Virtual power plant resource optimal configuration method and system

Abstract

The invention relates to the technical field of resource configuration optimization, in particular to a virtual power plant resource optimal configuration method and system. The method comprises the following steps: obtaining energy load storage resource data corresponding to each power generation unit in a virtual power plant, and carrying out power generation power prediction analysis and energy storage equipment charge and discharge state analysis on each power generation unit in the virtual power plant; the energy power generation power corresponding to each power generation unit and the charging and discharging operation state data of the energy storage equipment are obtained; performing resource operation load demand prediction analysis on the corresponding power generation units to obtain resource operation load demand prediction quantities corresponding to the power generation units; and carrying out resource configuration optimization target analysis on each power generation unit in the virtual power plant to obtain a virtual power plant power generation unit resource configuration optimization target and resource optimization configuration processing so as to generate a virtual power plant power generation unit resource optimal configuration scheme. According to the invention, efficient optimal configuration of each power generation unit resource in the virtual power plant can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of resource configuration optimization, and in particular to a virtual power plant resource optimization configuration method and system. Background Art

[0002] As an emerging power management model, Virtual Power Plant (VPP) is gradually becoming an important means to realize smart grid and sustainable energy utilization. Virtual power plant integrates a variety of distributed energy resources, such as solar energy, wind energy, energy storage system and controllable load, optimizes the scheduling and configuration of these resources to improve the overall efficiency and reliability of the power system. At the same time, with the development of big data and artificial intelligence technology, it has become a trend to optimize the allocation of resources using intelligent algorithms. However, due to the volatility and uncertainty of renewable energy, virtual power plants must consider multiple dynamic factors such as weather changes and load demand when scheduling resources. However, most of the existing virtual power plant resource optimization and configuration methods lack intelligence and mainly rely on manual scheduling and experience judgment. It is difficult to adapt to changes in market demand and resource availability in real time, resulting in low efficiency in power plant resource allocation. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for optimizing the configuration of virtual power plant resources to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a virtual power plant resource optimization configuration method includes the following steps:

[0005] Step S1: Obtain energy load storage resource data corresponding to each power generation unit in the virtual power plant, and perform power generation prediction analysis on each power generation unit in the virtual power plant based on the energy load storage resource data corresponding to each power generation unit to obtain the energy power generation corresponding to each power generation unit;

[0006] Step S2: Based on the energy generation power corresponding to each power generation unit, the energy storage device charging and discharging status analysis is performed on each power generation unit in the virtual power plant to obtain the energy storage device charging and discharging operation status data corresponding to each power generation unit; based on the energy generation power corresponding to each power generation unit and the energy storage device charging and discharging operation status data, the resource operation load demand forecast analysis is performed on the corresponding power generation unit to obtain the resource operation load demand forecast amount corresponding to each power generation unit;

[0007] Step S3: Analyze the resource allocation optimization target of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, and obtain the resource allocation optimization target of the virtual power plant power generation unit;

[0008] Step S4: performing resource optimization configuration processing on each power generation unit in the virtual power plant according to the resource configuration optimization target of the virtual power plant power generation unit to generate an optimal resource configuration plan for the virtual power plant power generation unit.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: Acquire energy storage resource data corresponding to each power generation unit in the virtual power plant;

[0011] Step S12: Obtain the energy resource input and energy resource output corresponding to each power generation unit through the energy load storage resource data corresponding to each power generation unit, and monitor the power generation of each power generation unit in the virtual power plant in real time through the integrated smart meter to obtain the energy resource power generation corresponding to each power generation unit; perform power generation capacity estimation analysis on the corresponding energy resource power generation based on the energy resource input and energy resource output corresponding to each power generation unit to obtain the energy power generation capacity estimation corresponding to each power generation unit;

[0012] Step S13: Acquire the meteorological change data of the virtual power plant, and perform seasonal change characteristic analysis on the meteorological change data of the virtual power plant to obtain the meteorological seasonal change characteristic data of the virtual power plant;

[0013] Step S14: performing environmental impact correction calculation on the estimated energy generation capacity corresponding to each power generation unit based on the virtual power plant meteorological seasonal variation characteristic data, and obtaining the corrected estimated actual generation capacity corresponding to each power generation unit;

[0014] Step S15: Perform power generation prediction analysis on the corresponding power generation units in the virtual power plant according to the actual power generation capacity correction estimate corresponding to each power generation unit, and obtain the energy power generation corresponding to each power generation unit.

[0015] Furthermore, the power generation capacity estimation analysis of the corresponding energy resource power generation based on the energy resource input and energy resource output corresponding to each power generation unit in step S12 includes the following steps:

[0016] Performing time-series synchronization processing on the energy resource input and energy resource output corresponding to each power generation unit to obtain the time-series change of resource input and the time-series change of resource output corresponding to each power generation unit;

[0017] Extract the time-series point input quantity of the resource input time-series change quantity corresponding to each power generation unit to obtain the resource input quantity corresponding to each time-series point of each power generation unit; calculate the input fluctuation difference between time-series points for the resource input quantity corresponding to each power generation unit at each time-series point to obtain the resource input change fluctuation difference between each adjacent time-series point of each power generation unit;

[0018] The resource input change gradient analysis is performed on the resource input change fluctuation difference between each adjacent time sequence point corresponding to each power generation unit to obtain the resource input change gradient corresponding to each power generation unit;

[0019] The resource output efficiency is evaluated and analyzed for the time series variation of resource output corresponding to each power generation unit to obtain the resource output efficiency corresponding to each power generation unit;

[0020] Based on the resource input change gradient and resource output efficiency of each power generation unit, the corresponding energy resource power generation is estimated and analyzed to obtain the energy power generation capacity estimate of each power generation unit.

[0021] Further, step S14 includes the following steps:

[0022] Step S141: Based on the seasonal variation characteristics of each meteorological factor in the virtual power plant meteorological seasonal variation characteristic data, an environmental impact factor identification analysis is performed on the estimated energy generation capacity corresponding to each power generation unit to obtain the environmental impact factor of the energy generation capacity corresponding to each power generation unit;

[0023] Step S142: quantifying the environmental impact coefficient of the energy generation capacity environmental impact factor corresponding to each power generation unit to obtain the power generation capacity impact correction coefficient of each power generation unit corresponding to the environmental impact factor;

[0024] Step S143: performing environmental impact correction calculation on the energy power generation capacity estimate corresponding to each power generation unit based on the power generation capacity impact correction coefficient of the environmental impact factor corresponding to each power generation unit, and obtaining the actual power generation capacity correction estimate corresponding to each power generation unit.

[0025] Further, step S2 includes the following steps:

[0026] Step S21: Analyze the time period fluctuation characteristics of the energy power generation corresponding to each power generation unit to obtain the power generation fluctuation characteristics of each power generation unit in different time periods, wherein the power generation fluctuation characteristics include the power generation fluctuation peak value, the power generation fluctuation valley value and the power generation fluctuation frequency;

[0027] Step S22: Based on the power fluctuation characteristics of each power generation unit in different time periods, the energy storage equipment corresponding to the power generation unit in the virtual power plant is identified and divided into operation demand time periods, so as to obtain the energy storage equipment charging demand operation time periods and energy storage equipment discharging demand operation time periods corresponding to each power generation unit;

[0028] Step S23: Designing charging and discharging strategies for energy storage devices corresponding to power generation units in the virtual power plant based on the charging demand operation period of energy storage devices corresponding to each power generation unit and the discharging demand operation period of energy storage devices, so as to generate a sequential charging and discharging strategy for energy storage devices corresponding to each power generation unit;

[0029] Step S24: analyzing the energy storage device charging and discharging status of each power generation unit in the virtual power plant according to the energy storage device timing charging and discharging strategy corresponding to each power generation unit, so as to obtain the energy storage device charging and discharging operation status data corresponding to each power generation unit;

[0030] Step S25: Based on the energy generation power corresponding to each power generation unit and the charging and discharging operation status data of the energy storage device, the corresponding power generation unit is subjected to resource operation load demand forecasting analysis to obtain the resource operation load demand forecast corresponding to each power generation unit.

[0031] Further, step S22 includes the following steps:

[0032] Based on the power fluctuation characteristics of each power generation unit in different time periods, the power generation fluctuation of the energy storage equipment corresponding to the power generation unit in the virtual power plant is visualized to obtain the power generation fluctuation curve of the energy storage equipment corresponding to each power generation unit;

[0033] The power generation fluctuation change curve corresponding to the energy storage device of each power generation unit is identified and divided into operation demand time periods. If the power generation fluctuation change frequency continues to rise in the time period before and after the power generation fluctuation peak in the power generation fluctuation change curve, the time period is identified and divided into the charging demand operation period, so as to obtain the charging demand operation period of the energy storage device corresponding to each power generation unit;

[0034] If the power generation fluctuation frequency continues to decrease in the time period before and after the power generation fluctuation valley value appears in the power generation fluctuation curve, the time period is identified and divided into the discharge demand operation period to obtain the energy storage device discharge demand operation period corresponding to each power generation unit.

[0035] Further, step S25 includes the following steps:

[0036] Step S251: Based on the energy generation power corresponding to each generation unit, a power generation field simulation analysis is performed on the corresponding generation unit in the virtual power plant to generate a resource operation power generation simulation field corresponding to each generation unit;

[0037] Step S252: Perform power generation load demand impact analysis on the resource operation power generation power simulation field corresponding to each power generation unit to obtain the power generation load demand impact factor corresponding to each power generation unit;

[0038] Step S253: Based on the charge and discharge operation status data of the energy storage device corresponding to each power generation unit, a charge and discharge state field simulation analysis is performed on the corresponding power generation unit in the virtual power plant to generate a resource operation charge and discharge state simulation field corresponding to each power generation unit;

[0039] Step S254: performing a charge and discharge load demand impact analysis on the resource operation charge and discharge state simulation field corresponding to each power generation unit, and obtaining an energy storage charge and discharge operation load demand impact factor corresponding to each power generation unit;

[0040] Step S255: Based on the power generation operation load demand influencing factors corresponding to each power generation unit and the energy storage charging and discharging operation load demand influencing factors, the resource operation load demand calculation formula is used to perform load demand forecasting calculations on the corresponding power generation units in the virtual power plant to obtain the resource operation load demand forecast corresponding to each power generation unit.

