CN114021851A - Multistage correlation section quota adaptability assessment and optimization calculation method - Google Patents

Multistage correlation section quota adaptability assessment and optimization calculation method Download PDF

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CN114021851A
CN114021851A CN202111420779.9A CN202111420779A CN114021851A CN 114021851 A CN114021851 A CN 114021851A CN 202111420779 A CN202111420779 A CN 202111420779A CN 114021851 A CN114021851 A CN 114021851A
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quota
section
stability
fault
adaptability
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任冲
马晓伟
柯贤波
王吉利
王智伟
刘鑫
霍超
程林
贺元康
高玉喜
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Northwest Branch Of State Grid Corp Of China
NR Engineering Co Ltd
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NR Engineering Co Ltd
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    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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    • G06—COMPUTING; CALCULATING OR COUNTING
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Abstract

The invention provides a multilevel correlation section quota adaptability assessment and optimization calculation method, which comprises the steps of firstly constructing an optimization model taking section quota adaptability optimization as a target, then providing a typical demand mode extraction method based on K-means clustering according to load, new energy prediction and market transaction data, and carrying out section quota and conveying demand analysis and adaptability assessment; and finally, calculating a matrix of the associated section quota and the dominant fault stability margin influence factor by using a parameter perturbation method, and carrying out section quota coordination optimization based on the quota adaptability condition and the stability margin influence factor to obtain the associated section quota with the optimal section conveying demand adaptability. The method can effectively reflect the operating characteristics of the high-proportion new energy power grid, provides reference for the optimization formulation of the section quota, and realizes the full play of the efficiency benefit of the power grid and the high-efficiency consumption of new energy.

Description

Multistage correlation section quota adaptability assessment and optimization calculation method
Technical Field
The invention relates to the field of electric power energy, in particular to a method for evaluating and optimizing adaptability of a multistage correlation section quota.
Background
With the advance of novel electric power system, the new forms of energy development will further accelerate, and the electric power system operation mode that uses the new forms of energy as the main part is complicated changeable, and electric power is protected and is supplied and new forms of energy consumption pressure further increases. Meanwhile, the ultrahigh voltage direct current is rapidly developed, the coupling degree of an alternating current and direct current power grid is continuously improved, the inter-section limit association degree and the power grid control complexity are improved, the traditional method for setting the power grid pre-control limit in advance according to an extreme typical mode is difficult to adapt to the complex and changeable characteristics of a novel power system mode and the requirement for supply and consumption protection, the limit adaptability evaluation and the optimization setting need to be actively adapted to the complex and changeable mode, and the power grid control flexibility is improved.
At present, researches aiming at the related section quota mainly focus on a power transmission section identification method and a limit transmission capacity calculation method under various safety constraints, such as a continuous power flow technology, a hierarchical clustering-based power grid partitioning method, a self-adaptive positioning technology, artificial intelligence and the like, and the section limit transmission capacity is solved. In the aspect of typical mode extraction, most researches are based on a power grid simulation sample generation method, user adjustment load flow behavior data in power grid simulation calculation offline data are collected and processed. At present, data improvement research is carried out on the basis of data-driven transient stability evaluation, and the data improvement research can be divided into an under-sampling method and an over-sampling method, wherein the under-sampling method has a limited application range because most types of data information can be lost.
The research aiming at the key section quota optimization of the complex power grid is comprehensive and meticulous, and the results are mainly focused on the research optimization of the available transmission capacity of the power grid. However, with the access of a large number of new energy sources such as wind power, distributed photovoltaic and the like, the source load uncertainty problem is increasingly prominent, and research on section demand adaptability evaluation optimization is less. In addition, extra-high voltage direct current is continuously put into operation, large-capacity fault impact and alternating current-direct current multi-section coupling are increased, the influence of the distribution combination of the tide on the stability characteristic after the fault is aggravated, and the relevance of the transmission quota among the sections is outstanding, so that a method is needed, which can fully consider the space-time complementary characteristic of new energy and carry out the adaptability evaluation and coordination optimization of the quota of the associated section on the basis of considering various uncertain factors.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the invention provides a method for evaluating and optimizing the adaptability of the multi-level associated section quota
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a method for adaptively evaluating and optimally calculating the quota of a multi-level correlation section comprises the following steps,
(1) constructing an optimization model with the optimal section quota adaptability as a target;
(2) determining an initial control limit and an initial stability limit according to a power system stability calculation rule and in combination with an optimization model;
(3) determining the conveying requirements of each section in different time periods of the next day according to the actual load, the forecast of new energy sources in the day and the situation of a transaction plan in the day, and determining a typical requirement mode;
(4) comparing the obtained typical demand mode with the initial stable quota, calculating the quota insufficiency index, if the initial quota does not meet the requirements of the typical mode, solving a section quota adjusting vector by using a power perturbation method based on an initial control quota scheme, and adjusting the section power quota to enable the balance change of the dominant fault stability margin to reach a critical value;
(5) and based on the quota adjusting vector and the obtained new section control quota scheme, carrying out time domain simulation in an adjusted quota mode, solving influence factors of section power and stability margin in the mode, and repeatedly generating the quota adjusting vector according to the step 4 until the adjusted quota meets the requirement of a typical mode or the stability margin of the leading fault reaches a critical value, thereby obtaining the next-day optimal section quota grading scheme.
