A power distribution network energy storage control method and system considering new energy consumption
By acquiring power generation and load data, calculating fluctuation coefficients and consistency coefficients, and adjusting droop control technology, the problem of insufficient flexibility of energy storage systems in distribution networks has been solved. This has enabled accurate tracking and efficient control of new energy power generation and load changes, thereby improving the overall performance of the distribution network.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID HENAN ELECTRIC POWER CO HUIXIAN CITY POWER SUPPLY CO
- Filing Date
- 2026-06-22
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies fail to fully consider the close coupling between the grid operation status and the energy storage system, resulting in insufficient flexibility in energy storage configuration, difficulty in timely and accurate tracking of new energy power generation and load changes, and impact on the control efficiency of the distribution network.
By acquiring power generation and load data, calculating the random fluctuation coefficient and comprehensive impact coefficient, analyzing the charging and discharging consistency, and adjusting the droop coefficient of the droop control technology, energy storage control can be optimized.
It improves the accuracy of energy storage systems in tracking new energy power generation and load changes, enhances the control efficiency and stability of the distribution network, and promotes the efficient consumption of new energy.
Smart Images
Figure CN122495510A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power grid energy storage control technology, specifically to a distribution network energy storage control method and system that takes into account the consumption of new energy sources. Background Technology
[0002] The role of energy storage systems in distribution networks is not only to provide power to consumer segments, but also to serve as an important means of reducing the volatility of renewable energy power generation. Their regulation performance is closely related to the power generation status, requiring high accuracy in predicting renewable energy power generation. However, weather and environmental factors make it difficult to maintain stable power output, and the charging and discharging status of energy storage systems also affects the stability of the distribution system. Existing methods fail to fully consider the close coupling between grid operation and the charging and discharging status of energy storage systems, making it difficult to track changes in renewable energy generation and load in a timely and accurate manner. This results in low control efficiency and ultimately affects the overall performance of energy storage systems in distribution networks.
[0003] The patent publication number CN114123259B describes an energy storage configuration method based on the evaluation of the inertial time constant of new energy sources connected to the distribution network. This method calculates the inertial time constant of nodes by real-time measurement of generator active power changes and node frequencies to compensate for the capacity of energy storage systems at nodes with low inertia. However, it fails to consider the dynamic characteristics of grid operation, resulting in insufficient flexibility in energy storage configuration and potentially failing to provide timely and effective compensation when the grid's operating state changes. Summary of the Invention
[0004] To address the aforementioned technical problems, the purpose of this application is to provide a distribution network energy storage control method and system that takes into account the consumption of new energy sources. The specific technical solution adopted is as follows: This application provides a distribution network energy storage control method that takes into account the consumption of new energy sources, including the following steps: Acquire data on the power generation, load power, and charging / discharging power of new energy sources in the power distribution network; The random fluctuation coefficients of power generation and load power are obtained based on the variation amplitude of adjacent peak values in power generation and load power, as well as the fluctuation degree of each peak value. The comprehensive influence coefficient of the energy storage system is obtained by combining the degree of difference between the predicted data corresponding to power generation and the predicted data corresponding to load power. The degree of consistency in the charging and discharging power changes of each energy storage device in the distribution network is analyzed to obtain the charging and discharging consistency coefficient of the hybrid energy storage. Based on the comprehensive influence coefficient of the energy storage system and the charging and discharging consistency coefficient, the energy storage control sensitivity value is obtained. The droop coefficient of the droop control technology is adjusted based on the energy storage control sensitivity value, and the energy storage in the distribution network is controlled in combination with the droop control technology.
[0005] Preferably, the method for calculating the random fluctuation coefficients of the power generation and load power is as follows: The formula for calculating the random fluctuation coefficient A of power generation is: In the formula, B is the sum of the differences between the amplitudes of all two adjacent peak power generation values within the current time period, C is the sum of the kurtosis of all peak power generation values within the current time period, and exp() represents an exponential function with the natural constant as the base. To avoid constants with a denominator of zero; Accordingly, for the load power in the current time period, the random fluctuation coefficient of the load power is obtained.
[0006] Preferably, the peak value is obtained by using an adaptive multi-scale peak detection algorithm.
