned method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling.

[0021] The beneficial effects of this application are as follows: By establishing a source-storage coupling model, this application defines the mapping relationship between the fluctuation characteristic index and the reserved capacity of the energy storage unit, realizing dynamic capacity assessment under multi-parameter coupling. This method breaks through the limitation of simple linear superposition of resource capabilities in traditional assessments. By removing the reserved capacity from the initial feasible domain of regulation capacity, the externally presented regulation capacity is more realistic and reliable, significantly improving the accuracy and robustness of virtual power plant regulation capacity assessment. Attached Figure Description

[0022] Figure 1 This is a flowchart of a virtual power plant regulation capacity assessment method based on multi-parameter coupling, according to an embodiment of this application.

[0023] Figure 2 This is a parameter variation diagram of a virtual power plant regulation capacity assessment method based on multi-parameter coupling according to an embodiment of this application.

[0024] Figure 3 This is a three-dimensional regulation capability evaluation surface diagram of a virtual power plant regulation capability evaluation method based on multi-parameter coupling according to an embodiment of this application.

[0025] Figure 4 This is a comprehensive regulation capability evaluation curve of a virtual power plant regulation capability evaluation method based on multi-parameter coupling according to an embodiment of this application.

[0026] Figure 5 This is a structural block diagram of a virtual power plant regulation capacity assessment system based on multi-parameter coupling, according to an embodiment of this application. Detailed Implementation

[0027] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Figure 1 The diagram shown is a flowchart of a virtual power plant regulation capacity assessment method based on multi-parameter coupling according to an embodiment of this application.

[0028] S1: Obtain the operating status data of each aggregation unit in the virtual power plant and calculate the fluctuation characteristic index.

[0029] First, the operating status data of each aggregation unit within the virtual power plant is acquired, and the fluctuation characteristic index is calculated. The virtual power plant comprises various types of aggregation units, mainly divided into two categories: power generation units and energy storage units. Power generation units include photovoltaic power generation units and wind power generation units, whose output power is highly random and fluctuating due to weather conditions. Energy storage units include lithium-ion battery energy storage, supercapacitor energy storage, etc., possessing rapid response and bidirectional regulation capabilities.

[0030] For each power unit, its historical output power sequence within a preset time window is acquired via a data acquisition terminal. In this embodiment, the time window is set to the past 24 hours, the sampling period is 15 minutes, and a total of 96 power sampling points are acquired. Let the historical output power sequence of the photovoltaic power generation unit be P1, P2, P3 to P96, in kilowatts.

[0031] Calculate the coefficient of variation (COP) of the historical output power sequence. First, calculate the mean of the power sequence by summing the power values ​​at all sampling points and dividing by the number of sampling points (96). Then, calculate the standard deviation of the power sequence by squared differences between the power value at each sampling point and the average power, summing the results, dividing by the number of sampling points, and taking the square root. The COP equals the standard deviation divided by the mean, reflecting the overall dispersion of the power output.

[0032] Calculate the power volatility of the historical output power sequence. For power changes at adjacent times, calculate P2 minus P1, P3 minus P2, up to P96 minus P95, resulting in 95 power changes. Calculate the root mean square value of all power changes, i.e., sum the squares of each change, divide by the number of changes, and take the square root to obtain the power volatility. Normalize the power volatility using the rated power of the power unit to obtain the normalized power volatility. Calculate the volatility characteristic index by weighting the power variation coefficient and the normalized power volatility. In this embodiment, the weight of the power variation coefficient is set to 0.6, and the weight of the normalized power volatility is set to 0.4. The formula for calculating the volatility characteristic index is as follows: ; Wherein, λ represents the fluctuation characteristic index, which is dimensionless; This represents the power variation coefficient, which is dimensionless. This represents the power fluctuation rate, measured in kilowatts. This indicates the rated power of the power supply unit, measured in kilowatts.

[0033] like Figure 2 The diagram shows parameter variations in a virtual power plant regulation capability assessment method based on multi-parameter coupling according to an embodiment of this application. The fluctuation characteristic index dynamically changes with the degree of power fluctuation, exhibiting a higher value during periods of rapid changes in photovoltaic output, and is mainly affected by wind power fluctuations during nighttime photovoltaic shutdowns. The energy storage SOC exhibits mean-recovery characteristics during regulation, reflecting the energy consumption and recovery of the energy storage unit in the process of smoothing internal fluctuations. The correlation between the two curves indicates that when the fluctuation characteristic index increases, the energy storage unit needs to operate more frequently to maintain internal balance, which is the physical basis for the source-storage coupling model proposed in this application.

[0034] S2: Construct the initial feasible region of adjustment capability and establish a source-storage coupling model.

