Energy storage capacity configuration method and system of virtual power plant

Abstract

The invention discloses an energy storage capacity configuration method and system for a virtual power plant. The method comprises the following steps: acquiring historical operation information of each distributed energy of an energy storage terminal in a target virtual power plant, and constructing each power generation model corresponding to each distributed energy; sampling the actual operation data of each power generation model by using an oversampling algorithm to obtain a power generation sampling data set corresponding to each distributed energy source; constructing a complementary power generation model among the distributed energy sources according to the power generation sampling data sets; and allocating the electric energy storage capacity of the target virtual power plant according to the filling generating capacity. The maximum fluctuation range interval of the power generation amount of each distributed power supply is obtained according to the power generation relevance among the distributed power supplies in the virtual power plant, so that the energy storage terminal of the virtual power plant is configured and regulated, supply and demand balance of power grid output and output is ensured, meanwhile, power waste is avoided, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy configuration, and in particular to a method and system for configuring energy storage capacity of a virtual power plant. Background Art

[0002] With the continuous development of new energy, new power systems based on new energy are gradually being used in power supply and dispatch.

[0003] Virtual power plants use information and communication technology to centralize decentralized, adjustable power sources and loads to form an energy system that can be uniformly dispatched, managed, and operated, which can improve the safety and efficiency of the power grid. However, in virtual power plants, multiple distributed power sources are aggregated, and the distributed power sources affect each other. If the influence of this factor is not taken into account, the monitoring and management of the power generation efficiency of the virtual power plant will be reduced, and the power grid will find it difficult to effectively respond to sudden imbalances in supply and demand, resulting in the collapse of the virtual power plant energy storage system.

[0004] It can be seen that how to reasonably allocate virtual power plants has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides a method and system for configuring energy storage capacity of a virtual power plant, so as to improve the efficiency of energy storage deployment of the virtual power plant.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for configuring energy storage capacity of a virtual power plant, the method comprising:

[0007] Obtain the historical operation information of each distributed energy source of the energy storage terminal in the target virtual power plant.

[0008] Each power generation model corresponding to each distributed energy source is constructed according to the historical operation information.

[0009] In actual operation, an oversampling algorithm is used to sample the operating data of each power generation model to obtain a power generation sampling data set corresponding to each distributed energy source.

[0010] A complementary power generation model between distributed energy sources is constructed based on each of the power generation sampling data sets, wherein the complementary power generation model reflects a critical range for filling the gap of each distributed energy source; and the filling power generation predicted by the complementary power generation model is obtained.

[0011] The electric energy storage capacity of the target virtual power plant is deployed according to the filling power generation.

[0012] Furthermore, the historical operation information includes power generation, charging and discharging records, scheduling response speed and power supply frequency fluctuations.

[0013] The obtaining of historical operation information of energy storage terminals in the target virtual power plant includes:

[0014] According to the historical database of the target virtual power plant, historical operating data of the target power plant is obtained.

[0015] The historical operation data is analyzed according to the monitoring system log file of the target virtual power plant to obtain the historical operation information.

[0016] Furthermore, the distributed energy includes photovoltaic power generation energy units, wind power generation energy units and tidal power generation energy units.

[0017] The oversampling algorithm is used to sample the operation data of each power generation model to obtain a power generation sampling data set corresponding to each distributed energy source, including:

[0018] Based on the acquired illumination data, a first sampling frequency of the photovoltaic power generation energy unit is determined.

[0019] Based on the acquired wind data, a second sampling frequency of the wind power generation energy unit is determined.

[0020] Based on the acquired ocean current data, a third sampling frequency of the tidal power generation energy unit is determined.

[0021] Based on the first sampling frequency, the second sampling frequency and the third sampling frequency, the operation data of each power generation model is sampled to obtain a power generation sampling data set.

[0022] Furthermore, after obtaining the power generation sampling data set, the method further includes:

[0023] The data in the sample data set is cleaned to remove outliers and missing values.

[0024] Furthermore, the complementary power generation model between the distributed energy sources is constructed according to each of the power generation sampling data sets, including:

[0025] According to the power generation sampling data set, the real-time total power generation of each distributed energy source in operation is obtained.

[0026] The power generation parameters of each distributed energy source are adjusted in turn to obtain a power generation missing curve caused by the real-time total power generation of the target virtual power plant when the power generation of each distributed energy source is insufficient.

