Distributed energy optimization scheduling method based on virtual power plant

Abstract

The invention discloses a distributed energy optimization scheduling method based on a virtual power plant. Comprising the following steps: analyzing data of unit time maximum / minimum output power, load tracking capability, equipment start-stop time and fault frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of distributed energy in a gateway integrated virtual power plant by utilizing edge statistics; the power plant energy centralized control center is used for adjusting to-be-managed data of distributed energy in unit time and unit area according to data in the virtual power plant, and the power plant energy centralized control center is used for adjusting the schedulable amount used by the to-be-managed data of the distributed energy in unit time and unit area; counting and analyzing whether the available schedulable amount of the distributed energy to-be-managed data adjustment performed by the power plant energy centralized control center exceeds a preset available schedulable interval boundary; according to the method, the accuracy of distributed energy scheduling in unit time and unit area is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy scheduling, and in particular relates to a distributed energy optimization scheduling method based on a virtual power plant. Background Art

[0002] With the large-scale access of renewable energy, the complexity and uncertainty of the power system have increased significantly, and the traditional centralized power dispatching method is difficult to cope with the challenges under the new situation. Virtual Power Plant (VPP) technology came into being, integrating distributed power generation and energy storage systems distributed in different locations and with different types of energy to achieve coordination and optimized dispatch of these resources. Virtual power plants use a centralized control platform to uniformly dispatch various types of distributed energy, optimize energy utilization efficiency, and improve the economy and stability of the system. However, due to the diversity and volatility of distributed energy, how to achieve accurate dispatch and management is still the core issue currently faced.

[0003] Existing distributed energy optimization scheduling methods based on virtual power plants usually rely on optimization algorithms, such as linear programming, mixed integer programming, etc., to adjust energy generation and consumption by setting fixed scheduling strategies. However, these methods have obvious defects in practice. First, the existing technology fails to fully consider the dynamic adjustment of distributed energy to be managed data per unit time and per unit area. The power generation characteristics of distributed energy at different time points and different geographical locations vary greatly, and these differences are affected by various factors such as weather and load changes. Traditional scheduling methods usually use relatively static prediction models, which cannot adjust and adapt to these rapidly changing data in a timely manner, resulting in delayed and inaccurate energy scheduling. Secondly, when processing a large amount of distributed energy data, the existing technology lacks efficient data processing and real-time scheduling mechanisms, resulting in low scheduling accuracy. Especially under the rapidly changing power demand and unstable renewable energy supply, the scheduling results may not meet the real-time supply and demand balance. Summary of the invention

[0004] Based on this, it is necessary to provide a distributed energy optimization scheduling method based on a virtual power plant to address the problem of randomly adjusting the distributed energy management data per unit time and unit area in the power plant energy control center.

[0005] A distributed energy optimization scheduling method based on a virtual power plant, comprising: Use edge statistics to analyze the maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability data of distributed energy in the gateway integrated virtual power plant; The power plant energy centralized control center uses the data of the maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and fault frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of the distributed energy in the virtual power plant to adjust the data to be managed for distributed energy per unit time and per unit area. The power plant energy centralized control center adjusts the data to be managed for distributed energy per unit time and per unit area, including grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters when the virtual power plant is running; The power plant energy centralized control center is used to adjust the dispatchable amount of distributed energy to be managed data per unit time and per unit area; Statistics and analysis of whether the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area exceeds the preset dispatchable interval boundary; When the statistics and analysis of the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area exceeds the preset boundary of the dispatchable interval that can be used, the power plant energy control center used to adjust the distributed energy to be managed data per unit time and unit area is dispatched to the data optimization platform at the abnormal load fluctuation of each distributed energy; when the statistics and analysis of the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area does not exceed the preset boundary of the dispatchable interval that can be used, the data statistics and analysis of the maximum / minimum output power per unit time of the distributed energy in the virtual power plant, the load tracking capability, the equipment start and stop time and the failure frequency, the energy supply stability and volatility, the energy storage equipment parameters, the energy quality and the voltage stability are cyclically performed, and the power plant energy control center is used to adjust the distributed energy to be managed data per unit time and unit area, and to count and analyze the dispatchable amount that can be used.

[0006] Beneficial effects: The present invention proposes a distributed energy optimization scheduling method based on a virtual power plant, a management method and management system for a power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area, statistics and analysis of the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area and compare it with the preset dispatchable interval boundary that can be used, so as to achieve the management of the distributed energy to be managed data per unit time and per unit area by managing the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area. Compared with the problems of troublesome prediction and low accuracy brought by the existing random scheduling, a distributed energy optimization scheduling method based on a virtual power plant in each embodiment of the present invention can achieve the management of the scheduling management difficulty, operability and the horizontal difference of the distributed energy to be managed data per unit time and per unit area by managing the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area, thereby improving the usability of the data optimization platform at the abnormal point of distributed energy load fluctuation and improving the accuracy of scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a flow chart of a distributed energy optimization scheduling method based on a virtual power plant of the present invention. DETAILED DESCRIPTION

[0008] It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments may be combined with each other. The present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0009] See also Figure 1 The present invention provides a distributed energy optimization scheduling method based on a virtual power plant according to an implementation mode, comprising: Step S100, using edge statistics, analyze the data of maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of distributed energy in the gateway integrated virtual power plant.

[0010] For dispatch management with specific content, the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters when the virtual power plant is running are preferably the factors with the greatest influence on dispatch management that are statistically analyzed using the dispatch management content. Generally, obtaining the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters when the virtual power plant is running should be conducive to completing the abnormal data of the abnormal load fluctuation of distributed energy set according to the dispatch management content. For example, for confrontational dispatch management, the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters when the virtual power plant is running can be set to the target characteristics or roles with the greatest combat effectiveness. The data of the maximum / minimum output power per unit time of distributed energy in the virtual power plant, load tracking capability, equipment start-stop time and fault frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability may include: the number, size, location, etc. of grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters when the virtual power plant is running.

[0011] Step S200, using the data of maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of distributed energy in the virtual power plant, the power plant energy control center adjusts the distributed energy to be managed per unit time and per unit area. The power plant energy control center adjusts the distributed energy to be managed per unit time and per unit area, including grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters when the virtual power plant is running.

