A demand response data interaction method, system and medium based on virtual power plant

Abstract

The present application provides a method, system and medium for interactive demand response data based on a virtual power plant. The method includes: obtaining real-time grid operation data and analyzing and processing it to obtain a grid balance demand index, performing a threshold comparison between the grid balance demand index and a preset grid balance demand threshold, determining the grid operation state according to the threshold comparison result, obtaining and analyzing the user's electricity consumption behavior characteristic data to obtain a demand response potential level, initiating a demand response invitation to the user based on the grid operation state and the demand response potential level, the invited user generating a response plan based on the demand response invitation and sending it to the virtual power plant management terminal for display; thereby achieving the goal of obtaining the grid operation state, analyzing the user's demand response potential, generating a demand response invitation, and improving the effectiveness and operability of the demand response.

Description

Technical Field

[0001] The present application relates to the field of power systems and energy management technology, and in particular to a demand response data interaction method, system and medium based on a virtual power plant. Background Art

[0002] With the continuous development of the power industry and the transformation of the energy structure, the proportion of renewable energy in the power grid has gradually increased. However, the intermittent and unstable nature of renewable energy has brought new challenges to the scheduling and balancing of the power system. Traditional power system scheduling models have difficulty adapting to this new environment. Demand response technology has emerged as a response to this situation. Demand response can achieve load balancing and stable operation of the power system by incentivizing users to reduce electricity consumption during peak demand periods or increase electricity consumption during off-peak periods. Virtual power plants, as an innovative power system operation model, integrate distributed energy, energy storage systems, and controllable load resources, and uniformly dispatch and manage them through information and communication technologies. In virtual power plants, demand response technology can effectively improve the flexibility and response speed of the system, quickly adjust the load when renewable energy output fluctuates, and reduce the impact on the power grid. However, how to improve the operability of demand response and accurately identify the effectiveness of user responses in a complex power market environment is an urgent problem that needs to be solved.

[0003] In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0004] The purpose of this application is to provide a demand response data interaction method, system and medium based on a virtual power plant. The method can obtain real-time power grid operation data and analyze and process it to obtain a power grid balance demand index, compare the power grid balance demand index with a preset power grid balance demand threshold, determine the power grid operation status according to the threshold comparison result, obtain the user's electricity consumption behavior characteristic data and analyze and process it to obtain the demand response potential level, initiate a demand response invitation to the user based on the power grid operation status and the demand response potential level, and the invited user generates a response plan based on the demand response invitation and sends it to the virtual power plant management end for display.

[0005] This application also provides a demand response data interaction method based on a virtual power plant, comprising the following steps:

[0006] Acquire real-time grid operation data and analyze and process it to obtain the grid balance demand index;

[0007] Comparing the grid balancing demand index with a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result;

[0008] Obtain and analyze the user's electricity consumption behavior characteristic data to obtain the demand response potential level;

[0009] Initiate a demand response invitation to the user based on the grid operation status and the demand response potential level;

[0010] The invited user generates a response plan based on the demand response invitation and sends it to the virtual power plant management terminal for display.

[0011] Optionally, in the virtual power plant-based demand response data interaction method described in the present application, acquiring real-time grid operation data and analyzing and processing it to obtain a grid balance demand index includes:

[0012] Obtain real-time grid operation data, including load change mean data, voltage fluctuation mean data, and frequency deviation mean data within a preset time period;

[0013] The load change mean data, voltage fluctuation mean data and frequency deviation mean data are input into a preset power grid balancing demand assessment model for processing to obtain a power grid balancing demand index.

[0014] Optionally, in the demand response data interaction method based on a virtual power plant described in the present application, performing a threshold comparison between the grid balancing demand index and a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result, includes:

[0015] Comparing the power grid balancing demand index with a preset power grid balancing demand threshold;

[0016] If it is less than or equal to the preset grid balancing demand threshold, it corresponds to a normal state;

[0017] If it is greater than the preset grid balancing demand threshold, it corresponds to an abnormal state.

[0018] Optionally, in the virtual power plant-based demand response data interaction method described in the present application, obtaining the user's electricity consumption behavior characteristic data and analyzing and processing it to obtain the demand response potential level includes:

[0019] Obtain user electricity usage behavior characteristic data, including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period;

[0020] Processing the adjustable power data, the adjustable rate, the historical response time average, and the response rate to obtain a demand response potential evaluation index corresponding to the user;

[0021] The demand response potential evaluation index is compared with a preset demand response potential evaluation threshold, and the demand response potential level is obtained according to the range to which the threshold comparison belongs.

[0022] Optionally, in the virtual power plant-based demand response data interaction method described in the present application, the processing according to the adjustable power data, the adjustable rate, the historical response time average, and the response rate to obtain the demand response potential evaluation index corresponding to the user includes:

[0023] According to the adjustable power data, the adjustable rate, the historical response time average and the response rate within the preset time period, the preset weight value list is searched to obtain the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value respectively;

[0024] The demand response potential evaluation index corresponding to the user is obtained by calculation based on the adjustable power data, adjustable rate, historical response time average and response rate, and the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value.

