A method for day-ahead active power dispatching of thermal power and energy storage in provincial power grids to cope with extreme scenarios

Abstract

This application discloses a day-ahead active power dispatching method for provincial power grids in response to extreme scenarios, relating to the field of power grid dispatching. The method includes: acquiring day-ahead forecast data sets for each preset node of the target provincial power grid and historical data within a preset historical period; for each preset node, dividing the historical data within the preset historical period into several groups and calculating the extreme index of each historical data group; performing clustering operations on each historical data group to determine extreme scenarios; determining negative and positive fluctuation boundary sets based on the extreme scenarios; further determining the upper and lower reserve capacity demand sets of the target provincial power grid; and finally calculating the day-ahead active power dispatching plan for the target provincial power grid. This application solves the problem of insufficient reserve capacity that easily occurs when traditional dispatching methods encounter extreme scenarios.

Description

Technical Field

[0001] This application relates to the field of power grid dispatching, and in particular to a method for day-ahead active power dispatching of provincial power grids in response to extreme scenarios. Background Technology

[0002] With the large-scale integration of new energy sources and the continuous increase in the peak-valley load difference, provincial power grids face the dual challenges of adapting to extreme scenarios and rationally allocating reserve capacity. Traditional dispatching methods generally rely on experience to set reserve capacity at a fixed ratio, which cannot adapt to the fluctuating characteristics of new energy sources. In particular, when the power grid system is subjected to extreme scenarios such as extreme cold waves, severe convective weather, or line faults, insufficient reserve capacity is likely to occur. Summary of the Invention

[0003] The purpose of this application is to provide a day-ahead active power dispatching method for provincial power grids that addresses extreme scenarios, enabling day-ahead dispatching of provincial power grids based on reasonable allocation of reserve capacity requirements under extreme scenarios.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] This application provides a method for day-ahead active power dispatching of provincial power grids for handling extreme scenarios, including:

[0006] The system acquires historical data for each preset node within a preset historical period and day-ahead forecast data for each preset node in the target provincial power grid. The preset nodes are divided into wind power nodes, photovoltaic nodes, and load nodes. The historical data includes historical forecast data and historical actual data.

[0007] For each preset node, the historical data within the preset historical period is divided into several groups, and the extreme degree index model is used to calculate the extreme degree index of each historical data group; wherein, each historical data group includes historical predicted data and historical actual data for each time period within a day; the extreme degree index model is a model that includes the daily maximum fluctuation, the daily peak-to-valley difference, and the daily maximum prediction deviation.

[0008] For each preset node, clustering is performed on each historical data group based on the extremeness index of each historical data group to determine extreme scenarios; the extreme scenarios include an extreme scenario data group and several extreme scenario subclass data groups.

[0009] For each preset node, a negative fluctuation boundary set and a positive fluctuation boundary set are determined based on a corresponding extreme scenario data set and several extreme scenario subclass data sets; the negative fluctuation boundary set includes the negative fluctuation boundary of each time period; the positive fluctuation boundary set includes the positive fluctuation boundary of each time period.

[0010] Based on the extreme scenario data sets, negative fluctuation boundary sets, and positive fluctuation boundary sets of each preset node, the upper reserve capacity demand set and lower reserve capacity demand set of the target provincial power grid are determined; the upper reserve capacity demand set includes the upper reserve capacity demand of the target provincial power grid for each time period; the lower reserve capacity demand set includes the lower reserve capacity demand of the target provincial power grid for each time period.

[0011] Based on the upper reserve capacity demand set, lower reserve capacity demand set, and day-ahead forecast data sets of each preset node of the target provincial power grid, the day-ahead active power dispatch plan of the thermal power and energy storage of the target provincial power grid is determined.

[0012] According to the specific embodiments provided in this application, this application has the following technical effects:

[0013] This application provides a method for day-ahead active power dispatching of a provincial power grid in response to extreme scenarios. Based on historical data (historical forecast data and historical actual data) from each wind power node, photovoltaic node, and load node within a preset historical period in the target provincial power grid, and utilizing a proposed extreme intensity index model and clustering method, an extreme scenario of the target provincial power grid is constructed. The reserve capacity demand configuration of the target provincial power grid under the extreme scenario is calculated, and then the day-ahead active power dispatching plan for the target provincial power grid is determined based on the reserve capacity demand configuration under the extreme scenario. Compared to traditional dispatching methods that rely on empirically based fixed-ratio reserves, this application achieves quantification of extreme scenarios based on historical data, thereby enabling more flexible and scientific configuration of reserve capacity demand and solving the problem of insufficient reserve capacity that easily occurs in traditional dispatching methods when encountering extreme scenarios. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a provincial power grid day-ahead active power dispatching method for dealing with extreme scenarios provided in an embodiment of this application;

[0016] Figure 2 A flowchart illustrating another method for scheduling day-ahead active power of provincial power grid thermal power storage in response to extreme scenarios, provided as an embodiment of this application;

[0017] Figure 3 A schematic diagram illustrating the design concept of a provincial power grid thermal power storage day-ahead active power dispatching method for dealing with extreme scenarios, provided in an embodiment of this application;

[0018] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] Traditional dispatching methods typically rely on experience to set reserve capacity at fixed ratios, which cannot adapt to the fluctuating characteristics of new energy sources. This is particularly problematic when the power grid experiences extreme scenarios such as extreme cold waves, severe convective weather, or line faults, leading to insufficient reserve capacity. This application addresses this issue by extracting extreme scenarios and making reserve capacity demand decisions that adapt to time-period fluctuations. It proposes a day-ahead optimized dispatching method for provincial power grid thermal power units that meets reserve capacity requirements, as well as a provincial power grid energy storage power decision-making method under N-1 security constraints. This provides day-ahead active power dispatching decision support for provincial power grids with high proportions of new energy integration, adapting to extreme scenarios.

