Demand side resource aggregation response regulation method for supporting low-carbon resilience of urban power grid

Abstract

The present application belongs to the technical field of power grid regulation, and specifically relates to a demand-side resource aggregation response regulation method for supporting the low-carbon resilience of urban power grids, which comprises the following steps: generating an urban power grid low-carbon resilience evaluation dataset; calculating the charging and discharging efficiency and cycle life index of energy storage devices, and the power conversion rate and renewable energy content of distributed power generation devices; calculating the urban power grid automatic recovery coefficient; constructing an anti-interference evaluation model to output the corresponding urban power grid anti-interference index; constructing a low-carbon operation evaluation model to obtain the urban power grid low-carbon operation index; constructing an urban power grid low-carbon resilience evaluation correction model to correct different types of urban power grid low-carbon resilience evaluation indexes; after correction, analyzing the low-carbon resilience of the urban power grid, issuing the corresponding response signal, and regulating the demand-side resources supporting the low-carbon resilience of the urban power grid. The present application can provide a scientific basis for the efficient configuration of various resources and the optimization of power grid low-carbon and high-resilience operation strategies.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power grid regulation, and particularly relates to a demand side resource aggregation response regulation method for supporting the low-carbon resilience of urban power grids. BACKGROUND

[0002] With the acceleration of urbanization and the promotion of energy transformation, urban power grids are facing the dual challenges of improving low-carbon and resilience, and existing regulation technologies are difficult to meet the demand; in terms of low carbon, urban power grids have high dependence on fossil energy, and there are difficulties in the consumption of distributed renewable energy, resulting in high carbon emissions; from the perspective of resilience, urban power grids are vulnerable to electromagnetic interference, network attacks and natural disasters, and once a disturbance occurs, the power supply recovery time is long and the power loss is large.

[0003] The traditional urban power grid regulation method only focuses on a single type of resource and local power grid operation indicators, and cannot integrate multi-type demand side resource data for comprehensive analysis, resulting in difficulty in accurately evaluating the low-carbon resilience of urban power grids, lack of accurate basis for demand side resource aggregation response regulation, poor regulation effect, and inability to effectively cope with complex and variable urban electricity demand and external interference, and there is an urgent need for innovative regulation methods to improve the comprehensive performance of urban power grids and to ensure the low-carbon resilience of urban power grids. Therefore, how to combine multi-type demand side resource data and urban power grid operation data and environmental data to evaluate the low-carbon resilience of urban power grids, and then realize the aggregation response regulation of demand side resources of urban power grids, is the problem to be solved by the present application. SUMMARY

[0004] In view of the deficiencies of the prior art, the purpose of the present application is to provide a demand side resource aggregation response regulation method for supporting the low-carbon resilience of urban power grids, which can accurately quantify the automatic recovery capability of urban power grids, and at the same time provide a scientific basis for the efficient configuration of various resources and the optimization of power grid low-carbon and high-resilience operation strategies.

[0005] To achieve the above purpose, the present application is implemented by the following technical solution: a demand side resource aggregation response regulation method for supporting the low-carbon resilience of urban power grids, comprising the following steps:

[0006] Collecting demand side resource evaluation data, including interruptible load data, energy storage device data, distributed power generation device data, flexible load data, and urban power grid operation data and environmental data; preprocessing the demand side resource evaluation data and aggregating to generate an urban power grid low-carbon resilience evaluation data set;

[0007] Using the preprocessed demand side resource evaluation data, calculating the charge and discharge efficiency and cycle life index of the energy storage device, and the power conversion rate and renewable energy content of the distributed power generation device, and aggregating the calculated data to the urban power grid low-carbon resilience evaluation data set;

[0008] Based on the urban power grid low-carbon resilience evaluation dataset, the urban power grid automatic recovery coefficient is calculated; an anti-interference evaluation model is constructed to output the corresponding urban power grid anti-interference index; a low-carbon operation evaluation model is constructed to obtain the urban power grid low-carbon operation index; the urban power grid automatic recovery coefficient, the urban power grid anti-interference index and the urban power grid low-carbon operation index constitute the low-carbon resilience evaluation index of different types of urban power grids;

[0009] A low-carbon resilience evaluation correction model of urban power grid is constructed to output the corresponding low-carbon resilience feedback coefficient of urban power grid, and the low-carbon resilience evaluation index of different types of urban power grid is corrected; after correction, the low-carbon resilience of urban power grid is analyzed, the corresponding response signal is sent out, and the demand side resource supporting the low-carbon resilience of urban power grid is correspondingly regulated.

[0010] Preferably, the process of collecting and preprocessing the demand side resource evaluation data comprises:

[0011] Different types of collection devices are deployed to collect demand side resource evaluation data, including smart meters, battery management systems, power sensors, power load management systems, voltage sensors, power analyzers, frequency measuring instruments, temperature sensors, humidity sensors, ultrasonic anemometers and tipping bucket rain gauges;

[0012] The interruptible load data includes the interruptible duration, interruptible capacity and interruptible recovery time of the interruptible load; the energy storage device data includes the real-time storage energy, real-time input energy, real-time output energy, real-time residual capacity, initial capacity, charge-discharge cycle number and charge-discharge response time of the energy storage device; the distributed power generation device data includes the output power, renewable energy power, renewable energy power generation and total power generation of the distributed power generation device; the elastic load data includes the elastic adjustment range size and elastic adjustment speed of the elastic load; the operation data of the urban power grid includes the voltage amplitude, active power, reactive power and frequency of the power grid equipment; the environmental data of the urban power grid includes the temperature, humidity, wind speed and precipitation of the urban power grid environment;

[0013] The collected demand side resource evaluation data is subjected to data cleaning and data standardization processing, and the preprocessed demand side resource evaluation data is aggregated to generate the urban power grid low-carbon resilience evaluation dataset.

