Provincial power grid fire storage day-ahead active power scheduling method for coping with extreme scene

Abstract

The invention discloses a provincial power grid fire storage day-ahead active power scheduling method for coping with an extreme scene, and relates to the field of power grid scheduling, and the method comprises the steps: obtaining a day-ahead prediction data set of each preset node of a target provincial power grid and historical data in a preset historical period; for each preset node, dividing historical data in a preset historical period into a plurality of groups, and calculating an extreme degree index of each historical data group; performing clustering operation on each historical data group to determine an extreme scene; determining a negative fluctuation boundary set and a positive fluctuation boundary set according to the extreme scene; and determining an upper reserve capacity demand set and a lower reserve capacity demand set of the target provincial power grid, and finally calculating to obtain a fire storage day-ahead active power scheduling plan of the target provincial power grid. The problem that the standby capacity is insufficient easily when a traditional scheduling method encounters an extreme scene is solved.

Description

Technical Field

[0001] The present application relates to the field of power grid dispatching, and in particular to a method for dispatching the day-ahead active power of thermal storage in a provincial power grid to cope with extreme scenarios. Background Art

[0002] With the large-scale integration of renewable energy and the increasing variance between peak and valley loads, provincial power grids face the dual challenges of adapting to extreme scenarios and properly allocating reserve capacity. Traditional dispatching methods, which typically rely on experience to set reserve capacity at a fixed ratio, are unable to adapt to the fluctuating nature of renewable energy. This is particularly true when the grid system is exposed to extreme scenarios such as extreme cold waves, severe convective weather, or line faults, and is prone to insufficient reserve capacity. Summary of the Invention

[0003] The purpose of this application is to provide a method for dispatching the day-ahead active power of thermal storage in a provincial power grid to cope with extreme scenarios, which can realize the day-ahead dispatching of thermal storage in a provincial power grid based on the reasonable configuration of spare capacity demand in extreme scenarios.

[0004] To achieve the above objectives, this application provides the following solutions: This application provides a method for dispatching day-ahead active power of thermal storage in a provincial power grid to cope with extreme scenarios, including: Obtain historical data within a preset historical period of each preset node of the target provincial power grid and a 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 include historical forecast data and historical actual data.

[0005] 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 the historical forecast data and historical actual data for each time period within 1 day; the extreme index model is a model that includes the daily maximum fluctuation, the daily peak-to-valley difference and the daily maximum forecast deviation.

[0006] For each preset node, a clustering operation is performed on each historical data group according to the extreme degree index of each historical data group to determine an extreme scenario; the extreme scenario includes an extreme scenario data group and several extreme scenario subclass data groups.

[0007] For each preset node, a negative fluctuation boundary set and a positive fluctuation boundary set are determined 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.

[0008] Based on the extreme scenario data group, negative fluctuation boundary set and positive fluctuation boundary set of each preset node, the upper spare capacity demand set and the lower spare capacity demand set of the target provincial power grid are determined; the upper spare capacity demand set includes the upper spare capacity demand of the target provincial power grid in each time period; the lower spare capacity demand set includes the lower spare capacity demand of the target provincial power grid in each time period.

[0009] According to the upper reserve capacity demand set, the lower reserve capacity demand set and the day-ahead forecast data group of each preset node of the target provincial power grid, the day-ahead active power dispatch plan of the thermal storage of the target provincial power grid is determined.

[0010] According to the specific embodiments provided in this application, this application has the following technical effects: The present application provides a method for dispatching the day-ahead active power of thermal storage in provincial power grids to cope with extreme scenarios. Based on the historical data (historical forecast data and historical actual data) of each wind power node, each photovoltaic node, and each load node in the target provincial power grid within a preset historical period, the extreme degree index model and clustering method proposed are used to construct the extreme scenario of the target provincial power grid, and calculate the reserve capacity demand configuration of the target provincial power grid under the extreme scenario. Then, based on the reserve capacity demand configuration under the extreme scenario, the day-ahead active power dispatching plan of thermal storage in the target provincial power grid is determined. Compared with the traditional dispatching method that relies on the empirical fixed ratio reserve method, the present application realizes the quantification of extreme scenarios based on historical data, so that the reserve capacity demand can be configured more flexibly and scientifically, solving the problem of insufficient reserve capacity when the traditional dispatching method encounters extreme scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flowchart of a method for dispatching active power from thermal storage in a provincial power grid to cope with extreme scenarios, provided in one embodiment of the present application; Figure 2 A flowchart of another method for dispatching active power from thermal storage in a provincial power grid to cope with extreme scenarios provided by one embodiment of the present application; Figure 3 A schematic diagram of the design concept of a method for dispatching active power from thermal storage in a provincial power grid to cope with extreme scenarios, provided in one embodiment of the present application; Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] Traditional dispatching methods generally rely on experience to set reserve capacity at a fixed ratio, which cannot adapt to the fluctuating characteristics of renewable energy. In particular, when the power grid system is subjected to extreme scenarios such as extreme cold waves, severe convective weather, or line faults, the problem of insufficient reserve capacity is prone to occur. In this application, by extracting extreme scenarios, a reserve capacity demand decision is made that adapts to the fluctuation characteristics of the time period, and a provincial power grid thermal power unit day-ahead optimization dispatching method that meets the reserve capacity demand and a provincial power grid energy storage power decision-making method under the N-1 safety constraint are proposed. This provides support for the day-ahead active power dispatching decision of thermal storage that adapts to extreme scenarios for provincial power grids with a high proportion of renewable energy access.