[0035] After calculating the fluctuation characteristic index, an initial feasible region for regulation capability is constructed, and a source-storage coupling model is established. For the energy storage unit, its rated power, rated capacity, current state of charge, and health parameters are obtained. In this embodiment, the lithium-ion battery energy storage unit has a rated power of 500 kW, a rated capacity of 1000 kWh, a current state of charge of 60%, and a health parameter of 95%.

[0036] The available charging and discharging energy are calculated based on the current state of charge. Available charging energy equals the difference between the rated capacity multiplied by 100% and the current state of charge; for example, 1000 kWh multiplied by 40% equals 400 kWh. Available discharging energy equals the rated capacity multiplied by the difference between the current state of charge and the minimum state of charge. Assuming the minimum state of charge is 10%, the available discharging energy is 1000 kWh multiplied by 50%, resulting in 500 kWh.

[0037] The rated power is attenuated and corrected based on the health parameter to obtain the actual usable power. The actual usable power equals the rated power multiplied by the health parameter, i.e., 500 kW multiplied by 95%, resulting in 475 kW. Using the actual usable power as the power boundary and the available charging energy and available discharging energy as the energy boundaries, an initial feasible region for regulation capability is constructed.

[0038] A source-storage coupling model is established, defining the mapping relationship between the fluctuation characteristic index and the reserved capacity in the energy storage unit used to balance internal fluctuations. The reserved capacity is positively correlated with the fluctuation characteristic index and is used to smooth the internal fluctuations of the power unit. The reservation coefficient function is set as a function of the nonlinear increase of the fluctuation characteristic index. When the fluctuation characteristic index is higher than the fluctuation threshold, the reservation coefficient increases nonlinearly with the increase of the fluctuation characteristic index. The formula for calculating the reservation coefficient is as follows: ; Where k represents the reserved coefficient, which is dimensionless; This represents the minimum basic value for the reserve coefficient, set to 0.05; λ represents the maximum base value of the reserved coefficient, set to 0.3; λ represents the volatility characteristic index. This represents the lower limit of the volatility characteristic index, set to 0.1; This represents the upper limit of the volatility characteristic index, set to 0.5.

[0039] The fluctuation characteristic index is input into the source-storage coupling model to obtain the reservation coefficient. The reserved capacity is defined as the product of the rated capacity and the reservation coefficient. The reserved capacity output by the source-storage coupling model is removed from the initial feasible region of adjustment capability to generate a corrected confidence feasible region.

[0040] S3: Calculate the adjustment power at different response time scales within the confidence feasible region.

[0041] After generating the corrected confidence feasible region, the maximum up-adjustment power and maximum down-adjustment power are calculated at different response time scales. This application divides the response time scale into three levels: second-level time scale, minute-level time scale, and hour-level time scale, which correspond to the response characteristics of different types of aggregation units.

[0042] The second-level timescale corresponds to the regulation capability of power-type energy storage units, mainly including supercapacitor energy storage and flywheel energy storage. These resources have millisecond-level response speeds, enabling them to complete power regulation actions in an extremely short time, making them suitable for fast-response scenarios such as grid frequency regulation. In this embodiment, the rated power of the supercapacitor energy storage unit is 200 kilowatts, and the response time is 50 milliseconds.

[0043] The minute-level timescale corresponds to the regulation capability of energy storage units, mainly including lithium-ion battery energy storage. These resources typically have response times between seconds and minutes, possess large energy capacity, and are suitable for scenarios such as load following and peak shaving. In this embodiment, the response time of the lithium-ion battery energy storage unit is 2 seconds.

[0044] The hourly timescale corresponds to the adjustment capability of energy storage units and controllable loads. Controllable loads include air conditioning loads, water heater loads, etc., whose response times are typically between minutes and hours, and whose adjustment duration is relatively long. In this embodiment, the adjustable power of the air conditioning load is 300 kilowatts, and the response time is 5 minutes.

[0045] For each time scale, aggregated units whose response times meet the requirements of that time scale are selected. The available power of each aggregated unit within the corrected confidence feasible region is obtained. During this process, an energy-power constraint verification is required: for an energy storage unit, its maximum available power at a specific time scale is limited by the corrected available energy (i.e., the remaining energy after deducting reserved capacity). Specifically, the ratio of the corrected available energy to the duration of the current time scale is calculated, and this ratio is compared with the rated power of the energy storage unit; the smaller of the two values ​​is taken as the actual available power at that time scale.

[0046] S4: Construct a three-dimensional evaluation model including the confidence dimension using a convolution algorithm, and output the comprehensive regulation capability evaluation curve of the virtual power plant.

[0047] After calculating the adjustment power at each time scale, a three-dimensional evaluation model including the confidence dimension is constructed.

