Accurate response method of virtual power plant considering bilateral uncertainty of source and load

Abstract

The application relates to the technical field of virtual power plant operation control, in particular to a virtual power plant accurate response method considering source and load bilateral uncertainty, which comprises the following steps: determining a supply-demand balance degree based on the discrete degree of power mismatch degree of all response intervals and the difference between power mismatch degree of any response interval and the next adjacent response interval; determining a load schedulable index based on the similarity between the power load and the predicted value of the power load at all time points in each response interval and the discrete degree of the predicted value of the power load at all time points, and combining the supply-demand balance degree to determine the response strength of the virtual power plant in each response interval; and judging whether the dispatching plan of the virtual power plant needs to be adjusted based on the response strength. The application improves the reliability of the virtual power plant in response to load fluctuation by considering the uncertainty between the power generation side and the load side.

Description

Technical Field

[0001] This application relates to the field of virtual power plant operation and control technology, specifically to a precise response method for virtual power plants that takes into account uncertainties on both the source and load sides. Background Technology

[0002] A virtual power plant is a virtual entity that aggregates distributed power sources (such as photovoltaic and wind power), energy storage, and controllable loads through digital technology to form a unified dispatchable system, achieving coordinated optimization of power generation, grid, load, and storage. The uncertainty on both the power source and load sides refers to the fluctuations and uncertainties in renewable energy output on the power source side and the random fluctuations in demand response on the load side. With the widespread application of renewable energy, precise response methods for virtual power plants that take into account these uncertainties can reduce the supply-demand imbalance in the power system, effectively promote the consumption of renewable energy, and ensure the reliable operation of virtual power plants.

[0003] The core function of a virtual power plant is to coordinate the dispatch of distributed energy resources and load resources. By aggregating multiple energy resources, it enhances the flexibility and reliability of the power system. However, the operation and dispatch of traditional virtual power plants fail to consider the uncertainties between the generation side and the load side. From the perspective of the single-sided evaluation of the virtual power plant, there is a lack of spatiotemporal correlation analysis between the output of new energy power and the load demand, which exacerbates the risk of power mismatch between the supply and demand sides. At the same time, due to the uncertainty of both the source and load sides of the virtual power plant, it is difficult to accurately assess the response potential strength between the output of new energy power and the user load demand. This makes it impossible to accurately assess the response reliability and flexibility of the virtual power plant on both the source and load sides, which may cause the operation and response dispatch of the virtual power plant to fall into a vicious cycle and affect the reliable operation of the virtual power plant. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a precise response method for virtual power plants that takes into account uncertainties on both the source and load sides, thereby resolving the existing issues.

[0005] The virtual power plant precise response method in this application, which takes into account the uncertainties on both the source and load sides, adopts the following technical solution:

[0006] One embodiment of this application provides a precise response method for a virtual power plant that takes into account uncertainties on both the source and load sides. The method includes the following steps:

[0007] Real-time acquisition of load on the load side and active power of each new energy power station during the operation of the virtual power plant;

[0008] Within each preset response interval, the power fluctuation of the virtual power plant in each response interval is determined by analyzing the dispersion of the differences between all peaks and adjacent troughs in the active power of each renewable energy power plant at all times. Combined with the degree of disorder in the sudden changes in electricity load data at all times within each response interval, the supply and demand fluctuation of the virtual power plant in each response interval is determined. Based on the correlation coefficient between the active power and electricity load of each renewable energy power plant at all times within each response interval and the differences in the trend of sudden changes in data, the supply and demand asymmetry of the virtual power plant in each response interval is determined. Combined with the supply and demand fluctuation, the power mismatch of the virtual power plant in each response interval is determined.

[0009] Within a preset time period before each response interval, the supply and demand balance of the virtual power plant under each response interval is determined based on the dispersion of the power mismatch degree of all response intervals and the difference in the power mismatch degree between any response interval and its adjacent subsequent response interval. Based on the changing trend of electricity load at all times within a preset time period before each time, the electricity load at each time is predicted. Based on the similarity between the electricity load at all times within each response interval and its predicted value, as well as the dispersion of the predicted value of the electricity load at all times, the load dispatchability index of the virtual power plant under each response interval is determined. Combined with the supply and demand balance degree, the response intensity of the virtual power plant under each response interval is determined.

[0010] Based on the response strength, it is determined whether the scheduling plan for the virtual power plant needs to be adjusted.

[0011] Preferably, the method for determining the power fluctuation of the virtual power plant in each response interval is as follows:

[0012] The degree of dispersion of the difference between all adjacent peaks and troughs in the active power of each new energy power station at all times within each response interval is denoted as the power dispersion of each new energy power station within each response interval.

[0013] The mean of the power dispersion of all new energy power plants in the virtual power plant within each response interval is taken as the power fluctuation of the virtual power plant in each response interval.

[0014] Preferably, the supply and demand fluctuation of the virtual power plant in each response interval is the result of the positive fusion of the information entropy and power fluctuation of all sudden data in the electricity load at all times on the load side within each response interval.

[0015] Preferably, the expression for the supply-demand asymmetry of the virtual power plant in each response interval is: In the formula, This represents the degree of supply-demand asymmetry of the virtual power plant in the i-th response interval; , represents the correlation coefficient between active power and electricity load of the j-th renewable energy power station at all times within the i-th response interval, and the difference in the trend of abrupt change data, respectively; J represents the number of all renewable energy power stations in the virtual power plant; exp() represents the exponential function with the natural constant as the base.

