Power grid integrated optimization control method and system for distributed energy resources

Abstract

The invention relates to the technical field of energy management, and discloses a distributed energy resource power grid integrated optimization control method and system, and the method comprises the steps: obtaining a to-be-processed distributed energy resource and a corresponding energy supply region, dispatching a region load data set of the energy supply region, and analyzing a load prediction amount corresponding to the energy supply region; calculating an effective productivity gain coefficient corresponding to the distributed energy resource, and determining the productivity pollution intensity corresponding to the distributed energy resource; querying a resource equipment cluster corresponding to the distributed energy resources, calculating maintenance management funds of the distributed energy resources, calculating an income discount net value corresponding to the distributed energy resources, and determining a scheduling priority of the distributed energy resources in a power grid; and executing integrated power generation control management on the distributed energy resources to obtain a power generation control result. The main purpose of the invention is to solve the problem of poor effect of power grid integrated optimization control of distributed energy resources.

Description

Technical Field

[0001] The present invention relates to a grid integration optimization control method and system for distributed energy resources, belonging to the technical field of energy management. Background Technique

[0002] In the current era of accelerating energy transformation, the proportion of distributed energy resources (such as solar energy, wind energy, biomass energy, etc.) in the power supply system is increasing day by day. Distributed energy resources have characteristics such as dispersion and intermittency. Their large-scale access to the power grid brings many challenges to the stable operation and efficient management of traditional power grids. In order to improve the overall operation efficiency, reliability and power quality of the power grid, it is necessary to perform grid integration optimization control processing on distributed energy resources.

[0003] The existing processing process is as follows: First, sensors are deployed at the distributed energy end and key nodes of the power grid to collect data such as power generation power, voltage, frequency, and power grid load, and transmit them to the control center for storage. Then, a professional software is used to build a power system model based on the data, analyze its impact on power grid power flow, stability and power quality, and then formulate strategies according to the power grid operation objectives with an optimization algorithm, such as instructions for adjusting distributed energy power, energy storage charge and discharge, and switching of reactive power compensation devices. Finally, the instructions are sent to each actuator for operation.

[0004] However, this method has many deficiencies. Data transmission is interfered by network factors, resulting in poor real-time performance and inaccurate data, affecting the accuracy of strategies; the model is difficult to accurately reproduce complex working conditions due to simplified assumptions; the algorithm has weak adaptability to new situations; and the coordination between various entities and devices of distributed energy resources is complex, and coordination and cooperation problems are likely to occur, thereby resulting in poor effects of grid integration optimization control of distributed energy resources. Summary of the Invention

[0005] The present invention provides a grid integration optimization control method and system for distributed energy resources, and its main purpose is to solve the problem of poor effects of grid integration optimization control of distributed energy resources.

[0006] To achieve the above object, a grid integration optimization control method for distributed energy resources provided by the present invention includes:

[0007] Obtain the distributed energy resources to be processed and their corresponding energy supply areas, schedule the regional load data sets of the energy supply areas, extract the load multi-dimensional attributes of the regional load data sets, and analyze the load prediction values corresponding to the energy supply areas based on the load multi-dimensional attributes and the regional load data sets;

[0008] Collect the resource production capacity data, resource transmission data, and energy supply data corresponding to the distributed energy resources. Combine the resource production capacity data, the resource transmission data, and the energy supply data to calculate the effective production capacity gain coefficient corresponding to the distributed energy resources. Combine the effective production capacity gain coefficient and the load prediction amount to determine the production capacity pollution intensity corresponding to the distributed energy resources.

[0009] Query the resource device cluster corresponding to the distributed energy resources. Based on the resource device cluster, calculate the maintenance and management funds of the distributed energy resources. Combine the maintenance and management funds and the resource device cluster to calculate the net present value of the revenue corresponding to the distributed energy resources. Combine the net present value of the revenue and the production capacity pollution intensity to determine the scheduling priority of the distributed energy resources in the power grid.

[0010] Dispatch the resource response data corresponding to the distributed energy resources. Based on the resource response data, evaluate the energy supply reliability corresponding to the distributed energy resources. Combine the load prediction amount, the energy supply reliability, and the scheduling priority to perform integrated power generation control and management on the distributed energy resources, and obtain the power generation control result.

[0011] Optionally, the extraction of the load multi-dimensional attributes of the regional load data set includes:

[0012] Identify the data time series of the regional load data set. Based on the data time series, perform sorting processing on the regional load data set to obtain sorted load data.

[0013] Construct a load data graph corresponding to the sorted load data. Based on the load data graph, analyze the load time series characteristics corresponding to the sorted load data.

[0014] Perform spatial clustering processing on the sorted load data to obtain clustered load data, and extract the spatial distribution characteristics corresponding to the clustered load data.

[0015] Analyze the load type characteristics corresponding to the clustered load data. Combine the load time series characteristics, the spatial distribution characteristics, and the load type characteristics to generate the load multi-dimensional attributes of the regional load data set.

[0016] Optionally, the analysis of the load prediction amount corresponding to the energy supply area based on the load multi-dimensional attributes and the regional load data set includes:

[0017] Perform data cleaning processing on the regional load data set to obtain regional target load data.

[0018] Perform data repair processing on the regional target load data to obtain repaired load data.

[0019] Calculate the attribute coupling degree among the multi-dimensional attributes of the load;

[0020] Construct an attribute-load curve graph corresponding to the multi-dimensional attributes of the load according to the repaired load data;

[0021] Perform curve optimization processing on the attribute-load curve graph to obtain an optimized load curve graph;

[0022] Analyze the load prediction quantity corresponding to the energy supply area in combination with the optimized load curve graph and the attribute coupling degree.

[0023] Optionally, the calculating the attribute coupling degree among the multi-dimensional attributes of the load includes:

[0024] Perform vectorization processing on the multi-dimensional attributes of the load to obtain a multi-dimensional attribute vector;

[0025] Calculate the attribute vector variance corresponding to the multi-dimensional attribute vector and calculate the vector covariance among the multi-dimensional attribute vectors;

[0026] Combine the attribute vector variance and the vector covariance, and calculate the attribute coupling degree among the multi-dimensional attributes of the load through the following formula:

[0027]

[0028] where A represents the attribute coupling degree among the multi-dimensional attributes of the load, cov(B a , B a+1 ) represents the vector covariance between the a-th attribute vector and the (a + 1)-th attribute vector in the multi-dimensional attribute vector, var(B a ) represents the attribute vector variance corresponding to the a-th attribute vector in the multi-dimensional attribute vector, var(B a+1 ) represents the attribute vector variance corresponding to the (a + 1)-th attribute vector in the multi-dimensional attribute vector, a represents the serial number of the multi-dimensional attribute vector, and n represents the number of multi-dimensional attribute vectors.