[0012] Specifically, the power plant energy centralized control center used shall include the grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters when the virtual power plant is running. The power plant energy centralized control center shall include the distributed energy to be dispatched to each distributed energy load fluctuation abnormal data optimization platform statistically analyzed by the dispatch management content to adjust the distributed energy to be managed per unit time and per unit area, where the grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters when the virtual power plant is running are included in the distributed energy to be dispatched to one or more distributed energy load fluctuation abnormal data optimization platform to adjust the distributed energy to be managed per unit time and per unit area. For example, when a virtual power plant is running, grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters should be used and included in the power plant energy control center used to adjust the distributed energy to be managed data per unit time and per unit area.

[0013] Step S300, statistics and analysis of the abnormal load fluctuations of each distributed energy source, the power plant energy centralized control center of the data optimization platform performs distributed energy management data adjustment per unit time and per unit area and the available dispatchable amount.

[0014] Specifically, the power plant energy centralized control center to be dispatched to each distributed energy load fluctuation abnormal data optimization platform can be used to adjust the distributed energy to be managed data per unit time and unit area, and the power plant energy centralized control center to be dispatched to the distributed energy load fluctuation abnormal data optimization platform can be used to adjust the distributed energy to be managed data per unit time and unit area. It can be understood that the power plant energy centralized control center generated by the random scheduling method performs the adjustment of the distributed energy to be managed data per unit time and unit area, and the differences in difficulty, operability, etc. are also random, so that the same scheduling management and the same period may have unexpected unevenness, and this unexpected unevenness is also one of the important reasons for the existing random scheduling principle to affect the user's prediction trouble and stickiness. Using an embodiment of the present invention, statistics and analysis of the power plant energy centralized control center of the distributed energy load fluctuation abnormal data optimization platform to adjust the distributed energy to be managed data per unit time and unit area can be used as an important means to manage the existing random scheduling principle.

[0015] Step S400, statistics and analysis of the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area.

[0016] By using an implementation mode of the present invention, the dispatchable amount that can be used can be counted and analyzed based on the dispatchable amount that can be used for the adjustment of the distributed energy to be managed data per unit time and per unit area by the power plant energy centralized control center of the data optimization platform for each distributed energy load fluctuation abnormality that is counted and analyzed in step S300. Of course, by using other feasible implementation modes, it is also possible to count and analyze the dispatchable amount that can be used for the adjustment of the distributed energy to be managed data per unit time and per unit area by performing preset operations on all the used power plant energy centralized control centers. In this case, it is also possible not to perform the statistics and analysis of the dispatchable amount that can be used for the adjustment of the distributed energy to be managed data per unit time and per unit area by the power plant energy centralized control center of the data optimization platform for each distributed energy load fluctuation abnormality in step S300.

[0017] In this embodiment, the available dispatchable amount reflects the difficulty and operability of the dispatching management content reflected by the power plant energy centralized control center adjusting the distributed energy to be managed data per unit time and per unit area, and the difference between the power plant energy centralized control center of the data optimization platform at each distributed energy load fluctuation abnormality to adjust the distributed energy to be managed data per unit time and per unit area. Through the available dispatchable amount, the difference in the difficulty and operability of the power plant energy centralized control center dispatched by the data optimization platform at each distributed energy load fluctuation abnormality to adjust the distributed energy to be managed data per unit time and per unit area can be obtained, which can be distinguished from the unexpected difficulty, operability and difference reflected in the existing random dispatching principle.

[0018] Step S500, statistics and analysis are performed to determine whether the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area exceeds the preset dispatchable interval boundary.

[0019] Specifically, the preset usable dispatchable interval boundary can be set by utilizing the various dispatching management elements contained in the adjustment of distributed energy to be managed data per unit time and unit area by the power plant energy control center, and by utilizing the required differences between the adjustment of distributed energy to be managed data per unit time and unit area by the power plant energy control center of the data optimization platform at the abnormal points of each distributed energy load fluctuation. In this embodiment, when the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and per unit area does not exceed the preset dispatchable interval boundary, it indicates that the difficulty and operability of the overall dispatching management and the difference between the power plant energy control center's adjustment of the distributed energy to be managed data per unit time and per unit area of ​​the data optimization platform at the abnormal fluctuation of each distributed energy load are beyond expectations, then it is necessary to loop through steps S100 to S400 to count and analyze the grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters during the operation of the virtual power plant, use the power plant energy control center to adjust the distributed energy to be managed data per unit time and per unit area, count and analyze the dispatchable amount that can be used, until the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and per unit area meets the expected difficulty, operability and difference expectations, that is, the dispatchable interval boundary that can be used exceeds the preset dispatchable interval boundary that can be used.

[0020] Step S600, if the statistics and analysis in step S500 on the dispatchable quantity that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and per unit area exceeds the preset usable dispatchable interval boundary, then the power plant energy control center used to adjust the distributed energy to be managed data per unit time and per unit area will be dispatched to the data optimization platform at each distributed energy load fluctuation abnormality.

[0021] In this embodiment, the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area reflects the overall difficulty, operability and individual differences of the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area to be dispatched to the data optimization platform for abnormal distributed energy load fluctuations. If the available dispatchable amount exceeds the preset usable dispatchable interval boundary, it indicates that the overall difficulty, operability and individual differences of the power plant energy control center of the data optimization platform for abnormal distributed energy load fluctuations to adjust the distributed energy to be managed data per unit time and unit area meet the required expectations, and each distributed energy load fluctuation data optimization platform can use the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area to start dispatching management.

[0022] In an optional implementation, before step S300 counts and analyzes the dispatchable amount that can be used by the power plant energy control center of each user to adjust the distributed energy to be managed data per unit time and unit area, or before step S400 counts and analyzes the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area, the power plant energy control center used can adjust the distributed energy to be managed data per unit time and unit area and dispatch it to the data optimization platform at each distributed energy load fluctuation abnormality. Therefore, after statistics and analysis of the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area does not exceed the preset dispatchable interval boundary, and it is necessary to cyclically perform data statistics and analysis on the maximum / minimum output power per unit time, load tracking capability, equipment start and stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of the distributed energy in the virtual power plant, and use the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area, it is necessary to delete or withdraw the dispatched power plant energy control center from the data optimization platform at each distributed energy load fluctuation abnormality to adjust the distributed energy to be managed data per unit time and unit area.

[0023] Another embodiment of the present invention is a process of a distributed energy optimization scheduling method based on a virtual power plant.

[0024] The risk signal is output regularly, and the risk signal indicates that the power plant energy control center should manage the randomness of the adjustment of the distributed energy to be managed data per unit time and per unit area.

[0025] Specifically, outputting the risk signal indicates that the present invention is different from the existing randomly dispatched power plant energy control center in adjusting the distributed energy to be managed data per unit time and unit area. That is, before the data optimization platform intends to enter the dispatching management content at the potential distributed energy load fluctuation abnormality, it will get a prompt about the way the dispatching management content is different from the existing randomly dispatched power plant energy control center in adjusting the distributed energy to be managed data per unit time and unit area.