[0014] Preferably, the process of calculating the charge-discharge efficiency and cycle life index of the energy storage device comprises:

[0015] The charge efficiency and discharge efficiency of the energy storage device are obtained through the ratio of real-time storage energy to real-time input energy and the ratio of real-time output energy to real-time storage energy of the energy storage device respectively, and the charge-discharge efficiency of the energy storage device is calculated in combination with the charge efficiency and discharge efficiency of the energy storage device;

[0016] The capacity retention rate of the energy storage device is obtained by using the proportion of the real-time residual capacity of the energy storage device in the initial capacity, weights are respectively assigned to the number of charge-discharge cycles of the energy storage device and the capacity retention rate, and the cycle life index of the energy storage device is calculated by using a weight summation method;

[0017] The charge-discharge efficiency and the cycle life index of the energy storage device are aggregated into the urban power grid low-carbon resilience evaluation dataset.

[0018] Preferably, the calculation process of the power conversion rate and the renewable energy content of the distributed power generation device comprises:

[0019] The power conversion rate of the distributed power generation device is obtained by using the proportion of the output power of the distributed power generation device in the renewable energy power;

[0020] The renewable energy content of the distributed power generation device is calculated by using the ratio of the renewable energy power generation amount of the distributed power generation device to the total power generation amount;

[0021] The power conversion rate and the renewable energy content of the distributed power generation device are aggregated into the urban power grid low-carbon resilience evaluation dataset.

[0022] Preferably, the calculation process of the urban power grid automatic recovery coefficient comprises:

[0023] The interruption capacity and interruption recovery time of the interruptible load, the charge-discharge efficiency and the cycle life index of the energy storage device, and the elastic adjustment range size of the elastic load in the urban power grid low-carbon resilience evaluation dataset are extracted;

[0024] The urban power grid automatic recovery coefficient mic is obtained by using the current extracted data:

[0025] ;

[0026] Wherein, is the interruption capacity of the interruptible load, is the interruption recovery time of the interruptible load, is the charge-discharge efficiency of the energy storage device, is the cycle life index of the energy storage device, is the elastic adjustment range size of the elastic load; t is a positive integer, indicating time.

[0027] Preferably, the process of constructing an anti-interference evaluation model and outputting a corresponding urban power grid anti-interference index comprises:

[0028] extract the interruptible duration of interruptible load, the charging and discharging response time of energy storage device and the elastic adjustment speed of elastic load in the low-carbon resilience evaluation dataset of urban power grid, convert the current extracted data into a first training set and a first test set;

[0029] Use the multiple linear regression algorithm, take the first training set data as input, take the urban power grid anti-interference index as output, learn the linear relationship between the interruptible duration of interruptible load, the charging and discharging response time of energy storage device, the elastic adjustment speed of elastic load and the urban power grid anti-interference index, train the anti-interference evaluation model;

[0030] Input the first test set data into the anti-interference evaluation model, evaluate the performance of the anti-interference evaluation model, adjust the regression coefficient and intercept term of the anti-interference evaluation model, optimize the anti-interference evaluation model, obtain the final anti-interference evaluation model, combine the interruptible duration of interruptible load, the charging and discharging response time of energy storage device and the elastic adjustment speed of elastic load, and output the corresponding urban power grid anti-interference index.

[0031] Preferably, the process of constructing the low-carbon operation evaluation model and then obtaining the urban power grid low-carbon operation index includes:

[0032] Extract the charging and discharging efficiency of energy storage device and the power conversion rate and renewable energy content of distributed power generation device in the low-carbon resilience evaluation dataset of urban power grid, convert the current extracted data into a second training set and a second test set;

[0033] Through the neural network algorithm, take the charging and discharging efficiency of energy storage device and the power conversion rate and renewable energy content of distributed power generation device as input, take the urban power grid low-carbon operation index as output, learn the nonlinear relationship between the charging and discharging efficiency of energy storage device, the power conversion rate and renewable energy content of distributed power generation device and the urban power grid low-carbon operation index, and train the low-carbon operation evaluation model;

[0034] Input the second test set data into the low-carbon operation evaluation model, adjust the parameters of the low-carbon operation evaluation model, optimize the performance of the low-carbon operation evaluation model, obtain the final low-carbon operation evaluation model, combine the current charging and discharging efficiency of energy storage device and the power conversion rate and renewable energy content of distributed power generation device, and output the corresponding urban power grid low-carbon operation index.

[0035] Preferably, the process of constructing the urban power grid low-carbon resilience evaluation correction model and outputting the corresponding urban power grid low-carbon resilience feedback coefficient includes:

[0036] Extract the operation data and environmental data of urban power grid in the low-carbon resilience evaluation dataset of urban power grid, convert the extracted data into a third training set and a third test set;

[0037] In combination with the third training set data and the multiple linear regression algorithm, the operation data and the environmental data of the urban power grid are taken as inputs, and the low-carbon resilience feedback coefficient of the urban power grid is taken as an output, a linear relationship between the operation data of the urban power grid, the environmental data of the urban power grid and the low-carbon resilience feedback coefficient of the urban power grid is learned, and a low-carbon resilience evaluation correction model of the urban power grid is trained;

[0038] The third test set data is input into the low-carbon resilience evaluation correction model of the urban power grid, the regression coefficient and the intercept term of the low-carbon resilience evaluation correction model of the urban power grid are adjusted, the low-carbon resilience evaluation correction model of the urban power grid is optimized, the final low-carbon resilience evaluation correction model of the urban power grid is obtained, and the corresponding low-carbon resilience feedback coefficient of the urban power grid is output in combination with the operation data and the environmental data of the current urban power grid.