[0041] Furthermore, the resource operation load demand calculation formula described in step S255 is specifically:

[0042]

[0043] Where, L i is the resource operation load demand forecast corresponding to the i-th power generation unit, n is the total number of power generation units in the virtual power plant, i is the item measurement parameter of the power generation unit, t is the time variable parameter, τ is the integral time variable parameter, P i (t) is the energy generation power of the i-th power generation unit at time t, η P is the power generation efficiency factor of the power generation unit, f i (t) is the influencing factor of the power generation load demand of the i-th power generation unit at time t, E i (t) is the charging and discharging operation state of the energy storage device of the i-th power generation unit at time t, η E is the charging and discharging efficiency factor of the energy storage device, g i (t) is the energy storage charging and discharging operation load demand influencing factor of the i-th power generation unit at time t, λ is the resource operation load demand attenuation factor, L(τ) is the time delay effect factor of the resource operation load demand at time τ, ξ is the correction coefficient of the resource operation load demand forecast.

[0044] Further, step S3 includes the following steps:

[0045] Step S31: Based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, the resource allocation potential of each power generation unit in the virtual power plant is evaluated and analyzed to obtain the energy resource allocation potential of each power generation unit under specific operation load demand conditions;

[0046] Step S32: performing resource configuration constraint analysis on the corresponding power generation units in the virtual power plant based on the energy resource configuration potential of each power generation unit under specific operating load demand conditions, so as to generate resource configuration constraint conditions corresponding to each power generation unit;

[0047] Step S33: performing resource optimization strategy analysis on the corresponding power generation units in the virtual power plant based on the resource configuration constraint conditions corresponding to each power generation unit, so as to generate a resource configuration optimization strategy corresponding to each power generation unit;

[0048] Step S34: According to the resource allocation optimization strategy corresponding to each power generation unit, the resource allocation optimization target analysis is performed on the corresponding power generation unit in the virtual power plant to obtain the resource allocation optimization target of the power generation unit of the virtual power plant.

[0049] Furthermore, the present invention also provides a virtual power plant resource optimization configuration system, which is used to execute the virtual power plant resource optimization configuration method as described above, and the virtual power plant resource optimization configuration system includes:

[0050] The virtual power plant sub-unit power generation prediction module is used to obtain the energy load storage resource data corresponding to each power generation unit in the virtual power plant, and perform power generation prediction analysis on each power generation unit in the virtual power plant based on the energy load storage resource data corresponding to each power generation unit, so as to obtain the energy power generation corresponding to each power generation unit;

[0051] The sub-unit resource load demand analysis module is used to analyze the charging and discharging status of the energy storage equipment of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit, so as to obtain the charging and discharging operation status data of the energy storage equipment corresponding to each power generation unit; based on the energy generation power corresponding to each power generation unit and the charging and discharging operation status data of the energy storage equipment, the corresponding power generation unit is predicted and analyzed for the resource operation load demand, so as to obtain the predicted amount of resource operation load demand corresponding to each power generation unit;

[0052] The virtual power plant resource allocation optimization target analysis module is used to analyze the resource allocation optimization target of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, so as to obtain the resource allocation optimization target of the virtual power plant power generation unit;

[0053] The resource configuration optimization output module is used to perform resource optimization configuration processing on each power generation unit in the virtual power plant according to the resource configuration optimization target of the virtual power plant power generation unit, so as to generate the optimal resource configuration plan for the virtual power plant power generation unit.

[0054] Beneficial effects of the present invention:

[0055] 1. Compared with the prior art, the virtual power plant resource optimization configuration method proposed in the present invention has the beneficial effect that by obtaining the energy storage resource data corresponding to each power generation unit in the virtual power plant, the resource status, power generation capacity and contribution of each power generation unit to the overall power grid can be fully understood. These data include battery energy storage status, fuel energy input and output, renewable energy generation capacity and other information, which can provide data support for subsequent processing. In addition, accurate data collection can promote real-time monitoring and maintenance of power generation capacity, timely discover potential problems, reduce failure rates, and thus improve power generation efficiency and safety. Through big data analysis, virtual power plant managers can conduct trend analysis based on energy input and output, identify efficient power generation units, optimize resource allocation, improve resource utilization, and ensure that the best power generation performance can be maintained under different meteorological conditions. At the same time, by predicting and analyzing the power generation of each power generation unit in the virtual power plant based on the energy storage resource data corresponding to each power generation unit, this step can provide a more accurate power generation forecast by combining resource power generation, real-time monitoring information and meteorological forecasts. Effective power forecasting not only helps the power dispatching center to reasonably allocate resources during peak load periods to ensure the stability and safety of power supply, but also reduces operating costs and improves the flexibility and response speed of the power grid, thereby providing basic data guarantee for the subsequent operation load demand forecasting process. Secondly, by analyzing the charging and discharging status of energy storage equipment for each power generation unit in the virtual power plant based on the energy power generation corresponding to each power generation unit, this process not only involves real-time monitoring of data, but also requires statistical analysis of energy power generation to evaluate the actual effect of the charging and discharging strategy. This step can obtain the operating status data of each power generation unit through in-depth analysis of the charging and discharging status of energy storage equipment, thereby evaluating the efficiency and reliability of energy storage equipment. The effectiveness of this step directly affects the operating efficiency of the entire process, especially when it is necessary to quickly respond to changes in the power market and load fluctuations, it can provide necessary support. This analysis not only improves the utilization rate of energy storage equipment, but also provides an important basis for optimizing the overall resource optimization configuration management. By conducting resource operation load demand forecasting and analysis on the corresponding power generation units based on the energy generation power corresponding to each power generation unit and the charging and discharging operation status data of the energy storage equipment, the load demand of each power generation unit in the future can be accurately predicted. Accurate load demand forecasting can not only help operators optimize the power generation mix and reduce operating costs, but also provide a basis for the resource optimization allocation strategy of the virtual power plant, help promote the use of renewable energy and reduce carbon emissions, and enhance the flexibility and adaptability of the virtual power plant in resource operation, thereby maintaining a competitive advantage in the increasingly complex electricity market.Then, by analyzing the resource allocation optimization targets of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, this process is the ultimate goal of the entire virtual power plant resource management chain. Through clear optimization targets, the virtual power plant can set clear resource optimization allocation targets and use them as the basis for subsequent evaluation and adjustment. This analysis not only helps to achieve short-term economic benefits, but also improves the overall efficiency and sustainability of each power generation unit in the virtual power plant in the long run. The setting of optimization targets helps to guide the scientific allocation of virtual power plant resources, and can adapt to changes in market demand and resource availability in real time, allowing managers to monitor and evaluate the operating status of each power generation unit in real time, thereby improving the efficiency of virtual power plant resource allocation. Finally, according to the virtual power plant power generation unit resource allocation optimization target obtained from the previous analysis, the resources of each power generation unit in the virtual power plant are optimized and configured. The main purpose of this step is to convert the theoretical optimization target into an actual operation plan to ensure that each power generation unit can reasonably allocate power plant resources according to market demand and its own status. This optimization configuration not only includes the adjustment of power generation capacity, but also covers the use strategy of energy storage equipment, allocation of spare parts and other aspects. It can significantly improve the overall efficiency of the virtual power plant. Through real-time scheduling and optimization, the virtual power plant can respond flexibly in different power market environments and ensure a stable power supply, thereby promoting the resource allocation optimization process of the entire virtual power system.

[0056] 2. The virtual power plant resource optimization configuration system proposed in the present invention is generally composed of a virtual power plant sub-unit power generation prediction module, a sub-unit resource load demand analysis module, a virtual power plant resource configuration optimization target analysis module and a resource configuration optimization output module. It can implement any virtual power plant resource optimization configuration method described in the present invention, and is used to combine the operations between computer programs running on each module to implement the virtual power plant resource optimization configuration method. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient virtual power plant resource optimization configuration process, thereby simplifying the operating procedures of the virtual power plant resource optimization configuration system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0058] Figure 1 A schematic diagram of the steps of the virtual power plant resource optimization configuration method of the present invention;

[0059] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0060] Figure 3 for Figure 2 Detailed step flow chart of step S14 in FIG. DETAILED DESCRIPTION

[0061] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for optimizing resource configuration of a virtual power plant, the method comprising the following steps:

[0065] Step S1: Obtain energy load storage resource data corresponding to each power generation unit in the virtual power plant, and perform power generation prediction analysis on each power generation unit in the virtual power plant based on the energy load storage resource data corresponding to each power generation unit to obtain the energy power generation corresponding to each power generation unit;

[0066] Step S2: Based on the energy generation power corresponding to each power generation unit, the energy storage device charging and discharging status analysis is performed on each power generation unit in the virtual power plant to obtain the energy storage device charging and discharging operation status data corresponding to each power generation unit; based on the energy generation power corresponding to each power generation unit and the energy storage device charging and discharging operation status data, the resource operation load demand forecast analysis is performed on the corresponding power generation unit to obtain the resource operation load demand forecast amount corresponding to each power generation unit;

[0067] Step S3: Analyze the resource allocation optimization target of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, and obtain the resource allocation optimization target of the virtual power plant power generation unit;

[0068] Step S4: performing resource optimization configuration processing on each power generation unit in the virtual power plant according to the resource configuration optimization target of the virtual power plant power generation unit to generate an optimal resource configuration plan for the virtual power plant power generation unit.