[0026] Receive risk feedback and determine whether the feedback indicates acceptance of the randomness of the adjustment of distributed energy to be managed data per unit time and per unit area by the power plant energy control center.

[0027] Specifically, after the risk signal indicates the information that the to-be-initiated scheduling management content is different from the existing random scheduling management content, the data optimization platform at the potential abnormal distributed energy load fluctuation can provide feedback based on the risk signal, and the feedback indicates whether to accept the management of the randomness of the distributed energy data to be managed per unit time and per unit area for the power plant energy centralized control center.

[0028] If the feedback indicates acceptance of the management of the randomness of the distributed energy data to be managed per unit time and per unit area for the power plant energy centralized control center, continue to use the power plant energy centralized control center to adjust the distributed energy data to be managed per unit time and per unit area and perform scheduling based on the adjusted distributed energy data to be managed per unit time and per unit area for the power plant energy centralized control center to initiate the scheduling management.

[0029] If the feedback indicates non-acceptance of the management of the randomness of the distributed energy data to be managed per unit time and per unit area for the power plant energy centralized control center, then the scheduling management content is not opened to the potential scheduling management user, and the process of regularly outputting the risk signal, receiving, and determining the risk feedback is carried out.

[0030] Using a distributed energy optimal scheduling method based on a virtual power plant in this embodiment, it can fully ensure that all parties using the scheduling management understand the management of the random scheduling method for adjusting the distributed energy data to be managed per unit time and per unit area of the existing power plant energy centralized control center by the distributed energy optimal scheduling method based on a virtual power plant of the present invention before the scheduling management starts, and ensure the full prior knowledge of the data optimization platform at the abnormal distributed energy load fluctuation before participating. In an alternative embodiment, the notification information can be sent to the data optimization platform at the abnormal distributed energy load fluctuation in the form of a dialog box or a prompt message, or can be reflected by setting a dedicated scheduling management area. In the dedicated scheduled scheduling management area, the information output can be reflected in ways such as the identification and prompt of the scheduling management area; receiving the feedback information can be reflected in whether the data optimization platform at the abnormal distributed energy load fluctuation enters or does not enter the dedicated scheduling management area.

[0031] The flow of a distributed energy optimal scheduling method based on a virtual power plant in another embodiment of the present invention.

[0032] In this embodiment, after statistics and analysis on whether the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area exceeds the preset usable dispatchable interval boundary, if statistics and analysis on the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area exceeds the preset usable dispatchable interval boundary, it is determined whether the conditions for dispatching the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area to the data optimization platform at the abnormal distributed energy load fluctuation location under the usable dispatchable interval boundary have reached the preset conditional interval boundary.

[0033] If statistics and analysis show that the conditions for the power plant energy control center to adjust and dispatch the distributed energy to be managed data per unit time and unit area to the data optimization platform for each distributed energy load fluctuation abnormality under the boundary of the usable dispatchable interval have not reached the preset condition interval boundary, the power plant energy control center used will adjust and dispatch the distributed energy to be managed data per unit time and unit area to the data optimization platform for each distributed energy load fluctuation abnormality.

[0034] If the conditions for scheduling determined by statistics and analysis have reached the boundaries of the preset conditional interval, then the boundaries of the new available scheduling interval will be analyzed and the available scheduling amount that can be used by the power plant energy control center to adjust the distributed energy management data per unit time and per unit area will be compared with the new available scheduling interval boundaries.

[0035] Through the distributed energy optimization scheduling method based on virtual power plant of the embodiment, the dynamic adjustment of the dispatchable interval boundary that can be used for the power plant energy control center to adjust the distributed energy to be managed data per unit time and per unit area can be realized. Optionally, since the distributed energy optimization scheduling method based on virtual power plant of the embodiment of the present invention only manages the distributed energy to be managed data per unit time and per unit area of ​​the power plant energy control center in terms of grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters, and the dispatchable amount that can be used when the virtual power plant is running, the dynamic management of the dispatchable interval boundary that can be used can utilize the randomness of the distributed energy to be managed data per unit time and per unit area of ​​the power plant energy control center in addition to the grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters when the virtual power plant is running to manage the distributed energy to be managed data per unit time and per unit area in random scheduling and formation, which reflects the further management of randomness.

[0036] Another embodiment of the present invention is a process of a distributed energy optimization scheduling method based on a virtual power plant.

[0037] In this implementation, if the statistics and analysis of the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area does not exceed the boundary of the usable dispatchable interval and it is necessary to cyclically perform statistics and analysis on the grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters when the virtual power plant is running, and use the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area, as well as statistics and analysis on the dispatchable amount that can be used, the current power plant energy control center performs the adjustment of the distributed energy to be managed data per unit time and unit area and the dispatchable amount that can be used by the corresponding parties.

[0038] Statistics and analysis are performed under the comparison round between the currently available dispatchable quantity and the boundary of the available dispatchable interval. The grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters are repeatedly performed when the virtual power plant is in operation. The power plant energy control center is used to adjust the distributed energy to be managed data per unit time and per unit area, and statistics and analysis are performed to see whether the repetition conditions of the available dispatchable quantity meet the preset repetition interval boundaries.

[0039] Here, the comparison round between the currently available dispatchable amount and the boundary of the available dispatchable interval refers to the comparison between the currently available dispatchable amount and the boundary of the available dispatchable interval conducted by the power plant energy control center for adjusting the distributed energy to be managed data per unit time and unit area since the last scheduling of the power plant energy control center for adjusting the distributed energy to be managed data per unit time and unit area.

[0040] Specifically, after using the data on the maximum / minimum output power per unit time of distributed energy in the virtual power plant, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability, and using the power plant energy control center including the grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters when the virtual power plant is running to adjust the distributed energy to be managed data per unit time and per unit area, if the available dispatchable amount does not exceed the preset boundary of the available dispatchable interval, it is necessary to reuse the edge statistics and analysis gateway to integrate the data on the maximum / minimum output power per unit time of distributed energy in the virtual power plant, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability, and then re-count and analyze the power plant energy control center to adjust the distributed energy to be managed data per unit time and per unit area, so as to count and analyze the corresponding available dispatchable amount. However, it is possible that after several consecutive re-counts and analyses of the grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters, and adjustments of the distributed energy to be managed data per unit time and per unit area by the power plant energy control center during the operation of the virtual power plant, the obtained usable dispatchable amount still cannot exceed the preset usable dispatchable interval boundary. In order to save the time required for re-counting and analyzing the grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters, and adjustments of the distributed energy to be managed data per unit time and per unit area by the power plant energy control center during the operation of the virtual power plant, it is necessary to limit such repetitive conditions.