Preferably, the step (1) is specifically to construct a multistage complex coupling section stable quota optimization model by taking the control quota adaptability to be optimal as a target under the condition of comprehensively considering various safety constraints according to the stable characteristics of the power grid under different operation modes, as shown in formula (1):
Figure BDA0003377305000000021
in the formula: g is power flow constraint of the power system; epsilon is the minimum margin requirement of power grid stability; etajThe system stability margin under fault j; pi requirementIs the delivery requirement of the ith section; pi quotaIs the delivery limit for the ith section; j represents the number of the dominant faults, and m represents the number of the dominant faults; t is t0Is the calculation analysis start time, t is the calculation analysis end time; k is K associated transmission sections.
Preferably, the step (2) specifically includes determining K associated power transmission sections and m dominant faults of the computing system according to a power system stability computing rule, determining an initial control limit based on time domain simulation according to stability characteristics of the dominant faults, gradually and uniformly increasing section power according to the section limit and a stability margin influence factor, and taking each associated section power when the dominant fault stability margin meets a critical stability condition as an initial stability limit.
Preferably, the step (3) is specifically to determine, according to the day-ahead new energy prediction and the day-ahead trading plan, a plurality of associated section transportation demand combinations at each time and having strong correlation with the key fault stability margin, integrate a plurality of section demand values at each time into a multidimensional vector, for the plurality of groups of associated section combination data, implement multidimensional vector clustering by using a K-means method, automatically calculate and search an optimal central point of each classification by using the K-means clustering method after setting a clustering class number, and then calculate an optimal classification number of the associated section by using an elbow rule, that is, calculate a square sum of errors, as shown in formula (2):
Figure BDA0003377305000000031
in the formula, CiIs the ith cluster, p is CiSample point of (1), miIs CiThe SSE is the clustering error of all samples and represents the quality of the clustering effect; the vector with the center point consisting of the centroid mi is multiplied by 1.15 times to obtain a value as a typical mode of demand.
Preferably, the step (4) specifically includes that different stability margin calculation methods need to be determined for different post-fault stability types, quantitative indicators of the stability margins under different faults are different according to different stability problems, and the post-fault stability margins are represented by voltage fluctuation integral values, as shown in formula (3):
Figure BDA0003377305000000032
in the formula etajIs a stable index after the fault j occurs; t is tcClearing the moment for the fault; u shapei,j(t) is the voltage amplitude of the bus i at the moment t after the fault j occurs, and the stability margin describes the area of the bus voltage lower than 0.9p.u. in the transient process after the fault is removed, which represents the stability degree of the quantifiable system;
when a plurality of fault-dominated associated section influence factors are analyzed, the stability margin influence factor of the section quota is expressed by a linear matrix J, as shown in the formulas (4) and (5):
Δη＝JΔP (4)
Figure BDA0003377305000000041
in the formula: Δ pkFor the adjustment variation of section k, Δ P is represented by Δ P1To Δ pkComponent section quota adjustment variation vector, Δ ηmAnd delta eta is a stability margin vector for the stability margin variable quantity after the fault m occurs, and after the matrix is solved, the matrix is used for guiding the quota adjustment vector and evaluating the disturbance rejection capacity of the system.
Preferably, the step (5) is specifically to generate the limit adjustment vector by increasing the section limit which does not meet the conveying requirement and reducing the section limit which exceeds the section requirement and has the highest sensitivity, and the limit adjustment step length is set to 10 ten thousand kilowatts.
