Real-time scheduling system for virtual power plant participating in frequency modulation auxiliary service

Abstract

The application discloses a kind of real-time scheduling systems of virtual power plant participating in frequency modulation auxiliary service, and the application relates to electric power technical field, by virtual power plant construction module, utilize internet to combine multiple generator sets to build virtual power plant, and by intelligent electric meter monitoring the active output power of each generator set, corresponding power sequence is constructed;Power grid monitoring module monitors the active consumption power and frequency of power grid, forms corresponding sequence;Fluctuation characteristic analysis module monitors power grid frequency sequence, determines fluctuation interval, and analyzes the change of active consumption power in fluctuation interval, locks the total active output power associated with fluctuation interval, and realizes the accurate control of the active output power of each generator set, to optimize power grid frequency stability, improve the frequency modulation auxiliary service capability of virtual power plant.

Description

Technical Field

[0001] This invention belongs to the field of power technology, specifically, it relates to a real-time dispatching system for virtual power plants participating in frequency regulation ancillary services. Background Technology

[0002] Virtual power plants have emerged as an innovative energy management concept. They integrate various distributed power generation resources and utilize advanced information technology and intelligent equipment to achieve efficient scheduling and optimized allocation of power generation resources.

[0003] In traditional virtual power plant (VPS)-based frequency regulation ancillary services dispatching systems, existing technologies often exhibit problems such as slow response, insufficient control precision, and poor adaptability. Specifically, existing systems typically monitor frequencies within a fixed allowable deviation range, failing to dynamically adjust thresholds based on the real-time operating status of the power grid. This results in the inability to promptly identify minor fluctuations when frequency stability is low, or oversensitivity triggering unnecessary controls when stability is high, increasing the risk to power grid operation. Furthermore, existing technologies rely heavily on simple single-point limit-crossing triggering mechanisms for identifying frequency fluctuation ranges, lacking analysis of the entire fluctuation process, such as precise determination of the start and end points of fluctuations. This can lead to inappropriate control timing, either intervening too early and increasing the system burden, or responding too late and amplifying frequency deviations, affecting frequency regulation effectiveness. In terms of power regulation, existing methods often employ extensive allocation strategies, ignoring the differences in the maximum active power output capacity of each unit, resulting in low control efficiency. This may lead to overload of some units while others are idle, failing to optimize the utilization of aggregated resources of the VPS, and lacking precise quantification methods for power imbalances. Consequently, they cannot effectively smooth out frequency fluctuations, reducing the overall reliability of frequency regulation ancillary services.

[0004] To address the aforementioned issues, this invention proposes a real-time scheduling system for virtual power plants participating in frequency regulation ancillary services. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a real-time scheduling system for virtual power plants participating in frequency regulation ancillary services, solving the problems of slow frequency regulation response, low accuracy, and poor resource utilization efficiency in existing technologies.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A real-time dispatching system for virtual power plants participating in frequency regulation ancillary services, the system comprising:

[0008] The virtual power plant construction module is based on the Internet to jointly construct a virtual power plant with several generator sets. It extracts the power grid that is supplied by the virtual power plant alone, uses smart meters to monitor the active power output of each generator set during the power supply process, and constructs the active power output sequence associated with each generator set.

[0009] The power grid monitoring module uses smart meters to monitor the active power consumption and power grid frequency associated with the power grid during the power supply process, and constructs an active power consumption sequence and power grid frequency sequence corresponding to the active power output sequence associated with each generator set.

[0010] The fluctuation characteristic analysis module monitors the power grid frequency sequence, determines the fluctuation range, and analyzes the changes in the active power consumption associated with the fluctuation range. Based on the changes in the active power consumption, it locks the total active power output associated with this fluctuation range in the virtual power plant and regulates the active power output.

[0011] As a further aspect of the present invention, the specific method by which the virtual power plant construction module jointly constructs a virtual power plant based on the Internet and several generator sets is as follows:

[0012] By connecting each generator set through the Internet and IoT interfaces, the maximum active power output of each generator set is extracted. The generator sets are then sorted in descending order according to their maximum active power output values ​​to construct a virtual power plant, represented as: D1, D2, ..., Dj, where j represents the total number of generator sets in the virtual power plant.

[0013] Extract the maximum active power output of each generator set and construct the maximum active power output sequence MP1, MP2, ..., MPj, where Di corresponds to MPi, and i is the counting index, with a value range from 1 to j.

[0014] As a further aspect of the present invention, the specific method for constructing the active power output sequence associated with each generator set in the virtual power plant construction module is as follows:

[0015] Obtain the power grid supplied solely by the virtual power plant and denote it as PG;

[0016] The active power output of each generator unit in the virtual power plant at any time t_rad during the process of the virtual power plant supplying power to the grid PG is monitored using a smart meter.

