Virtual power plant optimization regulation and control method and system considering power-carbon cooperation

Abstract

The invention discloses a virtual power plant optimization regulation and control method and system considering power-carbon cooperation, and is used for solving the problems that a current virtual power plant resource regulation and control method is not systematic and comprehensive enough, and is lack of systematic consideration on a carbon emission target. Generating set historical output data and external power data of a power system where the virtual power plant cluster is located are obtained, carbon emission factor prediction is carried out according to the generating set historical output data and the external power data, and carbon emission prediction information is obtained; performing carbon potential calculation according to the carbon emission prediction information to form a whole-network carbon emission flow; key feature parameters are calculated based on the whole network carbon emission flow, and a user electricity-carbon fusion image is drawn according to the key feature parameters; carrying out resource aggregation according to the key feature parameters, and drawing an electric-carbon coupling portrait of the virtual power plant; and in combination with a preset constraint condition, constructing a low-carbon scheduling optimization model considering carbon reduction and peak clipping at the same time, so as to perform virtual power plant resource optimization calling on the virtual power plant cluster based on the low-carbon scheduling optimization model.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated intelligent energy management, and in particular to an optimized regulation method for a virtual power plant considering the coordination of electricity and carbon, an optimized regulation system for a virtual power plant considering the coordination of electricity and carbon, an electronic device, and a storage medium. Background Art

[0002] As an integrated intelligent energy management system, a virtual power plant can aggregate, optimize, and manage a large number of distributed resources in the power grid, comprehensively balance the power supply side and the demand side, and coordinate and optimize the power grid stability. Thus, it can effectively solve problems such as the impact of new energy power generation grid connection on the power grid, peak load power supply, and local power grid congestion. However, the related research, system construction, and engineering practice of traditional virtual power plants are relatively simple, mainly focusing on the aggregation and scheduling of distributed resources. Currently, the main goal is to achieve the economy and stability of the power system, lacking systematic consideration of carbon emission targets and being difficult to meet the electricity-carbon coordination requirements.

[0003] A large amount of carbon emissions will be generated during the power production and consumption processes of the distributed flexible resources in the virtual power plant. Accurately mastering and managing the carbon emission situation helps the virtual power plant formulate scientific emission reduction strategies and reduce the carbon emission cost of the whole society. In the current situation of increasingly strict carbon emission constraints, achieving electricity-carbon coordinated management is crucial for the sustainable development of the virtual power plant. However, there is still a lack of effective methods and means to give full play to the electricity-carbon coordination benefits of resources during the aggregation process of the virtual power plant. Therefore, it is urgent to accelerate the research and development of support for resource aggregation and optimized regulation of virtual power plants considering electricity-carbon coordination, and to guide users to consume green electricity in an orderly manner. Summary of the Invention

[0004] The present invention provides an optimized regulation method for a virtual power plant considering the coordination of electricity and carbon, an optimized regulation system for a virtual power plant considering the coordination of electricity and carbon, an electronic device, and a storage medium, which are used to solve or partially solve the technical problem that the current virtual power plant resource regulation method is not systematic and comprehensive enough and lacks systematic consideration of carbon emission targets.

[0005] The present invention provides an optimized regulation method for a virtual power plant considering the coordination of electricity and carbon, which is applied to a virtual power plant cluster; the method includes:

[0006] Obtain the historical output data of the generating units and the imported electricity data of the power system where the virtual power plant cluster is located, and perform carbon emission factor prediction based on the historical output data of the generating units and the imported electricity data to obtain carbon emission prediction information;

[0007] Calculate the carbon potential according to the carbon emission prediction information to form the whole-network carbon emission flow;

[0008] Calculate the key characteristic parameters of the user's electricity-carbon and regulation characteristics based on the overall network carbon emission flow, and draw a user electricity-carbon fusion image according to the key characteristic parameters;

[0009] Aggregate resources according to the key characteristic parameters, and draw a virtual power plant electricity-carbon coupling portrait;

[0010] Combine preset constraint conditions to construct a low-carbon scheduling optimization model that simultaneously considers carbon reduction and peak shaving. The low-carbon scheduling optimization model is used to optimize the call of virtual power plant resources for the virtual power plant cluster.

[0011] Optionally, the imported electricity data includes the injected electricity of each imported line in the power grid within a preset historical time period and the external carbon emission factor of each imported line; the historical output data of the generating units includes the power generation information and power consumption information of the internal units of the power grid; the carbon emission factor prediction based on the historical output data to obtain carbon emission prediction information includes:

[0012] Predict the transmitted electricity of imported electricity for each sub-period within a preset future period according to the injected electricity of each imported line;

[0013] Predict the average imported carbon emission factor for each sub-period within a preset future period according to each external carbon emission factor, combined with the mean method;

[0014] Predict the output of local generating units for each sub-period within a preset future period according to the power generation information and the power consumption information;

[0015] Predict the local average carbon emission factor of each power source for each sub-period within a preset future period according to the power generation information and the power consumption information, combined with the mean method;

[0016] Use each transmitted electricity of imported electricity, each average imported carbon emission factor, each output of local generating units, and each local average carbon emission factor as the carbon emission prediction information of the virtual power plant.

[0017] Optionally, the carbon potential calculation based on the carbon emission prediction information to form the overall network carbon emission flow includes:

[0018] According to each transmitted electricity of imported electricity, each average imported carbon emission factor, each output of local generating units, and each local average carbon emission factor, based on the power grid topology structure, power grid power flow equation constraints and carbon emission balance principle of the virtual power plant cluster, start from the power source nodes in sequence to calculate the carbon potential of each node and line, and form the overall network carbon emission flow.

[0019] Optionally, the virtual power plant cluster includes multiple virtual power plants, and each virtual power plant includes multiple users; the key characteristic parameters include the peak-time electricity consumption ratio, load volatility, peak-time carbon ratio, and carbon load volatility; calculating the key characteristic parameters of the user's electricity-carbon and regulation characteristics based on the carbon emission flow of the entire network, and drawing a user electricity-carbon fusion image according to the key characteristic parameters, including:

[0020] For each virtual power plant, for each user in the virtual power plant, obtain the historical user load data of the user, and perform load forecasting based on the historical user load data to forecast the user's daily load curve;

[0021] Extract the peak load period and the user load at each moment from the user's daily load curve, and calculate the peak-time electricity consumption ratio according to the peak load period and the user load at each moment;

[0022] Calculate the load standard deviation value and the load mean value of the user's daily load curve respectively according to the user load at each moment, and calculate the load volatility according to the load standard deviation value and the load mean value;

[0023] Extract the node carbon potential information from the carbon emission flow of the entire network, calculate the carbon load at each moment according to the user load at each moment and the node carbon potential information, and generate the user's daily carbon load curve corresponding to the carbon load at each moment;

[0024] Calculate the peak-time carbon ratio based on the peak load period and the carbon load at each moment;

[0025] Calculate the carbon load standard deviation value and the carbon load mean value of the user's daily carbon load curve respectively according to the carbon load at each moment, and calculate the carbon load volatility according to the carbon load standard deviation value and the carbon load mean value;

[0026] Based on the peak-time electricity consumption ratio, the load volatility, the peak-time carbon ratio, and the carbon load volatility, draw an electricity-carbon fusion portrait through radar chart clustering analysis to generate the user's electricity-carbon fusion image.

