A quantitative evaluation method and system for ramping capability taking flexible resource status into account

Abstract

A method and system for quantitatively evaluating the climbing capacity of flexible resources taking into account their status, the method comprising: classifying the historical data of flexible resources according to different statuses to obtain sub-datasets in different status intervals; performing probability density estimation on the sub-datasets in different status intervals to obtain a climbing rate probability density function in each status interval; revising the climbing rate probability density in each status interval based on a preset confidence level to obtain a revised climbing rate set; calculating the expectation of the revised climbing rate set to obtain the climbing rate expectation in each status interval of the flexible resources; obtaining real-time data of the flexible resources, determining the status interval in which the flexible resources are located based on the status of the real-time data, determining the climbing rate expectation in the status interval in which the flexible resources are located as the climbing capacity of the flexible resources, and determining the final climbing capacity in combination with the status constraints of the flexible resources. This application takes a more comprehensive approach and improves the accuracy of the quantitative evaluation of the system's climbing capacity.

Description

Technical Field

[0001] The present application belongs to the field of power system operation and dispatching, and specifically relates to a method and system for quantitatively evaluating ramping capability taking into account the status of flexible resources. Background Art

[0002] With the rapid development and widespread grid integration of renewable energy, a new energy power system dominated by renewable energy is gradually taking shape. The increasing penetration of renewable energy poses a severe challenge to the flexibility of the power system. Its volatility and uncertainty increase the difficulty of grid operation and dispatch, leading to a continuously increasing demand for flexible resources in the power system. To ensure power balance, the power system needs to have strong up- and down-scaling capabilities.

[0003] At present, the flexibility demand for new energy consumption mainly relies on thermal power. At the same time, the system's ramping capacity does not take into account many factors such as the resource operation status (such as the limited adjustment capacity of the unit under deep peak regulation), physical characteristics (such as the energy storage adjustment capacity is restricted by SOC), and section limits (current exceeding the limit affects the actual adjustable output of the unit). The actual system's ramping capacity is lower than expected.

[0004] Existing solutions exist for analyzing the market demand for power system flexibility ramping. However, these solutions only consider the system ramping demand caused by the uncertainty of renewable energy output. By quantifying different demands, they form a stepped ramping demand model. However, these solutions fail to effectively analyze the uncertainty of the ramping capacity of the power source itself, such as the impact on system flexibility caused by the reduced ramping capacity of thermal power during deep peak regulation. Furthermore, these solutions do not provide detailed modeling for calculating the ramping rate under different peak regulation conditions. Furthermore, they only consider conventional power sources and are no longer suitable for new power systems that include large-scale energy storage.

[0005] How to build a more accurate quantitative assessment model for the ramping capacity of flexible resources is one of the effective measures to achieve the absorption of new energy and ensure the safe and stable operation of the power system. Summary of the Invention

[0006] In order to address the deficiencies in the existing technology, the present application provides a method and system for quantitatively evaluating the ramping capacity taking into account the flexible resource status. This method takes into account multiple factors such as the resource operating status and physical characteristics, and can improve the accuracy of the quantitative evaluation of the system's ramping capacity, providing data support for realizing the absorption of new energy and ensuring the safe and stable operation of the power system.

[0007] This application adopts the following technical solution.

[0008] A first aspect of the present application provides a method for quantitatively evaluating ramping capability taking into account the status of flexible resources, comprising:

[0009] Obtain historical data of flexible resources, classify the historical data of flexible resources according to different states, and obtain sub-data sets in different state intervals;

[0010] Probability density estimation is performed on the sub-data sets in different state intervals to obtain the probability density function of the ramp rate in each state interval;

[0011] Based on the preset confidence, the probability density of the climbing rate in each state interval is revised to obtain a revised climbing rate set, and the expectation of the revised climbing rate set is calculated to obtain the expected climbing rate in each state interval of the flexible resource;

[0012] Real-time data of flexible resources is obtained, and the state interval of the flexible resources is determined based on the status of the real-time data. The expected climbing rate in the state interval is determined as the climbing capacity of the flexible resources. The final climbing capacity is determined in combination with the state constraints of the flexible resources.

[0013] Optionally, the flexible resources include thermal power units and energy storage units; wherein,

[0014] The historical data of thermal power units are divided into sub-datasets of different state intervals according to unit temperature and unit power;

[0015] The historical data of each energy storage device under the energy storage unit is divided into sub-datasets of different status intervals according to temperature and remaining battery power; and a mapping relationship between temperature, remaining battery power and charging and discharging power is constructed based on the historical data.

[0016] Optionally, dividing the historical data of the thermal power unit into sub-datasets of different state intervals according to the unit temperature and the unit power includes:

[0017] When the unit power of the historical dispatch output data is greater than or equal to the first power threshold, it is classified into the sub-dataset corresponding to the normal peak-shaving state;

[0018] When the unit power of the historical dispatch output data is not 0 and is less than the first power threshold, it is classified into the sub-dataset corresponding to the deep peak load state;

[0019] When the unit power of the historical dispatch output data is 0 and the unit temperature is greater than or equal to the first temperature threshold, it is classified into the sub-dataset corresponding to the hot standby state;

[0020] When the unit power of the historical dispatch output data is 0, and the unit temperature is greater than the second temperature threshold and less than the first temperature threshold, it is classified into the sub-dataset corresponding to the warm standby state;

[0021] When the unit power of the historical dispatch output data is 0 and the unit temperature is equal to the second temperature threshold, it is classified into the sub-dataset corresponding to the cold standby state.

[0022] Optionally, performing probability density estimation on the sub-data sets in different state intervals to obtain a probability density function of the ramp rate in each state interval includes:

[0023] Calculate the climbing speed in each state interval to obtain the climbing rate of each sub-data set;

[0024] Each sub-data set is divided into an up-climbing data set and a down-climbing data set according to the positive and negative values ​​of the climbing rate;

[0025] Based on the up-climbing data set and the down-climbing data set, the non-parametric kernel density algorithm is used to obtain the probability density function of the climbing rate in the state power interval corresponding to each sub-data set.

[0026] Optionally, the step of calculating the expected value of the revised climbing rate set to obtain the expected climbing rate of the flexible resource in each state interval includes:

[0027] According to the preset confidence level, the upper quantile of the climbing rate and the lower quantile of the climbing rate are obtained.