[0025] Optionally, in the virtual power plant-based demand response data interaction method described in the present application, performing a threshold comparison between the demand response potential evaluation index and a preset demand response potential evaluation threshold, and obtaining a demand response potential level according to a range to which the threshold comparison belongs, includes:

[0026] Comparing the demand response potential evaluation index with a preset standard demand response potential evaluation index to obtain a relative value of demand response potential;

[0027] Performing a threshold comparison between the relative value of the demand response potential and a preset demand response potential assessment threshold, wherein the preset demand response potential assessment threshold includes a first preset demand response potential assessment threshold and a second preset demand response potential assessment threshold, and the first preset demand response potential assessment threshold is less than the second preset demand response potential assessment threshold;

[0028] If it is less than or equal to the first preset demand response potential assessment threshold, it corresponds to low potential;

[0029] If it is greater than the first preset demand response potential assessment threshold and less than or equal to the second preset demand response potential assessment threshold, it corresponds to medium potential;

[0030] If it is greater than a second preset demand response potential assessment threshold, it corresponds to high potential.

[0031] In a second aspect, the present application provides a demand response data interaction system based on a virtual power plant, the system comprising: a memory and a processor, the memory comprising a program for a demand response data interaction method based on a virtual power plant, the program for a demand response data interaction method based on a virtual power plant, when executed by the processor, implementing the following steps:

[0032] Acquire real-time grid operation data and analyze and process it to obtain the grid balance demand index;

[0033] Comparing the grid balancing demand index with a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result;

[0034] Obtain and analyze the user's electricity consumption behavior characteristic data to obtain the demand response potential level;

[0035] Initiate a demand response invitation to the user based on the grid operation status and the demand response potential level;

[0036] The invited user generates a response plan based on the demand response invitation and sends it to the virtual power plant management terminal for display.

[0037] Optionally, in the virtual power plant-based demand response data interaction system described in the present application, acquiring real-time power grid operation data and analyzing and processing it to obtain a power grid balance demand index includes:

[0038] Obtain real-time grid operation data, including load change mean data, voltage fluctuation mean data, and frequency deviation mean data within a preset time period;

[0039] The load change mean data, voltage fluctuation mean data and frequency deviation mean data are input into a preset power grid balancing demand assessment model for processing to obtain a power grid balancing demand index.

[0040] Optionally, in the demand response data interaction system based on a virtual power plant described in the present application, performing a threshold comparison between the grid balancing demand index and a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result, includes:

[0041] Comparing the power grid balancing demand index with a preset power grid balancing demand threshold;

[0042] If it is less than or equal to the preset grid balancing demand threshold, it corresponds to a normal state;

[0043] If it is greater than the preset grid balancing demand threshold, it corresponds to an abnormal state.

[0044] In a third aspect, the present application also provides a computer-readable storage medium, which stores a demand response data interaction method program based on a virtual power plant. When the demand response data interaction method program based on a virtual power plant is executed by a processor, the steps of the demand response data interaction method based on a virtual power plant as described in any one of the above items are implemented.

[0045] From the above, it can be seen that the present application provides a demand response data interaction method, system and medium based on a virtual power plant, which obtains real-time power grid operation data and analyzes and processes it to obtain a power grid balance demand index, compares the power grid balance demand index with a preset power grid balance demand threshold, determines the power grid operation status according to the threshold comparison result, obtains the user's electricity consumption behavior characteristic data and analyzes and processes it to obtain the demand response potential level, and initiates a demand response invitation to the user based on the power grid operation status and the demand response potential level. The invited user generates a response plan based on the demand response invitation and sends it to the virtual power plant management end for display.

[0046] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 A flowchart of a demand response data interaction method based on a virtual power plant provided in an embodiment of the present application;

[0049] Figure 2 A flowchart of obtaining a grid balance demand index according to a demand response data interaction method based on a virtual power plant provided in an embodiment of the present application;

[0050] Figure 3 A flowchart of obtaining a demand response potential level in a demand response data interaction method based on a virtual power plant provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0052] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0053] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for interacting with demand response data based on a virtual power plant in some embodiments of the present application. The method for interacting with demand response data based on a virtual power plant is used in a terminal device, such as a computer or mobile phone terminal. The method for interacting with demand response data based on a virtual power plant includes the following steps:

[0054] S11. Acquire real-time grid operation data and analyze and process it to obtain a grid balance demand index;

[0055] S12, comparing the grid balancing demand index with a preset grid balancing demand threshold, and determining a grid operation state according to the threshold comparison result;

[0056] S13. Obtaining and analyzing the user's electricity consumption behavior characteristic data to obtain a demand response potential level;

[0057] S14, initiating a demand response invitation to the user based on the grid operation status and the demand response potential level;

[0058] S15. The invited user generates a response plan based on the demand response invitation and sends it to the virtual power plant management terminal for display.

[0059] It should be noted that in order to realize the response invitation based on the grid balancing demand, the real-time grid operation data including load change mean data, voltage fluctuation mean data and frequency deviation mean data is obtained and analyzed and processed to obtain the grid balancing demand index, and the grid operation status is obtained by threshold comparison. If it is an abnormal state, it means that grid balancing needs to be implemented. The grid load demand reduction amount is obtained according to the abnormal state, and the power consumption behavior characteristic data including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period are further obtained and analyzed and processed to obtain the demand response potential level, including high potential, medium potential or low potential. According to the grid load demand reduction amount, the demand response invitation is sent to high-potential users first. When the load reduction capacity of high-potential users is insufficient, the demand response invitation is initiated to medium-potential or low-potential users to ensure the effectiveness of grid balancing. After receiving the demand response invitation, the user generates a corresponding response plan, including the load reduction amount and execution time, and implements grid balancing and sends it to the virtual power plant management end for display.

