Fast voltage and frequency regulation method integrating virtual power plant and energy storage system

Abstract

The present invention discloses a rapid voltage and frequency regulation method integrating a virtual power plant and an energy storage system, and relates to the technical field of voltage and frequency regulation, including setting a plurality of simulated charging states of energy storage systems, obtaining charging and discharging restriction influence data and system stability influence data of corresponding regulation simulation models; and constructing a virtual electric field and energy storage system voltage and frequency regulation evaluation model; obtaining energy storage system regulation bias data corresponding to a plurality of energy storage system working modes, and screening out a plurality of most similar historical data from a historical database used for model training according to the energy storage system regulation bias data; and performing data analysis on a set of charging state vectors to obtain an optimal charging state, so as to solve the problem that the efficiency of rapid voltage and frequency regulation is low due to the lack of evaluation and regulation of the maintenance state of the charging state of the energy storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage and frequency regulation, and more specifically, to a fast voltage and frequency regulation method for integrating a virtual power plant with an energy storage system. Background Art

[0002] A virtual power plant (VPP) is a power management system that, through intelligent control and coordination, integrates distributed energy resources (such as solar, wind, small gas turbines, and energy storage systems) into a large, virtualized power plant, capable of providing power and ancillary services similar to those of a traditional power plant. Unlike traditional centralized power plants, VPPs consist of multiple geographically dispersed small power generation units, leveraging information and communications technologies for real-time scheduling and control. By coordinating the management of distributed resources, VPPs can participate in electricity market transactions, providing services such as power balancing, load regulation, and voltage and frequency control, thereby enhancing the flexibility and stability of the power grid. The key advantage of VPPs is that they can fully leverage distributed renewable energy resources, improve energy efficiency, and reduce carbon emissions and reliance on traditional fossil fuel power plants.

[0003] Energy storage systems store electrical energy in various forms, such as batteries, flywheels, and pumped hydro, releasing it during periods of peak demand or grid instability to maintain supply and demand balance. Energy storage systems not only store excess energy during periods of excess power but also stabilize the grid during fluctuations in renewable energy sources, such as solar and wind power. They provide critical grid services such as frequency regulation, peak load regulation, emergency backup, and power quality control. When integrated with virtual power plants (VPPs), energy storage systems can be dispatched through intelligent control, responding to grid demand as part of the VPP. The integration of VPPs significantly enhances their flexibility and reliability, enabling faster voltage and frequency regulation, smoothing out volatile renewable energy output, and helping to maintain grid stability under varying load conditions. Such integrated systems can also participate in electricity markets, providing more cost-effective solutions for the grid.

[0004] This invention, patented under publication number CN113541173B, includes a cluster control device and method for a battery energy storage system under weak grid conditions. The device comprises a multi-function electricity meter module, a plant-level control algorithm module, a converter PCS power distribution module, a PCS virtual synchronization control algorithm module, and a Profinet communication module. Based on this operating mode setting and control algorithm, this cluster control device and method for a battery energy storage system under weak grid conditions implements power distribution between the energy storage converter PCSs. This system addresses the issue of stable and reliable operation of large-scale energy storage systems under weak grid conditions with low short-circuit capacity. It also achieves power control characteristics similar to those of traditional synchronous generator plants at the high-voltage bus connection point (PoC), enabling seamless switching between grid-connected and islanded operation. It also provides virtual inertia to the grid to stabilize the frequency change rate, enabling rapid and active support of transient frequency and voltage at photovoltaic / wind power plants.

[0005] This includes a battery energy storage system cluster control device and method for weak grid conditions, as disclosed in patent publication number CN113541173A. The device comprises a multi-function electricity meter module, a plant-level control algorithm module, a converter PCS power distribution module, a PCS virtual synchronization control algorithm module, and a Profinet communication module. Based on this operating mode setting and control algorithm, this battery energy storage system cluster control device and method for weak grid conditions implements power distribution between the energy storage converter PCSs. This system addresses the issue of stable and reliable operation of large-scale energy storage systems in weak grid conditions with low short-circuit capacity. It also achieves power control characteristics similar to those of traditional synchronous generator plants at the high-voltage bus connection point (PoC), enabling seamless switching between grid-connected and islanded operation. It also provides virtual inertia to the grid to stabilize the frequency change rate, enabling rapid and active support of transient frequency and voltage at photovoltaic / wind power plants.

[0006] The above-mentioned disclosed technical solution has at least the following technical problems: when integrating a virtual power plant with an energy storage system for rapid voltage and frequency regulation, the charging state of the energy storage system has a crucial impact on the effect of voltage and frequency regulation. When the charging state of the energy storage system is too high, the energy storage system will charge rapidly and lose the function of smoothing fluctuations; this will cause the grid frequency and voltage to exceed the allowable range, increasing the instability of the system; and when the charging state of the energy storage system is too low, it will not be able to provide effective support when the grid demand increases, which may cause the grid frequency to drop and affect the stability of the grid. In addition, batteries in a low power state are also prone to accelerated loss; and the existing technology does not evaluate and adjust the maintenance state of the charging state of the energy storage system, resulting in low efficiency in rapid voltage and frequency regulation.