[0028] Based on the upper and lower quantiles of the climbing rate, the climbing rate probability density in each state interval is integrated to obtain the expected climbing rate of the flexible resource in each state interval.

[0029] Optionally, the final ramping capability is determined in combination with the state constraints of the thermal power unit, including:

[0030] Determine whether the output of the thermal power unit has reached the maximum power value at the previous moment. If so, the unit's ramp-up rate value at the current moment is 0. Otherwise, the expectation of the ramp-up rate set within the state interval of the thermal power unit after correction is used as the unit's ramp-up rate value at the current moment.

[0031] Determine whether the output of the thermal power unit at the previous moment has reached the minimum power value under the conventional peak-shaving state of the thermal power unit. If so, the unit's ramp-down rate value at the current moment is 0. Otherwise, the expectation of the corrected ramp-down rate set in the state interval of the thermal power unit is used as the unit's ramp-down rate value at the current moment.

[0032] Optionally, the historical data of the energy storage device is divided into sub-datasets of different status intervals according to temperature and remaining battery power, including:

[0033] Divide the historical data of the energy storage device into n intervals according to the remaining battery power to form n first sub-sample sets;

[0034] The historical data in each first sub-sample set is divided into m intervals according to temperature to form A second sample set.

[0035] Optionally, determining the final ramp capability in combination with the state constraints of the energy storage device includes:

[0036] Based on the constructed mapping relationship between temperature, remaining battery capacity and charge and discharge power, combined with the energy storage device's capacity constraint, charge and discharge state constraint and maximum charge and discharge constraint on climbing capacity, the final climbing capacity of each energy storage device is obtained.

[0037] Optionally, obtaining the final climbing capability of each energy storage device includes:

[0038] Obtain the discharge power, charging power, and battery capacity of the energy storage device at the previous moment;

[0039] Based on the power constraint and the charge and discharge state constraint, the battery power at the current moment is obtained by solving the discharge power, charging power and battery power at the previous moment;

[0040] Finding the expected discharge value of the energy storage device at the current moment in the mapping relationship based on the current battery power, and finding the expected charge value of the energy storage device at the current moment in the mapping relationship based on the current battery power, wherein the expected discharge value of the energy storage device at the current moment and the expected charge value of the energy storage device at the current moment satisfy the maximum charge and discharge constraint condition;

[0041] The difference between the expected discharge value of the energy storage device at the current moment and the discharge power of the energy storage device at the previous moment is taken as the up-climbing capability of the energy storage device at the current moment; the difference between the expected charge value of the energy storage device at the current moment and the charge power of the energy storage device at the previous moment is taken as the down-climbing capability of the energy storage device at the current moment.

[0042] Optionally, the acquiring of historical data of the flexible resource and classifying the historical data of the flexible resource according to different states to obtain sub-data sets in different state intervals include:

[0043] Constructing a comprehensive feature vector representing the state and ramping capability of each flexible resource based on the influencing factors of the historical data of each flexible resource;

[0044] The comprehensive feature vectors are clustered based on the Euclidean distance to form sub-datasets in different state intervals.

[0045] Optionally, constructing a comprehensive feature vector representing the flexible resource status and ramping capability based on the influencing factors of the historical data of each flexible resource includes:

[0046] Normalize the historical data sample values ​​corresponding to each influencing factor;

[0047] The characteristic weight of each influencing factor is taken as the power of each influencing factor;

[0048] A comprehensive feature vector is constructed based on the sum of the powers of the historical data sample values ​​of each influencing factor after normalization.

[0049] Optionally, constructing a comprehensive feature vector representing the flexible resource status and ramping capability based on the influencing factors of the historical data of each flexible resource includes:

[0050] The historical data of each flexible resource is input into a pre-trained three-layer unsupervised neural network, and a comprehensive feature vector of the flexible resource status and climbing ability is output. The input layer dimension of the three-layer unsupervised neural network is the same as the dimension of the influencing factors.

[0051] A second aspect of the present application provides a system for quantitatively evaluating ramping capability taking into account flexible resource status, the system comprising:

[0052] Data collection module, used to obtain historical data of flexible resources;

[0053] The classification module is used to classify the historical data of flexible resources according to different states and obtain sub-data sets in different state intervals;

[0054] An estimation module is used to perform probability density estimation on sub-data sets in different state intervals to obtain the probability density function of the ramp rate in each state interval;

[0055] A revision module is used to revise the probability density of the climbing rate in each state interval based on a preset confidence level, obtain a revised climbing rate set, calculate the expectation of the revised climbing rate set, and obtain the expected climbing rate in each state interval of the flexible resource;

[0056] The quantitative evaluation module is used to obtain real-time data of flexible resources, determine the state interval of the flexible resources based on the status of the real-time data, determine the expected climbing rate in the state interval as the climbing capacity of the flexible resources, and determine the final climbing capacity in combination with the state constraints of the flexible resources.

[0057] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, the method for quantitatively evaluating the climbing capability taking into account the flexible resource status is implemented.

[0058] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for quantitatively evaluating climbing capability taking into account the flexible resource status.

[0059] Compared with the prior art, the beneficial effects of this application include at least:

[0060] This application effectively considers the impact of many factors such as resource operating status and physical characteristics on the system's climbing capability when constructing a quantitative assessment of the climbing capability of conventional power sources and energy storage power stations. By effectively considering the climbing capability of conventional power sources under different operating conditions and effectively considering the impact of SOC, temperature, and power on the charging and discharging power of energy storage power stations, a more comprehensive consideration is taken into account, and the constructed flexible resource climbing capability model is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] To more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0062] Figure 1 This is a flow chart of a method for quantitatively evaluating ramping capability taking into account flexible resource status, provided in an embodiment of the present application;

[0063] Figure 2 This is a schematic diagram of the probability distribution of the ramp rate of a conventional unit provided in an embodiment of the present application;

[0064] Figure 3 This is a schematic diagram of a data completion method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. The embodiments described in this application are only part of the embodiments of this application, not all of them. Based on the spirit of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0066] Combine Figure 1 As shown, embodiment 1 of the present application provides a method for quantitatively evaluating ramping capability taking into account flexible resource status, including the following contents:

[0067] S1: Obtain historical data of flexible resources, classify the historical data of flexible resources according to different states, and obtain sub-datasets in different state intervals.