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] In one exemplary embodiment, see [reference] Figure 1 and Figure 2 This paper provides a method for day-ahead active power dispatching of provincial power grids for handling extreme scenarios, including:

[0023] S101, acquire historical data of each preset node of the target provincial power grid within a preset historical period and the day-ahead forecast data group of each preset node; the preset nodes are divided into wind power nodes, photovoltaic nodes and load nodes; the historical data includes historical forecast data and historical actual data.

[0024] That is, the historical data for each wind power node includes: historical wind power forecast data and historical actual wind power data; the historical data for each photovoltaic node includes: historical photovoltaic forecast data and historical actual photovoltaic data; and the historical data for each load node includes: historical load forecast data and historical actual load data.

[0025] S102, for each preset node, the historical data within the preset historical period is divided into several groups, and the extreme degree index model is used to calculate the extreme degree index of each historical data group; wherein, each historical data group includes historical predicted data and historical actual data for each time period within a day; the extreme degree index model is a model that includes the daily maximum fluctuation, the daily peak-to-valley difference, and the daily maximum prediction deviation.

[0026] In some embodiments, the time granularity of historical data is 1 hour, meaning each time period is 1 hour long. Therefore, each historical data group includes 24 historical forecast data points and 24 historical actual data points. Correspondingly, the time granularity of the data in the daily forecast data group of each preset node is also 1 hour, and each daily forecast data group of the preset node includes 24 daily forecast data points within one day.

[0027] S103, for each preset node, clustering is performed on each historical data group according to the extremeness index of each historical data group to determine the extreme scenario; the extreme scenario includes an extreme scenario data group and several extreme scenario subclass data groups.

[0028] S104, for each preset node, determine the negative fluctuation boundary set and the positive fluctuation boundary set based on a corresponding extreme scenario data group and several extreme scenario subclass data groups; the negative fluctuation boundary set includes the negative fluctuation boundary of each time period; the positive fluctuation boundary set includes the positive fluctuation boundary of each time period.

[0029] S105, based on the extreme scenario data sets, negative fluctuation boundary sets, and positive fluctuation boundary sets of each preset node, determine the upper reserve capacity demand set and the lower reserve capacity demand set of the target provincial power grid; the upper reserve capacity demand set includes the upper reserve capacity demand of the target provincial power grid for each time period; the lower reserve capacity demand set includes the lower reserve capacity demand of the target provincial power grid for each time period.

[0030] S106. Based on the upper reserve capacity demand set, lower reserve capacity demand set, and day-ahead forecast data set of each preset node of the target provincial power grid, determine the day-ahead active power dispatch plan of the thermal power and energy storage of the target provincial power grid.

[0031] As an optional implementation, the mathematical expression of the extreme degree index model is:

[0032] (1);

[0033] (2);

[0034] (3);

[0035] (4);

[0036] in, Indicates the degree of extremeness; This indicates the maximum daily fluctuation, which is the maximum difference between historical actual data for adjacent time periods within a single day. This represents the daily peak-to-valley difference, which is the difference between the maximum and minimum values ​​of historical actual data within a single day. This represents the maximum daily forecast deviation, which is the maximum difference between historical forecast data and historical actual data within one day. , and They represent , and Weighting coefficients; , and They represent the first and second elements in the same historical data set. Historical actual data for the period, the first Historical actual data for the time period and the first Historical forecast data for the time period; This indicates the maximum operation; This indicates the minimum operation.

[0037] As an optional implementation, step S103 specifically includes:

[0038] S103.1, randomly select a preset number of historical data groups from each historical data group of the preset node, and determine each selected historical data group as the initial cluster center to obtain a preset number of initial cluster centers.

[0039] S103.2, based on a preset number of initial cluster centers, use the extrema index to perform clustering operations on other historical data groups of the preset node to obtain a preset number of initial clustering results; each initial clustering result is a clustering range that includes one initial cluster center.

[0040] When performing clustering operations, the K-means clustering algorithm can be used.

[0041] S103.3 For each initial clustering result, the average of all historical data groups except the initial cluster center in the initial clustering result is taken to obtain a new historical data group.

[0042] S103.4 For each new historical data group, determine whether the new historical data group is the same as the corresponding first historical data group; the corresponding first historical data group is the historical data group corresponding to the initial cluster center of the new historical data group.

[0043] S103.5, when the judgment result of each new historical data group is yes, the initial cluster center with the largest extreme index among the preset number of initial cluster centers is determined as the first cluster center, and the extreme scenario is determined according to the initial clustering result corresponding to the first cluster center; the data in the extreme scenario data group is the historical actual data in the historical data group corresponding to the first cluster center; the data in each extreme scenario subclass data group is the historical actual data in another historical data group other than the first cluster center in the initial clustering result corresponding to the first cluster center.

[0044] S103.6 If the judgment result of any new historical data group is negative, then the first operation is executed to determine the extreme scenario.

[0045] The specific execution process of the first operation is as follows:

[0046] S103.6.1 Calculate the extreme index of each new historical data group and determine each new historical data group as a new cluster center to obtain a preset number of new cluster centers.

[0047] S103.6.2, based on a preset number of new cluster centers, use the extrema index to perform clustering operations on each historical data group of the preset node to obtain a preset number of new clustering results.

[0048] S103.6.3 For each new clustering result, the average of all historical data groups in the new clustering result except for the new cluster center is taken to obtain the second new historical data group.

[0049] S103.6.4 Determine whether each second new historical data group is the same as the corresponding new historical data group.

[0050] S103.6.5, when the judgment result of each second new historical data group is yes, the new cluster center with the largest extreme index among the preset number of new cluster centers is determined as the second cluster center, and the extreme scenario is determined according to the new clustering result corresponding to the second cluster center; the data in the extreme scenario data group is the historical actual data in the new historical data group corresponding to the second cluster center; the data in each extreme scenario subclass data group is the historical actual data in a historical data group other than the second cluster center in the new clustering result corresponding to the second cluster center.

[0051] S103.6.6, when the judgment result of any second new historical data group is negative, the preset number of new historical data groups are updated to the preset number of second new historical data groups, and the process returns to the step "calculate the extreme index of each new historical data group and determine each new historical data group as a new cluster center to obtain the preset number of new cluster centers".

