CN112769124B - Power system rapid operation risk assessment method based on tide transfer and tracking - Google Patents
Power system rapid operation risk assessment method based on tide transfer and tracking Download PDFInfo
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- H—ELECTRICITY
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
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Abstract
The invention provides a power system rapid operation risk assessment method based on tide transfer and tracking. The method comprises the following steps: initializing parameters; simulating and extracting the running state of the system based on the non-sequential Monte Carlo; analyzing the system state by adopting an improved power flow calculation method and a load reduction model based on power flow tracking; and calculating an operation risk assessment index. The rapid load flow calculation is improved by deducing node transfer distribution factors and branch break distribution factors applicable to multi-branch break; the load reduction model based on the power flow tracking is established, the most effective control node set is screened out by adopting the power flow tracking theory, the optimization in the whole system range is converted into the optimization in the local range, and the improved rapid power flow calculation method and the load reduction model are applied to the power system operation risk assessment, so that the assessment precision is higher, and meanwhile, the assessment efficiency is greatly improved.
Description
Technical Field
The invention belongs to the field of power systems, and particularly relates to a power system rapid operation risk assessment method based on tide transfer and tracking.
Background
In recent years, a plurality of power system blackout accidents occur globally, and certain economic losses are caused for the country and residents. In order to reduce the occurrence of similar power accidents and ensure safe and stable operation of a power system, power system operation risk assessment is widely focused on by various countries. Because the power system has a complex structure and numerous elements, the risk assessment needs to perform state analysis on a large number of fault scenes, especially, the load flow iterative computation and the load reduction computation on multiple fault scenes cannot meet the timeliness of the operation risk assessment. Therefore, improving the efficiency of the state analysis is a key to improving the calculation efficiency of the risk assessment of the power system.
At present, in order to improve the efficiency of system state analysis, domestic and foreign scholars are studied from two aspects of tide calculation and load reduction calculation. In the aspect of tide calculation, zhang Jun, shen Chen, liu Feng and the like propose a calculation method of probability tide joint distribution of a power system comprising a plurality of wind power stations (patent number: CN 201610868051.5), in the power system comprising the wind power stations, line power change caused by wind power prediction errors is rapidly calculated through a power generation distribution transfer factor, but the lack of a node transfer factor describes line power change caused by power generation and load joint change; ren Jianwen, yi Chen and He Peicheng propose a multi-branch power flow transfer search algorithm (patent number: CN 201610894006.7) based on a virtual branch model and FTIL, based on double-branch break, the multi-branch fault is converted into a double-branch fault based on the virtual branch model, and the calculation of the branch break distribution factor is deduced, but the equivalent times are excessive and the calculation is complex when calculating the multi-branch break distribution factor; in terms of load shedding calculation, mo Wenxiong, wang Gong, luan Le and the like propose a method and a system for determining the load shedding amount of a power system (patent number: CN 201710743244.2), and high-sensitivity nodes are screened as a load shedding range by calculating the sensitivity of out-of-limit branch power to node injection power, so that the calculation efficiency of load shedding is improved, but the process for solving a sensitivity index is complex.
Disclosure of Invention
Based on the problems, the invention realizes rapid power flow calculation by deducing node transfer distribution factors and branch break distribution factors from the perspective of improving the system state analysis efficiency; and establishing a load reduction model based on load flow tracking, converting global optimization into local optimization, and realizing rapid running risk assessment based on rapid load flow calculation and load reduction model improvement. The effectiveness and accuracy of the invention are verified by introducing cut load risk indexes and line out-of-limit risk indexes through calculation examples.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
step 1, initializing parameters;
step 2, a generator and transmission line shutdown model is established, and sampling times k=1 are initialized;
step 3, obtaining the running state of the system by adopting non-sequential Monte Carlo sampling;
step 4, calculating the power flow of each line by adopting an improved rapid power flow calculation method, judging whether the power flow of each line is out of limit, if so, continuing the step 5, otherwise, continuing the step 6;
step 5, calculating load shedding amount by adopting a load shedding model based on power flow tracking, and returning to the step 4;
step 6, judging whether the sampling frequency of the system reaches the maximum sampling frequency, if so, calculating a risk index and continuing the step 7, otherwise, making k=k+1, and returning to the step 3;
and 7, outputting a system running risk assessment index.