[0068] Preferably, but not limited to, S1 specifically includes:

[0069] The historical data of thermal power units are divided into sub-datasets of different state intervals according to unit temperature and unit power;

[0070] The historical data of each energy storage device under the energy storage unit is divided into sub-datasets of different status intervals according to temperature and remaining battery power; and a mapping relationship between temperature, remaining battery power and charging and discharging power is constructed based on the historical data.

[0071] Preferably, but not limitatively, the historical data of the flexible resource is classified according to different states, and the sub-data sets in different state intervals are obtained, including:

[0072] The flexible resources are classified according to their historical data features to form sub-datasets in different states. Specifically, the comprehensive feature vectors representing the flexible resource states and ramping capabilities are constructed based on the influencing factors of the historical data of each flexible resource; and the comprehensive feature vectors are clustered based on the Euclidean distance to form sub-datasets in different state intervals.

[0073] It is understandable that the factors affecting the historical data of thermal power units include unit temperature, unit power, ramp rate, etc.; the factors affecting the historical data of energy storage units include temperature, remaining battery power, and charge and discharge power, etc.

[0074] It should be noted that clustering the comprehensive feature vector based on the Euclidean distance to form sub-datasets in different state intervals can add the comprehensive feature vector to the corresponding sample value sequence to form different sample sets containing the comprehensive feature vector. Clustering the comprehensive feature vector of different sample sets based on the Euclidean distance to form different clusters, that is, sub-datasets in different states.

[0075] In some embodiments, constructing a comprehensive feature vector representing the flexible resource status and ramping capability based on the influencing factors of the historical data of each flexible resource includes:

[0076] Normalize the historical data sample values ​​corresponding to each influencing factor;

[0077] The characteristic weight of each influencing factor is taken as the power of each influencing factor;

[0078] A comprehensive feature vector is constructed based on the sum of the powers of the historical data sample values ​​of each influencing factor after normalization.

[0079] The comprehensive feature vector is constructed as follows :

[0080]

[0081] in, is the influencing factor, there are n influencing factors in total; is the i-th influencing factor The feature weights can be obtained by solving the correlation;

[0082]

[0083] in, is the characteristic weight of a certain influencing factor, is the jth sample of the influencing factor, is the average value of N influencing factor samples; is the jth sample value of the ramp rate of a certain flexible resource; The average of N samples of the ramp-up rate of a flexible resource.

[0084] In this embodiment, by comprehensively considering multiple influencing factors to construct a comprehensive feature vector, the state of flexible resources at different points in time can be more comprehensively reflected, providing a data basis for subsequently more accurately characterizing the ramping capacity of flexible resources under different conditions. Furthermore, by determining feature weights based on the correlation between samples, the comprehensive feature vector can be weighted according to the characteristics of the data itself, making the assessment of the ramping capacity of flexible resources more adaptable to new data and different scenarios, thereby providing data support for ensuring the stable operation of the power system and improving the reliability and availability of resources.

[0085] In some embodiments, constructing a comprehensive feature vector representing the flexible resource status and ramping capability based on the influencing factors of the historical data of each flexible resource includes:

[0086] The historical data of each flexible resource is input into a pre-trained three-layer unsupervised neural network, and a comprehensive feature vector of the flexible resource status and climbing ability is output. The input layer dimension of the three-layer unsupervised neural network is the same as the dimension of the influencing factors.

[0087] Specifically, a three-layer unsupervised neural network is constructed, including an input layer, a weight layer, and an output layer. The input layer has the same n-dimensionality as the influencing factors, corresponding to an element of the input feature; the output layer is a one-dimensional neuron topology structure; the weight layer randomly assigns weights to each neuron, and these weights initially correspond to the variables in the data set; the neurons in the weight layer connect the input layer and the output layer, and their initial weights are randomly assigned.

[0088] Preferably, but not restrictively, during the training process, the three-layer unsupervised neural network continuously adjusts weights through a competitive learning mechanism so that the neurons in the output layer can gradually converge to the topological structure of the input data. The weight adjustment is expressed as follows:

[0089]

[0090]

[0091] in, is the t+1 training weight of the i-th neuron for the j-th flexible resource historical data sample value, is the t-time training weight of the j-th flexible resource historical data sample value of the i-th neuron, is the weight change, is the learning rate of the t-th training, is the neighborhood function of the t-th training of the i-th neuron, which is used to measure the neighborhood relationship between neuron i and the best matching unit neuron c. It is usually represented by a Gaussian function. The jth flexible resource historical data sample value is input in the tth training, and the comprehensive feature vector is output through multiple trainings .

[0092] Specifically, the weight change is first calculated based on the current training learning rate, neighborhood function, and the difference between the current training input value and the current training weight. The calculated weight change is then added to the current weight to obtain the new weight for the next round of training. In this way, the neurons in the three-layer unsupervised neural network gradually adjust their weights to better match the topological structure of the input flexible resource historical data.

[0093] Since this application is used to divide the status of various flexible resources, and the operating data of various resources are relatively small, in order to better achieve the classification effect, this application proposes a dynamic learning rate. In the initial stage of optimization, the learning rate is set to the maximum. A higher learning rate helps the model to jump out of the local optimal solution and explore a wider parameter space; as the training proceeds, the model gradually shifts from global exploration to local optimization, and the learning rate gradually decreases. The model can adjust parameters more finely to avoid parameter oscillations, and finally converge to the global optimal solution or an area close to the global optimal solution.

[0094] S2: Perform probability density estimation on the sub-data sets in different state intervals to obtain the probability density function of the climbing rate in each state interval.

[0095] S3: Revise the probability density of the climbing rate in each state interval based on the preset confidence level to obtain a revised climbing rate set, calculate the expectation of the revised climbing rate set, and obtain the expected climbing rate in each state interval of the flexible resource.

[0096] S4: Acquire real-time data of the flexible resource, determine its state interval based on the state of the real-time data, determine the expected climbing rate in the state interval as the climbing capacity of the flexible resource, and determine the final climbing capacity based on the state constraints of the flexible resource.