[0060] Please refer to Figure 2 , Figure 2 This is a flow chart of obtaining a grid balancing demand index for a method for interacting with demand response data based on a virtual power plant in some embodiments of the present application. According to an embodiment of the present invention, obtaining real-time grid operation data and analyzing and processing it to obtain a grid balancing demand index includes:

[0061] S21. Acquire real-time grid operation data, including load change mean data, voltage fluctuation mean data, and frequency deviation mean data within a preset time period;

[0062] S22: Input the load change mean data, voltage fluctuation mean data, and frequency deviation mean data into a preset power grid balance demand assessment model for processing to obtain a power grid balance demand index.

[0063] It should be noted that in order to determine whether grid balancing is required, by real-time monitoring of grid operation data, average load change data, average voltage fluctuation data, and average frequency deviation data within a preset time period are obtained based on preset sensors. The average load change data, average voltage fluctuation data, and average frequency deviation data within the preset time period are input into a preset grid balancing demand assessment model for processing to obtain a grid balancing demand index.

[0064] The calculation formula of the grid balancing demand index in the grid balancing demand evaluation model is:

[0065] ;

[0066] in, is the grid balancing demand index, 、 、 They are load change mean data, voltage fluctuation mean data and frequency deviation mean data, 、 、 is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset demand response data interaction platform).

[0067] According to an embodiment of the present invention, performing a threshold comparison between the grid balancing demand index and a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result, includes:

[0068] Comparing the power grid balancing demand index with a preset power grid balancing demand threshold;

[0069] If it is less than or equal to the preset grid balancing demand threshold, it corresponds to a normal state;

[0070] If it is greater than the preset grid balancing demand threshold, it corresponds to an abnormal state.

[0071] It should be noted that the obtained grid balancing demand index is compared with the preset grid balancing demand threshold. In this embodiment, the preset grid balancing demand threshold is set to (0, 0.55] and (0.55, 1], corresponding to the normal state and the abnormal state, respectively. For example, if the obtained grid balancing demand index is 0.4, it corresponds to the normal state and grid balancing does not need to be implemented; if the obtained grid balancing demand index is 0.6, it corresponds to the abnormal state and grid balancing needs to be implemented.

[0072] Please refer to Figure 3 , Figure 3 This is a flow chart of a method for interacting with demand response data based on a virtual power plant in some embodiments of the present application to obtain a demand response potential level. According to an embodiment of the present invention, obtaining and analyzing the user's electricity consumption behavior characteristic data to obtain the demand response potential level includes:

[0073] S31. Obtaining user electricity usage behavior characteristic data, including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period;

[0074] S32. Processing the adjustable power data, the adjustable rate, the historical response time average, and the response rate to obtain a demand response potential evaluation index corresponding to the user;

[0075] S33: Perform a threshold comparison between the demand response potential evaluation index and a preset demand response potential evaluation threshold, and obtain a demand response potential level according to a range to which the threshold comparison belongs.

[0076] It should be noted that demand response capability is an important factor in ensuring the effectiveness of grid balance implementation. Therefore, it is necessary to evaluate the user's demand response capability and process it by obtaining electricity consumption behavior characteristic data including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period. Among them, adjustable power data refers to the power consumption that the user can actively adjust or control under certain conditions, the adjustment rate refers to the ratio of power consumption that can be actively adjusted or controlled to the total power consumption, the historical response time average refers to the average time spent from receiving the demand response invitation to executing the demand response, and the response rate refers to the ratio of the actual number of executed responses to the number of received response invitations. The user's corresponding demand response potential evaluation index is obtained, and then the user's corresponding demand response potential level is finally determined through threshold comparison.

[0077] According to an embodiment of the present invention, the processing based on the adjustable power data, the adjustable rate, the historical response time average, and the response rate to obtain the demand response potential evaluation index corresponding to the user includes:

[0078] According to the adjustable power data, the adjustable rate, the historical response time average and the response rate within the preset time period, the preset weight value list is searched to obtain the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value respectively;

[0079] The demand response potential evaluation index corresponding to the user is obtained by calculation based on the adjustable power data, adjustable rate, historical response time average and response rate, and the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value.

[0080] It should be noted that, according to the obtained adjustable power data, adjustable rate, historical response time average and response rate within the preset time period, the preset weight value list is queried to obtain the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value, respectively. Among them, the preset weight value list is provided by the demand response data platform based on the virtual power plant, and the demand response potential evaluation index corresponding to the user is obtained by calculation based on the weight value;

[0081] The demand response potential evaluation index calculation formula is:

[0082] ;

[0083] in, is the demand response potential assessment index, 、 、 、 、 、 、 、 They are adjustable power data, adjustable rate, historical response time average within a preset time period, response rate, adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value. 、 、 、 is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset demand response data interaction platform).