[0007] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for rapid voltage and frequency regulation integrating a virtual power plant and an energy storage system. By analyzing the virtual power plant and the energy storage system under different charging states of the energy storage system, the problem of low efficiency of rapid voltage and frequency regulation caused by the lack of evaluation and adjustment of the maintenance state of the energy storage system's charging state is solved.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A rapid voltage and frequency regulation method integrating a virtual power plant and an energy storage system includes the following steps: simulating a scenario of voltage and frequency regulation of a virtual electric field and an energy storage system based on MATLAB to obtain a regulation simulation model; setting a number of charging states of simulated energy storage systems to obtain corresponding charge and discharge restriction impact data and system stability impact data of the regulation simulation model; constructing a voltage and frequency regulation evaluation model of the virtual electric field and energy storage system based on a BP neural network according to the charge and discharge restriction impact data and the system stability impact data; obtaining energy storage system regulation bias data corresponding to a number of energy storage system working modes, and screening out the most similar historical data from a historical database used for model training according to the energy storage system regulation bias data for training the voltage and frequency regulation evaluation model of the virtual electric field and energy storage system; constructing a charging state vector set based on the output of the voltage and frequency regulation evaluation model of the virtual electric field and energy storage system and the corresponding charging state, and performing data analysis on the charging state vector set to obtain the optimal charging state for application to the voltage and frequency regulation of the virtual electric field and energy storage system.

[0011] In a preferred embodiment, the charge and discharge restriction impact data includes a charge and discharge effect evaluation coefficient; the specific method for obtaining the charge and discharge effect evaluation coefficient is as follows: obtain the charging and discharging data of the energy storage system under different SoC conditions, the charging and discharging data including the current charging state of the energy storage system, the maximum charging power that the energy storage system can receive under a given SoC, the maximum discharge power that the energy storage system can provide under a given SoC, the voltage level expected to be achieved during the adjustment process of the energy storage system, and the frequency level expected during the adjustment process; calculate the charging restriction impact and the discharging restriction impact according to the charging and discharging data respectively; and set the charging restriction weight of the corresponding charging restriction impact and the discharging restriction weight of the corresponding discharging restriction impact according to the percentage of the charging state, and the sum of the charging restriction weight and the discharging restriction weight is 1; perform weighted summation of the charging restriction impact and the discharging restriction impact as the response capability under different charging states, and multiply them by the corresponding voltage and frequency deviations to calculate the charge and discharge effect evaluation coefficient.

[0012] In a preferred embodiment, the system stability influencing data includes an equipment pressure abnormality influence coefficient and a system stability limited influence coefficient; the specific method for obtaining the equipment pressure abnormality influence coefficient is as follows: obtain the size of the equipment pressure under different charging states, and mark the size of the equipment pressure under different charging states; set a monitoring interval, obtain the size of the equipment pressure at different time nodes in the monitoring interval, and mark the size of the equipment pressure at different time nodes in the monitoring interval; calculate the average value and standard deviation of the equipment pressure at different time nodes; calculate the coefficient of variation of the equipment pressure at different time nodes based on the average value and standard deviation of the equipment pressure; calculate the equipment pressure abnormality influence coefficient based on the coefficient of variation of the equipment pressure.

[0013] In a preferred embodiment, the specific method for obtaining the system stability limited influence coefficient is as follows: obtain the voltage and frequency wave signals when the virtual power plant and the energy storage system perform rapid voltage and frequency adjustment, and convert the voltage and frequency wave signals into frequency domain signals; extract the frequency domain characteristics, and the frequency domain characteristics include frequency proportion, signal fluctuation amplitude, and spectrum bandwidth; analyze the spectrum characteristics based on the frequency domain characteristics, and calculate the system stability limited influence coefficient in combination with the frequency domain characteristics.

[0014] In a preferred embodiment, the data analysis of the charging state vector set is specifically as follows: constructing a change line graph based on each data point in the charging state vector set, performing secondary derivatives on each peak of the line graph to obtain the change rate; taking the charging state corresponding to the peak with the change rate closest to zero as the optimal charging state, and applying it to the voltage and frequency regulation of the virtual electric field and energy storage system.

[0015] In a preferred embodiment, the virtual electric field and energy storage system voltage and frequency regulation evaluation model is constructed based on the BP neural network according to the charge and discharge restriction impact data and the system stability impact data. Specifically, the acquired charge and discharge effect evaluation coefficient, the equipment pressure abnormality impact coefficient and the system stability limitation impact coefficient are used to construct the virtual electric field and energy storage system voltage and frequency regulation evaluation model to generate the regulation effect evaluation coefficient.

[0016] A device for a fast voltage and frequency regulation method integrating a virtual power plant and an energy storage system, comprising a simulation module, a data acquisition module, a model construction module, a mode adjustment module and a charging state selection module, wherein the modules are connected; the simulation module is used to simulate the scenario of the virtual electric field and the energy storage system in voltage and frequency regulation based on MATLAB to obtain a regulation simulation model; the data acquisition module is used to set the charging states of several simulated energy storage systems and obtain the charge and discharge limit influence data and system stability influence data of the corresponding regulation simulation model; the model construction module is used to construct a mode adjustment module based on the BP neural network according to the charge and discharge limit influence data and the system stability influence data A virtual electric field and energy storage system voltage and frequency regulation evaluation model is established; a mode adjustment module is used to obtain energy storage system regulation bias data corresponding to several energy storage system working modes, and according to the energy storage system regulation bias data, several most similar historical data are screened out from the historical database used for model training to train the virtual electric field and energy storage system voltage and frequency regulation evaluation model; a charging state selection module is used to construct a charging state vector set based on the output of the virtual electric field and energy storage system voltage and frequency regulation evaluation model and the corresponding charging state, and perform data analysis on the charging state vector set to obtain the optimal charging state, which is applied to the voltage and frequency regulation of the virtual electric field and energy storage system.