[0039] Preferably, the process of correcting different types of low-carbon resilience evaluation indexes of the urban power grid comprises:

[0040] The low-carbon resilience feedback coefficient is used to correct the automatic recovery coefficient of the urban power grid, the anti-interference index of the urban power grid and the low-carbon operation index of the urban power grid.

[0041] When the low-carbon resilience feedback coefficient of the urban power grid is less than 0.4, the automatic recovery coefficient of the urban power grid is corrected; when the low-carbon resilience feedback coefficient of the urban power grid is between 0.4 and 0.7, the anti-interference index of the urban power grid is corrected; when the low-carbon resilience feedback coefficient of the urban power grid is greater than 0.7, the low-carbon operation index of the urban power grid is corrected, and then the corrected automatic recovery coefficient of the urban power grid, the anti-interference index of the urban power grid and the low-carbon operation index of the urban power grid are obtained.

[0042] Preferably, the process of analyzing the low-carbon resilience of the urban power grid, issuing a corresponding response signal and adjusting the demand-side resources supporting the low-carbon resilience of the urban power grid comprises:

[0043] The corrected different types of low-carbon resilience evaluation indexes of the urban power grid are set with a low-carbon resilience evaluation index range.

[0044] When three low-carbon resilience evaluation indexes of the urban power grid are located within the corresponding low-carbon resilience evaluation index range, it indicates that the current low-carbon resilience of the urban power grid is excellent, a green response signal is issued, and the monitoring of different types of low-carbon resilience evaluation indexes of the urban power grid is continued.

[0045] When two low-carbon resilience evaluation indexes of the urban power grid are located within the corresponding low-carbon resilience evaluation index range, it indicates that the current low-carbon resilience of the urban power grid is good, a yellow response signal is issued, and the current demand-side resource management measures are continuously promoted and optimized.

[0046] When one of the low-carbon resilience evaluation indexes of the urban power grid is located within the corresponding low-carbon resilience evaluation index range, it indicates that the current low-carbon resilience of the urban power grid is general, and an orange response signal is sent out, and the power consumption period and the charge-discharge plan of the energy storage device are optimized according to the actual operation of the urban power grid;

[0047] When none of the low-carbon resilience evaluation indexes of the urban power grid is located within the corresponding low-carbon resilience evaluation index range, it indicates that the current low-carbon resilience of the urban power grid is poor, and a red response signal is sent out, and the distributed renewable energy access and consumption efforts are increased, the operation mode of the power grid is optimized and adjusted, the emergency demand response mechanism is started, and economic incentives are set to encourage users to reduce power load during the set period.

[0048] The beneficial effects of the present application are:

[0049] In the present application, the data acquisition technology, algorithm model construction technology and urban power grid low-carbon resilience intelligent evaluation technology are closely combined with modern information technology, which can accurately capture interruptible load data, energy storage device data, distributed power generation equipment data, flexible load data, urban power grid operation data and environmental data, and then obtain the charge-discharge efficiency and cycle life index of the energy storage device, and the power conversion rate and renewable energy content of the distributed power generation equipment, based on the urban power grid low-carbon resilience evaluation data set, combined with multiple linear regression algorithm and neural network algorithm, the urban power grid automatic recovery coefficient, the urban power grid anti-interference index and the urban power grid low-carbon operation index are calculated, and through the construction of the urban power grid low-carbon resilience evaluation correction model, the effective monitoring of the urban power grid low-carbon resilience evaluation process is realized, thereby achieving the comprehensive evaluation of the urban power grid low-carbon resilience, and solving the problem that the traditional method is difficult to combine multiple types of demand side resource data and urban power grid operation data and environmental data.

[0050] The present application can evaluate the low-carbon resilience of the urban power grid, and then realize the aggregation response regulation and control of the demand side resources of the urban power grid, which can refine the dynamic monitoring standard of the demand side resource aggregation response regulation and control method for supporting the low-carbon resilience of the urban power grid in a more accurate range, so that the monitored data becomes a more accurate index under the same conditions, and the intelligent degree of the demand side resource aggregation response regulation and control process of the low-carbon resilience of the urban power grid is significantly enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The present application is a schematic diagram of the steps of the method;

[0052] Figure 2 The present application is a schematic diagram of the overall process of the method. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings.

[0054] Embodiment 1: as shown in Figure 1 and Figure 2 The demand-side resource aggregation response regulation method for supporting the low-carbon resilience of the urban power grid comprises the following steps:

[0055] Collecting demand-side resource evaluation data, including interruptible load data, energy storage device data, distributed power generation device data, elastic load data, and urban power grid operation data and environmental data;

[0056] Pretreating the demand-side resource evaluation data and aggregating them to generate an urban power grid low-carbon resilience evaluation dataset;

[0057] Using the pretreated demand-side resource evaluation data, calculating the charge-discharge efficiency and cycle life index of the energy storage device, and the power conversion rate and renewable energy content of the distributed power generation device, and aggregating the calculated data to the urban power grid low-carbon resilience evaluation dataset;

[0058] Based on the urban power grid low-carbon resilience evaluation dataset, calculating the urban power grid automatic recovery coefficient;

[0059] Combining the pretreated interruptible load data, energy storage device data, elastic load data, and multiple linear regression algorithm, constructing an anti-interference evaluation model, and outputting the corresponding urban power grid anti-interference index;

[0060] Using the power conversion rate and renewable energy content of the distributed power generation device, combining the neural network algorithm, constructing a low-carbon operation evaluation model, and obtaining the urban power grid low-carbon operation index;

[0061] The urban power grid automatic recovery coefficient, the urban power grid anti-interference index, and the urban power grid low-carbon operation index constitute different types of urban power grid low-carbon resilience evaluation indexes;

[0062] Through the urban power grid operation data and environmental data, combining the multiple linear regression algorithm, constructing an urban power grid low-carbon resilience evaluation correction model, outputting the corresponding urban power grid low-carbon resilience feedback coefficient, and correcting different types of urban power grid low-carbon resilience evaluation indexes;

[0063] Combining the corrected different types of urban power grid low-carbon resilience evaluation indexes, analyzing the low-carbon resilience of the urban power grid, issuing corresponding response signals, and regulating the demand-side resources supporting the low-carbon resilience of the urban power grid.