As a supplement to the above technical solution, the improved fast power flow calculation method mentioned in step 4 is as follows:
step 4-1, calculating a node branch admittance matrix, a node branch association matrix and a node transfer distribution factor of the system;
step 4-1, determining a generator, a load power change node set and a fault branch set;
step 4-3, calculating a branch break distribution factor;
step 4-4, calculating line power flow by using the node change power and the node transfer distribution factor:
wherein ,Gk-i Representing node transfer distribution factors of the node i to the branch k; p (P) l(k) Representing the power flow on the kth line after the injection power of the node i changes;representing the initial power flow of the kth line; m is M k A kth column vector representing a node-branch association matrix; x is X i An ith column vector representing the node impedance matrix X; x is x k The reactance value of the kth branch; ΔP i Representing the injection power variation value of the node i;
step 4-5, solving the non-fault line power flow by using the fault line power flow and the branch break distribution factor:
wherein ,after representing the line fault, the active power column vector of the non-fault line; />An initial active power column vector representing a non-faulty line; />An initial active power column vector representing a faulty line; d (D) M-O Representing the branch break distribution factor; x is X M A diagonal array of reactance values representing non-faulty lines; x is X O A diagonal array of reactance values representing a faulty line; phi represents a node-non-fault branch correlation matrix; psi represents a node-fault branch correlation matrix; b (B) 0 Is an initial node admittance matrix.
In addition to the above, the improved load shedding model construction method mentioned in step 5 is as follows:
step 5-1, adopting sequential tide tracking to calculate a power drawing coefficient of the power transmission line to the generator; calculating a power distribution coefficient of the power transmission line to the load node by adopting reverse sequence power flow tracking:
wherein ,PGG A diagonal matrix of generator node power; p (P) LL A diagonal matrix for the load node power; p (P) TT Injecting a power diagonal matrix for the node; p (P) Gi→s-t For line s-t pair generator G i Is used for drawing power; p (P) Li←s-t For line s-t to load L i Is allocated to the power distribution of the power supply;
step 5-2, determining a generator node flowing into the branch power and a load node flowing out of the branch power by utilizing a breadth first search algorithm (BFS) according to the association information of the overload branch and each generator/load node;
step 5-3, setting a threshold value, and screening out a generator node set and a load node set with larger drawing coefficients and distribution coefficients as an effective control node set;
step 5-4, using the screened load node set and generator node set as local optimizing ranges, converting optimizing in the whole system range into optimizing in the local range, and establishing a load reduction model based on tide tracking:
the objective function is:
wherein ,Cd Indicating the system load reduction amount; c (C) i A load reduction amount indicating a node i;
the constraint conditions are as follows:
a. system power balance constraint
Wherein NG and ND represent all generators and load node sets of the system;
b. generator output constraint
wherein ,PG Representing the generator node active vector; and />Representing P G Upper and lower limits of (2);
c. load shedding constraints
0≤C i ≤P D
wherein ,PD Representing the generator node active vector; c represents a node cut load vector;
d. line active power constraint
wherein ,T(Sj ) Is shown in the system state S j A lower line active vector; a (S) j ) Is shown in the system state S j Injecting an active relation matrix into the lower line active and the node; t (T) max Representing the maximum active vector that the line is allowed to pass through.
In addition to the above technical solution, the method for constructing the running risk assessment index mentioned in step 7 is as follows:
each system state E k The running risk of the system is the product of the state probability of the generator and the power transmission line and the severity of the consequences in the state, the running risk assessment index of the system is the sum of all the system state risk values, and the system load shedding risk index is as follows:
wherein ,Rcut Representing a cut load risk; sev ev-cut (E k ) Representing the severity of load shedding results in the kth system state; l (L) cut (E k ) Representing the per unit value of the cut load quantity in the kth system state; p (P) load Representing the current system load per unit value;
the active power out-of-limit risk index of the system line is as follows:
wherein ,Rol Representing the risk of line active power out-of-limit; sev ev-ol (E k ) Representing the kth systemThe severity of the line active out-of-limit consequences in the state; p (P) l Representing the active power of the line i,indicating the maximum active power allowed by line l.