[0007] Preferably, the method for calculating the comprehensive impact coefficient of the energy storage system is as follows: In the formula, F is the comprehensive influence coefficient of the system under energy storage, D is the DTW distance between the predicted sequence of power generation and the predicted sequence of load power, and E is the mean of the random fluctuation coefficient of power generation and the random fluctuation coefficient of load power.
[0008] Preferably, the method for obtaining the predicted power generation sequence and the predicted load power sequence is as follows: The power generation data within the current time period is decomposed into various modal components. Each modal component is predicted using a prediction algorithm to obtain a preset number of predicted values for each modal component after the corresponding time period. These predicted values form a sequence of predicted values for each modal component. The predicted value sequences of all modal components are then superimposed bit by bit to obtain the power generation prediction sequence. Accordingly, a load power prediction sequence is obtained for the load power of the current time period.
[0009] Preferably, the method for calculating the charge-discharge consistency coefficient of the hybrid energy storage is as follows: Obtain the charge and discharge power curves corresponding to each energy storage device, calculate the area of the region enclosed by each charge and discharge power curve and the time axis, and record it as the area of each region, as well as the intersection area between all the enclosed regions. The average value of the ratio of the intersection area to the area of each region is used as the charge and discharge consistency coefficient of the hybrid energy storage.
[0010] Preferably, the method for obtaining the charge and discharge power curves corresponding to each energy storage device is as follows: the charge and discharge power data of each energy storage device are fitted, with the horizontal axis being the time axis and the vertical axis being the power, to obtain the charge and discharge power curves corresponding to each energy storage device.
[0011] Preferably, the method for calculating the energy storage control sensitivity value is as follows: In the formula, L is the energy storage control sensitivity value, F is the comprehensive influence coefficient of the energy storage system, and H is the charging and discharging consistency coefficient of the hybrid energy storage.
[0012] Preferably, the specific method for adjusting the sag coefficient of the sag control technology is as follows: The minimum and maximum values of the droop coefficient are preset, and the difference between the maximum and minimum values of the droop coefficient is used as the maximum adjustment range. The product of the normalized result of the energy storage control sensitivity value and the maximum adjustment range is calculated, and the sum of the preset initial droop coefficient and the product is used as the adjusted droop coefficient.
[0013] This application also provides a distribution network energy storage control system that takes into account the consumption of new energy sources, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the distribution network energy storage control method that takes into account the consumption of new energy sources described above.
[0014] As can be seen from the above, the energy storage control method and system for distribution networks that takes into account the consumption of new energy sources provided in this application has at least the following beneficial effects: This application deeply analyzes the random variation characteristics of power generation and load power in the distribution network, and, in response to the complex nonlinear characteristics of power generation and load power under the combined influence of multiple factors, obtains the balance difference characteristics between power generation and load power by predicting each modal component separately. Its advantage is that it can accurately track the status of new energy power generation and load power consumption, and reduce the interference of uncertain changes. Further considering the consistent charging and discharging characteristics among different devices in a hybrid energy storage system, the energy storage control sensitivity value is calculated to optimize the parameters of the droop control technology, thereby enabling energy storage regulation of the distribution network. This helps to improve control efficiency and enhance the overall performance of the energy storage system in the distribution network. Attached Figure Description
[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 A flowchart illustrating the steps of a distribution network energy storage control method that takes into account the consumption of new energy sources, as provided in this application. Detailed Implementation
[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a distribution network energy storage control method and system considering new energy consumption proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0018] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a distribution network energy storage control method and system that takes into account the consumption of new energy sources, as provided in this application.
[0020] Please see Figure 1 The document illustrates a flowchart of a distribution network energy storage control method considering renewable energy consumption, according to an embodiment of this application, including the following steps: Step 1: Obtain the power generation, load power, and charging / discharging power data of new energy sources in the distribution network.
[0021] Energy storage systems are widely used in renewable energy generation due to their ability to effectively mitigate the adverse effects of renewable energy generation on the power grid. Reasonable control strategies and methods are crucial for maximizing their effectiveness. Power balance in the distribution network is fundamental to its stable operation, and renewable energy generation power and load power are key factors affecting this balance. Real-time acquisition of these two types of data enables the energy storage control system to accurately understand the current power supply and demand situation of the power grid.