[0014] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in 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.

[0015] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0016] In an exemplary embodiment, see Figure 1 and Figure 2 , provides a method for dispatching active power from thermal storage in provincial power grids to cope with extreme scenarios, including: S101, obtaining historical data of each preset node in the target provincial power grid within a preset historical period and a 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.

[0017] That is, the historical data of each wind power node includes: historical wind power forecast data and historical wind power actual data; the historical data of each photovoltaic node includes: historical photovoltaic forecast data and historical photovoltaic actual data; the historical data of each load node includes: historical load forecast data and historical load actual data.

[0018] S102, 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 the historical forecast data and historical actual data for each time period within 1 day; the extreme index model is a model including the daily maximum fluctuation, the daily peak-to-valley difference and the daily maximum forecast deviation.

[0019] In some embodiments, the time granularity of historical data is one hour, meaning each time period is one 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 each preset node's day-ahead forecast data group is also one hour, meaning each preset node's day-ahead forecast data group includes 24 day-ahead forecast data points for a single day.

[0020] S103 , for each preset node, performing a clustering operation on each historical data group according to the extreme degree index of each historical data group to determine an extreme scenario; the extreme scenario includes an extreme scenario data group and several extreme scenario subclass data groups.

[0021] S104, for each preset node, determine a negative fluctuation boundary set and a positive fluctuation boundary set based on a corresponding extreme scenario data group and several extreme scenario sub-category 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.

[0022] S105. Determine the upper standby capacity demand set and the lower standby capacity demand set of the target provincial power grid based on the extreme scenario data group, the negative fluctuation boundary set, and the positive fluctuation boundary set of each preset node; the upper standby capacity demand set includes the upper standby capacity demand of the target provincial power grid for each time period; the lower standby capacity demand set includes the lower standby capacity demand of the target provincial power grid for each time period.

[0023] S106 , determining a day-ahead active power dispatch plan for thermal storage of the target provincial power grid based on the upper reserve capacity demand set, the lower reserve capacity demand set, and the day-ahead forecast data set of each preset node of the target provincial power grid.

[0024] As an optional implementation, the mathematical expression of the extreme degree index model is: (1); (2); (3); (4); in, represents an extreme degree index; Indicates the maximum daily fluctuation, that is, the maximum difference between historical actual data of adjacent periods within 1 day; It represents the daily peak-to-valley difference, that is, the difference between the maximum and minimum values ​​of historical actual data within one day; Indicates the maximum daily forecast deviation, that is, the maximum difference between the historical forecast data and the historical actual data within 1 day; 、 and Respectively 、 and The weight coefficient of 、 and Respectively represent the Historical actual data of the period, Historical actual data for the period and Historical forecast data for the time period; Indicates the maximum operation; Indicates the minimum operation.

[0025] As an optional implementation, step S103 specifically includes: S103.1, randomly selecting a preset number of historical data groups from each historical data group of the preset node, and determining each selected historical data group as an initial cluster center to obtain a preset number of initial cluster centers.

[0026] S103.2. Based on a preset number of initial cluster centers, cluster other historical data groups of the preset node using an extreme degree index to obtain a preset number of initial cluster results; each initial cluster result is a cluster range including one initial cluster center.

[0027] When performing clustering operations, the K-means clustering algorithm can be used.

[0028] S103.3, for each initial clustering result, perform an averaging operation on all historical data groups in the initial clustering result except the initial cluster center to obtain a new historical data group.

[0029] 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 corresponding to the new historical data group.

[0030] S103.5. When the judgment results of each new historical data group are all yes, the initial cluster center with the largest extreme degree index among the preset number of initial cluster centers is determined as the first cluster center, and the extreme scenario is determined based on 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 of the extreme scenario subclass data groups is the historical actual data in another historical data group except the first cluster center in the initial clustering result corresponding to the first cluster center.

[0031] S103.6: When the judgment result of any new historical data group is negative, perform the first operation to determine the extreme scenario.

[0032] The specific execution process of the first operation is: S103.6.1. Calculate the extreme degree 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.

[0033] S103.6.2. Based on a preset number of new cluster centers, using an extreme degree index, perform a clustering operation on each historical data group of the preset node to obtain a preset number of new cluster results.

[0034] S103.6.3. For each new clustering result, perform an averaging operation on all historical data groups in the new clustering result except the new cluster center to obtain a second new historical data group.

[0035] S103.6.4, determine whether each second new historical data group is identical to the corresponding new historical data group.

[0036] S103.6.5, when the judgment results of each second new historical data group are all yes, the new cluster center with the largest extreme degree index among the preset number of new cluster centers is determined as the second cluster center, and the extreme scenario is determined based on 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 of the extreme scenario subclass data groups 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.

[0037] S103.6.6, when the judgment result of any second new historical data group is no, the preset number of new historical data groups are updated to the preset number of second new historical data groups, and return to the step of "calculating the extreme degree index of each new historical data group, and determining each new historical data group as a new cluster center to obtain a preset number of new cluster centers."