Compared with the prior art, the method and the device realize rapid power flow calculation by deducing the node transfer distribution factors and the branch break distribution factors; the global optimization is converted into the local optimization by establishing a load reduction model based on the load flow tracking, and the quick running risk assessment is realized based on quick load flow calculation and load reduction model improvement. The beneficial effects are as follows: the node transfer distribution factors and the branch break distribution factors are adopted to calculate the power flow, so that iterative calculation of the power flow is avoided; the load reduction model based on the power flow tracking is established, an effective control node set is screened out by adopting the power flow tracking theory, the optimization in the whole system range is converted into the local range optimization, and the running risk assessment by adopting the scheme provided by the invention has higher assessment precision, and meanwhile, the assessment efficiency is greatly improved.
Drawings
Fig. 1 is a flow chart of a fast running risk assessment of a power system.
Fig. 2 is a flow chart of an improved fast power flow calculation method.
Fig. 3 is a flow chart of a load shedding method based on load flow tracking.
FIG. 4 is a graph of the power draw coefficient of a portion of a line versus a generator node.
Fig. 5 is a graph of the power distribution coefficients of lines 11-13 for each load node.
Fig. 6 is a diagram of the IEEE RTS79 system configuration.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings.
The invention relates to a power system rapid operation risk assessment method based on power flow transfer and power flow tracking, which is known from a rapid operation risk assessment flow in fig. 1, and comprises the following specific steps:
step 1, initializing parameters;
the number of generators, rated capacity, active output and failure rate; the number of transmission lines, starting point and end point serial numbers, branch impedance parameters, transmission capacity limit and fault rate; load level of each node and the like; the total number of monte carlo samples K.
Step 2, a generator and transmission line shutdown model is established, and the specific calculation method comprises the following steps:
step 2-1, a generator shutdown model;
the simulation is performed using a two-state model, i.e. only two states, operational and faulty. In general, the operation risk assessment period is shorter than the repair time of the generator, so that the generator is considered to be unrepairable in the current assessment period, namely, the repair rate is considered to be zero. The real-time state probability of the generator set is as follows:
wherein ,pge The real-time state probability of the generator set is obtained; lambda (lambda) g The failure rate of the generator; m represents a generator in an off-line state; m' represents a generator in an operating state; p is p g Representing the real-time state probability of the generator.
Step 2-2, a power transmission line shutdown model;
the outage model of the power transmission line is similar to that of the generator, and the real-time state probability of the power transmission line is as follows:
wherein ,pli The real-time state probability of the power transmission line is obtained; lambda (lambda) l The fault rate of the power transmission line; n represents a generator in an off-stream state; n' represents a generator in an operating state; p is p l And representing the real-time state probability of the power transmission line.
And step 3, obtaining the running state of the system by adopting non-sequential Monte Carlo sampling, which specifically comprises the following steps:
step 3-1, modeling to generate mutually independent compliance [0,1 ]]Uniformly distributed n g The random numbers are screened out, and the random numbers smaller than the forced outage rate of the generator are selected, so that the running state of the generator is extracted;
wherein ,sg Representing the state of the generator g; "1" means a normal state; "0" indicates a fault condition.
Step 3-2, modeling to generate mutually independent compliance [0,1 ]]Uniformly distributed n l The random numbers are screened out, and the random numbers smaller than the forced outage rate of the power transmission line are selected, so that the running state of the power transmission line is extracted;
wherein ,sl Indicating the status of the transmission line l.