[0016] Preferably, the power mismatch degree of the virtual power plant in each response interval is the normalized value of the positive fusion result of the supply and demand fluctuation degree and the supply and demand asymmetry degree of the virtual power plant in each response interval.

[0017] Preferably, the expression for the supply-demand balance of the virtual power plant in each response interval is: In the formula, This represents the supply and demand balance of the virtual power plant in the i-th response interval; This indicates the degree of dispersion of power mismatch among all response intervals within a preset time period prior to the i-th response interval; This represents the difference in power mismatch between the k-th response interval and its adjacent next response interval within a preset time period preceding the i-th response interval. This represents the number of all response intervals within the preset time period before the i-th response interval; exp() represents an exponential function with the natural constant as the base. This indicates a constant that is pre-defined as being greater than 0.

[0018] Preferably, the prediction of electricity load at each time point includes:

[0019] The load on the load side at all times within a preset time period before each time point is used as the input to the smoothing algorithm, and the output smoothed value is used as the predicted value of the load at each time point.

[0020] Preferably, the load dispatchability index of the virtual power plant in each response interval is the result of the similarity between the power load at all times in each response interval and its predicted value, compared with the dispersion of the predicted power load at all times.

[0021] Preferably, the response strength of the virtual power plant in each response interval is the normalized value of the product of the supply and demand balance of the virtual power plant in each response interval and the load dispatchability index.

[0022] Preferably, the determination of whether the scheduling plan of the virtual power plant needs to be adjusted includes:

[0023] If the response intensity of the virtual power plant in the current response interval is greater than the preset response threshold, then there is no need to adjust the scheduling plan of the virtual power plant; otherwise, the scheduling plan of the virtual power plant needs to be adjusted.

[0024] This application has at least the following beneficial effects:

[0025] This application constructs a comprehensive power mismatch assessment index by quantitatively analyzing the uncertainty of the volatility and form matching degree between renewable energy output and load demand. This index accurately characterizes the supply and demand risks within the virtual power plant, enabling the dispatching system to predict unstable periods and formulate optimal control strategies in advance. This effectively avoids supply and demand imbalances caused by blind dispatching, thereby contributing to the reliability of virtual power plant operation and the safety and stability of the power grid. Furthermore, this application constructs a response strength by introducing supply and demand balance degree and load dispatchability index, accurately quantifying the response potential and capability of the virtual power plant. This method not only assesses the stability of historical operating states but also deeply analyzes the response under the current dispatching strategy. The predictability and recoverability of the load effectively overcome the dilemma of "the more you adjust, the more off-target" caused by the failure of traditional methods to consider the uncertainties between the generation side and the load side. It realizes the scientific prediction of the response potential of virtual power plants and provides strong support for the formulation of more accurate, reliable and flexible dispatch strategies. Furthermore, this application quantifies the response potential of virtual power plants based on response intensity, realizing precise trigger-based adjustment of dispatch plans. When the risk is high, priority is given to calling up power plants with strong response capabilities and coordinating with energy storage systems for regulation. This hierarchical dispatch strategy based on response intensity not only maximizes the utilization efficiency of new energy sources, but also improves the reliability of virtual power plants in dealing with load fluctuations. Attached Figure Description

[0026] 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.

[0027] Figure 1 A flowchart illustrating the steps of a virtual power plant precise response method considering both source and load uncertainties provided in one embodiment of this application;

[0028] Figure 2 This is a schematic diagram of the response intensity extraction process provided in one embodiment of this application. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive objective, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the virtual power plant precise response method considering both source and load uncertainties 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.

[0030] Unless otherwise defined, 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.

[0031] The following, in conjunction with the accompanying drawings, details the specific scheme of the virtual power plant precise response method for considering both source and load uncertainties provided in this application.

[0032] This application provides an embodiment of a precise response method for a virtual power plant that considers uncertainties on both the source and load sides. Specifically, the method is described below. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0033] Step S1: Real-time acquisition of the load on the load side and the active power of each new energy power station during the operation of the virtual power plant.

[0034] In this embodiment, intelligent terminals are deployed on each distributed renewable energy power station and the load side in collaboration with the virtual power plant. The intelligent terminal of each distributed renewable energy power station can be built using a Supervisory Control and Data Acquisition (SCADA) system, while the intelligent terminal on the load side can be built using smart meters. The intelligent terminals acquire the electricity load on the load side and the active power of each renewable energy power station in real time during the operation of the virtual power plant. The active power and electricity load are continuously acquired at a consistent acquisition frequency, and a time synchronization triggering mechanism is established, using Network Time Protocol (NTP) for time synchronization to ensure that the data from both the source and load sides of the virtual power plant are acquired at the same time segment.

[0035] To prevent data loss and the impact of different data units on subsequent analysis during the transmission of active power data and load data from various distributed renewable energy power plants, this embodiment uses the acquired active power and load data as input for data interpolation, performs missing value filling, and then normalizes the data after missing value filling. In this embodiment, z-score normalization is used for data normalization. In practical applications, as other implementation methods, implementers may also use other normalization methods such as maximum-minimum normalization method according to specific circumstances. This embodiment does not impose special restrictions on the selection of normalization methods.

[0036] It should be noted that the data interpolation method used in this embodiment is linear interpolation. In practical applications, as other implementation methods, implementers may also use other interpolation methods such as median interpolation or mean interpolation depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of interpolation methods.

