Virtual power plant optimization operation method and system based on energy storage capacity configuration regulation and control

Abstract

The invention relates to the technical field of electric energy storage, in particular to a virtual power plant optimization operation method and system based on energy storage capacity configuration regulation, and the method comprises the steps: building a comprehensive power generation model according to equipment information, obtaining a charge state model and a charge constraint condition of a lithium battery, and obtaining a compressed air energy storage principle model of compressed air energy storage equipment; calculating the daily generating capacity according to the real-time environment change parameters by using the comprehensive generating model; obtaining daily power output, and calculating a difference value between the power generation amount and the power output to obtain a power regulation amount change curve; according to the charge state model, the charge constraint condition and the compressed air energy storage principle model, capacity prediction operation based on a short period and a whole period is carried out on the electric power regulation variable change curve, and lithium battery recommended capacity and compressed air energy storage recommended capacity are obtained; and performing energy storage capacity configuration on the virtual power plant according to the lithium battery recommended capacity and the compressed air energy storage recommended capacity. The operation efficiency of the virtual power plant can be optimized.

Description

Technical Field

[0001] The present invention relates to the field of electric energy storage, and in particular to a virtual power plant optimization operation method and system based on energy storage capacity configuration and regulation. Background Art

[0002] With the depletion of fossil energy and the resulting environmental pollution, the development of renewable clean energy is unstoppable. Wind and solar power generation are now being developed on a large scale, but wind and solar power are greatly affected by the environment and have uncertainties, which poses challenges to the stability of the power system.

[0003] With the gradual development of the electricity spot market, operators with wind and solar assets not only need to control the stability of wind and solar output, but also need to face the increased risk of purchasing high-priced electricity when participating in the electricity market. At present, the industry mainly achieves stable output and eliminates risks by configuring energy storage capacity. However, the capacity configuration is small and cannot effectively stabilize the wind and solar output; and excessive capacity configuration will lead to increased operation and maintenance costs and reduce the economic effect of virtual power plants. Therefore, building a suitable energy storage capacity configuration plan has become a problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a virtual power plant optimization operation method based on energy storage capacity configuration regulation, the main purpose of which is to optimize the operation efficiency of the virtual power plant by configuring appropriate energy storage capacity.

[0005] To achieve the above-mentioned purpose, the present invention provides a virtual power plant optimization operation method based on energy storage capacity configuration control, comprising:

[0006] Acquire a clean energy cluster and an energy storage device cluster in a target virtual power plant, wherein the clean energy cluster includes wind energy and photovoltaic energy, and the energy storage device cluster includes lithium batteries and compressed air energy storage devices;

[0007] Obtaining equipment information of the clean energy cluster, and constructing a comprehensive power generation model based on the equipment information, and obtaining a charge state model and charge constraint conditions of the lithium battery, and obtaining a compressed air energy storage principle model of the compressed air energy storage device;

[0008] Acquire real-time environmental change parameters of each day within a preset time period, and calculate the daily power generation within the preset time period according to the real-time environmental change parameters using the comprehensive power generation model;

[0009] Obtaining the daily power output within the preset time period, and calculating the difference between the power output and the power generation, to obtain a power regulation amount change curve;

[0010] Based on the charge state model, charge constraint conditions, and compressed air energy storage principle model, perform short - cycle - based lithium - battery energy storage capacity prediction operations on the power regulation amount change curve to obtain the recommended lithium - battery capacity, and perform full - cycle - based compressed air energy storage capacity prediction operations on the power regulation amount change curve to obtain the recommended compressed air energy storage capacity;

[0011] Configure the energy storage capacity of the virtual power plant according to the recommended lithium - battery capacity and the recommended compressed air energy storage capacity.

[0012] Optionally, the constructing an integrated power generation model according to the device information includes:

[0013] Obtain the rated wind power, number of wind turbines, cut - in speed of wind turbines, cut - out speed of wind turbines, and rated wind speed in the device information, and measure the actual wind speed;

[0014] Construct a wind power generation model according to the actual wind speed, rated wind power, number of wind turbines, cut - in speed of wind turbines, cut - out speed of wind turbines, and rated wind speed;

[0015] Obtain the number of photovoltaic panels, standard light intensity under standard test conditions, rated photovoltaic power under standard test conditions, and photovoltaic system efficiency in the device information, and measure the actual light intensity;

[0016] Construct a photovoltaic power generation model according to the actual light intensity, number of photovoltaic panels, standard light intensity under standard test conditions, rated photovoltaic power under standard test conditions, and photovoltaic system efficiency;

[0017] Perform weighted summation on the wind power generation model and the photovoltaic power generation model to obtain an integrated power generation model.

[0018] Optionally, the constructing a wind power generation model according to the actual wind speed, rated wind power, number of wind turbines, cut - in speed of wind turbines, cut - out speed of wind turbines, and rated wind speed includes:

[0019] Construct the Weibull distribution of the actual wind speed to obtain the wind speed probability density function;

[0020] Construct a wind turbine power model according to the actual wind speed, rated wind power, number of wind turbines, cut - in speed of wind turbines, cut - out speed of wind turbines, and rated wind speed, where the wind turbine power model is expressed as:

[0021]

[0022] In the formula, i represents the i - th wind turbine, wind represents wind power generation, t represents time, represents the wind turbine power of the i - th wind turbine for wind power generation at time t, V tDenote the actual wind speed as V ci Denote the cut-in wind speed of the wind turbine as V co Denote the cut-out wind speed of the wind turbine as V rate Denote the rated wind speed, N denotes the number of wind turbines, Denote the rated power of the wind power;

[0023] Perform a product integration on the wind turbine power model and the wind speed probability density function to obtain a wind power generation model, where the wind power generation model is expressed as:

[0024]

[0025] In the formula, P wind Denote the wind power generation model, PDF(x) denotes the wind speed probability density function, x denotes the measurement point for the actual wind speed, a to b denote the distribution range of the wind turbines, k denotes the shape factor, and λ denotes the scale factor.

[0026] Optionally, the comprehensive power generation model is expressed as:

[0027] P = P wind + P pv

[0028]

[0029] In the formula, P denotes the comprehensive power generation model, P pv Denote the photovoltaic power generation model, η pv Denote the efficiency of the photovoltaic system, N pv Denote the number of photovoltaic panels, P STC Denote the rated power of the photovoltaic power, Denote the actual light intensity, I STC Denote the standard light intensity.