[0017] Determine the active power output of any generator set Di at time t_rad and continuously monitor it. Summarize the data in chronological order to construct the active power output sequence AP1, AP2, ..., APm associated with generator set Di, where m represents the total number of time points and m increases with time.

[0018] Similarly, determine the active power output sequence associated with each generator unit in the virtual power plant D1, D2, ..., Dj;

[0019] The active power output of each generator set is summed at each time point to obtain the total active power output sequence ZAP1, ZAP2, ..., ZAPm.

[0020] As a further aspect of the present invention, the specific method for constructing the active power consumption sequence corresponding to the active power output sequence time sequence associated with each generator set in the power grid monitoring module is as follows:

[0021] Obtain the active power output sequence AP1, AP2, ..., APm of any generator set Di, extract the time corresponding to m active power outputs, and arrange them in timeline order to obtain the timeline sequence, denoted as t1, t2, ..., tm;

[0022] At each time point in the timeline sequence t1, t2, ..., tm, the active power consumption associated with the power grid during the power supply process is monitored using smart meters to obtain the active power consumption corresponding to m time points, and arranged in timeline order as the active power consumption sequence XP1, XP2, ..., XPm.

[0023] As a further aspect of the present invention, the specific method for constructing the power grid frequency sequence corresponding to the active power output sequence time sequence associated with each generator set in the power grid monitoring module is as follows:

[0024] Extract the timeline sequence t1, t2, ..., tm, and based on the power grid frequency monitored by the smart meter during the power supply process, perform synchronization processing on the power grid frequency according to the steps of constructing the active power consumption sequence XP1, XP2, ..., XPm to obtain the power grid frequency sequence GF1, GF2, ..., GFm.

[0025] As a further aspect of the present invention, the specific method for monitoring the power grid frequency sequence and determining the fluctuation range in the fluctuation characteristic analysis module is as follows:

[0026] Obtain the standard power grid frequency F_std = 50.0Hz;

[0027] Determine the current time and backtrack a preset sliding time window T_win;

[0028] Calculate the statistical stability index S of the power grid frequency sequence GF1, GF2, ..., GFm within this sliding time window T_win, where S = σ / μ, σ is the standard deviation of the power grid frequency within the sliding time window T_win, and μ is the average value of the power grid frequency within the sliding time window T_win;

[0029] Based on the preset mapping relationship Q=f(S), the dynamic quality factor Q is determined. The dynamic quality factor Q is inversely proportional to the statistical stability index S. The inverse ratio is calibrated by the operator in conjunction with the mapping relationship Q=f(S). The larger the statistical stability index S, the worse the power grid frequency stability, and the smaller the dynamic quality factor Q, indicating that strict monitoring is required.

[0030] The allowable deviation range of the power grid frequency reference is determined based on the dynamic quality factor Q [F_std-ΔF_base*Q,F_std+ΔF_base*Q], where ΔF_base is the preset allowable deviation of the reference.

[0031] If, at any moment tn during the power supply process, the grid frequency GFn first exceeds the allowable deviation range of the grid frequency reference [F_std-ΔF_base*Q,F_std+ΔF_base*Q], then bidirectional backtracking is initiated to track the fluctuation range and determine the fluctuation range, where n is the counting index, with a value range of 1 to m.

[0032] As a further aspect of the present invention, the specific method for determining the fluctuation range in the fluctuation feature analysis module by initiating bidirectional backtracking to track the fluctuation range is as follows:

[0033] Starting from time tn, the power grid frequency sequence GF1, GF2, ..., GFm is retrieved backwards. When a time tk is found that satisfies |GFk-F_std|<=(ΔF_base*Q) / 2, time tk is locked as the starting point of the fluctuation. Here, GFk is the power grid frequency at time tk, and k is the counting index, which ranges from 1 to m.

[0034] Starting from time tn, continue to obtain u times including time tk to time tn to get time te, where u is the total number of times within a preset fluctuation interval, and e is the counting index, with a value range from 1 to m;

[0035] By summing the time intervals tk and te, we obtain the fluctuation range [tk, te].

[0036] As a further aspect of the present invention, the specific method by which the fluctuation characteristic analysis module locks the total active power output power associated with this fluctuation range in the virtual power plant based on the numerical change of active power consumption is as follows:

[0037] Determine the total number of times u within the fluctuation interval [tk, te].

[0038] Extract the active power consumption sequence XP1, XP2, ..., XPm at u time points within the fluctuation range [tk, te], and arrange them in chronological order to obtain the active power consumption subsequence XP1', XP2', ..., XPu';

[0039] Obtain the total active power output sequence ZAP1, ZAP2, ..., ZAPm within the fluctuation range [tk, te], and arrange them in time order to obtain the total active power output subsequence ZAP1', ZAP2', ..., ZAPu'.