[0041] If the repetition condition does not reach the preset repetition interval boundary, the process of statistics and analysis of grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters, and the power plant energy control center's adjustment of distributed energy management data per unit time and per unit area during the operation of the virtual power plant and its available dispatchable quantity are compared with the preset usable dispatchable interval boundary.

[0042] Statistics and analysis show that the repetition condition has reached the preset repetition interval boundary in the comparison round between the currently available dispatchable amount and the available dispatchable interval boundary. Then, the energy control center of the power plant with the largest archived available dispatchable amount adjusts the distributed energy to be managed data per unit time and unit area as the power plant energy control center to be dispatched. The distributed energy to be managed data per unit time and unit area adjusted by the statistics and analysis of the energy control center of the power plant to be dispatched is dispatched to the data optimization platform at the abnormal distributed energy load fluctuation point to start dispatching management.

[0043] By utilizing this implementation mode of the present invention, in order to avoid the management of the power plant energy control center performing adjustments to the distributed energy to be managed data per unit time and unit area being too lengthy and affecting the user experience, it is necessary to limit the conditions for repeatedly using the power plant energy control center to perform adjustments to the distributed energy to be managed data per unit time and unit area. If the distributed energy to be managed data per unit time and unit area is adjusted to the data optimization platform for each distributed energy load fluctuation abnormality since the last dispatch of the power plant energy control center, in order to avoid further statistics and analysis of the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters, and the time consumed in the process of using the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area during the operation of the virtual power plant, it is necessary to use the power plant energy control center with the largest usable dispatchable amount to adjust the distributed energy to be managed data per unit time and unit area among the adjustments of the distributed energy to be managed data per unit time and unit area used by the power plant energy control center after the last dispatch of the distributed energy to be managed data per unit time and unit area as the power plant energy control center to be dispatched to adjust the distributed energy to be managed data per unit time and unit area and dispatch it to the data optimization platform for each distributed energy load fluctuation abnormality.

[0044] By utilizing the above embodiments of the present invention, statistics and analysis are performed on the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area, and compared with the preset usable dispatchable interval boundary, so as to realize the management of the distributed energy to be managed data per unit time and unit area by the power plant energy control center through the management of the dispatchable amount that can be used for the distributed energy to be managed data per unit time and unit area. Compared with the existing random scheduling of the distributed energy to be managed data per unit time and unit area by the power plant energy control center, which brings about the prediction troubles and low accuracy problems, a distributed energy optimization scheduling method based on a virtual power plant in each embodiment of the present invention can realize the management of the scheduling management difficulty, operability and the horizontal differences of the distributed energy to be managed data per unit time and unit area by the power plant energy control center through the management of the dispatchable amount that can be used for the distributed energy to be managed data per unit time and unit area, thereby improving the usability of the data optimization platform at the abnormal point of distributed energy load fluctuation and improving the accuracy of scheduling.

[0045] In a management method for adjusting the distributed energy to be managed data per unit time and per unit area by a power plant energy control center in another embodiment of the present invention, a process is provided for statistics and analysis of the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and per unit area on a data optimization platform at abnormal points of distributed energy load fluctuations.

[0046] Statistics and analysis of grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and dispatchable quantities of model calibration parameters when the virtual power plant is running.

[0047] Specifically, the edge statistics and analysis gateway integrates the data of maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of distributed energy in the virtual power plant, and the power plant energy control center adjusts the distributed energy to be managed per unit time and per unit area, including the grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters when the virtual power plant is running, and then the power plant energy control center that plans to dispatch to the data optimization platform at the abnormal load fluctuation of each distributed energy to adjust the distributed energy to be managed per unit time and per unit area may include or not include the grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters when the virtual power plant is running. If the distributed energy load fluctuation abnormality data optimization platform's power plant energy centralized control center performs unit time and unit area distributed energy to be managed data adjustment including the virtual power plant operation grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters, then the virtual power plant operation grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters that can be used for scheduling are counted and analyzed, if the virtual power plant operation grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters are not included, then the next step is to count and analyze the available scheduling amount of abnormal data at the next distributed energy load fluctuation abnormality. Similarly, it can be understood that in the process of counting and analyzing the available scheduling amount of abnormal data at other distributed energy load fluctuation abnormalities, statistics and analysis are only performed when the distributed energy load fluctuation abnormality data optimization platform's power plant energy centralized control center performs unit time and unit area distributed energy to be managed data adjustment including the distributed energy load fluctuation abnormality.

[0048] Statistics and analysis are conducted on the available dispatchable amount of abnormal data at abnormal points where distributed energy load fluctuates abnormally.

[0049] As before, when the virtual power plant is running, the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters are usually the abnormal data of the abnormal load fluctuation of distributed energy resources that have a greater impact on the overall dispatch management progress and operation. Furthermore, after the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters are running in the virtual power plant, the available dispatchable amount of the abnormal data of the abnormal load fluctuation of distributed energy resources with a slightly weaker influence can also be sequentially counted and analyzed.

[0050] Statistics and analysis are conducted on the available dispatchable amount of abnormal data at the abnormal points of distributed energy load fluctuation.

[0051] Generally speaking, when the distributed energy to be managed data per unit time and unit area is adjusted in the energy centralized control center of a power plant that is to be dispatched to the distributed energy load fluctuation abnormality data optimization platform, multiple types of distributed energy load fluctuation abnormality data can be included. The available dispatchable quantities of various types of distributed energy load fluctuation abnormality data can be counted and analyzed in turn according to the influence of various types of distributed energy load fluctuation abnormality data.

[0052] The power plant energy control center of the distributed energy load fluctuation abnormal data optimization platform counts and analyzes the available dispatchable amount by using the statistics and analysis of the abnormal data of each distributed energy load fluctuation abnormal point to adjust the available dispatchable amount of distributed energy to be managed data per unit time and unit area.

[0053] Specifically, after counting and analyzing the available dispatchable amounts of abnormal data at various types of distributed energy load fluctuation abnormalities, the dispatchable amounts that can be used by the power plant energy centralized control center of the distributed energy load fluctuation abnormality data optimization platform to adjust the distributed energy to be managed data per unit time and per unit area can be counted and analyzed in a preset manner. In an optional implementation, the dispatchable amount that can be used by the power plant energy centralized control center of a distributed energy load fluctuation abnormality data optimization platform to adjust the distributed energy to be managed data per unit time and per unit area can be the sum of the available dispatchable amounts of all distributed energy load fluctuation abnormality data included in the distributed energy to be managed data adjustment per unit time and per unit area by the power plant energy centralized control center, or the weighted sum.