[0029] Optionally, the combining the resource production capacity data, the resource transmission data, and the energy supply data to calculate the effective production capacity gain coefficient corresponding to the distributed energy resource includes:

[0030] Extract the production capacity benchmark value corresponding to the distributed energy resource from the resource production capacity data, and calculate the energy production efficiency corresponding to the distributed energy resource based on the production capacity benchmark value;

[0031] Determine the starting transmission energy and the ending transmission energy corresponding to the distributed energy resource according to the resource transmission data;

[0032] Calculate the transmission loss rate corresponding to the distributed energy resource according to the transmission start energy and the transmission end energy;

[0033] Calculate the renewable utilization rate corresponding to the distributed energy resource according to the energy supply data;

[0034] Combining the energy production efficiency, the transmission loss rate and the renewable utilization rate, calculate the effective production gain coefficient corresponding to the distributed energy resource through the following formula:

[0035]

[0036] Where D represents the effective production gain coefficient corresponding to the distributed energy resource, α represents the energy production efficiency, β represents the renewable utilization rate, and μ represents the transmission loss rate.

[0037] Optionally, the calculating the transmission loss rate corresponding to the distributed energy resource according to the transmission start energy and the transmission end energy includes:

[0038] Collect the energy quality parameters corresponding to the transmission start energy and the transmission end energy respectively to obtain the start energy quality parameter and the end energy quality parameter;

[0039] Combine the start energy quality parameter and the end energy quality parameter to calculate the energy quality impact factor corresponding to the distributed energy resource;

[0040] Combining the energy quality impact factor, the transmission start energy and the transmission end energy, calculate the transmission loss rate corresponding to the distributed energy resource through the following formula:

[0041]

[0042] Where E represents the transmission loss rate corresponding to the distributed energy resource, ω represents the energy quality impact factor, F end represents the transmission end energy, F start represents the transmission start energy.

[0043] Optionally, the calculating the maintenance and management cost of the distributed energy resource based on the resource device cluster includes:

[0044] Query the device parameters corresponding to each device in the resource device cluster, and calculate the total power carrying capacity corresponding to the resource device cluster based on the device parameters;

[0045] Evaluate the unit capital investment corresponding to each device in the resource device cluster;

[0046] Combined with the total power carrying capacity and the capital investment per unit of the equipment, the maintenance and management funds of the distributed energy resources are calculated through the following formula:

[0047]

[0048] Wherein, G represents the maintenance and management funds of the distributed energy resources, H represents the total power carrying capacity, and K represents the capital investment per unit of the equipment.

[0049] Optionally, calculating the net present value of the income corresponding to the distributed energy resources by combining the maintenance and management funds and the resource equipment cluster includes:

[0050] Analyze the equipment status corresponding to the resource equipment cluster, and based on the equipment status, determine the remaining service life of the equipment corresponding to the resource equipment cluster;

[0051] Collect the equipment income data corresponding to the resource equipment cluster, and based on the equipment collection data, predict the annual resource income corresponding to the resource equipment cluster during the remaining service life of the equipment;

[0052] Combined with the remaining service life of the equipment, the maintenance and management funds, and the annual resource income, calculate the net present value of the income corresponding to the distributed energy resources through the following formula:

[0053]

[0054] Wherein, M represents the net present value of the income corresponding to the distributed energy resources, P t represents the annual resource income corresponding to the resource equipment cluster in the t-th year during the remaining service life of the equipment, T represents the remaining service life of the equipment, t represents the starting year of the remaining service life of the equipment, N t represents the maintenance and management funds corresponding to the resource equipment cluster in the t-th year during the remaining service life of the equipment, and h represents the market interest rate.

[0055] Optionally, evaluating the energy supply reliability corresponding to the distributed energy resources based on the resource response data includes:

[0056] Identify the data tags corresponding to the resource response data, and based on the data tags, determine the response identifier corresponding to the resource response data;

[0057] Based on the response identifier, perform data classification processing on the resource response data to obtain classified response data;

[0058] Count the response characteristics corresponding to the classified response data, and based on the response characteristics, analyze the identification response trend corresponding to the response identifier;

[0059] Evaluate the energy supply reliability corresponding to the distributed energy resource in combination with the response identifier and the identifier response situation.

[0060] A grid integration optimization control system for distributed energy resources, characterized in that the system comprises:

[0061] A load prediction analysis module, configured to obtain the distributed energy resource to be processed and its corresponding energy supply area, schedule the regional load data set of the energy supply area, extract the load multi-dimensional attributes of the regional load data set, and analyze the load prediction corresponding to the energy supply area based on the load multi-dimensional attributes and the regional load data set;

[0062] A pollution intensity analysis module, configured to collect the resource production capacity data, resource transmission data, and energy supply data corresponding to the distributed energy resource, calculate the effective production capacity gain coefficient corresponding to the distributed energy resource in combination with the resource production capacity data, the resource transmission data, and the energy supply data, and determine the production capacity pollution intensity corresponding to the distributed energy resource in combination with the effective production capacity gain coefficient and the load prediction;

[0063] A priority determination module, configured to query the resource device cluster corresponding to the distributed energy resource, calculate the maintenance and management cost of the distributed energy resource based on the resource device cluster, calculate the net present value of the revenue corresponding to the distributed energy resource in combination with the maintenance and management cost and the resource device cluster, and determine the scheduling priority of the distributed energy resource in the power grid in combination with the net present value of the revenue and the production capacity pollution intensity;

[0064] A power generation control management module, configured to schedule the resource response data corresponding to the distributed energy resource, evaluate the energy supply reliability corresponding to the distributed energy resource based on the resource response data, and perform integrated power generation control management on the distributed energy resource in combination with the load prediction, the energy supply reliability, and the scheduling priority to obtain a power generation control result.

[0065] Compared with the problems described in the background art, by extracting the load multi-dimensional attributes of the regional load data set, the present invention can obtain the description characteristics of the power loads in multiple different dimensions of the regional load data set, improving the analysis accuracy of the subsequent load prediction amount corresponding to the energy supply region. By combining the resource production capacity data, the resource transmission data, and the energy supply data, the present invention calculates the effective production capacity gain coefficient corresponding to the distributed energy resource, and can understand the production capacity efficiency corresponding to the distributed energy resource through the effective production capacity gain coefficient, thereby laying a foundation for determining the production capacity pollution intensity corresponding to the distributed energy resource subsequently. By calculating the maintenance and management funds of the distributed energy resource based on the resource equipment cluster, the present invention can obtain the quantitative index of the maintenance and management cost of the distributed energy resource, laying a basis for calculating the net present value of the subsequent income corresponding to the distributed energy resource. By evaluating the energy supply reliability corresponding to the distributed energy resource based on the resource response data, the present invention can understand the energy supply stability corresponding to the distributed energy resource through the energy supply reliability, avoiding the problem of untimely response of the distributed energy resource subsequently, and improving the grid integration optimization control effect of the distributed energy resource. Therefore, the present invention proposes a grid integration optimization control method and system for distributed energy resources to improve the grid integration optimization control effect of distributed energy resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 FIG. is a schematic flowchart of a grid integration optimization control method for distributed energy resources provided by an embodiment of the present invention;

[0067] Figure 2 FIG. is a functional module diagram of a grid integration optimization control system for implementing the distributed energy resource provided by an embodiment of the present invention.