Adopt the beneficial effect that above-mentioned technical scheme brought:
the invention provides a multilevel correlation section quota adaptability assessment and optimization calculation method, which comprises the steps of firstly constructing an optimization model taking section quota adaptability optimization as a target, then providing a typical demand mode extraction method based on K-means clustering according to load, new energy prediction and market transaction data, and carrying out section quota and conveying demand analysis and adaptability assessment; and finally, calculating a matrix of the associated section quota and the dominant fault stability margin influence factor by using a parameter perturbation method, carrying out section quota coordination optimization based on the quota adaptability condition and the stability margin influence factor, obtaining the associated section quota with the optimal section transportation demand adaptability, effectively reflecting the operation characteristics of a high-proportion new energy power grid, providing a reference for section quota optimization formulation, and realizing full play of power grid efficiency and benefit and high-efficiency consumption of new energy. Because the coordination optimization is carried out by using the principle of meeting the requirement to the maximum extent and reducing the initial quota to the minimum extent, the adjustment quota is obtained by using the method, the reduction of the integral quota of a plurality of sections is ensured to be minimum, the requirement of a typical mode is met to the maximum extent, and the efficiency benefit of the operation of the power grid is improved.
Drawings
FIG. 1 is a flow chart of a computational method of the present invention;
FIG. 2 is a flow chart of the clustering principle of the present invention;
FIG. 3 is a structure diagram of the network frame of the XJ power grid networking channel of the invention;
FIG. 4 is a diagram of an exemplary daily clustering scenario in accordance with the present invention;
FIG. 5 is an envelope of demand for the conventional classification scheme of the present invention;
fig. 6 is a fault voltage fluctuation curve after the JQ dc commutation fails in four ways according to the present invention.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
The invention is described in further detail below with reference to the accompanying drawings:
referring to the flowchart shown in fig. 1, in order to make up for the deficiencies of the conventional method, the present invention provides a method for adaptive evaluation and optimal calculation of multi-level correlation cross section quota. The method specifically comprises the following steps.
1) And according to the stability characteristics of the power grid in different operation modes, under the condition of comprehensively considering various safety constraints, and with the aim of optimal control quota adaptability, constructing a multi-stage complex coupling section stability quota optimization model as shown in the formula (1).
Figure BDA0003377305000000051
In the formula: g is power flow constraint of the power system; epsilon is the minimum margin requirement of power grid stability; etajThe system stability margin under fault j; pi requirementIs the delivery requirement of the ith section; pi quotaIs the delivery limit for the ith section; j represents the dominant fault number, and m represents the dominant fault number. 2) Determining K associated power transmission sections and m dominant faults according to operation experience and a stable calculation rule, and determining an initial control quota scheme based on a time domain simulation result according to the dominant faults; t is t0Is the calculation analysis start time, and t is the calculation analysis end time.
3) And determining the conveying requirements of each section in different time periods of the next day according to the load and the new energy prediction, and determining a main typical requirement mode by using a clustering method to avoid excessive quota.
As shown in fig. 2, according to the future new energy prediction and the future trading plan, a plurality of related section transportation demand combinations having strong correlation with the key fault stability margin at each time can be determined, and a plurality of section demand values at each time can be integrated into a multidimensional vector. Aiming at the multiple groups of associated section combined data, multi-dimensional vector clustering is realized by using a K-means method, the optimal central point of each classification can be automatically calculated and searched after the clustering class number is set by using the K-means clustering method, and then the optimal classification number of the associated sections is calculated by using an elbow rule, namely SSE (sum of the squared errors) is calculated, as shown in formula (2).
Figure BDA0003377305000000061
In the formula, CiIs the ith cluster, p is CiSample point of (1), miIs CiCenter of mass (C)iMean of all samples), SSE is the clustering error of all samples, and represents how good the clustering effect is. As the clustering number K increases, the sample division becomes finer, the aggregation degree of each cluster gradually increases, and then the sum of squared errors SSE naturally becomes smaller. And when K is smaller than the real cluster number, the aggregation degree of each cluster is greatly increased due to the increase of K, so that the descending amplitude of the SSE is large, and when K reaches the real cluster number, the return of the aggregation degree obtained by increasing K is rapidly reduced, so that the descending amplitude of the SSE is rapidly reduced and then tends to be gentle along with the continuous increase of the K value, that is, the relation graph of the SSE and the K is in the shape of an elbow, and the K value corresponding to the elbow is the optimal cluster number of the data. And taking factors such as the prediction accuracy of the section demand, the maximum value of the central point which probably does not belong to various combinations and the like into consideration, and taking the value obtained by multiplying the central point of each type by 1.15 times as a demand typical mode according to the operation experience.