[0037] Among them, linear interpolation and z-score normalization are well-known techniques, and the specific process of using them to fill in and normalize data will not be elaborated here.

[0038] Step S2: Within each preset response interval, by analyzing the dispersion of the differences between all peaks and adjacent troughs in the active power of each renewable energy power plant at all times, the power fluctuation of the virtual power plant in each response interval is determined. Combined with the disorder of the sudden change data in the electricity load at all times within each response interval, the supply and demand fluctuation of the virtual power plant in each response interval is determined. Based on the correlation coefficient between the active power and electricity load of each renewable energy power plant at all times within each response interval and the overall difference in the trend of sudden change data, the supply and demand asymmetry of the virtual power plant in each response interval is determined. Combined with the supply and demand fluctuation, the power mismatch of the virtual power plant in each response interval is determined.

[0039] During the operation and dispatch of virtual power plants, the output of renewable energy is significantly uncertain due to environmental factors, while user load demand is also highly uncertain due to the influence of electricity consumption time and power. Traditional virtual power plant operation and dispatch fail to take into account the spatiotemporal uncertainties between the generation side and the load side, which may lead to renewable energy processing being unable to meet the load demand of the electricity consumption side or incomplete renewable energy consumption, making it difficult to conduct timely response and dispatch of virtual power plants, exacerbating the risk of power mismatch between supply and demand, and in severe cases, affecting the operational safety of the power grid.

[0040] Specifically, during the virtual power plant operation response scheduling process, when the risk of power mismatch between the supply and demand sides is higher, the randomness of the active power output fluctuation amplitude caused by the uncertainty of the generation side of each new energy power plant is higher, the intensity of the periodicity of the load change on the load side is lower, and at the same time, the risk of power adaptability between the power plant supply and demand sides is higher, the reverse peak shaving situation of the load side is more serious, and the asymmetric coupling effect between new energy power and load demand is intensified, that is, the matching degree between new energy output and load demand is reduced, and the power output of each new energy power plant shows a relatively obvious negative correlation.

[0041] Based on the above analysis, this embodiment determines the power fluctuation of the virtual power plant in each response interval by analyzing the dispersion of the differences between all peaks and adjacent troughs in the active power of each renewable energy power plant at all times. Combined with the degree of disorder in the sudden changes in electricity load data at all times within each response interval, the supply and demand fluctuation of the virtual power plant in each response interval is determined. Based on the correlation coefficient between the active power and electricity load of each renewable energy power plant at all times within each response interval and the differences in sudden changes in data, the supply and demand asymmetry of the virtual power plant in each response interval is determined. Combined with the supply and demand fluctuation, the power mismatch of the virtual power plant in each response interval is determined. This is used to characterize the supply and demand fluctuation caused by uncertainties on both the source and load sides during the virtual power plant response scheduling process, as well as the intensified asymmetric coupling between renewable energy power and load demand. The specific process is as follows:

[0042] In this embodiment, the time period of length T is first used as a response interval. In this embodiment, T is 1 hour. In actual application, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0043] Furthermore, this embodiment determines the power fluctuation of the virtual power plant in each response interval by analyzing the dispersion of the difference between all adjacent peaks and troughs in the active power at all times of each new energy power station. Specifically:

[0044] In this embodiment, the active power of each renewable energy power station at all times within each response interval is used as the input of the Automatic Multiscale-based Peak Detection (AMPD) algorithm, and the output is all peaks and troughs in the active power of each renewable energy power station at all times within each response interval. The dispersion of the difference between all peaks and their adjacent troughs in the active power of each renewable energy power station at all times is recorded as the power dispersion of each renewable energy power station within each response interval. The mean of the power dispersion of all renewable energy power stations in the virtual power plant within each response interval is used as the power volatility of the virtual power plant within each response interval. This is used to characterize the overall volatility and randomness of the active power of all renewable energy power stations in the virtual power plant within each response interval. The greater the power volatility, the more drastic the output fluctuation of the renewable energy power station is, indicating that the generation side is extremely unstable and is prone to impacting the power grid and load side and causing power loss.

[0045] It should be noted that there are many methods to measure the dispersion of a set of data. In this embodiment, the coefficient of variation of the difference between all peaks and their adjacent troughs in the active power of each new energy power station at all times is used as the dispersion of the difference between all peaks and their adjacent troughs in the active power of each new energy power station at all times. In practical applications, as other implementation methods, implementers may also use other methods such as variance or standard deviation to measure the dispersion of data, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods to measure the dispersion of data.

[0046] The automatic multi-scale peak finding algorithm and the method for calculating the coefficient of variation are both well-known technologies. The specific process of obtaining peaks and troughs in active power using the automatic multi-scale peak finding algorithm and the calculation process of the coefficient of variation will not be elaborated here.

[0047] Furthermore, this embodiment is based on the power fluctuation of the virtual power plant in each response interval, and combined with the degree of disorder of sudden changes in the electricity load data at all times within each response interval, specifically as follows:

[0048] In this embodiment, the information entropy of all sudden changes in the power load at all times within each response interval is positively fused with the power fluctuation, and the result is used as the supply and demand fluctuation of the virtual power plant in each response interval.

[0049] The method for calculating information entropy is a well-known technique, and its specific calculation process will not be elaborated here.

[0050] It should be understood that positive fusion refers to combining two or more indicators through addition or multiplication to obtain a comprehensive indicator, thereby more comprehensively and accurately assessing a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analytical methods. Implementers can choose according to specific circumstances, and this embodiment does not impose any special restrictions.