[0068] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] The embodiment of the present application provides a method for optimizing and controlling the integrated power grid of distributed energy resources. The execution subject of the method for optimizing and controlling the integrated power grid of distributed energy resources includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for optimizing and controlling the integrated power grid of distributed energy resources can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0071] Embodiment 1:

[0072] Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing the integrated control of distributed energy resources in a power grid according to an embodiment of the present invention. In this embodiment, the method for optimizing the integrated control of distributed energy resources in a power grid includes:

[0073] S1. Obtain the distributed energy resources to be processed and their corresponding energy supply areas, schedule the regional load data set of the energy supply area, extract the load multidimensional attributes of the regional load data set, and analyze the load forecast corresponding to the energy supply area based on the load multidimensional attributes and the regional load data set.

[0074] The present invention can obtain the descriptive characteristics of the power load of multiple different dimensions of the regional load data set by extracting the multidimensional attributes of the load of the regional load data set, thereby improving the analysis accuracy of the load forecast corresponding to the subsequent energy supply area. It should be explained that the distributed energy resources are energy comprehensive utilization systems distributed at the user end, including solar power generation devices, small wind turbines, biomass power generation equipment and energy storage systems, etc., which can realize on-site production, storage and consumption of energy, effectively improve energy utilization efficiency and promote the widespread application of renewable energy. The energy supply area is the service area corresponding to the distributed energy resources, such as communities, factories and hospitals, etc. The regional load data set is a data set of multidimensional information such as power load size, change trend, load type, etc. in the energy supply area, including different times, different locations and various types of electricity users. The multidimensional attributes of the load are the descriptive characteristics of the power load of multiple different dimensions of the regional load data set. Furthermore, the scheduling of the regional load data set of the energy supply area can be achieved through a smart grid scheduling system, which relies on advanced information technology, communication technology and power system control technology.

[0075] In detail, the extracting of the load multi-dimensional attributes of the regional load data set includes:

[0076] Identify the data time series of the regional load dataset. Based on the data time series, sort the regional load dataset to obtain sorted load data;

[0077] Construct a load data graph corresponding to the sorted load data. Based on the load data graph, analyze the load time series characteristics corresponding to the sorted load data;

[0078] Perform spatial clustering on the sorted load data to obtain clustered load data, and extract the spatial distribution characteristics corresponding to the clustered load data;

[0079] Analyze the load type characteristics corresponding to the clustered load data. Combine the load time series characteristics, the spatial distribution characteristics, and the load type characteristics to generate the load multi-dimensional attributes of the regional load dataset.

[0080] It should be explained that the data time series is the relevant information about the time sequence of each data in the regional load dataset. The sorted load data is the data obtained after arranging and organizing the data in the regional load dataset according to the time sequence corresponding to the data time series. The load data graph is the visualization chart corresponding to the sorted load data. The load time series characteristics are the relevant content reflecting the characteristics in terms of time sequence corresponding to the sorted load data. The clustered load data is the data obtained after clustering the sorted load data. The spatial distribution characteristics are the situation regarding spatial distribution reflected by the clustered load data. The load type characteristics are the relevant characteristics corresponding to different types of the clustered load data.

[0081] Furthermore, the data time series of the regional load dataset can be identified through a time series recognition algorithm. Based on the data time series, the regional load dataset can be sorted through a bubble sort algorithm to obtain sorted load data. The load data graph corresponding to the sorted load data can be constructed through a visualization tool, such as the Visio drawing tool. Based on the load data graph, the load time series characteristics corresponding to the sorted load data can be analyzed through a statistical analysis method. The sorted load data can be spatially clustered through a long short-term memory network to obtain clustered load data. The spatial distribution characteristics corresponding to the clustered load data can be extracted through a density-based spatial clustering algorithm. The load type characteristics corresponding to the clustered load data can be analyzed through a geographic information system (GIS) spatial feature extraction technology, and the load time series characteristics, the spatial distribution characteristics, and the load type characteristics are merged to generate the load multi-dimensional attributes of the regional load dataset.

[0082] Based on the multi-dimensional attributes of the load and the regional load data set, the present invention analyzes the predicted load corresponding to the energy supply area. Since the multi-dimensional attributes of the load cover information such as time, space, and type, complex change laws can be captured, avoiding the limitation of a single factor, improving the accuracy of the predicted load, facilitating the power grid to plan the power generation plan in advance, optimize resource allocation, reduce the risk of supply-demand imbalance, ensure stable power supply, and reduce operating costs. It should be noted that the predicted load is a numerical representation of the power load magnitude and its changes that are expected to occur in a certain future time period corresponding to the energy supply area, taking into account various influencing factors.

[0083] Specifically, the analysis of the predicted load corresponding to the energy supply area based on the multi-dimensional attributes of the load and the regional load data set includes:

[0084] Perform data cleaning on the regional load data set to obtain regional target load data;

[0085] Perform data repair on the regional target load data to obtain repaired load data;

[0086] Calculate the attribute coupling degree between the multi-dimensional attributes of the load;

[0087] According to the repaired load data, construct an attribute-load curve corresponding to the multi-dimensional attributes of the load;

[0088] Perform curve optimization on the attribute-load curve to obtain an optimized load curve;

[0089] Analyze the predicted load corresponding to the energy supply area by combining the optimized load curve and the attribute coupling degree.

[0090] It should be noted that the regional target load data is the data determined after screening or specific target setting of the regional load data set, the repaired load data is the data obtained after repair processing of the regional target load data, the attribute coupling degree represents the tightness of the mutual association and mutual influence between the multi-dimensional attributes of the load, the attribute-load curve is a relationship curve drawn with the attribute as the variable and the load as the corresponding manifestation corresponding to the multi-dimensional attributes of the load, and the optimized load curve is a curve that can better reflect the relevant characteristics and relationships obtained after optimization adjustment of the attribute-load curve.