[0069] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a flow chart of the steps of the virtual power plant resource optimization configuration method of the present invention. In this example, the virtual power plant resource optimization configuration method includes the following steps:

[0070] Step S1: Obtain energy load storage resource data corresponding to each power generation unit in the virtual power plant, and perform power generation prediction analysis on each power generation unit in the virtual power plant based on the energy load storage resource data corresponding to each power generation unit to obtain the energy power generation corresponding to each power generation unit;

[0071] In an embodiment of the present invention, energy load storage resource data is collected by installing a sensor network in each power generation unit in a virtual power plant. These sensors monitor the operating status, energy storage level, and load demand of the power generation unit in real time to form a comprehensive energy load storage resource data set, thereby obtaining the energy load storage resource data corresponding to each power generation unit. By obtaining the energy resource input and energy resource output corresponding to each power generation unit from the previously collected energy load storage resource data, and using integrated smart meters to monitor the energy flow of each power generation unit in real time, these meters not only record the input energy (such as solar energy, wind energy or other renewable resource input) and output energy (electrical energy output during the power generation process) of the power generation unit, but also integrate these data to form a dynamic energy flow diagram. Through data collection and transmission protocols (such as MODBUS or MQTT), real-time update and accurate transmission of data are ensured, so that the energy resource power generation corresponding to each power generation unit is obtained, and according to the previously collected energy load storage resource data, the energy resource output corresponding to each power generation unit is obtained. The collected energy resource input and output are used to predict the power generation of the corresponding resource power generation in each power generation unit using statistical analysis methods. This step adopts the time series prediction method, which analyzes the impact of power generation data on resource input and output or environmental meteorology, and uses deep learning models (such as long short-term memory network LSTM) to predict and calculate future power generation. Through integrated learning methods (such as random forest and gradient boosting tree), the accuracy and stability of the prediction are improved. After the prediction is completed, the optimal configuration of virtual power plant resources will be ensured to achieve sustainable and efficient energy management. Finally, the energy power generation corresponding to each power generation unit is predicted and calculated.

[0072] Step S2: Based on the energy generation power corresponding to each power generation unit, the energy storage device charging and discharging status analysis is performed on each power generation unit in the virtual power plant to obtain the energy storage device charging and discharging operation status data corresponding to each power generation unit; based on the energy generation power corresponding to each power generation unit and the energy storage device charging and discharging operation status data, the resource operation load demand forecast analysis is performed on the corresponding power generation unit to obtain the resource operation load demand forecast amount corresponding to each power generation unit;

[0073] In an embodiment of the present invention, the power generation of each power generation unit is subjected to a time period fluctuation change characteristic analysis, so as to calculate the fluctuation peak and valley value of the power generation using a time series analysis algorithm. The specific method includes calculating the maximum and minimum values ​​of the power generation to obtain the fluctuation peak and valley value. In addition, the frequency analysis of the data is performed using a Fourier transform or a wavelet transform method to obtain the power generation fluctuation change frequency, and the power generation fluctuation change characteristics obtained by the previous fluctuation analysis are used to identify and divide the energy storage equipment of each power generation unit in the virtual power plant into operation demand periods, so as to determine when charging and discharging are required by setting the upper and lower thresholds of the power generation and comparing them with the fluctuation peak and valley values. For example, when the corresponding fluctuation change frequency continues to rise in the time period before and after the peak value of power generation fluctuation appears, the energy storage device enters the charging state; conversely, when the fluctuation change frequency continues to decrease in the time period before and after the valley value of power generation fluctuation appears, the energy storage device enters the discharging state. The time series analysis method is used to further identify the charging demand and discharging demand of each power generation unit in different time periods. At the same time, the charging and discharging strategies of the energy storage devices corresponding to the power generation units in the virtual power plant are designed by combining the charging demand operation time period and the discharging demand operation time period determined by the previous division. In this process, the charging and discharging strategies of the energy storage devices are optimized by using optimization algorithms (such as dynamic programming or genetic algorithms). The goal is to minimize operating costs and maximize resource utilization efficiency. Factors such as electricity price fluctuations, changes in load demand, and instability of renewable energy generation are taken into account during the design process to form a sequential charging and discharging strategy for each power generation unit. The charging and discharging status of the energy storage device is analyzed for each corresponding power generation unit based on the sequential charging and discharging strategy of the energy storage device generated by the previous design. In the specific implementation, the charging and discharging status of the energy storage device is tracked through a real-time monitoring system, and the charging time, discharging time, and charging and discharging efficiency are recorded. The recorded data are sorted and analyzed using data analysis tools (such as data analysis libraries in MATLAB or Python) to obtain the charging and discharging operation This process can intuitively reflect the operating status of the energy storage device. Then, by combining the energy generation power corresponding to each power generation unit obtained by previous statistical analysis and the charging and discharging operating status data of the energy storage device, the corresponding power generation unit is predicted and analyzed for resource operation load demand. This step is achieved by building a load prediction model (such as linear regression, support vector machine and other machine learning algorithms). The input data includes power generation power, operating status data of the energy storage device and other relevant meteorological data. The model will analyze the time series characteristics of the data, predict the resource operation load demand of each power generation unit in the future period, and finally obtain the resource operation load demand forecast corresponding to each power generation unit.

[0074] Step S3: Analyze the resource allocation optimization target of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, and obtain the resource allocation optimization target of the virtual power plant power generation unit;

[0075] In an embodiment of the present invention, a mathematical model is established by combining the energy generation power corresponding to each power generation unit obtained by previous statistical analysis and the resource operation load demand forecast, and data mining technology is used to analyze the power generation resource configuration potential of each power generation unit in the virtual power plant under different operation load demands, and by combining the energy resource configuration potential of each power generation unit under specific operation load demand conditions obtained by previous evaluation analysis, a resource configuration constraint analysis is performed on the corresponding power generation unit to analyze the resource configuration constraint conditions of each power generation unit in the virtual power plant, including factors such as the maximum load, minimum power generation capacity, and fuel supply capacity of the power generation unit. At the same time, by combining the resource configuration constraint conditions obtained by previous analysis, an optimization analysis of the resource optimization strategy is performed on the corresponding power generation unit in the virtual power plant, so as to generate a resource configuration optimization strategy for each power generation unit by using a genetic algorithm, a simulated annealing algorithm or other optimization algorithms. The specific implementation When constructing an objective function, such as minimizing the cost of power generation or maximizing economic benefits, the power generation power of each power generation unit is continuously adjusted through an iterative algorithm to meet the resource allocation constraints. MATLAB programming is used to realize the generation process of resource optimization strategies to ensure the effectiveness and feasibility of the resource allocation plan. Then, by combining the previously generated resource allocation optimization strategy, the corresponding power generation unit is identified and analyzed for the resource allocation optimization goals to set specific optimization goals, such as reducing the cost of power generation, improving operating efficiency, etc. Data analysis tools are used to combine historical data and optimization results to quantitatively analyze the resource allocation optimization goals of each power generation unit. In the specific implementation, statistical software is used for data regression analysis to generate an evaluation report for the optimization goals to provide a clear optimization direction and basis, provide reliable decision-making support for the resource optimization allocation method of the virtual power plant, and finally obtain the resource allocation optimization goals of the virtual power plant power generation unit.

[0076] Step S4: performing resource optimization configuration processing on each power generation unit in the virtual power plant according to the resource configuration optimization target of the virtual power plant power generation unit to generate an optimal resource configuration plan for the virtual power plant power generation unit.

[0077] In an embodiment of the present invention, a specific resource optimization configuration scheme is implemented for the corresponding power generation units in the virtual power plant by using the previously obtained resource configuration optimization target of the virtual power plant power generation unit. At this stage, an optimization scheduling tool (such as an electricity market trading system or a distributed energy management system) is required to allocate resources for each power generation unit according to the previous resource configuration optimization target. Through real-time data analysis, the optimal power generation plan is implemented to ensure that the output of the power generation unit is consistent with demand in the case of load fluctuations. For example, if the load is expected to increase in a certain period of time, the corresponding power generation unit will be instructed to increase the power generation output and adjust the charging and discharging state of the energy storage device. This process needs to take into account the stability and economy of the power grid, and finally generate the optimal resource configuration scheme for the virtual power plant power generation unit, improve the overall resource configuration optimization efficiency of the virtual power plant, and ensure the reliability and sustainability of power supply.

[0078] Further, step S1 includes the following steps:

[0079] Step S11: Acquire energy storage resource data corresponding to each power generation unit in the virtual power plant;

[0080] Step S12: Obtain the energy resource input and energy resource output corresponding to each power generation unit through the energy load storage resource data corresponding to each power generation unit, and monitor the power generation of each power generation unit in the virtual power plant in real time through the integrated smart meter to obtain the energy resource power generation corresponding to each power generation unit; perform power generation capacity estimation analysis on the corresponding energy resource power generation based on the energy resource input and energy resource output corresponding to each power generation unit to obtain the energy power generation capacity estimation corresponding to each power generation unit;

[0081] Step S13: Acquire the meteorological change data of the virtual power plant, and perform seasonal change characteristic analysis on the meteorological change data of the virtual power plant to obtain the meteorological seasonal change characteristic data of the virtual power plant;

[0082] Step S14: performing environmental impact correction calculation on the estimated energy generation capacity corresponding to each power generation unit based on the virtual power plant meteorological seasonal variation characteristic data, and obtaining the corrected estimated actual generation capacity corresponding to each power generation unit;

[0083] Step S15: Perform power generation prediction analysis on the corresponding power generation units in the virtual power plant according to the actual power generation capacity correction estimate corresponding to each power generation unit, and obtain the energy power generation corresponding to each power generation unit.