[0097] Among them, the state constraints include the minimum operating time constraint of the thermal power unit, the minimum power constraint, the power constraint of the energy storage equipment on the climbing ability, the charging and discharging state constraint, and the maximum charging and discharging constraint.

[0098] It should be noted that the flexible resources described include thermal power units and energy storage units. Thermal power units are conventional units, which are the primary power source for the power system, providing a stable baseload and partial peak-shaving capability. Energy storage power stations are energy storage units. Their primary function is to store and release electrical energy, acting as a buffer within the power system. They are primarily used for frequency regulation, peak shaving, backup power, and smoothing fluctuations in renewable energy generation. They primarily include battery energy storage systems and pumped-storage power stations.

[0099] In this embodiment, by considering the constraints of different state constraints of flexible resources on the ramp capability, the system ramp capability is estimated more comprehensively, and the accuracy of the system ramp capability estimation is further improved.

[0100] Embodiment 2 of the present application provides a method for quantitatively evaluating ramping capability taking into account flexible resource status, which is applied to a thermal power unit. The method includes:

[0101] S1: Obtain historical data of flexible resources, classify the historical data of flexible resources according to different states, and obtain sub-datasets in different state intervals.

[0102] Specifically, the historical dispatch output data are classified according to the unit temperature and unit power to obtain sub-datasets under different thermal power unit states.

[0103] Based on prior knowledge that the ramping ability of thermal power units is related to their state and operating temperature, the output data of the historical scheduling process is divided into normal peak regulation (RPR), deep peak regulation (DPR), hot standby (HS), warm standby (WS), and cold standby (CS).

[0104] Optionally, S1 divides the historical dispatch process output data into:

[0105] When the unit power of the historical dispatch output data is greater than or equal to the first power threshold, it is classified into the sub-dataset corresponding to the normal peak-shaving state;

[0106] When the unit power of the historical dispatch output data is not 0 and is less than the first power threshold, it is classified into the sub-dataset corresponding to the deep peak load state;

[0107] When the unit power of the historical dispatch output data is 0 and the unit temperature is greater than or equal to the first temperature threshold, it is classified into the sub-dataset corresponding to the hot standby state;

[0108] When the unit power of the historical dispatch output data is 0, and the unit temperature is greater than the second temperature threshold and less than the first temperature threshold, it is classified into the sub-dataset corresponding to the warm standby state;

[0109] When the unit power of the historical dispatch output data is 0 and the unit temperature is equal to the second temperature threshold, it is classified into the sub-dataset corresponding to the cold standby state;

[0110] Among them, the first power threshold is , is the maximum power of conventional units in the power system, is a constant coefficient, which can be set to 0.5; the second temperature threshold is room temperature, which can be set to 20°C; the first temperature threshold can be set to 300°C.

[0111] It is understandable that each sub-data set corresponds to a different state-power interval. By dividing the historical data according to different state-power intervals, a more accurate maximum climbing capacity of the thermal power unit can be obtained.

[0112] Optionally, data cleaning is performed on each sub-dataset after S1.

[0113] Specifically, data cleaning includes removing zero values ​​and erroneous values.

[0114] It should be noted that the range of zero values ​​and erroneous values ​​can be defined to determine whether they need to be eliminated based on specific business scenarios.

[0115] In this way, data cleaning can eliminate zero values ​​and erroneous values, improve the accuracy of data in each data set, and reduce the amount of data, thereby reducing the difficulty of subsequent data processing and increasing the speed of data processing.

[0116] S2: Perform probability density estimation on the sub-data sets in different state intervals in S1 to obtain the probability density function of the climbing rate in each state interval.

[0117] Preferably, but not limitatively, performing probability density estimation on the sub-data sets in different state intervals to obtain the probability density function in each state interval includes:

[0118] S2.1: Calculate the climbing speed in each state interval to obtain the climbing rate of each sub-data set.

[0119] Preferably, but not limiting, the ramp rate is calculated as follows: :

[0120]

[0121] Where, Indicates the status of the thermal power unit is The climbing rate, Indicates the status of the thermal power unit. Indicates normal peak load regulation status. Indicates deep peak regulation state, Indicates hot standby status, Indicates warm standby state. Indicates cold standby state. Indicates that the state of the thermal power unit at time t is The output value of Indicates that the state of the thermal power unit at time t-1 is The output value of Indicates the time interval between adjacent data, which can be 15 minutes.

[0122] S2.2: Divide each sub-data set into an up-climbing data set and a down-climbing data set according to the positive and negative values ​​of the climbing rate.

[0123] S2.3: Based on the up-climbing data set and the down-climbing data set, a non-parametric kernel density algorithm is used to obtain a probability density function of the climbing rate in the state power interval corresponding to each sub-data set.

[0124] Specifically, construct the ramp rate under each state power range The probability density function of Dataset The positive and negative values ​​in the code divide the up and down climbing data. 、 Set, use the non-parametric kernel density probability prediction method to solve the climbing rate in each state power interval 、 The probability density function of .

[0125] Specifically, the probability density function of the ramp rate within the state power interval corresponding to each sub-data set is calculated according to the following formula:

[0126]

[0127] in, express The probability density function of Indicates the status of the thermal power unit is The climbing rate includes 、 , Indicates the status of the thermal power unit is The sub-datasets in The climbing rate of samples, Indicates the status of the thermal power unit is The number of samples in the sub-dataset under h is the bandwidth parameter, A Gaussian kernel function can be used.

[0128] Preferably, but not restrictively, the bandwidth parameter is calculated according to the following formula:

[0129]

[0130] Where, is the bandwidth parameter, Indicates the status of the thermal power unit is The number of samples in the sub-dataset.

[0131] S3: Based on the preset confidence, the probability density of the climbing rate in each state interval in S2 is revised to obtain a revised climbing rate set. The expectation of the revised climbing rate set is calculated to obtain the expected climbing rate in each state interval of the flexible resource.

[0132] Preferably, but not limitatively, obtaining the expected climbing rate for each state interval of the flexible resource by calculating the expected climbing rate for the revised set of climbing rates includes:

[0133] According to the preset confidence level, the upper quantile of the climbing rate and the lower quantile of the climbing rate are obtained.

[0134] Based on the upper and lower quantiles of the climbing rate, the climbing rate probability density in each state interval is integrated to obtain the expected climbing rate of the flexible resource in each state interval.