[0084] According to an embodiment of the present invention, performing a threshold comparison between the demand response potential evaluation index and a preset demand response potential evaluation threshold, and obtaining a demand response potential level according to a range to which the threshold comparison belongs, includes:

[0085] Comparing the demand response potential evaluation index with a preset standard demand response potential evaluation index to obtain a relative value of demand response potential;

[0086] Performing a threshold comparison between the relative value of the demand response potential and a preset demand response potential assessment threshold, wherein the preset demand response potential assessment threshold includes a first preset demand response potential assessment threshold and a second preset demand response potential assessment threshold, and the first preset demand response potential assessment threshold is less than the second preset demand response potential assessment threshold;

[0087] If it is less than or equal to the first preset demand response potential assessment threshold, it corresponds to low potential;

[0088] If it is greater than the first preset demand response potential assessment threshold and less than or equal to the second preset demand response potential assessment threshold, it corresponds to medium potential;

[0089] If it is greater than a second preset demand response potential assessment threshold, it corresponds to high potential.

[0090] It should be noted that the obtained demand response potential evaluation index is compared with the preset standard demand response potential evaluation index to obtain the relative value of demand response potential. For example, the obtained demand response potential evaluation index is 7, and the preset standard demand response potential evaluation index is 10, then 7 / 10=0.7 is the relative value of demand response potential, and then the threshold is compared with the preset demand response potential evaluation threshold. In this embodiment, the demand response potential evaluation threshold is set to (0, 0.5], (0.5, 0.7], and (0.7, 1], which correspond to low potential, medium potential, and high potential, respectively. For example, if the obtained relative value of demand response potential is 0.3, the user's corresponding demand response potential level is low potential. If the obtained relative value of demand response potential is 0.6, the user's corresponding demand response potential level is medium potential. If the obtained relative value of demand response potential is 0.8, the user's corresponding demand response potential level is high potential.

[0091] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes:

[0092] Obtain the total load reduction data before and after the implementation of demand response, the average response time of different users, and the electricity price reduction rate after demand response;

[0093] The total load reduction data, the average response time, and the electricity price reduction rate are input into a preset demand response effectiveness evaluation model to obtain a demand response effectiveness index;

[0094] The demand response effectiveness index is compared with a preset demand response effectiveness level threshold to obtain a demand response effectiveness level, including first-level effectiveness, second-level effectiveness or third-level effectiveness, among which third-level effectiveness is the highest.

[0095] It should be noted that in order to evaluate the effectiveness of demand response implementation, we first obtain the total load reduction data before and after the implementation of demand response, the average response time of different users, and the electricity price reduction rate after demand response. The electricity price reduction rate after demand response refers to the electricity price before demand response minus the electricity price after demand response divided by the electricity price before demand response. These data are input into the preset demand response effectiveness evaluation model for processing to obtain the demand response effectiveness index.

[0096] The calculation formula of the demand response effectiveness index in the demand response effectiveness evaluation model is:

[0097] ;

[0098] in, is the demand response effectiveness index, 、 、 They are the total load reduction data, the average response time and the electricity price reduction rate, 、 is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset demand response data interaction platform);

[0099] The obtained demand response effectiveness index is compared with the preset demand response effectiveness level threshold. In this embodiment, the preset demand response effectiveness level threshold is set to (0, 0.65], (0.65, 0.85], and (0.85, 1], which correspond to the first-level effectiveness, the second-level effectiveness, and the third-level effectiveness, respectively. For example, if the obtained demand response effectiveness index is 0.55, it corresponds to the first-level effectiveness. If the obtained demand response effectiveness index is 0.75, it corresponds to the second-level effectiveness. If the obtained demand response effectiveness index is 0.9, it corresponds to the third-level effectiveness.

[0100] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes:

[0101] Obtain load reduction data corresponding to different users;

[0102] Divide the load reduction data by the total load reduction data to obtain the load reduction contribution rate corresponding to the user;

[0103] The user is incentivized according to the load reduction contribution rate and the demand response effectiveness level.

[0104] It should be noted that in order to evaluate the contribution of different users to the grid balance, give corresponding incentives based on different contributions, and improve the enthusiasm of users to participate, we first obtain the load reduction data corresponding to different users. The load reduction data refers to the load difference before and after the implementation of the user's demand response. For example, before the demand response event, the grid load is 1000 MW in a certain period of time. After the demand response is implemented, the load in this period is reduced to 800 MW, then the load reduction is 200. MW, the load reduction data corresponding to the user is divided by the total load reduction data to obtain the load reduction contribution rate corresponding to the user. For example, the load reduction of a user is 200 MW and the total load reduction is 1000 MW, then 200 / 1000=0.2 is the load reduction contribution rate. The user is incentivized according to the load reduction contribution rate combined with the demand response effectiveness level. For example, if the demand response effectiveness level is level one, the total incentive amount is multiplied by 1 times the load reduction contribution rate and allocated to the corresponding user. If the demand response effectiveness level is level two, the total incentive amount is multiplied by 1.1 times the load reduction contribution rate and allocated to the corresponding user. If the demand response effectiveness level is level three, the total incentive amount is multiplied by 1.3 times the load reduction contribution rate and allocated to the corresponding user. This improves the economic benefits of users and achieves the purpose of increasing the enthusiasm of users to participate in demand response.