[0040] As a further aspect of the present invention, the specific method for regulating the active power output in the fluctuation characteristic analysis module is as follows:

[0041] Construct a two-dimensional coordinate system with time as the horizontal axis and power value as the vertical axis, and label the u active power consumption subsequences XP1', XP2',..., XPu' as data points at the corresponding time in the two-dimensional coordinate system to obtain u data points. Then, use curve fitting to obtain the active power consumption fluctuation curve W1.

[0042] Based on the method of constructing the active power consumption fluctuation curve W1, construct the total active power output fluctuation curve W2 associated with the total active power output subsequence ZAP1', ZAP2', ..., ZAPu';

[0043] Construct straight lines perpendicular to the horizontal axis and parallel to the vertical axis, passing through the time tk and time te on the horizontal axis of the two-dimensional coordinate system, and simultaneously passing through the active power consumption fluctuation curve W1 and the total active power output fluctuation curve W2. These lines are denoted as line L1 and line L2, respectively.

[0044] Determine the area values ​​of all closed regions formed by straight lines L1 and L2, active power consumption fluctuation curve W1, and total active power output fluctuation curve W2.

[0045] The area of ​​the closed region above the total active power output fluctuation curve W2, which is the active power consumption fluctuation curve W1, is given a positive sign, and the area of ​​the closed region below the total active power output fluctuation curve W2, which is given a negative sign.

[0046] Summarize the area values ​​of all enclosed regions to obtain the total active power output to be regulated, denoted as TKP;

[0047] Extract the maximum active power output sequence MP1, MP2, ..., MPj and normalize it to obtain the normalized maximum active power output sequence MP1_g, MP2_g, ..., MPj_g;

[0048] The values ​​of the normalized maximum active power output in the normalized maximum active power output sequence MP1_g,MP2_g,...,MPj_g are used as the control ratio of the control active power output of the corresponding generator set.

[0049] The total active power output TKP to be controlled is allocated according to the control ratio of each generator set, thereby controlling the active power output of each generator set.

[0050] The beneficial effects of this invention are:

[0051] This invention integrates distributed generator sets via the internet to construct a virtual power plant, and utilizes smart meters to monitor active power output, power consumption, and grid frequency in real time, constructing corresponding sequences to achieve rapid identification and response to grid frequency fluctuations. Its core advantages lie in significantly improving grid stability and reliability. Through a fluctuation characteristic analysis module, it accurately identifies and dynamically regulates active power output associated with frequency fluctuations, effectively suppressing frequency deviations and optimizing supply and demand balance. Simultaneously, it fully utilizes distributed resources, improves frequency regulation efficiency, reduces traditional frequency regulation costs, and promotes the intensive use of energy resources.

[0052] This invention achieves efficient integration and optimized configuration of generator sets by associating them and extracting their maximum active power output, arranging them in descending order. Its advantages lie in its ability to remotely and automatically aggregate distributed generation resources, ensuring orderly generator set scheduling. Simultaneously, by using smart meters to monitor the active power output of each unit in real time and constructing active power output sequences and total active power output sequences, it enables precise monitoring, data-driven management, dynamic adjustment, and load balancing of the power grid supply process, thereby improving the stability and responsiveness of the power grid.

[0053] This invention acquires the active power output sequence of generator sets and extracts the corresponding time points to form a unified timeline sequence. Then, it uses smart meters to synchronously monitor the active power consumption and grid frequency at each time point, constructing a time-series-strictly-corresponding active power consumption sequence and grid frequency sequence. This ensures precise alignment of power output, consumption, and frequency data in the time dimension, eliminates data deviations caused by time sequence mismatches, thereby improving the real-time performance and data consistency of grid monitoring, enhancing the accuracy of grid operation status assessment, facilitating rapid detection of power fluctuations and frequency anomalies, and thus optimizing power generation scheduling.

[0054] This invention achieves real-time monitoring and accurate identification of power grid frequency fluctuations through dynamic quality factors and a two-way backtracking mechanism. It can adaptively adjust the allowable deviation range to ensure automatic enhanced monitoring when frequency stability decreases, thereby improving response speed and sensitivity. By locking the fluctuation range and analyzing the curve differences between active power consumption and total active power output, it accurately calculates the controllable quantities and intelligently allocates control tasks based on the normalized maximum output power of generator units. This optimizes resource utilization, enhances power grid stability, improves the automation level of frequency fluctuation management, and reduces human intervention. Attached Figure Description

[0055] The invention will now be further described with reference to the accompanying drawings.