[0030] Optionally, obtaining the charge state model and charge constraint conditions of the lithium battery includes:

[0031] Obtain the maximum power limit, charging power, discharging power and self-discharge energy of the lithium battery;

[0032] Construct a charge constraint condition according to the maximum power limit, where the charge constraint condition is expressed as:

[0033]

[0034] In the formula, Denote the discharging power, Denote the charging power, Denote the maximum power limit;

[0035] Obtain the initial charge state of the lithium battery, and construct a discharge formula and a charging formula according to the maximum power limit, charging power, and discharging power;

[0036] Construct a charge state model according to the initial charge state, discharge formula, charging formula, and self-discharge energy, where the charge state model is expressed as:

[0037]

[0038] In the formula, represents the charge state at time t + 1, represents the initial charge state at time t, η c represents the charging power, η d represents the discharging power, represents the self-discharge energy, and Δt represents the time period from time t to time t + 1.

[0039] Optionally, the compressed air energy storage principle model is expressed as:

[0040]

[0041] In the formula, p1 represents the low pressure, p2 represents the high pressure, W c represents the energy storage process of compressing air from low pressure p1 to high pressure p2, W r represents the energy release process of decompressing high pressure p2 to low pressure p1, m represents the air quality, c represents the specific heat capacity at constant pressure, T represents the ambient temperature, η caes represents the compressed air system efficiency, p0 represents the ambient pressure, and γ represents the specific heat ratio.

[0042] Optionally, based on the charge state model, charge constraint conditions, and compressed air energy storage principle model, perform a short-period-based lithium battery energy storage capacity prediction operation on the power regulation amount change curve to obtain the recommended lithium battery capacity, and perform a full-period-based compressed air energy storage capacity prediction operation on the power regulation amount change curve to obtain the recommended compressed air energy storage capacity, including:

[0043] Perform feature classification on the power regulation amount change curve based on the fluctuation frequency to obtain the characteristics of each short-period curve and the characteristics of the full-period curve;

[0044] Predict the real-time electricity of the lithium battery according to the characteristics of each short-period curve, the charge state model, and the charge constraint conditions to obtain the lithium battery capacity change curve corresponding to each short-period;

[0045] Obtain the maximum and minimum values of each lithium battery capacity change curve in the lithium battery capacity change curves corresponding to the respective short cycles, obtain multiple recommended sub-capacities of the lithium battery, and calculate the average value of the multiple recommended sub-capacities of the lithium battery to obtain the recommended capacity of the lithium battery;

[0046] According to the full-cycle curve characteristics and the compressed air energy storage principle model, perform power prediction on the compressed air energy storage device to obtain a compressed air energy storage change curve, and calculate the amplitude of the compressed air energy storage change curve to obtain the recommended capacity of the compressed air energy storage.

[0047] Optionally, the performing feature classification based on the fluctuation frequency on the power regulation amount change curve to obtain the respective short-cycle curve characteristics and the full-cycle curve characteristics includes:

[0048] Perform Fourier transform on the power regulation amount change curve to obtain frequency domain spectrum data;

[0049] According to the frequency domain spectrum data and a preset long-term and short-term segmentation frequency threshold, perform filtering processing on the power regulation amount change curve to obtain the respective short-cycle change curves and the full-cycle baseline curve;

[0050] Perform feature extraction operation based on the curve shape on the full-cycle baseline curve to obtain the full-cycle curve characteristics;

[0051] Perform feature extraction operation based on the curve shape on the respective short-cycle change curves to obtain the respective short-cycle curve characteristics.

[0052] Optionally, after performing energy storage capacity configuration on the virtual power plant according to the recommended capacity of the lithium battery and the recommended capacity of the compressed air energy storage, the method further includes:

[0053] Perform daily monitoring on the lithium battery to obtain the daily stored power and the daily released power, sum the daily stored power and the daily released power to obtain the daily lithium battery usage amount, and calculate the lithium battery usage rate according to the recommended capacity of the lithium battery and the daily lithium battery usage amount;

[0054] Calculate the average value of the lithium battery usage rates on a daily basis within a week to obtain the average lithium battery usage rate;

[0055] Perform weekly monitoring on the compressed air energy storage device to obtain the weekly stored power and the weekly released power, and calculate the compressed air energy storage usage rate according to the weekly stored power, the weekly released power, and the recommended capacity of the compressed air energy storage;

[0056] Perform weighted calculation on the average lithium battery usage rate and the compressed air energy storage usage rate to obtain the energy storage device usage rate, and determine whether the energy storage device usage rate is less than a preset effective threshold;

[0057] When the utilization rate of the energy storage device is less than the effective threshold, an energy storage capacity configuration alarm is generated.

[0058] To achieve the above object, the present invention further provides an optimized operation system for a virtual power plant based on energy storage capacity configuration regulation, including:

[0059] A virtual power plant device reading module, configured to obtain a clean energy cluster and an energy storage device cluster in a target virtual power plant, wherein the clean energy cluster includes wind energy and photovoltaic energy, and the energy storage device cluster includes lithium batteries and compressed air energy storage devices;

[0060] An equipment modeling module, configured to obtain the equipment information of the clean energy cluster, and construct an integrated power generation model according to the equipment information, and obtain the state-of-charge model and charge constraint conditions of the lithium battery, and obtain the compressed air energy storage principle model of the compressed air energy storage device;

[0061] An energy storage regulation amount identification module, configured to obtain the real-time environmental change parameters every day within a preset time period, use the integrated power generation model, calculate the daily power generation amount within the preset time period according to the real-time environmental change parameters, and obtain the daily power output amount within the preset time period, and calculate the difference between the power output amount and the power generation amount to obtain a power regulation amount change curve;

[0062] An energy storage capacity identification and configuration module, configured to perform short-cycle-based lithium battery energy storage capacity prediction operations on the power regulation amount change curve according to the state-of-charge model, charge constraint conditions, and compressed air energy storage principle model to obtain a recommended lithium battery capacity, and perform full-cycle-based compressed air energy storage capacity prediction operations on the power regulation amount change curve to obtain a recommended compressed air energy storage capacity, and configure the energy storage capacity of the virtual power plant according to the recommended lithium battery capacity and the recommended compressed air energy storage capacity.

[0063] To solve the above problems, the present invention further provides an electronic device, which includes:

[0064] A memory, storing at least one instruction; and

[0065] A processor, executing the instructions stored in the memory to implement the above-mentioned optimized operation method for a virtual power plant based on energy storage capacity configuration regulation.

[0066] To solve the above problems, the present invention further provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned optimized operation method for a virtual power plant based on energy storage capacity configuration regulation.