[0135] In this embodiment, combined with the preset confidence Obtain the corrected climbing rate set, and thus obtain the expected climbing rate in each state-power interval. Calculate the confidence interval according to the preset confidence level, and define the climbing rate outliers outside the confidence interval as climbing rate anomalies, thereby obtaining the corrected climbing rate set, where the preset confidence level can be set to 95%. In this way, the climbing rate in each state-power interval can be obtained. , ,in, E Express expectations, Represents the revised ramp rate set in this state-power interval.

[0136] Figure 2 is a schematic diagram of the probability distribution of the ramp rate of conventional units, as shown in Figure 2 As shown, the horizontal axis represents the climbing rate , the vertical axis represents The probability density function of , is the upper quantile, satisfying the probability , the probability that the climbing rate is less than or equal to the upper quantile is a / 2, P represents the probability of the climbing rate, and P is calculated as follows:

[0137]

[0138] Then, calculate the expected ramp rate in each state-power range according to the following formula:

[0139]

[0140] in, Indicates the status of the thermal power unit is Climbing rate expectations, is the upper quantile, is the lower quantile, express The probability density function of .

[0141] In this embodiment, the ramp rate is calculated based on the sub-data sets under different thermal power unit states, and each sub-data set is divided into an up-climbing data set and a down-climbing data set according to the positive and negative values ​​of the ramp rate. Based on the up-climbing data set and the down-climbing data set, a non-parametric kernel density algorithm is used to obtain the probability density function of the ramp rate in the state power interval corresponding to each sub-data set. Combined with the preset confidence, a corrected ramp rate set is obtained, thereby obtaining the expected ramp rate under each state-power interval. Compared with the prior art, when evaluating the ramping capability of conventional units (Thermal power units, TUs) in the power system, the ramp rate is usually regarded as a constant value, and the ramp rate is generally set to 1% to 2% of MW / min. This application takes into account that the climbing ability of the unit will change greatly under different states, so an expected model of the unit's climbing ability under each state interval is constructed, which can more accurately evaluate the climbing ability.

[0142] S4: Acquire real-time data of the flexible resource, determine its state interval based on the state of the real-time data, determine the expected climbing rate in the state interval as the climbing capacity of the flexible resource, and determine the final climbing capacity based on the state constraints of the flexible resource.

[0143] Specifically, the final unit climbing speed model is obtained by combining the unit state at each moment, the output value at the previous moment, and the minimum operating time of each state. The unit climbing speed model is used to quantify the unit's climbing ability.

[0144] Optionally, the final ramping capability is determined in combination with the state constraints of the thermal power unit, including:

[0145] Determine whether the output of the thermal power unit has reached the maximum power value at the previous moment. If so, the unit's ramp-up rate value at the current moment is 0. Otherwise, the expectation of the ramp-up rate set within the state interval of the thermal power unit after correction is used as the unit's ramp-up rate value at the current moment.

[0146] Determine whether the output of the thermal power unit at the previous moment has reached the minimum power value under the conventional peak-shaving state of the thermal power unit. If so, the unit's ramp-down rate value at the current moment is 0. Otherwise, the expectation of the corrected ramp-down rate set in the state interval of the thermal power unit is used as the unit's ramp-down rate value at the current moment.

[0147] Specifically, combined with the unit status at each moment , the output value at the previous moment , and the minimum running time of each state , the final conventional unit climbing speed model is obtained as follows:

[0148]

[0149]

[0150] Where, Indicates the unit's ramp rate at time t, Indicates the ramp rate value of the unit at time t, Indicates the output of the unit at time t-1, Indicates the maximum power of conventional units in the power system. Indicates the minimum power value of the unit during normal peak load regulation. Represents the set of climbing rates within a certain state interval after correction expectations, represents the climbing rate sample, Indicates the status is The following uphill sample collection. Represents the set of downward climbing rates within a certain state interval after correction expectations, represents the down-ramp rate sample, Indicates the status is The downhill sample collection.

[0151] When the output of the thermal power unit has reached the maximum power value at time t-1, the ramp rate value of the unit at time t is 0. Otherwise, the ramp rate set within a certain state interval after correction is As the expected value of the unit's ramp-up rate at time t. When the output of the thermal power unit at time t-1 reaches the minimum power value under the conventional peak-shaving state, the unit's ramp-down rate at time t is 0. Otherwise, the set of ramp-down rates in the corresponding state interval after correction is The expectation of is taken as the ramp rate value of the unit at time t.

[0152] As one of the outstanding substantive features of this application, by dividing historical data according to different states and performing probability density estimation in each state interval, the initial climbing rate is obtained, and then the final unit climbing rate capability value is obtained by combining the unit state at each moment, the output value at the previous moment, and the minimum operating time of each state. Compared with the existing technology that does not consider the impact of the climbing capability of conventional units on system flexibility, this application clearly describes how to calculate the climbing rate of different peak-shaving states under 5 states, thereby improving the accuracy of climbing capability evaluation.

[0153] Embodiment 3 of the present application provides a method for quantitatively evaluating ramping capability taking into account flexible resource status, which is applied to an energy storage unit. The method includes:

[0154] S1: Obtain historical data of flexible resources, classify the historical data of flexible resources according to different states, and obtain sub-datasets in different state intervals.

[0155] Specifically, the historical data of the energy storage device is divided into sub-datasets of different state intervals according to temperature and remaining battery power; and the mapping relationship between temperature, remaining battery power and charge and discharge power is constructed based on the historical data. .

[0156] Preferably, but not limited to, S1 specifically includes:

[0157] The historical data of the energy storage device is divided into n intervals according to the remaining battery power, forming n first sub-sample sets; each first sub-sample set is then classified according to temperature and divided into m intervals, thus forming A second sample set.

[0158] The historical data of energy storage equipment includes: temperature data, battery remaining power data, charging power, and discharging power.

[0159] S2: Estimating the probability density of the sub-data sets in different state intervals to obtain the probability density function of the climbing rate in each state interval

[0160] In S2, for each second sample set, the probability density function of each second sample set is obtained based on the non-parametric estimation method, which is expressed as follows:

[0161]

[0162] Where, for The probability density function of is the expected charging value under the temperature range and battery power range corresponding to the second sample set, is the i-th sample in the second sample set, is the number of samples in the second sample set, is the width parameter of the kernel function, As the kernel function, a Gaussian kernel function can be used.