[0056] Figure 1 This is a schematic diagram of the system described in this invention;

[0057] Figure 2 This is a flowchart illustrating the method described in Embodiment 2 of the present invention;

[0058] Figure 3 This is a flowchart illustrating the method described in Embodiment 4 of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1

[0061] A real-time dispatching system for virtual power plants participating in frequency regulation ancillary services, such as Figure 1 As shown, this system includes the following:

[0062] This system is a real-time dispatching system for virtual power plants participating in frequency regulation ancillary services. It is mainly used to address the frequency stability issues faced by modern power grids after a high proportion of renewable energy is connected to the grid. The system aims to aggregate dispersed and diversified power generation resources or distributed generator sets (such as distributed photovoltaics, energy storage batteries, controllable loads, small gas turbines, etc.) into a coordinated and unified virtual power plant through advanced information and communication technologies and control algorithms. This allows the virtual power plant to respond to grid frequency fluctuations in real time and provide fast and accurate frequency regulation ancillary services, just like a traditional large power plant.

[0063] This system mainly consists of three modules: virtual power plant construction module, power grid monitoring module, and fluctuation characteristic analysis module.

[0064] In the virtual power plant construction module, a virtual power plant is jointly constructed based on the Internet and several generator sets. The power grid supplied by the virtual power plant alone is extracted, and smart meters are used to monitor the active power output associated with each generator set during the power supply process, constructing an active power output sequence associated with each generator set. Specifically:

[0065] Based on the combination of Internet of Things and Internet technology, geographically dispersed generator sets are linked together through a high-speed, low-latency communication network to form a virtual power plant. In this virtual power plant, smart meters are installed on each generator set to form nodes that include metering functions, data acquisition, and control execution.

[0066] The smart meter collects the active power output of each generator set at a predetermined frequency, and obtains the total active power output associated with the virtual power plant based on the active power output of each generator set.

[0067] The power grid monitoring module utilizes smart meters to monitor the active power consumption and power grid frequency associated with the power grid during the power supply process, constructing an active power consumption sequence and a power grid frequency sequence corresponding to the active power output sequence associated with each generator set. Specifically:

[0068] By installing smart meters at the common connection point between the virtual power plant and the power grid, two core parameters of the power grid can be monitored in real time: total active power consumption and power grid frequency. It should be noted that the power plant must be supplied by the virtual power plant alone; otherwise, the subsequent steps will not be valid.

[0069] The total active power consumption represents the real-time total load demand of the area covered by the power grid, and the power grid frequency is taken as the standard power grid frequency of 50.0Hz.

[0070] Finally, based on the data collected by the smart meters, an active power consumption sequence and a power grid frequency sequence that are strictly synchronized with the active power output are constructed.

[0071] The fluctuation characteristic analysis module monitors the power grid frequency sequence, determines the fluctuation range, and analyzes the changes in the active power consumption associated with the fluctuation range. Based on the changes in the active power consumption, it locks the total active power output in the virtual power plant associated with this fluctuation range and regulates the active power output. Specifically:

[0072] This module mainly includes three core steps:

[0073] Step 1: Continuously monitor the power grid frequency sequence and construct a power grid frequency reference allowable deviation range based on the data changes of the power grid frequency sequence itself. Then, determine the power grid frequency based on the power grid frequency reference allowable deviation range to determine whether there is a fluctuation range in the power grid.

[0074] Step 2: Analyze the changes in the active power consumption within the fluctuation range, and then perform correlation analysis with the total active power output of the virtual power plant within the fluctuation range to finally determine the total active power output to be regulated by the virtual power plant.

[0075] Step 3: Combine the various generator sets in the virtual power plant with the total active power output to be controlled by the virtual power plant, and regulate each generator set to form a closed-loop control until the grid frequency is completely stable.

[0076] Example 2

[0077] This embodiment, based on Embodiment 1, further discloses a method for determining the active power output sequence associated with each generator unit in a virtual power plant, such as... Figure 2 As shown, it specifically includes the following:

[0078] As can be seen from the content described in Example 1, a virtual power plant is composed of several generator sets connected together. Therefore, the required generator sets can be connected through the Internet and IoT interfaces.

[0079] Then, obtain the maximum active power output of each generator set, and sort all generator sets in descending order according to the value of their maximum active power output. The virtual power plant is represented by the permutation and combination, specifically: D1, D2, ..., Dj, where j represents the total number of generator sets in the virtual power plant. The specific value is determined by the operator based on the actual situation.

[0080] Next, the maximum active power output of each of the j generator sets D1, D2, ..., Dj is extracted again, and sorted according to the order of D1, D2, ..., Dj to obtain the maximum active power output sequence MP1, MP2, ..., MPj. Here, any generator set Di corresponds to its associated maximum active power output MPi, where i is the counting index, and the value range is from 1 to j.

[0081] As described in Example 1, a power grid supplied solely by a virtual power plant is obtained and denoted as PG. The power grid PG can be a physically isolated microgrid or a logically defined power supply area, which is determined by the operator based on the actual situation.

[0082] Next, during the power supply process from the virtual power plant to the grid PG, the active power output of each generator unit in the virtual power plant is monitored in real time at any time t_rad using smart meters.