[0017] An electronic device, characterized in that the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute a rapid voltage and frequency regulation method for integrating a virtual power plant and an energy storage system.

[0018] A computer-readable storage medium stores a computer program, wherein the computer program is executed by a processor to implement a fast voltage and frequency regulation method for an integrated virtual power plant and an energy storage system.

[0019] The technical effects and advantages of the fast voltage and frequency regulation method of the integrated virtual power plant and energy storage system of the present invention are as follows:

[0020] 1. The present invention constructs an efficient regulation simulation model by simulating the voltage and frequency regulation scenarios in the MATLAB environment. The advantage of this model is that it can accurately evaluate and optimize the voltage and frequency regulation effects under different charging states, thereby ensuring the efficient and stable response of the system under various load conditions. By monitoring the charging state of the energy storage system in real time, low regulation efficiency caused by improper charging state can be avoided. At the same time, the method provides a basis for optimizing the regulation strategy by analyzing the charging and discharging effect evaluation coefficient and the equipment pressure abnormality influence coefficient, thereby enhancing the stability of the system and preventing performance bottlenecks caused by extreme charging states. This not only improves energy utilization efficiency, but also extends the service life of the equipment. Overall, this technology provides strong support for coping with frequent fluctuations and abnormal hidden dangers in the power grid, ensuring the safety and reliability of the power system.

[0021] 2. The present invention can effectively screen out historical data that is most similar to the current adjustment bias data by obtaining the adjustment bias data corresponding to the working modes of multiple energy storage systems, thereby providing an accurate basis for training the virtual electric field and energy storage system voltage and frequency adjustment evaluation model. This process makes the model training more in line with the actual operation requirements by analyzing the adjustment tendency and behavioral characteristics of the energy storage system under different working conditions, ensuring that the model has good accuracy and reliability; the screened similar historical data can help the model fully learn and reflect the actual adjustment requirements, and improve the effect and efficiency of voltage and frequency regulation. On this basis, step S5 constructs a charging state vector set by analyzing the output and charging state of the virtual electric field and energy storage system voltage and frequency adjustment evaluation model, and determines the optimal charging state through data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a structural diagram of the fast voltage and frequency regulation method of integrating a virtual power plant and an energy storage system according to the present invention.

[0023] Figure 2 This is a structural diagram of the fast voltage and frequency regulation device that integrates a virtual power plant and an energy storage system in the present invention. DETAILED DESCRIPTION

[0024] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] Example 1, Figure 1 The present invention provides a method for rapidly regulating voltage and frequency by integrating a virtual power plant with an energy storage system, comprising the following steps:

[0026] S1, based on MATLAB, simulates the scenario of the virtual electric field and energy storage system in voltage and frequency regulation to obtain the regulation simulation model.

[0027] In this embodiment, the specific method for obtaining the adjustment simulation model is as follows:

[0028] Establish a mathematical model of the virtual power plant and energy storage system, and define key parameters and input and output variables, including the charge state of the energy storage system, the power output of the virtual power plant, and the frequency response characteristics; the model is based on differential equations, state-space models, or data-driven models.

[0029] Using MATLAB's Simulink or Simscape tools, the subsystems of the virtual power plant and energy storage system are modularized; the energy storage system module is divided into sub-modules such as battery management, current control, and voltage control, while the virtual power plant module is divided into sub-modules such as load distribution, power scheduling, and frequency control.

[0030] The models of the virtual power plant and energy storage system are integrated together, enabling the two to exchange information and achieve collaborative control during the simulation process; using signal interfaces, variables such as charging status, load demand, and frequency deviation are transmitted between modules, thereby simulating the overall collaborative regulation process of the virtual power plant and energy storage system.

[0031] Configure simulation parameters, including sampling time, control gain, and initial state, to ensure the simulation model meets actual grid conditions. Set different scenario variables, including high and low state of charge, load change rate, grid disturbances, etc., to test the model's regulation response under different conditions;

[0032] The line regulation simulation model uses MATLAB and Simulink visualization tools to monitor the changes in voltage and frequency in real time during the simulation process; the dynamic response data of each variable, including voltage fluctuations, frequency deviations, and power changes of the energy storage system, is recorded to provide data support for subsequent analysis and optimization.

[0033] It's important to note that MATLAB is a high-level programming language and software environment widely used in scientific computing, data analysis, engineering modeling, and simulation. Developed by MathWorks in the United States, it is renowned for its powerful numerical and matrix operations. MATLAB offers a rich toolbox, encompassing areas such as control system design, signal processing, image processing, machine learning, and financial computing, greatly expanding its application across various fields. Its built-in Simulink tool is specifically designed for multi-domain system simulation and model design, enabling users to easily model complex systems and perform dynamic simulations in a graphical environment. As a mature technology, MATLAB, with its flexibility and powerful visualization capabilities, is widely used in a variety of industrial and scientific fields, including power systems, aerospace, automotive engineering, and communications technology. It not only supports rapid prototyping but also can be used for system performance analysis and optimization, making it a vital tool for modern engineers and researchers.