4) And counting that the leading fault stability margin meets the critical stability condition, calculating the stability margin under each section demand mode, and judging whether the stability margin meets the requirement under the main typical demand mode. If the fault stability margin does not meet the requirement, based on the initial control limit scheme, a power perturbation method is used for solving a section limit adjustment vector, and the section power limit is adjusted globally, so that the balance change of the dominant fault stability margin reaches a critical value.
Different stability margin calculation methods need to be determined for different stability types after faults, and according to different stability problems, the common methods include methods such as EEAC (energy efficiency access control), transient voltage margin and the like. The quantitative indexes of the stability margins under different faults are different, for example, the common transient voltage stability problem of the northwest power grid is taken as an example, the voltage fluctuation degree after the fault reflects the disturbance rejection capability of the power grid, and the stability margin after the fault can be represented by a voltage fluctuation integral value as shown in a formula (3).
Figure BDA0003377305000000071
In the formula etajIs a stable index after the fault j occurs; t is tcClearing the moment for the fault; u shapei,jAnd (t) is the voltage amplitude of the bus i at the time t after the fault j occurs. The stability margin describes the area of bus voltage lower than 0.9p.u. in the transient process after the fault is removed, and the system stability degree can be quantified.
Because the power system has strong time-varying property and nonlinearity, the relationship between the stability margin and the characteristic quantity is difficult to express by an analytic expression, the stability margin change is solved by perturbation of characteristic quantity parameters by adopting the neighborhood expansion in an operation mode, and therefore the influence factor of the characteristic quantity in a certain range on the stability margin is obtained. When analyzing the associated section influence factors of a plurality of fault dominance, the stability margin influence factor of the section quota can be represented by a linear matrix, as shown in formulas (4) and (5).
Δη＝JΔP (4)
Figure BDA0003377305000000072
In the formula: Δ pkFor the adjustment variation of section k, Δ P is represented by Δ P1To Δ pkComponent section quota adjustment variation vector, Δ ηmThe variation of the stability margin after the fault m occurs, and Δ η is a stability margin vector. The stability margin after the main constraint fault is related to the cascade section power, but the stability margin after the fault is affected differently by different section powers, and after the matrix is solved, the matrix can be used for guiding quota adjustment vectors and evaluating the disturbance rejection capability of the system.
5) The new section control limit scheme obtained based on solving is compared with the section requirement, the section power and stability margin influence factor is solved, the section limit which does not meet the conveying requirement is locally increased, the section limit which exceeds the section requirement and has the highest sensitivity is reduced, the difference value between the section limit and the section requirement is minimum, meanwhile, the whole section limit is reduced to the minimum, and the next-day optimal section limit grading scheme is obtained.
In order to verify the above proposed method for evaluating and calculating optimization of multilevel associated section quota, taking the analysis of the adaptability of the multilevel associated section quota of the XJ power grid as an example, fig. 3 shows a grid structure diagram of a networking channel of the XJ power grid. Because the XJ networking channel is a long chain type channel for relay power transmission, the channel requirement has great correlation with factors such as XJ outgoing power, HX new energy internet access, QHHX photovoltaic output and the like, and HX wind power and QHHX photovoltaic space-time distribution are different, the distribution of the section outgoing requirement is not uniform, and the conventionally specified grading control limit is difficult to meet the requirement.
Therefore, in order to improve the overall transmission efficiency of the networking channel, a typical delivery demand mode needs to be obtained according to prediction and market conditions, and overall optimization is developed. Firstly, market trading is predicted according to load and new energy, JQ direct current power, HX and SY section sending requirements and XJWS section requirements are determined, and a typical requirement mode is statistically mined by utilizing a clustering algorithm, as shown in table 1. According to the clustering result, the clustering result of a typical day is divided into 4 classes according to the elbow rule, as shown in fig. 4.