[0054] The process of counting and analyzing the available dispatchable amounts of the three types of abnormal data of the distributed energy load fluctuation abnormality of the distributed energy load fluctuation abnormality data optimization platform should be understood that for a dispatching management content, it may include more or fewer abnormal data of the distributed energy load fluctuation abnormality. In this case, the available dispatchable amounts of various abnormal data of the distributed energy load fluctuation abnormality can be similarly counted and analyzed, and the dispatchable amounts that can be used by the power plant energy control center of the distributed energy load fluctuation abnormality data optimization platform to adjust the distributed energy to be managed data per unit time and per unit area can be counted and analyzed. In addition, the process of counting and analyzing the available dispatchable amounts of various abnormal data of the distributed energy load fluctuation abnormality may not be in the order of the influence of the abnormal data of the distributed energy load fluctuation abnormality as above, and any other arbitrary order is also feasible.

[0055] Furthermore, the process of statistics and analysis of the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area can be statistically analyzed by using the dispatchable amount that can be used by the power plant energy centralized control center of the data optimization platform at each distributed energy load fluctuation abnormality to adjust the distributed energy to be managed data per unit time and per unit area, or by performing preset operations on all the used power plant energy centralized control centers to adjust the distributed energy to be managed data per unit time and per unit area, thereby statistics and analysis of the dispatchable amount that can be used by the used power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area. As an optional implementation method, the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area can be the dispatchable amount that can be used by the power plant energy centralized control center of the data optimization platform at each distributed energy load fluctuation abnormality to adjust the distributed energy to be managed data per unit time and per unit area and the algorithm for the difference in the dispatchable amount that can be used.

[0056] Using a further optional implementation of the present invention, the dispatchable amount that can be used for abnormal data at the abnormal fluctuation point of each type of distributed energy load is not fixed, but can be adjusted dynamically. The process of dynamically adjusting the dispatchable amount that can be used for abnormal data at the abnormal fluctuation point of distributed energy load using an implementation of the present invention. It should be understood that for multiple types of abnormal data at the abnormal fluctuation point of distributed energy load, the dynamic adjustment of the dispatchable amount that can be used for abnormal data at the abnormal fluctuation point of distributed energy load in the implementation can be applied respectively, and other possible dynamic adjustment methods are also applicable.

[0057] Statistics and analysis of the available dispatchable quantity and initial information of abnormal data at abnormal points of distributed energy load fluctuations.

[0058] Specifically, for a statistical and analytical dispatching management content, the expected frequency of occurrence of abnormal data at the abnormal fluctuation of distributed energy load is statistically analyzed, and the expected occurrence ratio is used as the initial information of the abnormal data at the abnormal fluctuation of distributed energy load.

[0059] After the power plant energy centralized control center has carried out the scheduling of the distributed energy to be managed data adjustment per unit time and per unit area under the preset conditions, the abnormal data of the abnormal load fluctuation of the distributed energy is counted and analyzed again.

[0060] It is understandable that although the predetermined frequency of occurrence of abnormal data at the abnormal point where the distributed energy load fluctuates abnormally is statistically analyzed as before, in the limited number of times that the power plant energy control center adjusts, uses and dispatches the distributed energy to be managed data per unit time and unit area, the occurrence of abnormal data at the abnormal point where the distributed energy load fluctuates abnormally may not completely comply with the predetermined frequency of occurrence. In the dispatching of the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area under the preset conditions, the probability of occurrence of abnormal data at the abnormal point where the distributed energy load fluctuates abnormally is statistically analyzed as the secondary information of the abnormal data at the abnormal point where the distributed energy load fluctuates abnormally.

[0061] By using the available dispatchable quantity, initial information and secondary information, the dispatchable quantity of new energy available for abnormal data at abnormal points of distributed energy load fluctuations is counted and analyzed.

[0062] Therefore, it can be understood that when the power plant energy control center under the preset conditions adjusts and dispatches the distributed energy to be managed data per unit time and unit area, if the probability of occurrence of abnormal data at the abnormal load fluctuation of the distributed energy exceeds expectations, the available dispatchable amount of the abnormal data at the abnormal load fluctuation of the distributed energy will be reduced.

[0063] Furthermore, if the dynamic adjustment of the available dispatchable quantity of abnormal data at the abnormal location of distributed energy load fluctuation has been carried out during the dispatch management process, the available dispatchable quantity and the probability of occurrence of abnormal data at the abnormal location of distributed energy load fluctuation in the previous preset power plant energy centralized control center for distributed energy to be managed data adjustment and dispatch per unit time and per unit area will be used as the available dispatchable quantity and the benchmark proportion of the abnormal data at the abnormal location of distributed energy load fluctuation in the new dynamic adjustment. That is, the available dispatchable quantity and the information in the previous dynamic adjustment will be used as the available dispatchable quantity and the initial information in the next dynamic adjustment.

[0064] By dynamically adjusting the available dispatchable amount of abnormal data at abnormal points where distributed energy load fluctuates abnormally in this embodiment, it is possible to avoid affecting operability due to the randomness of abnormal data at abnormal points where distributed energy load fluctuates abnormally during the adjustment and scheduling of distributed energy to be managed data per unit time and per unit area in the actual power plant energy control center, thereby further achieving optimization and management of randomness.

[0065] Using a further optional embodiment of the present invention, Statistics and analysis of the initial schedulable interval boundaries that can be used.

[0066] Specifically, before the scheduling management starts, the initially usable schedulable interval boundary is a set default initial value.

[0067] After the power plant energy control center carries out the scheduling of the adjustment of the distributed energy to be managed data per unit time and unit area under the preset conditions, the fluctuation error of the dispatchable quantity that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area in each scheduling is counted and analyzed.

[0068] Specifically, in the actual scheduling process of the power plant energy control center adjusting the distributed energy to be managed data per unit time and unit area, in fact, the distribution of the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area that meets the condition of exceeding the boundary of the usable dispatchable interval may be uneven. The fluctuation error of the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area in the scheduling of the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area under the preset conditions can reflect the scheduling status of the dispatchable amount that can be used in the scheduling of the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area under the preset conditions.

[0069] According to the fluctuation error between the initial usable dispatchable interval boundary and the usable dispatchable quantity, the new usable dispatchable interval boundary is statistically analyzed.