[0091] Further, the regional load data set can be processed for data cleaning through the box plot method to obtain regional target load data; the regional target load data can be processed for data repair through a data repair method based on an interpolation algorithm to obtain repaired load data; according to the repaired load data, an attribute-load curve graph corresponding to the multi-dimensional attributes of the load can be constructed through a data visualization drawing method; the attribute-load curve graph can be processed for curve optimization through a curve fitting optimization method to obtain an optimized load curve graph; combining the optimized load curve graph and the attribute coupling degree, the predicted load volume corresponding to the energy supply region can be analyzed through a regression analysis prediction method. Assume that the optimized load curve graph shows a trend of load change with a certain attribute. For example, with the change of the time attribute, the load has an upward or downward trend in a specific time period. The attribute coupling degree indicates the close association between different multi-dimensional attributes of the load. For example, the temperature attribute and the electricity consumption load attribute are highly coupled. When using the regression analysis prediction method, based on the historical data change law in the optimized load curve graph and considering the mutual influence relationship between attributes reflected by the attribute coupling degree, a suitable regression model, such as a linear regression or multiple regression model, will be constructed, and the currently known attribute data will be substituted into the model to calculate the predicted load volume corresponding to the energy supply region.

[0092] Further, as an optional embodiment of the present invention, calculating the attribute coupling degree between the multi-dimensional attributes of the load includes:

[0093] Perform vectorization processing on the multi-dimensional attributes of the load to obtain a multi-dimensional attribute vector;

[0094] Calculate the attribute vector variance corresponding to the multi-dimensional attribute vector and calculate the vector covariance between the multi-dimensional attribute vectors;

[0095] Combining the attribute vector variance and the vector covariance, calculate the attribute coupling degree between the multi-dimensional attributes of the load through the following formula:

[0096]

[0097] Among them, A represents the attribute coupling degree between the multi-dimensional attributes of the load, cov(B a , B a+1 ) represents the vector covariance between the a-th attribute vector and the (a + 1)-th attribute vector in the multi-dimensional attribute vector, var(B a ) represents the attribute vector variance corresponding to the a-th attribute vector in the multi-dimensional attribute vector, var(B a+1 ) represents the attribute vector variance corresponding to the (a + 1)-th attribute vector in the multi-dimensional attribute vector, a represents the serial number of the multi-dimensional attribute vector, and n represents the number of multi-dimensional attribute vectors.

[0098] It should be noted that the multi-dimensional attribute vector is a vector formed by combining the multi-dimensional attributes of the load in a certain order. The attribute vector variance is a measure of the degree to which each element in the multi-dimensional attribute vector deviates from its mean value. The vector covariance is a statistical measure of the mutual relationship between the multi-dimensional attribute vectors, indicating the degree of co-variation during their changes. Further, the multi-dimensional attributes of the load can be vectorized through an encoding algorithm, such as the one-hot encoding algorithm, to obtain the multi-dimensional attribute vector. The attribute vector variance corresponding to the multi-dimensional attribute vector can be calculated through the variance formula, and the vector covariance between the multi-dimensional attribute vectors can be calculated through the covariance formula.

[0099] S2. Collect the resource production capacity data, resource transmission data, and energy supply data corresponding to the distributed energy resource. Combine the resource production capacity data, the resource transmission data, and the energy supply data to calculate the effective production capacity gain coefficient corresponding to the distributed energy resource. Combine the effective production capacity gain coefficient and the load prediction amount to determine the production capacity pollution intensity corresponding to the distributed energy resource.

[0100] In the present invention, by combining the resource production capacity data, the resource transmission data, and the energy supply data, the effective production capacity gain coefficient corresponding to the distributed energy resource is calculated. Through the effective production capacity gain coefficient, the production capacity efficiency corresponding to the distributed energy resource can be understood, thereby laying a foundation for subsequently determining the production capacity pollution intensity corresponding to the distributed energy resource. It should be noted that the resource production capacity data is the data related to the energy output capacity in the production link corresponding to the distributed energy resource, including information such as theoretical production capacity and actual production capacity; the resource transmission data is the data related to losses, efficiency, transmission methods, etc. during the energy transmission process corresponding to the distributed energy resource; the energy supply data is the data related to aspects such as energy supply stability, energy supply quality, and energy supply efficiency during the stage of supplying energy to users or energy-consuming devices corresponding to the distributed energy resource; the effective production capacity gain coefficient is an index used to measure the actual effective improvement degree of production capacity in the entire energy supply process after comprehensively considering resource production capacity, transmission, and supply situations corresponding to the distributed energy resource. Further, the collection of the resource production capacity data, the resource transmission data, and the energy supply data corresponding to the distributed energy resource can be achieved through intelligent sensors, and these sensors include but are not limited to power sensors, flow sensors, pressure sensors, temperature sensors, etc.

[0101] Specifically, the combination of the resource production capacity data, the resource transmission data, and the energy supply data to calculate the effective production capacity gain coefficient corresponding to the distributed energy resource includes:

[0102] Extract the production capacity benchmark value corresponding to the distributed energy resource from the resource production capacity data, and calculate the energy production efficiency corresponding to the distributed energy resource based on the production capacity benchmark value;

[0103] Determine the starting energy and ending energy of transmission corresponding to the distributed energy resource according to the resource transmission data;

[0104] Calculate the transmission loss rate corresponding to the distributed energy resource according to the starting energy and the ending energy of transmission;

[0105] Calculate the renewable utilization rate corresponding to the distributed energy resource according to the energy supply data;

[0106] Combine the energy production efficiency, the transmission loss rate and the renewable utilization rate, and calculate the effective production capacity gain coefficient corresponding to the distributed energy resource through the following formula:

[0107]

[0108] Among them, D represents the effective production capacity gain coefficient corresponding to the distributed energy resource, α represents the energy production efficiency, β represents the renewable utilization rate, and μ represents the transmission loss rate.

[0109] It should be explained that the production capacity benchmark value is the ideal production capacity reference quantity corresponding to the distributed energy resource extracted from the resource production capacity data. The energy production efficiency represents the conversion degree of the actual production capacity and the ideal production capacity corresponding to the distributed energy resource. The starting energy and the ending energy of transmission are the energy quantity values at both ends of the transmission link corresponding to the distributed energy resource in the resource transmission data. The transmission loss rate represents the degree of energy loss during the transmission process corresponding to the distributed energy resource. The renewable utilization rate represents the proportion of the renewable energy corresponding to the distributed energy resource that is effectively utilized in the energy supply.