[0051] Preferably, as a specific implementation method, in this embodiment, the product of the information entropy of all sudden data in the power load at all times on the load side within each response interval and the power fluctuation is used as the supply and demand fluctuation of the virtual power plant in each response interval.

[0052] It should be noted that the method for obtaining the mutation data is detailed in the specific calculation process of the difference in mutation data change trends below, and will not be repeated here.

[0053] Based on the supply and demand volatility of the virtual power plant in each response interval, it can be understood that the supply and demand volatility reflects the randomness and disorder of supply and demand fluctuations under the combined effect of the generation side and the load side during the operation of the virtual power plant. If the power volatility of the virtual power plant is greater in the current response interval, it indicates that the volatility of the generation side is very strong, which directly leads to an increase in the randomness and disorder of the supply and demand relationship of the entire virtual power plant. Therefore, the corresponding supply and demand volatility is greater. At the same time, if the information entropy of all sudden changes in the electricity load data at all times in the current response interval is greater, it indicates that the changes on the load side are more random and irregular, which also exacerbates the disorder of the supply and demand relationship of the virtual power plant. Therefore, the corresponding supply and demand volatility is greater. This means that the supply and demand relationship in the virtual power plant is extremely unstable, difficult to predict and balance, and thus prone to power mismatch, causing shocks to the power grid.

[0054] Conversely, if the power fluctuation of the virtual power plant is smaller in the current response interval, it indicates that the volatility on the generation side is very weak, which directly reduces the randomness and disorder of the supply and demand relationship of the entire virtual power plant. Therefore, the corresponding supply and demand fluctuation is smaller. At the same time, if the information entropy of all sudden changes in the electricity load data at all times in the current response interval is smaller, it indicates that the changes on the load side are more regular and orderly, which also alleviates the disorder of the supply and demand relationship of the virtual power plant. Therefore, the corresponding supply and demand fluctuation is smaller, and the risk to the balance of supply and demand relationship of the virtual power plant is also reduced.

[0055] Furthermore, this embodiment determines the supply-demand asymmetry of the virtual power plant in each response interval based on the correlation coefficient between active power and electricity load at all times within each response interval, as well as the overall difference in abrupt change data. Specifically:

[0056] As one implementation method, in this embodiment, the supply-demand asymmetry of the virtual power plant in the i-th response interval is... The expression is: In the formula, , represents the correlation coefficient between active power and electricity load of the j-th renewable energy power station at all times within the i-th response interval, and the difference in the trend of abrupt change data, respectively; J represents the number of all renewable energy power stations in the virtual power plant; exp() represents the exponential function with the natural constant as the base.

[0057] It should be noted that, as a specific implementation method, the specific process for calculating the difference in the trend of mutation data in this embodiment is as follows:

[0058] The active power of each new energy power station at all times and the electricity load at all times on the load side within each response interval are used as inputs to the mutation point detection algorithm. The output is all mutation points of active power and all mutation points of electricity load. The mutation point detection algorithm in this embodiment adopts the Bayesian mutation point detection algorithm. In actual application, implementers may also adopt other methods such as the Pettt mutation point detection algorithm according to specific circumstances. This embodiment does not impose any special restrictions.

[0059] Furthermore, for each new energy power plant in each response interval, all abrupt changes in active power and all abrupt changes in electrical load at all times are fitted. In this embodiment, the least squares method is used for fitting, and the corresponding power fitting curve and load fitting curve are obtained respectively. The slopes at all fitting points on the power fitting curve and the load fitting curve are calculated and recorded as the power slope and the load slope respectively. The DTW distance between all power slopes and all load slopes is taken as the difference in the trend of abrupt change data between active power and electrical load at all times in each response interval and between each new energy power plant in each response interval.

[0060] Among them, the Bayesian mutation point detection algorithm, the least squares fitting method, and the DTW distance calculation method are all well-known technologies. The process of obtaining mutation points in the data using the Bayesian mutation point detection algorithm, the process of fitting the data using the least squares method, and the specific calculation process of the DTW distance will not be described in detail.

[0061] Furthermore, it should be understood that there are many methods for calculating the correlation coefficient between data groups. In this embodiment, the Pearson correlation coefficient between the active power and the electricity load of the j-th renewable energy power station at all times within the i-th response interval is used as the correlation coefficient between the active power and the electricity load of the j-th renewable energy power station at all times within the i-th response interval. In practical applications, implementers may also use other correlation coefficient calculation methods such as Kendall's rank correlation coefficient or Spearman's correlation coefficient, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of correlation coefficient calculation methods.

[0062] The calculation method for the Pearson correlation coefficient is a well-known technique, and its specific calculation process will not be elaborated here.

[0063] Based on the supply-demand asymmetry of virtual power plants in each response interval, it can be understood that supply-demand volatility reflects the matching degree between the output of renewable energy power plants and load demand, as well as the synergy among various renewable energy power plants. It is a key indicator for assessing the risk of power mismatch. If the correlation coefficient between the active power and electricity load of the j-th renewable energy power plant at all times in the i-th response interval is smaller, and the difference in the trend of abrupt changes in the data is greater, it indicates that the load change and the change in renewable energy output are not in sync, resulting in a poor matching degree. Therefore, the corresponding supply-demand volatility is greater. Conversely, if the correlation coefficient between the active power and electricity load of the j-th renewable energy power plant at all times in the i-th response interval is larger, and the difference in the trend of abrupt changes in the data is smaller, it indicates that the load change and the change in renewable energy output are in sync, resulting in a good matching degree. Therefore, the corresponding supply-demand volatility is smaller.