[0038] In this embodiment, for each preset node, a K-means clustering algorithm based on an extreme degree index is used for clustering to generate extreme scenarios. The number of clusters is set to J (i.e., the preset number), and J cluster centers are ultimately obtained. The cluster center with the largest extreme value (the first cluster center or the second cluster center) is selected from the J cluster centers. The historical actual data portion of the cluster center with the largest extreme value is the extreme scenario data, and the historical actual data portion of each historical data group that is divided into the cluster center with the largest extreme value is the extreme scenario subclass data. The process of the K-means clustering algorithm based on the extreme degree index is briefly described as follows: The first step is to randomly select J groups of data from each historical data group of the preset node as the initial cluster center; the second step is to calculate the daily maximum fluctuation, daily peak-to-valley difference and daily maximum prediction deviation of each historical data group and cluster center (the initial moment is the initial cluster center), further calculate the extreme index, and assign each historical data group to the cluster center closest to the 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 and use it as the new centroid, that is, 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.

[0039] Calculate the extreme degree index of each cluster center finally obtained, select the cluster center with the largest extreme degree index and the historical data group divided into this cluster center, take the historical actual data part, and obtain extreme scenario data and extreme scenario subclass data.

[0040] The convergence condition is: the current J cluster centers are the previous J cluster centers.

[0041] As an optional implementation, step S104 specifically includes: S104.1, based on an extreme scenario data group and several extreme scenario sub-category data groups corresponding to the preset node, determine a fluctuation feature set for each extreme scenario sub-category data group using a fluctuation feature calculation formula; the fluctuation feature calculation formula is: (5); in, Indicates the Extreme scene subclass data group Fluctuation characteristics of the time period; Indicates the In the extreme scene subclass data group Historical actual data for the time period; Indicates the extreme scenario data group Historical actual data for the period.

[0042] S104.2, based on the fluctuation characteristic set of each extreme scenario sub-category data group, determine the confidence set of each extreme scenario sub-category data group using a confidence calculation formula; the confidence calculation formula is: (6); in, Indicates the Extreme scene subclass data group confidence level of the time period; Indicates the basic confidence; represents the confidence adjustment amplitude; represents the tangent function; Indicates the baseline value of fluctuation characteristics.

[0043] S104.3. Determine an average confidence set based on the confidence sets of each extreme scenario subclass data group using an average confidence calculation formula; the average confidence calculation formula is: (7); in, Indicates the Average confidence level for the time period; Indicates the number of extreme scenario subclass data groups.

[0044] In this embodiment, extreme scenario data and extreme scenario subcategory data are used to calculate fluctuation characteristics, and the fluctuation characteristics are used to calculate confidence. Then, the confidence levels of the same time period are averaged to obtain the average confidence level of each time period, which is the final confidence level of each time period.

[0045] S104.4. 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 a kernel density estimation method; the probability density function set includes a probability density function for each time period.

[0046] The kernel function may be a Gaussian function.

[0047] S104.5. Determine a lower quantile set and an upper quantile set based on several extreme scenario subcategory data groups, average confidence sets, and probability density function sets corresponding to the preset nodes; the lower quantile set includes the lower quantiles of each time period; the upper quantile set includes the upper quantiles of each time period.

[0048] Among them, by using the confidence and probability density function of a time period, the lower quantile and upper quantile corresponding to the time period are obtained.

[0049] S104.6. Determine the lower quantile and upper quantile of each time period as the negative fluctuation boundary and the positive fluctuation boundary of each time period, respectively, to obtain a negative fluctuation boundary set and a positive fluctuation boundary set.

[0050] Among them, the negative fluctuation boundaries, extreme scenario data, and positive fluctuation boundaries corresponding to each preset node and time period of the target provincial power grid are as follows: (8); (9); (10); in, 、 and Respectively Wind power node No. Negative fluctuation boundaries, extreme scenario data, and positive fluctuation boundaries of the time period; 、 and Respectively Photovoltaic node No. Negative fluctuation boundaries, extreme scenario data, and positive fluctuation boundaries of the time period; 、 and Respectively Load node No. Negative fluctuation boundaries of the time period, extreme scenario data, and positive fluctuation boundaries.

[0051] In this article, the historical actual data in the extreme scenario data group is also called extreme scenario data; the historical actual data in the extreme scenario subcategory data group is also called extreme scenario subcategory data.

[0052] As an optional implementation, step S105 specifically includes: Based on the extreme scenario data sets, negative fluctuation boundary sets, and positive fluctuation boundary sets for each preset node, the upper and lower reserve capacity requirement sets for the target provincial power grid are determined using a fluctuation boundary and reserve capacity requirement conversion model. This converts the fluctuation boundary into reserve capacity requirements.

[0053] The mathematical expression of the fluctuation boundary and reserve capacity demand conversion model is: (11); (12); in, Indicates the target provincial power grid The upper reserve capacity requirement of the time period; Indicates the target provincial power grid The next reserve capacity requirement for the time period; Represents a set of wind power nodes; Represents a set of photovoltaic nodes; Represents a set of load nodes; Indicates the The negative fluctuation boundary of wind power node No. Negative volatility boundary of the period; Indicates the In the extreme scenario data set of wind power node No. Historical actual data for the time period; Indicates the The positive fluctuation boundary concentration of wind power node No. Positive volatility boundary of the period; Indicates the The negative fluctuation boundary of photovoltaic node No. Negative volatility boundary of the period; Indicates the In the extreme scenario data set of photovoltaic node No. Historical actual data for the time period; Indicates the The positive fluctuation boundary of photovoltaic node No. Positive volatility boundary of the period; Indicates the The negative fluctuation boundary of load node No. Negative volatility boundary of the period; Indicates the In the extreme scenario data group of load node No. Historical actual data for the time period; Indicates the The positive fluctuation boundary concentration of load node No. Positive fluctuation boundary of the time period.