[0027] Optionally, the resource aggregation according to the key characteristic parameters and the drawing of the electricity-carbon coupling portrait of the virtual power plant include:

[0028] For each virtual power plant, aggregate the peak-time electricity consumption ratios of all users in the virtual power plant to obtain the peak-time electricity consumption ratio of the virtual power plant;

[0029] Aggregate the load volatility ratios of all users in the virtual power plant to obtain the load volatility of the virtual power plant;

[0030] Aggregate the peak - time carbon ratios of all users in the virtual power plant to obtain the peak - time carbon ratio of the virtual power plant;

[0031] Aggregate the carbon load volatility of all users in the virtual power plant to obtain the carbon load volatility of the virtual power plant;

[0032] Based on the peak - time electricity consumption ratio of the virtual power plant, the load volatility of the virtual power plant, the peak - time carbon ratio of the virtual power plant, and the carbon load volatility of the virtual power plant, draw an electricity - carbon coupling portrait through radar chart clustering analysis to generate the electricity - carbon coupling portrait of the virtual power plant.

[0033] Optionally, the preset constraint conditions include node bus voltage constraints, power generator constraints, flexible load constraints, and power flow constraints; Combining the preset constraint conditions, constructing a low - carbon scheduling optimization model that simultaneously considers carbon reduction and peak shaving includes:

[0034] Take the load variable of the flexible load resources of the virtual power plant users in the response per unit time as the decision variable;

[0035] Based on the decision variable and the predicted load, construct a load fluctuation sub - objective function;

[0036] According to the decision variable and the carbon emission factors of adjacent nodes of the flexible load resources, calculate the carbon emissions of the flexible load resources, and based on the carbon emissions of the flexible load resources and the carbon emission values of the predicted load, construct a carbon emission sub - objective function;

[0037] According to the load fluctuation sub - objective function and the carbon emission sub - objective function, combined with the sub - function tuning parameters, construct a carbon - reduction - peak - shaving objective function;

[0038] According to the carbon - reduction - peak - shaving objective function, the node bus voltage constraints, the power generator constraints, the flexible load constraints, and the power flow constraints, construct a low - carbon scheduling optimization model that simultaneously considers carbon reduction and peak shaving.

[0039] Optionally, the process of optimizing the call of virtual power plant resources for the virtual power plant cluster through the low - carbon scheduling optimization model includes:

[0040] Sort each virtual power plant in the virtual power plant cluster in descending order of the carbon load volatility of the virtual power plant and then in descending order of the load volatility of the virtual power plant;

[0041] When the sub - function tuning parameter is less than or equal to the preset minimum tuning threshold, or when the carbon - reduction - peak - shaving objective function focuses on reducing carbon emissions, preferentially call the resources of the virtual power plants with higher rankings in the carbon load volatility of the virtual power plant to participate in the optimization;

[0042] When the sub-function tuning parameter is greater than or equal to the preset maximum tuning threshold, or when the carbon reduction-peak shaving objective function focuses on peak shaving, the virtual power plant resources with a higher ranking in terms of load volatility of the virtual power plant are preferentially called to participate in the optimization;

[0043] During the optimization call process, if the currently called virtual power plant cannot meet the optimization requirements, the next virtual power plant resource is sequentially called to continue participating in the optimization until the optimization requirements are met.

[0044] The present invention also provides a virtual power plant optimization and control system considering the coordination of electricity and carbon, which is applied to a virtual power plant cluster; the system includes:

[0045] A carbon emission factor prediction module, which is used to obtain the historical output data of the generating units and the imported electricity data of the power system where the virtual power plant cluster is located, and perform carbon emission factor prediction based on the historical output data of the generating units and the imported electricity data to obtain carbon emission prediction information;

[0046] A whole-network carbon emission flow carbon tracking module, which is used to calculate the carbon potential based on the carbon emission prediction information to form a whole-network carbon emission flow;

[0047] A user electricity-carbon integration portrait module, which is used to calculate the key characteristic parameters of the user's electricity-carbon and regulation characteristics based on the whole-network carbon emission flow, and draw a user electricity-carbon integration image according to the key characteristic parameters;

[0048] A resource aggregation module, which is used to perform resource aggregation according to the key characteristic parameters and draw a virtual power plant electricity-carbon coupling portrait;

[0049] A low-carbon optimal scheduling module, which is used to construct a low-carbon scheduling optimization model considering both carbon reduction and peak shaving in combination with preset constraint conditions, and the low-carbon scheduling optimization model is used to optimize the call of virtual power plant resources for the virtual power plant cluster.

[0050] The present invention also provides an electronic device, which includes a processor and a memory:

[0051] The memory is used to store program codes and transmit the program codes to the processor;

[0052] The processor is used to execute the virtual power plant optimization and control method considering the coordination of electricity and carbon as described in any one of the above according to the instructions in the program codes.

[0053] The present invention also provides a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the virtual power plant optimization and control method considering the coordination of electricity and carbon as described in any one of the above.

[0054] As can be seen from the above technical solutions, the present invention has the following advantages:

[0055] Provided is an optimized regulation method and system for a virtual power plant considering the coordination of electricity and carbon. First, historical output data of generator sets and imported electricity data of the power system where the virtual power plant cluster is located are obtained, and carbon emission factors are predicted based on the historical output data of generator sets and imported electricity data to obtain carbon emission prediction information; then, carbon potential is calculated based on the carbon emission prediction information to form a carbon emission flow across the entire network; thus, based on the embedded carbon emission flow algorithm, prediction and real-time online calculation and analysis of carbon emissions in all links of the power grid are realized. Then, key characteristic parameters of the electricity-carbon and regulation characteristics of users are calculated based on the carbon emission flow across the entire network, and an electricity-carbon fusion image of users is drawn according to the key characteristic parameters; thus, the electricity consumption characteristics of users are quantitatively analyzed from multiple perspectives such as carbon reduction potential and load regulation, so as to achieve precise classification of a large number of users. Then, resources are aggregated according to the key characteristic parameters, and an electricity-carbon coupling portrait of the virtual power plant is drawn; thus, large-scale distributed resources of the virtual power plant are aggregated to improve the management and control efficiency of a large number of distributed resources. Finally, combined with preset constraint conditions, a low-carbon scheduling optimization model considering both carbon reduction and peak shaving is constructed, and based on the low-carbon scheduling optimization model, the resources of the virtual power plant cluster are optimized and dispatched; thus, based on the analysis of the electricity-carbon coupling characteristics and the carbon reduction potential of users, the virtual power plant is optimized and scheduled to operate, and resources with better carbon reduction potential are selected from the user group to participate in low-carbon scheduling through the low-carbon optimization scheduling of the virtual power plant, providing a low-carbon operation strategy for the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0057] Figure 1 It is a schematic structural diagram of an optimized regulation system for a virtual power plant considering the coordination of electricity and carbon;

[0058] Figure 2 It is a flowchart of the steps of an optimized regulation method for a virtual power plant considering the coordination of electricity and carbon;

[0059] Figure 3 It is a schematic logical architecture diagram of an optimized regulation system for a virtual power plant considering the coordination of electricity and carbon. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] Embodiments of the present invention provide a virtual power plant optimization control method considering the coordination of electricity and carbon, a virtual power plant optimization control system considering the coordination of electricity and carbon, an electronic device, and a storage medium, which are used to solve or partially solve the technical problem that the current virtual power plant resource control method is not systematic and comprehensive enough and lacks systematic consideration of carbon emission targets.