[0084] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0085] Step S11: Acquire energy storage resource data corresponding to each power generation unit in the virtual power plant;

[0086] In an embodiment of the present invention, energy load storage resource data is collected by installing a sensor network in each power generation unit in the virtual power plant. These sensors monitor the operating status, energy storage level and load demand of the power generation unit in real time. The data acquisition system adopts high-precision intelligent sensors, which can capture the battery energy storage status, fuel energy input and output, renewable energy generation capacity, etc. of each unit. These data are preliminarily processed by edge computing equipment to ensure the efficiency and accuracy of data transmission, forming a comprehensive energy load storage resource data set, and finally obtaining the energy load storage resource data corresponding to each power generation unit.

[0087] Step S12: Obtain the energy resource input and energy resource output corresponding to each power generation unit through the energy load storage resource data corresponding to each power generation unit, and monitor the power generation of each power generation unit in the virtual power plant in real time through the integrated smart meter to obtain the energy resource power generation corresponding to each power generation unit; perform power generation capacity estimation analysis on the corresponding energy resource power generation based on the energy resource input and energy resource output corresponding to each power generation unit to obtain the energy power generation capacity estimation corresponding to each power generation unit;

[0088] In an embodiment of the present invention, the energy resource input and output corresponding to each power generation unit are obtained from the energy load storage resource data previously collected, and the energy flow of each power generation unit is monitored in real time by using integrated smart meters. These meters not only record the input energy (such as solar energy, wind energy or other renewable resource input) and output energy (electric energy output during the power generation process) of the power generation unit, but also integrate these data to form a dynamic energy flow diagram. Through data acquisition and transmission protocols (such as MODBUS or MQTT), the real-time update and accurate transmission of data are ensured, so as to obtain the energy resource power generation corresponding to each power generation unit. At the same time, according to the previously collected energy resource input and output, the resource power generation is estimated and analyzed by using statistical analysis methods. In implementation, regression analysis, time series analysis and other methods are used to combine historical power generation data and real-time data to establish a power generation capacity prediction model, and the model is trained using machine learning algorithms (such as support vector machines and random forests) so that it can predict future power generation capacity according to changes in input data, and finally obtain the energy power generation capacity estimation corresponding to each power generation unit.

[0089] Step S13: Acquire the meteorological change data of the virtual power plant, and perform seasonal change characteristic analysis on the meteorological change data of the virtual power plant to obtain the meteorological seasonal change characteristic data of the virtual power plant;

[0090] In an embodiment of the present invention, in terms of meteorological data acquisition, the virtual power plant needs to access the regional meteorological service system to obtain real-time meteorological change data of the virtual power plant, such as temperature, humidity, wind speed, precipitation, etc. These data are regularly updated through the API interface and stored in the database. At the same time, by using statistical software (such as R or MATLAB), the seasonal change characteristics of the virtual power plant meteorological change data are analyzed to identify the impact of meteorological conditions on energy production. Through Fourier transform or wavelet analysis and other methods, the seasonal characteristics of meteorological changes are extracted, and finally the seasonal change characteristic data of the virtual power plant meteorology are obtained.

[0091] Step S14: performing environmental impact correction calculation on the estimated energy generation capacity corresponding to each power generation unit based on the virtual power plant meteorological seasonal variation characteristic data, and obtaining the corrected estimated actual generation capacity corresponding to each power generation unit;

[0092] In an embodiment of the present invention, after completing the meteorological characteristic analysis, the environmental impact correction calculation is performed on the power generation capacity of each power generation unit based on the meteorological seasonal change characteristic data obtained above. The specific operation includes using the established correction model to combine the meteorological data with the power generation capacity estimate, and adjusting the model through weighted coefficients to reflect the actual power generation capacity under different meteorological conditions. This process can use mathematical optimization techniques (such as linear programming or nonlinear programming) to ensure the effectiveness and accuracy of the correction calculation, and finally obtain the corrected estimate of the actual power generation capacity corresponding to each power generation unit.

[0093] Step S15: Perform power generation prediction analysis on the corresponding power generation units in the virtual power plant according to the actual power generation capacity correction estimate corresponding to each power generation unit, and obtain the energy power generation corresponding to each power generation unit.

[0094] In an embodiment of the present invention, the power generation of the corresponding power generation unit is predicted and analyzed based on the revised estimate of the actual power generation capacity corresponding to each power generation unit obtained by the previous correction calculation. This step adopts a time series prediction method, by analyzing the relationship between historical power generation data and environmental factors, and using a deep learning model (such as a long short-term memory network LSTM) to predict and calculate future power generation. The accuracy and stability of the prediction are improved through an integrated learning method (such as a random forest and a gradient boosting tree). After the prediction is completed, the optimal allocation of virtual power plant resources will be ensured, and sustainable and efficient energy management will be achieved. Finally, the energy power generation corresponding to each power generation unit is predicted and calculated.

[0095] Furthermore, the power generation capacity estimation analysis of the corresponding energy resource power generation based on the energy resource input and energy resource output corresponding to each power generation unit in step S12 includes the following steps:

[0096] Performing time-series synchronization processing on the energy resource input and energy resource output corresponding to each power generation unit to obtain the time-series change of resource input and the time-series change of resource output corresponding to each power generation unit;

[0097] In an embodiment of the present invention, the energy resource input and energy resource output corresponding to each power generation unit are processed in time series synchronization, so that the real-time data of the power generation unit can be centrally collected by using a high-precision data acquisition system. In the specific implementation process, a data processing model is established to compare the input and output data of each power generation unit in the same time period to ensure time consistency, and the input and output data are marked with timestamps. Subsequently, the input and output of each power generation unit are normalized, and automatic data processing is achieved through programming to ensure that the resource input and output of each power generation unit are analyzed under the same time reference, and finally the resource input time series change and resource output time series change corresponding to each power generation unit are obtained.

[0098] Preferably, the time-series point input quantity extraction is performed on the time-series point input quantity corresponding to each power generation unit to obtain the resource input quantity corresponding to each power generation unit at each time-series point; the input fluctuation difference between time-series points is calculated for the resource input quantity corresponding to each power generation unit at each time-series point to obtain the resource input change fluctuation difference between each adjacent time-series point corresponding to each power generation unit;

[0099] In an embodiment of the present invention, by setting a specific time point, the resource input time series variation corresponding to each power generation unit is sampled and recorded, so as to select a suitable time window (for example, every hour or every minute) as a time series point, and use a data extraction tool (such as the Pandas library in Python) to read and filter out the resource input data of each power generation unit at these specific time points. After ensuring the data integrity of each time point, a time series data set is constructed, which can accurately reflect the resource input amount of each time series point, thereby obtaining the resource input amount of each power generation unit corresponding to each time series point. At the same time, by using the difference method to calculate the difference in resource input between adjacent time points, in the specific implementation, by setting a loop structure, the resource input amount of each power generation unit at adjacent time series points is compared one by one, the input fluctuation difference is calculated, and the calculation result is stored in the data frame, which can reveal the fluctuation characteristics of the resource input, and finally obtain the resource input change fluctuation difference between each adjacent time series point corresponding to each power generation unit.

[0100] Preferably, a resource input change gradient analysis is performed on the resource input change fluctuation difference between each adjacent time sequence point corresponding to each power generation unit to obtain the resource input change gradient corresponding to each power generation unit;

[0101] In an embodiment of the present invention, a statistical analysis of the resource input change gradient is performed on the resource input change fluctuation difference between each adjacent time point corresponding to each power generation unit, and the fluctuation difference is fitted and analyzed by using a linear regression model to calculate the gradient of the resource input change. In specific implementation, the fluctuation difference is used as the independent variable and time is used as the dependent variable. Regression analysis is performed through statistical software (such as R or MATLAB) to obtain the input change gradient of each power generation unit between each time point. This analysis result can clearly show the resource input change trend of each power generation unit over time, and finally the resource input change gradient corresponding to each power generation unit is obtained.

[0102] Preferably, the resource output efficiency evaluation and analysis is performed on the resource output time series variation corresponding to each power generation unit to obtain the resource output efficiency corresponding to each power generation unit;

[0103] In an embodiment of the present invention, the resource output efficiency is evaluated and calculated by calculating the time series changes in resource output corresponding to each power generation unit, and a calculation formula for the resource output efficiency is set, such as output efficiency = output amount / output duration. During the implementation process, a statistical method (such as weighted average) is applied to calculate the resource output efficiency of each power generation unit, and finally the resource output efficiency corresponding to each power generation unit is obtained.

[0104] Preferably, based on the resource input change gradient and resource output efficiency corresponding to each power generation unit, the corresponding energy resource power generation is analyzed for power generation capacity estimation to obtain the energy power generation capacity estimate corresponding to each power generation unit.

[0105] In an embodiment of the present invention, the power generation capacity of the corresponding resource power generation is estimated and analyzed by combining the resource input change gradient and the resource output efficiency corresponding to each power generation unit obtained by previous analysis. In specific implementation, the power generation capacity is estimated by combining the resource input change gradient and the output efficiency of each power generation unit. In this process, a regression model is constructed, and the input gradient and output efficiency are used as characteristic variables. The power generation capacity of each power generation unit is calculated in combination with the corresponding resource power generation prediction. By simulating different input conditions and output efficiencies, a more accurate power generation capacity estimate is obtained, and finally the energy power generation capacity estimate corresponding to each power generation unit is obtained.