[0064] The process of collecting and pretreating the demand-side resource evaluation data includes:

[0065] Different types of collection devices are deployed to collect demand-side resource assessment data, including smart meters, battery management systems, power sensors, power load management systems, voltage sensors, power analyzers, frequency meters, temperature sensors, humidity sensors, ultrasonic anemometers, and tipping-bucket rain gauges;

[0066] Interruptible load data includes interruptible duration, interruptible capacity, and interruptible recovery time of interruptible load; energy storage device data includes real-time stored energy, real-time input energy, real-time output energy, real-time residual capacity, initial capacity, number of charge-discharge cycles, and charge-discharge response time of energy storage device; distributed power generation device data includes output power, renewable energy power, renewable energy power generation, and total power generation of distributed power generation device; elastic load data includes elastic adjustment range size and elastic adjustment speed of elastic load; operation data of urban power grid includes voltage amplitude, active power, reactive power, and frequency of power grid equipment; environmental data of urban power grid includes temperature, humidity, wind speed, and precipitation of urban power grid environment;

[0067] Intelligent meters are used to collect interruptible duration, interruptible capacity, and interruptible recovery time of interruptible load, as well as renewable energy power generation and total power generation of distributed power generation device; battery management systems are used to collect charge-discharge response time of energy storage device; intelligent meters and battery management systems are combined to collect real-time stored energy, real-time input energy, real-time output energy, real-time residual capacity, initial capacity, and number of charge-discharge cycles of energy storage device; power sensors are used to collect output power and renewable energy power of distributed power generation device; power load management systems are used to collect elastic adjustment range size and elastic adjustment speed of elastic load; voltage sensors are used to collect voltage amplitude of power grid equipment; power analyzers are used to collect active power and reactive power of power grid equipment; frequency meters are used to collect frequency of power grid equipment; temperature sensors, humidity sensors, ultrasonic anemometers, and tipping-bucket rain gauges are combined to collect temperature, humidity, wind speed, and precipitation of urban power grid environment.

[0068] Power grid equipment covers multiple types of devices directly related to voltage amplitude, active power, reactive power, and frequency, mainly including:

[0069] Power generation equipment: synchronous generators (thermal power, hydroelectric power, nuclear power, etc.), new energy power generation grid-connected devices (photovoltaic inverters, wind power converters, etc.).

[0070] Power transmission and transformation equipment: power transmission lines (overhead lines, cables, etc.), transformers (including ordinary transformers, on-load voltage regulating transformers, etc.).

[0071] Reactive power regulation and compensation equipment: shunt capacitors / reactors, static var compensators (SVC), static synchronous compensators (STATCOM), synchronous phase modulators, etc.

[0072] Power side load type equipment: industrial load (such as motor, electric arc furnace, etc.), commercial / residential load (such as air conditioner, lighting, etc.).

[0073] Flexible control type equipment: thyristor controlled series capacitor (TCSC), unified power flow controller (UPFC) and other FACTS devices, etc.

[0074] The collected demand side resource evaluation data is subjected to data cleaning and data standardization processing, and the pre-processed demand side resource evaluation data is aggregated to generate the city power grid low-carbon resilience evaluation dataset.

[0075] The calculation process of the charge-discharge efficiency and cycle life index of the energy storage equipment includes:

[0076] The charge efficiency and discharge efficiency of the energy storage equipment are obtained through the ratio of real-time storage power to real-time input power and the ratio of real-time output power to real-time storage power of the energy storage equipment respectively, and the charge-discharge efficiency of the energy storage equipment is calculated in combination with the charge efficiency and discharge efficiency of the energy storage equipment :

[0077] ;

[0078] In the formula, , The charge efficiency and discharge efficiency of the energy storage equipment are respectively;

[0079] The capacity retention rate of the energy storage equipment is obtained by using the proportion of the real-time residual capacity in the initial capacity, the cycle number of charge-discharge of the energy storage equipment and the capacity retention rate are respectively assigned weights, and the cycle life index of the energy storage equipment is calculated by using the weight summation method :

[0080] ;

[0081] In the formula, , The cycle number of charge-discharge of the energy storage equipment and the capacity retention rate are respectively; , The weights of , ;

[0082] The charge-discharge efficiency and cycle life index of the energy storage equipment are aggregated to the city power grid low-carbon resilience evaluation dataset.

[0083] The calculation process of the power conversion rate and renewable energy content of the distributed power generation equipment includes:

[0084] The power conversion rate of the distributed power generation device is obtained by using the proportion of the output power of the distributed power generation device in the renewable energy power;

[0085] The renewable energy content of the distributed power generation device is calculated by the ratio of the renewable energy power generation amount of the distributed power generation device to the total power generation amount;

[0086] The power conversion rate and the renewable energy content of the distributed power generation device are aggregated into the urban power grid low-carbon resilience evaluation dataset.