[0061] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] As an example, the relevant research, system construction, and engineering practice of traditional virtual power plants are relatively simple, mainly focusing on the aggregation and scheduling of distributed resources. Currently, the main goal is to achieve the economy and stability of the power system, lacking systematic consideration of carbon emission targets and being difficult to meet the electricity-carbon coordination requirements. There is still a lack of effective methods and means for how to fully utilize the electricity-carbon coordination benefits of resources during the aggregation of virtual power plants. Therefore, it is urgent to accelerate the research and development of virtual power plant resource aggregation and optimization control support considering electricity-carbon coordination and orderly guide users to consume green electricity.

[0063] Therefore, one of the core inventive points of the embodiments of the present invention lies in: proposing a virtual power plant resource aggregation and optimal regulation support system and method considering the coordination of electricity and carbon (hereinafter referred to as virtual power plant optimal regulation). First, based on digital professional technologies, a large amount of grid-wide measurement data is aggregated, and based on the embedded carbon emission flow algorithm, the prediction and real-time online calculation and analysis of carbon emissions in all links of the power grid are realized. Secondly, the electricity consumption characteristics of users are quantitatively analyzed from multiple perspectives such as carbon reduction potential and load regulation, so as to achieve accurate classification of a large number of users. Then, the large-scale distributed resources of the virtual power plant are aggregated to improve the management and control efficiency of a large amount of distributed resources. Finally, based on the analysis of the electricity-carbon coupling characteristics and the carbon reduction potential of users, the virtual power plant is optimized and scheduled to operate. Through the low-carbon optimal scheduling of the virtual power plant, resources with better carbon reduction potential are selected from the user group to participate in the low-carbon scheduling, providing a low-carbon operation strategy for the virtual power plant, and at the same time evaluating the emission reduction effect of the low-carbon scheduling strategy, improving the efficiency of power grid scheduling and the economic and environmental benefits, thereby guiding energy conservation and carbon reduction on the power consumption side and promoting the consumption of green electricity, and providing data support and decision-making basis for power grid planning, construction, operation, etc. Thus, the present invention combines the carbon emission reduction mechanism of power consumption-side load users, and based on the topological structure of the power grid, carbon emission characteristics, and the analysis results of the electricity-carbon portrait of the virtual power plant, constructs a virtual power plant resource aggregation and optimal regulation model considering the coordination of electricity and carbon to achieve low-carbon optimal regulation of virtual power plant resources.

[0064] Referring to Figure 1 , a schematic structural diagram of a virtual power plant optimal regulation system considering the coordination of electricity and carbon provided by an embodiment of the present invention is shown.

[0065] The virtual power plant optimal regulation system 100 provided by the embodiments of the present invention is applied to a virtual power plant cluster. The virtual power plant cluster represents a cluster formed by multiple virtual power plants within a preset range. The virtual power plant optimal regulation system 100 may specifically include a carbon emission factor prediction module 101, a whole-network carbon emission flow carbon tracking module 102, a user electricity-carbon fusion portrait module 103, a resource aggregation module 104, and a low-carbon optimal scheduling module 105.

[0066] Among them, the carbon emission factor prediction module 101 is mainly used to obtain the historical output data and imported electricity data of the power system where the virtual power plant cluster is located, and predict the carbon emission factors based on the historical output data and imported electricity data of the generating units to obtain carbon emission prediction information.

[0067] In an optional embodiment, the imported electricity data may include the injected electricity of each imported line of the power grid within a preset historical time period and the external carbon emission factors of each imported line. The historical output data of the generating units may include the power generation information and electricity consumption information of the internal units of the power grid. Then, the carbon emission factor prediction module 101 may specifically be used for:

[0068] Predict the imported power transmission for each sub-period within a preset future period according to the injected power of each incoming line; predict the average imported carbon emission factor for each sub-period within a preset future period according to each external carbon emission factor and by combining the mean method; predict the output of local generator sets for each sub-period within a preset future period according to the power generation information and power consumption information; predict the local average carbon emission factor of each power source for each sub-period within a preset future period according to the power generation information and power consumption information and by combining the mean method; use each imported power transmission, each average imported carbon emission factor, each output of local generator sets, and each local average carbon emission factor as the carbon emission prediction information of the virtual power plant cluster.

[0069] The whole-network carbon emission flow carbon tracking module 102 is mainly used to calculate the carbon potential according to the carbon emission prediction information and form the whole-network carbon emission flow.

[0070] In an alternative embodiment, the whole-network carbon emission flow carbon tracking module 102 may specifically be used to: calculate the carbon potential of each node and line sequentially starting from the power source node based on each imported power transmission, each average imported carbon emission factor, each output of local generator sets, each local average carbon emission factor, the grid topology structure of the virtual power plant cluster, the grid power flow equation constraints, and the carbon emission balance principle, so as to form the whole-network carbon emission flow.

[0071] The user electricity-carbon integration portrait module 103 is mainly used to calculate the key characteristic parameters of the user's electricity-carbon and regulation characteristics based on the whole-network carbon emission flow, and draw the user electricity-carbon integration image according to the key characteristic parameters.

[0072] In an alternative embodiment, each virtual power plant may include multiple users. The key characteristic parameters include the peak-time electricity consumption ratio, load volatility, peak-time carbon ratio, and carbon load volatility. Then the user electricity-carbon integration portrait module 103 may specifically be used to:

[0073] For each virtual power plant, for each user within the virtual power plant, obtain the historical user load data of the user, perform accurate load forecasting based on the historical user load data to predict the daily load curve of the user; extract the peak load period and the user load at each moment from the daily load curve of the user, and calculate the peak-hour electricity consumption ratio based on the peak load period and the user load at each moment; calculate the load standard deviation value and the load mean value of the daily load curve of the user respectively according to the user load at each moment, and calculate the load volatility based on the load standard deviation value and the load mean value; extract the node carbon potential information from the whole-network carbon emission flow, calculate the carbon load at each moment according to the user load at each moment and the node carbon potential information, and generate the daily carbon load curve of the user corresponding to the carbon load at each moment; calculate the peak-hour carbon ratio based on the peak load period and the carbon load at each moment; calculate the carbon load standard deviation value and the carbon load mean value of the daily carbon load curve of the user respectively according to the carbon load at each moment, and calculate the carbon load volatility based on the carbon load standard deviation value and the carbon load mean value; based on the peak-hour electricity consumption ratio, the load volatility, the peak-hour carbon ratio, and the carbon load volatility, perform electro-carbon fusion portrait drawing through radar chart clustering analysis to generate the electro-carbon fusion image of the user.