[0067] To solve the problems described in the background art, the present invention first models the clean energy clusters and energy storage device clusters existing in the virtual power plant to obtain the state-of-charge model and charge constraint conditions, the compressed air energy storage principle model and the integrated power generation model. The daily power generation can be calculated through the integrated power generation model, and according to the daily power output of the virtual power plant, the change curve of the power regulation amount that needs the participation of energy storage devices in regulation can be calculated. The present invention identifies the long-term and short-term characteristics of the power regulation amount change curve, predicts the lithium battery capacity according to the short-term characteristics, and predicts the capacity of the compressed air energy storage device according to the long-term characteristics. Through the cooperation between the lithium battery and the compressed air energy storage device, the recommended capacity of the optimal energy storage device is obtained, thereby reducing the use and maintenance of unnecessary energy storage capacity and optimizing the operation efficiency of the virtual power plant. Therefore, the present invention can optimize the operation efficiency of the virtual power plant by configuring an appropriate energy storage capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 FIG. is a schematic flow chart of an optimized operation method for a virtual power plant based on energy storage capacity configuration regulation provided by an embodiment of the present invention;

[0069] Figure 2 FIG. is a functional module diagram of an optimized operation system for a virtual power plant based on energy storage capacity configuration regulation provided by an embodiment of the present invention;

[0070] Figure 3 FIG. is a schematic structural diagram of an electronic device for implementing the optimized operation method for a virtual power plant based on energy storage capacity configuration regulation provided by an embodiment of the present invention.

[0071] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] An embodiment of the present application provides an optimized operation method for a virtual power plant based on energy storage capacity configuration regulation. The execution subject of the optimized operation method for a virtual power plant based on energy storage capacity configuration regulation includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the optimized operation method for a virtual power plant based on energy storage capacity configuration regulation can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0074] Refer to Figure 1As shown in the figure, it is a schematic flowchart of an optimal operation method of a virtual power plant based on energy storage capacity configuration regulation provided by an embodiment of the present invention. In this embodiment, the optimal operation method of the virtual power plant based on energy storage capacity configuration regulation includes:

[0075] S1. Obtain the clean energy cluster and the energy storage device cluster in the target virtual power plant. Among them, the clean energy cluster includes wind energy and photovoltaic energy, and the energy storage device cluster includes lithium batteries and compressed air energy storage devices.

[0076] In the embodiment of the present invention, the target virtual power plant refers to integrating and managing dispersed power generation resources (such as wind power, solar energy, energy storage devices, etc.) through information technology to form a whole, simulating the operation mode of a centralized power plant, which can improve the energy use efficiency, increase the consumption capacity of renewable energy, and enhance the flexibility and stability of the power grid.

[0077] Specifically, in the embodiment of the present invention, the clean energy cluster in the target virtual power plant includes wind energy and photovoltaic energy, and the energy storage device cluster includes lithium batteries and compressed air energy storage devices.

[0078] Among them, the lithium battery has the advantages of high energy density, fast response, high efficiency, etc., and is suitable for short-term energy storage and scenarios with high power requirements, such as being suitable for applications such as balancing the power grid load fluctuation and adjusting the frequency; while the compressed air energy storage device has a large energy storage capacity and scalability, and is suitable for long-term energy storage and scenarios with high power requirements, such as being suitable for applications such as smoothing peak-valley loads and coping with seasonal energy fluctuations.

[0079] S2. Obtain the device information of the clean energy cluster, and based on the device information, construct a comprehensive power generation model, and obtain the state-of-charge model and charge constraint conditions of the lithium battery, and obtain the compressed air energy storage principle model of the compressed air energy storage device.

[0080] Among them, the device information refers to the parameters on the device when the energy operator builds the production capacity device (such as wind turbines, photovoltaic panels, etc.), such as geographical location, magnitude, applicable environment, quantity, and size, etc. In the embodiment of the present invention, based on the device information, a wind power generation model and a photovoltaic power generation model can be constructed, and then the comprehensive power generation model of the overall power generation of the target virtual power plant can be obtained by combining them.

[0081] Further, the charge constraint condition means that the charging power and discharging power of the charge cannot exceed the maximum power limit specified during the production of the lithium battery, otherwise it will affect the safety of power production.

[0082] Specifically, in the embodiment of the present invention, the constructing of the comprehensive power generation model based on the device information includes:

[0083] Obtain the rated wind power, number of wind turbines, cut-in speed of the wind turbines, cut-out speed of the wind turbines, and rated wind speed in the device information, and measure the actual wind speed;

[0084] Construct a wind power generation model based on the actual wind speed, rated wind power, number of wind turbines, cut-in speed of the wind turbines, cut-out speed of the wind turbines, and rated wind speed;

[0085] Obtain the number of photovoltaic panels, standard light intensity under standard test conditions, rated photovoltaic power under standard test conditions, and photovoltaic system efficiency in the device information, and measure the actual light intensity;

[0086] Construct a photovoltaic power generation model based on the actual light intensity, number of photovoltaic panels, standard light intensity under standard test conditions, rated photovoltaic power under standard test conditions, and photovoltaic system efficiency;

[0087] Perform weighted summation on the wind power generation model and the photovoltaic power generation model to obtain a comprehensive power generation model.

[0088] Specifically, in the process of constructing the comprehensive power generation model of the present invention, considering that the wind speed is greatly affected by the terrain, the wind speeds at different positions are different, and the power generation efficiencies of different wind turbines in the same area are different, while the photovoltaic power plant usually has an open view and uniform light intensity. Therefore, in the embodiments of the present invention, by constructing a wind speed probability density function, the power generation power between each wind turbine is adjusted, thereby constructing a wind power generation model; then a photovoltaic power generation model is constructed using the device information of the photovoltaic system.

[0089] Specifically, in the embodiments of the present invention, constructing the wind power generation model according to the actual wind speed, rated wind power, number of wind turbines, cut-in speed of the wind turbines, cut-out speed of the wind turbines, and rated wind speed includes:

[0090] Construct the Weibull distribution of the actual wind speed to obtain a wind speed probability density function;

[0091] Construct a wind turbine power model according to the actual wind speed, rated wind power, number of wind turbines, cut-in speed of the wind turbines, cut-out speed of the wind turbines, and rated wind speed, where the wind turbine power model is expressed as:

[0092]

[0093] In the formula, i represents the i-th wind turbine, wind represents wind power generation, t represents time, represents the wind turbine power of the i-th wind turbine for wind power generation at time t, V t represents the actual wind speed, V ci represents the cut-in wind speed of the wind turbine, V co represents the cut-out wind speed of the wind turbine, V raterepresents the rated wind speed, N represents the number of the wind turbines, represents the rated wind power of the wind power;

[0094] Performing product integration on the wind turbine power model and the wind speed probability density function to obtain a wind power generation model, where the wind power generation model is expressed as:

[0095]

[0096] In the formula, P wind represents the wind power generation model, PDF(x) represents the wind speed probability density function, x represents the measurement point for the actual wind speed, a to b represent the distribution range of the wind turbines, k represents the shape factor, and λ represents the scale factor.