[0054] As an optional implementation, step S106 specifically includes: S106.1, constructing a provincial power grid day-ahead optimal dispatch model based on the upper reserve capacity demand set, the lower reserve capacity demand set, and the day-ahead forecast data set of each preset node of the target provincial power grid.

[0055] S106.2. Utilize the dream optimization algorithm to solve the provincial power grid's day-ahead optimal dispatch model, and obtain a day-ahead active power data set of each thermal power unit node and a day-ahead load shedding power data set of each load node in the target provincial power grid.

[0056] S106.3. Construct a provincial power grid energy storage power decision model under N-1 safety constraints 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.

[0057] S106.4. Utilize the Magnificent Fairywren optimization algorithm to solve the provincial power grid energy storage power decision model under the N-1 safety constraint, and obtain the day-ahead charging power data set and the 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 the day-ahead downward adjustment power data set for each thermal power unit node.

[0058] As an optional implementation method, the provincial power grid day-ahead optimization dispatching model includes a first optimization objective and a first constraint condition; the first constraint condition includes thermal power unit operation constraint, flow safety constraint, power balance constraint and spare capacity constraint.

[0059] The mathematical expression of the first optimization objective is: (13); (14); in, Indicates the minimize operation; represents the total cost of day-ahead optimal scheduling; 、 and Respectively represent thermal power operation cost, load shedding cost and standby cost; 、 and All are Coal consumption coefficient of the node operation of thermal power unit No. Indicates the Unit power load shedding cost of load nodes; Indicates the The capacity cost of the unit spare capacity provided by the thermal power unit node; Indicates the The capacity cost of the unit reserve capacity provided by the thermal power unit node; Indicates the Thermal power unit node No. Active power during the time period; Indicates the Load node No. Load shedding power during the time period; and Respectively represent Thermal power unit node No. The upper and lower reserve capacities provided during the time period; Set for time period; is the node set of thermal power units; Represents a set of load nodes.

[0060] For each thermal power unit node, the active power of all periods within a day constitutes the day-ahead active power data set. For each load node, the load shedding power of all periods within a day constitutes the day-ahead load shedding power data set.

[0061] The mathematical expression of the thermal power unit operation constraint is: (15); in, and Respectively represent Thermal power unit node No. The minimum and maximum active power values ​​for each time period; Indicates the Thermal power unit node No. +1 period of active power; and Respectively represent The maximum climbing power and maximum descending power of the thermal power unit node.

[0062] The mathematical expression of the power flow safety constraint is: (16); in, For the Node No. to No. Power transfer distribution factor between signal lines; For the The first one in the day-ahead forecast data group of load node Load power during the time period; No. The first one in the day-ahead forecast data group of wind power node No. Wind power during the period; For the The first day of the forecast data set for photovoltaic node No. PV power during the time period; For the The power upper limit of line No. Line No. is any line in the target provincial power grid; Represents a set of wind power nodes; Represents a collection of photovoltaic nodes.

[0063] The mathematical expression of the power balance constraint is: (17).

[0064] In order to actively respond to the occurrence of extreme scenarios, the provincial power grid's day-ahead optimization dispatch model should ensure a certain amount of reserve capacity. The mathematical expression of the reserve capacity constraint is: (18); in, Indicates the concentration of upper reserve capacity demand of target provincial power grid The upper reserve capacity requirement of the time period; Indicates the concentration of reserve capacity demand of target provincial power grid The next spare capacity requirement for the time period.

[0065] In this embodiment, the Dream Optimization Algorithm (DOA) is used to solve the above-mentioned provincial power grid day-ahead optimization dispatch model.

[0066] The dream optimization algorithm simulates the characteristics of human dream memory retention and forgetting, and proposes memory strategy, forgetting and supplementation strategy to balance the algorithm's exploration and development functions, enhance the algorithm's ability to escape from local optimal solutions, and effectively improve the algorithm's global optimization ability. The dream optimization algorithm is divided into two stages. The first stage is the exploration stage (the number of iterations is from 0 to ), the second stage is the development stage (iteration number from arrive ). Among them, the exploration phase includes three types of strategies: memory strategy, forgetting and supplementing strategy, and dream sharing strategy; the development phase includes two types of strategies: memory strategy, forgetting and supplementing strategy. Each individual represents a solution, and the solution is composed of variables in the provincial power grid's day-ahead optimal dispatch model, including the active power of the thermal power unit node and the load shedding power of the load node.

[0067] In the exploration phase, all individuals are divided into The solution is updated and iterated in each group. The specific update strategy is as follows: (19); (20); (twenty one); in, For the The first iteration Individual number, For the At the iteration The best individual in the group; For the The first iteration Individual No. Dimensional information, For the At the iteration The best individual in the group Dimensional information; and To solve the space The lower and upper bounds of the dimension; and For the The first and second iterations The first iteration Individual No. Dimensional information. is the cosine function; is a random function.