[0087] The calculation process of the urban power grid automatic recovery coefficient includes:

[0088] The interruption capacity and interruption recovery time of the interruptible load, the charging and discharging efficiency and cycle life index of the energy storage device, and the elastic adjustment range size of the elastic load in the urban power grid low-carbon resilience evaluation dataset are extracted;

[0089] The urban power grid automatic recovery coefficient mic is obtained by using the current extracted data:

[0090] ;

[0091] Wherein, is the interruption capacity of the interruptible load, is the interruption recovery time of the interruptible load, is the elastic adjustment range size of the elastic load; t is a positive integer, representing the time index, used for summation operation.

[0092] The process of constructing the anti-interference evaluation model and outputting the corresponding urban power grid anti-interference index includes:

[0093] The interruptible duration of the interruptible load, the charging and discharging response time of the energy storage device, and the elastic adjustment speed of the elastic load in the urban power grid low-carbon resilience evaluation dataset are extracted, and the current extracted data is converted into a first training set and a first test set according to a ratio of 7:3;

[0094] Using multiple linear regression algorithm, taking the first training set data as input and the urban power grid anti-interference index as output, learning the linear relationship between the interruptible duration of the interruptible load, the charging and discharging response time of the energy storage device, the elastic adjustment speed of the elastic load and the urban power grid anti-interference index, training the anti-interference evaluation model;

[0095] The first test set data is input into the anti-interference evaluation model, the performance of the anti-interference evaluation model is evaluated, the regression coefficient and the intercept term of the anti-interference evaluation model are adjusted, the anti-interference evaluation model is optimized, and the final anti-interference evaluation model is obtained, which is represented as:

[0096] ;

[0097] mip is the urban power grid anti-interference index; is the interruptible duration of the interruptible load; is the charge-discharge response time of the energy storage device; is the elastic adjustment speed of the elastic load; is the error term of the anti-interference evaluation model; is the intercept term of the anti-interference evaluation model; , , are the regression coefficients of , ,

[0098] The urban power grid anti-interference index is output in combination with the interruptible duration of the current interruptible load, the charge-discharge response time of the energy storage device, and the elastic adjustment speed of the elastic load.

[0099] The process of constructing a low-carbon operation evaluation model to obtain the urban power grid low-carbon operation index includes:

[0100] The charge-discharge efficiency of the energy storage device and the power conversion rate and renewable energy content of the distributed power generation device in the urban power grid low-carbon resilience evaluation dataset are extracted, and the current extracted data is converted into a second training set and a second test set according to a ratio of 8:2;

[0101] The charge-discharge efficiency of the energy storage device and the power conversion rate and renewable energy content of the distributed power generation device are input as inputs, and the urban power grid low-carbon operation index is output as an output. The nonlinear relationship between the charge-discharge efficiency of the energy storage device, the power conversion rate and renewable energy content of the distributed power generation device, and the urban power grid low-carbon operation index is learned, and the low-carbon operation evaluation model is trained;

[0102] The second test set data is input into the low-carbon operation evaluation model, the parameters of the low-carbon operation evaluation model are adjusted, the performance of the low-carbon operation evaluation model is optimized, the final low-carbon operation evaluation model is obtained, and the corresponding urban power grid low-carbon operation index is output in combination with the current charge-discharge efficiency of the energy storage device and the power conversion rate and renewable energy content of the distributed power generation device.

[0103] The process of constructing a low-carbon resilience evaluation correction model to output the corresponding urban power grid low-carbon resilience feedback coefficient includes:

[0104] The operation data and environmental data of the urban power grid in the urban power grid low-carbon resilience evaluation dataset are extracted, and the extracted data is converted into a third training set and a third test set according to a ratio of 6:4;

[0105] ​The operation data and the environment data of the urban power grid are taken as inputs, and the low-carbon resilience feedback coefficient of the urban power grid is taken as output, the linear relationship between the operation data of the urban power grid, the environment data of the urban power grid and the low-carbon resilience feedback coefficient of the urban power grid is learned, and the low-carbon resilience evaluation correction model of the urban power grid is trained in combination with the third training set data and the multiple linear regression algorithm.

[0106] The third test set data is input into the low-carbon resilience evaluation correction model of the urban power grid, the regression coefficient and the intercept term of the low-carbon resilience evaluation correction model of the urban power grid are adjusted, the low-carbon resilience evaluation correction model of the urban power grid is optimized, and the final low-carbon resilience evaluation correction model of the urban power grid is obtained, which is represented as:

[0107] ;

[0108] In the formula, HI is the low-carbon resilience feedback coefficient of the urban power grid; 、 、 、 are the voltage amplitude, active power, reactive power and frequency of the power grid equipment respectively; 、 、 、 are the temperature, humidity, wind speed and precipitation of the environment of the urban power grid respectively; is the intercept term of the low-carbon resilience evaluation correction model of the urban power grid; is the error term of the low-carbon resilience evaluation correction model of the urban power grid; 、 、 、 、 、 、 、 are the regression coefficients of 、 、 、 、 、 、 、 .

[0109] In combination with the operation data and the environment data of the current urban power grid, the corresponding low-carbon resilience feedback coefficient of the urban power grid is output.

[0110] The process of correcting different types of low-carbon resilience evaluation indexes of the urban power grid includes:

[0111] The low-carbon resilience feedback coefficient is used to correct the automatic recovery coefficient of the urban power grid, the anti-interference index of the urban power grid and the low-carbon operation index of the urban power grid.