[0070] Furthermore, if the dynamic adjustment of the available dispatchable interval boundary has been carried out during the dispatch management process, the available dispatchable interval boundary used by the power plant energy centralized control center in the previous preset condition to adjust and dispatch the distributed energy to be managed data per unit time and per unit area is used as the initial available dispatchable interval boundary in the new dynamic adjustment. By dynamically adjusting the interval boundary, it is possible to avoid affecting the process of the power plant energy centralized control center adjusting and dispatching the distributed energy to be managed data per unit time and per unit area due to the interval boundary being set too high or too low.

[0071] The present invention also provides an operating system of a distributed energy optimization scheduling method based on a virtual power plant, comprising: The virtual power plant data acquisition module is used to utilize edge statistics and analyze the data of the maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of distributed energy in the virtual power plant integrated by the gateway.

[0072] For dispatch management with specific content, the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters when the virtual power plant is running are preferably the factors with the greatest influence on dispatch management that are statistically analyzed using the dispatch management content. Generally, obtaining the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters when the virtual power plant is running should be conducive to completing the abnormal data of the abnormal load fluctuation of distributed energy set according to the dispatch management content. For example, for confrontational dispatch management, the grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters when the virtual power plant is running can be set to the target characteristics or roles with the greatest combat effectiveness. The data of the maximum / minimum output power per unit time of distributed energy in the virtual power plant, load tracking capability, equipment start-stop time and fault frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability may include: the number, size, location, etc. of grid load and power flow, meteorological and environmental data, energy balance and power dispatch data, real-time prediction and model calibration parameters when the virtual power plant is running.

[0073] The statistical and analytical module of the power plant energy control center is used to use the data of the maximum / minimum output power per unit time, load tracking capability, equipment start-stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of distributed energy in the virtual power plant to adjust the distributed energy management data per unit time and per unit area. The power plant energy control center adjusts the distributed energy management data per unit time and per unit area when the virtual power plant is running. The data includes grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters.

[0074] Specifically, the power plant energy centralized control center used by the statistical and analytical module of the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area shall include the grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters when the virtual power plant is in operation. The power plant energy centralized control center shall adjust the distributed energy to be managed data per unit time and per unit area, which shall include the power plant energy centralized control center to be dispatched to each distributed energy load fluctuation abnormal data optimization platform that is statistically analyzed using the dispatching management content, and the distributed energy to be managed data per unit time and per unit area shall be adjusted, wherein the grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters when the virtual power plant is in operation are included in the power plant energy centralized control center to be dispatched to one or more distributed energy load fluctuation abnormal data optimization platforms. For example, when the virtual power plant is running, the grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters should be used and included in the power plant energy control center's statistics and analysis module when the power plant energy control center uses the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area.

[0075] The available dispatchable quantity module is used to count and analyze the dispatchable quantity that can be used by the power plant energy control center of the data optimization platform at each distributed energy load fluctuation abnormality to adjust the distributed energy to be managed data per unit time and unit area, and to count and analyze the dispatchable quantity that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area.

[0076] Specifically, the available dispatchable quantity module can use the power plant energy centralized control center statistics and analysis module to be dispatched to each distributed energy load fluctuation abnormal place data optimization platform to adjust the distributed energy to be managed data per unit time and unit area, and the power plant energy centralized control center that is statistically analyzed to be dispatched to the distributed energy load fluctuation abnormal place data optimization platform to adjust the available dispatchable quantity per unit time and unit area distributed energy to be managed data. It can be understood that the power plant energy centralized control center generated by the random scheduling method performs the adjustment of the distributed energy to be managed data per unit time and unit area, and the differences in difficulty, operability, etc. are also random, so that the same scheduling management and the same period may have unexpected unevenness, and this unexpected unevenness is also one of the important reasons for the existing random scheduling principle to affect the user prediction trouble and stickiness. Using an embodiment of the present invention, using the available dispatchable quantity module to count and analyze the power plant energy centralized control center of the distributed energy load fluctuation abnormal place data optimization platform to adjust the available dispatchable quantity per unit time and unit area distributed energy to be managed data is an important means to manage the existing random scheduling principle.

[0077] By using an implementation mode of the present invention, the available dispatchable quantity module can count and analyze the dispatchable quantity that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and unit area based on the statistics and analysis of the dispatchable quantity that can be used by the power plant energy centralized control center of the data optimization platform for each distributed energy load fluctuation abnormality. Of course, by using other feasible implementation modes, it is also possible to count and analyze the dispatchable quantity that can be used by the power plant energy centralized control center used by the power plant energy centralized control center statistics and analysis module to adjust the distributed energy to be managed data per unit time and unit area by performing preset operations on all the power plant energy centralized control centers used by the power plant energy centralized control center statistics and analysis module. In this case, the available dispatchable quantity module may not count and analyze the dispatchable quantity that can be used by the power plant energy centralized control center of the data optimization platform for each distributed energy load fluctuation abnormality to adjust the distributed energy to be managed data per unit time and unit area.

[0078] In this embodiment, the available dispatchable amount reflects the difficulty and operability of the dispatching management content reflected by the adjustment of the distributed energy to be managed data per unit time and per unit area by the power plant energy centralized control center, and the difference between the adjustment of the distributed energy to be managed data per unit time and per unit area by the power plant energy centralized control center of the data optimization platform at the abnormal location of each distributed energy load fluctuation. Through the available dispatchable amount, the difference in the difficulty and operability of the adjustment of the distributed energy to be managed data per unit time and per unit area by the power plant energy centralized control center dispatched by the data optimization platform at the abnormal location of each distributed energy load fluctuation can be obtained, which can be distinguished from the unexpected difficulty, operability and difference reflected in the existing random dispatching principle.

[0079] The available dispatchable quantity decision module is used to count and analyze whether the available dispatchable quantity for adjusting the distributed energy to be managed data per unit time and unit area by the power plant energy centralized control center exceeds the preset available dispatchable interval boundary.