Step 3-3, combining the extracted running states of the generator and the power transmission line to form a system state;
s＝(s 1 ，...，s g ，...，s l )
step 4, calculating the power flow of each line by adopting an improved rapid power flow calculation method, judging whether the power flow of each line is out of limit, if so, continuing the step 5, otherwise, continuing the step 6;
as shown in fig. 2, the improved power flow calculation method includes the following steps:
step 4-1, calculating a node branch admittance matrix, a node branch association matrix and a node transfer change factor of the system;
step 4-2, determining a generator, a load power change node set and a fault branch set;
step 4-3, calculating line power flow by using the node change power and the node transfer distribution factor;
wherein ,Gk-i Representing node transfer distribution factors of the node i to the branch k; p (P) l(k) Representing the power flow on the kth line after the injection power of the node i changes;representing the initial power flow of the kth line; m is M k A kth column vector representing a node-branch association matrix; x is X i An ith column vector representing the node impedance matrix X; x is x k The reactance value of the kth branch; ΔP i Indicating the node i injection power variation value.
And 4-4, calculating a branch break distribution factor, and solving the non-fault line power flow by using the fault line power flow and the branch break distribution factor.
After the line fails, the relationship between the failed line flow and the non-failed line flow is:
wherein ,after representing the line fault, the active power column vector of the non-fault line; />An initial active power column vector representing a non-faulty line; />An initial active power column vector representing a faulty line; d (D) M-O Representing the branch break distribution factor.
Relationship between node injection power and line power:
wherein ,XM A diagonal array of reactance values representing non-faulty lines; x is X O A diagonal array of reactance values representing a faulty line; phi represents a node-non-fault branch correlation matrix; psi represents a node-fault branch correlation matrix; b (B) 0 An initial node admittance matrix; b (B) c The node admittance matrix is the node admittance matrix after the line fault; p represents the node injection active power column vector.
Obtaining the relation of the node admittance matrix before and after line faults according to the compensation theorem:
and carrying the fourth expression and the fifth expression into a third expression to obtain a calculation expression of the branch break distribution factor:
D M-O ＝X M-O (E-X O-O ) -1
wherein ：
step 5, calculating load shedding amount by adopting a load shedding model based on power flow tracking, and returning to the step 4;
as shown in fig. 3, the load shedding model based on the load flow tracking includes the steps of:
step 5-1, as shown in fig. 4 and 5, calculating a power drawing coefficient of the power transmission line to the generator node by adopting sequential power flow tracking; calculating a power distribution coefficient of the power transmission line to the load node by adopting reverse sequence power flow tracking:
wherein ,PGG Diagonal matrix for generator node power, P GG ＝diag(P G1 ，P G2 ，...，P Gn )；P TT Injecting power into a diagonal matrix, P TT ＝diag(P T1 ，P T2 ，...，P Tn )；P LL For the diagonal matrix of load node power, P LL ＝diag(P L1 ，P L2 ，...，P Ln )；P Gi→s-t For line s-t pair generator G i Is used for drawing power; p (P) Li←s-t For line s-t to load L i Is used for drawing power;
step 5-2, determining the generator node flowing into the branch power and the load node flowing out of the branch power by using a breadth first search algorithm (BFS) according to the association information of the overload branch and each generator/load node:
wherein m and n represent the number of generators and load nodes associated with the overload branch;
step 5-3, based on an objective function and constraint conditions of a traditional load reduction model, taking the screened load node set and generator node set as local optimizing ranges, converting optimizing in a whole system range into optimizing in a local range, and establishing a load reduction model based on load flow tracking:
the objective function is:
wherein ,Cd Indicating the system load reduction amount; c (C) i A load reduction amount indicating a node i;
the constraint conditions are as follows:
a. system power balance constraint
Wherein NG and ND represent all generators and load node sets of the system;
b. generator output constraint
wherein ,PG Representing the generator node active vector; and />Representing P G Upper and lower limits of (2);
c. load shedding constraints
0≤C i ≤P D
wherein ,PD Representing the generator node active vector; c represents a node cut load vector;
d. line active power constraint
wherein ,T(Sj ) Is shown in the system state S j A lower line active vector; a (S) j ) Is shown in the system state S j Injecting an active relation matrix into the lower line active and the node; t (T) max Representing the maximum active vector that the line is allowed to pass through.