[0163] S3: Revise the probability density of the climbing rate in each state interval based on the preset confidence level to obtain a revised climbing rate set, calculate the expectation of the revised climbing rate set, and obtain the expected climbing rate in each state interval of the flexible resource.

[0164] Specifically, combined with the preset reliability Under this condition, the values ​​outside the confidence interval are defined as outliers, thereby obtaining a corrected data set. Based on this, the expected charging value under a certain temperature range and battery power range can be obtained. .

[0165] For each second sample set, the probability density function of each second sample set is obtained based on the non-parametric estimation method, and the corrected data set is obtained by combining the preset confidence, and the charging expectation value under different temperature ranges and battery power ranges can be obtained. .

[0166] Optionally, the expected charging value under the temperature range and battery power range is calculated as follows:

[0167]

[0168] Where -y and y represent the probability density function distribution The corresponding lower and upper values ​​in the confidence interval.

[0169] Repeat steps S1, S2, and S3 to calculate the n*m ​​sample sets and obtain the mapping relationship between each temperature, remaining power, and charge and discharge power, that is, obtain the expected charging value under different temperature ranges and battery power ranges.

[0170] Preferably, but not limitatively, the method further comprises: for an empty sample set, using an interpolation algorithm to fill it based on the calculated data.

[0171] Combine Figure 3 As shown, the known data points 、 、 、 ,but Estimate using the following formula and fill the empty sample set with the estimated result:

[0172]

[0173] Among them, the data points The coordinates of , ), data points The coordinates of , ), data points The coordinates of , ), data points The coordinates of , ), The coordinates of , ).

[0174] S4: Acquire real-time data of the flexible resource, determine its state interval based on the state of the real-time data, determine the expected climbing rate in the state interval as the climbing capacity of the flexible resource, and determine the final climbing capacity based on the state constraints of the flexible resource.

[0175] In S4, the final climbing capability is determined by combining the state constraints of the energy storage device, including:

[0176] Based on the constructed mapping relationship between temperature, remaining battery capacity and charge and discharge power, combined with the energy storage device's power constraint, state constraint and maximum charge and discharge constraint on climbing capacity, the final climbing capacity of each energy storage device is obtained.

[0177] Preferably, but not limitatively, based on the established mapping relationship between temperature, remaining battery capacity, and charge / discharge power, combined with the energy storage device's capacity constraint on climbing capacity, charge / discharge state constraint, and maximum charge / discharge constraint, obtaining the final climbing capacity of each energy storage device includes:

[0178] Obtain the discharge power, charging power, and battery capacity of the energy storage device at the previous moment;

[0179] Based on the power constraint and the charge and discharge state constraint, the battery power at the current moment is obtained by solving the discharge power, charging power and battery power at the previous moment;

[0180] Finding the expected discharge value of the energy storage device at the current moment in the mapping relationship based on the current battery power, and finding the expected charge value of the energy storage device at the current moment in the mapping relationship based on the current battery power, wherein the expected discharge value of the energy storage device at the current moment and the expected charge value of the energy storage device at the current moment satisfy the maximum charge and discharge constraint condition;

[0181] The difference between the expected discharge value of the energy storage device at the current moment and the discharge power of the energy storage device at the previous moment is taken as the up-climbing capability of the energy storage device at the current moment; the difference between the expected charge value of the energy storage device at the current moment and the charge power of the energy storage device at the previous moment is taken as the down-climbing capability of the energy storage device at the current moment.

[0182] Specifically, the mapping relationship built based on S1 , construct a temperature-battery remaining power and charge / discharge power mapping table. Combined with the energy storage device's ramp capacity constraints, charge / discharge state constraints, and maximum charge / discharge constraints, the ramp rate of each device is calculated as follows:

[0183] (1)

[0184] (2)

[0185] (3)

[0186] (4)

[0187] (5)

[0188] (6)

[0189] in, is the discharge state of the energy storage device s at time t, is the charging state of the energy storage device s at time t, For charging, the two are not equal to 1 at the same time;

[0190] is the battery capacity of energy storage device s at time t, is the battery capacity of energy storage device s at time t-1; is the discharge efficiency of the energy storage device s, is the charging efficiency of the energy storage device s, is the discharge power of energy storage device s at time t-1, is the charging power of energy storage device s at time t-1; Represents the time interval, that is, the time difference between time t and time t-1.

[0191] is the expected discharge value of energy storage device s at time t, which is less than the maximum discharge power of energy storage device s and the current discharge status product; is the expected charging value of energy storage device s at time t, which is less than the maximum charging power of energy storage device s and current charging status The product of is the temperature value of the energy storage device s at time t; Indicates that the discharge power of the energy storage device is obtained by querying the battery power and temperature values ​​in the mapping relationship. Indicates that the charging power of the energy storage device is obtained by querying the battery power and temperature values ​​in the mapping relationship; by querying the mapping relationship table of energy storage device power, temperature and charging and discharging power get and ;

[0192] is the climbing ability of energy storage device s at time t, is the ramping capacity of the energy storage power station at time t. The power station has ns energy storage devices. is the climbing ability of energy storage device s at time t, is the downhill climbing capability of the energy storage device s at time t, is the gradeability value of the entire station.

[0193] In this embodiment, the charging power of the energy storage device s at time t-1 is obtained. , discharge power , battery level , based on formulas (1) and (2), the current battery power is obtained , according to the current battery power and temperature in the mapping relationship to find 、 ,and 、 Satisfy the maximum charge and discharge constraints of formulas (3) and (4).

[0194] The expected discharge value of the energy storage device s at time t and the discharge power of energy storage device s at time t-1 The difference between the two is used as the climbing ability of the energy storage device s at time t The expected charge value of the energy storage device s at time t is and the charging power of energy storage device s at time t-1 The difference between the two is used as the downhill climbing capability of the energy storage device s at time t .

[0195] It should be noted that formula (1) is the charge and discharge state constraint, formula (2) is the power constraint, formula (3) and formula (4) are the maximum charge and discharge constraints, and formula (5) and formula (6) are the final climbing ability calculation formulas.