[0052] In this embodiment, for each preset node, a K-means clustering algorithm based on the extreme value index is used for clustering to generate extreme scenarios. The number of clusters is set to J (i.e., a preset number), ultimately resulting in J cluster centers. The cluster center with the largest extreme value (either the first or second cluster center) is selected from these J cluster centers. The historical data portion of this cluster center with the largest extreme value is the extreme scenario data, and the historical data portions of each historical data group assigned to this cluster center are the extreme scenario subclass data. The K-means clustering algorithm based on the extreme value index is briefly described below:

[0053] The first step is to randomly select J groups of data from the historical data groups of the preset nodes as the initial cluster centers. The second step is to calculate the daily maximum fluctuation, daily peak-to-valley difference, and daily maximum prediction deviation for each historical data group and cluster center (the initial cluster center at the initial time). Further, the extreme index is calculated, and each historical data group is assigned to the cluster center with the closest extreme index. The third step is to calculate the average value of all historical data groups in each clustering result to obtain a new historical data group, which is used as the new centroid, i.e., the new cluster center. The fourth step is to repeat the second and third steps until convergence, and finally obtain the final J cluster centers and the division of each historical data group.

[0054] The extreme index of each cluster center is calculated. The cluster center with the largest extreme index and the historical data group assigned to this cluster center are selected. The historical actual data part is taken to obtain the extreme scenario data and extreme scenario subclass data.

[0055] The convergence condition is: the current J cluster centers are the same as the previous J cluster centers.

[0056] As an optional implementation, step S104 specifically includes:

[0057] S104.1, based on the extreme scenario data group and several extreme scenario sub-data groups corresponding to the preset node, the fluctuation feature set of each extreme scenario sub-data group is determined using the fluctuation feature calculation formula; the fluctuation feature calculation formula is:

[0058] (5);

[0059] in, Indicates the first The first extreme scenario subclass data group Fluctuation characteristics over time periods; Indicates the first In the data set of the extreme scenario subclass, the first Historical data for the time period; Indicates the first in the extreme scenario data set Historical data for the specified time period.

[0060] S104.2, Based on the fluctuation feature set of each extreme scenario subclass data group, the confidence set of each extreme scenario subclass data group is determined using the confidence calculation formula; the confidence calculation formula is:

[0061] (6);

[0062] in, Indicates the first The first extreme scenario subclass data group Confidence level over a given time period; Indicates the base confidence level; Indicates the confidence level adjustment range; Represents the tangent function; This represents the baseline value for volatility characteristics.

[0063] S104.3, Based on the confidence sets of each extreme scenario subclass data group, determine the average confidence set using the average confidence calculation formula; the average confidence calculation formula is as follows:

[0064] (7);

[0065] in, Indicates the first Average confidence level over the time period; This indicates the number of data groups in the extreme scenario subclass.

[0066] In this embodiment, fluctuation characteristics are calculated using extreme scenario data and extreme scenario subclass data, and confidence is calculated using the fluctuation characteristics; then, for each confidence level in the same time period, an average operation is performed to obtain the average confidence level for each time period, which is the final confidence level for each time period.

[0067] S104.4 Based on the extreme scenario data group and several extreme scenario subclass data groups corresponding to the preset node, a probability density function set is constructed using the kernel density estimation method; the probability density function set includes the probability density function for each time period.

[0068] The kernel function can be a Gaussian function.

[0069] S104.5, based on the several extreme scenario subclass data groups, average confidence set, and probability density function set corresponding to the preset node, determine the lower quantile set and the upper quantile set; the lower quantile set includes the lower quantile of each time period; the upper quantile set includes the upper quantile of each time period.

[0070] Specifically, the lower and upper quantiles for a given time period are obtained by utilizing the confidence level and probability density function of that period.

[0071] S104.6, the lower quantile and upper quantile of each time period are determined as the negative fluctuation boundary and positive fluctuation boundary of each time period, respectively, to obtain the negative fluctuation boundary set and the positive fluctuation boundary set.

[0072] The negative fluctuation boundary, extreme scenario data, and positive fluctuation boundary for each preset node of the target provincial power grid at each time period are shown below:

[0073] (8);

[0074] (9);

[0075] (10);

[0076] in, , and The first Wind power node number Negative fluctuation boundaries, extreme scenario data, and positive fluctuation boundaries for different time periods; , and The first Photovoltaic node number Negative fluctuation boundaries, extreme scenario data, and positive fluctuation boundaries for different time periods; , and The first Load node number Negative fluctuation boundaries for different time periods, extreme scenario data, and positive fluctuation boundaries.

[0077] In this article, the historical actual data in the extreme scenario data group is also referred to as extreme scenario data; the historical actual data in the extreme scenario subclass data group is also referred to as extreme scenario subclass data.

[0078] As an optional implementation, step S105 specifically includes:

[0079] Based on the extreme scenario data sets, negative fluctuation boundary sets, and positive fluctuation boundary sets of each preset node, the upper and lower reserve capacity demand sets of the target provincial power grid are determined using the fluctuation boundary and reserve capacity demand conversion model. In other words, the fluctuation boundaries are converted into reserve capacity demand.

[0080] The mathematical expression for the fluctuation boundary and reserve capacity demand conversion model is as follows:

[0081] (11);

[0082] (12);

[0083] in, Indicates the target provincial power grid number Backup capacity requirements for different time periods; Indicates the target provincial power grid number Backup capacity requirements for the next time period; Represents the set of wind power nodes; Represents the set of photovoltaic nodes; Represents the set of load nodes; Indicates the first The negative oscillation boundary concentration of wind power node No. Negative fluctuation boundaries for a given period; Indicates the first The extreme scenario data set of the No. 1 wind power node Historical data for the time period; Indicates the first The positive wave boundary concentration of wind power node No. Positive fluctuation boundaries for the time period; Indicates the first The negative fluctuation boundary set of the photovoltaic node No. Negative fluctuation boundaries for a given period; Indicates the first The extreme scenario data set of the photovoltaic node No. Historical data for the time period; Indicates the first The positive fluctuation boundary concentration of the photovoltaic node No. Positive fluctuation boundaries for the time period; Indicates the first The negative fluctuation boundary set of load node number 1 Negative fluctuation boundaries for a given period; Indicates the first In the extreme scenario data set of the load node, the first Historical data for the time period; Indicates the first The positive fluctuation boundary set of load node number 1 The positive fluctuation boundary of the time period.