[0064] Furthermore, this embodiment determines the power mismatch degree of the virtual power plant within each response interval based on the supply-demand asymmetry of the virtual power plant in each response interval, and in conjunction with the supply-demand fluctuation degree, specifically as follows:

[0065] In this embodiment, the normalized value of the positive fusion result of the supply and demand fluctuation and supply and demand asymmetry of the virtual power plant in each response interval is used as the power mismatch degree of the virtual power plant in each response interval.

[0066] Preferably, as a specific implementation method, in this embodiment, the normalized value of the product of the supply and demand fluctuation degree and the supply and demand asymmetry degree of the virtual power plant in each response interval is used as the power mismatch degree of the virtual power plant in each response interval. In actual application, as other implementation methods, implementers may also adopt addition or other positive fusion methods according to specific circumstances. This embodiment does not impose any special restrictions.

[0067] Based on the power mismatch degree of the virtual power plant within each response interval, it can be understood that the power mismatch degree is used to measure the supply and demand mismatch risk caused by the uncertainty of both the source and load sides of the virtual power plant in each response interval. If the supply and demand volatility of the virtual power plant in the current response interval is greater, it indicates that the overall randomness and disorder of the generation side and the load side in the virtual power plant is very high, indicating that the virtual power plant itself is in a very unstable state, and the risk of power mismatch will inevitably increase. Therefore, the corresponding power matching degree is greater. At the same time, if the supply and demand asymmetry of the virtual power plant in the current response interval is greater, it indicates that the output of the new energy power station in the virtual power plant is seriously mismatched with the load demand, and the lack of coordination between the new energy power stations will also lead to serious power mismatch. Therefore, the corresponding power matching degree is greater.

[0068] Conversely, if the supply and demand fluctuation of the virtual power plant within the current response interval is smaller, it indicates that the overall randomness and disorder of the generation and load sides in the virtual power plant are very low, indicating that the virtual power plant itself is in a very stable state, and the risk of power mismatch will inevitably be reduced accordingly. Therefore, the corresponding power mismatch degree is smaller. At the same time, if the supply and demand asymmetry of the virtual power plant within the current response interval is smaller, it indicates that the output of the new energy power plants in the virtual power plant is well matched with the load demand, and the coordination between the new energy power plants is strong, which will also effectively suppress power mismatch. Therefore, the corresponding power mismatch degree is smaller.

[0069] Thus, this embodiment constructs a comprehensive power mismatch assessment index by quantitatively analyzing the uncertainty of the volatility and form matching degree between new energy output and load demand. This index accurately characterizes the supply and demand risks within the virtual power plant, enabling the dispatching system to predict unstable intervals and formulate optimal control strategies in advance. This effectively avoids supply and demand imbalances caused by blind dispatching, thereby contributing to the reliability of virtual power plant operation and the safety and stability of the power grid.

[0070] Step S3: Within a preset time period before each response interval, based on the dispersion of power mismatch in all response intervals and the difference in power mismatch between any response interval and its adjacent next response interval, determine the supply-demand balance of the virtual power plant in each response interval; based on the changing trend of electricity load at all times within a preset time period before each time, predict the electricity load at each time; based on the similarity between the electricity load at all times within each response interval and its predicted value, and the dispersion of the predicted value of the electricity load at all times, determine the load dispatchability index of the virtual power plant in each response interval; and combine the supply-demand balance to determine the response strength of the virtual power plant in each response interval.

[0071] In the process of virtual power plant operation response scheduling, relying solely on the assessment of the uncertainty on both the source and load sides of the virtual power plant based on the power mismatch risk status, and adjusting the precise response of the virtual power plant accordingly, still has certain drawbacks: namely, the lack of analysis on the response potential of renewable energy output on both the source and load sides to the electricity load makes it impossible to accurately assess the response reliability and flexibility of the virtual power plant, which may lead the operation response scheduling of the virtual power plant into a vicious cycle of "the more it is adjusted, the more it deviates", affecting the reliable operation of the virtual power plant.

[0072] Specifically, during the operation and response scheduling of virtual power plants, the greater the potential of the renewable energy output on both the source and load sides of the virtual power plant to respond to the electricity load, the more obvious the downward trend of the historical power mismatch risk of the virtual power plant becomes on a more stable basis. At the same time, the higher response potential of renewable energy output on the load side of the virtual power plant makes the peak shaving and valley filling response capability of the electricity load data more significant, that is, the more predictable the electricity load data is, and the more recoverable the response prediction of the electricity load is.