[0106] Further, step S14 includes the following steps:

[0107] Step S141: Based on the seasonal variation characteristics of each meteorological factor in the virtual power plant meteorological seasonal variation characteristic data, an environmental impact factor identification analysis is performed on the estimated energy generation capacity corresponding to each power generation unit to obtain the environmental impact factor of the energy generation capacity corresponding to each power generation unit;

[0108] Step S142: quantifying the environmental impact coefficient of the energy generation capacity environmental impact factor corresponding to each power generation unit to obtain the power generation capacity impact correction coefficient of each power generation unit corresponding to the environmental impact factor;

[0109] Step S143: performing environmental impact correction calculation on the energy power generation capacity estimate corresponding to each power generation unit based on the power generation capacity impact correction coefficient of the environmental impact factor corresponding to each power generation unit, and obtaining the actual power generation capacity correction estimate corresponding to each power generation unit.

[0110] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S14 in the embodiment, step S14 includes the following steps:

[0111] Step S141: Based on the seasonal variation characteristics of each meteorological factor in the virtual power plant meteorological seasonal variation characteristic data, an environmental impact factor identification analysis is performed on the estimated energy generation capacity corresponding to each power generation unit to obtain the environmental impact factor of the energy generation capacity corresponding to each power generation unit;

[0112] In an embodiment of the present invention, by collecting and collating the characteristic data of seasonal meteorological changes in the virtual power plant, and by adopting a variety of meteorological observation tools, such as meteorological stations, satellite remote sensing technology and meteorological simulation software, the meteorological factors related to each power generation unit, including temperature, humidity, wind speed, precipitation and sunshine duration, etc., are obtained. These meteorological data will be classified by season and statistically analyzed. The time series analysis method is used to identify the seasonal fluctuation characteristics of each meteorological factor, and its impact on the energy generation capacity of the power generation unit in different seasons is determined. Statistical analysis software (such as SPSS or R) is further used to perform correlation analysis to identify environmental influencing factors. For example, it is found that under high temperature in summer, the power generation capacity of photovoltaic power generation units is significantly improved, while under low temperature in winter, the power generation capacity of wind power generation units is significantly reduced. These analysis results will form the environmental impact factors corresponding to the power generation units, and finally the environmental impact factors of the energy generation capacity corresponding to each power generation unit are obtained.

[0113] Step S142: quantifying the environmental impact coefficient of the energy generation capacity environmental impact factor corresponding to each power generation unit to obtain the power generation capacity impact correction coefficient of each power generation unit corresponding to the environmental impact factor;

[0114] In an embodiment of the present invention, the environmental impact coefficient of the energy generation capacity of each power generation unit is quantitatively calculated according to the environmental impact factors previously identified, so as to adopt the weighted average method to calculate the impact coefficient of each power generation unit under different meteorological conditions, and the statistical results of the environmental impact factors are input into the table using tools such as Excel tables, and the data analysis function is applied to calculate the power generation capacity impact correction coefficient of each power generation unit under specific meteorological conditions, that is, the corresponding environmental impact factor is multiplied by the weight and the average value is calculated. In this process, it is necessary to consider the different degrees of influence of various meteorological factors on the power generation capacity of the power generation unit, and determine the weight of each factor in combination with actual meteorological data. For example, for photovoltaic power generation units, the influence weight of temperature will be higher than that of humidity, so the calculated power generation capacity impact correction coefficient can truly reflect the actual impact of environmental changes on its power generation capacity. This process ensures that the obtained impact coefficient has practical operability, and finally the power generation capacity impact correction coefficient of each power generation unit corresponding to the environmental impact factor is obtained.

[0115] Step S143: performing environmental impact correction calculation on the energy power generation capacity estimate corresponding to each power generation unit based on the power generation capacity impact correction coefficient of the environmental impact factor corresponding to each power generation unit, and obtaining the actual power generation capacity correction estimate corresponding to each power generation unit.

[0116] In an embodiment of the present invention, the energy power generation capacity estimate previously estimated and calculated is corrected by combining the power generation capacity impact correction coefficient of each power generation unit corresponding to the environmental impact factor obtained by previous quantitative calculation. In specific implementation, the original power generation capacity estimate of each power generation unit and its corresponding environmental impact correction coefficient are first summarized to form a data set. Subsequently, a programming tool (such as Python or MATLAB) is used for batch calculation to multiply the environmental impact correction coefficient by the original power generation capacity to obtain a corrected actual power generation capacity estimate. The obtained corrected estimate shows that the power generation capacity of a photovoltaic power generation unit under specific meteorological conditions is increased by 20%, and finally the corrected estimate of the actual power generation capacity corresponding to each power generation unit is obtained.

[0117] Further, step S2 includes the following steps:

[0118] Step S21: Analyze the time period fluctuation characteristics of the energy power generation corresponding to each power generation unit to obtain the power generation fluctuation characteristics of each power generation unit in different time periods, wherein the power generation fluctuation characteristics include the power generation fluctuation peak value, the power generation fluctuation valley value and the power generation fluctuation frequency;

[0119] In an embodiment of the present invention, by analyzing the time period fluctuation characteristics of the power generation of each power generation unit, first, the power generation data of each power generation unit in a certain time period is collected through a data acquisition system. The data acquisition can be performed using high-precision power monitoring equipment. In the data processing process, a timing analysis algorithm is used to calculate the fluctuation peak and valley values ​​of the power generation. The specific method includes calculating the maximum and minimum values ​​of the power generation to obtain the fluctuation peak and valley values. In addition, a Fourier transform or wavelet transform method is used to perform frequency analysis on the data to obtain the power generation fluctuation frequency. Through these analyses, the power generation fluctuation characteristics of each power generation unit in different time periods are finally obtained, including the power generation fluctuation peak value, the power generation fluctuation valley value and the power generation fluctuation frequency.

[0120] Step S22: Based on the power fluctuation characteristics of each power generation unit in different time periods, the energy storage equipment corresponding to the power generation unit in the virtual power plant is identified and divided into operation demand time periods, so as to obtain the energy storage equipment charging demand operation time periods and energy storage equipment discharging demand operation time periods corresponding to each power generation unit;

[0121] In an embodiment of the present invention, the energy storage device of each power generation unit in the virtual power plant is identified and divided into operation demand time periods by using the power generation fluctuation change characteristics obtained by the previous fluctuation analysis, so as to determine when charging and discharging are needed by setting the upper and lower thresholds of the power generation and comparing with the fluctuation peak and valley values. For example, when the corresponding fluctuation change frequency continues to rise in the time period before and after the power generation fluctuation peak value, the energy storage device enters the charging state; conversely, when the fluctuation change frequency continues to decrease in the time period before and after the power generation fluctuation valley value, the energy storage device enters the discharging state, and the time series analysis method is used to further identify the charging demand and discharging demand of each power generation unit in different time periods, and finally obtain the charging demand operation period of the energy storage device and the discharging demand operation period of the energy storage device corresponding to each power generation unit.

[0122] Step S23: Designing charging and discharging strategies for energy storage devices corresponding to power generation units in the virtual power plant based on the charging demand operation period of energy storage devices corresponding to each power generation unit and the discharging demand operation period of energy storage devices, so as to generate a sequential charging and discharging strategy for energy storage devices corresponding to each power generation unit;

[0123] In an embodiment of the present invention, a charging and discharging strategy for energy storage devices corresponding to power generation units in a virtual power plant is designed by combining the previously determined energy storage device charging demand operation time period and the energy storage device discharging demand operation time period. In this process, an optimization algorithm (such as dynamic programming or genetic algorithm) is used to model the charging and discharging strategy of the energy storage device. The goal is to minimize operating costs and maximize resource utilization efficiency. Factors such as electricity price fluctuations, load demand changes, and instability of renewable energy generation are considered in the design process to form a sequential charging and discharging strategy for each power generation unit. These strategies will guide the energy storage device to charge and discharge at appropriate time periods, thereby optimizing resource allocation, and finally designing and generating a sequential charging and discharging strategy for the energy storage device corresponding to each power generation unit.

[0124] Step S24: analyzing the energy storage device charging and discharging status of each power generation unit in the virtual power plant according to the energy storage device timing charging and discharging strategy corresponding to each power generation unit, so as to obtain the energy storage device charging and discharging operation status data corresponding to each power generation unit;

[0125] In an embodiment of the present invention, the charge and discharge status of the energy storage device is analyzed for each corresponding power generation unit according to the sequential charge and discharge strategy of the energy storage device designed and generated previously. In a specific implementation, the charge and discharge status of the energy storage device is tracked by a real-time monitoring system, the charging time, the discharging time and the charge and discharge efficiency are recorded, and the recorded data are sorted and analyzed using a data analysis tool (such as a data analysis library in MATLAB or Python) to obtain the charge and discharge operation status data. This process can intuitively reflect the operation status of the energy storage device, help judge the effectiveness and rationality of the charge and discharge strategy, and finally obtain the charge and discharge operation status data of the energy storage device corresponding to each power generation unit.

[0126] Step S25: Based on the energy generation power corresponding to each power generation unit and the charging and discharging operation status data of the energy storage device, the corresponding power generation unit is subjected to resource operation load demand forecasting analysis to obtain the resource operation load demand forecast corresponding to each power generation unit.

[0127] In an embodiment of the present invention, by combining the energy generation power corresponding to each power generation unit obtained by previous statistical analysis and the charging and discharging operation status data of the energy storage device, the resource operation load demand of the corresponding power generation unit is predicted and analyzed. This step is achieved by constructing a load prediction model (such as linear regression, support vector machine and other machine learning algorithms). The input data includes the generation power, the operation status data of the energy storage device and other relevant meteorological data. The model will analyze the time series characteristics of the data, predict the resource operation load demand of each power generation unit in the future period, and finally obtain the resource operation load demand forecast corresponding to each power generation unit.