[0084] As an optional implementation, step S106 specifically includes:

[0085] S106.1, Based on the upper reserve capacity demand set, lower reserve capacity demand set and day-ahead forecast data set of each preset node of the target provincial power grid, construct the day-ahead optimized scheduling model of the provincial power grid.

[0086] S106.2, Using the dream optimization algorithm, solve the day-ahead optimization scheduling model of the provincial power grid to obtain the day-ahead active power data set of each thermal power unit node and the day-ahead load shedding power data set of each load node in the target provincial power grid.

[0087] S106.3, Based on the day-ahead active power data set of each thermal power unit node and the day-ahead load shedding power data set of each load node in the target provincial power grid, construct a provincial power grid energy storage power decision model under N-1 security constraints.

[0088] S106.4 Using the Magnificent Slender-tailed Warbler optimization algorithm, solve the provincial power grid energy storage power decision model under the N-1 security constraints to obtain the day-ahead charging power data set and day-ahead discharging power data set for each energy storage node in the target provincial power grid, as well as the day-ahead upward adjustment power data set and day-ahead downward adjustment power data set for each thermal power unit node.

[0089] As an optional implementation, the provincial power grid day-ahead optimization dispatch model includes a first optimization objective and a first constraint; the first constraint includes thermal power unit operation constraints, power flow safety constraints, power balance constraints, and reserve capacity constraints.

[0090] The mathematical expression for the first optimization objective is:

[0091] (13);

[0092] (14);

[0093] in, This indicates a minimize operation; This indicates the total cost of optimizing the scheduling process recently. , and These represent the operating cost, load shedding cost, and standby cost of thermal power plants, respectively. , and All are the first Coal consumption coefficient of No. 1 thermal power unit during node operation; Indicates the first The unit power load shedding cost of load node No. 1; Indicates the first The node of thermal power unit provides the capacity cost per unit of standby capacity; Indicates the first The node provides the capacity cost per unit of standby capacity for thermal power units; Indicates the first No. 1 thermal power unit node Active power during a given time period; Indicates the first Load node number Load shedding power during the time period; and They represent the first No. 1 thermal power unit node The upper and lower reserve capacity provided during the time period; For time periods; For the set of nodes of thermal power units; This represents the set of load nodes.

[0094] For each thermal power unit node, the active power for all time periods within a day constitutes the day-ahead active power data set. For each load node, the load shedding power for all time periods within a day constitutes the day-ahead load shedding power data set.

[0095] The mathematical expression for the operating constraints of the thermal power unit is:

[0096] (15);

[0097] in, and They represent the first No. 1 thermal power unit node Minimum and maximum active power during the time period; Indicates the first No. 1 thermal power unit node Active power during the +1 time period; and They represent the first The maximum ramping power and maximum ramp-descent power of the No. 1 thermal power unit node.

[0098] The mathematical expression for the power flow safety constraint is:

[0099] (16);

[0100] in, For the first From node number to the... Power transfer distribution factor between lines; For the first The day-ahead forecast data group of load node number 1 Load power during a given time period; No. The day-ahead forecast data set of the No. 1 wind power node Wind power output during a given time period; For the first The day-ahead forecast data set of the photovoltaic node No. Photovoltaic power during a given time period; For the first The power limit of line number 1; the aforementioned line number 2 Line No. 1 is any line in the target provincial power grid; Represents the set of wind power nodes; This represents the set of photovoltaic nodes.

[0101] The mathematical expression for the power balance constraint is:

[0102] (17).

[0103] To proactively address extreme scenarios, the provincial power grid's recent optimized dispatch model should guarantee a certain level of reserve capacity. The mathematical expression for this reserve capacity constraint is:

[0104] (18);

[0105] in, This indicates the concentrated demand for reserve capacity in the target provincial power grid. Backup capacity requirements for different time periods; This indicates the concentrated reserve capacity demand of the target provincial power grid. Backup capacity requirements for a given time period.

[0106] In this embodiment, the Dream Optimization Algorithm (DOA) is used to solve the above-mentioned provincial power grid day-ahead optimal scheduling model.

[0107] The dream optimization algorithm simulates the partial memory retention and forgetting characteristics of human dreams, proposing memory, forgetting, and replenishment strategies to balance the algorithm's exploration and development functions. This enhances the algorithm's ability to escape local optima and effectively improves its global optimization capability. The dream optimization algorithm consists of two phases: the first phase is the exploration phase (iteration count from 0 to...). The second phase is the development phase (the number of iterations starts from...). arrive The dream optimization algorithm comprises three phases: the exploration phase includes three strategies: memory strategies, forgetting and replenishment strategies, and dream sharing strategies; the development phase includes two strategies: memory strategies, forgetting and replenishment strategies. The dream optimization algorithm incorporates... Each individual represents a solution, which is composed of variables in the provincial power grid day-ahead optimization dispatch model. The variables include the active power of thermal power unit nodes and the load shedding power of load nodes.

[0108] The exploration phase divides all individuals into Within each group, the solution is updated iteratively, and the specific update strategy is as follows:

[0109] (19);

[0110] (20);

[0111] (twenty one);

[0112] in, For the first During the nth iteration Individual number 1 For the first During the next iteration The best individual in the group; For the first During the nth iteration Individual No. Dimensional information, For the first During the next iteration The best individual in the group Information about dimensions; and To solve the space of the first The lower and upper bounds of a dimension; and For the first During the nth iteration and the 1st iteration During the nth iteration Individual No. Information about dimensions. It is a cosine function; It is a random function.

[0113] Among them, the Individual No. and No. Individuals numbered 1 belong to the same group.