TABLE 1 typical demand patterns derived from clustering
Figure BDA0003377305000000081
The typical demand mode generated by multiplying the center point of the four-category mode by 1.1 times is shown in the following table, and it can be seen that only the typical demand mode 1 completely meets the conventional mode grading, and the conventional limits of the other 3 typical demand modes cannot be well met. The envelope of the conventional way set limit to demand way is shown in fig. 5, and in order to meet the transportation demand, the conventional initial limit needs to be coordinated and optimized. In order to evaluate the quota adaptability, a quota insufficiency index is provided, as shown in formula (6).
Figure BDA0003377305000000091
In the formula, l is the number of the sections with the section requirement larger than the initial limit of the sections, m is the number of the sections with the section requirement smaller than the initial limit of the sections, and Pi requirement、Pi quotaRespectively the requirements and the limits of the section i under the characteristic requirement mode. The quota insufficiency indexes of the four typical demand modes are respectively 0, 0.29/2.5, 0.49/0.84 and 0.34/0.48 according to the formula (6), so that the demand modes 2, 3 and 4 have optimization spaces, meanwhile, the shortage part of the demand mode 2 is less, the surplus part is larger and is easy to optimize, the proportion of the surplus part and the shortage part of the demand mode 4 is relative, and the optimization difficulty is the greatest.
And then optimizing the limits of the XJWS section, the DY section, the GY section, the QY section, the HY section and the HXSC section. In the above networking channel calculation example, the main fault constraints are a JQ direct-current high-power commutation failure fault and a low-voltage problem caused by power flow transfer after a HW double-circuit line N-2 same-pole different-phase fault, and therefore, a stability margin and a section power influence factor matrix are obtained by taking a transient voltage fluctuation integral quantity after the fault as a stability margin quantization index.
Taking optimization in the demand mode 2 as an example, the quota adjustment vector in each step is obtained by combining the section influence factor matrix and evaluation of demand and quota fitness, the specific iteration process is shown in table 2, and after x iterations, the quota optimized in the demand mode 2 is obtained, as shown in table 3. The indexes of the insufficient degree of the section requirements of the optimized typical mode are all 0, which shows that the optimized limitation can meet the sending requirements of the typical day in the relevant time period, the problem of new energy consumption and sending of the typical day can be effectively solved, and the effectiveness and the practicability of the method are verified.
TABLE 2 iterative computation procedure
Figure BDA0003377305000000092
Figure BDA0003377305000000101
TABLE 3 iterative computation procedure
XJWS DY GY QY HY HXWS
Conventional grading mode 250 460 440 460 630 320
Typical demand pattern 2 309 305 216 229 227 40
Optimizing quota for demand mode 2 320 400 380 340 520 220
Typical requirement mode 3 363 401 352 452 657 160
Optimizing quota for demand mode 3 375 430 430 460 685 198
Typical demand pattern 4 275 400 323 511 580 360
Optimizing quota for demand mode 4 298 430 367 535 590 382
For further research on the disturbance rejection capability of the operation mode of the power grid after the optimization of the quota, taking JQ direct current commutation failure as an example, it is shown that the voltage fluctuation trends in the four quota modes are similar, and the stability margin meets the requirement, as shown in FIG. 6.
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.

Claims (6)

1. A multi-level correlation section quota adaptability assessment and optimization calculation method is characterized by comprising the following steps,
(1) constructing an optimization model with the optimal section quota adaptability as a target;
(2) determining an initial control limit and an initial stability limit according to a power system stability calculation rule and in combination with an optimization model;
(3) determining the conveying requirements of each section in different time periods of the next day according to the actual load, the forecast of new energy sources in the day and the situation of a transaction plan in the day, and determining a typical requirement mode;
(4) comparing the obtained typical demand mode with the initial stable quota, calculating the quota insufficiency index, if the initial quota does not meet the requirements of the typical mode, solving a section quota adjusting vector by using a power perturbation method based on an initial control quota scheme, and adjusting the section power quota to enable the balance change of the dominant fault stability margin to reach a critical value;
(5) and (3) based on the quota adjusting vector and the obtained new section control quota scheme, carrying out time domain simulation in an adjusted quota mode, solving influence factors of section power and stability margin in the mode, and repeatedly generating the quota adjusting vector according to the step (4) until the adjusted quota meets the requirement of a typical mode or the stability margin of the leading fault reaches a critical value, thereby obtaining the next-day optimal section quota grading scheme.