[0097] Specifically, in the embodiment of the present invention, first, according to the actual wind speed, the rated wind power, the number of wind turbines, the cut-in speed of the wind turbines, the cut-out speed of the wind turbines, and the rated wind speed, a wind turbine power model is constructed. Among them, the relationship between the wind turbine power and the wind speed is a complex non-linear relationship. Therefore, the present invention is expressed in a segmented form to describe the output power of the wind turbine under different wind speeds. Among them, when the actual wind speed is below the cut-in power or above the cut-out power, in order to ensure the stability of the power system, wind power generation is not connected to the power system at this time.

[0098] Furthermore, in the embodiment of the present invention, according to the actual wind speed, a Weibull distribution of the actual wind speed is constructed to obtain a wind speed probability density function. Among them, the Weibull distribution is a continuous probability distribution. Since the distribution of the wind speed usually shows specific shape characteristics, and the Weibull distribution can effectively capture these characteristics, it is more suitable for wind power modeling.

[0099] It can be understood that since the relationship between the probability density function and the wind power output is obtained by substituting the wind speed value into the power curve. Therefore, the present invention uses the probability density function to calculate the probability within a certain wind speed range, and then multiplies the probability by the corresponding wind turbine output power within this range to obtain the wind power output within this range. Among them, the output is a common term in the power industry and can be understood as the actual generated electric power.

[0100] In the embodiment of the present invention, the measurement point of the actual wind speed is x. In the energy storage device cluster of the target virtual power plant of the present invention, each wind turbine can be located by the number i. Therefore, the position coordinates corresponding to each wind turbine can be directly queried according to the number i of each wind turbine, and then according to the relative position between the position coordinates and the measurement point x, the wind force on each wind turbine can be calculated, so as to obtain the wind power generation model of the entire wind energy.

[0101] Furthermore, the present invention can construct a photovoltaic power generation model based on the actual light intensity, the number of photovoltaic panels, the standard light intensity under the standard test environment, the rated photovoltaic power under the standard test environment, and the photovoltaic system efficiency, and then merge it with the wind power generation model to obtain a comprehensive power generation model.

[0102] Specifically, in the embodiment of the present invention, the comprehensive power generation model is expressed as:

[0103] P = P wind + P pv

[0104]

[0105] In the formula, P represents the comprehensive power generation model, P pv represents the photovoltaic power generation model, η pv represents the photovoltaic system efficiency, N pv represents the number of photovoltaic panels, P STC represents the rated photovoltaic power, represents the actual light intensity, I STC represents the standard light intensity.

[0106] Furthermore, after the comprehensive power generation model is constructed in the embodiment of the present invention, mathematical modeling can be performed on the energy storage devices in the virtual power plant.

[0107] Specifically, in the embodiment of the present invention, obtaining the charge state model and charge constraint conditions of the lithium battery includes:

[0108] Obtaining the maximum power limit, charging power, discharging power, and self-discharge energy of the lithium battery;

[0109] According to the maximum power limit, constructing a charge constraint condition, where the charge constraint condition is expressed as:

[0110]

[0111] In the formula, represents the discharging power, represents the charging power, represents the maximum power limit;

[0112] Obtaining the initial charge state of the lithium battery, and constructing a discharging formula and a charging formula according to the maximum power limit, charging power, and discharging power;

[0113] Constructing a charge state model according to the initial charge state, discharging formula, charging formula, and self-discharge energy, where the charge state model is expressed as:

[0114]

[0115] In the formula, represents the charge state at time t + 1, represents the initial charge state at time t, and η c represents the charging power, and η d represents the discharging power, represents the self-discharge energy, and Δt represents the time period from time t to time t + 1.

[0116] Among them, represents the charging formula, represents the charging formula. In the present invention, the charge state at time t is defaulted to the initial charge state, which can be measured and read through a charge meter.

[0117] Specifically, in the embodiments of the present invention, when constructing the charge state model of the lithium battery, the principle of safe production needs to be followed. Therefore, by querying the maximum power limit, the working state of the lithium battery is limited to obtain the charge constraint conditions.

[0118] Then, the present invention queries the charging power, discharging power, and self-discharge energy in the device information of the lithium battery. According to the method of adding the charging process, subtracting the discharging process, and then subtracting the self-discharge energy based on the initial charge state, a charge state model is constructed.

[0119] Furthermore, in the embodiments of the present invention, according to the principle of the energy required when the air pressure rises from the low pressure p1 to the high pressure p2, a compressed air energy storage principle model is constructed.

[0120] Specifically, in the embodiments of the present invention, the compressed air energy storage principle model is expressed as:

[0121]

[0122] In the formula, p1 represents the low pressure, p2 represents the high pressure, and W c represents the energy storage process of compressing the air from the low pressure p1 to the high pressure p2, and W r represents the energy release process of reducing the pressure of the high pressure p2 to the low pressure p1, m represents the air quality, c represents the specific heat capacity at constant pressure, T represents the ambient temperature, and η caes represents the efficiency of the compressed air system, p0 represents the ambient pressure, and γ represents the specific heat ratio.

[0123] Specifically, in the embodiments of the present invention, the compressed air energy storage device compresses the air into a high-pressure state and stores it in a gas storage tank, and then when energy needs to be released, the compressed air is put into a generator to release energy by burning fuel or combining with a high-temperature heat source to drive the generator to generate electricity.

[0124] S3. Obtain the real-time environmental change parameters for each day within a preset time period, and use the comprehensive power generation model to calculate the power generation for each day within the preset time period according to the real-time environmental change parameters.

[0125] Among them, the real-time environmental change parameters are the wind power and light intensity for each day. In the embodiments of the present invention, by substituting the real-time environmental change parameters into the comprehensive power generation model, the power generation for each day within the preset time period can be calculated.

[0126] When necessary, the present invention can also measure the real-time environmental change parameters twice a day (i.e., in the morning and afternoon) to improve the prediction accuracy of power generation.