[0105] The present invention also discloses a demand response data interaction system based on a virtual power plant, comprising a memory and a processor. The memory comprises a demand response data interaction method program based on a virtual power plant. When the demand response data interaction method program based on a virtual power plant is executed by the processor, the following steps are implemented:

[0106] Acquire real-time grid operation data and analyze and process it to obtain the grid balance demand index;

[0107] Comparing the grid balancing demand index with a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result;

[0108] Obtain and analyze the user's electricity consumption behavior characteristic data to obtain the demand response potential level;

[0109] Initiate a demand response invitation to the user based on the grid operation status and the demand response potential level;

[0110] The invited user generates a response plan based on the demand response invitation and sends it to the virtual power plant management terminal for display.

[0111] It should be noted that in order to realize the response invitation based on the grid balancing demand, the real-time grid operation data including load change mean data, voltage fluctuation mean data and frequency deviation mean data is obtained and analyzed and processed to obtain the grid balancing demand index, and the grid operation status is obtained by threshold comparison. If it is an abnormal state, it means that grid balancing needs to be implemented. The grid load demand reduction amount is obtained according to the abnormal state, and the power consumption behavior characteristic data including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period are further obtained and analyzed and processed to obtain the demand response potential level, including high potential, medium potential or low potential. According to the grid load demand reduction amount, the demand response invitation is sent to high-potential users first. When the load reduction capacity of high-potential users is insufficient, the demand response invitation is initiated to medium-potential or low-potential users to ensure the effectiveness of grid balancing. After receiving the demand response invitation, the user generates a corresponding response plan, including the load reduction amount and execution time, and implements grid balancing and sends it to the virtual power plant management end for display.

[0112] According to an embodiment of the present invention, acquiring real-time power grid operation data and performing analysis and processing to obtain a power grid balancing demand index includes:

[0113] Obtain real-time grid operation data, including load change mean data, voltage fluctuation mean data, and frequency deviation mean data within a preset time period;

[0114] The load change mean data, voltage fluctuation mean data and frequency deviation mean data are input into a preset power grid balancing demand assessment model for processing to obtain a power grid balancing demand index.

[0115] It should be noted that in order to determine whether grid balancing is required, by real-time monitoring of grid operation data, average load change data, average voltage fluctuation data, and average frequency deviation data within a preset time period are obtained based on preset sensors. The average load change data, average voltage fluctuation data, and average frequency deviation data within the preset time period are input into a preset grid balancing demand assessment model for processing to obtain a grid balancing demand index.

[0116] The calculation formula of the grid balancing demand index in the grid balancing demand evaluation model is:

[0117] ;

[0118] in, is the grid balancing demand index, 、 、 They are load change mean data, voltage fluctuation mean data and frequency deviation mean data, 、 、 is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset demand response data interaction platform).

[0119] According to an embodiment of the present invention, performing a threshold comparison between the grid balancing demand index and a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result, includes:

[0120] Comparing the power grid balancing demand index with a preset power grid balancing demand threshold;

[0121] If it is less than or equal to the preset grid balancing demand threshold, it corresponds to a normal state;

[0122] If it is greater than the preset grid balancing demand threshold, it corresponds to an abnormal state.

[0123] It should be noted that the obtained grid balancing demand index is compared with the preset grid balancing demand threshold. In this embodiment, the preset grid balancing demand threshold is set to (0, 0.55] and (0.55, 1], corresponding to the normal state and the abnormal state, respectively. For example, if the obtained grid balancing demand index is 0.4, it corresponds to the normal state and grid balancing does not need to be implemented; if the obtained grid balancing demand index is 0.6, it corresponds to the abnormal state and grid balancing needs to be implemented.

[0124] According to an embodiment of the present invention, obtaining the user's electricity usage behavior characteristic data and performing analysis and processing to obtain the demand response potential level includes:

[0125] Obtain user electricity usage behavior characteristic data, including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period;

[0126] Processing the adjustable power data, the adjustable rate, the historical response time average, and the response rate to obtain a demand response potential evaluation index corresponding to the user;

[0127] The demand response potential evaluation index is compared with a preset demand response potential evaluation threshold, and the demand response potential level is obtained according to the range to which the threshold comparison belongs.

[0128] It should be noted that demand response capability is an important factor in ensuring the effectiveness of grid balance implementation. Therefore, it is necessary to evaluate the user's demand response capability and process it by obtaining electricity consumption behavior characteristic data including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period. Among them, adjustable power data refers to the power consumption that the user can actively adjust or control under certain conditions, the adjustment rate refers to the ratio of power consumption that can be actively adjusted or controlled to the total power consumption, the historical response time average refers to the average time spent from receiving the demand response invitation to executing the demand response, and the response rate refers to the ratio of the actual number of executed responses to the number of received response invitations. The user's corresponding demand response potential evaluation index is obtained, and then the user's corresponding demand response potential level is finally determined through threshold comparison.