[0022] Therefore, this embodiment takes wind power generation as an example. The control system of the inverter connected to the wind power generator acquires the power generation data of each generator set in real time, and the smart meter at the load end collects the load power data in real time. In this embodiment, the energy storage system of the distribution network is a hybrid energy storage system of energy storage batteries and supercapacitors, acquiring the charging and discharging power data corresponding to each energy storage device. In this embodiment, all data within each time period are collected in real time, with the time interval for each data collection set to 1 minute. In this embodiment, one time period is one day; however, the implementer can set this value according to the actual application scenario.
[0023] Thus, based on the above process in this embodiment, wind power generation data, load power data, and energy storage device charging and discharging power data in the power distribution network can be obtained.
[0024] Step 2: Based on the variation range of adjacent peak values in power generation and load power, as well as the fluctuation degree of each peak value, obtain the random fluctuation coefficients of power generation and load power. Combined with the degree of difference between the predicted data corresponding to power generation and the predicted data corresponding to load power, obtain the comprehensive influence coefficient of the energy storage system.
[0025] Energy storage systems in power distribution networks primarily function as "regulators" and "stabilizers." They store excess energy during periods of overcapacity in renewable energy generation, preventing wind and solar power curtailment, and release energy during periods of undercapacity or peak load, achieving peak shaving and valley filling to ensure grid supply and demand balance. Simultaneously, energy storage systems provide inertia support, enhancing grid frequency and power stability to promote efficient renewable energy consumption and contribute to the safe, stable operation and efficient, economical power supply of the distribution network. Wind power output is significantly affected by geographical environment, climate, and season, resulting in considerable uncertainty across different time scales. Furthermore, frequent start-ups and shutdowns of industrial and residential electrical equipment, such as the intermittent operation of machine tools or large appliances, cause the load power of the distribution network to fluctuate continuously in short periods, exhibiting high short-term randomness. Given the high uncertainty in both power generation and load power, achieving efficient and rapid peak shaving and valley filling requires accurate tracking and prediction of power generation and load power to adjust the power supply and charging capacity of the energy storage system in a timely manner. Therefore, this application analyzes the variation characteristics of power generation and load power, as well as the differences between the two, as detailed below.
[0026] Because load power exhibits diurnal time-varying characteristics, it changes systematically throughout the day. Therefore, the length of a day is used as a time period to assess the uncertainties in power generation and consumption within the distribution network. Since wind power generation relies on wind energy, changes in wind speed and direction directly affect power generation. During certain periods of the day, local weather conditions, such as the passage of cold air or increased wind speed due to terrain, can cause sudden increases in wind speed. As wind speed increases, the output power of wind turbines rises rapidly, and when wind speed decreases, the output power decreases accordingly. This results in short-term peaks in wind power output, leading to an irregular multi-peak pattern in the daily output power data. The steepness and magnitude of these peaks reflect the uncertainty of wind power output.
[0027] Therefore, this embodiment uses an adaptive multi-scale peak detection algorithm to obtain the peak value of the power generation data in the current time period, the difference between all adjacent peak points, and the kurtosis corresponding to each peak. It should be noted that the peak detection and peak kurtosis calculation processes are existing known technologies and will not be described in detail in this embodiment.
[0028] Furthermore, in this embodiment, the random fluctuation coefficient of power generation will be obtained, and the specific calculation formula is as follows: In the formula, A is the random fluctuation coefficient of power generation, B is the sum of the differences between the amplitudes of all two adjacent peaks of power generation in the current time period, which reflects the magnitude of power generation change; C is the sum of the kurtosis of all peaks of power generation in the current time period, which reflects the speed of fluctuation of peak power generation; exp() represents an exponential function with the natural constant as the base, C may be negative, and thus an exponential function is used for mapping. To avoid a constant with a denominator of zero, this embodiment takes a value of 1. The larger the obtained A, the more significant the random variation of the irregular multi-peak pattern of the power generation data during a day's change, and the stronger the uncertainty of the power generation efficiency.
[0029] Furthermore, under the combined influence of various factors such as environment, climate, and season, power generation data exhibits complex non-stationary and nonlinear characteristics, and also displays a mixture of high-frequency fluctuations and low-frequency trends, thus increasing the difficulty of tracking power generation status. Therefore, in this embodiment, an empirical mode decomposition algorithm is used to decompose the power generation data within the current time period. In this embodiment, the number of mode components is set to 6, and the output consists of mode components corresponding to different frequency characteristics. It should be noted that there are many methods for mode decomposition, and implementers can choose according to their own needs. The specific mode decomposition process is existing technology and will not be described in detail in this embodiment.