[0074] The resource aggregation module 104 is mainly used to perform resource aggregation according to key feature parameters and draw the electro-carbon coupling portrait of the virtual power plant.

[0075] In an optional embodiment, the resource aggregation module 104 may be specifically used for:

[0076] For each virtual power plant, aggregate the peak-hour electricity consumption ratios of all users within the virtual power plant to obtain the peak-hour electricity consumption ratio of the virtual power plant; aggregate the load volatility ratios of all users within the virtual power plant to obtain the load volatility of the virtual power plant; aggregate the peak-hour carbon ratios of all users within the virtual power plant to obtain the peak-hour carbon ratio of the virtual power plant; aggregate the carbon load volatilities of all users within the virtual power plant to obtain the carbon load volatility of the virtual power plant; based on the peak-hour electricity consumption ratio of the virtual power plant, the load volatility of the virtual power plant, the peak-hour carbon ratio of the virtual power plant, and the carbon load volatility of the virtual power plant, perform electro-carbon coupling portrait drawing through radar chart clustering analysis to generate the electro-carbon coupling portrait of the virtual power plant.

[0077] In addition, the resource aggregation module 104 can also be used to integrate various types of distributed energy resource information within the virtual power plant, including real-time operation data, power generation / consumption capabilities, geographical locations, etc. of distributed power sources (such as solar photovoltaics, wind power, etc.), energy storage devices, and adjustable loads (such as industrial loads, electric vehicle charging and discharging facilities, etc.). By establishing a resource information database, these resources are uniformly managed and classified. At the same time, based on intelligent algorithms and related technical means, the collected distributed energy resources are aggregated to form virtual power plant resource pools. Then, according to the analysis method of the user's electricity-carbon integration portrait, the overall electricity-carbon coupling portrait after the aggregation of distributed resources within the jurisdiction of the virtual power plant is constructed.

[0078] The low-carbon optimization scheduling module 105 is mainly used to construct a low-carbon scheduling optimization model that simultaneously considers carbon reduction and peak shaving in combination with preset constraint conditions. The low-carbon scheduling optimization model is used to optimize the virtual power plant resources of the virtual power plant cluster.

[0079] In an alternative embodiment, the preset constraint conditions include node bus voltage constraints, power generator constraints, flexible load constraints, and power flow constraints. The low-carbon optimization scheduling module 105 can be specifically used for:

[0080] Taking the load variable of the virtual power plant's electricity user's flexible load resource in a unit time period during the response as the decision variable; constructing a load fluctuation sub-objective function based on the decision variable and the predicted load; calculating the carbon emissions of the flexible load resource according to the decision variable and the carbon emission factors of the adjacent nodes of the flexible load resource, and constructing a carbon emission sub-objective function based on the carbon emissions of the flexible load resource and the carbon emission value of the predicted load; constructing a carbon reduction-peak shaving objective function according to the load fluctuation sub-objective function and the carbon emission sub-objective function, in combination with the sub-function tuning parameters; constructing a low-carbon scheduling optimization model that simultaneously considers carbon reduction and peak shaving according to the carbon reduction-peak shaving objective function, node bus voltage constraints, power generator constraints, flexible load constraints, and power flow constraints.

[0081] In an alternative embodiment, the process of optimizing the virtual power plant resources of the virtual power plant cluster by the low-carbon optimization scheduling module 105 through the low-carbon scheduling optimization model may include:

[0082] Sort each virtual power plant in the virtual power plant cluster in descending order of the carbon load volatility of the virtual power plant and in descending order of the load volatility of the virtual power plant; when the sub-function tuning parameter is less than or equal to the preset minimum tuning threshold, or when the carbon emission reduction - peak shaving objective function focuses on carbon emission reduction, preferentially call the virtual power plant resources with higher carbon load volatility ranking in the virtual power plant to participate in the optimization; when the sub-function tuning parameter is greater than or equal to the preset maximum tuning threshold, or when the carbon emission reduction - peak shaving objective function focuses on peak shaving, preferentially call the virtual power plant resources with higher load volatility ranking in the virtual power plant to participate in the optimization; during the optimization call process, if the currently called virtual power plant cannot meet the optimization requirements, then sequentially call the next virtual power plant resource to continue to participate in the optimization until the optimization requirements are met.

[0083] In addition, the virtual power plant optimization and control system 100 provided by the embodiments of the present invention also has a visualization display function. The dispatching department can view the electricity consumption before and after optimization, the carbon emissions before and after optimization, and the corresponding carbon emission reduction potential (expressed in terms of carbon emission reduction amount) of the virtual power plant under different dispatching schemes. Thus, it assists the power grid dispatching department in formulating and issuing corresponding dispatching requirements during actual dispatching.

[0084] Therefore, an embodiment of the present invention provides a virtual power plant optimization and control system considering the coordination of electricity and carbon. Through this virtual power plant optimization and control system, combined with the carbon emission reduction mechanism of electricity consumption side load users, based on the topological structure of the power grid, carbon emission characteristics, and the analysis results of the electricity-carbon portrait of the virtual power plant, a virtual power plant resource aggregation and optimization and control model considering the coordination of electricity and carbon is constructed with peak shaving and carbon emissions as the objectives to achieve low-carbon optimization and control of virtual power plant resources.

[0085] Refer to Figure 2 , which shows a step flow chart of a virtual power plant optimization and control method considering the coordination of electricity and carbon provided by an embodiment of the present invention. The method is applied to a virtual power plant cluster, and the virtual power plant cluster includes multiple virtual power plants; the method specifically may include the following steps:

[0086] Step 201, obtain the historical output data of the power system where the virtual power plant cluster is located and the imported power data, and perform carbon emission factor prediction based on the historical output data of the generator sets and the imported power data to obtain carbon emission prediction information;

[0087] In this step, mainly based on the historical output data of clean energy output and conventional fossil energy unit output and the imported power data, the mean method is used to predict the carbon emission factors in the future time period (such as 24 hours), including the carbon emission factor situations of local power sources and imported power, as the basic data for calculation.

[0088] In some embodiments, the imported power data mainly includes the injected power of each imported line in the power grid within a preset historical time period and the external carbon emission factors of each imported line. The historical output data of the generating units mainly includes the power generation information and power consumption information of the internal units of the power grid. Then, the process of predicting the carbon emission factors based on the historical output data of the generating units and the imported power data to obtain the carbon emission prediction information can be achieved by performing the following sub-steps 2011 to 2015:

[0089] Step 2011: Predict the imported power transmission power for each sub-period within a preset future period according to the injected power of each imported line;

[0090] Step 2012: Predict the average imported carbon emission factor for each sub-period within a preset future period according to each external carbon emission factor, in combination with the mean method;

[0091] Step 2013: Predict the output of the local generating units for each sub-period within a preset future period according to the power generation information and power consumption information;

[0092] Step 2014: Predict the local average carbon emission factor of each power source for each sub-period within a preset future period according to the power generation information and power consumption information, in combination with the mean method;

[0093] Taking a single power source as an example, the carbon emission factor at a certain moment = the amount of carbon dioxide emissions generated at a certain moment / the power generated and fed into the grid.