2. The method for evaluating and calculating the multistage correlation section quota adaptability according to claim 1, wherein the step (1) is specifically to construct a multistage complex coupling section stability quota optimization model with the objective of optimal control quota adaptability under the condition of comprehensively considering various safety constraints according to the stability characteristics of the power grid under different operation modes, as shown in the formula (1):
Figure FDA0003377304990000011
in the formula: g is power flow constraint of the power system; epsilon is the minimum margin requirement of power grid stability; etajThe system stability margin under fault j; pi requirementIs the delivery requirement of the ith section; pi quotaIs the delivery limit for the ith section; j represents the number of the dominant faults, and m represents the number of the dominant faults; t is t0Is the calculation analysis start time, t is the calculation analysis end time; k is K associated transmission sections.
3. The multi-level associated section quota adaptability assessment and optimization calculation method according to claim 1, wherein the step (2) specifically comprises determining K associated transmission sections and m dominant faults of the calculation system according to a power system stability calculation rule, determining an initial control quota based on time domain simulation according to stability characteristics of the dominant faults, gradually balancing and increasing section power according to section quota and stability margin influence factors, and taking each associated section power when the stability margin of the dominant fault meets a critical stability condition as an initial stability quota.
4. The method for the adaptive evaluation and optimization calculation of the multistage correlation section quota according to claim 1, wherein the step (3) is specifically that according to the prediction of new energy resources in the day ahead and the day ahead trading plan, a plurality of correlation section transportation demand combinations which are strongly related to the key fault stability margin at each moment can be determined, a plurality of section demand values at each moment are integrated into a multidimensional vector, for the plurality of groups of correlation section combination data, the multidimensional vector clustering is realized by using a K-means method, after the clustering category number is set, the optimal central point of each classification can be automatically calculated and found by using a K-means clustering method, then the optimal classification number of the correlation section is found by using an elbow rule, namely, the square sum of errors is calculated, and the formula (2) shows that:
Figure FDA0003377304990000021
in the formula, CiIs the ith cluster, p is CiSample point of (1), miIs CiThe SSE is the clustering error of all samples and represents the quality of the clustering effect; with the central point as the mass center miThe vector of the composition is multiplied by 1.15 times as much as a typical way of demand.
5. The method for adaptive evaluation and optimization calculation of the multi-level correlated fracture surface quota according to claim 1, wherein the step (4) is specifically a calculation method for determining different stability margins according to different stability problems, quantization indexes of the stability margins under different faults are different according to different stability problems, and the stability margin after the fault is represented by a voltage fluctuation integral value as shown in formula (3):
Figure FDA0003377304990000031
in the formula etajIs a stable index after the fault j occurs; t is tcClearing the moment for the fault; u shapei,j(t) is the voltage amplitude of the bus i at the moment t after the fault j occurs, and the stability margin describes the area of the bus voltage lower than 0.9p.u. in the transient process after the fault is removed, which represents the stability degree of the quantifiable system;
when a plurality of fault-dominated associated section influence factors are analyzed, the stability margin influence factor of the section quota is expressed by a linear matrix J, as shown in the formulas (4) and (5):
Δη＝JΔP (4)
Figure FDA0003377304990000032
in the formula: Δ pkFor the adjustment variation of section k, Δ P is represented by Δ P1To Δ pkComponent section quota adjustment variation vector, Δ ηmAnd delta eta is a stability margin vector for the stability margin variable quantity after the fault m occurs, and after the matrix is solved, the matrix is used for guiding the quota adjustment vector and evaluating the disturbance rejection capacity of the system.
6. The adaptive evaluation and optimization calculation method for the multilevel correlation section limit according to claim 1, wherein the step (5) is specifically to generate the limit adjustment vector by increasing the section limit which does not meet the transmission requirement and reducing the section limit which exceeds the section requirement and has the highest sensitivity, and the limit adjustment step is set to 10 ten thousand kilowatts.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117374986A (en) * 2023-09-15 2024-01-09 国家电网有限公司华东分部 Power grid thermal stability quota calculation method, device, equipment and medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117374986A (en) * 2023-09-15 2024-01-09 国家电网有限公司华东分部 Power grid thermal stability quota calculation method, device, equipment and medium
CN117374986B (en) * 2023-09-15 2024-03-08 国家电网有限公司华东分部 Power grid thermal stability quota calculation method, device, equipment and medium

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