[0196] It is understandable that when online monitoring / ultra-short-term prediction of 15-min ramping capability is performed, the charging power of the energy storage device s at time t-1 is , discharge power If it is a short-term, for example, 24-hour plan, to predict the climbing ability, the charging power of the energy storage device s at time t-1 is , discharge power is the expected value corresponding to the previous moment.

[0197] It can be seen that the ramping capability of the energy storage power station is limited by the current state, remaining capacity and charging and discharging rate of each energy storage device s at the previous moment.

[0198] As one of the outstanding substantive features of this application, by constructing a mapping relationship between temperature, battery remaining power and charge and discharge power The historical data of energy storage equipment is divided according to the remaining battery power and temperature, and probability estimation is performed on the divided data. In the probability estimation process, multiple factors such as resource operating status and physical characteristics are taken into account, making the evaluation more comprehensive, thereby improving the system's climbing ability to plan the accuracy of the evaluation.

[0199] Embodiment 4 of the present application provides a system for quantitatively evaluating climbing capability taking into account flexible resource status, which runs the method for quantitatively evaluating climbing capability taking into account flexible resource status as described in Embodiment 1, 2, or 3. The system includes:

[0200] Data collection module, used to obtain historical data of flexible resources;

[0201] The classification module is used to classify the historical data of flexible resources according to different states and obtain sub-data sets in different state intervals;

[0202] An estimation module is used to perform probability density estimation on sub-data sets in different state intervals to obtain the probability density function of the ramp rate in each state interval;

[0203] A revision module is used to revise the probability density of the climbing rate in each state interval based on a preset confidence level, obtain a revised climbing rate set, calculate the expectation of the revised climbing rate set, and obtain the expected climbing rate in each state interval of the flexible resource;

[0204] The quantitative evaluation module is used to obtain real-time data of flexible resources, determine the state interval of the flexible resources based on the status of the real-time data, determine the expected climbing rate in the state interval as the climbing capacity of the flexible resources, and determine the final climbing capacity in combination with the state constraints of the flexible resources.

[0205] Regarding the system in the above embodiment, the specific manner in which each unit performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0206] Embodiment 5 of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the method for quantitatively evaluating climbing capability taking into account the flexible resource status described in Embodiments 1, 2, or 3 is implemented.

[0207] Embodiment 6 of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for quantitatively evaluating the climbing capability taking into account the flexible resource status according to Embodiment 1, 2 or 3.

[0208] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0209] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present application can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present application should be included in the scope of protection of the claims of the present application.

Claims

1. A quantitative evaluation method for ramping capability taking into account flexible resource status, characterized in that: include: Obtain historical data of flexible resources, classify the historical data of flexible resources according to different states, and obtain sub-data sets in different state intervals; Probability density estimation is performed on the sub-data sets in different state intervals to obtain the probability density function of the ramp rate in each state interval; Based on the preset confidence, the probability density of the climbing rate in each state interval is revised to obtain a revised climbing rate set, and the expectation of the revised climbing rate set is calculated to obtain the expected climbing rate in each state interval of the flexible resource; Obtain real-time data of the flexible resource, determine its state interval based on the status of the real-time data, determine the expected climbing rate in the state interval as the climbing capacity of the flexible resource, and determine the final climbing capacity based on the state constraints of the flexible resource; The flexible resources include thermal power units and energy storage units; The historical data of thermal power units are divided into sub-datasets of different state intervals according to unit temperature and unit power; The historical data of each energy storage device under the energy storage unit is divided into sub-datasets with different status intervals according to temperature and remaining battery power; and the mapping relationship between temperature, remaining battery power and charge and discharge power is constructed based on the historical data; The method of performing probability density estimation on the sub-data sets in different state intervals to obtain the probability density function of the ramp rate in each state interval includes: Calculate the climbing speed in each state interval to obtain the climbing rate of each sub-data set; Each sub-data set is divided into an up-climbing data set and a down-climbing data set according to the positive and negative values ​​of the climbing rate; Based on the up-climbing data set and the down-climbing data set, a non-parametric kernel density algorithm is used to obtain the probability density function of the climbing rate in the state power interval corresponding to each sub-data set; The final ramp capability is determined by combining the state constraints of the thermal power unit, including: Determine whether the output of the thermal power unit has reached the maximum power value at the previous moment. If so, the unit's ramp-up rate value at the current moment is 0. Otherwise, the expectation of the ramp-up rate set within the state interval of the thermal power unit after correction is used as the unit's ramp-up rate value at the current moment. Determine whether the output of the thermal power unit at the previous moment has reached the minimum power value under the conventional peak-shaving state of the thermal power unit. If so, the current unit ramp-down rate value is 0. Otherwise, the expected value of the ramp-down rate set in the state interval of the thermal power unit after correction is used as the current unit ramp-down rate value. The final climbing capacity is determined by combining the state constraints of the energy storage device, including: Based on the constructed mapping relationship between temperature, remaining battery capacity and charge and discharge power, combined with the energy storage device's capacity constraint, charge and discharge state constraint and maximum charge and discharge constraint on climbing capacity, the final climbing capacity of each energy storage device is obtained.

2. The method for quantitatively evaluating ramping capability taking flexible resource status into account according to claim 1, characterized in that: The historical data of the thermal power unit is divided into sub-data sets of different state intervals according to the unit temperature and the unit power, including: When the unit power of the historical dispatch output data is greater than or equal to the first power threshold, it is classified into the sub-dataset corresponding to the normal peak-shaving state; When the unit power of the historical dispatch output data is not 0 and is less than the first power threshold, it is classified into the sub-dataset corresponding to the deep peak load state; When the unit power of the historical dispatch output data is 0 and the unit temperature is greater than or equal to the first temperature threshold, it is classified into the sub-dataset corresponding to the hot standby state; When the unit power of the historical dispatch output data is 0, and the unit temperature is greater than the second temperature threshold and less than the first temperature threshold, it is classified into the sub-dataset corresponding to the warm standby state; When the unit power of the historical dispatch output data is 0 and the unit temperature is equal to the second temperature threshold, it is classified into the sub-dataset corresponding to the cold standby state.