[0094] Exemplarily, the carbon emission factor at a certain future moment can be taken as the average value of the carbon emission factors at the same moment in the past 5 days.

[0095] The carbon emission factors of different unit power sources such as coal, gas, hydropower, nuclear power, wind power, and photovoltaic power can be calculated using relevant type standards for carbon emission factors.

[0096] Step 2015: Take each imported power transmission power, each average imported carbon emission factor, each output of the local generating units, and each local average carbon emission factor as the carbon emission prediction information of the virtual power plant.

[0097] Step 202: Calculate the carbon potential according to the carbon emission prediction information to form the whole-network carbon emission flow;

[0098] In some embodiments, calculating the carbon potential according to the carbon emission prediction information to form the whole-network carbon emission flow can be: based on each imported power transmission power, each average imported carbon emission factor, each output of the local generating units, and each local average carbon emission factor, and based on the power grid topology structure of the virtual power plant, the constraints of the power grid power flow equation, and the carbon emission balance principle, start from the power source nodes in sequence to calculate the carbon potential of each node and line to form the whole-network carbon emission flow.

[0099] Among them, for a power generation node, the carbon emission factor of the generator on the node is the carbon potential of the node. For a load node, the node carbon potential is the electricity consumption carbon emission factor of the load on the node.

[0100] The constraints that need to be satisfied for carbon potential calculation are as follows:

[0101]

[0102]

[0103] In the formula, is the set of all branches (i.e., lines); is the carbon emission factor at node at time, that is, the carbon potential; is the carbon flow density of branch at time; is the carbon emission intensity of the generator set at time; represents the downstream line of node ; represents the upstream line of node ; is the power generation output of the generator set at time; is the power flow of branch at 、 are respectively the sets of branches injecting power flow and flowing out power flow to node ; is the set of generator set equipment of node ;

[0104] Thus, by tracking the real-time operation data of the power grid through real-time carbon tracking, using digital professional technologies, aggregating a large amount of measurement data of the entire power grid area, and based on the embedded carbon emission flow algorithm, the real-time online calculation and analysis of carbon emissions in all links of the power grid are realized. At the same time, the carbon emission flow calculation links the carbon emissions in all links of the power system, including power generation, transmission, distribution, and power consumption, to achieve the coupling of electricity and carbon.

[0105] Step 203, based on the carbon emission flow of the entire network, calculate the key characteristic parameters of the user's electricity-carbon and regulation characteristics, and draw a user electricity-carbon fusion image according to the key characteristic parameters;

[0106] In this step, based mainly on the carbon emission flow across the network obtained in the previous step, key characteristic parameters of user electricity-carbon and regulation characteristics are calculated, including the peak-hour carbon proportion, carbon load volatility, peak-hour electricity consumption proportion, and load volatility, and a radar chart is used to draw an electricity-carbon integration portrait. Further, different user groups with different carbon reduction potential levels can be obtained according to different numerical values of the characteristic parameters, providing a reference for subsequent low-carbon optimal scheduling.

[0107] Each virtual power plant can include multiple users. In some embodiments, the process of calculating the key characteristic parameters of user electricity-carbon and regulation characteristics based on the carbon emission flow across the network and drawing an electricity-carbon integration image of the user according to the key characteristic parameters can be implemented by performing the following sub-steps 2031 to 2037:

[0108] Step 2031: For each virtual power plant, for each user within the virtual power plant, obtain the historical user load data of the user, and perform load forecasting based on the historical user load data to predict the daily load curve of the user;

[0109] Step 2032: Extract the peak load period and the user load at each moment from the user's daily load curve, and calculate the peak-hour electricity consumption proportion according to the peak load period and the user load at each moment;

[0110] Peak-hour electricity consumption proportion Indicates the proportion of the electricity consumption during the peak load period in the total daily electricity consumption in the user's daily load curve. The higher the peak-hour electricity consumption proportion, the more concentrated the user's electricity consumption period, and the better the effect of peak-shifting regulation. The expression is as follows:

[0111]

[0112] Among them, Represents the user set, where Represents the th user; the value 1 represents the first feature. Is the th user at moment load; Is the peak load time interval; Is the total number of time points calculated on the current day; Is the minimum time granularity for calculation.

[0113] Step 2033: Calculate the load standard deviation and the load mean of the user's daily load curve respectively according to the user load at each moment, and calculate the load volatility according to the load standard deviation and the load mean;

[0114] Load volatility It is the ratio of the standard deviation to the mean of the user's daily load curve. It reflects the degree of dispersion of the load on the time scale. The greater the load volatility, the greater the fluctuation of the user's load curve and the higher the reliability of the user's emergency peak shaving. The expression of

[0115]

[0116] is as follows: is the standard deviation of the user's weekday load; is the load mean. and The expressions of

[0117]

[0118] Step 2034: Extract the node carbon potential information from the whole-network carbon emission flow. According to the user load and node carbon potential information at each moment, calculate the carbon load at each moment and generate the user's daily carbon load curve corresponding to the carbon load at each moment;

[0119] Step 2035: Calculate the carbon proportion during peak hours based on the peak load period and the carbon load at each moment;

[0120] Carbon proportion during peak hours refers to the proportion of the carbon emissions during the peak load period in the total daily carbon emissions in the user's daily carbon load curve. The higher the carbon proportion during peak hours, the more concentrated the user's carbon emission period and the better the carbon reduction effect of peak-shifting regulation. The expression of

[0121]

[0122] is as follows: is the instantaneous carbon emission of the load of the th user at the moment; The instantaneous carbon emission of the load at the downstream node of the branch (hereinafter referred to as the carbon load) can be calculated by multiplying the load by the node carbon emission factor : The calculation is as follows:

[0123]

[0124] Step 2036: Calculate the standard deviation value and the mean value of the carbon load of the user's daily carbon load curve respectively according to the carbon load at each moment, and calculate the carbon load volatility according to the standard deviation value and the mean value of the carbon load;

[0125] Carbon load volatility It is the ratio of the standard deviation to the mean of the user's daily carbon load curve, which reflects the degree of dispersion of the carbon load on the time scale. The greater the carbon load volatility, the greater the fluctuation of the user's carbon load curve, and the higher the reliability of the user's emergency peak shaving at the carbon emission level. The expression of

[0126]

[0127] In the formula, is the standard deviation of the user's daily carbon load, is the mean value of the carbon load. The expressions of and

[0128]

[0129] Step 2037: Based on the peak-time electricity consumption ratio, load volatility, peak-time carbon ratio, and carbon load volatility, draw an electricity-carbon fusion portrait through radar chart clustering analysis to generate the user's electricity-carbon fusion image.