[0080] Specifically, the preset usable dispatchable interval boundary can be set by utilizing the various dispatching management elements contained in the distributed energy to be managed data adjustment per unit time and per unit area by the power plant energy control center, and utilizing the differences between the distributed energy to be managed data adjustment per unit time and per unit area by the power plant energy control center of the data optimization platform at each distributed energy load fluctuation abnormality. In this embodiment, when the usable dispatchable quantity decision module counts and analyzes the dispatchable quantity that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and per unit area and does not exceed the preset usable dispatchable interval boundary, it indicates that the difficulty and operability of the overall dispatching management and the differences between the distributed energy to be managed data adjustment per unit time and per unit area by the power plant energy control center of the data optimization platform at each distributed energy load fluctuation abnormality are beyond expectations, and then it is necessary to notify the virtual power plant data acquisition module, the power plant energy control center statistics and analysis module, and the usable dispatchable quantity module to respectively conduct the above again. The process of statistics and analysis of grid load and power flow, meteorological and environmental data, energy balance and power dispatching data, real-time prediction and model calibration parameters when the virtual power plant is running, and the use of the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area, statistics and analysis of the available dispatchable amount, until the available dispatchable amount decision module statistics and analysis of the used power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area meets the expected difficulty, operability and difference expectations, that is, the boundary of the usable dispatchable interval exceeds the preset boundary of the usable dispatchable interval.

[0081] The data transmission module for abnormal distributed energy load fluctuations is connected to the statistical and analysis module of the power plant energy centralized control center and the schedulable quantity decision-making module that can be used, and is used to utilize the power plant energy centralized control center from the schedulable quantity decision-making module that can be used to adjust the schedulable quantity that can be used for the distributed energy to be managed per unit time and per unit area, and when the schedulable quantity that can be used exceeds the preset schedulable interval boundary signal, the power plant energy centralized control center statistical and analysis module is used to adjust the distributed energy to be managed per unit time and per unit area to be respectively scheduled to each distributed energy load fluctuation abnormal data optimization platform.

[0082] The abnormal data monitoring module at the abnormal distributed energy load fluctuation location is used to adjust the schedulable quantity of the abnormal data at each distributed energy load fluctuation location required for the statistical and analysis of the schedulable quantity of the distributed energy to be managed per unit time and per unit area by the power plant energy centralized control center in the schedulable quantity module that can be used. Among them, the abnormal data monitoring module at the abnormal distributed energy load fluctuation location is used to statistically analyze the schedulable quantity and initial information of the abnormal data at the abnormal distributed energy load fluctuation location, and is used to utilize the occurrence probability of the abnormal data at the abnormal distributed energy load fluctuation location in each adjustment of the distributed energy to be managed per unit time and per unit area by the power plant energy centralized control center statistical and analysis module to statistically analyze the recurrence information of the abnormal data at the abnormal distributed energy load fluctuation location, and to statistically analyze the new schedulable quantity of the abnormal data at the abnormal distributed energy load fluctuation location by using the schedulable quantity, initial information, and recurrence information.

[0083] In this embodiment, the schedulable quantity that can be used for the adjustment of the distributed energy to be managed per unit time and per unit area by the power plant energy centralized control center reflects the overall difficulty, operability, and differences between individuals of the distributed energy to be managed per unit time and per unit area by the power plant energy centralized control center to be scheduled to each distributed energy load fluctuation abnormal data optimization platform. If the schedulable quantity that can be used exceeds the preset schedulable interval boundary, it indicates that the overall difficulty, operability, and differences between individuals of the distributed energy to be managed per unit time and per unit area by the power plant energy centralized control center of each distributed energy load fluctuation abnormal data optimization platform meet the required expectations, and each distributed energy load fluctuation abnormal data optimization platform can start scheduling management by using the distributed energy to be managed per unit time and per unit area by the power plant energy centralized control center.

[0084] In an optional implementation, a register may also be included, the register is connected to the power plant energy centralized control center statistics and analysis module, and is used to store the power plant energy centralized control center used by the power plant energy centralized control center statistics and analysis module to adjust the distributed energy to be managed data per unit time and per unit area. Thus, when the available dispatchable quantity decision module statistics and analyzes the available dispatchable quantity for the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area exceeds the preset usable dispatchable interval boundary, the distributed energy load fluctuation abnormality data transmission module can take out the stored power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area from the register, and send the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area to the data optimization platform for each distributed energy load fluctuation abnormality.

[0085] In an optional embodiment, the data transmission module at the point where the distributed energy load fluctuates abnormally may not rely on the indication signal of the available dispatchable quantity decision module, but may directly dispatch the distributed energy to be managed data per unit time and unit area adjusted by the power plant energy control center used by the statistics and analysis module of the power plant energy control center, or the distributed energy to be managed data per unit time and unit area stored in the register of the power plant energy control center to each data optimization platform at the point where the distributed energy load fluctuates abnormally. Therefore, when the available dispatchable quantity decision module counts and analyzes the available dispatchable quantity for the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area, and the available dispatchable quantity does not exceed the preset usable dispatchable interval boundary, and it is necessary to cyclically perform data statistics and analysis on the maximum / minimum output power per unit time, load tracking capability, equipment start and stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of the distributed energy in the virtual power plant, and use the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area, the distributed energy load fluctuation abnormality data transmission module needs to issue a command to each distributed energy load fluctuation abnormality data optimization platform to delete or withdraw the dispatched power plant energy control center from each distributed energy load fluctuation abnormality data optimization platform to adjust the distributed energy to be managed data per unit time and unit area.

[0086] Using the above embodiments of the present invention, the schedulable amount that can be used for adjusting the distributed energy management data per unit time and per unit area in the power plant energy centralized control center is statistically analyzed and compared with the preset boundary of the schedulable range that can be used, so as to realize the management of the distributed energy management data adjustment per unit time and per unit area in the power plant energy centralized control center through the management of the schedulable amount that can be used for adjusting the distributed energy management data per unit time and per unit area in the power plant energy centralized control center. Compared with the problems of prediction trouble and low accuracy caused by the existing random scheduling of the distributed energy management data adjustment per unit time and per unit area in the power plant energy centralized control center, the distributed energy management data adjustment management system per unit time and per unit area in the power plant energy centralized control center of each embodiment of the present invention can realize the management of the scheduling management difficulty, operability and horizontal difference of the distributed energy management data adjustment per unit time and per unit area in the power plant energy centralized control center through the management of the schedulable amount that can be used for adjusting the distributed energy management data per unit time and per unit area in the power plant energy centralized control center, improve the usability of the data optimization platform for abnormal distributed energy load fluctuations, and improve the accuracy of scheduling.

[0087] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various equivalent changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalent scope.