[0027] According to the power generation missing curve of each distributed energy source, the maximum fluctuation range of the power generation of each distributed energy source is obtained. According to each maximum fluctuation range of the power generation, a complementary power generation model is constructed to reflect the critical range of the target virtual power plant to fill the missing of each distributed energy source.

[0028] Another embodiment of the present invention provides a system for configuring energy storage capacity of a virtual power plant, including:

[0029] The data acquisition module is used to obtain the historical operation information of each distributed energy source of the energy storage terminal in the target virtual power plant.

[0030] The power generation model building module is used to build each power generation model corresponding to each distributed energy source according to the historical operation information.

[0031] The data sampling module is used to sample the operating data of each power generation model using an oversampling algorithm in actual operation to obtain a power generation sampling data set corresponding to each distributed energy source.

[0032] A complementary model construction module is used to construct a complementary power generation model between each distributed energy source based on each of the power generation sampling data sets, wherein the complementary power generation model reflects the critical range for filling the missing of each distributed energy source; and obtain the filling power generation predicted by the complementary power generation model.

[0033] An energy storage allocation module is used to allocate the electric energy storage capacity of the target virtual power plant according to the filling power generation.

[0034] Furthermore, the historical operation information includes power generation, charging and discharging records, scheduling response speed and power supply frequency fluctuations.

[0035] The data acquisition module comprises:

[0036] A data acquisition unit is used to acquire the historical operation data of the target power plant based on the historical database of the target virtual power plant.

[0037] A data processing unit is used to analyze the historical operation data according to the monitoring system log file of the target virtual power plant to obtain the historical operation information.

[0038] Furthermore, the distributed energy includes photovoltaic power generation energy units, wind power generation energy units and tidal power generation energy units.

[0039] The data sampling module comprises:

[0040] The first sampling frequency determination unit is used to determine the first sampling frequency of the photovoltaic power generation energy unit based on the acquired illumination data.

[0041] The second sampling frequency determination unit is used to determine the second sampling frequency of the wind power generation energy unit based on the acquired wind data.

[0042] The third sampling frequency determination unit is used to determine the third sampling frequency of the tidal power generation energy unit based on the acquired ocean current data.

[0043] The data sampling unit is used to sample the operation data of each power generation model based on the first sampling frequency, the second sampling frequency and the third sampling frequency to obtain a power generation sampling data set.

[0044] Furthermore, the system further comprises:

[0045] The data processing module is used to clean the data in the sampled data set and remove abnormal values ​​and missing values ​​after obtaining the power generation sampled data set.

[0046] Furthermore, the complementary model building module includes:

[0047] The total power generation acquisition unit is used to obtain the real-time total power generation of each distributed energy source in operation according to the power generation sampling data set.

[0048] The missing power calculation unit is used to adjust the power generation parameters of each distributed energy source in turn to obtain the power generation missing curve caused by the real-time total power generation of the target virtual power plant when the power generation of each distributed energy source is insufficient.

[0049] The model construction unit is used to obtain the maximum fluctuation range of the power generation of each distributed energy according to the power generation missing curve of each distributed energy, and to construct a complementary power generation model that reflects the critical range of the target virtual power plant to fill the missing of each distributed energy according to each maximum fluctuation range of the power generation.

[0050] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0051] (1) Using the oversampling algorithm to collect the power generation data of each distributed power source can better restore the original signal from the sampled signal and improve the accuracy of power generation data collection.

[0052] (2) Based on the power generation correlation between distributed power sources in the virtual power plant, the maximum fluctuation range of the power generation of each distributed power source is obtained, so as to configure and regulate the energy storage terminal of the virtual power plant to ensure the supply and demand balance of the power grid output, avoid power waste and improve energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flowchart of the steps of the energy storage capacity configuration method of a virtual power plant provided in an embodiment of the present invention;

[0054] Figure 2 A structural block diagram of the energy storage capacity configuration system of a virtual power plant provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0057] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0058] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0059] An embodiment of the present invention provides a method for configuring energy storage capacity of a virtual power plant. For details, see Figure 1 , Figure 1 The flowchart is a method for configuring the energy storage capacity of a virtual power plant in one embodiment of the present invention, including steps S11 to S15:

[0060] S11, obtaining historical operation information of each distributed energy source of the energy storage terminal in the target virtual power plant.