[0114] Development phase:

[0115] (twenty two);

[0116] (twenty three);

[0117] in, For the first The best individual among all individuals in the next iteration. For the first In the nth iteration, the best individual among all individuals is... Information about dimensions.

[0118] The specific solution process is as follows: First, initialize the variables according to the upper and lower boundaries of the variables in the provincial power grid day-ahead optimal dispatch model, generate a population, and divide the individuals in the population into groups. One group; secondly, enter the exploration phase, update according to the memory strategy formula (19), if If the value is less than 0.9, execute formula (20); otherwise, execute formula (21) to update and record the current best individual and its corresponding target value. Third, when the number of iterations reaches... When the development phase begins, the memory strategy formula (22) is executed, followed by the forgetting and replenishment strategy formula (23), and the current best individual and its corresponding target value are updated and recorded. Finally, the loop terminates when the maximum number of iterations is reached through continuous iteration.

[0119] As an optional implementation, the provincial power grid energy storage power decision model under N-1 security constraints includes a second optimization objective and a second constraint; the second constraint includes power flow security constraints under N-1 security constraints, power balance constraints under N-1 security constraints, energy storage model constraints, and thermal power unit regulation constraints.

[0120] The mathematical expression for the second optimization objective is:

[0121] (twenty four);

[0122] (25);

[0123] in, To represent the decision-making and dispatching cost of the target provincial power grid's energy storage capacity; and These represent the regulation cost of thermal power and the operating cost of energy storage, respectively. and They represent the first Scenario 1 No. 1 thermal power unit node The upward and downward adjustment power during the time period; and They represent the first Energy storage node No. Charging and discharging power during the same period; Indicates the first Operating cost per unit power of energy storage node; and To represent the first The capacity cost of providing a unit of upper reserve capacity and the capacity cost of providing a unit of lower reserve capacity for thermal power unit node; A collection of energy storage nodes; For N-1 safety-constrained circuit sets; Indicates the number of lines in the N-1 security constraint line cluster; the first The scenario represents a target provincial power grid containing only power lines. The scenario where the connection is broken.

[0124] For each energy storage node, the charging power and discharging power for all time periods within a day constitute the day-ahead charging power data group and the day-ahead discharging power data group, respectively. For each thermal power unit node, the upward adjustment power and downward adjustment power for all time periods within a day constitute the day-ahead upward adjustment power data group and the day-ahead downward adjustment power data group, respectively.

[0125] The mathematical expression for the power flow security constraint under the N-1 security constraint is:

[0126] (26);

[0127] in, For the first Scenario 1 From node number to the... Power transfer distribution factor between lines.

[0128] The mathematical expression for the power balance constraint under the N-1 security constraint is:

[0129] (27).

[0130] The mathematical expression for the constraints of the energy storage model is:

[0131] (28);

[0132] in, and They represent the first Energy storage node No. Minimum and maximum power during the time period; Indicates the first The rated energy of the energy storage node; and They represent the first The first energy storage node State of charge during the period and the first State of charge during the +1 time period; and They represent the first Initial period of energy storage node No. State of charge and end of dispatch period The state of charge; and They represent the first Energy storage node No. The minimum and maximum states of charge during a given time period; and They represent the first The charging and discharging efficiency of the energy storage node.

[0133] The mathematical expression for the adjustment constraint of the thermal power unit is:

[0134] (29).

[0135] As an optional implementation, the process for determining the N-1 security constraint circuit set is as follows:

[0136] 1) For each line in the target provincial power grid, determine whether the critical line inequality is satisfied, and obtain the judgment result.

[0137] 2) When all lines in the target provincial power grid have been judged, the set of all lines with a judgment result of "yes" is determined as the N-1 safety constraint line set.

[0138] The mathematical expression for the critical path determination inequality is:

[0139] (30);

[0140] in, This is the threshold for determining the critical path.

[0141] In other words, before constructing the provincial power grid energy storage power decision model under N-1 security constraints, the day-ahead active power dispatch plan, load shedding plan, and line power flow status of thermal power units are first obtained based on the provincial power grid day-ahead optimization dispatch model. Critical lines are selected based on the line power flow status. If a line satisfies formula (30), it is considered a critical line and requires consideration of N-1 security constraints; therefore, the line is included in the N-1 security constraint line set. .

[0142] In this embodiment, the Superb Fairy-wren Optimization Algorithm (SFOA) is used to solve the provincial power grid energy storage power decision model under N-1 security constraints in order to obtain the target provincial power grid energy storage day-ahead charging and discharging power plan.

[0143] The optimization algorithm for the magnificent swan warbler is divided into three stages: the juvenile growth stage, the breeding and nurturing stage, and the predator avoidance stage. In the juvenile growth stage, through extensive learning and positional changes, the candidate solutions move extensively within the solution space, achieving broad exploration and thus enhancing the global search capability, as shown in formula (31). The breeding and nurturing stage enables the candidate solutions to perform local fine-grained searches, as shown in formula (32). The predator avoidance stage gives the candidate solutions the ability to escape local optima, as shown in formula (33). The candidate solutions are variables in the provincial power grid energy storage power decision model, including the charging and discharging power of energy storage, the upward adjustment power of thermal power units, and the downward adjustment power of thermal power units.

[0144] (31);

[0145] (32);

[0146] (33);

[0147] in, and For the first The second iteration and the first At the +1st iteration, the... Individual No. Information about dimensions; For the first The best individual at the nth iteration Information about dimensions; and To solve the space of the first The lower and upper bounds of a dimension; This is a value used to determine the proportion of juveniles within a population. Risk factors; It is a random function; To characterize a function that gradually increases with the number of iterations; The step size is random. This is an adaptive balance factor.

[0148] The specific solution process is as follows: First, the variables are randomly initialized according to the provincial power grid energy storage power decision model under N-1 security constraints to generate a population; second, according to and In this case, the candidate solution is selected to enter the position update stage; finally, the optimal position information of the candidate solution is updated, and the loop is terminated when the maximum number of iterations is reached.