[0048] A power output probability density function for each aggregation unit within the stated confidence feasible region is constructed. For each aggregation unit, a probability distribution model of the power output is established based on its historical response data and device characteristics. In this embodiment, the power output of the lithium-ion battery energy storage unit follows a normal distribution with a mean of 95% of the rated power and a standard deviation of 5% of the rated power. The power output of the supercapacitor energy storage unit follows a uniform distribution, ranging from 90% to 100% of the rated power.

[0049] For multiple aggregation units at the same time scale, a convolution algorithm is used to calculate their probability density functions, yielding the probability distribution curve of the overall regulating power of the virtual power plant at that time scale. The convolution algorithm can correctly handle the uncertainty of each resource response and their mutual influence. The probability distribution curve describes the probability that the virtual power plant can achieve different regulating power values ​​at a specific time scale.

[0050] The probability distribution curves of regulating power at different time scales are stitched together along the time dimension to generate a three-dimensional regulating capacity evaluation surface with the time scale as the X-axis, regulating power as the Y-axis, and confidence probability as the Z-axis. This surface visually displays the regulating capacity boundary of the virtual power plant at different time scales and confidence levels. Figure 3 The image shows a three-dimensional regulation capability assessment surface plot of a virtual power plant regulation capability assessment method based on multi-parameter coupling according to an embodiment of this application. The three-dimensional surface exhibits a ridge shape, with the highest confidence level near zero regulation power. As the absolute value of the regulation power increases, the confidence level gradually decreases. The ridge width of the surface expands with increasing time scale, reflecting the characteristics of increased available resources and improved regulation capability under longer response times. The contour projection clearly shows the regulation power boundary at different confidence levels, providing an intuitive basis for subsequent boundary curve extraction.

[0051] Contour lines corresponding to a preset confidence threshold are extracted from the three-dimensional regulation capability evaluation surface. These contour lines are then mapped onto a two-dimensional plane of time scale and regulation power to generate the final comprehensive regulation capability evaluation curve. In this embodiment, the confidence threshold is set to 95%, meaning that the regulation capability that the virtual power plant can stably provide at a 95% confidence level is extracted. The comprehensive regulation capability evaluation curve can be used by the virtual power plant to formulate market declaration strategies, ensuring that the declared regulation capability has high execution reliability.

[0052] like Figure 4The figure shows a comprehensive regulation capacity assessment curve of a virtual power plant regulation capacity assessment method based on multi-parameter coupling according to an embodiment of this application. The data in this figure is extracted from a three-dimensional regulation capacity assessment surface. Three confidence thresholds of 99%, 95%, and 90% are selected, and the surface is cut along the time scale axis to obtain the corresponding regulation power boundary values. The banded area between the three boundary curves visually demonstrates the uncertainty range of the regulation capacity, providing a quantitative basis for virtual power plants to formulate differentiated market application strategies.

[0053] According to a second aspect of this application, this application also provides a virtual power plant regulation capacity assessment system based on multi-parameter coupling. Figure 5 This is a structural block diagram of a virtual power plant regulation capacity assessment system based on multi-parameter coupling, according to an embodiment of this application. Figure 5 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the virtual power plant regulation capacity assessment method based on multi-parameter coupling according to the first aspect of this application. The system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their configuration and functions are known in the art and will not be described further here.

[0054] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in this application, based on the technical solution and inventive concept of this application, should be within the scope of protection of this application.