3. The method for quantitatively evaluating ramping capability taking flexible resource status into account according to claim 1, characterized in that: The method of calculating the expected value of the revised climbing rate set to obtain the expected climbing rate of the flexible resource in each state interval includes: According to the preset confidence level, the upper quantile of the climbing rate and the lower quantile of the climbing rate are obtained. Based on the upper and lower quantiles of the climbing rate, the climbing rate probability density in each state interval is integrated to obtain the expected climbing rate of the flexible resource in each state interval.

4. The method for quantitatively evaluating ramping capability taking flexible resource status into account according to claim 1, characterized in that: The historical data of the energy storage device is divided into sub-datasets of different status intervals according to temperature and remaining battery power, including: Divide the historical data of the energy storage device into n intervals according to the remaining battery power to form n first sub-sample sets; The historical data in each first sub-sample set is divided into m intervals according to temperature to form A second sample set.

5. The method for quantitatively evaluating ramping capability taking flexible resource status into account according to claim 1, characterized in that: Obtaining the final climbing capability of each energy storage device includes: Obtain the discharge power, charging power, and battery capacity of the energy storage device at the previous moment; Based on the power constraint and the charge and discharge state constraint, the battery power at the current moment is obtained by solving the discharge power, charging power and battery power at the previous moment; Finding the expected discharge value of the energy storage device at the current moment in the mapping relationship based on the current battery power, and finding the expected charge value of the energy storage device at the current moment in the mapping relationship based on the current battery power, wherein the expected discharge value of the energy storage device at the current moment and the expected charge value of the energy storage device at the current moment satisfy the maximum charge and discharge constraint condition; The difference between the expected discharge value of the energy storage device at the current moment and the discharge power of the energy storage device at the previous moment is taken as the up-climbing capability of the energy storage device at the current moment; the difference between the expected charge value of the energy storage device at the current moment and the charge power of the energy storage device at the previous moment is taken as the down-climbing capability of the energy storage device at the current moment.

6. The method for quantitatively evaluating ramping capability taking flexible resource status into account according to claim 1, characterized in that: The acquisition of historical data of flexible resources, classifying the historical data of flexible resources according to different states, and obtaining sub-data sets in different state intervals include: Constructing a comprehensive feature vector representing the state and ramping capability of each flexible resource based on the influencing factors of the historical data of each flexible resource; The comprehensive feature vectors are clustered based on the Euclidean distance to form sub-datasets in different state intervals.

7. The method for quantitatively evaluating ramping capability taking flexible resource status into account according to claim 6, characterized in that: The comprehensive feature vector representing the flexible resource status and ramping capability is constructed based on the influencing factors of the historical data of each flexible resource, including: Normalize the historical data sample values ​​corresponding to each influencing factor; The characteristic weight of each influencing factor is taken as the power of each influencing factor; A comprehensive feature vector is constructed based on the sum of the powers of the historical data sample values ​​of each influencing factor after normalization.

8. The method for quantitatively evaluating ramping capability taking flexible resource status into account according to claim 6, characterized in that: The comprehensive feature vector representing the flexible resource status and ramping capability is constructed based on the influencing factors of the historical data of each flexible resource, including: The historical data of each flexible resource is input into a pre-trained three-layer unsupervised neural network, and a comprehensive feature vector of the flexible resource status and climbing ability is output. The input layer dimension of the three-layer unsupervised neural network is the same as the dimension of the influencing factors.

9. A quantitative evaluation system for climbing capability taking into account flexible resource status, characterized in that: The system comprises: Data collection module, used to obtain historical data of flexible resources; The classification module is used to classify the historical data of flexible resources according to different states and obtain sub-data sets in different state intervals; An estimation module is used to perform probability density estimation on sub-data sets in different state intervals to obtain the probability density function of the ramp rate in each state interval; A revision module is used to revise the probability density of the climbing rate in each state interval based on a preset confidence level, obtain a revised climbing rate set, calculate the expectation of the revised climbing rate set, and obtain the expected climbing rate in each state interval of the flexible resource; The quantitative evaluation module is used to obtain real-time data of flexible resources, determine the state interval of the flexible resources based on the status of the real-time data, determine the expected ramp rate in the state interval as the ramp capacity of the flexible resources, and determine the final ramp capacity based on the state constraints of the flexible resources; The flexible resources include thermal power units and energy storage units; The classification module divides the historical data of thermal power units into sub-datasets of different state intervals according to unit temperature and unit power; The historical data of each energy storage device under the energy storage unit is divided into sub-datasets with different status intervals according to temperature and remaining battery power; and the mapping relationship between temperature, remaining battery power and charge and discharge power is constructed based on the historical data; The estimation module is used to perform probability density estimation on sub-data sets in different state intervals. The probability density function of the ramp rate in each state interval is obtained by: Calculate the climbing speed in each state interval to obtain the climbing rate of each sub-data set; Each sub-data set is divided into an up-climbing data set and a down-climbing data set according to the positive and negative values ​​of the climbing rate; Based on the up-climbing data set and the down-climbing data set, a non-parametric kernel density algorithm is used to obtain the probability density function of the climbing rate in the state power interval corresponding to each sub-data set; The quantitative evaluation module is used to determine the final ramp capability in combination with the state constraints of the thermal power unit and includes: Determine whether the output of the thermal power unit has reached the maximum power value at the previous moment. If so, the unit's ramp-up rate value at the current moment is 0. Otherwise, the expectation of the ramp-up rate set within the state interval of the thermal power unit after correction is used as the unit's ramp-up rate value at the current moment. Determine whether the output of the thermal power unit at the previous moment has reached the minimum power value under the conventional peak-shaving state of the thermal power unit. If so, the current unit ramp-down rate value is 0. Otherwise, the expected value of the ramp-down rate set in the state interval of the thermal power unit after correction is used as the current unit ramp-down rate value. The quantitative evaluation module is used to determine the final climbing capability by combining the state constraints of the energy storage device, including: Based on the constructed mapping relationship between temperature, remaining battery capacity and charge and discharge power, combined with the energy storage device's capacity constraint, charge and discharge state constraint and maximum charge and discharge constraint on climbing capacity, the final climbing capacity of each energy storage device is obtained.

10. An electronic device comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method for quantitatively evaluating ramping capability taking flexible resource status into account according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for quantitatively evaluating the ramping capability taking into account the flexible resource status as described in any one of claims 1 to 8 are implemented.

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