[0129] According to an embodiment of the present invention, the processing based on the adjustable power data, the adjustable rate, the historical response time average, and the response rate to obtain the demand response potential evaluation index corresponding to the user includes:

[0130] According to the adjustable power data, the adjustable rate, the historical response time average and the response rate within the preset time period, the preset weight value list is searched to obtain the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value respectively;

[0131] The demand response potential evaluation index corresponding to the user is obtained by calculation based on the adjustable power data, adjustable rate, historical response time average and response rate, and the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value.

[0132] It should be noted that, according to the obtained adjustable power data, adjustable rate, historical response time average and response rate within the preset time period, the preset weight value list is queried to obtain the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value, respectively. Among them, the preset weight value list is provided by the demand response data platform based on the virtual power plant, and the demand response potential evaluation index corresponding to the user is obtained by calculation based on the weight value;

[0133] The demand response potential evaluation index calculation formula is:

[0134] ;

[0135] in, is the demand response potential assessment index, 、 、 、 、 、 、 、 They are adjustable power data, adjustable rate, historical response time average within a preset time period, response rate, adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value. 、 、 、 is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset demand response data interaction platform).

[0136] According to an embodiment of the present invention, performing a threshold comparison between the demand response potential evaluation index and a preset demand response potential evaluation threshold, and obtaining a demand response potential level according to a range to which the threshold comparison belongs, includes:

[0137] Comparing the demand response potential evaluation index with a preset standard demand response potential evaluation index to obtain a relative value of demand response potential;

[0138] Performing a threshold comparison between the relative value of the demand response potential and a preset demand response potential assessment threshold, wherein the preset demand response potential assessment threshold includes a first preset demand response potential assessment threshold and a second preset demand response potential assessment threshold, and the first preset demand response potential assessment threshold is less than the second preset demand response potential assessment threshold;

[0139] If it is less than or equal to the first preset demand response potential assessment threshold, it corresponds to low potential;

[0140] If it is greater than the first preset demand response potential assessment threshold and less than or equal to the second preset demand response potential assessment threshold, it corresponds to medium potential;

[0141] If it is greater than a second preset demand response potential assessment threshold, it corresponds to high potential.

[0142] It should be noted that the obtained demand response potential evaluation index is compared with the preset standard demand response potential evaluation index to obtain the relative value of demand response potential. For example, the obtained demand response potential evaluation index is 7, and the preset standard demand response potential evaluation index is 10, then 7 / 10=0.7 is the relative value of demand response potential, and then the threshold is compared with the preset demand response potential evaluation threshold. In this embodiment, the demand response potential evaluation threshold is set to (0, 0.5], (0.5, 0.7], and (0.7, 1], which correspond to low potential, medium potential, and high potential, respectively. For example, if the obtained relative value of demand response potential is 0.3, the user's corresponding demand response potential level is low potential. If the obtained relative value of demand response potential is 0.6, the user's corresponding demand response potential level is medium potential. If the obtained relative value of demand response potential is 0.8, the user's corresponding demand response potential level is high potential.

[0143] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes:

[0144] Obtain the total load reduction data before and after the implementation of demand response, the average response time of different users, and the electricity price reduction rate after demand response;

[0145] The total load reduction data, the average response time, and the electricity price reduction rate are input into a preset demand response effectiveness evaluation model to obtain a demand response effectiveness index;

[0146] The demand response effectiveness index is compared with a preset demand response effectiveness level threshold to obtain a demand response effectiveness level, including first-level effectiveness, second-level effectiveness or third-level effectiveness, among which third-level effectiveness is the highest.

[0147] It should be noted that in order to evaluate the effectiveness of demand response implementation, we first obtain the total load reduction data before and after the implementation of demand response, the average response time of different users, and the electricity price reduction rate after demand response. The electricity price reduction rate after demand response refers to the electricity price before demand response minus the electricity price after demand response divided by the electricity price before demand response. These data are input into the preset demand response effectiveness evaluation model for processing to obtain the demand response effectiveness index.

[0148] The calculation formula of the demand response effectiveness index in the demand response effectiveness evaluation model is:

[0149] ;

[0150] in, is the demand response effectiveness index, 、 、 They are the total load reduction data, the average response time and the electricity price reduction rate, 、 is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset demand response data interaction platform);

[0151] The obtained demand response effectiveness index is compared with the preset demand response effectiveness level threshold. In this embodiment, the preset demand response effectiveness level threshold is set to (0, 0.65], (0.65, 0.85], and (0.85, 1], which correspond to the first-level effectiveness, the second-level effectiveness, and the third-level effectiveness, respectively. For example, if the obtained demand response effectiveness index is 0.55, it corresponds to the first-level effectiveness. If the obtained demand response effectiveness index is 0.75, it corresponds to the second-level effectiveness. If the obtained demand response effectiveness index is 0.9, it corresponds to the third-level effectiveness.

[0152] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes:

[0153] Obtain load reduction data corresponding to different users;

[0154] Divide the load reduction data by the total load reduction data to obtain the load reduction contribution rate corresponding to the user;

[0155] The user is incentivized according to the load reduction contribution rate and the demand response effectiveness level.