[0061] According to the historical database of the target virtual power plant, multiple types of historical operation information such as power generation, charging and discharging records, dispatch response speed and power supply frequency fluctuation of each distributed energy source are obtained.

[0062] The system monitoring logs of the target virtual power plant record various events and states during the operation of the virtual power plant. Through detailed analysis of these logs, errors or inconsistencies in the historical operation information can be discovered.

[0063] The integrity and continuity of historical operation information are verified by monitoring the timestamps, event sequences and other information in the system monitoring log files. Inconsistent information is checked and corrected to obtain the historical operation data of the target virtual power plant and ensure the integrity and accuracy of the data.

[0064] S12: constructing power generation models corresponding to distributed energy sources according to the historical operation information.

[0065] The distributed energy in the virtual power plant is a diversified collection, including distributed energy of various resource types. Specifically, this embodiment constructs various power generation models of distributed energy such as at least photovoltaic power generation energy units, wind power generation energy units and tidal power generation energy units according to the historical operation data of the target virtual power plant obtained in S11, which are used to simulate the power generation operation process in the target virtual power plant.

[0066] S13, in actual operation, using an oversampling algorithm to sample the operation data of each power generation model to obtain a power generation sampling data set corresponding to each distributed energy source.

[0067] The oversampling algorithm can effectively reduce the error generated when quantifying the power generation situation into data by setting the sampling frequency much higher than the highest frequency of the signal, making the collected data as close as possible to the actual power generation situation, thereby improving the efficiency of subsequent regulation of the virtual power plant energy storage terminal.

[0068] Since the sources of power generation resources of various distributed energy sources connected to the virtual power plant vary greatly, the efficiency and stability of power generation also vary greatly. The power generation efficiency of some distributed energy sources that rely on natural resources or weather resources also fluctuates greatly with changes in external conditions. For example, photovoltaic energy generators have higher power generation and more stable power generation efficiency in weather with sufficient sunlight, while power generation is reduced in cloudy weather and the power generation efficiency is extremely unstable. In addition, different distributed energy sources rely on different external conditions. Therefore, the sampling frequency of the generator set corresponding to each distributed energy source needs to be set according to the actual situation.

[0069] Specifically, this embodiment determines the first sampling frequency of the photovoltaic power generation energy unit according to the acquired external conditions that have an impact on the power generation efficiency and based on the acquired illumination data.

[0070] Based on the acquired wind data, a second sampling frequency of the wind power generation energy unit is determined.

[0071] Based on the acquired ocean current data, a third sampling frequency of the tidal power generation energy unit is determined.

[0072] Based on the first sampling frequency, the second sampling frequency and the third sampling frequency, the operation data of each power generation model is sampled to obtain a power generation sampling data set.

[0073] After obtaining the power generation sampling data set, in order to further improve the efficiency and accuracy of subsequent deployment, it is necessary to clarify the data in the data set and remove outliers and missing values ​​to reduce the risk of configuration decision errors caused by inaccurate or misleading data.

[0074] S14, constructing a complementary power generation model between distributed energy sources based on each of the power generation sampling data sets, wherein the complementary power generation model reflects the critical range for filling the gap of each distributed energy source; and obtaining the filling power generation predicted by the complementary power generation model.

[0075] According to the power generation sampling data set obtained for each power generation model in S13, the real-time total power generation of each corresponding distributed energy in operation is obtained.

[0076] Since there is a resource complementarity between the power generation tasks of different types of distributed energy in the virtual power plant, for example, in continuous rainy weather, other distributed energy sources such as gas-fired power generation units may be needed to fill in and compensate for the power generation gap caused by photovoltaic power generation units.

[0077] However, the ability of each distributed energy source to make up for the power generation shortfall of other distributed energy sources is limited, and as the amount of power generation to be filled increases, the filling cost per unit of power generation and the required resources and configurations will gradually increase. Therefore, it is necessary to calculate the complementary power generation between each distributed energy source.

[0078] This embodiment analyzes the power generation shortage of each distributed energy source by calculating the power generation shortage curve of each distributed energy source. Specifically, when calculating the power generation shortage of each distributed energy source, the power generation parameters of other distributed energy sources are fixed unchanged, the power generation parameters of the power generation model corresponding to the current distributed energy source are adjusted, and the power generation shortage curve caused by the current distributed energy source to the real-time total power generation of the target virtual power plant when the power generation is insufficient is obtained.