Step 6, judging whether the sampling frequency of the system reaches the maximum sampling frequency, if so, calculating a risk index and continuing the step 7, otherwise, making k=k+1, and returning to the step 3;
and 7, outputting a system running risk assessment index.
The specific construction method of the running risk assessment index in the step 7 is as follows:
each system state E k The running risk of the system is the product of the state probability of the generator and the power transmission line and the severity of the consequences in the state, the running risk assessment index of the system is the sum of all the system state risk values, namely the running risk assessment model is as follows:
wherein ,pg (E k )、p l (E k ) Representing system state E k Real-time state probability of the generator and the transmission line; sev ev (E k ) Represented in system state E k Severity of system failure consequences at time;
the system load shedding risk index is:
wherein ,Rcut Representing a cut load risk; sev ev-cut (E k ) Representing the severity of load shedding results in the kth system state; l (L) cut (E k ) Representing the per unit value of the cut load quantity in the kth system state; p (P) load Representing the current system load per unit value;
the active power out-of-limit risk index of the system line is as follows:
wherein ,Rol Representing the risk of line active power out-of-limit; sev ev-ol (E k ) Representing the severity of the active out-of-limit consequences of the line in the kth system state; p (P) l Representing the active power of the line i,indicating the maximum active power allowed by line l.
The method designed by the invention is verified by a simulation example.
The IEEE-RTS79 test system is taken as an example system to carry out simulation verification on the rapid operation risk assessment method designed by the invention, fig. 6 is a structural diagram of the simulation system, and a non-sequential Monte Carlo sampling method is adopted to carry out risk assessment on the power system, wherein the sampling frequency is 8000.
Calculation example 1:
sampling scenario 1: line 12-13 failure, generator 12 failure;
sampling scenario 2: line 1-3 failure, line 12-23 failure, line 17-18 failure, generator 21 failure.
The method and the traditional method designed by the invention are adopted to carry out power flow calculation, the power flow calculation results of the two are shown in tables 1 and 2, and the calculation efficiency is shown in table 3.
As can be seen from the calculation results of tables 1 and 2, the calculation results of the fast power flow calculation method related by the invention and the calculation results of the alternating current power flow calculation method are very close, the error is very small, and the fast power flow calculation method provided by the invention is verified to be suitable for power flow calculation in single-branch or multi-branch faults within the error allowable range, thereby meeting the accuracy requirement.
As can be seen from the calculation efficiency of the table 3, the method of the design of the invention adopts the node transfer distribution factor and the branch break distribution factor to carry out the load flow calculation on the basis of the original load flow, thereby avoiding the problem of load flow iterative calculation, saving about 90 percent of time and having high calculation efficiency.
Calculation example 2:
sampling scenario 1: line 3-9 failure, line 10-11 failure, generator 32 failure;
sampling scenario 2: line 6-10 failure, line 18-21 failure, generator 21 failure;
sampling scene 3: line 9-11 failure, line 9-12 failure, generator 4 failure.
The load shedding amount of each sampling scene was calculated using the modified load shedding model and the normal load shedding model, the calculation results are shown in table 4, and the calculation efficiency is shown in table 5.
As can be seen from the load shedding calculation results of table 4, the load shedding amount calculated by the present invention is very close to the optimal solution obtained by optimizing the conventional model based on the whole system. The load reduction range is an effective control node set screened by adopting a tide tracking theory, and the model provided by the invention comprises the most effective control node set of the conventional model.
As can be seen from the calculation efficiency of Table 5, compared with the conventional model, the calculation load reduction method has the advantages that the number of optimized variables and the nodes to be regulated are greatly reduced, the time can be saved by 45% and more, and the calculation efficiency is very high.
Calculation example 3: the rapid operation risk assessment method and the conventional operation risk assessment are adopted to calculate the cut load risk index and the line out-of-limit risk index, the risk index calculation result is shown in table 6, and the calculation efficiency comparison result is shown in table 7.