[0130] Step 204: Aggregate resources according to the key characteristic parameters and draw the electricity-carbon coupling portrait of the virtual power plant;

[0131] In this step, based on the user's electricity-carbon fusion portrait, the parameter values of each resource are aggregated (summed) to obtain the corresponding key characteristic parameters of the virtual power plant, and referring to the radar chart clustering analysis method of the user's electricity-carbon fusion portrait, the electricity-carbon coupling portrait of the virtual power plant is drawn.

[0132] In some embodiments, the process of aggregating resources according to the key characteristic parameters and drawing the electricity-carbon coupling portrait of the virtual power plant can be implemented by executing the following sub-steps 2041 to 2045:

[0133] Step 2041: For each virtual power plant, aggregate the peak-time electricity consumption ratios of all users within the virtual power plant to obtain the peak-time electricity consumption ratio of the virtual power plant;

[0134] The th peak-time electricity consumption ratio of the virtual power plant

[0135]

[0136] where represents that the th virtual power plant has

[0137] Step 2042: Aggregate the load volatility ratios of all users within the virtual power plant to obtain the load volatility of the virtual power plant;

[0138] The load volatility of the nth

[0139]

[0140]

[0141] The sum of the standard deviations of the weekday loads of all users in the nth virtual power plant;

[0142] is the sum of the mean values of the loads of all users in the

[0143] nth virtual power plant. is:

[0144]

[0145] Step 2043: Aggregate the peak-time carbon ratios of all users within the virtual power plant to obtain the peak-time carbon ratio of the virtual power plant;

[0146] The peak-time carbon ratio of the nth

[0147]

[0148]

[0149] The sum of the standard deviations of the daily carbon loads of all users in the nth virtual power plant,

[0150] is the sum of the mean values of the carbon loads of all users in the

[0151] Step 2045: Based on the peak-time electricity consumption ratio of the virtual power plant, the load volatility of the virtual power plant, the peak-time carbon ratio of the virtual power plant, and the carbon load volatility of the virtual power plant, draw an electro-carbon coupling portrait through radar chart clustering analysis to generate the electro-carbon coupling portrait of the virtual power plant.

[0152] The low-carbon dispatch optimization of virtual power plants needs to combine the carbon emission reduction mechanism of electricity load users on the demand side. Based on the carbon emission characteristics of the power grid and the analysis results of the electricity-carbon coupling portrait of virtual power plants, the carbon potential of system nodes is used as the guiding signal for low-carbon dispatch. A low-carbon optimization goal is introduced to deeply explore the flexibility potential of distributed resources on the demand side, guiding users to actively change their electricity consumption behaviors to cooperate with the system's low-carbon dispatch, realizing the collaborative interaction between the supply and demand sides and enhancing users' enthusiasm for participating in carbon emission reduction.

[0153] First, it is necessary to construct a user low-carbon dispatch optimization model. Among them, the constructed objective function includes two sub-objectives: carbon reduction and peak shaving. By adjusting the weights in the objective function, various actual demands can be matched (for example, if the current demand focuses more on carbon reduction, the weight of the carbon reduction sub-objective function is increased. Assuming that the current demand focuses more on reducing the burden on the power grid, that is, peak shaving and valley filling, the weight of the peak shaving sub-objective function is increased).

[0154] In some embodiments, the preset constraint conditions may include node bus voltage constraints, power generator constraints, flexible load constraints, and power flow constraints.

[0155] The node bus voltage constraints are as follows:

[0156]

[0157] Among them, and are respectively the lower and upper limits of the square of the bus voltage; is the set of power grid nodes.

[0158] The generator operation constraints include active power constraints, ramp rate constraints, and reactive power constraints, and the expressions are as follows respectively:

[0159]

[0160]

[0161]

[0162] In the formula, 、 are the lower and upper limits of the active power generation of the distributed generator ; is the power of the th generator at the 、 are the lower and upper limits of the reactive power generation of the distributed generator ; is the set of all generators; is the generator ramp rate power constraint.

[0163] The flexible load constraints are as follows:

[0164]

[0165]

[0166]

[0167]

[0168]

[0169] where is the lower limit of flexible power consumption, representing the amount of inflexible load; is the set of all virtual power plant users participating in the dispatching; is the set of all resources of the th user, represents the th resource among them, which needs to meet the self-use load constraints of individual resources (corresponding to the 3rd to 5th formulas of the flexible load constraints); is the minimum energy demand of all resources of the virtual power plant in all time periods (i.e., the target to be met for the call). By coordinating small flexible load-side resources to shape the overall demand, significant flexibility and carbon reduction potential can be provided for the power system, thereby improving network efficiency and energy economy.

[0170] The power flow constraints (i.e., the linear distribution network power flow model) are as follows:

[0171]

[0172]

[0173] where represents the active power output of the renewable energy connected to the th node; is the active power generation of the non-renewable energy distributed generator corresponding to the th node; , are respectively the equivalent load of the virtual power plant and other non-virtual power plant loads corresponding to the th node; and represent the active and reactive power flows of the distribution line (the line connecting busbars , ); , represent the voltages at busbars , ; and represent the resistance and reactance of the line segment; represents the set of distribution network busbars.

[0174] Based on the introduction of the aforementioned relevant constraint conditions and combined with the preset constraint conditions, the process of constructing a low-carbon scheduling optimization model that simultaneously considers carbon reduction and peak shaving can be achieved by executing the following sub-steps 2051 to 2055:

[0175] Step 2051: Take the load variable of the flexible load resource of the virtual power plant's electricity users in a unit time period during the response as the decision variable;

[0176] The decision variable in the embodiment of the present invention is the load variable of the flexible load resource of the virtual power plant's electricity users in a unit time period during the response .

[0177] Step 2052: Based on the decision variable and the predicted load, construct a load fluctuation sub-objective function;

[0178] Load fluctuation sub-objective function is as follows:

[0179]

[0180] where, is the predicted load value at time; is the considered time interval; is the th flexible load resource decision variable; is the number of resources considered.

[0181] Step 2053: According to the decision variable and the carbon emission factor of the adjacent nodes of the flexible load resource, calculate the carbon emissions of the flexible load resource, and based on the carbon emissions of the flexible load resource and the carbon emission value of the predicted load, construct a carbon emission sub-objective function;

[0182] Carbon emission sub-objective function is as follows:

[0183]

[0184]

[0185] where, is the carbon emission value corresponding to the predicted load at time, obtained by multiplying the corresponding predicted load by the carbon emission factor of the whole network average load; is the carbon emissions of the th flexible load resource; is the Flexible load resource adjacent nodes of the carbon emission factor.

[0186] Step 2054: According to the load fluctuation sub-objective function and the carbon emission sub-objective function, combined with the sub-function tuning parameters, construct a carbon reduction-peak shaving objective function;

[0187] The carbon reduction-peak shaving objective function is considered from two perspectives of peak shaving and carbon emissions, and the expression is as follows:

[0188]

[0189] where is the sub-function tuning parameter, and its value is greater than 0 and less than 1.