[0079] The above method is used to obtain the missing power generation curves of all distributed energy sources in turn, and the missing power generation curves are integrated. According to the overlap of each power generation curve, the maximum fluctuation range of the power generation of each distributed energy is obtained. According to the fluctuation range of each power generation under actual conditions, a complementary power generation model is constructed to reflect the critical range of each distributed energy missing by the target virtual power plant.

[0080] S15: adjusting the electric energy storage capacity of the target virtual power plant according to the filling power generation.

[0081] In the actual process of allocating the energy storage capacity of a virtual power plant, the complementary power generation model is used to predict the power generation data of the virtual power plant based on the actual operating conditions of each distributed energy source, and the point energy storage capacity of the virtual power plant is adjusted and configured based on the power generation data of the virtual power plant.

[0082] The energy storage capacity configuration method of the virtual power plant of the present invention uses an oversampling algorithm to collect the power generation data of each distributed power source, which can better restore the original signal from the sampled signal and improve the accuracy of power generation data collection. According to the power generation correlation between the distributed power sources in the virtual power plant, the maximum fluctuation range of the power generation of each distributed power source is obtained, so as to configure and regulate the energy storage terminal of the virtual power plant, ensure the supply and demand balance of the power grid output and output, avoid power waste, and improve energy utilization efficiency.

[0083] The embodiment of the present invention further provides a system for configuring energy storage capacity of a virtual power plant, which is used to execute the energy storage capacity configuration method of the virtual power plant as described above. Figure 2 This is a structural block diagram of the energy storage capacity configuration system of a virtual power plant according to an embodiment of the present invention, and the system includes:

[0084] The data acquisition module 21 is used to obtain the historical operation information of each distributed energy source of the energy storage terminal in the target virtual power plant.

[0085] The power generation model building module 22 is used to build each power generation model corresponding to each distributed energy source according to the historical operation information.

[0086] The data sampling module 23 is used to sample the operation data of each power generation model using an oversampling algorithm in actual operation to obtain a power generation sampling data set corresponding to each distributed energy source.

[0087] The complementary model construction module 24 is used to construct a complementary power generation model between each distributed energy source based on each of the power generation sampling data sets, wherein the complementary power generation model reflects the critical range for filling the missing of each distributed energy source; and obtain the filling power generation predicted by the complementary power generation model.

[0088] The energy storage allocation module 25 is used to allocate the electric energy storage capacity of the target virtual power plant according to the filling power generation.

[0089] The technical features and technical effects of the system proposed in the embodiment of the present invention are the same as those of the method proposed in the embodiment of the present invention, and are not described in detail here. Each module in the above system can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0090] In summary, the energy storage capacity configuration method and system of a virtual power plant provided by the embodiments of the present invention have the following beneficial effects compared with the prior art:

[0091] (1) Using the oversampling algorithm to collect the power generation data of each distributed power source can better restore the original signal from the sampled signal and improve the accuracy of power generation data collection.

[0092] (2) Based on the power generation correlation between distributed power sources in the virtual power plant, the maximum fluctuation range of the power generation of each distributed power source is obtained, so as to configure and regulate the energy storage terminal of the virtual power plant to ensure the supply and demand balance of the power grid output, avoid power waste and improve energy utilization efficiency.

[0093] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for configuring energy storage capacity of a virtual power plant, characterized in that: include: Obtain the historical operation information of each distributed energy source in the energy storage terminal of the target virtual power plant; constructing respective power generation models corresponding to respective distributed energy sources according to the historical operation information; In actual operation, an oversampling algorithm is used to sample the operation data of each power generation model to obtain a power generation sampling data set corresponding to each distributed energy source; Constructing a complementary power generation model between distributed energy sources according to each of the power generation sampling data sets, wherein the complementary power generation model reflects the critical range for filling the missing of each distributed energy source; obtaining the filling power generation predicted by the complementary power generation model; The electric energy storage capacity of the target virtual power plant is deployed according to the filling power generation.

2. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The historical operation information includes power generation, charging and discharging records, dispatch response speed and power supply frequency fluctuation; The obtaining of historical operation information of energy storage terminals in the target virtual power plant includes: Acquiring historical operation data of the target virtual power plant according to a historical database of the target virtual power plant; The historical operation data is analyzed according to the monitoring system log file of the target virtual power plant to obtain the historical operation information.

3. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The distributed energy includes photovoltaic power generation energy units, wind power generation energy units and tidal power generation energy units; The oversampling algorithm is used to sample the operation data of each power generation model to obtain a power generation sampling data set corresponding to each distributed energy source, including: Determining a first sampling frequency of the photovoltaic power generation energy unit based on the acquired light data; Determining a second sampling frequency of the wind power generation energy unit based on the acquired wind power data; Determining a third sampling frequency of the tidal power generation energy unit based on the acquired ocean current data; Based on the first sampling frequency, the second sampling frequency and the third sampling frequency, the operation data of each power generation model is sampled to obtain a power generation sampling data set.

4. The method for configuring energy storage capacity of a virtual power plant according to claim 3, characterized in that: After obtaining the power generation sampling data set, the method further includes: The data in the sample data set is cleaned to remove outliers and missing values.

5. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The constructing of a complementary power generation model between distributed energy sources according to each of the power generation sampling data sets includes: According to the power generation sampling data set, obtaining the real-time total power generation of each distributed energy source in operation; Adjusting the power generation parameters of each distributed energy source in turn to obtain a power generation shortage curve caused by the real-time total power generation of the target virtual power plant when the power generation of each distributed energy source is insufficient; According to the power generation missing curve of each distributed energy source, the maximum fluctuation range of the power generation of each distributed energy source is obtained. According to each maximum fluctuation range of the power generation, a complementary power generation model is constructed to reflect the critical range of the target virtual power plant to fill the missing of each distributed energy source.

6. A system for configuring energy storage capacity of a virtual power plant, characterized in that: The system comprises: A data acquisition module is used to obtain the historical operation information of each distributed energy source of the energy storage terminal in the target virtual power plant; A power generation model building module, used to build each power generation model corresponding to each distributed energy source according to the historical operation information; A data sampling module is used to sample the operation data of each power generation model using an oversampling algorithm in actual operation to obtain a power generation sampling data set corresponding to each distributed energy source; A complementary model building module is used to build a complementary power generation model between each distributed energy source according to each of the power generation sampling data sets, wherein the complementary power generation model reflects the critical range of filling the missing of each distributed energy source; and obtain the filling power generation predicted by the complementary power generation model; An energy storage allocation module is used to allocate the electric energy storage capacity of the target virtual power plant according to the filling power generation.

7. The energy storage capacity configuration system of a virtual power plant according to claim 6, characterized in that: The historical operation information includes power generation, charging and discharging records, dispatch response speed and power supply frequency fluctuation; The data acquisition module comprises: A data acquisition unit, configured to acquire historical operation data of the target power plant according to a historical database of the target virtual power plant; A data processing unit is used to analyze the historical operation data according to the monitoring system log file of the target virtual power plant to obtain the historical operation information.

8. The energy storage capacity configuration system of a virtual power plant according to claim 6, characterized in that: The distributed energy includes photovoltaic power generation energy units, wind power generation energy units and tidal power generation energy units; The data sampling module comprises: A first sampling frequency determination unit, used to determine a first sampling frequency of the photovoltaic power generation energy unit based on the acquired illumination data; A second sampling frequency determination unit, configured to determine a second sampling frequency of the wind power generation energy unit based on the acquired wind power data; A third sampling frequency determination unit, used to determine a third sampling frequency of the tidal power generation energy unit based on the acquired ocean current data; The data sampling unit is used to sample the operation data of each power generation model based on the first sampling frequency, the second sampling frequency and the third sampling frequency to obtain a power generation sampling data set.

9. The energy storage capacity configuration system of a virtual power plant according to claim 8, characterized in that: The system further comprises: The data processing module is used to clean the data in the sampled data set and remove abnormal values ​​and missing values ​​after obtaining the power generation sampled data set.

10. The energy storage capacity configuration system of a virtual power plant according to claim 6, characterized in that: The complementary model building module comprises: A total power generation acquisition unit, used to obtain the real-time total power generation of each distributed energy source in operation according to the power generation sampling data set; A missing power calculation unit, used to adjust the power generation parameters of each distributed energy source in turn, and obtain a power generation missing curve caused by the real-time total power generation of the target virtual power plant when the power generation of each distributed energy source is insufficient; The model construction unit is used to obtain the maximum fluctuation range of the power generation of each distributed energy according to the power generation missing curve of each distributed energy, and to construct a complementary power generation model that reflects the critical range of the target virtual power plant to fill the missing of each distributed energy according to each maximum fluctuation range of the power generation.

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