[0127] S4. Obtain the power output for each day within the preset time period, and calculate the difference between the power output and the power generation to obtain the power regulation amount change curve.

[0128] It can be understood that due to the influence of the market, although the power output of the target virtual power plant fluctuates daily, it can be accurately determined, and the power output of the target virtual power plant can be queried through transaction information.

[0129] It should be noted that since the energy output enterprise conducts power generation according to market orders every day, when the daily power generation is large, it can be stored through energy storage devices for the operation and backup use of the power plant itself, and can also be stored and sold when the price rises; while when the daily power generation is small, it can be retrieved from the energy storage device and supplemented into the daily power generation.

[0130] Therefore, the power regulation amount change curve obtained by calculating the difference between the power output and the power generation can accurately express the capacity demand of the target virtual power plant for the energy storage device. However, limited by the operation and maintenance costs, the more energy storage devices are not necessarily better, and the energy storage capacity needs to be adjusted according to the utilization rate of the energy storage device.

[0131] S5. Based on the charge state model, charge constraint conditions, and compressed air energy storage principle model, perform short-cycle-based lithium battery energy storage capacity prediction operations on the power regulation amount change curve to obtain the recommended lithium battery capacity, and perform full-cycle-based compressed air energy storage capacity prediction operations on the power regulation amount change curve to obtain the recommended compressed air energy storage capacity.

[0132] In the embodiments of the present invention, two energy storage devices, namely lithium batteries and compressed air energy storage devices, are constructed through the charge state model, charge constraint conditions, and compressed air energy storage principle model.

[0133] In the embodiments of the present invention, when the power generation power changes at a high frequency, the charge and discharge operations are completed by the lithium battery. When the power generation power changes greatly, the charge and discharge operations are completed by the compressed air energy storage device. Therefore, small waveform changes on the power adjustment amount change curve need to be adjusted by the lithium battery, and large waveform changes need to be adjusted by the compressed air energy storage device.

[0134] Specifically, in the embodiments of the present invention, the operation of predicting the lithium battery energy storage capacity based on a short period for the power adjustment amount change curve, obtaining the recommended lithium battery capacity, and the operation of predicting the compressed air energy storage capacity based on a full period for the power adjustment amount change curve, obtaining the recommended compressed air energy storage capacity, include:

[0135] Performing feature classification based on the fluctuation frequency on the power adjustment amount change curve to obtain each short-period curve feature and full-period curve feature;

[0136] Predicting the real-time power of the lithium battery according to each short-period curve feature, the state-of-charge model, and the charge constraint conditions to obtain the lithium battery capacity change curve corresponding to each short period;

[0137] Obtaining the maximum value and minimum value of each lithium battery capacity change curve in the lithium battery capacity change curves corresponding to each short period, obtaining multiple recommended sub-capacities of the lithium battery, and calculating the average value of the multiple recommended sub-capacities of the lithium battery to obtain the recommended lithium battery capacity;

[0138] Predicting the power of the compressed air energy storage device according to the full-period curve feature and the compressed air energy storage principle model to obtain the compressed air energy storage amount change curve, and calculating the amplitude of the compressed air energy storage amount change curve to obtain the recommended compressed air energy storage capacity.

[0139] In the embodiments of the present invention, first, the power adjustment amount change curve is grouped into short-period curve features and full-period curve features through feature classification. Then, through the energy storage principle of each energy storage device, the curve features of the short period or full period are equivalently replaced with the capacity change curves of the lithium battery or the compressed air energy storage device. Among them, the larger the amplitude of the curve feature, the larger the value on the capacity change curve.

[0140] Specifically, in the embodiments of the present invention, the operation of performing feature classification based on the fluctuation frequency on the power adjustment amount change curve to obtain each short-period curve feature and full-period curve feature includes:

[0141] Performing Fourier transform on the power adjustment amount change curve to obtain frequency-domain spectrum data;

[0142] Filter the power adjustment amount change curve according to the frequency-domain spectrum data and the preset long-term and short-term segmentation frequency thresholds to obtain each short-period change curve and the full-period baseline curve;

[0143] Perform feature extraction operations based on the curve shape on the full-period baseline curve to obtain full-period curve features;

[0144] Perform feature extraction operations based on the curve shape on each short-period change curve to obtain each short-period curve feature.

[0145] In the embodiment of the present invention, the time-domain signal is transformed into a frequency-domain signal through Fourier transform. Since the frequency of the power fluctuation that needs to be adjusted by the lithium battery is relatively high, usually changing at all times, while the frequency of the power fluctuation that needs to be adjusted by the compressed air energy storage device is relatively low, usually once or twice a day.

[0146] Therefore, the present invention divides the frequency-domain spectrum data by configuring long-term and short-term segmentation frequency thresholds, such as twice a day, to obtain short-period change curves and full-period baseline curves, and then performs curve shape feature extraction through a neural network to obtain each short-period curve feature and full-period curve feature.

[0147] Since each short-period curve is caused by various different reasons, the power fluctuation intensity is different and the time distribution is random. Therefore, the present invention separately calculates the lithium battery capacity for each short-period curve feature to obtain each lithium battery capacity change curve, and then obtains each lithium battery recommended sub-capacity. Then, an average is taken over a long time period to obtain the lithium battery recommended capacity.

[0148] In the embodiment of the present invention, the compressed air energy storage capacity is directly calculated based on the full-period curve features to obtain a compressed air energy storage amount change curve, and then the compressed air energy storage recommended capacity is calculated according to the compressed air energy storage amount change curve.

[0149] Furthermore, when calculating the recommended capacities of the compressed air energy storage device and the lithium battery, the present invention uses the method of querying the difference (amplitude) between the maximum value and the minimum value of the curve for capacity recommendation to ensure the normal operation of the compressed air energy storage device and the lithium battery.

[0150] S6. Configure the energy storage capacity of the virtual power plant according to the lithium battery recommended capacity and the compressed air energy storage recommended capacity.

[0151] In the embodiment of the present invention, configuring the energy storage capacity of the virtual power plant according to the lithium battery recommended capacity and the compressed air energy storage recommended capacity can achieve an energy storage device cluster with the lowest cost, achieve the stability of the power plant power in the long cycle and short time, and optimize the operation efficiency of the virtual power plant.

[0152] Specifically, in the embodiments of the present invention, after the energy storage capacity of the virtual power plant is configured according to the recommended capacity of the lithium battery and the recommended capacity of the compressed air energy storage, the method further includes:

[0153] Monitor the lithium battery daily to obtain the daily stored power and the daily released power, sum the daily stored power and the daily released power to obtain the daily lithium battery usage, and calculate the lithium battery usage rate according to the recommended capacity of the lithium battery and the daily lithium battery usage.