[0068] Among them, Individual No. and Individuals belong to the same group.

[0069] Development stage: (twenty two); (twenty three); in, For the The best individual among all individuals at the iteration, For the The best individual among all individuals at the iteration Dimensional information.

[0070] The specific solution process is as follows: First, initialize the variables according to the upper and lower bounds of the variables in the provincial power grid day-ahead optimization dispatch model, generate a population, and divide the individuals in the population into groups; secondly, enter the exploration phase and update according to the memory strategy formula (19). If If it is less than 0.9, execute formula (20), otherwise execute formula (21), update and record the current best individual and the corresponding target value ( ); Third, when the number of iterations reaches , it enters the development phase, executes the memory strategy formula (22), and further executes the forgetting and supplementing strategy formula (23), updates and records the current best individual and the corresponding target value; finally, through continuous iteration, the loop is terminated when the maximum number of iterations is reached.

[0071] As an optional implementation, the provincial power grid energy storage power decision model under the N-1 safety constraint includes a second optimization objective and a second constraint condition; the second constraint condition includes a flow safety constraint under the N-1 safety constraint, a power balance constraint under the N-1 safety constraint, an energy storage model constraint, and a thermal power unit regulation constraint.

[0072] The mathematical expression of the second optimization objective is: (twenty four); (25); in, represents the target provincial power grid energy storage power decision dispatch cost; and represent the thermal power regulation cost and energy storage operation cost respectively; and Respectively represent Scene Thermal power unit node No. Upward and downward power regulation for time periods; and Respectively represent Energy storage node No. Charging power and discharging power during the time period; Indicates the The energy storage unit power operation cost of the energy storage node; and To represent the The capacity cost of providing upper unit spare capacity and the capacity cost of providing lower unit spare capacity for the thermal power unit node No. is a collection of energy storage nodes; is the N-1 security constraint line set; Represents the number of N-1 safety constraint line sets; The scenario indicates that only the lines in the target provincial power grid Disconnected scene.

[0073] For each energy storage node, the charging power and discharging power for all periods within a day are composed of the day-ahead charging power data set and the day-ahead discharging power data set, respectively. For each thermal power unit node, the upward and downward power adjustments for all periods within a day are composed of the day-ahead upward power adjustment data set and the day-ahead downward power adjustment data set, respectively.

[0074] The mathematical expression of the power flow safety constraint under the N-1 safety constraint is: (26); in, For the Scene Node No. to No. The power transfer distribution factor between signal lines.

[0075] The mathematical expression of the power balance constraint under the N-1 safety constraint is: (27).

[0076] The mathematical expression of the energy storage model constraint is: (28); in, and Respectively represent Energy storage node No. Minimum power and maximum power for each time period; Indicates the Rated energy of the energy storage node; and Respectively represent Energy storage node No. The state of charge of the period and the +1 period of charge state; and Respectively represent The initial period of the energy storage node State of charge and the end of the dispatch period State of charge; and Respectively represent Energy storage node No. The minimum state of charge and maximum state of charge for the time period; and Respectively represent The charging efficiency and discharging efficiency of the energy storage node.

[0077] The mathematical expression of the thermal power unit regulation constraint is: (29).

[0078] As an optional implementation manner, the process of determining the N-1 security constraint circuit set is as follows: 1) For each line in the target provincial power grid, determine whether it satisfies the key line determination inequality and obtain the judgment result.

[0079] 2) When all lines in the target provincial power grid have been judged, the set consisting of all lines with a judgment result of yes is determined as the N-1 safety-constrained line set.

[0080] The mathematical expression of the critical path determination inequality is: (30); in, The threshold for determining the critical line.

[0081] That is to say, before constructing the provincial power grid energy storage power decision model under the N-1 safety constraint, first obtain the day-ahead active power dispatch plan, load shedding plan, and line flow of the thermal power units according to the provincial power grid day-ahead optimization dispatch model. Select the key lines according to the line flow. If the formula (30) is satisfied, the line is considered to be a key line and needs to consider the N-1 safety constraint. Then the line is included in the N-1 safety constraint line set. .

[0082] In this embodiment, the Superb Fairy-wren Optimization Algorithm (SFOA) is used to solve the provincial power grid energy storage power decision model under the N-1 security constraint to obtain the target provincial power grid energy storage day-ahead charging and discharging power plan.

[0083] The optimization algorithm for the magnificent fairy-wren is divided into the chick growth stage, the breeding and rearing stage, and the predator avoidance stage. In this algorithm, the chick growth stage, through extensive learning and position changes, allows candidate solutions to move around the solution space over a large range, achieving extensive exploration, thereby improving the global exploration capability of the search, as shown in Formula (31). The breeding and rearing stage enables candidate solutions to conduct local fine search, as shown in Formula (32). The predator avoidance stage gives candidate solutions the ability to escape the local optimal solution, as shown in Formula (33). Candidate solutions are variables in the provincial power grid energy storage power decision model, including the charging and discharging power of the energy storage, the upward adjustment power of the thermal power unit, and the downward adjustment power of the thermal power unit.

[0084] (31); (32); (33); in, and For the The iteration and +1 iteration Individual No. Dimensional information; For the The best individual at the iteration Dimensional information; and To solve the space The lower and upper bounds of the dimension; is the determination value of the proportion of young birds within the population; is a risk factor; is a random function; is a function that represents the number of iterations that gradually increases; is a random step size; is the adaptive balancing factor.