Claims

1. A distributed energy optimization scheduling method based on a virtual power plant, characterized in that: include: Use edge statistics to analyze the maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability data of distributed energy in the gateway integrated virtual power plant; The power plant energy centralized control center uses the data of the maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and fault frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of the distributed energy in the virtual power plant to adjust the data to be managed for distributed energy per unit time and per unit area. The power plant energy centralized control center adjusts the data to be managed for distributed energy per unit time and per unit area, including grid load and power flow, meteorological and environmental data, energy balance and power scheduling data, real-time prediction and model calibration parameters when the virtual power plant is running; Use the power plant energy centralized control center to adjust the dispatchable amount of distributed energy to be managed per unit time and per unit area; Statistics and analysis of the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy management data per unit time and per unit area to see if it exceeds the preset dispatchable interval boundary; When statistics and analysis of the dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area exceeds the preset dispatchable interval boundary, the power plant energy centralized control center used to adjust the distributed energy to be managed data per unit time and per unit area will be dispatched to the data optimization platform at each distributed energy load fluctuation abnormality; When statistics and analysis of the power plant energy centralized control center's adjustment of distributed energy management data per unit time and per unit area show that the available dispatchable amount does not exceed the preset dispatchable interval boundary, the maximum / minimum output power per unit time of distributed energy in the virtual power plant, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability data statistics and analysis are performed cyclically, and the power plant energy centralized control center is used to adjust the distributed energy management data per unit time and per unit area, as well as to statistics and analyze the available dispatchable amount.

2. The distributed energy optimization scheduling method based on virtual power plant according to claim 1 is characterized in that: When statistics and analysis are performed on the distributed energy to be managed data per unit time and per unit area by the power plant energy centralized control center, and the available dispatchable amount does not exceed the preset dispatchable interval boundary, it also includes: Archive the current power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and unit area and its corresponding data optimization platform for abnormal load fluctuations of each distributed energy. The power plant energy centralized control center can adjust the distributed energy to be managed data per unit time and unit area. The dispatchable amount that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and unit area; Statistics and analysis: after the last time the power plant energy centralized control center adjusted the data of distributed energy to be managed per unit time and per unit area, the data statistics and analysis of the maximum / minimum output power per unit time, load tracking capability, equipment start / stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of the distributed energy in the virtual power plant are repeated, and the power plant energy centralized control center is used to adjust the data of distributed energy to be managed per unit time and per unit area, as well as statistics and analysis on whether the repetition conditions of the available dispatchable quantity meet the preset repetition interval boundaries; When the statistics and analysis of the repetition conditions have reached the preset repetition interval boundary, the energy control center of the power plant with the largest available dispatchable amount that is archived and analyzed adjusts the distributed energy to be managed data per unit time and per unit area as the energy control center of the power plant to be dispatched to adjust the distributed energy to be managed data per unit time and per unit area; When the statistics and analysis of the repetition conditions do not reach the preset repetition interval boundary, the data statistics and analysis of the maximum / minimum output power per unit time, load tracking capability, equipment start and stop time and failure frequency, energy supply stability and volatility, energy storage equipment parameters, energy quality and voltage stability of the distributed energy in the gateway integrated virtual power plant are cyclically performed, and the power plant energy control center is used to adjust the distributed energy to be managed data per unit time and per unit area, and to count and analyze the available dispatchable amount.

3. The distributed energy optimization scheduling method based on virtual power plant according to claim 1 is characterized in that: After the statistics and analysis of the power plant energy centralized control center for adjusting the distributed energy to be managed data per unit time and per unit area and the available dispatchable amount exceeds the preset dispatchable interval boundary, it also includes: Determine whether the conditions for adjusting and dispatching the distributed energy to be managed data per unit time and per unit area to the data optimization platform at the abnormal load fluctuation location of each distributed energy source have reached the preset condition interval boundary under the boundary of the available dispatchable interval; When the conditions for scheduling based on statistics and analysis have reached the preset condition interval boundary, new available scheduling interval boundaries are analyzed based on statistics and analysis.

4. According to claim 3, a distributed energy optimization scheduling method based on a virtual power plant is characterized in that: The statistics and analysis of the new usable schedulable interval boundaries include: Statistics and analysis of the initial schedulable interval boundaries that can be used; Statistics and analysis of fluctuation errors in dispatchable quantities that can be used by the power plant energy centralized control center to adjust the distributed energy to be managed data per unit time and per unit area during each dispatch; The new usable schedulable interval boundary is statistically analyzed based on the fluctuation error between the initial usable schedulable interval boundary and the usable schedulable amount, wherein the new usable schedulable interval boundary is the fluctuation error between the initial usable schedulable interval boundary and the usable schedulable amount.

5. The distributed energy optimization scheduling method based on virtual power plant according to claim 1 is characterized in that: The step of using the power plant energy centralized control center to adjust the dispatchable amount of distributed energy to be managed data per unit time and per unit area includes: Statistics and analysis of abnormal load fluctuations of distributed energy sources are carried out by optimizing the power plant energy control center on the platform to adjust the available dispatchable amount of distributed energy to be managed per unit time and per unit area; The algorithm of the dispatchable amount that can be used by the power plant energy control center of the data optimization platform at the abnormal distributed energy load fluctuation point to adjust the distributed energy to be managed data per unit time and unit area and the difference in the dispatchable amount that can be used is used as the dispatchable amount that can be used by the power plant energy control center to adjust the distributed energy to be managed data per unit time and unit area.

6. The distributed energy optimization scheduling method based on virtual power plant according to claim 1 is characterized by: Before the step of adjusting the distributed energy to be managed data per unit time and per unit area by the power plant energy centralized control center and dispatching them to the data optimization platform for each distributed energy load fluctuation abnormality, statistics and analysis are performed on the corresponding available dispatchable quantities and initial information of the abnormal data at each distributed energy load fluctuation abnormality included in the adjustment of the distributed energy to be managed data per unit time and per unit area by the power plant energy centralized control center, wherein the initial information is the predetermined occurrence frequency of the abnormal data at the corresponding distributed energy load fluctuation abnormality; After the power plant energy centralized control center performs the scheduling of the adjustment of the distributed energy to be managed data per unit time and per unit area under the preset conditions, the corresponding secondary information of the abnormal data at the abnormal load fluctuation of each distributed energy source is counted and analyzed, and the secondary information is the probability of occurrence of the abnormal data at the abnormal load fluctuation of the corresponding distributed energy source in the scheduling of the adjustment of the distributed energy to be managed data per unit time and per unit area under the preset conditions of the power plant energy centralized control center; By using the available dispatchable quantity, initial information and secondary information, statistics and analysis are conducted on the dispatchable quantity of new energy available for abnormal data at abnormal places of distributed energy load fluctuation; The power plant energy control center adjusts the distributed energy to be managed data per unit time and unit area, including abnormal data at the abnormal load fluctuations of each distributed energy. The power plant energy control center adjusts the distributed energy to be managed data per unit time and unit area and can use the dispatchable amount that can be used to count and analyze the abnormal data at the abnormal load fluctuations of each distributed energy.

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