[0154] Calculate the average value of the lithium battery usage rates on a daily basis within a week to obtain the average lithium battery usage rate.

[0155] Monitor the compressed air energy storage device weekly to obtain the weekly stored power and the weekly released power, and calculate the compressed air energy storage usage rate according to the weekly stored power, the weekly released power, and the recommended capacity of the compressed air energy storage.

[0156] Perform weighted calculation on the average lithium battery usage rate and the compressed air energy storage usage rate to obtain the energy storage device usage rate, and determine whether the energy storage device usage rate is less than a preset effective threshold.

[0157] When the energy storage device usage rate is less than the effective threshold, generate an energy storage capacity configuration alarm.

[0158] Specifically, in the embodiments of the present invention, the usage rate is calculated by the method of [power usage amount / recommended capacity × 100%]. Among them, both the charging and discharging processes belong to the usage process, and the power usage amount is obtained by superimposing the absolute values of the charging amount and the discharging amount.

[0159] In the embodiments of the present invention, the lithium battery and the compressed air energy storage device are used in combination. Therefore, the usage rates of the two are weighted and calculated to obtain the energy storage device usage rate. The present invention can configure the effective threshold according to factors such as enterprise scale, photoelectric production capacity configuration, and power stability. For example, 3.2, that is, when the usage rates of the two energy storage devices do not exceed 1.6 (charging 0.8, discharging 0.8), it is determined that the power usage rate is low and recalculation and configuration are required, so as to ensure that the virtual power plant can change the capacity configuration according to seasonal factors such as seasonal alternation.

[0160] To solve the problems described in the background art, the present invention first models the clean energy cluster and energy storage device cluster existing in the virtual power plant to obtain the charge state model and charge constraint conditions, the compressed air energy storage principle model, and the integrated power generation model. Through the integrated power generation model, the daily power generation can be calculated, and according to the daily power output of the virtual power plant, the change curve of the power regulation amount that needs the energy storage device to participate in regulation can be calculated. The present invention identifies the long-term and short-term characteristics of the power regulation amount change curve, predicts the lithium battery capacity according to the short-term characteristics, predicts the capacity of the compressed air energy storage device according to the long-term characteristics, and through the cooperation between the lithium battery and the compressed air energy storage device, obtains the recommended capacity of the optimal energy storage device, thereby reducing the use and maintenance of unnecessary energy storage capacity and optimizing the operation efficiency of the virtual power plant. Therefore, the present invention can optimize the operation efficiency of the virtual power plant by configuring an appropriate energy storage capacity.

[0161] As Figure 2 shown, it is a functional module diagram of a virtual power plant optimized operation system based on energy storage capacity configuration regulation provided by an embodiment of the present invention.

[0162] The virtual power plant optimized operation system 100 based on energy storage capacity configuration regulation described in the present invention can be installed in an electronic device. According to the functions achieved, the virtual power plant optimized operation system 100 based on energy storage capacity configuration regulation can include a virtual power plant device reading module 101, a device modeling module 102, an energy storage regulation amount identification module 103, and an energy storage capacity identification and configuration module 104. The modules described in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0163] The virtual power plant device reading module 101 is used to obtain the clean energy cluster and energy storage device cluster in the target virtual power plant, wherein the clean energy cluster includes wind energy and photovoltaic energy, and the energy storage device cluster includes lithium batteries and compressed air energy storage devices;

[0164] The device modeling module 102 is used to obtain the device information of the clean energy cluster, and according to the device information, construct an integrated power generation model, and obtain the charge state model and charge constraint conditions of the lithium battery, and obtain the compressed air energy storage principle model of the compressed air energy storage device;

[0165] The energy storage regulation amount identification module 103 is used to obtain the real-time environmental change parameters every day within a preset time period, use the integrated power generation model, calculate the daily power generation within the preset time period according to the real-time environmental change parameters, and obtain the daily power output within the preset time period, and calculate the difference between the power output and the power generation to obtain the power regulation amount change curve;

[0166] The energy storage capacity identification and configuration module 104 is configured to perform short-cycle-based lithium battery energy storage capacity prediction operations on the power regulation amount change curve according to the state of charge model, charge constraint conditions, and compressed air energy storage principle model to obtain the recommended lithium battery capacity, perform full-cycle-based compressed air energy storage capacity prediction operations on the power regulation amount change curve to obtain the recommended compressed air energy storage capacity, and configure the energy storage capacity of the virtual power plant according to the recommended lithium battery capacity and the recommended compressed air energy storage capacity.

[0167] Specifically, each module in the virtual power plant optimal operation system 100 based on energy storage capacity configuration regulation in the embodiments of the present invention uses the same technical means as those Figure 1 described in the virtual power plant optimal operation method based on energy storage capacity configuration regulation above and can produce the same technical effects, which will not be elaborated here.

[0168] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing the virtual power plant optimal operation method based on energy storage capacity configuration regulation provided by an embodiment of the present invention.

[0169] The electronic device 1 may include a processor 10, a memory 11, and a bus 12, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a virtual power plant optimal operation method program based on energy storage capacity configuration regulation.

[0170] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disks, multimedia cards, card-type memories (such as SD or DX memories, etc.), magnetic memories, magnetic disks, optical disks, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as the mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 also includes the internal storage unit of the electronic device 1 and the external storage device. The memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code of the virtual power plant optimal operation method program based on energy storage capacity configuration regulation, but also be used to temporarily store data that has been output or will be output.

[0171] In some embodiments, the processor 10 may be composed of an integrated circuit. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as the virtual power plant optimization operation method program based on energy storage capacity configuration regulation, etc.), and by calling the data stored in the memory 11, to perform various functions of the electronic device 1 and process data.

[0172] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable connection and communication between the memory 11 and at least one processor 10, etc.

[0173] Figure 3 Only the electronic device with components is shown. Those skilled in the art can understand that Figure 3 The shown structure does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have a different component arrangement.