Claims

1. A method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling, characterized in that, The evaluation method includes: acquiring the operating status data of each aggregation unit within the virtual power plant, wherein the aggregation unit includes at least a power supply unit and an energy storage unit; calculating a fluctuation characteristic index based on the historical output power of the power supply unit, and constructing an initial regulation capability feasible region based on the state of charge and health of the energy storage unit; constructing a source-storage coupling model, wherein the source-storage coupling model defines the mapping relationship between the fluctuation characteristic index and the reserved capacity in the energy storage unit used to balance internal fluctuations; inputting the fluctuation characteristic index into the source-storage coupling model, and removing the reserved capacity output by the source-storage coupling model from the initial regulation capability feasible region to generate a corrected confidence feasible region; within the confidence feasible region, calculating the maximum upward adjustment power and the maximum downward adjustment power at different response time scales, and constructing a three-dimensional evaluation model including a confidence dimension using a convolution algorithm, and outputting the comprehensive regulation capability evaluation curve of the virtual power plant; The process of constructing the source-storage coupling model includes: establishing a positive correlation function between the volatility characteristic index and the reservation coefficient; defining the reserved capacity as the product of the rated capacity and the reservation coefficient; and constructing the source-storage coupling model, wherein the input of the source-storage coupling model is the volatility characteristic index and the output is the reserved capacity. The reserved coefficient has the following functional relationship: when the volatility characteristic index is lower than the preset volatility threshold, the reserved coefficient is zero or maintains the minimum basic value; when the volatility characteristic index is higher than the volatility threshold, the reserved coefficient increases non-linearly with the increase of the volatility characteristic index until it reaches the preset reserved upper limit. A source-storage coupling model is established, defining the mapping relationship between the fluctuation characteristic index and the reserved capacity in the energy storage unit used to balance internal fluctuations. The reserved capacity is positively correlated with the fluctuation characteristic index and is used to smooth the internal fluctuations of the power unit. The reserved coefficient function is set as a function of the nonlinear increase of the fluctuation characteristic index. When the fluctuation characteristic index is higher than the fluctuation threshold, the reserved coefficient increases nonlinearly with the increase of the fluctuation characteristic index. The calculation formula of the reserved coefficient is as follows: ； Where k represents the reserved coefficient, which is dimensionless; This represents the minimum basic value for the reserve coefficient, set to 0.05; λ represents the maximum base value of the reserved coefficient, set to 0.3; λ represents the volatility characteristic index. This represents the lower limit of the volatility characteristic index, set to 0.1; This represents the upper limit of the volatility characteristic index, set to 0.5; The fluctuation characteristic index is input into the source-storage coupling model to obtain the reservation coefficient; the reserved capacity is defined as the product of the rated capacity and the reservation coefficient; the reserved capacity output by the source-storage coupling model is removed from the initial feasible region of adjustment capability to generate the corrected confidence feasible region.

2. The method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling according to claim 1, characterized in that, The calculation of the fluctuation characteristic index includes: obtaining the historical output power sequence of the power supply unit within a preset time window; calculating the ratio of the standard deviation to the mean of the historical output power sequence to obtain the power variation coefficient; calculating the root mean square value of the power change at adjacent times based on the historical output power sequence to obtain the power volatility; normalizing the power volatility using the rated power of the power supply unit to obtain the normalized power volatility; and weighting and summing the power variation coefficient and the normalized power volatility to obtain the fluctuation characteristic index.

3. The method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling according to claim 1, characterized in that, The construction of the initial regulation capability feasible region includes: obtaining the rated power, rated capacity, current state of charge, and health parameters of the energy storage unit; calculating the available charging energy and available discharging energy based on the current state of charge; performing attenuation correction on the rated power based on the health parameters to obtain the actual available power; and constructing the initial regulation capability feasible region with the actual available power as the power boundary and the available charging energy and available discharging energy as the energy boundary.

4. The method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling according to claim 1, characterized in that, The different response time scales include second-level time scales, minute-level time scales, and hour-level time scales; the second-level time scale corresponds to the regulation capability of power-type energy storage units; the minute-level time scale corresponds to the regulation capability of energy-type energy storage units; and the hour-level time scale corresponds to the regulation capability of energy-type energy storage units and controllable loads.

5. The method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling according to claim 1, characterized in that, The calculation of the maximum up-adjustment power and maximum down-adjustment power under different response time scales includes: for each time scale, screening aggregate units whose response time meets the requirements of that time scale; obtaining the available power of each aggregate unit within the corrected confidence feasible region after screening; and summing the available power of each aggregate unit algebraically to obtain the theoretical maximum up-adjustment power and theoretical maximum down-adjustment power under that time scale.

6. The method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling according to claim 1, characterized in that, The construction process of the three-dimensional evaluation model includes: constructing the power output probability density function of each aggregation unit within the confidence feasible region; for multiple aggregation units at the same time scale, using a convolution algorithm to operate on their probability density functions to obtain the probability distribution curve of the overall regulation power of the virtual power plant at the current time scale; the probability distribution curve describes the probability that the virtual power plant can achieve different regulation power values ​​at a specific time scale.

7. The method for evaluating the regulation capacity of a virtual power plant based on multi-parameter coupling according to claim 6, characterized in that, The comprehensive regulation capability evaluation curve of the output virtual power plant includes: stitching together the regulation power probability distribution curves corresponding to different time scales according to the time dimension to generate a three-dimensional regulation capability evaluation surface with the time scale as the X-axis, the regulation power as the Y-axis, and the confidence probability as the Z-axis; intercepting the contour lines corresponding to the preset confidence thresholds on the three-dimensional regulation capability evaluation surface, mapping the contour lines to a two-dimensional plane of time scale and regulation power to generate the final comprehensive regulation capability evaluation curve.

8. A virtual power plant regulation capacity assessment system based on multi-parameter coupling, characterized in that, include: processor; A memory storing a computer program; wherein the processor is configured to execute the computer program to implement a virtual power plant regulation capacity assessment method based on multi-parameter coupling as described in any one of claims 1 to 7.

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