[0030] Different modal components reflect the changing patterns and characteristics of power generation data at different time scales. For example, high-frequency modal components mainly reflect short-term random changes in power generation, such as the short-term impact of sudden changes in weather and environment on power generation; low-frequency modal components, on the other hand, reflect the long-term trend of power generation, such as the overall power generation status under the current climate conditions. When tracking power generation, directly predicting the raw power generation data is often difficult to accurately capture all the changing patterns and characteristic information in the data due to complexity and the intertwined influence of multiple factors. Furthermore, in this embodiment, the PSO-LSSNM prediction model is used to predict each modal component separately, outputting a preset number of predicted values after the corresponding time period for each modal component, forming a predicted value sequence for each modal component. In this embodiment, the number of predictions is 10. Then, the predicted value sequences of all modal components are superimposed position by position to obtain the power generation prediction sequence, that is, the element value at the same position in all predicted value sequences is added together to obtain the element value at the same position in the power generation prediction sequence.
[0031] Accordingly, for load power data, the above calculation steps can be used to obtain the random fluctuation coefficient of the corresponding load power and the load power prediction sequence.
[0032] During the process of renewable energy consumption, when renewable energy generation in the distribution network is insufficient and the load is too high, an energy storage system is needed to supply power; conversely, when renewable energy generation is sufficient and the load is low, the energy storage system can be charged. Therefore, the balance between power generation and load in the distribution network affects the regulation state of the energy storage system. Predicted values considering the combined effects of multiple factors can better reflect the differences in power supply and consumption over a future period. Therefore, the DTW distance between the predicted power generation sequence and the predicted load power sequence is calculated. For ease of understanding and expression, in this embodiment, the DTW distance is used as the balance difference value between renewable energy generation and load consumption in the distribution network. The larger this value, the more unbalanced the power generation and consumption in the distribution network, and the greater the need for peak shaving and valley filling by the energy storage system.
[0033] Meanwhile, considering that the uncertainty of power generation and load power will also affect the efficiency of energy storage charging and discharging control, the mean of the random fluctuation coefficient of power generation and the random fluctuation coefficient of load power is denoted as E. This mean reflects the comprehensive random fluctuation characteristics of power generation and load in the distribution network.
[0034] Furthermore, based on the DTW distance between the predicted power generation sequence and the predicted load power sequence, and combined with the stochastic fluctuation coefficients of power generation and load power, the formula for the comprehensive impact coefficient of the energy storage system is obtained as follows: In the formula, F is the comprehensive influence coefficient of the energy storage system, D is the DTW distance between the predicted power generation sequence and the predicted load power sequence, and E is the mean of the random fluctuation coefficients of power generation and load power. The larger the obtained F, the greater the influence of changes in power generation and load power on the control of the energy storage system.
[0035] Step 3: Analyze the consistency of the charging and discharging power changes of each energy storage device in the distribution network to obtain the charging and discharging consistency coefficient of the hybrid energy storage. Based on the comprehensive influence coefficient of the energy storage system and the charging and discharging consistency coefficient, obtain the energy storage control sensitivity value.
[0036] Furthermore, the energy storage system of the distribution network is typically a hybrid energy storage system. In this embodiment, the energy storage devices used are energy storage batteries and supercapacitors for charging and discharging the distribution network. Due to the performance differences of different energy storage devices and the influence of remaining power, hybrid energy storage systems often experience simultaneous charging and discharging states, leading to unnecessary energy charging and discharging of the energy storage. For example, at one moment, the energy storage battery may be charging while the supercapacitor is discharging. This inconsistent charging and discharging characteristic of hybrid energy storage reduces the overall operating efficiency of the system and increases the difficulty of coordinating the power of various energy storage devices, thereby reducing control efficiency. Therefore, it is necessary to obtain the charging and discharging consistency characteristics of the hybrid energy storage system.
[0037] Taking energy storage batteries as an example, their charge / discharge power data is positive when charging and negative when discharging, and each energy storage device is constantly alternating between charging and discharging throughout the day. Based on these characteristics, the following processing is performed: polynomial fitting technology is used to fit the charge / discharge power data of each energy storage device, with the horizontal axis representing time and the vertical axis representing power, resulting in corresponding charge / discharge power curves. If the hybrid energy storage system is in a state of alternating charging and discharging, the smaller the area of intersection between the two charge / discharge power curves and the time axis, the greater the impact on energy storage regulation efficiency.