As can be seen from the data in tables 6 and 7, the running risk assessment indexes calculated by the two models are very close, compared with the conventional risk assessment model, the model can save 69.39% of time, and the rapid running risk assessment model designed by the invention is verified to have higher assessment precision and faster assessment efficiency.
Table 1 shows the comparison of the power flow calculations for sample scenario 1 of example 1.
Table 2 shows the comparison of the power flow calculations for sample scenario 2 of example 1.
Table 3 shows the comparison of the calculation efficiencies of sample scenario 1 and sample scenario 2 of example 1.
Table 4 shows the load reduction comparison results for sample scenario 1, 2, and 3 of example 2.
Table 5 shows the comparison of the calculation efficiencies of sample scenario 1, 2, and 3 in example 2.
Table 6 shows the risk index comparison results.
Table 7 shows the results of the risk assessment calculation efficiency.
TABLE 1
TABLE 2
TABLE 3 Table 3
TABLE 4 Table 4
TABLE 5
TABLE 6
TABLE 7
From the results, the invention adopts the theory of power flow transfer and power flow tracking to realize the rapid running risk assessment of the power system, and is a new attempt. According to the method, the node transfer distribution factors and the branch break distribution factors suitable for breaking a plurality of branches are deduced, the factors are adopted to realize rapid power flow calculation on the basis of the original power flow of the system, so that iterative calculation of power flow is avoided, and the calculation speed is improved while the calculation accuracy is ensured; the most effective control node set for the overload branch is screened out by adopting a power flow tracking theory, the control node set is used as a load reduction range, a load reduction model based on power flow tracking is established, and the optimization in the whole system range is converted into the local range optimization, so that the load reduction calculation efficiency is greatly improved; the running risk assessment by adopting the scheme of the invention has higher assessment precision, and simultaneously, the assessment efficiency is greatly improved.
Claims (3)
1. A power system rapid operation risk assessment method based on tide transfer and tracking is characterized in that: the method comprises the following steps:
step 1, initializing parameters;
step 2, a generator and transmission line shutdown model is established, and sampling times k=1 are initialized;
step 3, obtaining the running state of the system by adopting non-sequential Monte Carlo sampling;
step 4, calculating the power flow of each line by adopting an improved rapid power flow calculation method, judging whether the power flow of each line is out of limit, if so, continuing the step 5, otherwise, continuing the step 6;
step 5, calculating load shedding amount by adopting a load shedding model based on power flow tracking, and returning to the step 4;
step 6, judging whether the sampling frequency of the system reaches the maximum sampling frequency, if so, calculating a risk index and continuing the step 7, otherwise, making k=k+1, and returning to the step 3;
step 7, outputting a system running risk assessment index; the improved rapid power flow calculation method in the step 4 is to realize rapid power flow calculation by adopting node transfer distribution factors and branch break distribution factors on the basis of the original power flow of the system, and comprises the following specific steps:
step 4-1, calculating a node admittance matrix, a node-branch correlation matrix and a node transfer distribution factor of the system;
step 4-2, determining a generator, a load power change node set and a fault branch set;
step 4-3, calculating a branch break distribution factor;
step 4-4, calculating line power flow by using the node change power and the node transfer distribution factor:
wherein ,Gm-i Representing node transfer distribution factors of the node i to the mth branch; p (P) l(m) Representing the power flow on the mth branch after the injection power of the node i changes;representing the initial power flow of the mth branch; m is M m An mth column vector representing the node-branch association matrix; x is X i An ith column vector representing the node impedance matrix X; x is x m The reactance value of the mth branch; ΔP i Representing the injection power variation value of the node i;
step 4-5, solving the non-fault line power flow by using the fault line power flow and the branch break distribution factor:
wherein ,after representing the line fault, the active power column vector of the non-fault line; />An initial active power column vector representing a non-faulty line; />An initial active power column vector representing a faulty line; d (D) M-O Representing the branch break distribution factor; x is X M A diagonal array of reactance values representing non-faulty lines; x is X O A diagonal array of reactance values representing a faulty line; phi represents a node-non-fault branch correlation matrix; psi represents a node-fault branch correlation matrix; b (B) 0 An initial node admittance matrix;
the node transfer distribution factor considers the condition that the power of the generator node and the power of the load node are changed simultaneously; the branch break distribution factors can be simultaneously suitable for the conditions of single-branch break and multi-branch break, the node transfer distribution factors and the branch break distribution factors are adopted to carry out power flow calculation, and when the node injection power changes and the multi-branch faults, the power flow transferred to the non-fault branch can be directly calculated, so that the iterative calculation of the power flow is avoided, and the rapid power flow calculation is realized.