[0128] Further, step S22 includes the following steps:

[0129] Based on the power fluctuation characteristics of each power generation unit in different time periods, the power generation fluctuation of the energy storage equipment corresponding to the power generation unit in the virtual power plant is visualized to obtain the power generation fluctuation curve of the energy storage equipment corresponding to each power generation unit;

[0130] In an embodiment of the present invention, the power fluctuation of the energy storage device in the virtual power plant is visualized by using a data visualization tool in combination with the power fluctuation change characteristics of each power generation unit in different time periods obtained by previous statistical analysis, so as to obtain the real-time power generation data of each power generation unit in a certain time period through the data acquisition system, and store these data in a database. Then, data analysis software (such as Python combined with Pandas and Matplotlib libraries) is used for data processing, and a program is written to analyze the change trend of the power generation, and the power generation fluctuation change curves of each power generation unit are drawn. These curves not only show the change of the power generation of each power generation unit over time, but also help identify the peaks and troughs of the power generation, and finally the power generation fluctuation change curves of the energy storage device corresponding to each power generation unit are obtained.

[0131] Preferably, the power generation fluctuation change curve corresponding to the energy storage device of each power generation unit is identified and divided into operation demand time periods. If the power generation fluctuation change frequency continues to rise in the time period before and after the power generation fluctuation peak in the power generation fluctuation change curve, the time period is identified and divided into the charging demand operation period, so as to obtain the charging demand operation period of the energy storage device corresponding to each power generation unit;

[0132] In an embodiment of the present invention, the operation demand time period is identified and divided for the previously generated power generation fluctuation change curve, and an algorithm is set to analyze the power generation fluctuation peak in the curve, determine the time point of the peak and the time period before and after the peak, and calculate the power generation fluctuation change frequency in these time periods to determine whether it continues to rise. If the power generation fluctuation frequency in the time period before and after the peak increases significantly, these time periods are marked as charging demand operation time periods. In specific implementation, signal processing tools can be used for frequency analysis, and mathematical methods such as Fourier transform can be used to extract characteristic frequencies, and judgment can be made in combination with set thresholds, and finally the energy storage device charging demand operation time periods corresponding to each power generation unit can be identified and divided.

[0133] Preferably, if the power generation fluctuation frequency continues to decrease in the time period before and after the power generation fluctuation valley value appears in the power generation fluctuation change curve, the time period is identified and divided into the discharge demand operation period to obtain the energy storage device discharge demand operation period corresponding to each power generation unit.

[0134] In an embodiment of the present invention, by analyzing the valley value in the power generation fluctuation curve, the time point of the valley value and the time period before and after the valley value are determined, and by evaluating whether the power generation fluctuation frequency continues to decrease in these time periods, it is judged whether the time period meets the characteristics of the discharge demand. If the power generation fluctuation frequency in the time period before and after the valley value continues to decrease, it is marked as the discharge demand operation period. In this step, the statistical analysis software is used to perform regression analysis on the fluctuation trend, determine the specific parameters of the time period, and generate a detailed report on the discharge demand time period, and finally identify and divide the energy storage equipment discharge demand operation period corresponding to each power generation unit.

[0135] Further, step S25 includes the following steps:

[0136] Step S251: Based on the energy generation power corresponding to each generation unit, a power generation field simulation analysis is performed on the corresponding generation unit in the virtual power plant to generate a resource operation power generation simulation field corresponding to each generation unit;

[0137] In an embodiment of the present invention, a simulation analysis of power change scenarios is performed on corresponding power generation units by combining the energy power generation corresponding to each power generation unit obtained by previous statistical analysis, so as to establish a change field space model using simulation software (such as MATLAB / Simulink or PVSYST), and input characteristic parameters and environmental factors of wind power generation, solar power generation and traditional power generation units. During the simulation process, the influence of different meteorological conditions (such as wind speed and light intensity) on power generation is considered, and the power output distribution of each power generation unit under specific conditions is obtained by numerical calculation methods. Finally, a simulation field of resource operation power generation corresponding to each power generation unit is generated by simulation.

[0138] Step S252: Perform power generation load demand impact analysis on the resource operation power generation power simulation field corresponding to each power generation unit to obtain the power generation load demand impact factor corresponding to each power generation unit;

[0139] In an embodiment of the present invention, after the power generation simulation field is generated, the load demand impact analysis is performed on the power output of each power generation unit, and the historical load data and the simulated power generation data are cross-analyzed using statistical analysis tools (such as pandas and numpy libraries in Python), the power response coefficients of different power generation units under different load conditions are calculated, and the influencing factors of the power generation operation load demand of each power generation unit are determined by constructing a regression model. The specific steps include data cleaning, feature extraction and model fitting, so as to obtain the demand response characteristics of the power output of the power generation unit, and finally obtain the power generation operation load demand impact factors corresponding to each power generation unit.

[0140] Step S253: Based on the charge and discharge operation status data of the energy storage device corresponding to each power generation unit, a charge and discharge state field simulation analysis is performed on the corresponding power generation unit in the virtual power plant to generate a resource operation charge and discharge state simulation field corresponding to each power generation unit;

[0141] In an embodiment of the present invention, by combining the charging and discharging operation status data of the energy storage device corresponding to each power generation unit obtained by previous analysis, a simulation analysis of the charging and discharging state change scenario is performed on the corresponding power generation unit to collect the charging and discharging history records of the energy storage device of each power generation unit, and a simulation tool (such as ANSYS or OpenDSS) is used to build a dynamic model of the energy storage system, and relevant current, voltage and efficiency of the energy storage device and other parameters are input to simulate the charging and discharging state of the energy storage device under different operating conditions. During the analysis process, the external load changes and the real-time fluctuations of the generated power are considered to obtain the charging and discharging state field corresponding to each power generation unit, thereby revealing the operating efficiency and responsiveness of the energy storage device, and finally generating a resource operation charging and discharging state simulation field corresponding to each power generation unit.

[0142] Step S254: performing a charge and discharge load demand impact analysis on the resource operation charge and discharge state simulation field corresponding to each power generation unit, and obtaining an energy storage charge and discharge operation load demand impact factor corresponding to each power generation unit;

[0143] In an embodiment of the present invention, after completing the simulation of the charge and discharge state field, an impact assessment analysis of the charge and discharge load demand is carried out, so as to conduct an in-depth analysis of the charge and discharge simulation results by using a timing analysis tool, and combine the load demand model to consider the impact of external factors (such as electricity price, market demand) on the charge and discharge state. By setting the load demand threshold and the charge and discharge strategy, the charge and discharge operation load demand impact factor of each power generation unit under a specific load demand is calculated, forming a quantitative description of the charge and discharge behavior of the energy storage device, and finally obtaining the energy storage charge and discharge operation load demand impact factor corresponding to each power generation unit.

[0144] Step S255: Based on the power generation operation load demand influencing factors corresponding to each power generation unit and the energy storage charging and discharging operation load demand influencing factors, the resource operation load demand calculation formula is used to perform load demand forecasting calculations on the corresponding power generation units in the virtual power plant to obtain the resource operation load demand forecast corresponding to each power generation unit.

[0145] In an embodiment of the present invention, a suitable resource operation load demand calculation formula is formed by combining time variable parameters, energy generation power, generation efficiency factor of generation unit, generation power operation load demand influencing factor, energy storage device charging and discharging operation status, energy storage device charging and discharging efficiency factor, energy storage charging and discharging operation load demand influencing factor, resource operation load demand attenuation factor, resource operation load demand time delay effect factor and related parameters to predict the load demand of the corresponding power generation unit in the virtual power plant, so as to integrate the power demand of each power generation unit and the charging and discharging demand of the energy storage device, predict the future load demand, obtain the load demand forecast of each power generation unit, and finally obtain the resource operation load demand forecast corresponding to each power generation unit.

[0146] Furthermore, the resource operation load demand calculation formula described in step S255 is specifically:

[0147]

[0148] Where, L i is the resource operation load demand forecast corresponding to the i-th power generation unit, n is the total number of power generation units in the virtual power plant, i is the item measurement parameter of the power generation unit, t is the time variable parameter, τ is the integral time variable parameter, P i (t) is the energy generation power of the i-th power generation unit at time t, η P is the power generation efficiency factor of the power generation unit, f i (t) is the influencing factor of the power generation load demand of the i-th power generation unit at time t, E i (t) is the charging and discharging operation state of the energy storage device of the i-th power generation unit at time t, η E is the charging and discharging efficiency factor of the energy storage device, g i (t) is the energy storage charging and discharging operation load demand influencing factor of the i-th power generation unit at time t, λ is the resource operation load demand attenuation factor, L(τ) is the time delay effect factor of the resource operation load demand at time τ, ξ is the correction coefficient of the resource operation load demand forecast.