[0083] For example: Obtain any generator set Di, monitor and collect the active power output of generator set Di at time t_rad, continuously monitor and collect the active power output of generator set Di, and sort the collected active power output of generator set Di in chronological order to obtain the active power output sequence associated with generator set Di, represented as: AP1, AP2, ..., APm, where m represents the total number of time points, and the value of m increases with the passage of time.

[0084] Repeat the steps above to determine the active power output sequence AP1, AP2, ..., APm associated with generator set Di, and determine the active power output sequence associated with each generator set in virtual power plant D1, D2, ..., Dj.

[0085] For any given moment, the active power output of each generator unit at that moment is summed to obtain the total active power output sequence associated with the virtual power plant at that moment. By repeating this process, the total active power output sequence corresponding to the virtual power plant and the time sequence can be determined, denoted as: ZAP1, ZAP2, ..., ZAPm.

[0086] Example 3

[0087] This embodiment, based on embodiment 2, further discloses a method for constructing a power grid frequency sequence and an active power consumption sequence associated with the power grid, specifically including the following:

[0088] Based on the content described in Example 2, extract any generator set Di from the determined virtual power plants D1, D2, ..., Dj, and the active power output sequence AP1, AP2, ..., APm associated with generator set Di;

[0089] Then, timestamps are extracted from the active power output sequence AP1, AP2, ..., APm associated with generator Di, that is, the times corresponding to m active power outputs. The extracted m timestamps are arranged in the order of the timeline to obtain the timeline sequence, and denoted as t1, t2, ..., tm.

[0090] At each moment in the timeline sequence t1, t2, ..., tm, the active power consumption associated with the power grid during the power supply process is monitored using a pre-built smart meter. Finally, m active power consumption values ​​corresponding to m moments in the timeline sequence t1, t2, ..., tm are obtained. The obtained m active power consumption values ​​are then sorted according to the timeline sequence t1, t2, ..., tm to obtain the active power consumption sequence associated with the power grid, denoted as XP1, XP2, ..., XPm.

[0091] Next, following the method of constructing the active power consumption sequence XP1, XP2, ..., XPm, the grid frequencies corresponding to the time line sequence t1, t2, ..., tm collected by smart meters are processed in the same way to obtain the grid frequency sequence GF1, GF2, ..., GFm associated with the grid.

[0092] Example 4

[0093] This embodiment, based on embodiment 3, further discloses a method for regulating the active power output of generator sets in a virtual power plant, such as... Figure 3 As shown, it specifically includes the following:

[0094] First, obtain the standard power grid frequency specified in Example 1 and label it as F_std, where F_std = 50.0 Hz. Subsequent operations are based on this standard power grid frequency. If the power grid has different requirements, the operator can make adaptive adjustments to the value of F_std according to the actual situation.

[0095] Because the power grid state is continuously changing, rather than only observing an instantaneous state, the following processing scheme is proposed based on this characteristic:

[0096] First, determine the current time (the current time refers to the time when the power grid and virtual power plant are controlled and processed using this solution, not simply the current time), and then use the current time as the end time to backtrack a sliding time window T_win preset by the operator.

[0097] The determined sliding time window T_win is a time interval. The grid frequency within this time interval is obtained from the grid frequency sequence GF1, GF2, ..., GFm, and the statistical stability index S within this time interval (sliding time window T_win) is calculated. Here, S = σ / μ, where σ is the standard deviation of the grid frequency within the sliding time window T_win, which measures the fluctuation amplitude of the grid frequency within the sliding time window T_win, and μ is the average value of the grid frequency within the sliding time window T_win, which measures the average deviation level of the grid frequency within the sliding time window T_win. The statistical stability index S can be understood as a coefficient of variation, which takes into account both the amplitude of fluctuation and the degree of dispersion relative to the center. The larger the value of the statistical stability index S, the more unstable the grid is in the near future.

[0098] Then, by using the mapping relationship Q=f(S) preset by the operator, the dynamic quality factor Q associated with the statistical stability index S is determined. The dynamic quality factor Q is inversely proportional to the statistical stability index S. The inverse ratio is calibrated by the operator in conjunction with the mapping relationship Q=f(S), and f(S) is an inverse proportional function.

[0099] When S is large, it indicates that the power grid is unstable, and Q is small, indicating that the tolerance for frequency deviation is reduced. Even a small frequency deviation may indicate a greater risk, so it is necessary to monitor it closely and start the frequency regulation response earlier to prevent the situation from deteriorating.

[0100] When S hours represent a stable power grid, a large Q indicates a relatively lenient threshold, avoiding unnecessary over-regulation of small, self-recovering fluctuations and saving frequency regulation resources.