[0112] When the low-carbon resilience feedback coefficient of the urban power grid is less than 0.4, the urban power grid automatic recovery coefficient is corrected; when the low-carbon resilience feedback coefficient of the urban power grid is between 0.4 and 0.7, the urban power grid anti-interference index is corrected; when the low-carbon resilience feedback coefficient of the urban power grid is greater than 0.7, the urban power grid low-carbon operation index is corrected, and then the corrected urban power grid automatic recovery coefficient, the urban power grid anti-interference index and the urban power grid low-carbon operation index are obtained. The specific correction process is:

[0113] ;

[0114] ;

[0115] ;

[0116] In the formula, , , respectively, the corrected urban power grid automatic recovery coefficient, the urban power grid anti-interference index and the urban power grid low-carbon operation index; miu is the urban power grid low-carbon operation index;

[0117] The initial acquisition of the three evaluation indexes mic, mip and miu of the low-carbon resilience of the urban power grid is only based on demand side resource data (such as load, energy storage, distributed power generation), without fully considering the influence of actual operation data (such as voltage, power, frequency) and environmental data (such as temperature and humidity, wind speed, precipitation) of the power grid. The setting of the low-carbon resilience feedback coefficient HI is a quantitative embodiment of the influence of the two types of data on resilience, and the correction logic is to accurately compensate the deviation of the three indexes caused by operation data and environmental data, so that the evaluation is more in line with the actual working condition.

[0118] The range of the low-carbon resilience feedback coefficient HI of the urban power grid is based on the influence of operation and environmental factors on resilience. When HI is less than 0.4, it represents that the operation data (such as low voltage amplitude, insufficient active power) or environmental data (such as extreme temperature and humidity, heavy precipitation) of the power grid is at a poor level, at this time the core influence dimension of these factors on resilience is the recovery ability after failure. Therefore, when HI is low, the recovery ability is the most critical short board, and mic needs to be corrected first to ensure the accuracy of the basic dimension of resilience evaluation.

[0119] When HI is between 0.4 and 0.7, it represents that the operation and environmental factors are at a medium level, at this time the core dimension of the influence is the ability to resist external disturbance. Therefore, when HI is medium, the anti-interference ability becomes the intermediate short board, and correcting mip can avoid the deviation of regulation caused by anti-interference misjudgment.

[0120] When HI is greater than 0.7, it represents that the operation data (voltage stability, normal frequency) and environmental data (suitable temperature and humidity, moderate wind speed) are at an excellent level, and the core dimension affected is the low-carbon operation capability. When HI is high, the recovery and anti-interference capability has reached the standard, so the low-carbon capability becomes the main optimization direction, and the corrected miu can accurately match the actual low-carbon operation level.

[0121] The three correction formulas are linear formulas because HI itself is constructed by a multiple linear regression algorithm, and the linear formula can maintain the consistency of the influence quantization.

[0122] For , mic is positively correlated with HI. The higher the HI (the better the operating environment), the higher the efficiency of calling interruptible loads and energy storage after grid failure, and the stronger the recovery capability. (1+HI) is a positive compensation coefficient that quantifies the gain amplitude of operation and environmental factors on recovery capability. When HI is lower than 0.4, the mic is improved through correction to compensate for the underestimation of recovery capability caused by low HI.

[0123] For , mip is positively correlated with HI. When HI is moderate, the influence of operation and environmental factors on the anti-interference threshold is linear, and HI is directly used as a calibration coefficient to quantify the actual discount rate of ideal anti-interference capability of operation and environmental factors. When HI is between 0.4 and 0.7, mip is reduced through correction to avoid overestimating the anti-interference level.

[0124] For , miu is negatively correlated with HI. The higher the HI, the lower the dependence of the grid on fossil energy, and the actual low-carbon level has reached the standard. The initial calculated value of miu may be redundant and overestimated. (1-HI) is a redundancy compression coefficient that quantifies the actual optimization space of operation and environmental factors on low-carbon capability. When HI is greater than 0.7, miu is significantly reduced through correction to reflect that the low-carbon capability of the grid has exceeded the standard in an excellent operating environment, and there is no need to maintain a high miu value to avoid wasting regulation resources due to overestimating low-carbon demand.

[0125] The process of analyzing the low-carbon resilience of urban power grids, sending corresponding response signals, and adjusting the demand-side resources that support the low-carbon resilience of urban power grids includes:

[0126] The low-carbon resilience evaluation index range is set for the corrected low-carbon resilience evaluation index of different types of urban power grids:

[0127] The urban power grid automatic recovery coefficient between 0.7 and 1 is within the low-carbon resilience evaluation index range; the urban power grid anti-interference index between 0.6 and 1 is within the low-carbon resilience evaluation index range; the urban power grid low-carbon operation index between 0.5 and 0.9 is within the low-carbon resilience evaluation index range;

[0128] When three urban power grid low-carbon resilience evaluation indexes are located within the corresponding low-carbon resilience evaluation index range, it indicates that the current urban power grid low-carbon resilience is excellent, and a green response signal is sent, and the monitoring of different types of urban power grid low-carbon resilience evaluation indexes is continued;

[0129] When two urban power grid low-carbon resilience evaluation indexes are located within the corresponding low-carbon resilience evaluation index range, it indicates that the current urban power grid low-carbon resilience is good, and a yellow response signal is sent, and the current demand side resource management measures are continuously promoted and optimized;

[0130] When one urban power grid low-carbon resilience evaluation index is located within the corresponding low-carbon resilience evaluation index range, it indicates that the current urban power grid low-carbon resilience is general, and an orange response signal is sent, and the power consumption period and the charging and discharging plan of the energy storage device are optimized according to the actual operation of the urban power grid;

[0131] When no urban power grid low-carbon resilience evaluation index is located within the corresponding low-carbon resilience evaluation index range, it indicates that the current urban power grid low-carbon resilience is poor, and a red response signal is sent, and the distributed renewable energy access and consumption efforts are increased, the operation mode of the power grid is optimized and adjusted, the emergency demand response mechanism is started, and certain economic incentives are set to encourage users to reduce power load during the set period.