[0034] The regulation simulation model combines the coordinated control of the virtual power plant and the energy storage system, and can accurately evaluate and optimize the voltage and frequency regulation effects under different charging states, so that the system can maintain efficient and stable response under various load conditions. By monitoring and adjusting the charging state of the energy storage system in real time, this method ensures that the energy storage equipment is always in the optimal state when providing frequency response and voltage support, thereby avoiding the problem of low regulation efficiency caused by insufficient or excessive charging state. Specifically, the regulation simulation model can dynamically adjust the output power of the energy storage system according to actual needs, achieve fast and stable voltage and frequency regulation, improve the regulation efficiency and reliability of the overall system, and help to cope with the challenges of frequent fluctuations and abnormal hidden dangers in the power grid.

[0035] S2, setting a number of simulated energy storage system charging states, and obtaining corresponding charging and discharging limit impact data and system stability impact data of the regulation simulation model.

[0036] The charge and discharge restriction impact data includes a charge and discharge effect evaluation coefficient; the system stability impact data includes an equipment pressure abnormality impact coefficient and a system stability restriction impact coefficient.

[0037] The acquisition of charge-discharge effect evaluation coefficients primarily focuses on the impact of different states of charge on the energy storage system's charge and discharge capabilities. This data includes the charge-discharge effect evaluation coefficient, which quantifies the energy storage system's charge and discharge capabilities under different SoC conditions and its contribution to voltage and frequency regulation. By analyzing the charge-discharge effect evaluation coefficients, the energy storage system's discharge regulation capabilities under high and low SoC conditions, as well as its ability to absorb excess energy, can be effectively evaluated. This assessment not only helps identify the energy storage system's regulation performance under specific states of charge but also guides how to optimize charge-discharge strategies to achieve more efficient voltage and frequency regulation, ensuring system stability and reliability in the face of grid fluctuations.

[0038] Analyzing the impact of charge and discharge limits has the following advantages for resolving the issue of inefficient rapid voltage and frequency regulation resulting from a failure to assess and adjust the energy storage system's state of charge:

[0039] Optimizing regulation strategies: By quantifying the charge and discharge capabilities under different SoC states, the charge and discharge limit impact data can provide a benchmark for optimizing energy storage system regulation. This helps select the optimal charge and discharge strategy under different charge states, enabling rapid response when adjusting voltage and frequency, and improving the overall regulation efficiency of the system.

[0040] Enhanced system stability: Charge and discharge limit impact data can reveal the stability of the energy storage system at different states of charge. For example, a low SoC may limit discharge capacity, while an excessively high SoC may affect charging performance. By evaluating this data, we can better avoid the adverse effects of the charge state on voltage and frequency regulation, ensuring stable system operation.

[0041] Preventing extreme conditions: Data analysis can help identify potential performance bottlenecks of energy storage systems under extreme charging conditions (such as overcharge or low charge), providing an important basis for maintaining optimal voltage and frequency regulation capabilities. This helps prevent overload or underload conditions during regulation and improves the system's ability to cope with sudden fluctuations.

[0042] Improved energy efficiency: By understanding the impact of charge and discharge limits, the energy distribution and scheduling of the energy storage system can be better managed, preventing energy waste caused by overcharging or discharging. This not only improves the efficiency of the energy storage system but also helps extend the life of the equipment, thereby improving the overall efficiency and sustainability of voltage and frequency regulation.

[0043] The specific method for obtaining the charge and discharge effect evaluation coefficient is as follows:

[0044] Obtain charging and discharging data of the energy storage system under different SoC conditions. The charging and discharging data includes the current charge state of the energy storage system, the maximum charging power that the energy storage system can receive under a given SoC, the maximum discharge power that the energy storage system can provide under a given SoC, the expected voltage level to be achieved during the energy storage system regulation, and the expected frequency level during the regulation process.

[0045] Calculate the charge limit effect and discharge limit effect based on the charge and discharge data respectively;

[0046] And according to the percentage of the charging state, the charging limit weight of the corresponding charging limit influence and the discharging limit weight of the corresponding discharging limit influence are set respectively, and the sum of the charging limit weight and the discharging limit weight is 1;

[0047] The charge and discharge limit effects are weighted and summed to obtain the response capability under different charge states. The sum is then multiplied by the corresponding voltage and frequency deviations to obtain the charge and discharge effect evaluation coefficient.

[0048] The specific calculation formula for the charging restriction effect is:

[0049]

[0050] The specific calculation formula for the discharge limitation effect is:

[0051]

[0052] The specific calculation formula of the charge and discharge effect evaluation coefficient is:

[0053]

[0054] Where, Due to the charging limitation, Correction factors related to internal resistance and temperature when charging the energy storage system, In charging state, is the charging power, Voltage change, is the current voltage, For the influence of power limitation, is the correction factor related to the internal resistance and temperature of the energy storage system during discharge, is the discharge power, is the frequency change, Current frequency, is the charge and discharge effect evaluation coefficient, is the total capacity of the energy storage system, is the charging weight coefficient.

[0055] The device pressure anomaly impact coefficient is used to assess the impact of changes in the battery charge management system's pressure on the voltage and frequency regulation of the energy storage system at different charge states. Specifically, this coefficient reflects the abnormal pressure on the device's operation caused by factors such as the battery's internal temperature, current, and voltage at different charge and discharge rates and charge states. For example, at a higher state of charge, the battery may face greater temperature rise and current pressure, which may affect the accuracy and efficiency of regulation. By monitoring the device pressure anomaly impact coefficient, the pressure the device withstands at a specific charge state can be quantified to ensure the safety and stability of the system during voltage and frequency regulation and effectively protect battery life.