[0149] This application also provides an application scenario in which the above-described method for dispatching day-ahead active power of provincial power grids in response to extreme scenarios is applied. Specifically, the method for dispatching day-ahead active power of provincial power grids in response to extreme scenarios provided in this embodiment can be applied in provincial power grid dispatching scenarios. A provincial power grid dispatching scenario includes a provincial power grid dispatching plan stage and a provincial power grid dispatching stage; the provincial power grid dispatching plan stage generates a provincial power grid dispatching plan, and the provincial power grid dispatching stage applies the provincial power grid dispatching plan to perform provincial power grid dispatching. The method for dispatching day-ahead active power of provincial power grids in response to extreme scenarios provided in this embodiment belongs to the provincial power grid dispatching plan stage.

[0150] See Figure 3 The design concept of this application is as follows:

[0151] First, an extreme degree index is proposed, and a k-means clustering algorithm based on the extreme degree index is used to generate extreme scenario data and extreme scenario subclass data. Second, a fluctuation characteristic calculation method is proposed based on extreme scenarios, and a reserve capacity demand decision method based on fluctuation characteristics is used to generate upper and lower reserve capacity demands. Third, based on the reserve capacity demand and the day-ahead forecast data of wind power, photovoltaic power, and load, a day-ahead optimization scheduling model for the provincial power grid is established, and the Dream Optimization Algorithm is used to solve it. Finally, based on the day-ahead active power scheduling plan of thermal power units, a provincial power grid energy storage power decision model under N-1 security constraints is established, and the Magnificent Wren Optimization Algorithm is used to solve it.

[0152] This application has the following beneficial effects:

[0153] (1) This application proposes an extreme scene generation method based on extreme degree, which uses the k-means clustering algorithm based on extreme degree index to quickly screen extreme scenes and improve the representativeness of extreme scenes.

[0154] (2) This application proposes a reserve capacity demand decision method based on fluctuation characteristics. The reserve capacity demand is calculated based on fluctuation characteristics, adapts to the fluctuation characteristics of different time periods, avoids the waste of capacity resources caused by fixed reserve ratio, and improves the reserve capacity during critical periods to cope with extreme changes.

[0155] (3) This application uses the dream optimization algorithm to solve the day-ahead optimization scheduling model of the provincial power grid. The algorithm has fast convergence characteristics, improves the computational efficiency of the scheduling plan, is suitable for the optimization problem of the provincial power grid, and enhances the escape ability of the algorithm from the local optimal solution, effectively improving the global optimization ability of the algorithm.

[0156] (4) This application uses the magnificent slender-tailed warbler optimization algorithm to solve the provincial power grid energy storage power decision model under N-1 security constraints, ensuring that the energy storage power decision can still maintain the stability of the power grid under line faults. At the same time, the algorithm has excellent local search capabilities and is suitable for handling fine optimization problems under N-1 security constraints.

[0157] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores historical data and day-ahead forecast data sets. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a day-ahead active power dispatching method for provincial power grids to cope with extreme scenarios.

[0158] Those skilled in the art will understand that Figure 4 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0159] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0160] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0161] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0162] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0163] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0164] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0165] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for day-ahead active power dispatching of provincial power grids in response to extreme scenarios, characterized in that, include: Acquire historical data for each preset node of the target provincial power grid within a preset historical period, as well as day-ahead forecast data sets for each preset node; The preset nodes are divided into wind power nodes, photovoltaic nodes, and load nodes; the historical data includes historical forecast data and historical actual data. For each preset node, the historical data within the preset historical period is divided into several groups, and the extreme index model is used to calculate the extreme index of each historical data group; wherein, each historical data group includes historical predicted data and historical actual data for each time period within a day; the extreme index model is a model that includes the daily maximum fluctuation, the daily peak-to-valley difference, and the daily maximum prediction deviation. For each preset node, clustering is performed on each historical data group based on the extremity index of each historical data group to determine the extreme scenarios; the extreme scenarios include an extreme scenario data group and several extreme scenario subclass data groups; For each preset node, based on a corresponding extreme scenario data group and several extreme scenario subclass data groups, a negative fluctuation boundary set and a positive fluctuation boundary set are determined; the negative fluctuation boundary set includes the negative fluctuation boundary of each time period; the positive fluctuation boundary set includes the positive fluctuation boundary of each time period. Based on the extreme scenario data sets, negative fluctuation boundary sets, and positive fluctuation boundary sets of each preset node, the upper reserve capacity demand set and lower reserve capacity demand set of the target provincial power grid are determined; the upper reserve capacity demand set includes the upper reserve capacity demand of the target provincial power grid for each time period; the lower reserve capacity demand set includes the lower reserve capacity demand of the target provincial power grid for each time period. Based on the upper reserve capacity demand set, lower reserve capacity demand set, and day-ahead forecast data sets of each preset node of the target provincial power grid, the day-ahead active power dispatch plan of the thermal power and energy storage of the target provincial power grid is determined.

2. The provincial power grid thermal power-storage day-ahead active power dispatching method for extreme scenarios as described in claim 1, characterized in that, The mathematical expression for the extreme degree index model is: ； ； ； ； in, Indicates the degree of extremeness; Indicates the maximum daily fluctuation; Indicates the daily peak-to-valley difference; Indicates the maximum daily forecast deviation; , and They represent , and Weighting coefficients; , and They represent the first and second elements in the same historical data set. Historical actual data for the period, the first Historical actual data for the time period and the first Historical forecast data for the time period; This indicates the maximum operation; This indicates the minimum operation.