[0110] Furthermore, the production capacity benchmark value corresponding to the distributed energy resource can be extracted from the resource production capacity data through an extraction function. The extraction function is compiled by a scripting language, such as the JS scripting language; calculate the ratio of the production capacity benchmark value to the theoretical production capacity value to obtain the energy production efficiency corresponding to the distributed energy resource. The theoretical production capacity value can be obtained according to business standards; determine the starting energy and the ending energy of transmission corresponding to the distributed energy resource according to the transmitted energy recorded in the resource transmission data; calculate the renewable utilization rate corresponding to the distributed energy resource according to the energy supply data. Find out the total amount of renewable energy generated and the total amount of all energy (including renewable and non-renewable) used from the energy supply data, divide the total amount of renewable energy generated by the total amount of all energy used, and then multiply the result by 100% to calculate the renewable utilization rate corresponding to the distributed energy resource.

[0111] Further, as an alternative embodiment of the present invention, calculating the transmission loss rate corresponding to the distributed energy resource according to the transmission start energy and the transmission end energy includes:

[0112] Collect the energy quality parameters corresponding to the transmission start energy and the transmission end energy respectively to obtain the start energy quality parameter and the end energy quality parameter;

[0113] Combine the start energy quality parameter and the end energy quality parameter to calculate the energy quality impact factor corresponding to the distributed energy resource;

[0114] Combine the energy quality impact factor, the transmission start energy and the transmission end energy, and calculate the transmission loss rate corresponding to the distributed energy resource through the following formula:

[0115]

[0116] wherein, E represents the transmission loss rate corresponding to the distributed energy resource, ω represents the energy quality impact factor, F end represents the transmission end energy, F start represents the transmission start energy.

[0117] It should be explained that the start energy quality parameter and the end energy quality parameter are respectively indicators for measuring the energy quality characteristics (such as voltage, temperature, etc.) corresponding to the transmission start energy and the transmission end energy. The energy quality impact factor represents the quantification degree of the impact on the effective value of the energy due to the energy quality change corresponding to the distributed energy resource. Further, the energy quality parameters corresponding to the transmission start energy and the transmission end energy can be collected respectively by a dedicated energy quality monitoring device to obtain the start energy quality parameter and the end energy quality parameter, such as a power analyzer; the energy quality impact factor corresponding to the distributed energy resource can be obtained by performing a standardization process on the start energy quality parameter and the end energy quality parameter and calculating the parameter ratio between the standardized parameters.

[0118] By combining the effective production capacity gain coefficient and the load prediction amount, the present invention determines the production capacity pollution intensity corresponding to the distributed energy resource, which can accurately evaluate the environmental protection performance of the distributed energy resource. This helps to optimize the energy configuration, minimize pollution while meeting the load demand in energy production, and promote the sustainable development of distributed energy. It should be noted that the production capacity pollution intensity is a quantitative value of the pollution generated by the distributed energy resource when providing energy. Further, by combining the effective production capacity gain coefficient and the load prediction amount, to determine the production capacity pollution intensity corresponding to the distributed energy resource, first, according to the effective production capacity gain coefficient, analyze the improvement range of the distributed energy resource production capacity, and then combine the load prediction amount to estimate the total energy production to match the demand. Through comprehensive consideration of the two, query the pollution emission data of this energy resource to determine the pollution intensity at the corresponding production capacity.

[0119] S3. Query the resource device cluster corresponding to the distributed energy resource. Based on the resource device cluster, calculate the maintenance and management funds of the distributed energy resource. Combine the maintenance and management funds and the resource device cluster to calculate the discounted net value of the income corresponding to the distributed energy resource. Combine the discounted net value of the income and the production capacity pollution intensity to determine the scheduling priority of the distributed energy resource in the power grid.

[0120] By calculating the maintenance and management funds of the distributed energy resource based on the resource device cluster, the present invention can obtain a quantitative index of the maintenance and management cost of the distributed energy resource, which lays a basic foundation for the subsequent calculation of the discounted net value of the income corresponding to the distributed energy resource. It should be noted that the resource device cluster is a set of hardware facilities corresponding to the distributed energy resource, and the maintenance and management funds are the quantitative values of the annual maintenance and management costs corresponding to the distributed energy resource.

[0121] Specifically, the calculation of the maintenance and management funds of the distributed energy resource based on the resource device cluster includes:

[0122] Query the device parameters corresponding to each device in the resource device cluster. Based on the device parameters, calculate the total power carrying capacity corresponding to the resource device cluster;

[0123] Evaluate the unit capital investment of each device in the resource device cluster;

[0124] Combine the total power carrying capacity and the unit capital investment of the device, and calculate the maintenance and management funds of the distributed energy resource through the following formula:

[0125]

[0126] Among them, G represents the maintenance and management expenses of distributed energy resources, H represents the total power carrying capacity, and K represents the unit investment capital of equipment.

[0127] It should be explained that the equipment parameters are the technical performance indicators corresponding to each device in the resource equipment cluster, the total power carrying capacity is the upper limit of the overall power processing capacity corresponding to the resource equipment cluster, and the equipment unit investment capital is the capital investment required for each unit production capacity corresponding to each device in the resource equipment cluster. Furthermore, the equipment parameters corresponding to each device in the resource equipment cluster can be queried through the official website of the equipment, and the equipment power of each device in the resource equipment cluster can be obtained through the equipment parameters. The power of the equipment is summed to obtain the total power carrying capacity corresponding to the resource equipment cluster; the equipment unit investment capital corresponding to each device in the resource equipment cluster can be evaluated according to the industry standard reference method, and refer to relevant industry standards and specifications. Many industries have rough estimation standards for equipment investment costs. For example, in the power industry, for a certain type of generator set, the industry standard may stipulate that its unit capacity investment cost is between 10,000 and 15,000 yuan / kW.

[0128] The present invention calculates the discounted net value of the income corresponding to the distributed energy resources by combining the maintenance and management expenses and the resource equipment cluster, so as to obtain the specific situation of the income corresponding to the distributed energy resources, and then understand the economic feasibility of the distributed energy resources. It should be explained that the discounted net value of the income is the net income indicator of the distributed energy resources corresponding to the future income discounted to the current value after considering the time value of money and deducting the maintenance and management expenses.

[0129] Specifically, the calculation of the discounted net value of the benefits corresponding to the distributed energy resources by combining the maintenance and management expenses with the resource equipment cluster includes:

[0130] Analyze the device status corresponding to the resource device cluster, and determine the remaining service life of the devices corresponding to the resource device cluster based on the device status;

[0131] Collecting equipment revenue data corresponding to the resource equipment cluster, and predicting the annual resource revenue corresponding to the resource equipment cluster in the remaining life of the equipment according to the equipment collection data;

[0132] Combined with the remaining life of the equipment, the maintenance and management expenses and the annual income of the resources, the discounted net value of the income corresponding to the distributed energy resources is calculated by the following formula:

[0133]

[0134] Where M represents the net discounted value of the benefits of distributed energy resources, P trepresents the annual resource income corresponding to the t-th year in the remaining service life of the resource equipment cluster, T represents the remaining service life of the equipment, t represents the starting year of the remaining service life of the equipment, N t represents the maintenance and management funds corresponding to the t-th year in the remaining service life of the resource equipment cluster, and h represents the market interest rate.