[0073] Based on the above analysis, this embodiment determines the supply-demand balance of the virtual power plant in each response interval based on the dispersion of power mismatch in all response intervals and the difference in power mismatch between any response interval and its adjacent subsequent response interval. Based on the changing trend of electricity load at all times within a preset time period prior to each time point, the electricity load at each time point is predicted. Based on the similarity between the electricity load at all times within each response interval and its predicted value, as well as the dispersion of the predicted value of the electricity load at all times, the load dispatchability index of the virtual power plant in each response interval is determined. Combined with the supply-demand balance, the response strength of the virtual power plant in each response interval is determined. This strength is used to characterize the decreasing trend of power mismatch risk on the supply and demand side and the recovery status of predicted electricity load response in any response interval during the virtual power plant's operation and response scheduling process. The specific process is as follows:

[0074] First, this embodiment determines the supply-demand balance of the virtual power plant in each response interval based on the dispersion of power mismatch across all response intervals and the difference in power mismatch between any response interval and its adjacent next response interval. Specifically:

[0075] In this embodiment, the supply and demand balance of the virtual power plant in the i-th response interval The expression is: In the formula, This indicates the degree of dispersion of power mismatch among all response intervals within a preset time period prior to the i-th response interval; This represents the difference in power mismatch between the k-th response interval and its adjacent next response interval within a preset time period preceding the i-th response interval. This represents the number of all response intervals within the preset time period before the i-th response interval; exp() represents an exponential function with the natural constant as the base. This indicates a preset constant greater than 0, used to prevent the denominator from being 0. The value is set manually, in this embodiment. The value of is 0.01. Provided that the denominator is not zero and does not excessively affect the calculation result, the implementer may also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0076] Specifically, if the i-th response interval contains half a response interval within the preset time period, it will not be considered.

[0077] It should be noted that there are many methods to measure the degree of data dispersion. In this embodiment, the standard deviation of the power mismatch of all response intervals within a preset time period before the i-th response interval is taken as the degree of dispersion of the power mismatch of all response intervals within a preset time period before the i-th response interval. In practical applications, as other implementation methods, implementers may also use other methods such as variance or coefficient of variation to measure the degree of data dispersion in combination with specific circumstances. This embodiment does not impose any special restrictions on the selection of methods for measuring the degree of data dispersion.

[0078] It should be understood that there are many methods to measure the difference between data. In this embodiment, the difference between the power mismatch between the k-th response interval and its adjacent next response interval within a preset time period before the i-th response interval is taken as the difference between the power mismatch between the k-th response interval and its adjacent next response interval within a preset time period before the i-th response interval. In practical applications, implementers may also use other methods to measure the difference between data, such as the square or ratio of the difference, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods to measure the difference between data.

[0079] Based on the supply-demand balance of the virtual power plant under each response interval, it can be understood that the supply-demand balance is used to evaluate the stability and regularity of the supply and demand on both the source and load sides during the historical operation of the virtual power plant before each response interval. If the dispersion of the power mismatch degree of all response intervals within the preset time before the i-th response interval is smaller, it indicates that the power mismatch risk of the virtual power plant has been highly volatile in the past, making it difficult to assess the degree of power mismatch. Therefore, the corresponding supply-demand balance degree is smaller. At the same time, if the difference in power mismatch degree between the k-th response interval and its adjacent next response interval within the preset time before the i-th response interval is smaller, it indicates that the power mismatch risk of the virtual power plant has been slowly improved in the past, and the response potential of the virtual power plant has not been effectively released, possibly falling into a dispatch bottleneck. Therefore, the corresponding supply-demand balance degree is relatively small.

[0080] Conversely, if the dispersion of power mismatch among all response intervals within the preset time period before the i-th response interval is smaller, it indicates that the virtual power plant has historically exhibited very low volatility in power mismatch risk, and the degree of power mismatch is stable and predictable. Therefore, the corresponding supply-demand balance is greater. At the same time, if the difference in power mismatch between the k-th response interval and its adjacent subsequent response interval within the preset time period before the i-th response interval is greater, it indicates that the virtual power plant's historical power mismatch risk is continuously and significantly decreasing. This suggests that the control strategy is effective, the response potential of the virtual power plant has been fully released, and the dispatch strategy is flexible and effective. Therefore, the corresponding supply-demand balance is relatively large.

[0081] Furthermore, this embodiment predicts the electricity load at each time point based on the changing trend of electricity load at all times within a preset time period prior to each time point. Based on the similarity between the electricity load at all times within each response interval and its predicted value, as well as the dispersion of the predicted values ​​of the electricity load at all times, the load dispatchability index of the virtual power plant in each response interval is determined, specifically as follows:

[0082] In one implementation method, in this embodiment, the power load on the load side at all times within a preset time period before each time is used as the input of the smoothing algorithm, and the output smoothed value is used as the predicted value of the power load at each time.

[0083] It should be noted that there are many commonly used smoothing algorithms. In this embodiment, the exponential moving average (EMA) algorithm is used to obtain the smoothed value. The size of the smoothing window is set to 1 minute. In actual applications, implementers may also use other smoothing algorithms according to specific circumstances, and may also set the smoothing window size according to specific circumstances. This embodiment does not impose any special restrictions.

[0084] The exponential moving average algorithm is a well-known technique, and the specific process of using it to analyze and predict data trends will not be elaborated here.

[0085] Furthermore, in this embodiment, the similarity between the electricity load at all times within each response interval and its predicted value is compared with the dispersion of the predicted electricity load at all times, and the result is used as the load dispatchability index of the virtual power plant in each response interval.

[0086] It should be noted that there are many methods to measure the similarity between data groups. In this embodiment, the cosine similarity between the power load and its predicted value at all times within each response interval is used as the similarity between the power load and its predicted value at all times within each response interval. In practical applications, as other implementation methods, implementers may also use other methods such as the reciprocal of the Euclidean distance to measure the similarity between data groups, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods for measuring the similarity between data.