2. The power system rapid operation risk assessment method based on power flow transfer and tracking as claimed in claim 1, wherein the method comprises the following steps:
the load shedding model based on the trend tracking in the step 5 specifically comprises the following steps:
step 5-1, adopting sequential tide tracking to calculate a power drawing coefficient of the power transmission line to the generator node; calculating a power distribution coefficient of the power transmission line to the load node by adopting reverse sequence power flow tracking:
wherein ,PGG A diagonal matrix of generator node power; p (P) LL A diagonal matrix for the load node power; p (P) TT Injecting a power diagonal matrix for the node; p (P) Gi→s-t For line s-t pair generator G i Is used for drawing power; p (P) Li←s-t For line s-t to load L i Is allocated to the power distribution of the power supply;
step 5-2, determining a generator node set flowing into the overload branch power and a load node set flowing out of the overload branch power by utilizing a breadth first search algorithm (BFS) according to the association information of the overload branch and each generator/load node;
step 5-3, setting a threshold value, and screening out a generator node set and a load node set with larger drawing coefficients and distribution coefficients as an effective control node set;
step 5-4, using the screened load node set and generator node set as local optimizing ranges, converting optimizing in the whole system range into optimizing in the local range, and establishing a load reduction model based on tide tracking:
the objective function is:
wherein ,Cd Indicating the system load reduction amount; c (C) i A load reduction amount indicating a node i;
the constraint conditions are as follows:
a. system power balance constraint
Wherein NG and ND represent all generators and load node sets of the system;
b. generator output constraint
wherein ,PG Representing the generator node active vector; and />Representing P G Upper and lower limits of (2);
c. load shedding constraints
0≤C i ≤P D
wherein ,PD Representing the generator node active vector;
d. line active power constraint
wherein ,T(Sj ) Is shown in the system state S j A lower line active vector; a (S) j ) Is shown in the system state S j Injecting an active relation matrix into the lower line active and the node; t (T) max Representing the maximum active vector that the line is allowed to pass through; c represents a node cut load vector;
the generator nodes and the load node set screened by adopting the tide tracking theory are used as load reduction optimizing ranges, the most effective control node set of the conventional model is included, the calculating precision is ensured, meanwhile, the number of optimized variables and the nodes needing to be regulated are greatly reduced, and the calculating efficiency is improved.
3. The power system rapid operation risk assessment method based on power flow transfer and tracking as claimed in claim 1, wherein the method comprises the following steps: the specific calculation method of the operation risk assessment index in step 7 is that each system state E k The running risk of the system is the product of the state probability of the generator and the power transmission line and the severity of the consequences in the state, the running risk assessment index of the system is the sum of all the system state risk values, and the system load shedding risk index is as follows:
wherein ,Rcut Representing a cut load risk; sev ev-cut (E k ) Representing the severity of load shedding results in the kth system state; l (L) cut (E k ) Representing the per unit value of the cut load quantity in the kth system state; p (P) load Representing the current system load per unit value;
the active power out-of-limit risk index of the system line is as follows:
wherein ,Rol Representing the risk of line active power out-of-limit; sev ev-ol (E k ) Representing the severity of the active out-of-limit consequences of the line in the kth system state; p (P) l Representing the active power of line l, P l max Indicating the maximum active power allowed by line l.
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