[0085] 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 the N-1 safety constraint to generate a population; secondly, according to and In the case of a problem, the candidate solution is judged and the position update phase is entered; finally, the optimal position information of the candidate solution is updated, and the iteration is continued until the maximum iteration is reached and the loop is terminated.

[0086] The present application also provides an application scenario, which applies the above-mentioned method for dispatching active power of thermal storage of provincial power grids on the day before to deal with extreme scenarios. Specifically: the method for dispatching active power of thermal storage of provincial power grids on the day before to deal with extreme scenarios provided in this embodiment can be applied in provincial power grid dispatching scenarios. The provincial power grid dispatching scenario includes a provincial power grid dispatching plan link and a provincial power grid dispatching link; the provincial power grid dispatching plan link generates a provincial power grid dispatching plan, and the provincial power grid dispatching link applies the provincial power grid dispatching plan to perform provincial power grid dispatching. The method for dispatching active power of thermal storage of provincial power grids on the day before to deal with extreme scenarios provided in this embodiment belongs to the provincial power grid dispatching plan link.

[0087] See Figure 3 , the design concept of this application is as follows: 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 sub-category data; second, a fluctuation characteristic calculation method is proposed based on extreme scenarios, and a reserve capacity demand decision-making 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 provincial power grid day-ahead optimization dispatch model is established, and the dream optimization algorithm is used to solve it; finally, based on the day-ahead active power dispatch plan of thermal power units, a provincial power grid energy storage power decision-making model under N-1 safety constraints is established, and the magnificent fairy-wren optimization algorithm is used to solve it.

[0088] This application has the following beneficial effects: (1) This application proposes an extreme scenario generation method based on extreme degree, and uses the k-means clustering algorithm based on the extreme degree index to quickly screen extreme scenarios and improve the representativeness of extreme scenarios.

[0089] (2) This application proposes a method for making decisions on reserve capacity demand based on fluctuation characteristics. The method calculates the reserve capacity demand based on the fluctuation characteristics, adapts to the fluctuation characteristics of different time periods, avoids the waste of capacity resources caused by fixed-ratio reserve, and increases the reserve capacity in critical periods to cope with extreme changes.

[0090] (3) This application adopts the dream optimization algorithm to solve the provincial power grid day-ahead optimization dispatching model. The algorithm has the characteristics of fast convergence, improves the computational efficiency of the dispatching plan, is suitable for provincial power grid optimization problems, and enhances the ability of the algorithm to escape from the local optimal solution, effectively improving the algorithm's global optimization ability.

[0091] (4) This application uses the magnificent fairy-wren optimization algorithm to solve the provincial power grid energy storage power decision model under the N-1 safety constraint, 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 the N-1 safety constraint.

[0092] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store historical data and day-ahead forecast data sets. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a day-ahead active power scheduling method for thermal storage in a provincial power grid to cope with extreme scenarios.

[0093] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0094] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0095] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0096] 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, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0097] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0098] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0099] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, 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.

[0100] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for dispatching active power of thermal storage in a provincial power grid in advance to cope with extreme scenarios, characterized by: include: Obtain historical data for each preset node in the target provincial power grid within a preset historical period and a day-ahead forecast data set for each preset node; The preset nodes are divided into wind power nodes, photovoltaic nodes and load nodes; the historical data includes historical prediction 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 of each historical data group is calculated using an extreme index model; wherein each historical data group includes the historical forecast data and historical actual data for each period within a day; the extreme index model includes the daily maximum fluctuation, the daily peak-to-valley difference, and the daily maximum forecast deviation; For each preset node, clustering operations are performed on each historical data group according to the extreme degree 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; For each preset node, a negative fluctuation boundary set and a positive fluctuation boundary set are determined 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; Determine an upper reserve capacity requirement set and a lower reserve capacity requirement set of the target provincial power grid based on the extreme scenario data set, the negative fluctuation boundary set, and the positive fluctuation boundary set of each preset node; the upper reserve capacity requirement set includes the upper reserve capacity requirement of the target provincial power grid for each time period; the lower reserve capacity requirement set includes the lower reserve capacity requirement of the target provincial power grid for each time period; According to the upper reserve capacity demand set, the lower reserve capacity demand set and the day-ahead forecast data group of each preset node of the target provincial power grid, the day-ahead active power dispatch plan of the thermal storage of the target provincial power grid is determined.

2. The method for dispatching active power of thermal power storage in a provincial power grid in response to extreme scenarios according to claim 1, characterized in that: The mathematical expression of the extreme degree index model is: ； ； ； ； in, represents an extreme degree index; Indicates the maximum daily fluctuation; represents the daily peak-to-valley difference; represents the maximum daily forecast deviation; 、 and Respectively 、 and The weight coefficient of 、 and Respectively represent the Historical actual data of the period, Historical actual data for the period and Historical forecast data for the time period; Indicates the maximum operation; Indicates the minimum operation.