[0135] It should be explained that the equipment status is the operating conditions of each equipment corresponding to the resource equipment cluster, the remaining service life of the equipment is the time that each equipment corresponding to the resource equipment cluster can still be used, the equipment income data is the income generated by each equipment corresponding to the resource equipment cluster, the annual resource income is the income generated by the resource equipment cluster every year in the remaining service life of the equipment, and the market interest rate is the cost ratio of borrowing funds in the market. The average interest rate level of similar energy projects in the market can be referred to. For example, for solar distributed energy projects, the financing interest rate conditions of other built solar projects in the market can be investigated. If the average financing interest rate of similar projects in the market is 5%, then r can be set at about 5% in the preliminary estimate. Further, the equipment status corresponding to the resource equipment cluster can be analyzed through the equipment status monitoring system. Based on the equipment status, the remaining service life of the equipment corresponding to the resource equipment cluster can be determined through the reliability evaluation model; the equipment income data corresponding to the resource equipment cluster can be collected through the Internet of Things data acquisition platform. According to the data collected by the equipment, the annual resource income corresponding to the resource equipment cluster in the remaining service life of the equipment can be predicted through the income prediction model, such as the autoregressive model.

[0136] The present invention determines the scheduling priority of the distributed energy resources in the power grid by combining the discounted net income and the production pollution intensity, which can achieve the comprehensive consideration of economic and environmental benefits, prompt the power grid scheduling to take into account pollution reduction while pursuing the maximization of economic benefits, and help optimize the power grid energy structure, guide the reasonable access of distributed energy resources, and enhance the sustainability and stability of the overall operation of the power grid. It should be explained that the scheduling priority is the priority level of the distributed energy resources used in the power grid scheduling. Further, by combining the discounted net income and the production pollution intensity, the scheduling priority of the distributed energy resources in the power grid is determined. For example, if the discounted net income of a certain distributed energy resource is relatively high and the production pollution intensity is extremely low, in the power grid scheduling, compared with the energy resources with low income and high pollution, it will be given a higher scheduling priority, and this resource will be preferentially scheduled in subsequent use.

[0137] S4. Schedule the resource response data corresponding to the distributed energy resources. Based on the resource response data, evaluate the energy supply reliability corresponding to the distributed energy resources. Combine the load prediction amount, the energy supply reliability, and the scheduling priority to perform integrated power generation control management on the distributed energy resources, and obtain a power generation control result.

[0138] In the present invention, by evaluating the energy supply reliability corresponding to the distributed energy resources based on the resource response data, the energy supply stability corresponding to the distributed energy resources can be understood through the energy supply reliability, avoiding the problem of untimely response of the distributed energy resources subsequently. To improve the grid integration optimization control effect of the distributed energy resources, it should be noted that the resource response data is data related to the feedback made by the distributed energy resources when facing various internal and external influencing factors (such as changes in load demand, changes in environmental conditions, etc.). The energy supply reliability represents the reliable degree of the distributed energy resources to continuously, stably, and qualitatively and quantitatively supply energy. Further, the scheduling of the resource response data corresponding to the distributed energy resources can be realized through the above-mentioned Internet of Things data acquisition platform.

[0139] Specifically, the evaluating the energy supply reliability corresponding to the distributed energy resources based on the resource response data includes:

[0140] Identify the data tags corresponding to the resource response data, and based on the data tags, determine the response identifier corresponding to the resource response data;

[0141] Based on the response identifier, perform data classification processing on the resource response data to obtain classified response data;

[0142] Statistically analyze the response characteristics corresponding to the classified response data, and based on the response characteristics, analyze the identification response trend corresponding to the response identifier;

[0143] Combine the response identifier and the identification response trend to evaluate the energy supply reliability corresponding to the distributed energy resources.

[0144] It should be noted that the data tag is a classification identifier corresponding to the resource response data for distinguishing different natures, sources, uses, etc. The response identifier is a symbolic sign representing the specific response state or behavior of the resource corresponding to the resource response data. The classified response data is a data set obtained by classifying and integrating the resource response data according to their respective represented categories based on the response identifier. The response characteristic is the relevant manifestation corresponding to the classified response data that can reflect its internal law and attribute characteristics. The identification response trend is the dynamic trend and overall state presented by the response identifier corresponding to changes in factors such as time and external conditions.

[0145] Furthermore, the data tags corresponding to the resource response data can be identified by a tag identification tool, and the tag identification tool is compiled by a programming language; the key characters of the data tags can be extracted, and according to the key characters of the tags, the response identifier corresponding to the resource response data can be determined; the resource response data can be processed by a classification algorithm for data classification to obtain classified response data, such as a decision tree algorithm; the response characteristics corresponding to the classified response data can be statistically analyzed, such as calculating the average value, variance, standard deviation, and frequency corresponding to the classified response data, so as to obtain the response characteristics; based on the response characteristics, the identification response trend corresponding to the response identifier can be analyzed; combining the response identifier and the identification response trend, the energy supply reliability corresponding to the distributed energy resource can be evaluated, and according to the identification response trend, the change rule and stability degree of the response identifier can be determined. For example, if the identification response trend is stable, it indicates that the change of the response identifier is orderly. Then, the relevant characteristics of the response identifier are quantified, such as the frequency of occurrence and duration of the identifier. Considering the influence weights of different identifiers and their trends on the energy supply, the influence weights are multiplied by the corresponding quantified response identifier data and summed to obtain the reliability value. According to the reliability value, the energy supply reliability corresponding to the distributed energy resource is evaluated.

[0146] The present invention performs integrated power generation control management on the distributed energy resources by combining the load prediction amount, the energy supply reliability, and the scheduling priority, thereby improving the efficiency of the integrated power generation control management of the distributed energy resources. Furthermore, the integrated power generation control management of the distributed energy resources is performed by combining the load prediction amount, the energy supply reliability, and the scheduling priority: the scale and change trend of the power demand are clarified based on the load prediction amount, and then the stable and reliable distributed energy resources are selected with reference to the energy supply reliability. The power generation access and regulation order of each resource are determined by combining the scheduling priority. Subsequently, precise power generation control strategies, such as power distribution and start-stop timing, are formulated according to this information to perform integrated power generation control management on the distributed energy resources, and finally, a power generation control result that meets the load demand and ensures stable and efficient energy supply is obtained.