[0056] Analyzing the impact coefficient of equipment pressure anomalies has the following advantages for resolving the problem of inefficient rapid voltage and frequency regulation caused by a failure to evaluate and adjust the energy storage system's state of charge:

[0057] Improving the accuracy of voltage and frequency regulation: The device pressure abnormality impact coefficient provides real-time information on the internal pressure experienced by the battery under different charge states. By monitoring this coefficient, it is possible to effectively identify situations in which excessive device pressure, under high load or low charge conditions, can lead to reduced regulation performance. This helps the regulation system make more precise control decisions and avoid voltage or frequency deviations caused by excessive charge and discharge.

[0058] Improved regulation efficiency and response speed: During voltage and frequency regulation, excessive equipment stress can slow battery response, even leading to delays or anomalies. By monitoring the impact coefficient of pressure anomalies, we can assess equipment stress in real time, appropriately reduce equipment load, and optimize battery charge and discharge rates under different charge states. This process improves the regulation speed of the energy storage system, enabling it to more quickly respond to frequency or voltage fluctuations.

[0059] Extending device life: Operating batteries under excessive pressure can lead to accelerated aging and performance degradation, impacting overall system reliability. By monitoring the impact coefficient of pressure anomalies, we can proactively identify and prevent device damage caused by overcharging or discharging under different charging states, keeping the battery operating within the optimal pressure range. This not only improves battery stability during conditioning but also extends device life in the long term.

[0060] Improved overall system stability: After comprehensively evaluating the charge state and pressure conditions, the device pressure abnormality impact coefficient can help adjust the system to operate at the appropriate charge state, avoiding overall system instability caused by device pressure imbalance. This balance helps reduce frequent and large adjustments, ensuring system stability during rapid adjustments, especially in high-load adjustment scenarios, ensuring smooth and reliable output.

[0061] The specific method for obtaining the equipment pressure abnormality influence coefficient is as follows:

[0062] Obtain the magnitude of device pressure at different charging states and mark the magnitude of device pressure at different charging states;

[0063] Set the monitoring interval, obtain the size of the equipment pressure at the nodes at different times within the monitoring interval, and mark the size of the equipment pressure at the nodes at different times within the monitoring interval;

[0064] Calculate the mean and standard deviation of equipment pressure at nodes at different times;

[0065] Calculate the coefficient of variation of equipment pressure at nodes at different times based on the mean and standard deviation of equipment pressure;

[0066] Calculate the equipment pressure abnormality impact coefficient based on the equipment pressure variation coefficient.

[0067] The specific calculation formula for the standard deviation of the equipment pressure is as follows:

[0068]

[0069] The specific calculation formula for the average value of the equipment pressure is as follows:

[0070]

[0071] The specific calculation formula for the equipment pressure abnormality influence coefficient is as follows:

[0072]

[0073] Where, is the standard deviation of the equipment pressure, is the average value of the equipment pressure, is the total number of pressure data numbers, is the number of the pressure data, is the equipment pressure, is the pressure ratio, is the influence coefficient of equipment pressure abnormality.

[0074] The system stability limited impact coefficient is used to evaluate the impact of load changes on the overall stability of the system during voltage and frequency regulation under different charging states. This coefficient pays special attention to the responsiveness and stability of the grid frequency when the load increases, that is, whether the system can quickly and smoothly maintain within the preset frequency range when the load suddenly increases or changes. Through this coefficient, the impact of the charging state on the stability of the grid frequency can be quantified. For example, when the charging state is low, the energy storage system may find it difficult to provide sufficient power to support the grid frequency, resulting in an increased risk of frequency fluctuations. The system stability limited impact coefficient provides a quantitative basis for optimizing voltage and frequency regulation strategies, helps to ensure the stability of the grid during the regulation process, and avoids system instability caused by excessive frequency fluctuations.

[0075] Analyzing the impact of charge and discharge limits has the following advantages for resolving the issue of inefficient rapid voltage and frequency regulation resulting from a failure to assess and adjust the energy storage system's state of charge:

[0076] Quantifying charging capacity: The charge-discharge performance evaluation coefficient accurately quantifies the energy storage system's charging and discharging capabilities under different charging states. This allows the system to flexibly adjust discharge strategies based on the real-time charging state during voltage and frequency regulation to better meet grid needs, thereby improving regulation efficiency.

[0077] Optimizing Regulation Strategies: By analyzing data on charge and discharge limitations, the system can identify limiting factors at specific states of charge and optimize regulation strategies accordingly. For example, when the state of charge is low, the system might adjust the discharge rate or prioritize the use of other energy storage resources to avoid frequency fluctuations caused by insufficient state of charge. This optimization ensures that the energy storage system can maximize its regulation capabilities under different operating conditions.

[0078] Enhanced system adaptability: In the event of grid frequency fluctuations, charge and discharge limit impact data enables the system to adjust its charge state in real time to adapt to changing load demands. This dynamic adaptability not only improves the responsiveness of voltage and frequency regulation but also reduces the system's vulnerability to sudden load changes, thereby ensuring grid stability.

[0079] Preventing Equipment Overload: By monitoring data affecting charge and discharge limits, it is possible to promptly identify and predict overload risks that may occur under certain charging conditions, preventing equipment failure due to excessive stress. This not only protects the safety of energy storage equipment, but also improves overall regulation efficiency, ensuring smooth voltage and frequency regulation.

[0080] Supporting Decision Making: Data on the impact of charge and discharge constraints provides a crucial decision-making tool for power system dispatchers. By assessing the real-time effects of charge and discharge, more targeted regulation plans can be developed, further improving the efficiency and stability of the power system under high load and frequency fluctuations.