[0101] Then, the determined dynamic quality factor Q is extracted, and the grid frequency reference allowable deviation range [F_std-ΔF_base*Q,F_std+ΔF_base*Q] is constructed in real time. Here, ΔF_base is the reference allowable deviation preset by the operator. The grid frequency reference allowable deviation range is no longer fixed, but is dynamically adjusted according to the real-time health status of the grid, so as to achieve adaptive regulation and scheduling.

[0102] Next, if, during the power supply process, the grid frequency GFn at any time tn exceeds the grid frequency reference allowable deviation range [F_std-ΔF_base*Q,F_std+ΔF_base*Q] for the first time, then bidirectional backtracking to track the fluctuation range is initiated, where n is the counting index, with a value range of 1 to m;

[0103] The specific steps for bidirectional backtracking to track fluctuation ranges are as follows:

[0104] Starting from the determined time tn, retrieve the power grid frequency sequence GF1, GF2, ..., GFm in the past time interval. For example, if n=50, then retrieve the power grid frequency sequence GF1, GF2, ..., GFm in the past time interval t1-t49.

[0105] When a time tk is retrieved, and the power grid frequency GFk at time tk satisfies: |GFk-F_std|<=(ΔF_base*Q) / 2, then time tk is determined to be the starting point of the fluctuation, where GFk is the power grid frequency at time tk, and k is the counting index, with a value range from 1 to m.

[0106] Then, starting from time tn, obtain u times including times tk to tn from the subsequent times of time tn, to get the uth time with time tk as the first time, denoted as te, and lock time te as the end point of the fluctuation, where u is the total number of times in a preset fluctuation interval, and e is the counting index, with a value range from 1 to m.

[0107] Based on the determined fluctuation start point: time tk and fluctuation end point: time te, the fluctuation range [tk, te] is determined.

[0108] Based on the determined fluctuation range [tk,te], u time points within the fluctuation range [tk,te] can be obtained;

[0109] Then, extract u active power consumption values ​​corresponding to u time points within the fluctuation interval [tk,te] from the active power consumption sequence XP1, XP2, ..., XPm, and arrange the u active power consumption values ​​in time sequence to obtain the active power consumption subsequence, represented as: XP1', XP2', ..., XPu'.

[0110] Then, extract the u total active power outputs corresponding to the u times within the fluctuation interval [tk,te] from the total active power output sequence ZAP1, ZAP2, ..., ZAPm, and arrange the u total active power outputs in time sequence to obtain the total active power output subsequence, denoted as: ZAP1', ZAP2', ..., ZAPu'.

[0111] Next, extract the active power consumption subsequence XP1', XP2', ..., XPu' and the total active power output subsequence ZAP1', ZAP2', ..., ZAPu';

[0112] A two-dimensional coordinate system is constructed with the time line as the horizontal axis and the power value as the vertical axis. Then, u active power consumption values ​​are extracted from the active power consumption subsequence XP1', XP2',..., XPu'. These u active power consumption values ​​are plotted as data points in the two-dimensional coordinate system according to the time correspondence, resulting in u data points corresponding to the u active power consumption values. By fitting the u data points with a curve, the active power consumption fluctuation curve W1 associated with the active power consumption subsequence XP1', XP2',..., XPu' can be obtained.

[0113] Next, extract u total active power outputs from the total active power output subsequence ZAP1', ZAP2', ..., ZAPu', and plot these u total active power outputs as data points in a two-dimensional coordinate system according to time correspondence, obtaining u data points corresponding to the u total active power outputs. By fitting the u data points with a curve, the total active power output fluctuation curve W2 associated with the total active power output subsequence ZAP1', ZAP2', ..., ZAPu' can be obtained.

[0114] Construct a straight line perpendicular to the horizontal axis and parallel to the vertical axis, passing through the first time point tk and the last time point te on the horizontal axis of the two-dimensional coordinate system, and simultaneously passing through the active power consumption fluctuation curve W1 and the total active power output fluctuation curve W2. These two straight lines are denoted as line L1 and line L2, respectively, where line L1 is the line at time tk and line L2 is the line at time te.

[0115] At this point, straight lines L1 and L2, along with the active power consumption fluctuation curve W1 and the total active power output fluctuation curve W2, will form one or more closed regions. The area values ​​of all closed regions formed by straight lines L1 and L2, along with the active power consumption fluctuation curve W1 and the total active power output fluctuation curve W2, will be determined.

[0116] The area of ​​the closed region above the total active power output fluctuation curve W2, which is the active power consumption fluctuation curve W1, is assigned a positive sign, and the area of ​​the closed region below the total active power output fluctuation curve W2, which is assigned a negative sign.

[0117] When W1 is at the top, it indicates a total active power output deficit, where the grid consumes more energy than the generation side provides. This energy deficit is supplemented by the rotational kinetic energy of the grid, resulting in a decrease in frequency. The area value is positive.