[0147] Compared with the problems described in the background art, by extracting the load multi-dimensional attributes of the regional load data set, the present invention can obtain the description characteristics of the electric power loads in multiple different dimensions of the regional load data set, improving the analysis accuracy of the subsequent load prediction amount corresponding to the energy supply region. By combining the resource production capacity data, the resource transmission data, and the energy supply data, the present invention calculates the effective production capacity gain coefficient corresponding to the distributed energy resource, and can understand the production capacity efficiency corresponding to the distributed energy resource through the effective production capacity gain coefficient, thereby laying a foundation for determining the production capacity pollution intensity corresponding to the distributed energy resource subsequently. By calculating the maintenance and management funds of the distributed energy resource based on the resource equipment cluster, the present invention can obtain the quantitative index of the maintenance and management cost of the distributed energy resource, laying a basis for calculating the net present value of the subsequent income corresponding to the distributed energy resource. By evaluating the energy supply reliability corresponding to the distributed energy resource based on the resource response data, the present invention can understand the energy supply stability corresponding to the distributed energy resource through the energy supply reliability, avoiding the problem of untimely response of the distributed energy resource subsequently, and improving the grid integration optimization control effect of the distributed energy resource. Therefore, the present invention proposes a grid integration optimization control method for distributed energy resources to improve the grid integration optimization control effect of distributed energy resources.

[0148] Embodiment 2:

[0149] As Figure 2 shown, it is a functional module diagram of a grid integration optimization control system for distributed energy resources provided by an embodiment of the present invention.

[0150] The grid integration optimization control system 100 for distributed energy resources according to the present invention can be installed in an electronic device. According to the functions achieved, the grid integration optimization control system 100 for distributed energy resources can include a load prediction amount analysis module 101, a pollution intensity analysis module 102, a priority determination module 103, and a power generation control management module 104. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0151] In this embodiment, the functions of each module / unit are as follows:

[0152] The load prediction amount analysis module 101 is used to obtain the distributed energy resource to be processed and its corresponding energy supply region, schedule the regional load data set of the energy supply region, extract the load multi-dimensional attributes of the regional load data set, and analyze the load prediction amount corresponding to the energy supply region based on the load multi-dimensional attributes and the regional load data set;

[0153] The pollution intensity analysis module 102 is configured to collect resource production capacity data, resource transmission data, and energy supply data corresponding to the distributed energy resource, calculate an effective production capacity gain coefficient corresponding to the distributed energy resource by combining the resource production capacity data, the resource transmission data, and the energy supply data, and determine the production capacity pollution intensity corresponding to the distributed energy resource by combining the effective production capacity gain coefficient and the load prediction amount;

[0154] The priority determination module 103 is configured to query a resource device cluster corresponding to the distributed energy resource, calculate the maintenance and management cost of the distributed energy resource based on the resource device cluster, calculate a net present value of the income corresponding to the distributed energy resource by combining the maintenance and management cost and the resource device cluster, and determine the scheduling priority of the distributed energy resource in the power grid by combining the net present value of the income and the production capacity pollution intensity;

[0155] The power generation control and management module 104 is configured to schedule resource response data corresponding to the distributed energy resource, evaluate the energy supply reliability corresponding to the distributed energy resource based on the resource response data, and perform integrated power generation control and management on the distributed energy resource by combining the load prediction amount, the energy supply reliability, and the scheduling priority to obtain a power generation control result.

[0156] Specifically, each module in the grid integration optimization control system 100 of a distributed energy resource described in the embodiments of the present application adopts the same technical means as those in the above Figure 1 and can produce the same technical effects, which will not be elaborated here.

[0157] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A grid integration optimization control method for distributed energy resources, characterized in that The method includes: Obtain the distributed energy resources to be processed and their corresponding energy supply areas, schedule the regional load data sets of the energy supply areas, extract the load multi-dimensional attributes of the regional load data sets, and analyze the load prediction values corresponding to the energy supply areas based on the load multi-dimensional attributes and the regional load data sets; Collect the resource production capacity data, resource transmission data, and energy supply data corresponding to the distributed energy resources, calculate the effective production capacity gain coefficient corresponding to the distributed energy resources by combining the resource production capacity data, the resource transmission data, and the energy supply data, and determine the production capacity pollution intensity corresponding to the distributed energy resources by combining the effective production capacity gain coefficient and the load prediction values; Query the resource equipment clusters corresponding to the distributed energy resources, calculate the maintenance and management funds of the distributed energy resources based on the resource equipment clusters, calculate the net present value of the benefits corresponding to the distributed energy resources by combining the maintenance and management funds and the resource equipment clusters, and determine the scheduling priority of the distributed energy resources in the power grid by combining the net present value of the benefits and the production capacity pollution intensity; Schedule the resource response data corresponding to the distributed energy resources, evaluate the energy supply reliability corresponding to the distributed energy resources based on the resource response data, and perform integrated power generation control management on the distributed energy resources by combining the load prediction values, the energy supply reliability, and the scheduling priority to obtain a power generation control result.

2. The grid integration optimization control method for distributed energy resources according to claim 1, wherein The extraction of the load multi-dimensional attributes of the regional load data sets includes: Identify the data time series of the regional load data sets, perform sorting processing on the regional load data sets based on the data time series to obtain sorted load data; Construct a load data graph corresponding to the sorted load data, and analyze the load time series characteristics corresponding to the sorted load data based on the load data graph; Perform spatial clustering processing on the sorted load data to obtain clustered load data, and extract the spatial distribution characteristics corresponding to the clustered load data; Analyze the load type characteristics corresponding to the clustered load data, and generate the load multi-dimensional attributes of the regional load data sets by combining the load time series characteristics, the spatial distribution characteristics, and the load type characteristics.

3. The grid integration optimization control method for distributed energy resources according to claim 1, characterized in that, The analysis of the load prediction values corresponding to the energy supply areas based on the load multi-dimensional attributes and the regional load data sets includes: Perform data cleaning processing on the regional load data sets to obtain regional target load data; Perform data repair processing on the regional target load data to obtain repaired load data; Calculate the attribute coupling degree between the load multi-dimensional attributes; Construct an attribute-load curve graph corresponding to the load multi-dimensional attributes according to the repaired load data; Perform curve optimization processing on the attribute-load curve graph to obtain an optimized load curve graph; Analyze the load prediction values corresponding to the energy supply areas by combining the optimized load curve graph and the attribute coupling degree.