[0190] Step 2055: According to the carbon reduction-peak shaving objective function, node bus voltage constraints, power generator constraints, flexible load constraints and power flow constraints, construct a low-carbon scheduling optimization model that simultaneously considers carbon reduction and peak shaving.

[0191] The low-carbon scheduling optimization model constructed in the embodiments of the present invention can give the user the best low-carbon operation strategy, that is, the peak shaving amount, load transfer amount, economic benefits and carbon emission reduction benefits obtained from low-carbon optimal scheduling at any time, providing a reference for decision-makers.

[0192] Based on the low-carbon scheduling optimization model, the following are the virtual power plant resource optimization invocation principles:

[0193] (1) When the tuning parameter is small or the carbon reduction-peak shaving objective function focuses on reducing carbon emissions, give priority to invoking the resources of virtual power plants with a higher carbon load volatility ranking (that is, is larger); when the requirements cannot be met by invoking one virtual power plant, sequentially invoke the resources of the next virtual power plant to participate in the optimization.

[0194] (2) When the tuning parameter is large or the carbon reduction-peak shaving objective function focuses on peak shaving, give priority to invoking the resources of virtual power plants with a higher load volatility ranking (that is, is larger); when the requirements cannot be met by invoking one virtual power plant, sequentially invoke the resources of the next virtual power plant to participate in the optimization.

[0195] Based on the invocation principles introduced above, in the specific implementation, the process of optimizing and invoking virtual power plant resources for the virtual power plant cluster through the low-carbon scheduling optimization model can be:

[0196] First, sort each virtual power plant in the virtual power plant cluster in descending order of virtual power plant carbon load volatility and descending order of virtual power plant load volatility.

[0197] When the tuning parameter of the sub-function is less than or equal to the preset minimum tuning threshold, or when the carbon emission reduction - peak shaving objective function focuses on carbon emission reduction, virtual power plant resources with a higher ranking in terms of carbon load volatility are preferentially called to participate in the optimization.

[0198] When the tuning parameter of the sub-function is greater than or equal to the preset maximum tuning threshold, or when the carbon emission reduction - peak shaving objective function focuses on peak shaving, virtual power plant resources with a higher ranking in terms of load volatility are preferentially called to participate in the optimization.

[0199] During the optimization call process, if the currently called virtual power plant cannot meet the optimization requirements, the next virtual power plant resource is sequentially called to continue participating in the optimization until the optimization requirements are met.

[0200] Thus, through the above-mentioned built-in virtual power plant low-carbon optimization scheduling algorithm in the embodiments of the present invention, not only can the day-ahead - intra-day virtual power plant optimization operation plan under different objective functions be formulated, and the comparison of electricity consumption, day-ahead - intra-day load power curves, electro-carbon factor curves, and carbon emission situations before and after virtual power plant optimization scheduling be given, but also the carbon emission reduction potential of virtual power plants participating in optimization scheduling can be analyzed to assist the grid dispatching department in actual virtual power plant optimization scheduling. At the same time, the scheduling plan for virtual power plant resources can be formulated and released according to different scheduling requirements (such as reducing peak load, reducing carbon emissions, etc.) to achieve the low-carbon optimized operation of the power system.

[0201] Based on the relevant content of the virtual power plant optimization control method considering the coordination of electricity and carbon introduced above, the schematic diagram of the logical architecture of the virtual power plant optimization control system in the embodiments of the present invention can be referred to Figure 3 .

[0202] In the embodiments of the present invention, a virtual power plant optimization control method considering the coordination of electricity and carbon is provided. First, based on digital professional technologies, a large amount of grid-wide measurement data is aggregated, and based on the embedded carbon emission flow algorithm, the prediction and real-time online calculation and analysis of carbon emissions in all links of the grid are realized. Secondly, the electricity consumption characteristics of users are quantitatively analyzed from multiple angles such as carbon emission reduction potential and load regulation, so as to achieve the accurate classification of a large number of users. Then, the large-scale distributed resources of the virtual power plant are aggregated to improve the management and control efficiency of a large amount of distributed resources. Finally, based on the analysis of the electro-carbon coupling characteristics and the carbon emission reduction potential of users, the virtual power plant is optimized and scheduled to operate. Through the low-carbon optimization scheduling of the virtual power plant, resources with better carbon emission reduction potential are selected from the user group to participate in low-carbon scheduling, providing a low-carbon operation strategy for the virtual power plant, and at the same time evaluating the emission reduction effect of the low-carbon scheduling strategy, improving the efficiency of grid scheduling and the economic and environmental benefits, and further guiding the energy conservation and carbon reduction on the power consumption side and promoting the consumption of green electricity, and providing data support and decision-making basis for grid planning, construction, operation, etc.

[0203] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory:

[0204] The memory is used to store program code and transmit the program code to the processor;

[0205] The processor is used to execute the virtual power plant optimization control method considering the coordination of electricity and carbon in any embodiment of the present invention according to the instructions in the program code.

[0206] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the virtual power plant optimization control method considering the coordination of electricity and carbon in any embodiment of the present invention.

[0207] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0208] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, indirect couplings or communication connections of devices or units, and can be in electrical, mechanical or other forms.

[0209] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0210] In addition, each functional unit in various embodiments of the present invention can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0211] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0212] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A virtual power plant optimization control method considering the coordination of electricity and carbon, characterized in that Applied to a virtual power plant cluster; the method includes: Obtain the historical output data of the generating units and the imported power data of the power system where the virtual power plant cluster is located, and perform carbon emission factor prediction based on the historical output data of the generating units and the imported power data to obtain carbon emission prediction information; Perform carbon potential calculation according to the carbon emission prediction information to form a whole-network carbon emission flow; Calculate the key characteristic parameters of the user's electricity-carbon and regulation characteristics based on the whole-network carbon emission flow, and draw a user electricity-carbon fusion image according to the key characteristic parameters; Perform resource aggregation according to the key characteristic parameters and draw a virtual power plant electricity-carbon coupling portrait; Combined with preset constraint conditions, construct a low-carbon scheduling optimization model that simultaneously considers carbon reduction and peak shaving, and the low-carbon scheduling optimization model is used to optimize the call of virtual power plant resources for the virtual power plant cluster.

2. The virtual power plant optimal regulation method considering the coordination of electricity and carbon according to claim 1, wherein The imported power data includes the injected power of each imported line in the power grid within a preset historical time period and the external carbon emission factor of each imported line; the historical output data of the generating units includes the power generation information and power consumption information of the internal units of the power grid; The carbon emission factor prediction based on the historical output data of the generating units and the imported power data to obtain carbon emission prediction information includes: Predict the imported power transmission power of each sub-period within a preset future period according to the injected power of each imported line; Predict the average external carbon emission factor of each sub-period within a preset future period according to each external carbon emission factor, combined with the mean method; Predict the output of the local generating units of each sub-period within a preset future period according to the power generation information and the power consumption information; Predict the local average carbon emission factor of each power source of each sub-period within a preset future period according to the power generation information and the power consumption information, combined with the mean method; Take each imported power transmission power, each average external carbon emission factor, each local generating unit output, and each local average carbon emission factor as the carbon emission prediction information of the virtual power plant cluster.