[0087] Furthermore, it should be understood that in this embodiment, the variance of the predicted electricity load at all times is taken as the degree of dispersion of the predicted electricity load at all times. In actual application, implementers may also use other methods such as standard deviation or coefficient of variation to measure the degree of data dispersion, depending on the specific circumstances. This embodiment does not impose any special restrictions.

[0088] The method for calculating cosine similarity is a well-known technique, and its specific calculation process will not be elaborated here.

[0089] Based on the load dispatchability index of the virtual power plant in each response interval, it can be understood that the load dispatchability index is used to characterize the controllability and dispatchability of the electricity load within the response interval. If the similarity between the electricity load at all times in the current response interval and its predicted value is greater, it indicates that the actual electricity load change is highly predictable. This means that the adjustment of the output of the new energy power plant is effective, successfully transforming the originally irregular load change pattern into a predictable pattern. The load is highly predictable, therefore, the corresponding load dispatchability index is larger. At the same time, if the dispersion of the predicted electricity load at all times in the current response interval is smaller, it indicates that the fluctuation of the electricity load is smaller. The predicted electricity load not only matches the reality, but its value itself is also very stable with small fluctuations. This indicates that the current virtual power plant is in a stable state that is easy to manage. Therefore, the corresponding load dispatchability index is larger.

[0090] Conversely, if the similarity between the electricity load at all times within the current response interval and its predicted value is smaller, it indicates that the actual electricity load changes significantly deviate from the load expectation. This suggests that the adjustment of the output of the renewable energy power plant is ineffective, failing to transform the originally irregular load change pattern into a predictable one, resulting in poor load predictability. Therefore, the corresponding load dispatchability index is smaller. At the same time, if the dispersion of the predicted electricity load at all times within the current response interval is greater, it indicates that the electricity load is more volatile. The predicted electricity load not only deviates significantly from the actual load but also exhibits highly volatile values. This indicates that the current virtual power plant is in a chaotic and unmanageable state, and therefore, the corresponding load dispatchability index is smaller.

[0091] Furthermore, this embodiment determines the response strength of the virtual power plant in each response interval based on the load dispatchability index of the virtual power plant in each response interval, combined with the supply-demand balance degree, specifically as follows:

[0092] As a specific implementation method, in this embodiment, the response strength of the virtual power plant in each response interval is the normalized value of the product of the supply and demand balance degree of the virtual power plant in each response interval and the load dispatchability index, and is used as the response strength of the virtual power plant in each response interval.

[0093] Preferably, the schematic diagram of the response intensity extraction process provided in this embodiment is as follows: Figure 2 As shown.

[0094] Based on the response intensity of virtual power plants in each response interval, it can be understood that the response intensity reflects the potential and capacity reserves of virtual power plants to respond accurately within the response interval. If the supply and demand balance of virtual power plants is greater in the current response interval, it indicates that virtual power plants have established and maintained a good supply and demand balance trend in the past period, the foundation of virtual power plants is solid, and the response potential is sufficient. Therefore, the corresponding response intensity is greater. At the same time, if the load dispatchability index of virtual power plants is greater in the current response interval, it indicates that source-load coordination has produced significant effects in the current response interval, the load has become predictable and easy to recover, indicating that the current dispatching methods of virtual power plants are effective, and the response potential is being effectively released and verified. Therefore, the corresponding response intensity is greater.

[0095] Conversely, if the supply-demand balance of the virtual power plant is smaller in the current response interval, it indicates that the virtual power plant has had a poor supply-demand balance trend in the past period, its operating status is unstable, and its response potential is limited. Therefore, the corresponding response intensity is smaller. At the same time, if the load dispatchability index of the virtual power plant is smaller in the current response interval, it indicates that the source-load coordination effect is poor in the current response interval, the load is difficult to predict and control, and the current dispatching methods of the virtual power plant are inefficient or ineffective. The response potential cannot be effectively released. Therefore, the corresponding response intensity is smaller.

[0096] Thus, this embodiment constructs a response strength by introducing supply and demand balance and load dispatchability index, accurately quantifying the response potential and capability of the virtual power plant. This method not only assesses the stability of historical operating states but also deeply analyzes the predictability and recoverability of the load under the current dispatch strategy. This effectively overcomes the dilemma of "the more you adjust, the more biased it becomes" caused by the failure of traditional methods to consider the uncertainties between the generation side and the load side. It achieves a scientific prediction of the response potential of the virtual power plant and provides strong support for formulating more accurate, reliable, and flexible dispatch strategies.

[0097] Step S4: Based on the response strength, determine whether the scheduling plan of the virtual power plant needs to be adjusted.

[0098] Based on the response intensity obtained in step S3, the necessity of adjusting the virtual power plant dispatch plan is determined. The specific process is as follows:

[0099] If the response strength of the virtual power plant in the current response interval is greater than the preset response threshold, it indicates that the power mismatch risk on both the supply and demand sides of the virtual power plant is minor within the response interval time range, and the output of new energy power has a high response potential to the electricity load, which can effectively respond to the peak shaving and valley filling demand of the electricity load on the virtual power plant's side. In this case, there is no need to adjust the dispatch plan of the virtual power plant. Conversely, if the response strength of the virtual power plant in the current response interval is less than or equal to the preset response threshold, it indicates that the power mismatch risk on both the supply and demand sides of the virtual power plant is significant within the response interval time range, and new energy power cannot effectively respond to the peak shaving and valley filling demand of the electricity load on the virtual power plant's side. In this case, the dispatch plan of the virtual power plant needs to be adjusted, and the specific adjustment method is as follows:

[0100] The response intensity of all new energy power plants in the virtual power plant within the current response interval is sorted. When the load on the electricity consumption side of the virtual power plant fluctuates, the new energy power plants with greater response intensity are selected first to respond in order to meet the current demand on the electricity consumption side. The remaining new energy power plants that do not participate in the response store their power generation in the EMS energy storage management system and are given priority for use in the new energy output process when the load on the electricity consumption side fluctuates in the next response.