4. The grid integration optimization control method for distributed energy resources according to claim 3, characterized in that, The calculation of the attribute coupling degree between the load multi-dimensional attributes includes: Vectorize the multi-dimensional attributes of the load to obtain a multi-dimensional attribute vector; Calculate the variance of the attribute vector corresponding to the multi-dimensional attribute vector, and calculate the vector covariance between the multi-dimensional attribute vectors; Combine the variance of the attribute vector and the vector covariance, and calculate the attribute coupling degree between the multi-dimensional attributes of the load through the following formula: Among them, A represents the attribute coupling degree between multi-dimensional attributes of the load, and cov(B a , B a+1 ) represents the vector covariance between the a-th attribute vector and the (a + 1)-th attribute vector in the multi-dimensional attribute vector, var(B a ) represents the attribute vector variance corresponding to the a-th attribute vector in the multi-dimensional attribute vector, var(B a+1 ) represents the attribute vector variance corresponding to the (a + 1)-th attribute vector in the multi-dimensional attribute vector, a represents the serial number of the multi-dimensional attribute vector, and n represents the number of multi-dimensional attribute vectors.

5. The grid integration optimization control method for distributed energy resources according to claim 1, characterized in that, The calculation of the effective production capacity gain coefficient corresponding to the distributed energy resource by combining the resource production capacity data, the resource transmission data, and the energy supply data includes: Extract the production capacity benchmark value corresponding to the distributed energy resource from the resource production capacity data, and calculate the energy production efficiency corresponding to the distributed energy resource based on the production capacity benchmark value; Determine the starting transmission energy and the ending transmission energy corresponding to the distributed energy resource according to the resource transmission data; Calculate the transmission loss rate corresponding to the distributed energy resource according to the starting transmission energy and the ending transmission energy; Calculate the renewable utilization rate corresponding to the distributed energy resource according to the energy supply data; Combine the energy production efficiency, the transmission loss rate, and the renewable utilization rate, and calculate the effective production capacity gain coefficient corresponding to the distributed energy resource through the following formula: Where D represents the effective production capacity gain coefficient corresponding to the distributed energy resource, α represents the energy production efficiency, β represents the renewable utilization rate, and μ represents the transmission loss rate.

6. The grid integration optimization control method for distributed energy resources according to claim 5, characterized in that The calculation of the transmission loss rate corresponding to the distributed energy resource according to the starting transmission energy and the ending transmission energy includes: Collect the energy quality parameters corresponding to the starting transmission energy and the ending transmission energy respectively to obtain the starting energy quality parameter and the ending energy quality parameter; Combine the starting energy quality parameter and the ending energy quality parameter to calculate the energy quality impact factor corresponding to the distributed energy resource; Combine the energy quality impact factor, the starting transmission energy, and the ending transmission energy, and calculate the transmission loss rate corresponding to the distributed energy resource through the following formula: Among them, E represents the transmission loss rate corresponding to distributed energy resources, ω represents the energy quality impact factor, F end represents the energy at the transmission end point, F start represents the energy at the transmission starting point.

7. The grid integration optimization control method for distributed energy resources according to claim 1, characterized in that, The calculation of the maintenance and management cost of the distributed energy resource based on the resource equipment cluster includes: Query the equipment parameters corresponding to each device in the resource equipment cluster, and calculate the total power carrying capacity corresponding to the resource equipment cluster based on the equipment parameters; Evaluate the unit investment capital of each device in the resource equipment cluster; Combine the total power carrying capacity and the unit investment capital of the device, and calculate the maintenance and management cost of the distributed energy resource through the following formula: Where G represents the maintenance and management cost of the distributed energy resource, H represents the total power carrying capacity, and K represents the unit investment capital of the device.

8. The grid integration optimization control method for distributed energy resources according to claim 1, characterized in that, The calculation of the discounted net value of the income corresponding to the distributed energy resource by combining the maintenance and management cost and the resource equipment cluster includes: Analyze the device status corresponding to the resource equipment cluster, and determine the remaining service life of the devices corresponding to the resource equipment cluster based on the device status; Collect the device revenue data corresponding to the resource device cluster, and based on the device collection data, predict the annual resource revenue corresponding to the resource device cluster in the remaining device life. Combine the remaining device life, the maintenance management funds, and the annual resource revenue, and calculate the discounted net value of the revenue corresponding to the distributed energy resource through the following formula: Among them, M represents the net present value of the discounted revenue corresponding to the distributed energy resources, and P t represents the annual resource revenue corresponding to the resource equipment cluster in the t-th year during the remaining service life of the equipment, T represents the remaining service life of the equipment, t represents the starting year of the remaining service life of the equipment, and N t represents the maintenance and management funds corresponding to the resource equipment cluster in the t-th year during the remaining service life of the equipment, and h represents the market interest rate.

9. The grid integration optimization control method for distributed energy resources according to claim 1, characterized in that, Evaluating the energy supply reliability corresponding to the distributed energy resource based on the resource response data includes: Identify the data tags corresponding to the resource response data, and based on the data tags, determine the response identifier corresponding to the resource response data; Based on the response identifier, perform data classification processing on the resource response data to obtain classified response data; Count the response characteristics corresponding to the classified response data, and based on the response characteristics, analyze the identifier response trend corresponding to the response identifier; Combine the response identifier and the identifier response trend to evaluate the energy supply reliability corresponding to the distributed energy resource.

10. Grid integration optimization control system for distributed energy resources, characterized in that, The system includes: A load prediction analysis module, configured to obtain the distributed energy resource to be processed and its corresponding energy supply area, schedule the regional load data set of the energy supply area, extract the load multi-dimensional attributes of the regional load data set, and analyze the load prediction corresponding to the energy supply area based on the load multi-dimensional attributes and the regional load data set; A pollution intensity analysis module, configured to collect the resource production data, resource transmission data, and energy supply data corresponding to the distributed energy resource, calculate the effective production gain coefficient corresponding to the distributed energy resource by combining the resource production data, the resource transmission data, and the energy supply data, and determine the production pollution intensity corresponding to the distributed energy resource by combining the effective production gain coefficient and the load prediction; A priority determination module, configured to query the resource device cluster corresponding to the distributed energy resource, calculate the maintenance management funds of the distributed energy resource based on the resource device cluster, calculate the discounted net value of the revenue corresponding to the distributed energy resource by combining the maintenance management funds and the resource device cluster, and determine the scheduling priority of the distributed energy resource in the power grid by combining the discounted net value of the revenue and the production pollution intensity; A power generation control management module, configured to schedule the resource response data corresponding to the distributed energy resource, evaluate the energy supply reliability corresponding to the distributed energy resource based on the resource response data, and perform integrated power generation control management on the distributed energy resource by combining the load prediction, the energy supply reliability, and the scheduling priority to obtain a power generation control result.

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