[0081] The specific method for obtaining the system stability limited influence coefficient is as follows:

[0082] Obtain voltage and frequency wave signals when the virtual power plant and energy storage system perform rapid voltage and frequency regulation, and convert the voltage and frequency wave signals into frequency domain signals; extract frequency domain characteristics, including frequency proportion, signal fluctuation amplitude, and spectrum bandwidth; analyze spectrum characteristics based on the frequency domain characteristics, and calculate the system stability limitation influence coefficient based on the frequency domain characteristics;

[0083] The specific calculation formula for converting the voltage and frequency wave signals into the frequency domain is:

[0084]

[0085] The formula for calculating the difference between the peak frequency and the center frequency is:

[0086]

[0087] Then the calculation formula of the signal fluctuation amplitude is:

[0088]

[0089] Then the calculation formula of the frequency domain characteristics is:

[0090]

[0091] The calculation formula of the system stability limited influence coefficient is as follows:

[0092]

[0093] Where, is the frequency domain conversion formula, are the voltage and frequency wave signals at time t, is the wavelet function, is the scale of the wavelet function, is the translation parameter of the wavelet function; is the difference between the peak frequency and the center frequency, is the center frequency, and are the maximum and minimum values ​​of the signal fluctuation amplitude, The value of is the peak frequency minus half the spectrum bandwidth, The value of is the peak frequency plus half the spectrum bandwidth, is the signal fluctuation amplitude, is the frequency domain characteristic, is the maximum energy within the spectrum, is the minimum energy within the spectrum range, is the limited influence coefficient of system stability.

[0094] Example 2, S3, constructs a virtual electric field and energy storage system voltage and frequency regulation evaluation model based on the BP neural network according to the charge and discharge restriction impact data and the system stability impact data.

[0095] The virtual electric field and energy storage system voltage and frequency regulation evaluation model is constructed based on the BP neural network according to the charge and discharge restriction impact data and the system stability impact data, specifically:

[0096] The obtained charging and discharging effect evaluation coefficients, equipment pressure abnormality impact coefficients, and system stability limitation impact coefficients are used to construct a virtual electric field and energy storage system voltage and frequency regulation evaluation model to generate a regulation effect evaluation coefficient.

[0097] The specific calculation formula of the adjustment effect evaluation coefficient is as follows:

[0098]

[0099] Where, is the adjustment effect evaluation coefficient, It is the preset proportional coefficient of the charge and discharge effect evaluation coefficient. It is the preset proportional coefficient of the equipment pressure abnormality influence coefficient. is the preset proportional coefficient of the system stability limiting influence coefficient, is the charge and discharge effect evaluation coefficient, is the influence coefficient of equipment pressure abnormality, is the limited influence coefficient of system stability.

[0100] This embodiment constructs an efficient regulation simulation model by simulating the voltage and frequency regulation scenarios in the MATLAB environment. The advantage of this model is that it can accurately evaluate and optimize the voltage and frequency regulation effects under different charging states, thereby ensuring the efficient and stable response of the system under various load conditions. By monitoring the charging state of the energy storage system in real time, the low regulation efficiency caused by improper charging state can be avoided. At the same time, the method provides a basis for optimizing the regulation strategy by analyzing the charging and discharging effect evaluation coefficient and the equipment pressure abnormality influence coefficient, thereby enhancing the stability of the system and preventing performance bottlenecks caused by extreme charging states. This not only improves energy utilization efficiency, but also extends the service life of the equipment. Overall, this technology provides strong support for coping with frequent fluctuations and abnormal hidden dangers in the power grid, ensuring the safety and reliability of the power system.

[0101] It's important to note that when selecting the most similar historical data, it's first necessary to define the characteristic indicators of the energy storage system's regulation bias data, such as voltage, frequency, power response speed, regulation amplitude, and other system state parameters. These characteristics are then used to establish a mathematical similarity assessment model, using algorithms such as Euclidean distance, cosine similarity, or dynamic time warping (DTW), to quantify the similarity between the current regulation bias data and historical data. Specifically, for the current regulation bias data, its characteristic values ​​are compared with each data item in the historical database to obtain a similarity score. Data with higher scores are more similar to the current situation. Finally, the historical data items with the highest similarity scores are selected as input for model training, ensuring that the selected data best reflects the characteristics of the current regulation demand, thereby improving the accuracy and adaptability of model training.

[0102] In Example 2, S4, energy storage system regulation bias data corresponding to several energy storage system operating modes are obtained, and the most similar historical data are screened out from the historical database used for model training based on the energy storage system regulation bias data for training the virtual electric field and energy storage system voltage and frequency regulation evaluation model.

[0103] In this embodiment, the most similar historical data are selected from the historical database used for model training based on the energy storage system adjustment bias data, specifically:

[0104] By analyzing the operating modes of multiple energy storage systems, corresponding regulation bias data is obtained; this data reflects the regulation tendencies and behavioral characteristics of the energy storage systems under different operating conditions; then, the system selects historical data sets that are most similar to the current regulation bias data from a pre-established historical database to ensure the accuracy and reliability of model training; these similar historical data will be used to train the virtual electric field and energy storage system voltage and frequency regulation evaluation model, ensuring that the model can fully learn and reflect the regulation requirements in actual operation, thereby improving the effectiveness and efficiency of voltage and frequency regulation.