[0118] When W1 is at the bottom, it indicates that the total active power output is excessive, the power generation is more than the consumption, the excess energy is converted into kinetic energy, which leads to an increase in frequency, and the area value is negative.

[0119] After assigning a sign, the area values ​​of all closed regions (including values ​​with positive and negative signs) are obtained, and accumulated to obtain a final value, which is used as the total active power output to be regulated and recorded as TKP. The value of TKP is the total active power output that needs to be increased or decreased, which is the total imbalance of net total active power output during the entire fluctuation event.

[0120] By balancing the total active power output to be regulated, TKP, the grid frequency is brought back to the standard value.

[0121] Extract the maximum active power output sequence MP1,MP2,...,MPj associated with all generator sets in the virtual power plant, and normalize the maximum active power output associated with each generator set j to obtain the normalized maximum active power output sequence MP1_g,MP2_g,...,MPj_g;

[0122] Then, the values ​​of the normalized maximum active power output in the normalized maximum active power output sequence MP1_g,MP2_g,...,MPj_g are used as the control ratio of the control active power output of the corresponding generator set, which is equivalent to the weighting coefficient of each generator set, in line with the fair principle that the greater the capacity, the greater the responsibility.

[0123] Finally, the total active power output TKP to be controlled is allocated according to the control ratio of each generator set, and the active power output of each generator set is controlled. It should be noted that if any generator set cannot be controlled, the uncontrollable part will be redistributed according to the control ratio of each generator set to the remaining controllable generator sets.

[0124] All data in the formulas described above are numerical calculations performed after removing their dimensions. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0125] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

[0126] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A real-time dispatching system for virtual power plants participating in frequency regulation ancillary services, characterized in that, The system includes: The virtual power plant construction module, based on the Internet, enables the joint construction of a virtual power plant by several generator sets. Specifically: By connecting each generator set through the Internet and IoT interfaces, the maximum active power output of each generator set is extracted. The generator sets are then sorted in descending order according to their maximum active power output values ​​to construct a virtual power plant, represented as: D1, D2, ..., Dj, where j represents the total number of generator sets in the virtual power plant. Extract the maximum active power output of each generator set and construct the maximum active power output sequence MP1, MP2, ..., MPj, where Di corresponds to MPi, and i is the counting index, with a value range from 1 to j; Extract the power grid supplied solely by the virtual power plant, and use smart meters to monitor the active power output associated with each generator set during the power supply process. Construct an active power output sequence associated with each generator set, specifically: Obtain the power grid supplied solely by the virtual power plant and denote it as PG; The active power output of each generator unit in the virtual power plant at any time t_rad during the process of the virtual power plant supplying power to the grid PG is monitored using a smart meter. Determine the active power output of any generator set Di at time t_rad and continuously monitor it. Summarize the data in chronological order to construct the active power output sequence AP1, AP2, ..., APm associated with generator set Di, where m represents the total number of time points and m increases with time. Similarly, determine the active power output sequence associated with each generator unit in the virtual power plant D1, D2, ..., Dj; The active power output of each generator unit is summed up at each time point to obtain the total active power output sequence ZAP1, ZAP2, ..., ZAPm; The power grid monitoring module uses smart meters to monitor the active power consumption and power grid frequency associated with the power grid during the power supply process, and constructs an active power consumption sequence and power grid frequency sequence corresponding to the active power output sequence associated with each generator set. The fluctuation characteristic analysis module monitors the power grid frequency sequence, determines the fluctuation range, and analyzes the changes in the active power consumption associated with the fluctuation range. Based on the changes in the active power consumption, it locks the total active power output associated with this fluctuation range in the virtual power plant and regulates the active power output.

2. The system according to claim 1, characterized in that, In the power grid monitoring module, the specific method for constructing the active power consumption sequence corresponding to the active power output sequence time sequence associated with each generator unit is as follows: Obtain the active power output sequence AP1, AP2, ..., APm of any generator set Di, extract the time corresponding to m active power outputs, and arrange them in timeline order to obtain the timeline sequence, denoted as t1, t2, ..., tm; At each time point in the timeline sequence t1, t2, ..., tm, the active power consumption associated with the power grid during the power supply process is monitored using smart meters to obtain the active power consumption corresponding to m time points, and arranged in timeline order as the active power consumption sequence XP1, XP2, ..., XPm.

3. The system according to claim 2, characterized in that, In the power grid monitoring module, the specific method for constructing the power grid frequency sequence corresponding to the active power output sequence time series associated with each generator unit is as follows: Extract the timeline sequence t1, t2, ..., tm, and based on the power grid frequency monitored by the smart meter during the power supply process, perform synchronization processing on the power grid frequency according to the steps of constructing the active power consumption sequence XP1, XP2, ..., XPm to obtain the power grid frequency sequence GF1, GF2, ..., GFm.