[0101] It should be noted that the preset response threshold is set manually. In this embodiment, the preset response threshold is set to 0.4. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0102] Thus, this embodiment quantifies the response potential of virtual power plants based on response strength, enabling precise trigger-based adjustments to the scheduling plan. When the risk is high, priority is given to calling power plants with strong response capabilities and coordinating with energy storage systems for regulation. This hierarchical scheduling strategy based on response strength not only maximizes the utilization efficiency of new energy sources but also significantly improves the reliability of virtual power plants in coping with load fluctuations.

[0103] It should be noted 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, specific embodiments of this specification have been described above. 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.

[0104] 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.

[0105] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A precise response method for a virtual power plant considering uncertainties on both the source and load sides, characterized in that, The method includes the following steps: Real-time acquisition of load on the load side and active power of each new energy power station during the operation of the virtual power plant; Within each preset response interval, the power fluctuation of the virtual power plant in each response interval is determined by analyzing the dispersion of the differences between all peaks and adjacent troughs in the active power of each renewable energy power plant at all times. Combined with the degree of disorder in the sudden changes in electricity load data at all times within each response interval, the supply and demand fluctuation of the virtual power plant in each response interval is determined. Based on the correlation coefficient between the active power and electricity load of each renewable energy power plant at all times within each response interval and the differences in the trend of sudden changes in data, the supply and demand asymmetry of the virtual power plant in each response interval is determined. Combined with the supply and demand fluctuation, the power mismatch of the virtual power plant in each response interval is determined. Within a preset time period before each response interval, the supply and demand balance of the virtual power plant under each response interval is determined based on the dispersion of the power mismatch degree of all response intervals and the difference in the power mismatch degree between any response interval and its adjacent subsequent response interval. Based on the changing trend of electricity load at all times within a preset time period before each time, the electricity load at each time is predicted. Based on the similarity between the electricity load at all times within each response interval and its predicted value, as well as the dispersion of the predicted value of the electricity load at all times, the load dispatchability index of the virtual power plant under each response interval is determined. Combined with the supply and demand balance degree, the response intensity of the virtual power plant under each response interval is determined. Based on the response strength, it is determined whether the scheduling plan for the virtual power plant needs to be adjusted.

2. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The method for determining the power fluctuation of the virtual power plant in each response interval is as follows: The degree of dispersion of the difference between all adjacent peaks and troughs in the active power of each new energy power station at all times within each response interval is denoted as the power dispersion of each new energy power station within each response interval. The mean of the power dispersion of all new energy power plants in the virtual power plant within each response interval is taken as the power fluctuation of the virtual power plant in each response interval.

3. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The supply and demand fluctuation of the virtual power plant in each response interval is the result of the positive fusion of the information entropy and power fluctuation of all sudden data in the electricity load at all times on the load side within each response interval.

4. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The expression for the supply-demand asymmetry of the virtual power plant in each response interval is as follows: In the formula, This represents the degree of supply-demand asymmetry of the virtual power plant in the i-th response interval; , represents the correlation coefficient between active power and electricity load of the j-th renewable energy power station at all times within the i-th response interval, and the difference in the trend of abrupt change data, respectively; J represents the number of all renewable energy power stations in the virtual power plant; exp() represents the exponential function with the natural constant as the base.

5. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The power mismatch degree of the virtual power plant in each response interval is the normalized value of the positive fusion result of the supply and demand fluctuation and supply and demand asymmetry of the virtual power plant in each response interval.

6. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The expression for the supply-demand balance of the virtual power plant in each response interval is as follows: In the formula, This represents the supply and demand balance of the virtual power plant in the i-th response interval; This indicates the degree of dispersion of power mismatch among all response intervals within a preset time period prior to the i-th response interval; This represents the difference in power mismatch between the k-th response interval and its adjacent next response interval within a preset time period preceding the i-th response interval. This represents the number of all response intervals within the preset time period before the i-th response interval; exp() represents an exponential function with the natural constant as the base. This indicates a constant that is pre-defined as being greater than 0.

7. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The prediction of electricity load at each time point includes: The load on the load side at all times within a preset time period before each time point is used as the input to the smoothing algorithm, and the output smoothed value is used as the predicted value of the load at each time point.

8. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The load dispatchability index of the virtual power plant in each response interval is the result of the similarity between the power load at all times in each response interval and its predicted value, divided by the dispersion of the predicted power load at all times.

9. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The response strength of the virtual power plant in each response interval is the normalized value of the product of the supply and demand balance degree of the virtual power plant in each response interval and the load dispatchability index.

10. The virtual power plant precise response method considering both source and load uncertainties as described in claim 1, characterized in that, The determination of whether the scheduling plan for the virtual power plant needs to be adjusted includes: If the response intensity of the virtual power plant in the current response interval is greater than the preset response threshold, then there is no need to adjust the scheduling plan of the virtual power plant; otherwise, the scheduling plan of the virtual power plant needs to be adjusted.

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