[0174] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0175] Furthermore, the electronic device 1 may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0176] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0177] The virtual power plant optimal operation method program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can implement:

[0178] Obtain a clean energy cluster and an energy storage device cluster in a target virtual power plant, where the clean energy cluster includes wind energy and photovoltaic energy, and the energy storage device cluster includes lithium batteries and compressed air energy storage devices;

[0179] Obtain the device information of the clean energy cluster, and according to the device information, construct a comprehensive power generation model, obtain the state-of-charge model and charge constraint conditions of the lithium battery, and obtain the compressed air energy storage principle model of the compressed air energy storage device;

[0180] Obtain the real-time environmental change parameters of each day within a preset time period, and use the comprehensive power generation model to calculate the daily power generation within the preset time period according to the real-time environmental change parameters;

[0181] Obtain the daily power output within the preset time period, and calculate the difference between the power output and the power generation to obtain a power adjustment amount change curve;

[0182] According to the state-of-charge model, charge constraint conditions, and compressed air energy storage principle model, perform a short-cycle-based lithium battery energy storage capacity prediction operation on the power adjustment amount change curve to obtain a recommended lithium battery capacity, and perform a full-cycle-based compressed air energy storage capacity prediction operation on the power adjustment amount change curve to obtain a recommended compressed air energy storage capacity;

[0183] Configure the energy storage capacity of the virtual power plant according to the recommended lithium battery capacity and the recommended compressed air energy storage capacity.

[0184] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 the description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0185] Furthermore, if the modules / cells integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory).

[0186] The present invention also provides a computer-readable storage medium storing a computer program, which when executed by a processor of an electronic device, can implement:

[0187] Obtain a clean energy cluster and a energy storage device cluster in a target virtual power plant, where the clean energy cluster includes wind energy and photovoltaic energy, and the energy storage device cluster includes lithium batteries and compressed air energy storage devices;

[0188] Obtain device information of the clean energy cluster, and based on the device information, construct a comprehensive power generation model, and obtain a state-of-charge model and charge constraint conditions of the lithium battery, and obtain a compressed air energy storage principle model of the compressed air energy storage device;

[0189] Obtain real-time environmental change parameters for each day within a preset time period, and use the comprehensive power generation model to calculate the daily power generation within the preset time period according to the real-time environmental change parameters;

[0190] Obtain the daily power output within the preset time period, and calculate the difference between the power output and the power generation to obtain a power regulation amount change curve;

[0191] Based on the state-of-charge model, charge constraint conditions and compressed air energy storage principle model, perform short-cycle-based lithium battery energy storage capacity prediction operations on the power regulation amount change curve to obtain a recommended lithium battery capacity, and perform full-cycle-based compressed air energy storage capacity prediction operations on the power regulation amount change curve to obtain a recommended compressed air energy storage capacity;

[0192] Configure the energy storage capacity of the virtual power plant according to the recommended lithium battery capacity and the recommended compressed air energy storage capacity.

[0193] In several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and there can be other division methods in actual implementation.

[0194] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] In addition, each functional module in various embodiments of the present invention may be integrated in a processing unit, may also be physically present separately in each unit, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a hardware plus software functional module.

[0196] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A virtual power plant optimization operation method based on energy storage capacity configuration control, characterized in that: The method comprises: Acquire a clean energy cluster and an energy storage device cluster in a target virtual power plant, wherein the clean energy cluster includes wind energy and photovoltaic energy, and the energy storage device cluster includes lithium batteries and compressed air energy storage devices; Obtaining equipment information of the clean energy cluster, and constructing a comprehensive power generation model based on the equipment information, and obtaining a charge state model and charge constraint conditions of the lithium battery, and obtaining a compressed air energy storage principle model of the compressed air energy storage device; Acquire real-time environmental change parameters of each day within a preset time period, and calculate the daily power generation within the preset time period according to the real-time environmental change parameters using the comprehensive power generation model; Obtaining the daily power output within the preset time period, and calculating the difference between the power output and the power generation, to obtain a power regulation amount change curve; According to the charge state model, charge constraint conditions and compressed air energy storage principle model, the power regulation amount change curve is subjected to a short-cycle lithium battery energy storage capacity prediction operation to obtain a lithium battery recommended capacity, and the power regulation amount change curve is subjected to a full-cycle compressed air energy storage capacity prediction operation to obtain a compressed air energy storage recommended capacity; The energy storage capacity of the virtual power plant is configured according to the recommended capacity of the lithium battery and the recommended capacity of the compressed air energy storage.

2. The virtual power plant optimization operation method based on energy storage capacity configuration control according to claim 1 is characterized in that: The step of constructing a comprehensive power generation model according to the device information includes: Obtain wind power rated power, number of wind turbines, wind turbine cut-in speed, wind turbine cut-out speed and rated wind speed from the equipment information, and measure the actual wind speed; Constructing a wind power generation model according to the actual wind speed, wind power rated power, number of wind turbines, wind turbine cut-in speed, wind turbine cut-out speed and rated wind speed; Obtain the number of photovoltaic panels, the standard light intensity under the standard test environment, the photovoltaic rated power and the photovoltaic system efficiency under the standard test environment in the equipment information, and measure the actual light intensity; Constructing a photovoltaic power generation model according to the actual light intensity, the number of photovoltaic panels, the standard light intensity under the standard test environment, the photovoltaic rated power under the standard test environment, and the photovoltaic system efficiency; The wind power generation model and the photovoltaic power generation model are weighted and summed to obtain a comprehensive power generation model.

3. The virtual power plant optimization operation method based on energy storage capacity configuration control according to claim 2 is characterized in that: The wind power generation model is constructed according to the actual wind speed, wind power rated power, number of wind turbines, wind turbine cut-in speed, wind turbine cut-out speed and rated wind speed, including: Constructing a Weibull distribution of the actual wind speed to obtain a wind speed probability density function; A wind turbine power model is constructed according to the actual wind speed, wind power rated power, number of wind turbines, wind turbine cut-in speed, wind turbine cut-out speed and rated wind speed, wherein the wind turbine power model is expressed as: In the formula, i represents the i-th wind turbine, wind represents wind power generation, and t represents time. represents the wind turbine power of the i-th wind turbine at time t, V represents the actual wind speed, V ci represents the fan cut-in wind speed, V co Indicates the fan cut-out wind speed, V rate represents the rated wind speed, N represents the number of fans, represents the rated power of the wind power; The wind turbine power model and the wind speed probability density function are multiplied and integrated to obtain a wind power generation model, wherein the wind power generation model is expressed as: Where P wind represents the wind power generation model, PDF(x) represents the wind speed probability density function, x represents the measurement point for the actual wind speed, a to b represent the distribution range of wind turbines, k represents the shape factor, and λ represents the scale factor.