[0038] Thus, the charge / discharge power curves corresponding to each energy storage device can be obtained, and the area enclosed by each charge / discharge power curve and the time axis can be calculated, as well as the intersection area between all enclosed areas. Preferably, in this embodiment, energy storage batteries and supercapacitors are used to charge and discharge the power distribution network. Therefore, the areas enclosed by the charge / discharge power curves of the energy storage batteries and supercapacitors and the time axis are obtained by calculating the integral, and recorded as the area of each area, as well as the intersection area between two areas.
[0039] Furthermore, the ratio between the intersecting area and the area of each region is calculated separately, and the average value of all ratios is taken as the charging and discharging consistency coefficient of the hybrid energy storage, denoted as H. The charging and discharging consistency coefficient is used to reflect the degree of consistency of the energy storage system in the distribution network during the charging and discharging process.
[0040] Finally, the more pronounced the random variations and balance differences in power generation and load in the distribution network, the worse the charging and discharging consistency of the hybrid energy storage system. Therefore, the response speed to system changes during energy storage control should be faster to efficiently maintain the stability of the distribution network system. Thus, in this embodiment, based on the comprehensive influence coefficient of the energy storage system and the charging and discharging consistency coefficient, the formula for calculating the energy storage control sensitivity value is as follows: In the formula, L is the energy storage control sensitivity value, F is the comprehensive influence coefficient of the energy storage system, and H is the charging and discharging consistency coefficient of the hybrid energy storage. The larger the obtained L, the more susceptible the energy storage control is to the random changes in power generation and load power, and also to the interference of inconsistent charging and discharging states of energy storage devices.
[0041] Step 4: Adjust the droop coefficient of the droop control technology according to the energy storage control sensitivity value, and combine the droop control technology to control the energy storage of the distribution network.
[0042] In this embodiment, by deeply analyzing the random variation characteristics of power generation and load power in the distribution network and the balance difference between them, and further considering the charging and discharging consistency characteristics between hybrid energy storage devices, an energy storage control sensitivity value L is obtained. This energy storage control sensitivity value can effectively reflect the response speed to system changes during energy storage control. When controlling the energy storage system, this embodiment employs droop control technology and optimizes the initial droop parameters in the original droop control technology. A minimum and maximum value for the droop coefficient are preset. Generally, the control range of the droop coefficient is set between 1% and 5%, and the difference between the maximum and minimum values is recorded as the maximum adjustment range. It should be noted that the specific process of using droop control technology for energy storage control is a well-known prior art, and will not be described in detail in this embodiment.
[0043] The larger the energy storage control sensitivity value, the faster and more agile the distribution network system needs to adapt to system changes, and the larger the droop coefficient. Conversely, the smaller the energy storage control sensitivity value, the smaller the random changes in power generation and load and the smaller the balance difference. At the same time, the larger the charge and discharge consistency coefficient, the more consistent the charge and discharge state of the energy storage device, and the less interference to the system. The corresponding droop coefficient is smaller to avoid system oscillation.
[0044] Furthermore, in this embodiment, the initial droop coefficient is set to 1%. When calculating the droop coefficient adjustment, the obtained energy storage control sensitivity value L is first normalized using the sigmoid function, and the normalized result is calculated in relation to the maximum adjustment range. For ease of understanding and description, this product is denoted as the droop coefficient adjustment amount in this embodiment. This method ensures that the droop coefficient is always within the control range of [1%, 5%]. Finally, the sum of the initial droop coefficient and the droop coefficient adjustment amount is used as the adjusted droop coefficient. Based on the optimization results, droop control technology is used to control the energy storage system, thereby improving the system's stability and energy storage efficiency.
[0045] Based on the same inventive concept as the above method, this application embodiment also provides a distribution network energy storage control system that takes into account the consumption of new energy sources, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described distribution network energy storage control methods that take into account the consumption of new energy sources.
[0046] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0047] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0048] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.