3. The method for dispatching active power of thermal storage in a provincial power grid in advance to cope with extreme scenarios according to claim 1, characterized in that: For each preset node, clustering operations are performed on each historical data group based on the extreme degree index of each historical data group to determine extreme scenarios, including: Randomly selecting a preset number of historical data groups from each historical data group of the preset node, and determining each selected historical data group as an initial cluster center to obtain a preset number of initial cluster centers; Based on a preset number of initial cluster centers, clustering operations are performed on other historical data groups of the preset node using an extreme degree index to obtain a preset number of initial cluster results; each initial cluster result is a cluster range including one initial cluster center; For each initial clustering result, averaging all historical data groups in the initial clustering result except the initial cluster center 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 corresponding to the new historical data group; When the judgment results of each new historical data group are all yes, the initial cluster center with the largest extreme degree index among the preset number of initial cluster centers is determined as the first cluster center, and the extreme scenario is determined based on 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 of the extreme scenario subclass data groups is the historical actual data in another historical data group except the first cluster center in the initial clustering result corresponding to the first cluster center; When the judgment result of any new historical data group is negative, the first operation is performed to determine the extreme scenario; The specific execution process of the first operation is: Calculate the extreme degree index of each new historical data group, and determine each new historical data group as a new cluster center, and obtain a preset number of new cluster centers; According to a preset number of new cluster centers, using an extreme degree index, a clustering operation is performed on each historical data group of the preset node to obtain a preset number of new cluster results; For each new clustering result, averaging all historical data groups in the new clustering result except the new cluster center to obtain a second new historical data group; determining whether each second new historical data group is identical to the corresponding new historical data group; When the judgment results of each second new historical data group are all yes, the new cluster center with the largest extreme degree 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; When the judgment result of any second new historical data group is no, the preset number of new historical data groups are updated to the preset number of second new historical data groups, and return to the step of "calculating the extreme degree index of each new historical data group, and determining each new historical data group as a new cluster center to obtain a preset number of new cluster centers." 4. The method for dispatching active power of thermal storage in a provincial power grid in advance to cope with extreme scenarios according to claim 1, characterized in that: 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, specifically including: According to an extreme scene data group and several extreme scene sub-category data groups corresponding to the preset node, a fluctuation feature set of each extreme scene sub-category data group is determined using a fluctuation feature calculation formula; the fluctuation feature calculation formula is: ； in, Indicates the Extreme scenario subclass data group Fluctuation characteristics of the time period; Indicates the In the extreme scene subclass data group Historical actual data for the time period; Indicates the extreme scenario data group Historical actual data for the time period; According to the fluctuation feature set of each extreme scenario sub-category data group, the confidence set of each extreme scenario sub-category data group is determined using the confidence calculation formula; the confidence calculation formula is: ； in, Indicates the Extreme scenario subclass data group confidence level of the time period; Indicates the basic confidence; represents the confidence adjustment amplitude; represents the tangent function; Indicates the fluctuation characteristic benchmark value; According to 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: ； in, Indicates the Average confidence level for the time period; Indicates the number of extreme scenario subclass data groups; According to 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 a kernel density estimation method; the probability density function set includes a probability density function for each time period; Determine a lower quantile set and an upper quantile set according to the plurality of extreme scenario subclass data groups, average confidence sets, and probability density function sets corresponding to the preset nodes; the lower quantile set includes the lower quantiles of each time period; the upper quantile set includes the upper quantiles of each time period; The lower quantile and the upper quantile of each time period are respectively determined as the negative fluctuation boundary and the positive fluctuation boundary of each time period, and a negative fluctuation boundary set and a positive fluctuation boundary set are obtained.

5. The method for dispatching active power of thermal power storage in a provincial power grid in response to extreme scenarios according to claim 1, characterized in that: Based on the extreme scenario data set, negative fluctuation boundary set, and positive fluctuation boundary set of each preset node, the upper reserve capacity requirement set and lower reserve capacity requirement set of the target provincial power grid are determined, specifically including: Based on the extreme scenario data set, negative fluctuation boundary set, and positive fluctuation boundary set 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 of the fluctuation boundary and reserve capacity demand conversion model is: ； ； in, Indicates the target provincial power grid The upper reserve capacity requirement of the time period; Indicates the target provincial power grid The next reserve capacity requirement for the time period; Represents a set of wind power nodes; Represents a set of photovoltaic nodes; Represents a set of load nodes; Indicates the The negative fluctuation boundary of wind power node No. Negative volatility boundary of the period; Indicates the In the extreme scenario data set of wind power node No. Historical actual data for the time period; Indicates the The positive fluctuation boundary concentration of wind power node No. Positive volatility boundary of the period; Indicates the The negative fluctuation boundary of photovoltaic node No. Negative volatility boundary of the period; Indicates the In the extreme scenario data set of photovoltaic node No. Historical actual data for the time period; Indicates the The positive fluctuation boundary of photovoltaic node No. Positive volatility boundary of the period; Indicates the The negative fluctuation boundary of load node No. Negative volatility boundary of the period; Indicates the In the extreme scenario data group of load node No. Historical actual data for the time period; Indicates the The positive fluctuation boundary concentration of load node No. Positive fluctuation boundary of the time period.