[0149] The present invention obtains a resource operation load demand calculation formula by using a specific mathematical model and verifying it, which is used to predict and calculate the load demand of the corresponding power generation unit in the virtual power plant. i (t) represents the energy generation power of the i-th power generation unit at time t, reflecting the current power generation capacity of the power generation unit; η P It represents the power generation efficiency factor of the power generation unit, reflecting the ratio between the actual power generation and the theoretical power generation; f i(t) represents the load demand influencing factor of power generation, reflecting the influence of load demand on power generation, which may change over time; E i (t) represents the charging and discharging state of the energy storage device of the i-th power generation unit at time t, indicating whether the energy storage device is charging or discharging; η E Indicates the charging and discharging efficiency factor of the energy storage device, which refers to the proportion of energy loss during the charging or discharging process; g i (t) represents the load demand influencing factor of energy storage charging and discharging operation, reflecting the degree of influence of energy storage operation status on load demand; λ represents the load demand attenuation factor of resource operation, reflecting the attenuation speed of load demand; L(τ) represents the time delay effect factor of resource operation load demand at time τ, considering the influence of historical load demand on current load. This calculation formula combined with the above parameters can reflect the operation status of each power generation unit and the change of external load demand in real time, so that the virtual power plant can quickly adjust the power generation strategy and optimize the power supply. By considering factors such as power generation efficiency, energy storage status, and time delay, the accuracy of load demand forecasting can be significantly improved, thereby improving the reliability of the power system. Using the load demand forecasting results, the virtual power plant can optimize resource allocation, reasonably arrange the operation of power generation and energy storage equipment, reduce waste and improve economic benefits. At the same time, by introducing the time delay effect of historical load demand, the dynamic characteristics of the system can be better captured and future power dispatching decisions can be optimized. In addition, by introducing the correction coefficient ξ, a certain flexibility can be provided for load demand forecasting, so that it can adapt to uncertain market conditions and user needs. This load demand calculation formula provides strong support for the energy management of virtual power plants by comprehensively considering the various operating states, historical influencing factors and dynamic changes of power generation units. This will improve the flexibility, economy and sustainability of virtual power systems, especially in the context of the growing renewable energy. It has important practical significance. In summary, this formula fully considers the resource operation load demand forecast L corresponding to the i-th power generation unit i , the total number of power generation units in the virtual power plant n, the item measurement parameter i of the power generation unit, the time variable parameter t, the integral time variable parameter τ, the energy generation power P of the i-th power generation unit at time t i (t), power generation efficiency factor η P , the power generation load demand influencing factor f of the i-th power generation unit at time t i (t), the charging and discharging operation state E of the energy storage device of the i-th power generation unit at time t i (t), energy storage device charging and discharging efficiency factor η E , the energy storage charging and discharging operation load demand influencing factor g of the i-th power generation unit at time t i(t), resource operation load demand attenuation factor λ, resource operation load demand time delay effect factor L(τ) at time τ, resource operation load demand forecast correction coefficient ξ, resource operation load demand forecast L corresponding to the i-th power generation unit i The correlation between the above parameters forms a functional relationship:

[0150]

[0151] This formula can realize the load demand forecasting calculation process for the corresponding power generation units in the virtual power plant. At the same time, by introducing the correction coefficient ξ of the resource operation load demand forecast, it can be adjusted according to the errors occurring in the calculation process, thereby improving the accuracy and applicability of the resource operation load demand calculation formula.

[0152] Further, step S3 includes the following steps:

[0153] Step S31: Based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, the resource allocation potential of each power generation unit in the virtual power plant is evaluated and analyzed to obtain the energy resource allocation potential of each power generation unit under specific operation load demand conditions;

[0154] In an embodiment of the present invention, a mathematical model is established by combining the energy generation power corresponding to each power generation unit obtained by previous statistical analysis and the predicted amount of resource operation load demand, and data mining technology is used to analyze the power generation resource allocation potential of each power generation unit in the virtual power plant under different operation load demands. By using linear programming and nonlinear programming algorithms, the power generation units under different load demands are evaluated to determine the resource allocation potential of each power generation unit. In specific implementation, tools such as Python or MATLAB are used to calculate the power generation capacity of each power generation unit under specific load demand conditions by writing algorithm codes, thereby obtaining the corresponding resource allocation potential, and finally obtaining the energy resource allocation potential of each power generation unit under specific operation load demand conditions.

[0155] Step S32: performing resource configuration constraint analysis on the corresponding power generation units in the virtual power plant based on the energy resource configuration potential of each power generation unit under specific operating load demand conditions, so as to generate resource configuration constraint conditions corresponding to each power generation unit;

[0156] In an embodiment of the present invention, by combining the energy resource allocation potential of each power generation unit under specific operating load demand conditions obtained by previous evaluation and analysis, a resource allocation constraint analysis is performed on the corresponding power generation unit to analyze the resource allocation constraints of each power generation unit in the virtual power plant, including factors such as the maximum load, minimum power generation capacity, and fuel supply capacity of the power generation unit, and determine the specific restrictions on resource allocation. In the specific implementation process, the optimization algorithm is used to derive the constraints to ensure that each power generation unit performs resource allocation within its working range. Excel can be used for data sorting, and the Solver plug-in can be used to solve the resource allocation constraints of each power generation unit, and finally the resource configuration constraints corresponding to each power generation unit are generated.

[0157] Step S33: performing resource optimization strategy analysis on the corresponding power generation units in the virtual power plant based on the resource configuration constraint conditions corresponding to each power generation unit, so as to generate a resource configuration optimization strategy corresponding to each power generation unit;

[0158] In an embodiment of the present invention, an optimization analysis of the resource optimization strategy is performed on the corresponding power generation units in the virtual power plant by combining the resource configuration constraints obtained by the previous analysis, so as to generate a resource configuration optimization strategy for each power generation unit by using a genetic algorithm, a simulated annealing algorithm or other optimization algorithms. In specific implementation, an objective function is constructed, such as minimizing the power generation cost or maximizing the economic benefit, and the power generation power of each power generation unit is continuously adjusted through an iterative algorithm to meet the resource configuration constraints. MATLAB programming is used to implement the resource optimization strategy generation process to ensure the effectiveness and feasibility of the resource configuration plan, and finally generate the resource configuration optimization strategy corresponding to each power generation unit.

[0159] Step S34: According to the resource allocation optimization strategy corresponding to each power generation unit, the resource allocation optimization target analysis is performed on the corresponding power generation unit in the virtual power plant to obtain the resource allocation optimization target of the power generation unit of the virtual power plant.

[0160] In an embodiment of the present invention, by combining the resource allocation optimization strategies corresponding to each power generation unit previously generated, the resource allocation optimization targets of the corresponding power generation units are identified and analyzed to set specific optimization targets, such as reducing power generation costs, improving operating efficiency, etc., and using data analysis tools, combined with historical data and optimization results, the resource allocation optimization targets of each power generation unit are quantitatively analyzed. In the specific implementation, statistical software is used to perform data regression analysis and generate an evaluation report for the optimization targets to provide a clear optimization direction and basis, provide reliable decision-making support for the resource optimization allocation method of the virtual power plant, and finally obtain the resource allocation optimization targets of the virtual power plant power generation units.

[0161] Furthermore, the present invention also provides a virtual power plant resource optimization configuration system, which is used to execute the virtual power plant resource optimization configuration method as described above, and the virtual power plant resource optimization configuration system includes:

[0162] The virtual power plant sub-unit power generation prediction module is used to obtain the energy load storage resource data corresponding to each power generation unit in the virtual power plant, and perform power generation prediction analysis on each power generation unit in the virtual power plant based on the energy load storage resource data corresponding to each power generation unit, so as to obtain the energy power generation corresponding to each power generation unit;

[0163] The sub-unit resource load demand analysis module is used to analyze the charging and discharging status of the energy storage equipment of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit, so as to obtain the charging and discharging operation status data of the energy storage equipment corresponding to each power generation unit; based on the energy generation power corresponding to each power generation unit and the charging and discharging operation status data of the energy storage equipment, the corresponding power generation unit is predicted and analyzed for the resource operation load demand, so as to obtain the predicted amount of resource operation load demand corresponding to each power generation unit;

[0164] The virtual power plant resource allocation optimization target analysis module is used to analyze the resource allocation optimization target of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, so as to obtain the resource allocation optimization target of the virtual power plant power generation unit;

[0165] The resource configuration optimization output module is used to perform resource optimization configuration processing on each power generation unit in the virtual power plant according to the resource configuration optimization target of the virtual power plant power generation unit, so as to generate the optimal resource configuration plan for the virtual power plant power generation unit.

[0166] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0167] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A virtual power plant resource optimization configuration method, characterized in that: The following steps are involved: Step S1: Obtain energy load storage resource data corresponding to each power generation unit in the virtual power plant, and perform power generation prediction analysis on each power generation unit in the virtual power plant based on the energy load storage resource data corresponding to each power generation unit to obtain the energy power generation corresponding to each power generation unit; Step S2: Based on the energy generation power corresponding to each power generation unit, the energy storage device charging and discharging status analysis is performed on each power generation unit in the virtual power plant to obtain the energy storage device charging and discharging operation status data corresponding to each power generation unit; based on the energy generation power corresponding to each power generation unit and the energy storage device charging and discharging operation status data, the resource operation load demand forecast analysis is performed on the corresponding power generation unit to obtain the resource operation load demand forecast amount corresponding to each power generation unit; Step S3: Analyze the resource allocation optimization target of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, and obtain the resource allocation optimization target of the virtual power plant power generation unit; Step S4: performing resource optimization configuration processing on each power generation unit in the virtual power plant according to the resource configuration optimization target of the virtual power plant power generation unit to generate an optimal resource configuration plan for the virtual power plant power generation unit.

2. The virtual power plant resource optimization configuration method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire energy storage resource data corresponding to each power generation unit in the virtual power plant; Step S12: Obtain the energy resource input and energy resource output corresponding to each power generation unit through the energy load storage resource data corresponding to each power generation unit, and monitor the power generation of each power generation unit in the virtual power plant in real time through the integrated smart meter to obtain the energy resource power generation corresponding to each power generation unit; perform power generation capacity estimation analysis on the corresponding energy resource power generation based on the energy resource input and energy resource output corresponding to each power generation unit to obtain the energy power generation capacity estimation corresponding to each power generation unit; Step S13: Acquire the meteorological change data of the virtual power plant, and perform seasonal change characteristic analysis on the meteorological change data of the virtual power plant to obtain the meteorological seasonal change characteristic data of the virtual power plant; Step S14: performing environmental impact correction calculation on the estimated energy generation capacity corresponding to each power generation unit based on the virtual power plant meteorological seasonal variation characteristic data, and obtaining the corrected estimated actual generation capacity corresponding to each power generation unit; Step S15: Perform power generation prediction analysis on the corresponding power generation units in the virtual power plant according to the actual power generation capacity correction estimate corresponding to each power generation unit, and obtain the energy power generation corresponding to each power generation unit.