4. The virtual power plant optimization operation method based on energy storage capacity configuration control according to claim 3 is characterized in that: The comprehensive power generation model is expressed as: P=P wind +P pv Where, P represents the comprehensive power generation model, P pv represents the photovoltaic power generation model, η pv represents the efficiency of the photovoltaic system, N pv represents the number of photovoltaic panels, P STC represents the photoelectric rated power, Indicates the actual light intensity, I STC Represents the standard light intensity.

5. The virtual power plant optimization operation method based on energy storage capacity configuration control according to claim 4 is characterized in that: The obtaining of the charge state model and charge constraint conditions of the lithium battery includes: Obtaining the maximum power limit, charging power, discharging power and self-discharge energy of the lithium battery; According to the maximum power limit, a charge constraint condition is constructed, wherein the charge constraint condition is expressed as: In the formula, represents the discharge power, represents the charging power, represents the maximum power limit; Obtaining the initial charge state of the lithium battery, and constructing a discharge formula and a charge formula according to the maximum power limit, the charging power, and the discharging power; A charge state model is constructed according to the initial charge state, the discharge formula, the charge formula and the self-discharge energy, wherein the charge state model is expressed as: In the formula, represents the charge state at time t+1, represents the initial charge state at time t, η c represents the charging power, η d represents the discharge power, represents the self-discharge energy, and Δt represents the time period from time t to time t+1.

6. The virtual power plant optimization operation method based on energy storage capacity configuration control according to claim 5 is characterized in that: The compressed air energy storage principle model is expressed as: In the formula, p1 represents low pressure, p2 represents high pressure, W c It represents the energy storage process of compressing air from low pressure p1 to high pressure p2, W r represents the energy release process of reducing high pressure p2 to low pressure p1, m represents the air mass, c represents the constant pressure specific heat capacity, T represents the ambient temperature, η caes represents the efficiency of the compressed air system, p0 represents the ambient pressure, and γ represents the specific heat ratio.

7. The virtual power plant optimization operation method based on energy storage capacity configuration control according to claim 6 is characterized in that: According to the charge state model, charge constraint conditions and compressed air energy storage principle model, the power regulation amount change curve is subjected to a short-cycle lithium battery energy storage capacity prediction operation to obtain a lithium battery recommended capacity, and the power regulation amount change curve is subjected to a full-cycle compressed air energy storage capacity prediction operation to obtain a compressed air energy storage recommended capacity, including: The power regulation amount change curve is subjected to characteristic classification based on the fluctuation frequency to obtain the characteristics of each short-period curve and the characteristics of the full-period curve; According to the characteristics of each short-cycle curve, the charge state model and the charge constraint condition, the real-time power of the lithium battery is predicted to obtain the lithium battery capacity change curve corresponding to each short cycle; Obtaining the maximum value and the minimum value of each of the lithium battery capacity change curves corresponding to the respective short cycles, obtaining a plurality of lithium battery recommended sub-capacities, and calculating the average of the plurality of lithium battery recommended sub-capacities to obtain the lithium battery recommended capacity; According to the full-cycle curve characteristics and the compressed air energy storage principle model, the power consumption of the compressed air energy storage device is predicted to obtain a compressed air energy storage change curve, and the amplitude of the compressed air energy storage change curve is calculated to obtain the recommended capacity of the compressed air energy storage.

8. The virtual power plant optimization operation method based on energy storage capacity configuration control according to claim 7 is characterized in that: The characteristic classification of the power regulation amount change curve based on the fluctuation frequency is performed to obtain the characteristics of each short-period curve and the characteristics of the full-period curve, including: Performing Fourier transformation on the electric power regulation amount change curve to obtain frequency domain spectrum data; According to the frequency domain spectrum data and the preset long-short term segmentation frequency threshold, the power regulation amount change curve is filtered to obtain each short-period change curve and a full-period baseline curve; Performing a feature extraction operation based on the curve shape on the full-cycle baseline curve to obtain full-cycle curve features; A feature extraction operation based on the curve shape is performed on each short-period variation curve to obtain each short-period curve feature.

9. The method for optimizing operation of a virtual power plant based on energy storage capacity configuration control according to claim 8, characterized in that: After configuring the energy storage capacity of the virtual power plant according to the recommended capacity of the lithium battery and the recommended capacity of the compressed air energy storage, the method further includes: Perform daily monitoring on the lithium battery to obtain daily stored electricity and daily released electricity, sum the daily stored electricity and daily released electricity to obtain daily lithium battery usage, and calculate the lithium battery usage rate based on the recommended capacity of the lithium battery and the daily lithium battery usage; Calculate the average lithium battery usage rate every day within a week to obtain the average lithium battery usage rate; Perform weekly monitoring on the compressed air energy storage device to obtain weekly stored electricity and weekly released electricity, and calculate the compressed air energy storage utilization rate based on the weekly stored electricity, weekly released electricity and the recommended capacity of the compressed air energy storage; Performing weighted calculation on the average utilization rate of the lithium battery and the utilization rate of the compressed air energy storage to obtain the utilization rate of the energy storage device, and determining whether the utilization rate of the energy storage device is less than a preset effective threshold; When the energy storage device usage rate is less than the effective threshold, an energy storage capacity configuration alarm is generated.

10. A virtual power plant optimization operation system based on energy storage capacity configuration control, characterized in that: The system comprises: A virtual power plant equipment reading module, used to obtain a clean energy cluster and an energy storage equipment cluster in a target virtual power plant, wherein the clean energy cluster includes wind energy and photovoltaic energy, and the energy storage equipment cluster includes lithium batteries and compressed air energy storage equipment; An equipment modeling module is used to obtain equipment information of the clean energy cluster, and to construct a comprehensive power generation model based on the equipment information, and to obtain a charge state model and charge constraint conditions of the lithium battery, and to obtain a compressed air energy storage principle model of the compressed air energy storage device; The energy storage regulation amount identification module is used to obtain the real-time environmental change parameters of each day within a preset time period, calculate the daily power generation within the preset time period according to the real-time environmental change parameters using the comprehensive power generation model, obtain the daily output within the preset time period, and calculate the difference between the output and the power generation to obtain the power regulation amount change curve; The energy storage capacity identification and configuration module is used to perform a short-cycle lithium battery energy storage capacity prediction operation on the power regulation quantity change curve according to the charge state model, charge constraint conditions and compressed air energy storage principle model to obtain the recommended capacity of the lithium battery, and to perform a full-cycle compressed air energy storage capacity prediction operation on the power regulation quantity change curve to obtain the recommended capacity of the compressed air energy storage, and to configure the energy storage capacity of the virtual power plant according to the recommended lithium battery capacity and the recommended compressed air energy storage capacity.

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