[0156] It should be noted that in order to evaluate the contribution of different users to the grid balance, give corresponding incentives based on different contributions, and improve the enthusiasm of users to participate, we first obtain the load reduction data corresponding to different users. The load reduction data refers to the load difference before and after the implementation of the user's demand response. For example, before the demand response event, the grid load is 1000 MW in a certain period of time. After the demand response is implemented, the load in this period is reduced to 800 MW, then the load reduction is 200. MW, the load reduction data corresponding to the user is divided by the total load reduction data to obtain the load reduction contribution rate corresponding to the user. For example, the load reduction of a user is 200 MW and the total load reduction is 1000 MW, then 200 / 1000=0.2 is the load reduction contribution rate. The user is incentivized according to the load reduction contribution rate combined with the demand response effectiveness level. For example, if the demand response effectiveness level is level one, the total incentive amount is multiplied by 1 times the load reduction contribution rate and allocated to the corresponding user. If the demand response effectiveness level is level two, the total incentive amount is multiplied by 1.1 times the load reduction contribution rate and allocated to the corresponding user. If the demand response effectiveness level is level three, the total incentive amount is multiplied by 1.3 times the load reduction contribution rate and allocated to the corresponding user. This improves the economic benefits of users and achieves the purpose of increasing the enthusiasm of users to participate in demand response.

[0157] The third aspect of the present invention provides a readable storage medium, which stores a demand response data interaction method program based on a virtual power plant. When the demand response data interaction method program based on a virtual power plant is executed by a processor, the steps of the demand response data interaction method based on a virtual power plant as described in any one of the above items are implemented.

[0158] The present invention discloses a demand response data interaction method, system and medium based on a virtual power plant. The method obtains real-time power grid operation data and performs analysis and processing to obtain a power grid balance demand index, performs a threshold comparison between the power grid balance demand index and a preset power grid balance demand threshold, determines the power grid operation status according to the threshold comparison result, obtains the user's electricity consumption behavior characteristic data and performs analysis and processing to obtain a demand response potential level, initiates a demand response invitation to the user according to the power grid operation status and the demand response potential level, and the invited user generates a response plan according to the demand response invitation and sends it to the virtual power plant management end for display.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0160] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0161] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0162] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware related to program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0163] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A demand response data interaction method based on a virtual power plant, characterized in that: The following steps are involved: Acquire real-time grid operation data and analyze and process it to obtain the grid balance demand index; Comparing the grid balancing demand index with a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result; Obtain and analyze the user's electricity consumption behavior characteristic data to obtain the demand response potential level; Initiate a demand response invitation to the user based on the grid operation status and the demand response potential level; The invited user generates a response plan based on the demand response invitation and sends it to the virtual power plant management terminal for display; The acquiring of real-time grid operation data and performing analysis and processing to obtain a grid balance demand index includes: Obtain real-time grid operation data, including load change mean data, voltage fluctuation mean data, and frequency deviation mean data within a preset time period; Inputting the load change mean data, voltage fluctuation mean data and frequency deviation mean data into a preset power grid balancing demand assessment model for processing to obtain a power grid balancing demand index; The step of obtaining the user's electricity consumption behavior characteristic data and analyzing and processing the data to obtain the demand response potential level includes: Obtain user electricity usage behavior characteristic data, including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period; Processing the adjustable power data, the adjustable rate, the historical response time average, and the response rate to obtain a demand response potential evaluation index corresponding to the user; Comparing the demand response potential evaluation index with a preset demand response potential evaluation threshold, and obtaining a demand response potential level according to a range within which the threshold comparison falls; Also includes: Obtain the total load reduction data before and after the implementation of demand response, the average response time of different users, and the electricity price reduction rate after demand response; The total load reduction data, the average response time, and the electricity price reduction rate are input into a preset demand response effectiveness evaluation model to obtain a demand response effectiveness index; Comparing the demand response effectiveness index with a preset demand response effectiveness level threshold to obtain a demand response effectiveness level, including first-level effectiveness, second-level effectiveness, or third-level effectiveness, where third-level effectiveness is the highest; The calculation formula of the demand response effectiveness index in the demand response effectiveness evaluation model is: ； in, is the demand response effectiveness index, 、 、 They are the total load reduction data, the average response time and the electricity price reduction rate, 、 is the preset characteristic coefficient; The step of comparing the grid balancing demand index with a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result, includes: Comparing the power grid balancing demand index with a preset power grid balancing demand threshold; If it is less than or equal to the preset grid balancing demand threshold, it corresponds to a normal state; If it is greater than the preset grid balancing demand threshold, it corresponds to an abnormal state; The processing according to the adjustable power data, the adjustable rate, the historical response time average and the response rate to obtain the demand response potential evaluation index corresponding to the user includes: According to the adjustable power data, the adjustable rate, the historical response time average and the response rate within the preset time period, the preset weight value list is searched to obtain the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value respectively; Calculate the user's corresponding demand response potential assessment index based on the adjustable power data, adjustable rate, historical response time average and response rate, and the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value; The demand response potential evaluation index calculation formula is: ； in, is the demand response potential assessment index, 、 、 、 、 、 、 、 They are adjustable power data, adjustable rate, historical response time average within a preset time period, response rate, adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value. 、 、 、 is the preset characteristic coefficient; The step of comparing the demand response potential evaluation index with a preset demand response potential evaluation threshold, and obtaining a demand response potential level according to a range of the threshold comparison, includes: Comparing the demand response potential evaluation index with a preset standard demand response potential evaluation index to obtain a relative value of demand response potential; Performing a threshold comparison between the relative value of the demand response potential and a preset demand response potential assessment threshold, wherein the preset demand response potential assessment threshold includes a first preset demand response potential assessment threshold and a second preset demand response potential assessment threshold, and the first preset demand response potential assessment threshold is less than the second preset demand response potential assessment threshold; If it is less than or equal to the first preset demand response potential assessment threshold, it corresponds to low potential; If it is greater than the first preset demand response potential assessment threshold and less than or equal to the second preset demand response potential assessment threshold, it corresponds to medium potential; If it is greater than a second preset demand response potential assessment threshold, it corresponds to high potential.