3. The provincial power grid thermal power-storage day-ahead active power dispatching method for extreme scenarios as described in claim 1, characterized in that, For each preset node, clustering is performed on each historical data group based on its extreme intensity index to identify extreme scenarios. Specifically, this includes: A preset number of historical data groups are randomly selected from each historical data group of the preset node, and each selected historical data group is determined as an initial cluster center, thus obtaining a preset number of initial cluster centers. Based on a preset number of initial cluster centers, the other historical data groups of the preset nodes are clustered using an extrema index to obtain a preset number of initial clustering results; each initial clustering result is a clustering range that includes one initial cluster center; For each initial clustering result, the average of all historical data groups in the initial clustering result except for the initial cluster centers is taken to obtain a new historical data group; For each new historical data group, determine whether the new historical data group is the same as the corresponding first historical data group; the corresponding first historical data group is the historical data group corresponding to the initial cluster center of the new historical data group. When the judgment result of each new historical data group is yes, the initial cluster center with the largest extreme index among the preset number of initial cluster centers is determined as the first cluster center, and the extreme scenario is determined according to the initial clustering result corresponding to the first cluster center; the data in the extreme scenario data group is the historical actual data in the historical data group corresponding to the first cluster center; the data in each extreme scenario subclass data group is the historical actual data in another historical data group other than the first cluster center in the initial clustering result corresponding to the first cluster center; If the judgment result of any new historical data set is negative, then the first operation is executed to determine the extreme scenario; The specific execution process of the first operation is as follows: Calculate the extreme index for each new historical data group and determine each new historical data group as a new cluster center, thus obtaining a preset number of new cluster centers; Based on a preset number of new cluster centers, the historical data groups of the preset nodes are clustered using the extrema index to obtain a preset number of new clustering results. For each new clustering result, the average of all historical data groups in the new clustering result, excluding the new cluster center, is taken to obtain the second new historical data group. Determine whether each second new historical data set is the same as the corresponding new historical data set; When the judgment result of each second new historical data group is yes, the new cluster center with the largest extreme index among the preset number of new cluster centers is determined as the second cluster center, and the extreme scenario is determined according to the new clustering result corresponding to the second cluster center; the data in the extreme scenario data group is the historical actual data in the new historical data group corresponding to the second cluster center; the data in each extreme scenario subclass data group is the historical actual data in one historical data group other than the second cluster center in the new clustering result corresponding to the second cluster center; If the judgment result of any second new historical data group is negative, then update the preset number of new historical data groups to the preset number of second new historical data groups, and return to the step "Calculate the extreme index of each new historical data group and determine each new historical data group as a new cluster center to obtain the preset number of new cluster centers".

4. The provincial power grid day-ahead active power dispatching method for extreme scenarios according to claim 1, characterized in that, For each preset node, based on a corresponding extreme scenario data set and several extreme scenario subclass data sets, the negative fluctuation boundary set and the positive fluctuation boundary set are determined, specifically including: Based on one extreme scenario data group and several extreme scenario sub-data groups corresponding to the preset node, the fluctuation feature set of each extreme scenario sub-data group is determined using the fluctuation feature calculation formula; the fluctuation feature calculation formula is as follows: ； in, Indicates the first The first extreme scenario subclass data group Fluctuation characteristics over time periods; Indicates the first In the data set of the extreme scenario subclass, the first Historical data for the time period; Indicates the first in the extreme scenario data set Historical data for the time period; Based on the fluctuation feature set of each extreme scenario subclass data group, the confidence set of each extreme scenario subclass data group is determined using the confidence calculation formula; the confidence calculation formula is as follows: ； in, Indicates the first The first extreme scenario subclass data group Confidence level over a given time period; Indicates the base confidence level; Indicates the confidence level adjustment range; Represents the tangent function; Indicates the baseline value for volatility characteristics; Based on the confidence sets of each extreme scenario subclass data group, the average confidence set is determined using the average confidence calculation formula; the average confidence calculation formula is as follows: ； in, Indicates the first Average confidence level over the time period; Indicates the number of subclass data groups in the extreme scenario; Based on an extreme scenario data group and several extreme scenario subclass data groups corresponding to the preset node, a probability density function set is constructed using the kernel density estimation method; the probability density function set includes the probability density function for each time period; Based on the data sets of several extreme scenario subclasses, the average confidence set, and the probability density function set corresponding to the preset node, the lower quantile set and the upper quantile set are determined; the lower quantile set includes the lower quantile for each time period; the upper quantile set includes the upper quantile for each time period. The lower quantile and upper quantile of each time period are determined as the negative fluctuation boundary and positive fluctuation boundary of each time period, respectively, to obtain the negative fluctuation boundary set and the positive fluctuation boundary set.

5. The provincial power grid day-ahead active power dispatching method for extreme scenarios according to claim 1, characterized in that, Based on the extreme scenario data sets, negative fluctuation boundary sets, and positive fluctuation boundary sets of each preset node, the upper and lower reserve capacity demand sets of the target provincial power grid are determined, specifically including: Based on the extreme scenario data sets, negative fluctuation boundary sets, and positive fluctuation boundary sets of each preset node, the upper and lower reserve capacity demand sets of the target provincial power grid are determined using the fluctuation boundary and reserve capacity demand conversion model. The mathematical expression for the fluctuation boundary and reserve capacity demand conversion model is as follows: ； ； in, Indicates the target provincial power grid number Backup capacity requirements for different time periods; Indicates the target provincial power grid number Backup capacity requirements for the next time period; Represents the set of wind power nodes; Represents the set of photovoltaic nodes; Represents the set of load nodes; Indicates the first The negative oscillation boundary concentration of wind power node No. Negative fluctuation boundaries for a given period; Indicates the first The extreme scenario data set of the No. 1 wind power node Historical data for the time period; Indicates the first The positive wave boundary concentration of wind power node No. Positive fluctuation boundaries for the time period; Indicates the first The negative fluctuation boundary set of the photovoltaic node No. Negative fluctuation boundaries for a given period; Indicates the first The extreme scenario data set of the photovoltaic node No. Historical data for the time period; Indicates the first The positive fluctuation boundary concentration of the photovoltaic node No. Positive fluctuation boundaries for the time period; Indicates the first The negative fluctuation boundary set of load node number 1 Negative fluctuation boundaries for a given period; Indicates the first In the extreme scenario data set of the load node, the first Historical data for the time period; Indicates the first The positive fluctuation boundary set of load node number 1 The positive fluctuation boundary of the time period.