[0105] It should be noted that regulation bias data refers to a collection of data that reflects the energy storage system's regulation tendencies and behavioral characteristics for voltage and frequency under different operating conditions. This data typically includes the energy storage system's response characteristics under specific operating modes, such as its reaction speed, regulation amplitude, and specific regulation strategy under varying loads, environmental conditions, or power demand. By capturing the energy storage system's operational preferences under these conditions, regulation bias data can reveal the system's behavioral patterns and regulation directions in voltage and frequency control.

[0106] For example, when an energy storage system operates under high load, its regulation bias data may reflect a preference for maintaining stable voltage, whereas under low load, the system may prefer to maintain frequency stability. This data not only describes the current regulation behavior of the energy storage system but also serves as historical data for comparison with current regulation requirements, screening out historical data most similar to the current situation to more accurately predict and adapt to new regulation requirements. Therefore, regulation bias data plays a key role in the optimal control and model training of energy storage systems, ensuring the accuracy and adaptability of voltage and frequency regulation evaluation models.

[0107] It should be noted that

[0108] S5, based on the output of the virtual electric field and energy storage system voltage and frequency regulation evaluation model and the corresponding charging state, construct a charging state vector set, and perform data analysis on the charging state vector set to obtain the optimal charging state, which is applied to the voltage and frequency regulation of the virtual electric field and energy storage system.

[0109] The data analysis of the charge state vector set is specifically as follows:

[0110] A change line graph is constructed based on each data point in the charge state vector set, and the change rate is obtained by performing a secondary derivative on each peak of the line graph;

[0111] The charging state corresponding to the peak value with the change rate closest to zero is taken as the optimal charging state, which is applied to the voltage and frequency regulation of the virtual electric field and energy storage system.

[0112] By acquiring the regulation bias data corresponding to the operating modes of multiple energy storage systems, this embodiment can effectively screen out the historical data that is most similar to the current regulation bias data, thereby providing an accurate basis for training the virtual electric field and energy storage system voltage and frequency regulation evaluation model. This process makes the model training more in line with actual operating needs by analyzing the regulation tendencies and behavioral characteristics of the energy storage system under different operating conditions, ensuring that the model has good accuracy and reliability; the screened similar historical data can help the model fully learn and reflect the actual regulation needs, and improve the effect and efficiency of voltage and frequency regulation. On this basis, step S5 constructs a set of charging state vectors by analyzing the output and charging state of the virtual electric field and energy storage system voltage and frequency regulation evaluation model, and determines the optimal charging state through data analysis.

[0113] Example 3, Figure 2 , a schematic diagram of a device for a fast voltage and frequency regulation method integrating a virtual power plant and an energy storage system, comprising a simulation module, a data acquisition module, a model building module, a mode adjustment module, and a charge state selection module, with connections between the modules;

[0114] The simulation module is used to simulate the voltage and frequency regulation scenarios of the virtual electric field and energy storage system based on MATLAB to obtain a regulation simulation model;

[0115] A data acquisition module is used to set the charging status of several simulated energy storage systems and obtain the corresponding charging and discharging limit impact data and system stability impact data of the adjustment simulation model;

[0116] A model building module is used to build a virtual electric field and energy storage system voltage and frequency regulation evaluation model based on the BP neural network according to the charge and discharge limit impact data and system stability impact data;

[0117] A mode adjustment module is used to obtain energy storage system regulation bias data corresponding to several energy storage system operating modes, and to filter out the most similar historical data from the historical database used for model training based on the energy storage system regulation bias data, and to train the virtual electric field and energy storage system voltage and frequency regulation evaluation model;

[0118] The charging state selection module is used to construct a charging state vector set based on the output of the virtual electric field and energy storage system voltage and frequency regulation evaluation model and the corresponding charging state, and perform data analysis on the charging state vector set to obtain the optimal charging state for application in the voltage and frequency regulation of the virtual electric field and energy storage system.

[0119] The present application also includes an electronic device, comprising:

[0120] at least one processor; and,

[0121] a memory communicatively connected to the at least one processor; wherein,

[0122] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fast voltage and frequency regulation method of the integrated virtual power plant and energy storage system.

[0123] The present application also includes a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the method for rapid voltage and frequency regulation of the integrated virtual power plant and energy storage system.