6. The method for dispatching active power of thermal storage in a provincial power grid in advance to cope with extreme scenarios according to claim 1, characterized in that: Based on the upper reserve capacity demand set and lower reserve capacity demand set of the target provincial power grid and the day-ahead forecast data set of each preset node, the day-ahead active power dispatch plan of the thermal storage of the target provincial power grid is determined, specifically including: Based on the upper and lower reserve capacity demand sets of the target provincial power grid and the day-ahead forecast data set of each preset node, a provincial power grid day-ahead optimization dispatch model is constructed; Using the dream optimization algorithm, the provincial power grid day-ahead optimization dispatch model 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 of each thermal power unit node and the day-ahead load shedding power data of each load node in the target provincial power grid, a provincial power grid energy storage power decision-making model under N-1 safety constraints is constructed. The magnificent fairywren optimization algorithm is used to solve the provincial power grid energy storage power decision-making model under the N-1 safety constraint, obtaining 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 power data set and day-ahead downward power data set of each thermal power unit node.

7. The method for dispatching active power of thermal power storage in a provincial power grid in advance to cope with extreme scenarios according to 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 a thermal power unit operation constraint, a power flow safety constraint, a power balance constraint, and a reserve capacity constraint; The mathematical expression of the first optimization objective is: ； ； in, Indicates the minimize operation; represents the total cost of day-ahead optimal scheduling; 、 and Respectively represent thermal power operation cost, load shedding cost and standby cost; 、 and All are Coal consumption coefficient of the node operation of thermal power unit No. Indicates the Unit power load shedding cost of load nodes; Indicates the The capacity cost of the unit spare capacity provided by the thermal power unit node; Indicates the The capacity cost of the unit reserve capacity provided by the thermal power unit node; Indicates the Thermal power unit node No. Active power during the time period; Indicates the Load node No. Load shedding power during the time period; and Respectively represent Thermal power unit node No. The upper and lower reserve capacities provided during the time period; Set for time period; is the node set of thermal power units; Represents a set of load nodes; The mathematical expression of the thermal power unit operation constraint is: ； in, and Respectively represent Thermal power unit node No. The minimum and maximum active power values ​​for each time period; Indicates the Thermal power unit node No. +1 period of active power; and Respectively represent Maximum ramp-up power and maximum ramp-down power of the thermal power unit node; The mathematical expression of the power flow safety constraint is: ； in, For the Node No. to No. Power transfer distribution factor between signal lines; For the The first one in the day-ahead forecast data group of load node Load power during the time period; No. The first one in the day-ahead forecast data group of wind power node No. Wind power during the period; For the The first day of the forecast data set for photovoltaic node No. PV power during the time period; For the The power upper limit of line No. Line No. is any line in the target provincial power grid; Represents a set of wind power nodes; Represents a set of photovoltaic nodes; The mathematical expression of the power balance constraint is: ； The mathematical expression of the reserve capacity constraint is: ； in, Indicates the concentration of upper reserve capacity demand of target provincial power grid The upper reserve capacity requirement of the time period; Indicates the concentration of reserve capacity demand of target provincial power grid The next spare capacity requirement for the time period.

8. The method for dispatching active power of thermal power storage in a provincial power grid in advance to cope with extreme scenarios according to claim 7, characterized in that: The provincial power grid energy storage power decision model under the N-1 safety constraint includes a second optimization objective and a second constraint condition; the second constraint condition includes a power flow safety constraint under the N-1 safety constraint, a power balance constraint under the N-1 safety constraint, an energy storage model constraint, and a thermal power unit regulation constraint; The mathematical expression of the second optimization objective is: ； ； in, represents the target provincial power grid energy storage power decision dispatch cost; and represent the thermal power regulation cost and energy storage operation cost respectively; and Respectively represent Scene Thermal power unit node No. Upward and downward power regulation for time periods; and Respectively represent Energy storage node No. Charging power and discharging power during the time period; Indicates the The energy storage unit power operation cost of the energy storage node; and To represent the The capacity cost of providing upper unit spare capacity and the capacity cost of providing lower unit spare capacity for the thermal power unit node No. is a collection of energy storage nodes; is the N-1 security constraint line set; Represents the number of N-1 safety constraint line sets; The scenario indicates that only the lines in the target provincial power grid disconnected scenes; The mathematical expression of the power flow safety constraint under the N-1 safety constraint is: ； in, For the Scene Node No. to No. Power transfer distribution factor between signal lines; The mathematical expression of the power balance constraint under the N-1 safety constraint is: ； The mathematical expression of the energy storage model constraint is: ； in, and Respectively represent Energy storage node No. Minimum power and maximum power for each time period; Indicates the Rated energy of the energy storage node; and Respectively represent Energy storage node No. The state of charge of the period and the +1 period of charge state; and Respectively represent The initial period of the energy storage node State of charge and the end of the dispatch period State of charge; and Respectively represent Energy storage node No. The minimum state of charge and maximum state of charge for the time period; and Respectively represent Charging efficiency and discharging efficiency of energy storage nodes; The mathematical expression of the thermal power unit regulation constraint is: 。 9. The method for dispatching active power of thermal power storage in a provincial power grid in advance to cope with extreme scenarios according to claim 8, characterized in that: The process of determining the N-1 security constraint circuit set is as follows: For each line in the target provincial power grid, determine whether the key line determination inequality is satisfied and obtain the judgment result; When all lines in the target provincial power grid are judged to be complete, a set consisting of all lines with a judgment result of yes is determined as an N-1 safety-constrained line set.

10. The method for dispatching active power of thermal power storage in a provincial power grid in advance to cope with extreme scenarios according to claim 9, characterized in that: The mathematical expression of the critical path determination inequality is: ； in, The threshold for determining the critical line.

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