3. The virtual power plant optimization and control method considering the coordinated operation of electricity and carbon according to claim 2, wherein The carbon potential calculation according to the carbon emission prediction information to form a whole-network carbon emission flow includes: Based on the imported power transmission power of each, each average external carbon emission factor, each local generating unit output, and each local average carbon emission factor, and based on the power grid topology structure, power grid power flow equation constraints, and carbon emission balance principle of the virtual power plant cluster, start from the power source node in sequence to perform carbon potential calculation for each node and line to form a whole-network carbon emission flow.

4. The virtual power plant optimization regulation method considering the coordination of electricity and carbon according to claim 2, characterized in that, The virtual power plant cluster includes multiple virtual power plants, and each virtual power plant includes multiple users; the key characteristic parameters include the peak-time electricity consumption ratio, load volatility, peak-time carbon ratio, and carbon load volatility; the calculation of the key characteristic parameters of the user's electricity-carbon and regulation characteristics based on the whole-network carbon emission flow, and the drawing of a user electricity-carbon fusion image according to the key characteristic parameters includes: For each virtual power plant, for each user in the virtual power plant, obtain the historical user load data of the user, and perform load prediction according to the historical user load data to predict the user's daily load curve; Extract the peak load period and the user load at each moment from the user daily load curve, and calculate the peak-hour electricity consumption ratio according to the peak load period and the user load at each moment; Calculate the load standard deviation and the load mean value of the user daily load curve respectively according to the user load at each moment, and calculate the load volatility according to the load standard deviation and the load mean value; Extract the node carbon potential information from the whole-network carbon emission flow, calculate the carbon load at each moment according to the user load at each moment and the node carbon potential information, and generate a user daily carbon load curve corresponding to the carbon load at each moment; Calculate the peak-hour carbon ratio based on the peak load period and the carbon load at each moment; Calculate the carbon load standard deviation and the carbon load mean value of the user daily carbon load curve respectively according to the carbon load at each moment, and calculate the carbon load volatility according to the carbon load standard deviation and the carbon load mean value; Based on the peak-hour electricity consumption ratio, the load volatility, the peak-hour carbon ratio and the carbon load volatility, draw an electricity-carbon fusion portrait through radar chart clustering analysis, and generate the user's electricity-carbon fusion image.

5. The virtual power plant optimization and regulation method considering the coordination of electricity and carbon according to claim 4, characterized in that The aggregating resources according to the key feature parameters and drawing the electricity-carbon coupling portrait of the virtual power plant includes: For each virtual power plant, aggregate the peak-hour electricity consumption ratios of all users in the virtual power plant to obtain the peak-hour electricity consumption ratio of the virtual power plant; Aggregate the load volatility ratios of all users in the virtual power plant to obtain the load volatility of the virtual power plant; Aggregate the peak-hour carbon ratios of all users in the virtual power plant to obtain the peak-hour carbon ratio of the virtual power plant; Aggregate the carbon load volatilities of all users in the virtual power plant to obtain the carbon load volatility of the virtual power plant; Based on the peak-hour electricity consumption ratio of the virtual power plant, the load volatility of the virtual power plant, the peak-hour carbon ratio of the virtual power plant and the carbon load volatility of the virtual power plant, draw an electricity-carbon coupling portrait through radar chart clustering analysis, and generate the electricity-carbon coupling portrait of the virtual power plant.

6. The virtual power plant optimization control method considering the coordination of electricity and carbon according to claim 4 or 5, characterized in that, The preset constraint conditions include node bus voltage constraints, power generator constraints, flexible load constraints and power flow constraints; the constructing a low-carbon scheduling optimization model considering both carbon reduction and peak shaving in combination with the preset constraint conditions includes: Take the load variable of the flexible load resources of the virtual power plant users in the response per unit time as the decision variable; Based on the decision variable and the predicted load, construct a load fluctuation sub-objective function; Calculate the carbon emissions of the flexible load resources according to the decision variable and the carbon emission factors of the adjacent nodes of the flexible load resources, and construct a carbon emission sub-objective function based on the carbon emissions of the flexible load resources and the carbon emission value of the predicted load; According to the load fluctuation sub-objective function and the carbon emission sub-objective function, combine the sub-function tuning parameters to construct a carbon reduction-peak shaving objective function; According to the carbon reduction-peak shaving objective function, the node bus voltage constraint, the power generator constraint, the flexible load constraint and the power flow constraint, construct a low-carbon scheduling optimization model considering both carbon reduction and peak shaving.

7. The virtual power plant optimization and regulation method considering the coordination of electricity and carbon according to claim 6, characterized in that, The process of optimizing and invoking virtual power plant resources of the virtual power plant cluster through the low-carbon scheduling optimization model includes: Sorting each virtual power plant in the virtual power plant cluster in descending order of the virtual power plant carbon load volatility and then in descending order of the virtual power plant load volatility; When the sub-function tuning parameter is less than or equal to the preset minimum tuning threshold, or when the carbon emission reduction - peak shaving objective function focuses on reducing carbon emissions, preferentially invoking the resources of the virtual power plants with higher rankings in the virtual power plant carbon load volatility to participate in the optimization; When the sub-function tuning parameter is greater than or equal to the preset maximum tuning threshold, or when the carbon emission reduction - peak shaving objective function focuses on peak shaving, preferentially invoking the resources of the virtual power plants with higher rankings in the virtual power plant load volatility to participate in the optimization; During the optimization and invocation process, if the currently invoked virtual power plant cannot meet the optimization requirements, then sequentially invoking the next virtual power plant resource to continue participating in the optimization until the optimization requirements are met.

8. A virtual power plant optimization and regulation system considering the coordination of electricity and carbon, characterized in that, Applied to a virtual power plant cluster; the system includes: A carbon emission factor prediction module, configured to obtain the historical output data of the generating units and the imported power data of the power system where the virtual power plant cluster is located, and perform carbon emission factor prediction based on the historical output data of the generating units and the imported power data to obtain carbon emission prediction information; A whole-network carbon emission flow carbon tracking module, configured to calculate the carbon potential based on the carbon emission prediction information to form a whole-network carbon emission flow; A user electricity-carbon integration portrait module, configured to calculate the key characteristic parameters of the user's electricity-carbon sum and regulation characteristics based on the whole-network carbon emission flow, and draw a user electricity-carbon integration image according to the key characteristic parameters; A resource aggregation module, configured to perform resource aggregation according to the key characteristic parameters and draw a virtual power plant electricity-carbon coupling portrait; A low-carbon optimization scheduling module, configured to construct a low-carbon scheduling optimization model that simultaneously considers carbon emission reduction and peak shaving in combination with preset constraint conditions, and the low-carbon scheduling optimization model is used to optimize and invoke virtual power plant resources of the virtual power plant cluster.

9. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the virtual power plant optimization and control method considering electricity-carbon coordination according to any one of claims 1-7 based on the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code, and the program code is used to execute the virtual power plant optimization and control method considering electricity-carbon coordination according to any one of claims 1-7.

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