4. The system according to claim 3, characterized in that, The fluctuation characteristic analysis module monitors the power grid frequency sequence and determines the fluctuation range in the following specific way: Obtain the standard power grid frequency F_std = 50.0Hz; Determine the current time and backtrack a preset sliding time window T_win; Calculate the statistical stability index S of the power grid frequency sequence GF1, GF2, ..., GFm within this sliding time window T_win, where S = σ / μ, σ is the standard deviation of the power grid frequency within the sliding time window T_win, and μ is the average value of the power grid frequency within the sliding time window T_win; Based on the preset mapping relationship Q=f(S), the dynamic quality factor Q is determined. The dynamic quality factor Q is inversely proportional to the statistical stability index S. The inverse ratio is calibrated by the operator in conjunction with the mapping relationship Q=f(S). The larger the statistical stability index S, the worse the power grid frequency stability, and the smaller the dynamic quality factor Q, indicating that strict monitoring is required. The allowable deviation range of the power grid frequency reference is determined based on the dynamic quality factor Q [F_std-ΔF_base*Q,F_std+ΔF_base*Q], where ΔF_base is the preset allowable deviation of the reference. If, at any moment tn during the power supply process, the grid frequency GFn first exceeds the allowable deviation range of the grid frequency reference [F_std-ΔF_base*Q,F_std+ΔF_base*Q], then bidirectional backtracking is initiated to track the fluctuation range and determine the fluctuation range, where n is the counting index, with a value range of 1 to m.

5. The system according to claim 4, characterized in that, In the fluctuation characteristic analysis module, the specific method for determining the fluctuation range by initiating bidirectional backtracking is as follows: Starting from time tn, the power grid frequency sequence GF1, GF2, ..., GFm is retrieved backwards. When a time tk is found that satisfies |GFk-F_std|<=(ΔF_base*Q) / 2, time tk is locked as the starting point of the fluctuation. Here, GFk is the power grid frequency at time tk, and k is the counting index, which ranges from 1 to m. Starting from time tn, continue to obtain u times including time tk to time tn to get time te, where u is the total number of times within a preset fluctuation interval, and e is the counting index, with a value range from 1 to m; By summing the time intervals tk and te, we obtain the fluctuation range [tk, te].

6. The system according to claim 5, characterized in that, In the fluctuation characteristic analysis module, the specific method for locking the total active power output associated with this fluctuation range in the virtual power plant based on the numerical change of active power consumption is as follows: Determine the total number of times u within the fluctuation interval [tk, te]. Extract the active power consumption sequence XP1, XP2, ..., XPm at u time points within the fluctuation range [tk, te], and arrange them in chronological order to obtain the active power consumption subsequence XP1', XP2', ..., XPu'; Obtain the total active power output sequence ZAP1, ZAP2, ..., ZAPm within the fluctuation range [tk, te], and arrange them in time order to obtain the total active power output subsequence ZAP1', ZAP2', ..., ZAPu'.

7. The system according to claim 6, characterized in that, The specific method for regulating the active power output in the fluctuation characteristic analysis module is as follows: Construct a two-dimensional coordinate system with time as the horizontal axis and power value as the vertical axis, and label the u active power consumption subsequences XP1', XP2',..., XPu' as data points at the corresponding time in the two-dimensional coordinate system to obtain u data points. Then, use curve fitting to obtain the active power consumption fluctuation curve W1. Based on the method of constructing the active power consumption fluctuation curve W1, construct the total active power output fluctuation curve W2 associated with the total active power output subsequence ZAP1', ZAP2', ..., ZAPu'; Construct straight lines perpendicular to the horizontal axis and parallel to the vertical axis, passing through the time tk and time te on the horizontal axis of the two-dimensional coordinate system, and simultaneously passing through the active power consumption fluctuation curve W1 and the total active power output fluctuation curve W2. These lines are denoted as line L1 and line L2, respectively. Determine the area values ​​of all closed regions formed by straight lines L1 and L2, active power consumption fluctuation curve W1, and total active power output fluctuation curve W2. The area of ​​the closed region above the total active power output fluctuation curve W2, which is the active power consumption fluctuation curve W1, is assigned a positive sign, and the area of ​​the closed region below the total active power output fluctuation curve W2, which is assigned a negative sign. Summarize the area values ​​of all enclosed regions to obtain the total active power output to be regulated, denoted as TKP; Extract the maximum active power output sequence MP1, MP2, ..., MPj and normalize it to obtain the normalized maximum active power output sequence MP1_g, MP2_g, ..., MPj_g; The values ​​of the normalized maximum active power output in the normalized maximum active power output sequence MP1_g,MP2_g,...,MPj_g are used as the control ratio of the control active power output of the corresponding generator set. The total active power output TKP to be controlled is allocated according to the control ratio of each generator set, thereby controlling the active power output of each generator set.

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