6. The provincial power grid day-ahead active power dispatching method for extreme scenarios according to claim 1, characterized in that, Based on the target provincial power grid's upper and lower reserve capacity demand sets and the day-ahead forecast data sets for each preset node, the day-ahead active power dispatch plan for the target provincial power grid's thermal power and energy storage is determined, specifically including: Based on the upper reserve capacity demand set, lower reserve capacity demand set, and day-ahead forecast data set of each preset node of the target provincial power grid, a day-ahead optimized scheduling model for the provincial power grid is constructed. Using the dream optimization algorithm, the day-ahead optimization scheduling model of the provincial power grid is solved to obtain the day-ahead active power data set of each thermal power unit node and the day-ahead load shedding power data set of each load node in the target provincial power grid; Based on the day-ahead active power data set of each thermal power unit node and the day-ahead load shedding power data set of each load node in the target provincial power grid, a provincial power grid energy storage power decision model under N-1 security constraints is constructed. Using the Magnificent Wren-Wren optimization algorithm, the provincial power grid energy storage power decision model under the N-1 security constraint is solved, and the day-ahead charging power data set and day-ahead discharging power data set of each energy storage node in the target provincial power grid, as well as the day-ahead upward adjustment power data set and day-ahead downward adjustment power data set of each thermal power unit node are obtained.

7. The provincial power grid thermal power-storage day-ahead active power dispatching method for extreme scenarios as described in claim 6, characterized in that, The provincial power grid day-ahead optimization dispatch model includes a first optimization objective and a first constraint condition; the first constraint condition includes thermal power unit operation constraints, power flow safety constraints, power balance constraints, and reserve capacity constraints. The mathematical expression for the first optimization objective is: ； ； in, This indicates a minimize operation; This indicates the total cost of optimizing the scheduling process recently. , and These represent the operating cost, load shedding cost, and standby cost of thermal power plants, respectively. , and All are the first Coal consumption coefficient of No. 1 thermal power unit during node operation; Indicates the first The unit power load shedding cost of load node No. 1; Indicates the first The node of thermal power unit provides the capacity cost per unit of standby capacity; Indicates the first The node provides the capacity cost per unit of standby capacity for thermal power units; Indicates the first No. 1 thermal power unit node Active power during a given time period; Indicates the first Load node number Load shedding power during the time period; and They represent the first No. 1 thermal power unit node The upper and lower reserve capacity provided during the time period; For time periods; For the set of nodes of thermal power units; Represents the set of load nodes; The mathematical expression for the operating constraints of the thermal power unit is: ； in, and They represent the first No. 1 thermal power unit node Minimum and maximum active power during the time period; Indicates the first No. 1 thermal power unit node Active power during the +1 time period; and They represent the first The maximum ramping power and maximum ramp-reducing power of the No. 1 thermal power unit node; The mathematical expression for the power flow safety constraint is: ； in, For the first From node number to the... Power transfer distribution factor between lines; For the first The day-ahead forecast data group of load node number 1 Load power during a given time period; No. The day-ahead forecast data set of the No. 1 wind power node Wind power output during a given time period; For the first The day-ahead forecast data set of the photovoltaic node No. Photovoltaic power during a given time period; For the first The power limit of line number 1; the aforementioned line number 2 Line No. 1 is any line in the target provincial power grid; Represents the set of wind power nodes; Represents the set of photovoltaic nodes; The mathematical expression for the power balance constraint is: ； The mathematical expression for the reserve capacity constraint is: ； in, This indicates the concentrated demand for reserve capacity in the target provincial power grid. Backup capacity requirements for different time periods; This indicates the concentrated reserve capacity demand of the target provincial power grid. Backup capacity requirements for a given time period.

8. The provincial power grid thermal power-storage day-ahead active power dispatching method for extreme scenarios as described in claim 7, characterized in that, The provincial power grid energy storage power decision model under N-1 security constraints includes a second optimization objective and a second constraint; the second constraint includes power flow security constraints under N-1 security constraints, power balance constraints under N-1 security constraints, energy storage model constraints, and thermal power unit regulation constraints. The mathematical expression for the second optimization objective is: ； ； in, To represent the decision-making and dispatching cost of the target provincial power grid's energy storage capacity; and These represent the regulation cost of thermal power and the operating cost of energy storage, respectively. and They represent the first Scenario 1 No. 1 thermal power unit node The upward and downward adjustment power during the time period; and They represent the first Energy storage node No. Charging and discharging power during the same period; Indicates the first Operating cost per unit power of energy storage node; and To represent the first The capacity cost of providing a unit of upper reserve capacity and the capacity cost of providing a unit of lower reserve capacity for thermal power unit node; A collection of energy storage nodes; For N-1 safety-constrained circuit sets; Indicates the number of lines in the N-1 security constraint line cluster; the first The scenario represents a target provincial power grid containing only power lines. A scenario where the connection is lost; The mathematical expression for the power flow security constraint under the N-1 security constraint is: ； in, For the first Scenario 1 From node number to the... Power transfer distribution factor between lines; The mathematical expression for the power balance constraint under the N-1 security constraint is: ； The mathematical expression for the constraints of the energy storage model is: ； in, and They represent the first Energy storage node No. Minimum and maximum power during the time period; Indicates the first The rated energy of the energy storage node; and They represent the first The first energy storage node State of charge during the period and the first State of charge during the +1 time period; and They represent the first Initial period of energy storage node No. State of charge and end of dispatch period The state of charge; and They represent the first Energy storage node No. The minimum and maximum states of charge during a given time period; and They represent the first The charging and discharging efficiency of the energy storage node; The mathematical expression for the adjustment constraint of the thermal power unit is: 。 9. The provincial power grid thermal power-storage day-ahead active power dispatching method for extreme scenarios as described in claim 8, characterized in that, The process for determining the N-1 security constraint circuit set is as follows: For each line in the target provincial power grid, determine whether the critical line inequality is satisfied, and obtain the judgment result; When all lines in the target provincial power grid have been judged, the set of all lines with a judgment result of "yes" is determined as the N-1 safety constraint line set.

10. The provincial power grid day-ahead active power dispatching method for extreme scenarios according to claim 9, characterized in that, The mathematical expression for the critical path determination inequality is: ； in, This is the threshold for determining the critical path.

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