3. The virtual power plant resource optimization configuration method according to claim 2, characterized in that: The power generation capacity estimation analysis of the corresponding energy resource power generation based on the energy resource input and energy resource output corresponding to each power generation unit in step S12 includes the following steps: Performing time-series synchronization processing on the energy resource input and energy resource output corresponding to each power generation unit to obtain the time-series change of resource input and the time-series change of resource output corresponding to each power generation unit; Extract the time-series point input quantity of the resource input time-series change quantity corresponding to each power generation unit to obtain the resource input quantity corresponding to each time-series point of each power generation unit; calculate the input fluctuation difference between time-series points for the resource input quantity corresponding to each power generation unit at each time-series point to obtain the resource input change fluctuation difference between each adjacent time-series point of each power generation unit; The resource input change gradient analysis is performed on the resource input change fluctuation difference between each adjacent time sequence point corresponding to each power generation unit to obtain the resource input change gradient corresponding to each power generation unit; The resource output efficiency is evaluated and analyzed for the time series variation of resource output corresponding to each power generation unit to obtain the resource output efficiency corresponding to each power generation unit; Based on the resource input change gradient and resource output efficiency of each power generation unit, the corresponding energy resource power generation is estimated and analyzed to obtain the energy power generation capacity estimate of each power generation unit.

4. The virtual power plant resource optimization configuration method according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: Based on the seasonal variation characteristics of each meteorological factor in the virtual power plant meteorological seasonal variation characteristic data, an environmental impact factor identification analysis is performed on the estimated energy generation capacity corresponding to each power generation unit to obtain the environmental impact factor of the energy generation capacity corresponding to each power generation unit; Step S142: quantifying the environmental impact coefficient of the energy generation capacity environmental impact factor corresponding to each power generation unit to obtain the power generation capacity impact correction coefficient of each power generation unit corresponding to the environmental impact factor; Step S143: performing environmental impact correction calculation on the energy power generation capacity estimate corresponding to each power generation unit based on the power generation capacity impact correction coefficient of the environmental impact factor corresponding to each power generation unit, and obtaining the actual power generation capacity correction estimate corresponding to each power generation unit.

5. The method for optimizing resource configuration of a virtual power plant according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Analyze the time period fluctuation characteristics of the energy power generation corresponding to each power generation unit to obtain the power generation fluctuation characteristics of each power generation unit in different time periods, wherein the power generation fluctuation characteristics include the power generation fluctuation peak value, the power generation fluctuation valley value and the power generation fluctuation frequency; Step S22: Based on the power fluctuation characteristics of each power generation unit in different time periods, the energy storage equipment corresponding to the power generation unit in the virtual power plant is identified and divided into operation demand time periods, so as to obtain the energy storage equipment charging demand operation time periods and energy storage equipment discharging demand operation time periods corresponding to each power generation unit; Step S23: Designing charging and discharging strategies for energy storage devices corresponding to power generation units in the virtual power plant based on the charging demand operation period of energy storage devices corresponding to each power generation unit and the discharging demand operation period of energy storage devices, so as to generate a sequential charging and discharging strategy for energy storage devices corresponding to each power generation unit; Step S24: analyzing the energy storage device charging and discharging status of each power generation unit in the virtual power plant according to the energy storage device timing charging and discharging strategy corresponding to each power generation unit, so as to obtain the energy storage device charging and discharging operation status data corresponding to each power generation unit; Step S25: Based on the energy generation power corresponding to each power generation unit and the charging and discharging operation status data of the energy storage device, the corresponding power generation unit is subjected to resource operation load demand forecasting analysis to obtain the resource operation load demand forecast corresponding to each power generation unit.

6. The method for optimizing resource configuration of a virtual power plant according to claim 5, characterized in that: Step S22 includes the following steps: Based on the power fluctuation characteristics of each power generation unit in different time periods, the power generation fluctuation of the energy storage equipment corresponding to the power generation unit in the virtual power plant is visualized to obtain the power generation fluctuation curve of the energy storage equipment corresponding to each power generation unit; The power generation fluctuation change curve corresponding to the energy storage device of each power generation unit is identified and divided into operation demand time periods. If the power generation fluctuation change frequency continues to rise in the time period before and after the power generation fluctuation peak in the power generation fluctuation change curve, the time period is identified and divided into the charging demand operation period, so as to obtain the charging demand operation period of the energy storage device corresponding to each power generation unit; If the power generation fluctuation frequency continues to decrease in the time period before and after the power generation fluctuation valley value appears in the power generation fluctuation curve, the time period is identified and divided into the discharge demand operation period to obtain the energy storage device discharge demand operation period corresponding to each power generation unit.

7. The method for optimizing resource configuration of a virtual power plant according to claim 5, characterized in that: Step S25 includes the following steps: Step S251: Based on the energy generation power corresponding to each generation unit, a power generation field simulation analysis is performed on the corresponding generation unit in the virtual power plant to generate a resource operation power generation simulation field corresponding to each generation unit; Step S252: Perform power generation load demand impact analysis on the resource operation power generation power simulation field corresponding to each power generation unit to obtain the power generation load demand impact factor corresponding to each power generation unit; Step S253: Based on the charge and discharge operation status data of the energy storage device corresponding to each power generation unit, a charge and discharge state field simulation analysis is performed on the corresponding power generation unit in the virtual power plant to generate a resource operation charge and discharge state simulation field corresponding to each power generation unit; Step S254: performing a charge and discharge load demand impact analysis on the resource operation charge and discharge state simulation field corresponding to each power generation unit, and obtaining an energy storage charge and discharge operation load demand impact factor corresponding to each power generation unit; Step S255: Based on the power generation operation load demand influencing factors corresponding to each power generation unit and the energy storage charging and discharging operation load demand influencing factors, the resource operation load demand calculation formula is used to perform load demand forecasting calculations on the corresponding power generation units in the virtual power plant to obtain the resource operation load demand forecast corresponding to each power generation unit.

8. The method for optimizing resource configuration of a virtual power plant according to claim 7, characterized in that: The resource operation load demand calculation formula described in step S255 is specifically: Where, L i is the resource operation load demand forecast corresponding to the i-th power generation unit, n is the total number of power generation units in the virtual power plant, i is the item measurement parameter of the power generation unit, t is the time variable parameter, τ is the integral time variable parameter, P i (t) is the energy generation power of the i-th power generation unit at time t, η P is the power generation efficiency factor of the power generation unit, f i (t) is the influencing factor of the power generation load demand of the i-th power generation unit at time t, E i (t) is the charging and discharging operation state of the energy storage device of the i-th power generation unit at time t, η E is the charging and discharging efficiency factor of the energy storage device, g i (t) is the energy storage charging and discharging operation load demand influencing factor of the i-th power generation unit at time t, λ is the resource operation load demand attenuation factor, L(τ) is the time delay effect factor of the resource operation load demand at time τ, ξ is the correction coefficient of the resource operation load demand forecast.

9. The method for optimizing resource configuration of a virtual power plant according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, the resource allocation potential of each power generation unit in the virtual power plant is evaluated and analyzed to obtain the energy resource allocation potential of each power generation unit under specific operation load demand conditions; Step S32: performing resource configuration constraint analysis on the corresponding power generation units in the virtual power plant based on the energy resource configuration potential of each power generation unit under specific operating load demand conditions, so as to generate resource configuration constraint conditions corresponding to each power generation unit; Step S33: performing resource optimization strategy analysis on the corresponding power generation units in the virtual power plant based on the resource configuration constraint conditions corresponding to each power generation unit, so as to generate a resource configuration optimization strategy corresponding to each power generation unit; Step S34: According to the resource allocation optimization strategy corresponding to each power generation unit, the resource allocation optimization target analysis is performed on the corresponding power generation unit in the virtual power plant to obtain the resource allocation optimization target of the power generation unit of the virtual power plant.

10. A virtual power plant resource optimization configuration system, characterized in that: Used to execute the virtual power plant resource optimization configuration method according to claim 1, the virtual power plant resource optimization configuration system comprises: The virtual power plant sub-unit power generation prediction module is used to obtain the energy load storage resource data corresponding to each power generation unit in the virtual power plant, and perform power generation prediction analysis on each power generation unit in the virtual power plant based on the energy load storage resource data corresponding to each power generation unit, so as to obtain the energy power generation corresponding to each power generation unit; The sub-unit resource load demand analysis module is used to analyze the charging and discharging status of the energy storage equipment of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit, so as to obtain the charging and discharging operation status data of the energy storage equipment corresponding to each power generation unit; based on the energy generation power corresponding to each power generation unit and the charging and discharging operation status data of the energy storage equipment, the corresponding power generation unit is predicted and analyzed for the resource operation load demand, so as to obtain the predicted amount of resource operation load demand corresponding to each power generation unit; The virtual power plant resource allocation optimization target analysis module is used to analyze the resource allocation optimization target of each power generation unit in the virtual power plant based on the energy generation power corresponding to each power generation unit and the resource operation load demand forecast, so as to obtain the resource allocation optimization target of the virtual power plant power generation unit; The resource configuration optimization output module is used to perform resource optimization configuration processing on each power generation unit in the virtual power plant according to the resource configuration optimization target of the virtual power plant power generation unit, so as to generate the optimal resource configuration plan for the virtual power plant power generation unit.

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