Claims
1. A power distribution network energy storage control method considering new energy consumption, characterized in that, Includes the following steps: Acquire data on the power generation, load power, and charging / discharging power of new energy sources in the power distribution network; The random fluctuation coefficients of power generation and load power are obtained based on the variation amplitude of adjacent peak values in power generation and load power, as well as the fluctuation degree of each peak value. The comprehensive influence coefficient of the energy storage system is obtained by combining the degree of difference between the predicted data corresponding to power generation and the predicted data corresponding to load power. The degree of consistency in the charging and discharging power changes of each energy storage device in the distribution network is analyzed to obtain the charging and discharging consistency coefficient of the hybrid energy storage. Based on the comprehensive influence coefficient of the energy storage system and the charging and discharging consistency coefficient, the energy storage control sensitivity value is obtained. The droop coefficient of the droop control technology is adjusted based on the energy storage control sensitivity value, and the energy storage in the distribution network is controlled in combination with the droop control technology.
2. The distribution network energy storage control method considering renewable energy consumption as described in claim 1, characterized in that, The method for calculating the random fluctuation coefficients of the power generation and load power is as follows: The formula for calculating the random fluctuation coefficient A of power generation is: In the formula, B is the sum of the differences between the amplitudes of all two adjacent peak power generation values within the current time period, C is the sum of the kurtosis of all peak power generation values within the current time period, and exp() represents an exponential function with the natural constant as the base. To avoid constants with a denominator of zero; Accordingly, for the load power in the current time period, the random fluctuation coefficient of the load power is obtained.
3. The distribution network energy storage control method considering renewable energy consumption as described in claim 2, characterized in that, The peak value is obtained by extracting it using an adaptive multi-scale peak detection algorithm.
4. The distribution network energy storage control method considering renewable energy consumption as described in claim 1, characterized in that, The method for calculating the comprehensive impact coefficient of the energy storage system is as follows: In the formula, F is the comprehensive influence coefficient of the system under energy storage, D is the DTW distance between the predicted sequence of power generation and the predicted sequence of load power, and E is the mean of the random fluctuation coefficient of power generation and the random fluctuation coefficient of load power.
5. The distribution network energy storage control method considering renewable energy consumption as described in claim 4, characterized in that, The methods for obtaining the predicted power generation sequence and the predicted load power sequence are as follows: The power generation data within the current time period is decomposed into various modal components. Each modal component is predicted using a prediction algorithm to obtain a preset number of predicted values for each modal component after the corresponding time period. These predicted values form a sequence of predicted values for each modal component. The predicted value sequences of all modal components are then superimposed bit by bit to obtain the power generation prediction sequence. Accordingly, a load power prediction sequence is obtained for the load power of the current time period.
6. The distribution network energy storage control method considering renewable energy consumption as described in claim 1, characterized in that, The calculation method for the charge-discharge consistency coefficient of the hybrid energy storage is as follows: Obtain the charge and discharge power curves corresponding to each energy storage device, calculate the area of the region enclosed by each charge and discharge power curve and the time axis, and record it as the area of each region, as well as the intersection area between all the enclosed regions. The average value of the ratio of the intersection area to the area of each region is used as the charge and discharge consistency coefficient of the hybrid energy storage.
7. The distribution network energy storage control method considering renewable energy consumption as described in claim 6, characterized in that, The method for obtaining the charge and discharge power curves corresponding to each energy storage device is as follows: the charge and discharge power data of each energy storage device are fitted, with the horizontal axis being the time axis and the vertical axis being the power axis, to obtain the charge and discharge power curves corresponding to each energy storage device.
8. The distribution network energy storage control method considering renewable energy consumption as described in claim 1, characterized in that, The method for calculating the energy storage control sensitivity value is as follows: In the formula, L is the energy storage control sensitivity value, F is the comprehensive influence coefficient of the energy storage system, and H is the charging and discharging consistency coefficient of the hybrid energy storage.
9. The distribution network energy storage control method considering renewable energy consumption as described in claim 1, characterized in that, The specific method for adjusting the sag coefficient of the sag control technology is as follows: The minimum and maximum values of the droop coefficient are preset, and the difference between the maximum and minimum values of the droop coefficient is used as the maximum adjustment range. The product of the normalized result of the energy storage control sensitivity value and the maximum adjustment range is calculated, and the sum of the preset initial droop coefficient and the product is used as the adjusted droop coefficient.
10. A distribution network energy storage control system considering renewable energy consumption, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the distribution network energy storage control method that takes into account the consumption of new energy sources as described in any one of claims 1-9.