2. A demand response data interaction system based on virtual power plant, characterized in that: The system comprises a memory and a processor, wherein the memory comprises a program of a method for interacting with demand response data based on a virtual power plant, and when the program of the method for interacting with demand response data based on a virtual power plant is executed by the processor, the following steps are implemented: Acquire real-time grid operation data and analyze and process it to obtain the grid balance demand index; Comparing the grid balancing demand index with a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result; Obtain and analyze the user's electricity consumption behavior characteristic data to obtain the demand response potential level; Initiate a demand response invitation to the user based on the grid operation status and the demand response potential level; The invited user generates a response plan based on the demand response invitation and sends it to the virtual power plant management terminal for display; The acquiring of real-time grid operation data and performing analysis and processing to obtain a grid balance demand index includes: Obtain real-time grid operation data, including load change mean data, voltage fluctuation mean data, and frequency deviation mean data within a preset time period; Inputting the load change mean data, voltage fluctuation mean data and frequency deviation mean data into a preset power grid balancing demand assessment model for processing to obtain a power grid balancing demand index; The step of obtaining the user's electricity consumption behavior characteristic data and analyzing and processing the data to obtain the demand response potential level includes: Obtain user electricity usage behavior characteristic data, including adjustable power data, adjustable rate, historical response time average and response rate within a preset time period; Processing the adjustable power data, the adjustable rate, the historical response time average, and the response rate to obtain a demand response potential evaluation index corresponding to the user; Comparing the demand response potential evaluation index with a preset demand response potential evaluation threshold, and obtaining a demand response potential level according to a range within which the threshold comparison falls; Also includes: Obtain the total load reduction data before and after the implementation of demand response, the average response time of different users, and the electricity price reduction rate after demand response; The total load reduction data, the average response time, and the electricity price reduction rate are input into a preset demand response effectiveness evaluation model to obtain a demand response effectiveness index; Comparing the demand response effectiveness index with a preset demand response effectiveness level threshold to obtain a demand response effectiveness level, including first-level effectiveness, second-level effectiveness, or third-level effectiveness, where third-level effectiveness is the highest; The calculation formula of the demand response effectiveness index in the demand response effectiveness evaluation model is: ； in, is the demand response effectiveness index, 、 、 They are the total load reduction data, the average response time and the electricity price reduction rate, 、 is the preset characteristic coefficient; The step of comparing the grid balancing demand index with a preset grid balancing demand threshold, and determining the grid operation state according to the threshold comparison result, includes: Comparing the power grid balancing demand index with a preset power grid balancing demand threshold; If it is less than or equal to the preset grid balancing demand threshold, it corresponds to a normal state; If it is greater than the preset grid balancing demand threshold, it corresponds to an abnormal state; The processing according to the adjustable power data, the adjustable rate, the historical response time average and the response rate to obtain the demand response potential evaluation index corresponding to the user includes: According to the adjustable power data, the adjustable rate, the historical response time average and the response rate within the preset time period, the preset weight value list is searched to obtain the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value respectively; Calculate the user's corresponding demand response potential assessment index based on the adjustable power data, adjustable rate, historical response time average and response rate, and the corresponding adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value; The demand response potential evaluation index calculation formula is: ； in, is the demand response potential assessment index, 、 、 、 、 、 、 、 They are adjustable power data, adjustable rate, historical response time average within a preset time period, response rate, adjustable power weight value, adjustable rate weight value, response time weight value and response rate weight value. 、 、 、 is the preset characteristic coefficient; The step of comparing the demand response potential evaluation index with a preset demand response potential evaluation threshold, and obtaining a demand response potential level according to a range of the threshold comparison, includes: Comparing the demand response potential evaluation index with a preset standard demand response potential evaluation index to obtain a relative value of demand response potential; Performing a threshold comparison between the relative value of the demand response potential and a preset demand response potential assessment threshold, wherein the preset demand response potential assessment threshold includes a first preset demand response potential assessment threshold and a second preset demand response potential assessment threshold, and the first preset demand response potential assessment threshold is less than the second preset demand response potential assessment threshold; If it is less than or equal to the first preset demand response potential assessment threshold, it corresponds to low potential; If it is greater than the first preset demand response potential assessment threshold and less than or equal to the second preset demand response potential assessment threshold, it corresponds to medium potential; If it is greater than a second preset demand response potential assessment threshold, it corresponds to high potential.

3. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a demand response data interaction method program based on a virtual power plant. When the demand response data interaction method program based on a virtual power plant is executed by a processor, the steps of a demand response data interaction method based on a virtual power plant as claimed in claim 1 are implemented.

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