[0132] The urban power grid automatic recovery coefficient reflects the ability of the urban power grid to restore power supply after failure or disturbance through self-healing control, rapid load recovery and distributed power support. A high automatic recovery coefficient can reduce power outage time and carbon emissions caused by power outage, thereby improving the low-carbon resilience of the urban power grid;

[0133] The urban power grid anti-interference index is used to measure the ability of the urban power grid to resist external disturbances such as electromagnetic interference, network attacks and natural disasters. A high anti-interference index can reduce equipment damage or power outage caused by interference and avoid high-carbon emission emergency repair, thereby indirectly improving the low-carbon resilience of the urban power grid;

[0134] The urban power grid low-carbon operation index evaluates the low-carbon index of the urban power grid during operation. A high low-carbon operation index means that the dependence of the urban power grid on fossil energy is reduced.

[0135] Embodiment 2: A demand side resource aggregation response regulation device for supporting the low-carbon resilience of an urban power grid, comprising:

[0136] one or more processors;

[0137] a memory for storing one or more computer programs;

[0138] When one or more programs are executed by one or more processors, the one or more processors execute the method in embodiment 1.

[0139] Example 3: A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method in Example 1.

Claims

1. A demand-side resource aggregation response control method to support the low-carbon resilience of urban power grids, characterized by the following steps: include: Collect demand-side resource assessment data, including interruptible load data, energy storage device data, distributed generation device data, resilient load data, and urban power grid operation data and environmental data; Preprocess and aggregate demand-side resource assessment data to generate a low-carbon resilience assessment dataset for urban power grids. Using preprocessed demand-side resource assessment data, the charge-discharge efficiency and cycle life index of energy storage devices, as well as the power conversion rate and renewable energy content of distributed generation devices, are calculated, and the calculated data are aggregated into the urban power grid low-carbon resilience assessment dataset. Based on the urban power grid low-carbon resilience assessment dataset, the automatic recovery coefficient of the urban power grid is calculated; an anti-interference assessment model is constructed to output the corresponding urban power grid anti-interference index; a low-carbon operation assessment model is constructed to obtain the urban power grid low-carbon operation index; the urban power grid automatic recovery coefficient, the urban power grid anti-interference index, and the urban power grid low-carbon operation index constitute the low-carbon resilience assessment indicators for different types of urban power grids. A low-carbon resilience assessment and correction model for urban power grids is constructed, and the corresponding low-carbon resilience feedback coefficients of urban power grids are output to correct the low-carbon resilience assessment indicators of different types of urban power grids. After correction, the low-carbon resilience of urban power grids is analyzed, corresponding response signals are issued, and demand-side resources supporting the low-carbon resilience of urban power grids are regulated accordingly.

2. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 1, characterized in that, The process of collecting and preprocessing the demand-side resource assessment data includes: Deploy different types of data acquisition devices to collect demand-side resource assessment data. These devices include smart meters, battery management systems, power sensors, power load management systems, voltage sensors, power analyzers, frequency meters, temperature sensors, humidity sensors, ultrasonic anemometers, and tipping bucket rain gauges. Interruptible load data includes interruptible load interruptibility duration, interruption capacity, and interruption recovery time; energy storage device data includes real-time stored energy, real-time input energy, real-time output energy, real-time remaining capacity, initial capacity, charge / discharge cycle count, and charge / discharge response time; distributed generation device data includes distributed generation device output power, renewable energy power, renewable energy generation, and total generation; resilient load data includes the resilient load adjustment range and adjustment speed; urban power grid operation data includes voltage amplitude, active power, reactive power, and frequency of grid equipment; urban power grid environmental data includes temperature, humidity, wind speed, and precipitation of the urban power grid environment. The collected demand-side resource assessment data is cleaned and standardized. The preprocessed demand-side resource assessment data is then aggregated to generate a low-carbon resilience assessment dataset for urban power grids.

3. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 2, characterized in that, The calculation process for the charge / discharge efficiency and cycle life index of the energy storage device includes: The charging efficiency and discharging efficiency of the energy storage device are obtained by measuring the ratio of real-time stored energy to real-time input energy and the ratio of real-time output energy to real-time stored energy, respectively. The charging and discharging efficiency of the energy storage device is then calculated by combining the charging efficiency and discharging efficiency of the energy storage device. The capacity retention rate of the energy storage device is obtained by using the proportion of the real-time remaining capacity of the energy storage device to the initial capacity. Weights are assigned to the charge-discharge cycle number and capacity retention rate of the energy storage device, and the cycle life index of the energy storage device is calculated by using the weighted summation method. The charging and discharging efficiency and cycle life index of energy storage devices are aggregated into the urban power grid low-carbon resilience assessment dataset.

4. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 2, characterized in that, The calculation process for the power conversion efficiency and renewable energy content of the distributed generation equipment includes: The power conversion rate of distributed generation equipment is obtained by utilizing the proportion of its output power in the renewable energy power. The renewable energy content of distributed generation equipment is calculated by the ratio of renewable energy generation to total power generation. The power conversion rate and renewable energy content of distributed generation equipment are aggregated into the urban power grid low-carbon resilience assessment dataset.

5. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 2, characterized in that, The calculation process for the automatic recovery coefficient of the urban power grid includes: Extract the interruption capacity and recovery time of interruptible loads, the charging and discharging efficiency and cycle life index of energy storage devices, and the elastic adjustment range of elastic loads from the urban power grid low-carbon resilience assessment dataset; Using the currently extracted data, obtain the city power grid automatic recovery coefficient (mic): ； in, Interruption capacity for interruptible loads, The interruption recovery time for interruptible loads. The charging and discharging efficiency of energy storage devices. This refers to the cycle life index of energy storage devices. The elastic load represents the range of elastic adjustment; t is a positive integer representing time.

6. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 2, characterized in that, The process of constructing an anti-interference assessment model and outputting the corresponding urban power grid anti-interference index includes: Extract the interruptible duration of interruptible loads, the charging and discharging response time of energy storage devices, and the elastic adjustment speed of elastic loads from the urban power grid low-carbon resilience assessment dataset, and convert the currently extracted data into the first training set and the first test set. Using a multiple linear regression algorithm, the first training set data is used as input and the urban power grid anti-interference index is used as output. The linear relationship between the interruptible duration of interruptible loads, the charging and discharging response time of energy storage devices, the elastic adjustment speed of elastic loads and the urban power grid anti-interference index is learned, and the anti-interference assessment model is trained. The first test set data is input into the anti-interference assessment model to evaluate its performance. The regression coefficients and intercept terms of the anti-interference assessment model are adjusted to optimize it and obtain the final anti-interference assessment model. The corresponding urban power grid anti-interference index is output by combining the interruptible duration of the current interruptible load, the charging and discharging response time of the energy storage device, and the elastic adjustment speed of the elastic load.

7. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 1, characterized in that, The process of constructing a low-carbon operation assessment model and then obtaining the low-carbon operation index of the urban power grid includes: Extract the charging and discharging efficiency of energy storage devices, as well as the power conversion rate and renewable energy content of distributed generation devices from the urban power grid low-carbon resilience assessment dataset, and convert the currently extracted data into a second training set and a second test set; Using a neural network algorithm, the charging and discharging efficiency of energy storage devices, the power conversion rate and renewable energy content of distributed generation devices are taken as inputs, and the urban power grid low-carbon operation index is taken as output. The nonlinear relationship between the charging and discharging efficiency of energy storage devices, the power conversion rate and renewable energy content of distributed generation devices and the urban power grid low-carbon operation index is learned, and a low-carbon operation evaluation model is trained. The second test set data is input into the low-carbon operation assessment model. The parameters of the low-carbon operation assessment model are adjusted, the performance of the low-carbon operation assessment model is optimized, and the final low-carbon operation assessment model is obtained. Combining the current charging and discharging efficiency of energy storage devices, the power conversion rate of distributed generation devices, and the renewable energy content, the corresponding urban power grid low-carbon operation index is output.

8. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 1, characterized in that, The process of constructing a low-carbon resilience assessment and correction model for urban power grids and outputting the corresponding low-carbon resilience feedback coefficients for urban power grids includes: The operation data and environmental data of the urban power grid in the urban power grid low-carbon resilience assessment dataset are extracted, and the extracted data are converted into a third training set and a third test set. Combining the third training set data and the multiple linear regression algorithm, the operation data and environmental data of the urban power grid are used as inputs, and the low-carbon resilience feedback coefficient of the urban power grid is used as output. The linear relationship between the operation data and environmental data of the urban power grid and the low-carbon resilience feedback coefficient of the urban power grid is learned, and the low-carbon resilience assessment and correction model of the urban power grid is trained. The data from the third test set is input into the urban power grid low-carbon resilience assessment and correction model. The regression coefficients and intercept terms of the urban power grid low-carbon resilience assessment and correction model are adjusted to optimize the urban power grid low-carbon resilience assessment and correction model and obtain the final urban power grid low-carbon resilience assessment and correction model. Combined with the current urban power grid operation data and environmental data, the corresponding urban power grid low-carbon resilience feedback coefficients are output.

9. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 1, characterized in that, The process of correcting the low-carbon resilience assessment indicators for different types of urban power grids includes: The urban power grid low-carbon resilience feedback coefficient is used to correct the urban power grid automatic recovery coefficient, urban power grid anti-interference index, and urban power grid low-carbon operation index. When the low-carbon resilience feedback coefficient of the urban power grid is below 0.4, the automatic recovery coefficient of the urban power grid is corrected; when the low-carbon resilience feedback coefficient of the urban power grid is between 0.4 and 0.7, the anti-interference index of the urban power grid is corrected; when the low-carbon resilience feedback coefficient of the urban power grid is greater than 0.7, the low-carbon operation index of the urban power grid is corrected, and then the corrected automatic recovery coefficient, anti-interference index, and low-carbon operation index of the urban power grid are obtained.

10. The demand-side resource aggregation response control method for supporting the low-carbon resilience of urban power grids according to claim 1, characterized in that, The process of analyzing the low-carbon resilience of urban power grids, issuing corresponding response signals, and regulating demand-side resources that support the low-carbon resilience of urban power grids includes: To set the range of low-carbon resilience assessment indicators for different types of urban power grids after correction. When three urban power grid low-carbon resilience assessment indicators are within the corresponding low-carbon resilience assessment indicator range, it indicates that the current urban power grid has excellent low-carbon resilience and sends out a green response signal. Continue to monitor the low-carbon resilience assessment indicators of different types of urban power grids. When two urban power grid low-carbon resilience assessment indicators are within the corresponding low-carbon resilience assessment indicator range, it indicates that the current urban power grid has good low-carbon resilience, and a yellow response signal is issued to continuously promote and optimize the current demand-side resource management measures. When a city's power grid low-carbon resilience assessment index is within the corresponding low-carbon resilience assessment index range, it indicates that the current low-carbon resilience of the city's power grid is average, and an orange response signal is issued. Based on the actual operation of the city's power grid, the electricity consumption period and the charging and discharging plan of energy storage equipment are optimized. When no urban power grid low-carbon resilience assessment indicator falls within the corresponding low-carbon resilience assessment indicator range, it indicates that the current urban power grid has poor low-carbon resilience. A red response signal is issued, which calls for increased efforts to connect and absorb distributed renewable energy, optimization and adjustment of the power grid operation mode, activation of the emergency demand response mechanism, setting economic incentives, and encouraging users to reduce electricity load during designated periods.

Images