[0124] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0125] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0126] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0128] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0129] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A fast voltage and frequency regulation method integrating a virtual power plant and an energy storage system, characterized in that: The steps include: Based on MATLAB, the scenario of the virtual power plant and energy storage system in voltage and frequency regulation is simulated to obtain the regulation simulation model; Setting a number of simulated energy storage system charging states, and obtaining corresponding charging and discharging limit impact data and system stability impact data of the regulation simulation model; The charge and discharge restriction impact data includes a charge and discharge effect evaluation coefficient; the specific method for obtaining the charge and discharge effect evaluation coefficient is as follows: Obtain the charging and discharging data of the energy storage system under different SoC conditions. The charging and discharging data include the current charging state of the energy storage system, the maximum charging power that the energy storage system can receive under a given SoC, the maximum discharging power that the energy storage system can provide under a given SoC, the voltage level expected to be achieved during the regulation of the energy storage system, and the frequency level expected during the regulation process; Calculate the charge limit effect and the discharge limit effect respectively based on the charge and discharge data; And according to the percentage of the charging state, the charging restriction weight of the corresponding charging restriction influence and the discharging restriction weight of the discharging restriction influence are set respectively, and the sum of the charging restriction weight and the discharging restriction weight is 1; The charge limit effect and the discharge limit effect are weighted and summed as the response capacity under different charging states, and multiplied by the corresponding voltage and frequency deviations to calculate the charge and discharge effect evaluation coefficient; The system stability impact data includes the equipment pressure abnormality impact coefficient and the system stability limited impact coefficient; the specific method for obtaining the equipment pressure abnormality impact coefficient is as follows: Obtain the magnitude of device pressure in different charging states, and mark the magnitude of device pressure in different charging states; Set a monitoring interval, obtain the size of the equipment pressure at different time nodes in the monitoring interval, and mark the size of the equipment pressure at different time nodes in the monitoring interval; Calculate the mean and standard deviation of equipment pressure at nodes at different times; Calculate the coefficient of variation of equipment pressure at nodes at different times based on the mean value and standard deviation of equipment pressure; Calculate the equipment pressure abnormality influence coefficient based on the equipment pressure variation coefficient; The specific method for obtaining the system stability limited influence coefficient is as follows: Obtain voltage and frequency wave signals when the virtual power plant and the energy storage system perform rapid voltage and frequency regulation, and convert the voltage and frequency wave signals into frequency domain signals; Extracting frequency domain characteristics, wherein the frequency domain characteristics include frequency proportion, signal fluctuation amplitude, and spectrum bandwidth; Analyze the spectrum characteristics based on the frequency domain characteristics, and calculate the system stability restriction influence coefficient based on the frequency domain characteristics; According to the charging and discharging restriction impact data and system stability impact data, a voltage and frequency regulation evaluation model for virtual power plants and energy storage systems is constructed based on the BP neural network; Obtaining energy storage system regulation bias data corresponding to several energy storage system working modes, and selecting several most similar historical data from a historical database used for model training based on the energy storage system regulation bias data for training a virtual power plant and energy storage system voltage and frequency regulation evaluation model; Regulation bias data refers to a data set that reflects the regulation tendency and behavior characteristics of the voltage and frequency of the energy storage system under different operating conditions; According to the output of the voltage and frequency regulation evaluation model of the virtual power plant and energy storage system and the corresponding charging status, a charging state vector set is constructed, and data analysis is performed on the charging state vector set to obtain the optimal charging state, which is applied to the voltage and frequency regulation of the virtual power plant and energy storage system.

2. The fast voltage and frequency regulation method of the integrated virtual power plant and energy storage system according to claim 1, characterized in that: The data analysis of the charging state vector set is specifically as follows: A change line graph is constructed according to each data point in the charge state vector set, and a second derivative is performed on each peak value of the line graph to obtain the change rate; The charging state corresponding to the peak value with the change rate closest to zero is taken as the optimal charging state, and is applied to the voltage and frequency regulation of virtual power plants and energy storage systems.

3. The fast voltage and frequency regulation method of the integrated virtual power plant and energy storage system according to claim 2 is characterized in that: The voltage and frequency regulation evaluation model of the virtual power plant and the energy storage system is constructed based on the BP neural network according to the charge and discharge restriction impact data and the system stability impact data, specifically: The acquired charging and discharging effect evaluation coefficient, equipment pressure abnormality impact coefficient and system stability limitation impact coefficient are used to construct a voltage and frequency regulation evaluation model for the virtual power plant and energy storage system to generate a regulation effect evaluation coefficient.

4. The fast voltage and frequency regulation method of the integrated virtual power plant and energy storage system according to claim 3 is characterized in that: The specific calculation formula for the charging restriction effect is: The specific calculation formula for the discharge limitation effect is: The specific calculation formula of the charge and discharge effect evaluation coefficient is: In the formula, Due to the charging limitation, Correction factors related to internal resistance and temperature when charging the energy storage system, For charging status, is the charging power, Voltage change, is the current voltage, Discharge is limited by electricity, is the correction factor related to the internal resistance and temperature of the energy storage system during discharge, is the discharge power, is the frequency change, Current frequency, is the charge and discharge effect evaluation coefficient, is the total capacity of the energy storage system, is the charging weight coefficient.

5. A device using the fast voltage and frequency regulation method of the integrated virtual power plant and energy storage system as described in any one of claims 1 to 4, characterized in that: It includes a simulation module, a data acquisition module, a model building module, a mode adjustment module and a charging state selection module, and there are connections between the modules; The simulation module is used to simulate the voltage and frequency regulation scenarios of the virtual power plant and the energy storage system based on MATLAB to obtain a regulation simulation model; A data acquisition module, used to set the charging states of a number of simulated energy storage systems, and obtain the charge and discharge limit impact data and system stability impact data of the corresponding adjustment simulation model; A model building module is used to build a voltage and frequency regulation evaluation model for a virtual power plant and energy storage system based on a BP neural network according to the charging and discharging restriction impact data and the system stability impact data; A mode adjustment module is used to obtain energy storage system regulation bias data corresponding to a number of energy storage system working modes, and select the most similar historical data from the historical database used for model training based on the energy storage system regulation bias data, so as to train the virtual power plant and energy storage system voltage and frequency regulation evaluation model; The charging state selection module is used to construct a charging state vector set based on the output of the voltage and frequency regulation evaluation model of the virtual power plant and energy storage system and the corresponding charging state, and perform data analysis on the charging state vector set to obtain the optimal charging state, which is applied to the voltage and frequency regulation of the virtual power plant and energy storage system.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the rapid voltage and frequency regulation method of the integrated virtual power plant and energy storage system as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the rapid voltage and frequency regulation method of an integrated virtual power plant and energy storage system as described in any one of claims 1 to 4.

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